Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering (...) class='Hi'>algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth (...) residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms. (shrink)

Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling (...) processes is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition. (shrink)

In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from (...) a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the (...) features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms. (shrink)

Sentence boundary detection (SBD) represents an important first step in natural language processing since accurately identifying sentence boundaries significantly impacts downstream applications. Nevertheless, detecting sentence boundaries within legal texts poses a unique and challenging problem due to their distinct structural and linguistic features. Our approach utilizes deep learning models to leverage delimiter and surrounding context information as input, enabling precise detection of sentence boundaries in English legal texts. We evaluate various deep learning models, including domain-specific transformer models like (...) LegalBERT and CaseLawBERT. To assess the efficacy of our deep learning models, we compare them with a state-of-the-art domain-specific statistical conditional random field (CRF) model. After considering model size, F1-score, and inference time, we identify the Convolutional Neural Network Model (CNN) as the top-performing deep learning model. To further enhance performance, we integrate the features of the CNN model into the subsequent CRF model, creating a hybrid architecture that combines the strengths of both models. Our experiments demonstrate that the hybrid model outperforms the baseline model, achieving a 4% improvement in the F1-score. Additional experiments showcase the superiority of the hybrid model over SBD open-source libraries when confronted with an out-of-domain test set. These findings underscore the importance of efficient SBD in legal texts and emphasize the advantages of employing deep learning models and hybrid architectures to achieve optimal performance. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for (...) brain MRI images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise (...) of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network and long short-term memory with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. (shrink)

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. (...) This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides. (shrink)

In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game where players have to survive in a post-apocalyptic world filled with zombies. We used OpenFace software to extract action unit (AU) intensities of facial expressions characteristic of decision-making processes and then we implemented a simple convolution neural network (CNN) to see which AUs are predictive of decision-making. Our results provide evidence that the pre-decision variations in action units 17 (chin raiser), 23 (...) (lip tightener), and 25 (parting of lips) are predictive of decision-making processes. Furthermore, when combined, their predictive power increased up to 0.81 accuracy on the test set; we offer speculations about why it is that these particular three AUs were found to be connected to decision-making. Our results also suggest that machine learning methods in combination with video games may be used to accurately and automatically identify complex decision-making processes using AU intensity alone. Finally, our study offers a new method to test specific hypotheses about the relationships between higher-order cognitive processes and behavior, which relies on both narrative video games and easily accessible software, like OpenFace. (shrink)

Artificial intelligence techniques in general and convolutional neural networks in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with (...) image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. (shrink)

The use of technologies like artificial intelligence can drive productivity growth, efficiency and innovation. The goal of this study is to develop an anomaly detection method for locating flaws on the surface of sandwich panels using YOLOv5. The proposed algorithm extracts information locally from an image through a prediction system that creates bounding boxes and determines whether the sandwich panel surface contains flaws. It attempts to reject or accept a product based on quality levels specified in the standard. To (...) evaluate the proposed method, a comparison was made with a sandwich panel damage detection method based on a convolutional neural network and methods based on thresholding. The findings show that the proposed method, which is based on an object detector, is more accurate than the alternatives. The characteristics of the model, which can reject or accept a product according to the standard and limit allowable manufacturing flaws to obtain a quality product, also enable this system to improve industrial standards for producing sandwich panels while increasing speed. (shrink)

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance (...) of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method. (shrink)

The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD. Secondly, Hough line transform (...) class='Hi'>algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion (...) of synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, (...) namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly (...) detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. (shrink)

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which (...) is at least 3.15% higher than that of other algorithms. Besides, the Recall is at least 7.13% higher than that of other algorithms, and the recognition rate is not less than 90%. Evidently, the improved CNN-BiLSTM algorithm can achieve good recognition results, and provide significant experimental reference for research on learners’ emotion recognition and graphic visualization of expressions in an intelligent learning environment. (shrink)

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other (...) known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts. (shrink)

Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to (...) extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks models demonstrated recognition rates of 97.147%, 3DMultiHeaded_CNN models showed rates of 98.553%, and rates of 99.234% were demonstrated by CNN_LSTM models. The collected data contained sufficient information to distinguish the bowing techniques studied, and deep learning methods were capable of learning the movement patterns that distinguish these techniques. Each of the learning algorithms investigated produced high classification accuracies which supported the feasibility of training classifiers. The resulting classifiers may provide the foundation of a digital assistant to enhance musicians' time spent practicing alone, providing real-time feedback on the accuracy and consistency of their musical gestures in performance. (shrink)

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) for sequential (...) decoding of EEG events with deep learning-based postprocessing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application. (shrink)

In this paper, we study the face recognition and emotion recognition algorithms to monitor the emotions of preschool children. For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. There are not only innovations in applications, but also algorithmic enhancements. And face annotation is performed on the dataset, (...) while a hierarchical sampling method is designed to alleviate the data imbalance phenomenon that exists in the dataset. A new feature descriptor, called “oriented gradient histogram from three orthogonal planes,” is proposed to characterize facial appearance variations. A new efficient geometric feature is also proposed to capture facial contour variations, and the role of audio methods in emotion recognition is explored. Multifeature fusion can be used to optimally combine different features. The experimental results show that the method is very effective compared to other recent methods in dealing with facial expression recognition problems about videos in both laboratory-controlled environments and outdoor environments. The method performed experiments on expression detection in a facial expression database. The experimental results are compared with data from previous studies and demonstrate the effectiveness of the proposed new method. (shrink)

The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength of environmentally friendly concrete containing recycled aggregate is important for understanding sustainable structures’ concrete behaviour. In this research, the capability of the deep learning neural network approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence approaches named multivariate adaptive (...) regression spline, extreme learning machines, and random forests. The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error. The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete. (shrink)

As the development of artificial intelligence technology, the deep-learning -based Virtual Reality technology, and DL technology are applied in human-computer interaction, and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based (...) on the convolutional neural network algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically. (shrink)

Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its (...) use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search forgenerative model-based andgeometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs intoexplicitandimplicitmethods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs. (shrink)

Bioassessment is the process of using living organisms to assess the ecological health of a particular ecosystem. It typically relies on identifying specific organisms that are sensitive to changes in environmental conditions. Benthic macroinvertebrates are widely used for examining the ecological status of freshwaters. However, a time-consuming process of species identification that requires high expertise represents one of the key obstacles to more precise bioassessment of aquatic ecosystems. Partial automation of this process using deep learning-based image classification is the (...) goal of an ongoing project AIAQUAMI we are participating in. One of the project goals is to develop software support for image classification with visualization and reporting. For that purpose, we developed desktop and web applications that we open-sourced as Imagelytics Suite. Both desktop and web applications rely on a convolutional neural network (CNN) to classify images and the Grad-CAM algorithm to produce heatmaps of the image areas that mostly influenced the network decision. Along with the source code of the applications, we also open-sourced scripts that can be used to train CNN on an arbitrary dataset and produce required metadata, so it can be used with Imagelytics applications. In this article, we presented technical details regarding the design of the applications and the training method that will enable their general use for image classification tasks. As a part of the evaluation, we will show a use case related to species identification of non-biting midges (Diptera: Chironomidae). (shrink)

Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and (...) again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models. (shrink)

Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep learning has brought a turnaround (...) for this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm. (shrink)

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant (...) research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number (...) of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

Understanding students’ psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students’ normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In addition, a variety of experimental (...) methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive–compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. For the optimization of ant colony algorithm computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized. (shrink)

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning, given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their (...) trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios. (shrink)

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples (...) the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same. (shrink)

This paper focuses on the theme of the application of deep learning in the field of basketball sports, using research methods such as literature research, video analysis, comparative research, and mathematical statistics to explore deep learning in real-time analysis of basketball sports data. The basketball posture action recognition and analysis system proposed for basketball movement is composed of two parts serially. The first part is based on the bottom-up posture estimation method to locate the joint points and is (...) used to extract the posture sequence of the target in the video. The second part is the analysis and research of the action recognition algorithm based on the convolution of the space-time graph. According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. The method can actively correct technical movements, train players to form muscle memory, and improve their abilities. Through the research of this article, it provides a theoretical basis for promoting the application of deep learning in the field of basketball and also provides a theoretical reference for the wider application of deep learning in the field of sports. At the same time, the designed real-time analysis system of basketball data also provides more actual reference values for coaches and athletes. (shrink)

This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength (...) between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods. (shrink)

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network and Convolutional Neural Network is constructed. The method’s effectiveness is verified by comparing the (...) prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. (...) Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant (...) increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Convolutional neural networks, which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic signals, especially for decoding brain states in brain-computer interfaces. Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects (...) of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales in an efficient and optimized way, and was validated on three P300 datasets. The CNN was trained using different strategies and was compared with several state-of-the-art algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs. (shrink)

This study combines the discovery methods and training of innovative talents, China’s requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning, neural networks, and related concepts of the seasonal difference Autoregressive Moving Average Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation. (...) Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model’s convergence speed, superiority, and versatility. Then, particle swarm optimization algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model’s versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts. (shrink)

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods (...) are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. (shrink)

The convenience of big data processing technology has played a great advantage in many scenarios, and its deep learning can effectively mine different types of data in data sets. Applying this method to mining psychological prediction data set of legal anomie behavior can effectively prevent the occurrence of illegal behavior. The effective analysis of its psychological characteristics and the changes of psychological emotions will have hidden dangers, so it is necessary to extract this kind of data in such cases. (...) Therefore, the forest algorithm under deep learning uses weight factors, simple sampling, and other ideas to build the intervention mechanism, which will improve psychological quality to prevent anomie. The experimental results of this paper are as follows: In the case of testing psychological fluctuation by three algorithms, the forest algorithm calculates the psychological change area steadily, with the increase of the number of samples. In the data sample extraction of different dimensions, the test value increases with the increase of dimensions, and it is stable at 12 dimensions. Preventive intervention is extremely important to construct the mechanism before the anomie behavior occurs, and the longest running time of the roll neural network is as much as 950 s. The data mining technology for constructing the intervention mechanism accounts for 15.4% of counseling, which also shows that the auxiliary measures for preventing behavior are particularly important. (shrink)

Background: Detecting and classifying fish diseases is crucial for maintaining the health and sustainability of aquaculture systems. This study employs deep learning techniques, particularly Convolutional Neural Networks (CNNs), to automate the detection of various fish diseases using image data. Methods: The study utilizes a carefully curated dataset sourced from the Kaggle database, comprising images representing seven distinct types of fish diseases, along with images of healthy fish. Data preprocessing techniques, including resizing, rescaling, denoising, sharpening, and smoothing, are applied to (...) enhance image quality and facilitate accurate disease detection. Data augmentation is employed to increase the model's ability to generalize to unseen data. The CNN architecture is designed with cascading convolutional layers, ReLU activation functions, and pooling operations to extract high-level features associated with fish infections. The model architecture, implemented using the Keras Sequential API, includes convolutional layers, max pooling layers, and densely connected layers for classification. Results: Experimental results demonstrated promising performance across various disease categories, with high accuracy and balanced precision and recall values for most classes. The study also discussed the impact of climate change on fish disease incidence and underscores the importance of effective monitoring and management practices facilitated by technological innovations such as Big Data, IoT, sensors, and robotics in ensuring sustainable fisheries management. (shrink)

This paper studies the feature extraction and middle-level expression of Convolutional Neural Network convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of (...) the depth descriptor, Hellinger kernel and Principal Component Analysis are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral- laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high-speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four-frame plate glass is supported by sharp dagger-like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four-point supporting glass specimen is generally stronger than that of the four-frame supporting glass. (shrink)

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (...) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results. (shrink)