Worldwide, cardiovascular and chronic respiratory diseases have approximately million deaths each year. Evidence indicates that the ongoing COVID-19 pandemic directly contributed to increase blood pressure, cholesterol, and blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aimed to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was utilized to predict vital signs for the next 180 seconds. With lead time of 180 seconds, medical professionals can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, Naïve Bayes classification model, Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outperforms previous attempts for vital sign prediction. Compared with alternative methods, the Facebook Prophet model had the mean sqaure error for predicting vital signs. Hyperparameter tunning was utilized to refine model, yielding improved short- and long-term outcomes for each vital sign. From the results, it showed that the designed model acheives higher F-measure performance. The incorporation of additional elements, such as momentum indicators, can increase the flexibility of the model with calibration. The findings of this study demonstrated that the proposed model is more accurate in predicting vital signs and trends.

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

Social media platforms have made increasing use of irony in recent years. Users can express their ironic thoughts with audio, video, and images attached to text content. When you use irony, you are making fun of a situation or trying to make a point. It can also express frustration or highlight the absurdity of a situation. The use of irony in social media is likely to continue to increase, no matter the reason. By using syntactic information in conjunction with semantic exploration, we show that attention networks can be enhanced. Using learned embedding, unsupervised learning encodes word order into a joint space. By evaluating the entropy of an example class and adding instances, the active learning method uses the shared representation as a query to retrieve semantically similar sentences from a knowledge base. In this way, the algorithm can identify the instance with the maximum uncertainty and extract the most informative example from the training set. An ironic network trained for each labelled record is used to train a classifier (model). The partial training model and the original labelled data generate pseudo-labels for the unlabeled data. To correctly predict the label of a dataset, a classifier (attention network) updates the pseudo-labels for the remaining datasets. After the experimental evaluation of the 1,021 annotated texts, the proposed model performed better than the baseline models, achieving an F1 score of 0.63 on ironic tasks and 0.59 on non-ironic tasks. We also found that the proposed model generalized well to new instances of datasets.

Hate speech is demonstrably aimed at social tension and violence. Detection becomes increasingly difficult as overlapping emotional feelings occur. However, there are still several unresolved issues with informal and indirect targeting of negative communication, including sarcasm, misrepresentation, and praise for the target's or society's immoral behavior. In this study, we proposed a method for instance selection based on attention network visualization. The goal is to categorize, modify, and expand the number of training instances. To this end, we first used the lexicons of hate speech and online forums to train the embedding using transfer learning. Then, we used synonym expansion to the semantic vectors. The active learning approach was used to train the task using the result-label pairs. The entropy-based selection and visualization techniques help select unlabeled text for each active learning cycle. The approach is improved, and the number of training instances is increased to improve the model's accuracy. The active learning cycles are repeated until all unlabeled texts are converted to labeled text. The semantic embedding and lexicon expansion improve the model receiver operating characteristics (ROCs) from 0.89 to 0.91. The bidirectional LSTM with attention and active learning achieved 0.90 for precision-recall. The learned model can visualize the position-weighted terms to illustrate why hate speech is classified.

Current research has examined the use of user-generated data from online media to identify and diagnose depression as a serious mental health issue that can significantly impact an individual's daily life. To this end, many studies examined words in personal statements to identify depression. In addition to aiding in the diagnosis and treatment of depression, this study uses and utilizes a Graph Attention Network (GAT) model for the classification of depression from online media. The model is based on masked self-attention layers, that assigns different weight to each node in a neighborhood without costly matrix operations. In addition, an emotion lexicon was extended using hypernyms to improve the model performance. Furthermore, embedding of the model was used to illustrate the contribution of the activated words to each symptom and to obtain qualitative agreement from psychiatrists. This technique uses previously learned embedding to illustrate the contribution of activated words to depressive symptoms in online forums. A significant improvement was observed in the model's performance through the use of the lexicon extension method, resulting in an increase in the ROC performance. The performance was also enhanced by an increase in vocabulary and the adoption of a graph-based curriculum. The lexicon expansion method involves the generation of additional words with similar semantic attributes, utilizing similarity metrics to reinforce lexical features. The graph-based curriculum learning also utilized to handle more challenging training samples, allowing the model to develop increasing expertise in learning complex correlations between input data and output labels.

Abstract Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

Abstract Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning.

The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news article retrieval model based on active learning (AL) in a semisupervised learning setting. This model has the advantages of limited communication requirements, strong scalability, increased data privacy, and a time-dependent retrieval model. We use lexicon expansion, content segmentation, and temporal events to generate a bidirectional encoder representations from transformer (BERT) attention embedding query for the temporal understanding of sequential news articles. To generate pseudo-labels, we combine the partially trained model with the original tagged data. An attention network is used to update pseudo-labels of data samples when the label of a sample is correctly or incorrectly predicted. Finally, the modified classifiers are combined to make predictions. Experimental results indicate that the proposed model has 81% performance, showing that co-training and semisupervised learning can improve the performance of temporal expansion and profiling algorithms.

Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers to overcome the depression text categorization problem. The networks distribute weight to each node in a neighborhood based on neighbors’ properties/emotions without using expensive matrix operations like similarity or architectural knowledge. This study expands the emotional vocabulary through the use of hypernyms. As a result, our architecture outperforms the competition. Our experimental results show that the emotion lexicon combined with an attention network achieves receiver operating characteristic (ROC)-0.87 while staying interpretable and transparent. After obtaining qualitative agreement from the psychiatrist, the learned embedding is used to show the contribution of each symptom to the activated word. By utilizing unlabeled forum text, the approach increases the rate of detecting depression symptoms from online data.

Cooperative intelligent transportation systems (ITS) are on the rise in the field of transportation. The trajectory-based knowledge graph enables the ITS to have semantic and connectivity capabilities. This article presents the approach of embedding trajectory deviation points and deep clustering. We constructed the structural embedding by maintaining the relationship between the nodes based on the network structure and the neighbors of the nodes. This approach was then used to learn the latent representation based on the deviation points in the road network structure. We generated a set of sequences using a hierarchical multilayer network and a biased random walk. This research proposes a fuzzy contrast-based model that identifies deviation points using the deep network for weighted position nodes. This sequence is used to fine-tune the embedding of the nodes. We then averaged the embedding values of the nodes to obtain the travel embedding. Next, we extracted the embedding of the contrast set using a pairwise classification approach based on similarity metrics. Numerical studies show that the proposed learning trajectory embedding approach successfully captures the structural identity and outperforms competing strategies. The deep contrast set approach enables highly accurate detection of outliers in the trajectory and deviation locations.

Renewable energy is critical to the energy industry, even more so given the recent surge in oil and gas prices. As a result, the oil and gas sector is constantly preparing for the coming energy transition. This raises the question of when the oil and gas industry should invest in renewable energy sources. This article examines the history of oil company involvement in renewable energy to address this question. It also establishes and quantifies a relationship between renewable energy investment and oil and gas prices, followed by a projection of renewable energy investment based on oil and gas prices. A decision support system based on crude oil prices and renewable energy stocks is developed to predict renewable energy investment decisions. According to the experiments, the prediction model used provides favorable mean square values compared to other models. An algorithm based on the price of oil determines whether to invest in shares of renewable energy companies. The performance of a company is evaluated based on its expected profitability.

With advanced 5G/6G networks, data-driven interconnected devices will increase exponentially. As a result, the Industrial Internet of Things (IIoT) requires data secure information extraction to apply digital services, medical diagnoses, and financial forecasting. This introduction of high-speed network mobile applications will also adapt. As a consequence, the scale and complexity of Android malware are rising. Detection of malware classification is vulnerable to attacks. A fabricated feature can force misclassification to produce the desired output. This article proposes a subset feature selection method to evade fabricated attacks in the IIoT environment. The method extracts application-aware features from a single android application to train an independent classification model. Ensemble-based learning is then used to train the distinct classification models. Finally, the collaborative ML classifier makes independent decisions to fight against adversarial evasion attacks. We compare and evaluate the benchmark Android malware dataset. The proposed method achieved 91% accuracy with 14 fabricated input features.

Internet-Delivered Psychological Treatment (IDPT) has become necessary in the medical field. Deep neural networks (DNNs) require large, diverse patient populations to train models that achieve clinician-level performance. However, DNN models trained on limited datasets have poor clinical performance when used in a new location with different data. Thus, increasing the availability of diverse as well as distinct training data is vital. This study proposes a structural hypergraph as well as an emotional lexicon for word representation. An embedding model based on federated learning was developed for mental health symptom detection. The model treats text data as a collection of consecutive words. The model then learns a low-dimensional continuous vector while maintaining contextual linkage. The generated models with attention-based mechanisms as well as federated learning are then tested experimentally. Our strategy is suitable for vocabulary diversification, grammatical word representation, as well as dynamic lexicon analysis. The goal is to create semantic word representations using an attention network model. Later, clinical processes are used to mark the text by embedding it. Experimental results show the encoding of emotional words using the structural hypergraph. The 0.86 ROC was achieved using the bidirectional LSTM architecture with an attention mechanism.

Depression is a serious illness that significantly affects the lives of those affected. Recent studies have looked at the possibility of detecting and diagnosing this mental disorder using user-generated data from various forms of online media. Therefore, we address the issue of detecting sadness in social media by focusing on terms in personal remarks. To overcome the limitations in classifying depression texts, this study aims to develop attention networks that use covert levels of self-attention. Since nodes/words can express properties/emotions of their neighbors, this paper naturally assigns each node in a neighborhood a weight without performing costly matrix operations such as similarity or network architecture knowledge. This paper extends the emotion lexicon by using hypernyms. For this reason, our method is superior to the performance of other designs. According to the results of our experiments, the emotion lexicon combined with an attention network achieves an ROC of 0.87 while maintaining its interpretability and transparency level. Subsequently, the learned embedding is used to display the contribution of each symptom to the activated word, and the psychiatrist is polled to obtain his qualitative agreement with this representation. By using unlabeled forum language, the method increases the rate at which depression symptoms can be identified from information in Internet forums.

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.

Cyber-manufacturing Systems (CMS) have been growing in popularity, transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Therefore, it is likely that this new manufacturing model will become increasingly popular. By building new technologies on top of existing CMS, these systems will ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim; some challenges remain to be overcome. In general, the use of CMS can revolutionize the manufacturing industry. This study comprehensively analyzes these systems and their potential applications and implications. An overview of the field is then given and various aspects of CMS are also explored with more details. A taxonomy of the most common and current approaches to CMS is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, our survey identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, we identify several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. In summary, this paper presents a comprehensive overview of current technology and valuable insights are provided for the potential impact of CMS on society and industry.

In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89.

A serious issue in today’s society is Depression, which can have a devastating impact on a person’s ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, this paper investigates the language used in individuals’ personal expressions to identify depressive symptoms via social media. Graph Attention Networks (GATs) are used in this study as a solution to the problems associated with text classification of depression. These GATs can be constructed using masked self-attention layers. Rather than requiring expensive matrix operations such as similarity or knowledge of network architecture, this study implicitly assigns weights to each node in a neighbourhood. This is possible because nodes and words can carry properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the use of hypernyms. As a result, our method performs better when applied to data from the Reddit subreddit Depression. Our experiments show that the emotion lexicon constructed by using the Graph Attention Network ROC achieves 0.91 while remaining simple and interpretable.

Existing algorithms for sequential patterns do not address data heterogeneity. These algorithms can mine unusual patterns in the database and use them for the prediction. Traditional mining methods ignore pattern usefulness concerning heterogeneity and uncertainty. Some patterns may often exist in the database but may not add to its overall usefulness. Due to the changing database, this field can undertake more effective mining and prediction. Data security has become a priority with more data and user privacy problems. Laws and regulations help develop a safe data society, but performing pattern analysis without sharing data presents a new problem. Now, legally mandated data protection rules create issues with new data regulations and laws that require more attention. First, we have investigated conventional sequential mining methods that can improve effectiveness and efficiency in a heterogeneous environment. Second, we investigate learning and evolution methods for privacy protection. In the following, we briefly describe the insights we gained by publishing the defined research.

In this study, we present a fuzzy contrast-based model that classifies mental patient authored text into different symptoms by using an attention network for position-weighted words. Then, the mental data are labeled using the trained embedding. After that, the lexicons of the attention network are extended to allow the use of transfer learning methods. Our proposed approach classifies weighted attention words using similarity as well as contrast sets. The fuzzy model then classifies mental health data into different groups. To illustrate the performance of the proposed model, the approach is compared with the non-embedding as well as standard approaches. From the demonstrated results, the feature vector has a high Receiver Operating Characteristic Curve (ROC)-curve of 0.82 for 9 different symptom problems.

Dynamic traffic in a software-defined network (SDN) causes explosive data to flow from one system to another. The explosive data affects the functionality of system parameters, network-level configuration, routing parameters, network characteristics, and system load factors. Adapting to the traffic flow is a key research area in SDN in today’s big data world. Load balance vehicular sensor accessibility reduces delays, lowers energy consumption, and decreases the execution time. This paper combines the entropy-based active learning model to identify intrusion patterns efficiently, which is a packet-level intrusion detection model. The developed afterload balancing model can track the attack on the network. We then proposed a load balancing algorithm that optimizes the vehicular sensor usability by using sensor computing capability and source needs. We make use of a convergence-based mechanism to achieve high resource utilization. We then perform experiments on the state-of-the-art intrusion detection dataset. Our experimental results show that the load balancing mechanism can achieve 2× in performance improvements compared to traditional approaches. Thus, we can see that the designed model can help improve the decision boundary by increasing the training instance through pooling strategy and entropy uncertainty measure.

The Internet of Things (IoT) and smart cities are flourishing with distributed systems in mobile wireless networks. As a result, an enormous amount of data are being generated for devices at the network edge. This results in privacy concerns, sensor data management issues, and data utilization issues. In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point reached. The transaction data used to train the attention model that gives a low dimensional embedding. Afterwards, we share the convergence model among the client/stores. Then, efficient pattern mining methods known as a clustering-based dynamic method (CBDM) are applied. For experimentation, we used retail store data to cluster the customer based on purchase behaviour. The proposed clustering method used semantic embedding to extract and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.

Internet-delivered Psychological Treatment (IDPT) has been shown to be an effective method for improving psychological disorders. Natural language processing (NLP) requires an appropriate set of linguistic features for word representation and emotion segmentation. For psychological applications, models must be trained on extensive and diverse datasets to achieve expert-level performance. Labeling psychological texts authorized by patients is challenging because emotional biases can lead to incorrect segmentation of emotions and labeling emotional data is time consuming. In this paper, we propose an assistance tool for psychologists to explore the emotional aspects of mentally ill individuals. We first use an NLP-based method to create emotional lexicon embeddings, and then apply attention-based deep clustering. The learned representation is then used to visualize the emotional aspect of the text authorized by patients. We expand the patient authored text using synonymous semantic expansion. A latent semantic representation based on context is clustered using EANDC, which is a Explainable Attention Network-based Deep adaptive Clustering model. We use similarity metrics to select a subset of the text and then improve the explainability of learning using a curriculum-based optimization method. The experimental results show that synonym expansion based on the emotion lexicon increases accuracy without affecting the results. The attention method with bidirectional LSTM architecture achieved 0.81 ROC in a blind test. The self-learning based embedding then visualizes the weighted attention words and helps the psychiatrist to improve his explanatory power of the qualitative match for clinical notes and the remedy. The method helps in labeling text and improves the recognition rate of symptoms of mental disorders.

Abstract In the Internet of Medical Things (IoMT), collaboration among institutes can help complex medical and clinical analysis of disease. Deep neural networks (DNN) require training models on large, diverse patients to achieve expert clinician-level performance. Clinical studies do not contain diverse patient populations for analysis due to limited availability and scale. DNN models trained on limited datasets are thereby constraining their clinical performance upon deployment at a new hospital. Therefore, there is significant value in increasing the availability of diverse training data. This research proposes institutional data collaboration alongside an adversarial evasion method to keep the data secure. The model uses a federated learning approach to share model weights and gradients. The local model first studies the unlabeled samples classifying them as adversarial or normal. The method then uses a centroid-based clustering technique to cluster the sample images. After that, the model predicts the output of the selected images, and active learning methods are implemented to choose the sub-sample of the human annotation task. The expert within the domain takes the input and confidence score and validates the samples for the model’s training. The model re-trains on the new samples and sends the updated weights across the network for collaboration purposes. We use the InceptionV3 and VGG16 model under fabricated inputs for simulating Fast Gradient Signed Method (FGSM) attacks. The model was able to evade attacks and achieve a high accuracy rating of 95%.

In this paper, we propose a structure hypergraph and an emotional lexicon for word representation. Our method can solve problems related to vocabulary size, grammatical representation of words, and the lack of an emotional lexicon. Natural Language Processing (NLP) and attention-based curriculum learning are then used in the developed model. The goal is to achieve semantic word representations using a graph model. Later, embedding is used to label the text using clinical procedures. The experimental results show the emotional word representation with the structure hypergraph. The bidirectional Long Short Term Memory (LSTM) architecture with an attention mechanism achieved a Receiver Operating Characteristic (ROC) value of 0.96. The learning method can help psychiatrists in note taking and contributes to the detection rate of depression symptoms.

Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. Our method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC curve of 0.82 with problems associated with nine symptoms.

Cyber-physical systems (CPS) consist of a variety of multicore architectures, including central processing units (CPU) and graphical processing units (GPU). In general, programmers assign sequential programs to the CPU while parallel applications are assigned to the GPU. This article provides a method for mapping an OpenCL application to a heterogeneous multicore architecture using active fuzzy learning to determine the adequacy and processing capabilities of the application. During learning, subsamples are created by developing a machine learning-based device suitability classifier that predicts which processors would have excessive computational compatibility for running OpenCL programs. In addition, this study integrates an active learning model based on entropy with a fuzzification model to find nonoverlapping patterns. To minimize rule generation, the fuzzification-based weighted probabilistic technique is presented. The defuzzification process is optimized by using uncertainty values in conjunction with classification probability. In addition, 20 different features are proposed for extraction using the newly developed LLVM-based static analyzer. The correlation analysis approach is used to determine the optimal subset of features. The synthetic minority oversampling approach with and without feature selection is used to differentiate the class imbalance problem. Instead of manually modifying the machine learning classifier, a tree-based pipeline construction approach is used to determine the optimal classifier and associated hyperparameters. Experiments are then conducted on a set of benchmarks to verify the performance of the designed model. The results show that by increasing the number of training examples and including an entropy uncertainty measure, the proposed model is able to support and improve decision boundaries. We achieved a high F-measure of 0.77 and a ROC of 0.92 by optimizing and reducing the feature subsets.

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increasedF-measureby 5.06% and 2.4% while compared with genericNode2VecandStruct2Vecmethods.

In this paper, we provide a technique for identifying outliers based on embedding trajectory deviation points and deep clustering. We begin by constructing the network topology and the neighbors of the nodes to create a structural embedding while capturing the interactions of the nodes. We then develop a strategy to determine the hidden representation of distraction points in the road network topology. To create a collection of sequences from a hierarchical multilayer network, a biased random walk is used. This sequence is used to fine tune the embedding of the nodes. The trip embedding was then determined by averaging the node embedding values. Finally, the embeddings are clustered using an LSTM-based pairwise classification strategy based on similarity metrics. The experimental results show that compared to the generic techniques Node2Vec and Struct2Vec, the proposed embedding learning trajectory captures the structural identity and improves the F-measure by 5.06% and 2.4%, respectively.

In this paper, we propose a fuzzy classification deep attention-based model that expands emotional lexicons by using linguistic properties of actual patient authored texts. The active learning methods can expand the trained dataset and fuzzy rules over some time. As a result, the model itself can reduce its labeling efforts for mental health application. Thus, the designed model can solve issues related to vocabulary sizes per class, data sources, methods of creation, and create a baseline for human performance levels. This paper also gives fuzzy explainability by visualizing weighted words. Our proposed method uses a similarity-based method that includes a subset of unstructured data as the training set. Next, using an active learning mechanism cycle, our method updates the training model using new training points. This cycle is repeatedly performed until an optimal solution is reached. The designed model also converts all unlabeled texts into the training set. Our in-depth experimental results show that the emotion-based expansion enhances the testing accuracy and helps to build quality rules.

This paper proposes a framework that can be employed to mitigate adversarial evasion attacks on Android malware classifiers. It extracts multiple discriminating feature subsets from a single Android app such that each subset has the potential to classify a huge dataset of malicious and benign Android apps independently. Moreover, it incorporates an ensemble of ML classifiers where each classifier is trained on different features subset. Finally, the ensemble model formulates a collaborative classification decision that is resilient against adversarial evasion attacks. Results showed that the designed model achieves good performance compared to the existing models.

In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model is used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point is reached. The transaction data is used to train the attention model that gives a low dimensional embedding. Afterward, we share the convergence model among the client/stores. Then, efficient clustering-based dynamic method is then utilized. For experimentation, we used retail store data to cluster the customer based on purchase behavior. The proposed clustering method used semantic embedding to extract centroid and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.

In this paper, we propose a load balancing algorithm that optimizes the sensor usability by using sensor computing capability and source needs for intrusion detection, which is able to track the attack on the network. This paper combines the entropy-based active learning model to identify intrusion patterns efficiently. From the results, we can see that the designed model can help and improve the decision boundary by increasing the training instance through pooling strategy and entropy uncertainty measure.

Discovering important knowledge that may be available from databases while preserving the privacy of sensitive information can be considered a hot research subject of data mining in recent times. With the establishment of strong Internet of Things (IoT) networks globally, several data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and as edge computing has grown more important, applications running over networks require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In the past, many heuristics and meta heuristics-based approaches were revealed to sensitize sensitive information in privacy-preserving data mining (PPDM). They perturb the original database to hide sensitive information using addition or deletion operations. This is a known NP-hard problem. In this paper, we propose data privacy of IoT connected devices over heterogeneous networks. A deep re-enforcement learning-based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. Furthermore, minimizing known side effects that can be caused in the sanitization process is also be considered. Substantial experiments are conducted on both synthetic and real-world datasets. Results are evaluated based on sanitization side effects which include failing to hide sensitive items as well as choosing not to hide sensitive items. The proposed approach shows significant performance improvement compared to meta-heuristics (Genetic Algorithm, Particle Swarm Optimization) and heuristics (Greedy) approaches by our evaluation.

Summary In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource‐Aware Load Balancer for the Heterogeneous Cluster (RALB‐HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB‐HC uses supervised machine learning approach to classify applications using the static code‐features. The RALB‐HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource‐aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real‐world and synthetic workloads show that the RALB‐HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes.

Abstract Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F -measure, precision, recall and $$R^2$$ R 2 . The optimized and reduced feature subset model achieved a high F -measure of 0.91 and $$R^2$$ R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility.

Background: Internet-Delivered Psychological Treatment (IDPT) systems have the potential to provide evidence-based mental health treatments for a far-reaching population at a lower cost. However, most of the current IDPT systems follow a tunnel-based treatment process and do not adapt to the needs of different patients'. In this paper, we explore the possibility of applying Natural Language Processing (NLP) for personalizing mental health interventions. Objective: The primary objective of this study is to present an adaptive strategy based on NLP techniques that analyses patient-authored text data and extract depression symptoms based on a clinically established assessment questionnaire, PHQ-9. Method: We propose a novel word-embedding (Depression2Vec) to extract depression symptoms from patient authored text data and compare it with three state-of-the-art NLP techniques. We also present an adaptive IDPT system that personalizes treatments for mental health patients based on the proposed depression symptoms detection technique. Result: Our results indicate that the performance of proposed embedding Depression2Vec is comparable to WordNet, but in some cases, the former outperforms the latter with respect to extracting depression symptoms from the patient-authored text. Conclusion: Although the extraction of symptoms from text is challenging, our proposed method can effectively extract depression symptoms from text data, which can be used to deliver personalized intervention.

Abstract High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

As process technology scales, the design effort and nonrecurring engineering (NRE) costs associated with the development of integrated circuits is becoming extremely high. Structured ASICs offer one solution to these problems. However, to realize their full potential, their performance and cost advantages, architectures, and CAD must be fully understood. We believe that this can lead to wider adoption of structured ASICs. In this paper, we take a step in this direction and investigate the area, delay, power, and cost tradeoffs in metal-programmable structured ASICs (MPSAs). In particular, we quantify the impact of the number of user-defined (custom) metal mask layers on these metrics. Results indicate that for lowest cost, the number of custom layers should be minimized, especially for small die sizes (e.g., less than 100${\hbox {mm}}^{2}$). Delay and power, however, can be improved by a few additional custom layers. With two custom metal layers, MPSAs can be 2$\times$–10$\times$cheaper than cell-based ICs (CBICs).

In this paper, we evaluate the performance of an FPGA-like interconnect fabric for structured ASICs which is based upon fixed metal and programmable vias. We call this type of device a via-programmed structured ASIC or VPSA. We look at two different types of VPSA routing fabrics: one uses jumper wiring and the other uses crossover wiring. The performance of these fabrics is compared against an ASIC-like interconnect fabric, otherwise known as a metal-programmed structured ASIC or MPSA, which can be configured by customizing metal and via layers. We study the impact of these routing fabrics on cost, area, power and delay metrics. The results for different fabrics span a wide range, suggesting the routing architecture plays a very important role in their overall performance and it should be thoroughly researched.