Huge Amount Of Data Research Articles

Biochemistry of Redox-Active Sulphur Compounds in Mammalian Cells and Approaches to Detecting Them

The discovery of new classes of regulatory molecules in human and animal metabolism always leads to a large-scale study of their properties in the context of biochemistry, physiology, and pharmacology. About 20 years ago, hydrogen sulfide (H2S) and its derivatives – active sulfur forms (ASFs): persulfides, polysulfides, nitrosothiols, sulfenic acids, etc. – became one of such classes of molecules. The participation of ASFs in a variety of physiological and pathological processes, such as regulation of vascular tone, inflammation, long-term potentialization in the central nervous system, etc., has been shown. Changes in ASF levels or patterns of modification of their targets are associated with a wide range of pathologies: cardiovascular, oncologic, neurodegenerative, and others. For a part of these processes, mechanisms have been studied that involve direct modification of regulatory (NF-κB, Keap1) or effector (GAFD, eNOS, TRPA1) proteins through reactions of cysteine residues and metal-containing centers with APS. The presence of different regulated enzymatic systems producing APS and numerous molecular targets allows us to consider H2S and its derivatives as an important class of small regulatory molecules. H2S is counted among the so-called “gas transmitters”, along with nitric oxide(II) and carbon monoxide. Over the last 20 years, a huge amount of data on the biochemistry of these compounds and approaches to their study has been accumulated.

Safety assessment of sapropterin dihydrochloride: real-world adverse event analysis based on the FDA adverse event reporting system (FAERS)

ObjectiveSapropterin dihydrochloride is the first drug for the therapy of phenylketonuria, which is a rare disease that occurs one of 10,000–15,000 newborns. As a result, detailed and comprehensive reports on the safety of sapropterin in large, real-world populations are required. The purpose of this study is to undertake a complete analysis of sapropterin’s adverse events (AEs) using the FDA Adverse Event Reporting System (FAERS) database.MethodsWe retrieved reports of adverse events with sapropterin as the principal suspect from FAERS between the first quarter of 2008 and the first quarter of 2024. The Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN) were utilized to detect AE signals.ResultsThe study collected 4,953 suspected AE cases from the FAERS database, with sapropterin as the major suspect. A total of 130 positive signals were obtained utilizing the ROR, PRP, and BCPNN. The FAERS database revealed that common clinical AEs of sapropterin included vomiting, upper respiratory infection, rhinorrhea, and a reduction in amino acid concentrations. Furthermore, we detected probable unexpected adverse events (AEs) using disproportionality analysis, including gastroesophageal reflux disease, flatulence, influenza, ear infection, viral infection, pharyngitis streptococcal, spontaneous abortion, and nephrolithiasis.ConclusionBy analyzing huge amounts of real-world data from the FAERS database, we found potential novel AEs of sapropterin using disproportionate analysis. It is advantageous for healthcare professionals and pharmacists to focus on efficiently managing sapropterin’s high-risk adverse events, improving drug levels in clinical settings, and ensuring patient medication safety.

Lossless Recompression of Vector Quantization Index Table for Texture Images Based on Adaptive Huffman Coding Through Multi-Type Processing

With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy, compression technology is becoming increasingly important. Based on different application scenarios, it is divided into lossless data compression and lossy data compression, which allows a certain degree of compression. Vector quantization (VQ) is a widely used lossy compression technology. Building upon VQ compression technology, we propose a lossless compression scheme for the VQ index table. In other words, our work aims to recompress VQ compression technology and restore it to the VQ compression carrier without loss. It is worth noting that our method specifically targets texture images. By leveraging the spatial symmetry inherent in these images, our approach generates high-frequency symbols through difference calculations, which facilitates the use of adaptive Huffman coding for efficient compression. Experimental results show that our scheme has better compression performance than other schemes.

CoFFEE: a codec-based forensic feature extraction and evaluation software for H.264 videos

The forensic analysis of digital videos is becoming increasingly relevant to deal with forensic cases, propaganda, and fake news. The research community has developed numerous forensic tools to address various challenges, such as integrity verification, manipulation detection, and source characterization. Each tool exploits characteristic traces to reconstruct the video life-cycle. Among these traces, a significant source of information is provided by the specific way in which the video has been encoded. While several tools are available to analyze codec-related information for images, a similar approach has been overlooked for videos, since video codecs are extremely complex and involve the analysis of a huge amount of data. In this paper, we present a new tool designed for extracting and parsing a plethora of video compression information from H.264 encoded files, including macroblocks structure, prediction residuals, and motion vectors. We demonstrate how the extracted features can be effectively exploited to address various forensic tasks, such as social network identification, source characterization, and double compression detection. We provide a detailed description of the developed software, which is released free of charge to enable its use by the research community to create new tools for forensic analysis of video files.

REGULAÇÃO LEGAL DAS REDES SOCIAIS: PROTEÇÃO DE DADOS E LIBERDADE DE EXPRESSÃO

The legal regulation of social networks is a field in constant evolution, which seeks to balance two fundamental pillars in the digital era: data protection and freedom of expression. Social networks manage a huge amount of personal data, which makes the existence of legal frameworks that protect users’ privacy imperative. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the General Data Protection Law (LGPD) in Brazil establish strict standards on how platforms must manage personal information. These laws not only allow users to access, correct or delete their data, but also impose sanctions on companies that do not comply with these requirements, ensuring a high level of security and transparency. At the same time, freedom of expression is a fundamental right that must be protected in the digital environment. However, exercising this right on social media often conflicts with the need to moderate content that may be considered offensive, dangerous or even illegal. Platforms implement moderation policies to mitigate the spread of hate speech, misinformation, and other harmful content. This, however, creates significant challenges in relation to censorship and the delimitation of permitted speech, which can affect the free expression of ideas and opinions. The challenge lies in finding an appropriate balance between protecting users’ privacy and data and enabling robust freedom of expression. An excessive focus on data protection can limit users’ ability to express themselves freely, while unrestricted freedom of expression can put individuals’ security and privacy at risk. As social media becomes more deeply integrated into everyday life and becomes a key platform for communication and the exchange of ideas, it is essential that regulations continue to adapt to address these dilemmas in a balanced and fair way. The future of regulation in this field will depend on the ability of legislators and platforms to adapt to rapid technological and social changes. ensuring that both rights are protected effectively and equitably.

Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents.

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Comprehensive approach to clinical decision-making strategy, illustrated by the Gulf War.

Throughout the history of medicine, clinical decision-making strategies have largely been dependent on the implementation of novel technologies. Artificial intelligence (AI) has not only made a leap into a new dimension of medical measures & decisions' shaping but has also served as a strategic backup of medical practice. Due to its nature, and military technologies in use, the Gulf War (1990-1991) is considered to be the verge of the contemporary warfare era. Soldiers had been engaged into a highly complex military operation theatre contaminated with both chemical and radiological noxious agents, and burdened with side-effects of prophylactic measurements. The aim of this review is to present a comprehensive approach to clinical decision-making strategy using theGulf War veterans' syndrome as an example. The model is based on the processing of all data coming from the military operation theatre and their unification with medical data, so as to obtain the final product, i.e.,the decision needed for a personalised therapeutic approach. Due to the complexity and a huge amount of data gathered at a given location in a given time, the functioning of this model unanimously calls for the interlace between military system AI and that of the medical sector.

Acetylxylan esterase is the key to the host specialization of wood-decay fungi predicted by random forest machine-learning algorithm

Wood-decay fungi produce extracellular enzymes that metabolize wood components such as cellulose, hemicellulose and lignin. Each fungus has a preference of wood species as the host, but identification of these preferences requires a huge amount of cultivation data. Here, we developed a method of predicting the wood species preference, Angiosperm specialist or Gymnosperm specialist or generalist, of wood-decay fungi using the random forest machine-learning algorithm, trained on the numbers of families associated with host specialization in the Carbohydrate-Active enZymes database. The accuracy of the prediction was about 80%, which is lower than that of the classification of white- and brown-rot fungi (more than 98%) by the same method, but the reason for this may be the ambiguity of the definition of “preference” and “generalists”. Carbohydrate esterase (CE) family 1 acetylxylan esterase was the most significant contributor to the prediction of host specialization, followed by family 1 carbohydrate-binding module and CE family 15, mainly containing glucuronoyl esterases. These results suggest that the ability to degrade glucuronoacetylxylan, a major hemicellulose of Angiosperm, is the key factor determining the host specialization of wood-decay fungi.

Deep Reinforcement Learning based Running-Track Path Design for Fixed-Wing UAV Assisted Mobile Relaying Network

This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13% of energy and 65.93% of flight time when the ground source and the ground destination are located 2000 meters apart and need to transfer 5000bit/Hz of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.

Implementation of Artificial Intelligence on Personnel Management in a Dynamic Society

The bedrock of this study is Implementation of Artificial Intelligence (AI) on Personnel Management in a Dynamic Society.. The rapid development and advancement of the digital environment, the increase in computing power, the processing of huge amounts of data and advanced algorithms have not only made AI exceptional and indispensable, but also emerged as a key tool in talent management. Methodologically, the study used secondary data collection sources such as textbooks, magazines, newspapers, journals, and periodical reports. The study found, among other things, that the development of AI leads to errors caused by machines due to programming errors and persistence of discrimination/preferential treatment in recruitment. Therefore, AI is not able to recognize corporate culture and values when evaluating applicants. The paper recommends that companies should focus on updating and training their current employees to avoid job losses due to the emergence of AI and adapt to the current framework conditions, etc. Thus, the paper concludes that the increasing use of AI will have a social and economic impact in this millennium, as it aims at digitalization and development by utilizing AI-inspired decision-making and integrating advanced information systems into multidimensional areas of activity, with a significant impact on various sectors around the world.

USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging

There is an increasing number of large-scale cross-site database collections of neuroimaging markers (sMRI and fMRI) for studying neurodevelopmental illnesses (NDDs). Although a huge amount of data favors machine learning-based categorization algorithms, the unique heterogeneity of each site can impair cross-site generalization capacity. It is critical to create Unsupervised domain adaption methods for NDDs because obtaining appropriate diagnoses or labeling for NDDs might be problematic. In our work, we focus on Attention-deficit/hyperactivity disorder, which is the most common and frequently co-occurring NDD. We present an unsupervised multisource domain adaptation network (USMDA) with four primary components: Domain Alignment Module, Discrepancy Estimator, Pre-trained Model Generator, and Unsupervised Network. The Domain Alignment module is intended to incrementally and effectively align graph representations of the source and target domains. At the same time, the binary cross entropy regularizer is introduced for the first time during the training of a model learned on multiple source domains to improve existing feature alignment methods such as Transfer Joint Matching (TJM) and Joint Distribution Adaptation (JDA) by learning good unsupervised features. In an unsupervised network, the grid search optimization technique generates the optimal pseudo labels for unlabeled target data. We validate our proposed technique first on existing feature-level DA methods such as JDA, TJM, and Correlation alignment (CORAL), on the publically accessible dataset ADHD-200 and then by using binary cross-entropy in existing DA methods such as TJMCE and JDACE. The experimental results show that our proposed USMDAJDACE method when applied to multisite sMRI and fMRI ADHD data, can significantly outperform competitive methods for multi-center ADHD diagnosis.

Fuzzy Petri Nets for Traffic Node Reliability.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node's safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases.

Review of AI-Driven Solutions in Business Value and Operational Efficiency

Introduction. Artificial intelligence (AI) refers to a wide spectrum of breakthroughs that offer multiple advantages to companies in terms of increased sales. Considering the abundance of data and the significant increase in computational resources, organisations have rapidly turned to artificial intelligence (AI) to create financial benefits. Nevertheless, businesses continue to discover it is challenging to implement and employ AI in their everyday activities. Therefore, a comprehensive understanding is required due to the absence of an integrated comprehension of how artificial intelligence creates business value and what kind of corporate worth is anticipated. Aim and tasks. The study aims to review value-generating methods and explain how enterprises might use AI technology in their business activities. To accomplish its main aims, this study offers a thorough literature review. The working hypothesis claims that the use of AI can increase business value. Results. This study examines the research capabilities of AI, its use in the corporate environment, and its initial and secondary impacts. The impact of AI includes process efficiency, generating insights hidden in huge amounts of data, and transforming business processes in terms of procedural actions, operational efficiency, financial efficiency, market efficiency, and sustainability in terms of company profile. In addition to the favourable impacts, several recent cases have shown that unwanted and undesired consequences may develop in the absence of effective management procedures. These effects hurt the reputation of the businesses concerned and, in certain cases, resulted in huge fines and financial losses. Such findings increase the responsibility of AI enterprises to incorporate solutions that reduce the bias in data and algorithms at every stage of implementation. Conclusions. The role of artificial intelligence in the corporate environment in value creation and operational efficiency is extending. AI technologies can be used by companies to increase automation of corporate processes without direct interaction with customers, including applications that mean the use of AI in customer-facing services and products. Learning about the means by which AI might be employed will assist businesses in generating rational choices regarding the strength of implementing technologies in the supply chain. Assessing the possible implications of AI acceptance of artificial intelligence may enable firms to plan more successfully on a technology’s launch.

Machine Learning in Medicare Fraud Detection: Safeguarding Public Resources

Fraud in receipt and provision of Medicare is one of the most dangerous threats to public healthcare delivery systems, wasting billions of dollars annually and distorting the foundations upon which healthcare solutions are based. Conventional approaches to identifying fraud have become ineffective owing to the new and complex techniques undertaken by fraudsters. Through this paper, an effort is made to discuss the role of ML in identifying and combating Medicare fraud, specifically to preserve public assets. Using supervised learning, unsupervised learning, and deep learning are promising methods to detect patterns that are possibly related to fraud activities. Applying these techniques can help analyze a huge amount of data, learn from precedents, and identify elaborate and sophisticated trends that are hardly discernable using traditional approaches. This paper will provide an extensive investigation of various ML approaches to Medicare fraud detection. At this step, we experimentally analyze the most popular and effective ones, like decision trees, random forests, SVM, creative neural networks, and clusters. From the results obtained, it is clear that these advanced ML techniques can enhance the performance of fraud detection methods by dramatically minimizing false positives and enhancing the early detection of fraudsters in claims processing. In addition, the ethical concerns, future prospects, and difficulties of employing ML in this particular field. The use of machine learning in Medicare fraud prevention and identification mechanisms to prevent fraud greatly has the potential to transform the protection of public resources, which is crucial to ensuring that healthcare funds are used optimally.

Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy

In recent years, we have witnessed both artificial intelligence obtaining remarkable results in clinical decision support systems (CDSSs) and explainable artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domain due to its ease of interpretation, even for less technically proficient staff. However, the generation of high-quality counterfactuals relies on generative models for guidance. Unfortunately, training such models requires a huge amount of data that is beyond the means of ordinary hospitals. In this paper, we therefore propose to use federated learning to allow multiple hospitals to jointly train such generative models while maintaining full data privacy. We demonstrate the superiority of our approach compared to locally generated counterfactuals. Moreover, we prove that generative models for counterfactual generation that are trained using federated learning in a suitable environment perform only marginally worse compared to centrally trained ones while offering the benefit of data privacy preservation. Finally, we integrate our method into a prototypical CDSS for treatment recommendation for sepsis patients, thus providing a proof of concept for real-world application as well as insights and sanity checks from clinical application.

USE OF IIoT TECHNOLOGIES IN ENGINE MONITORING AND CONTROL SYSTEMS

Determining the operating parameters of engine systems is very important to ensure its long-term reliability. Modern power plants use a large number of modern electronic components (sensors, controllers, etc.). All systems generate a huge volume of data that must be tracked, processed and analyzed, which in turn creates calls to gateways and servers. However, the current state of development of microcontrollers and their power allow the use of fog computing, which can lead to more efficient use of both the power plant and data transmission systems. Also, the use of modern microcontrollers and fog computing allows modernizing old power plants with minimal effort and creating new information nodes for monitoring the state of nodes and engine systems and easily combining them with systems already existing on the engine. The selection of a microcontroller and justification of its type for the modernization of the fuel system of a diesel engine are considered. Rapid processes occur in the high-pressure fuel system of diesel engines, which can be determined by changes in fuel pressure. For detailed measurement of processes in the fuel system, measurements should be made with a frequency of at least 24 kHz, i.e.: if the crankshaft rotation frequency is 4000 min-1, then 24,000 measurements should be made every second (when registering data after 1 degree of crankshaft rotation). Another indicator that imposes its requirements is the bit rate of the analog-to-digital converter (ADC), which depends on the range of recorded values. For example, an ADC with a resolution of 8 bits is capable of outputting 256 discrete values (0…255). That is, the higher the bit rate, the more sensitive the converter is to signal changes, and 8 bits may not be enough. Thus, even a small number of sensors can generate a huge amount of data. Transmitting large amounts of data is irrational and can lead to data loss and "clogging" of transmission channels, which challenges the computational performance of the microprocessor.

Top-k sentiment analysis over spatio-temporal data.

In recent years, social media has become much more popular to use to express people's feelings in different forms. Social media such as X (i.e., Twitter) provides a huge amount of data to be analyzed by using sentiment analysis tools to examine the sentiment of people in an understandable way. Many works study sentiment analysis by taking in consideration the spatial and temporal dimensions to provide the most precise analysis of these data and to better understand people's opinions. But there is a need to facilitate and speed up the searching process to allow the user to find the sentiment analysis of recent top-k tweets in a specified location including the temporal aspect. This work comes with the aim of providing a general framework of data indexing and search query to simplify the search process and to get the results in an efficient way. The proposed query extends the fundamental spatial distance query, commonly used in spatial-temporal data analysis. This query, coupled with sentiment analysis, operates on an indexed dataset, classifying temporal data as positive, negative, or neutral. The proposed query demonstrates over a tenfold improvement in query time compared to the baseline index with various parameters such as top-k, query distance, and the number of query keywords.

Fuzzy deep visual panic detection

Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users’ emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user’s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users’ stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users’ profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user’s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.