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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer Science 2024年第5期5卷 1-10页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature engineering

Comprehensive Heart Attack Prediction Model Using Stacked En...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jayabalan, Anandhi Uma, R. Padmakala, S. Sri Sai Ram Engineering College Department of Computer Science Engineering Chennai India Saveetha School of Engineering Saveetha Institute Of Medical and Technical Sciences Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331509828

Cardiovascular disease continues to be a predominant cause of mortality globally, requiring precise and effective strategies for early identification. This work examines the application of clinical datasets to explore ensemble models and machine learning for predicting heart attacks. The dataset includes patient information about factors such as age, diabetics, levels of cholesterol, blood pressure, and smoking status. The Label Encoder is utilized to encode categorical variables, while class imbalance is tackled through the application of the Synthetic Minority Over-sampling Technique (SMOTE). The top five features were identified through the application of feature importance in Random Forest analysis. To enhance prediction accuracy, various classification techniques, including Random Forest (RF), Gradient Boosting (GB), XG Boost, and Light GBM, along with an ensemble Voting Classifier, are utilized. The best model performance was attained by intensive hyperparameter optimization using Grid Search CV. The performance of the proposed system was assessed through a stacked ensemble methodology. The Random Forest model revealed an accuracy of 87.25% and a ROC-AVC of 0.91. The results for Gradient Boosting and XG Boost showed accuracies of 85.60% and 86.80%, accompanied by ROC-AUC scores of 0.89 and 0.90, respectively. The ensemble Voting Classifier enhanced performance, reaching an accuracy of 88.15% and a ROC-AUC of 0.92. The findings indicate that utilizing an ensemble method alongside hyperparameter optimization significantly enhances prediction accuracy and facilitates the early identification of heart attack risks. © 2025 IEEE.

关键词： Blood pressure

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Optimizing Menstruation Symptom Management: A Machine Learning Approach to Personalized Nutrition Recommendations

Optimizing Menstruation Symptom Management: A Machine Learni...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Logapriya, E. Surendran, R. Sathish Kumar, J.P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals' day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly. © 2024 IEEE.

关键词： Support vector machines

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification 24

CounterFair: Group Counterfactuals for Bias Detection, Mitig...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Kuratomi, Alejandro Lee, Zed Tsaparas, Panayiotis Junior, Guilherme Dinis Pitoura, Evaggelia Lindgren, Tony Papapetrou, Panagiotis Stockholm University Department of Computer and Systems Sciences Sweden University of Ioannina and Archimedes/Athena RC Department of Computer Science and Engineering Greece

ISBN: (纸本)9798331506681

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization. © 2024 IEEE.

关键词： Algorithmic Fairness Counterfactual explanations Group counterfactuals Local explainability

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Impact of Human Vulnerabilities on Cybersecurity

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computer Systems Science & engineering 2022年第3期40卷 1153-1166页

作者： Maher Alsharif Shailendra Mishra Mohammed AlShehri Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Today,security is a major challenge linked with computer network companies that cannot defend against *** vulnerable factors increase security risks and cyber-attacks,including viruses,the internet,communications,and *** of Things(IoT)devices are more effective,and the number of devices connected to the internet is constantly increasing,and governments and businesses are also using these technologies to perform business activities ***,the increasing uses of technologies also increase risks,such as password attacks,social engineering,and phishing *** play a major role in the field of *** is observed that more than 39%of security risks are related to the human factor,and 95%of successful cyber-attacks are caused by human error,with most of them being insider *** major human factor issue in cybersecurity is a lack of user awareness of cyber *** study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different *** study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal ***,the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity,such as phishing attacks,passwords,attacks,and social engineering,are major problems that need to be addressed and reduced through proper awareness and training.

关键词： Cybersecurity phishing attack password attack social engineering cybersecurity awareness security risk

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ReLUs Are Sufficient for Learning Implicit Neural Representations 41

ReLUs Are Sufficient for Learning Implicit Neural Representa...

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41st International Conference on Machine Learning, ICML 2024

作者： Shenouda, Joseph Zhou, Yamin Nowak, Robert D. Department of Electrical and Computer Engineering University of Wisconsin-Madison United States Department of Computer Sciences University of Wisconsin-Madison United States

Motivated by the growing theoretical understanding of neural networks that employ the Rectified Linear Unit (ReLU) as their activation function, we revisit the use of ReLU activation functions for learning implicit neural representations (INRs). Inspired by second order B-spline wavelets, we incorporate a set of simple constraints to the ReLU neurons in each layer of a deep neural network (DNN) to remedy the spectral bias. This in turn enables its use for various INR tasks. Empirically, we demonstrate that, contrary to popular belief, one can learn state-of-the-art INRs based on a DNN composed of only ReLU neurons. Next, by leveraging recent theoretical works which characterize the kinds of functions ReLU neural networks learn, we provide a way to quantify the regularity of the learned function. This offers a principled approach to selecting the hyperparameters in INR architectures. We substantiate our claims through experiments in signal representation, super resolution, and computed tomography, demonstrating the versatility and effectiveness of our method. The code for all experiments can be found at https://***/joeshenouda/relu-inrs. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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A novel approach to identify parkinson’s disease and other similar neural stress by analysing keystrokes on modern active devices with ensemble classification

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Multimedia Tools and Applications 2025年 1-40页

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector - III Saltlake City Kolkata700106 India Department of Computer and System Sciences Visva-Bharati University Santiniketan731235 India

Motor skills (related to the motor nerve) and neurocognitive disorders affect humans’ typing ability to an extent that is noticeable while using a keyboard, smartphone, or other electronic gadgets. These two medical conditions are Parkinson’s disease (PD), caused by malfunctions of the motor nerve, and neurocognitive disorder, caused by a deficiency of organismic responses to stimuli. A mild symptom of PD, change in fine motor skills during typing, is reflected heavily in keystroke patterns during the early stages. Similarly, Emotional stress (ES) expresses a neurocognitive disorder that affects cognitive abilities as well. Early symptoms of this disorder are reflected in the keystroke patterns according to their severity. As there is no such pathological examination, it is challenging to perceive and measure the development of such disorders already developed in human behaviour as a disease. Furthermore, early screening of these diseases is essential for future diagnosis and preventing fatal consequences, since both are progressive illnesses. A modest attempt is made here to detect two such neurodegenerative disorders in humans using the way they type, formally known as Keystroke dynamics (KD). In this study, a bootstrapped-based homogeneous ensemble classification method has been proposed to address the uncertain performance and uneven distribution of classes for the detection of such medical conditions using users’ typing tendencies. For this purpose, two recent benchmark datasets were used for the validation and confirmation of operational improvements of the proposed method, which have been validated qualitatively and quantitatively. As a result, sensitivity/specificity of 0.82/0.78 in detecting PD and 0.98/0.98 in ES have been achieved, which is robust and accurate in a more realistic evaluation. The proposed framework explores the possibilities of implementing it in web-based systems, which has significant benefits. A better diagnosis, early detection at home

关键词： Neurodegenerative diseases

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PDIFT++: System-Wide Memory Tracking Using a Single-Process Memory Tracker

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SN computer Science 2024年第2期5卷 1-11页

作者： Kiperberg, Michael Zaidenberg, Nezer Department of Software Engineering Shamoon College of Engineering Beer-Sheva Israel Department of Computer Sciences Ariel University Ariel Israel

Information-flow tracking is useful for preventing malicious code execution and sensitive information leakage. Unfortunately, the performance penalty of the currently available solutions is too high for real-world applications. This paper presents PDIFT++, a hybrid system-wide dynamic information-flow tracker. PDIFT++ uses a hypervisor for coarse memory tracking and an emulator for fine memory tracking. The switching between the two modes allows PDIFT++ to achieve high performance without compromising the memory tracking precision. In addition, PDIFT++ provides system-wide tracking by monitoring system calls that can transmit information between two processes and between a process and a file system. The results show that PDIFT++ induces a performance penalty of 26% on average. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DIFT Emulator Hypervisor Virtualization

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Web Security:Emerging Threats and Defense

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computer Systems Science & engineering 2022年第3期40卷 1233-1248页

作者： Abdulwahed Awad Almutairi Shailendra Mishra Mohammed AlShehri Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Web applications have become a widely accepted method to support the internet for the past *** they have been successfully installed in the business activities and there is a requirement of advanced functionalities,the configuration is growing and becoming more *** growing demand and complexity also make these web applications a preferred target for intruders on the *** with the support of security specialists,they remain highly problematic for the complexity of penetration and code reviewing *** requires considering different testing patterns in both codes reviewing and penetration *** a result,the number of hacked websites is increasing day by *** of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding *** scanners for web applications can detect a few vulnerabilities in a dynamic *** are quite easy to use;however,these often miss out on some of the unique critical vulnerabilities in a different and static *** these are time-consuming,they can find complex vulnerabilities and improve developer knowledge in coding and best *** scanners choose both dynamic and static approaches,and the developers can select them based on their requirements and *** research explores and provides details of SQL injection,operating system command injection,path traversal,and cross-site scripting vulnerabilities through dynamic and static *** also examines various security measures in web applications and selected five tools based on their features for scanning PHP,and JAVA code focuses on SQL injection,cross-site scripting,Path Traversal,operating system ***,this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.

关键词： SQL injection attack cross-site scripting attack command injection attack path traversal attack

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