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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2689-2706页

作者： Brij B.Gupta Akshat Gaurav razaz waheeb attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia School of Science and Technology Hong Kong Metropolitan University(HKMU)Hong KongChina

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall *** balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification *** proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly *** model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and *** study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

关键词： LSTM neural networks anomaly detection smart home health-care elderly fall prevention

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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Computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav razaz waheeb attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Risk Evaluation in Blood Donation Using Failure Mode and Effective Analysis

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Journal of Biomedical Science and Engineering 2021年第4期14卷 177-184页

作者： razaz M. Salih Eltahir M. Husein Elias Hassan Department of Biomedical Engineering Sudan University of Science and Technology Khartoum Sudan

The recognition and management of risk in donation process and blood product is critical to ensure donor and patient safety. To achieve this goal, the failure mode and effects analysis (FMEA) is a convenient method;moreover it was used to prevent the occurrence of adverse events and look at what could go strong at each step. This study aimed to utilize FMEA in central blood bank in Khartoum to evaluate the potential risk and adverse event that may occur during the donation process. According to the severity, occurrence and the detection of each failure mode, the risk priority number (RPN) was calculated to determine which of the failures should take priority to find a solution and applying corrective action to reduce the failure risk. The statistical package for social sciences (SPSS) version 11 was used as descriptive and analytical statistics tool. The FMEA technique provides a systematic method for finding vulnerabilities in a process before they result in an error, and in this study a satisfactory outcome was reached.

关键词： Donation Blood Product Donor Failure Mode and Effective Analysis Adverse Event Risk Priority Number

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母亲肥胖与子代心血管疾病的风险:一项基于人群的队列研究和同胞对照研究

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中华预防医学杂志 2020年第12期54卷 1395-1395页

作者： razaz N Villamor E Muraca GM 马传伟(编译) 杨丽丽(编译) 不详山东大学齐鲁医学院公共卫生学院

母亲超重肥胖可能会增加子代肥胖、心血管疾病和代谢疾病的风险。该研究探究了母亲超重肥胖的严重程度与子代心血管疾病风险之间的关联。在这项基于人群的队列研究中,作者使用了瑞典医疗出生登记册中记录的单胎活产婴儿的数据。根据首... 详细信息

母亲超重肥胖可能会增加子代肥胖、心血管疾病和代谢疾病的风险。该研究探究了母亲超重肥胖的严重程度与子代心血管疾病风险之间的关联。在这项基于人群的队列研究中,作者使用了瑞典医疗出生登记册中记录的单胎活产婴儿的数据。根据首次产前就诊时孕妇自我报告的身高和体重计算了孕早期BMI。使用多变量Cox比例风险回归来估计调整后的风险比(HRs)和95%置信区间(95%CI)。

关键词：心血管疾病队列研究超重肥胖代谢疾病置信区间自我报告孕早期同胞对照

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Risk Evaluation in Blood Donation Using Failure Mode and Effective Analysis

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Journal of Biomedical Science and Engineering（生物医学工程） 2021年第4期

作者： razaz M. Salih Eltahir M. Husein Elias Hassan

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