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Classification and Comprehension of Software Requirements Using Ensemble Learning

Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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The dark side of cyclooctatetraene(COT):Photophysics in the singlet states of“self-healing”dyes

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Chinese Chemical Letters 2024年第8期35卷 225-228页

作者： Supphachok Chanmungkalakul Syed Ali abbas Abedi Federico J.Hernández Jianwei Xu Xiaogang Liu Fluorescence Research Group Singapore University of Technology and DesignSingapore 487372Singapore Department of Chemistry University College LondonLondon WC1H 0AJUnited Kingdom Institute of Sustainability for Chemicals Energy and Environment(ISCE2)Agency for ScienceTechnology and Research(A^(∗)STAR)Jurong Island 627833Singapore

Cyclooctatetraene(COT)attachment to fluorophores(“self-healing”dyes)is known for quenching reac-tive triplet states via triplet-state energy transfer(TET),enhancing ***,COT’s impact on singlet states remains *** calculations reveal that COT induces energy transfer to dark states in deep blue dyes while promoting photoinduced electron transfer(PET)and intersystem crossing(ISC)in visible dyes,potentially compromising brightness and/or *** address this,we pro-pose the use of △E descriptor to optimize COT’s *** findings uncover COT’s multifaceted *** insights will guide the development of superior triplet state quenchers and photostable dyes.

关键词： Cyclooctatetraene Photostability Triplet state quencher Energy transfer Electron transfer

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Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs

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Energy Geoscience 2024年第4期5卷 291-305页

作者： Mohammed A.abbas Watheq J.Al-Mudhafar Aqsa Anees David A.Wood Basrah Oil Company BasrahIraq Institute of International Rivers and Eco-Security Yunnan UniversityKunming650500China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental ScienceYunnan UniversityKunming650500China DWA Energy Limited LincolnUK

Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern *** observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr *** latent variables include shale volume and water *** EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied *** optimized EM model developed successfully predicts the core-derived facies classification in two of the studied *** EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir *** results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were *** NMR results confirm the effectiveness and accuracy of the EM model in predicting *** utilization of the EM algorithm for electrofacies classification/cluster analysis is ***,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering *** EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not ***,once calibrated with core data in some wells,the model is suitable for

关键词： Cluster analysis Electrofacies classification Expectation-maximization(EM)algorithm Clastic reservoir Maximum likelihood estimate(MLE)

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Is there a need for universal double reflex testing of HBsAg-positive individuals for hepatitis D infection?

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World Journal of Hepatology 2024年第3期16卷 300-303页

作者： Zaigham abbas Minaam abbas Department of Hepatogastroenterology and Liver Transplantation Dr.Ziauddin University HospitalKarachi 75600SindhPakistan Department of Medicine University of CambridgeCambridge CB20SPUnited Kingdom

Hepatitis D virus(HDV)can infect HBsAg-positive individuals,causing rapid fibrosis progression,early decompensation,increased hepatocellular carcinoma risk,and higher mortality than hepatitis B virus(HBV)*** countries lack high-quality HDV prevalence data,and the collection techniques employed often bias published *** recent meta-analyses,HDV prevalence in HBsAg-positive patients reaches 5%-15%and is even significantly higher in endemic *** HBV vaccination programs were implemented,HDV prevalence has decreased among younger ***,owing to immigrant influx,it has increased in some Western *** current practice of HDV screening in HBsAg-positive individuals is stepwise,based on physician’s discretion,and limited to at-risk populations and may require numerous *** reflex testing,which includes anti-HDV testing in all HBsAg-positive individuals and then HDV RNA testing for anti-HDV-positive ones,is *** testing can identify more HDV infection cases and link identified patients to further care and ***,laboratory-based double reflex screening is less biased than physician-led ***,health-care providers should learn about reflex testing,and federal and provincial hepatitis control programs should implement laboratory-based double reflex testing to obtain reliable HDV prevalence *** test’s cost-effectiveness depends on the number of HBV-positive patients screened to identify one HDV-positive *** testing may be viable in areas with low HBsAg but high HDV ***,its economic impact on areas with low HDV prevalence needs further study.

关键词： Anti-hepatitis D virus antibody HBsAg Hepatitis D virus RNA Hepatitis B Hepatitis D Reflex testing

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Use of ketamine for severe asthma exacerbation in a pregnant patient with persistent bronchospasm:a case report

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Emergency and Critical Care Medicine 2024年第3期4卷 130-133页

作者： Melanie Johnson Malak abbas Jaber Abdo Mansour Mansour Jessica Jones Pharmacy Department Beaumont HealthDearbornMIUSA Emergency Center Beaumont HealthDearbornMIUSA

Background:Estimated 4%-8%of pregnancies are complicated by *** management in this population is critical to minimize *** presenting with asthma exacerbation are typically managed with standard bronchodilators and systemic ***,additional agents may be used in patients with refractory asthma *** has been used in refractory bronchospasm,although its efficacy in published literature is *** Presentation:We present a case of a pregnant patient with severe asthma exacerbation refractory to standard and salvage treatment who achieved termination of bronchospasm with ketamine ***:After receiving ketamine infusion for several days,the patient had improved air flow and achieved successful extubation,without experiencing rebound *** this individual clinical case alone cannot change guidelines or directives to use in refractory asthma exacerbations,it offers a possible treatment option to patients and providers in unusually severe cases with extenuating risk factors.

关键词： Asthma Bronchospasms Case Report Ketamine Pregnancy

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Uncovering the epidemiology of bladder cancer in the Arab world: A review of risk factors, molecular mechanisms, and clinical features

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Asian Journal of Urology 2024年第3期11卷 406-422页

作者： Noura F.abbas Marc R.Aoude Hampig R.Kourie Humaid OAl-Shamsi Department of Hematology-Oncology Hotel Dieu De France HospitalSaint Joseph University of BeirutRiad El SolhLebanon Department of Oncology Burjeel Cancer InstituteBurjeel Medical CityAbu DhabiUnited Arab Emirates Innovation and Research Center Burjeel Cancer InstituteBurjeel Medical CityAbu DhabiUnited Arab Emirates College of Medicine University of SharjahSharjahUnited Arab Emirates Emirates Oncology Society DubaiUnited Arab Emirates College of Medicine Gulf Medical UniversityAjmanUnited Arab Emirates

Objective:Bladder cancer(BC)is a significant public health concern in the Middle East and North Africa,but the epidemiology and clinicopathology of the disease and contributors to high mortality in this region remain poorly *** aim of this systematic review was to investigate the epidemiological features of BC in the Arab world and compare them to those in Western countries in order to improve the management of this ***:An extensive electronic search of the PubMed/PMC and Cochrane Library databases was conducted to identify all articles published until May 2022,following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.A total of 95 articles were included in the final analysis after title,abstract,and full-text screening,with additional data obtained from the GLOBOCAN and WHO 2020 databases.

关键词： Bladder cancer Epidemiology Risk factor Biomarker Schistosomiasis Arab world Urology

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： Syed Konain abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Analysis and Control of the Vehicle Roll Dynamics Using Sum of Squares Polynomial Approach

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Journal of Systems Science & Complexity 2024年

作者： IBEN AMMAR Imen DOUMIATI Moustapha TALJ Reine CHOKOR abbas MACHMOUM Mohamed ESEO-IREENA Institut de Recherche en énergie électrique de Nantes Atlantique IREENA UR 4642 37 Boulevard de l’Université-BP406 Sorbonne Universités Université de Technologie de Compiègne CNRS Institut de Recherche en énergie électrique de Nantes Atlantique IREENA UR 4642 37 Boulevard de l’Université-BP 406

The safety of vehicle travel relies on good stability performance, making vehicle motion control a vital technology in vehicles. This paper focuses on investigating the impact of roll control on vehicle performance, particularly in terms of avoiding rollover and ensuring lateral stability. By introducing a feedback roll moment, the roll motion can be effectively controlled. The paper considers two roll reference generators: A static one aimed at zero roll, and a dynamic one based on the vehicle's lateral acceleration. The static roll reference generator enhances stability by employing a fixed reference, particularly beneficial during routine driving conditions. In contrast, the dynamic roll reference generator continually adapts the roll angle reference in response to real-time vehicle dynamics and driving conditions. These proposed reference generators can be paired with varying suspension systems — Static reference could be achieved using semi-active suspensions, while the dynamic one is integrated into advanced active suspension systems, offering heightened adaptability and *** address the roll control objectives, this paper proposes a novel sum of squares(SOS) integral polynomial tracking control. The proposed controller satisfies control bounds and considers control constraints during the design phase. The effectiveness and robustness of the proposed control scheme are evaluated through numerical simulations using a full vehicle nonlinear model in Matlab/Simulink. The results of these simulations are compared to super-twisting sliding mode and Lyapunov-based controllers.

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