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A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： farhan Ullah Xuexia Zhang Mansoor.khan Muhammad Abid Abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance *** approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering *** approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Eraretrospective Analysis for research and Applications (MErrA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of root Mean Squared Error (rMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for rMSE for MErrAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and rMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

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Computers, Materials & Continua 2024年第9期80卷 3507-3521页

作者： farhan Amin Gyu Sang Choi School of Computer Science and Engineering Yeungnam UniversityGyeongsan38541Republic of Korea

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s *** call detail records(CDr)of a mobile network are used to identify the network’s efficacy and the mobile user’s *** is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom *** addition,CPS is used to provide valuable services in smart *** general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of *** this aspect,data storage,analysis,and processing are the key *** solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet *** proposed multilevel system has three levels and each level has a specific ***,raw Call Detail Data(CDr)was collected at the first ***,the data preprocessing,cleaning,and error removal operations were *** the second level,data processing,cleaning,reduction,integration,processing,and storage were ***,suggested internet activity record measures were *** proposed system initially constructs a graph and then performs network *** proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.

关键词： Cyber-physical social systems big data cyber-physical systems pervasive computing smart city big data management techniques

Autologous hematopoietic stem cell transplantation conditioning regimens and chimeric antigen receptor T cell therapy in various diseases

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World Journal of Transplantation 2024年第1期14卷 32-41页

作者： Shahzaib Maqbool Maryam farhan Baloch Muhammad Abdul Khaliq khan Azeem Khalid Kiran Naimat Department of Medicine Holy Family HospitalRawalpindi 46000Pakistan Department of Community Medicine Allama Iqbal Medical CollegeLahore 45000Pakistan Department of Community Dentistry Baqai Medical UniversityKarachi 43000Pakistan Department of Medicine Allama lqbal Medical CollegeLahore 45000Pakistan Department of MedicineLiaquat University of Medical and Health Sciences Karachi 43000Pakistan

Conditioning regimens employed in autologous stem cell transplantation have been proven useful in various hematological disorders and underlying malignancies;however,despite being efficacious in various instances,negative consequences have also been *** conditioning regimens were extracted from various literature searches from databases like PubMed,Google scholar,EMBASE,and *** regimens for each disease were compared by using various end points such as overall survival(OS),progression free survival(PFS),and leukemia free survival(LFS).Variables were presented on graphs and analyzed to conclude a more efficacious conditioning *** multiple myeloma,the most effective regimen was high dose melphalan(MEL)given at a dose of 200/mg/*** comparative results of acute myeloid leukemia were presented and the regimens that proved to be at an admirable position were busulfan(BU)+MEL regarding OS and BU+VP16 regarding *** case of acute lymphoblastic leukemia(ALL),BU,fludarabine,and etoposide(BuFluVP)conferred good disease control not only with a paramount improvement in survival rate but also low risk of ***,for ALL,chimeric antigen receptor(CAr)T cell therapy was preferred in the context of better OS and *** respect to Hodgkin’s lymphoma,mitoxantrone(MITO)/MEL overtook carmustine,VP16,cytarabine,and MEL in view of PFS and vice versa regarding ***-Hodgkin’s lymphoma patients were administered MITO(60 mg/m2)and MEL(180 mg/m2)which showed promising ***,amyloidosis was considered,and the regimen that proved to be competent was MEL 200(200 mg/m2).This review article demonstrates a comparison between various conditioning regimens employed in different diseases.

关键词： Conditioning regimens Multiple myeloma Lymphoma Hodgkin Non-Hodgkin Acute leukemia

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

rOBO-SPOT:Detecting robocalls by Understanding User Engagement and Connectivity Graph

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Big Data Mining and Analytics 2024年第2期7卷 340-356页

作者： Muhammad Ajmal Azad Junaid Arshad farhan riaz College of Comnputer Science Birmingham City UniversityBirminghamB55JUUK School of Computer Science University of LincolnLincolnLN67TSUK

robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory *** calls not only trick users into disclosing their private and financial information,but also affect their productivity through unwanted phone ringing.A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential ***,this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a *** trust score of the callers is then used along with machine learning models to classify them as legitimate or *** use a large anonymized dataset(call detailed records)from a large telecommunication provider containing more than 1 billion records collected over 10 *** have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error ***,the proposed features when used collectively achieve a true-positive rate of around 97%with a false-positive rate of less than 0.01%.

关键词： social network analysis reputation unwanted calls robo-callers telephone network Spam Over Internet Technology(SPIT)

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

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Structural Durability & Health Monitoring 2024年第6期18卷 763-783页

作者： Yadong Xu Weixing Hong Mohammad Noori Wael A.Altabey Ahmed Silik Nabeel S.D.farhan Nanjing Highway Development Center Changzhou211106China Nanjing Zhixing Information Technology Co. Ltd.Nanjing210000China Department of Mechanical Engineering California Polytechnic State UniversitySan Luis ObispoCA 93405USA Department of Mechanical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Civil Engineering Nyala UniversityP.O.Box 155NyalaSudan School of Civil Engineering University of LeedsLeedsLS29JTUK

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element *** ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge *** CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency *** integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge *** diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter *** approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

关键词： Structural Health Monitoring(SHM) bridges big model Convolutional Neural Network(CNN) Finite Element Method(FEM)

Overall Assessment of Heat Transfer for a rarefied Flow in a Microchannel with Obstacles Using Lattice Boltzmann Method

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工程与科学中的计算机建模(英文) 2024年第1期138卷 273-299页

作者： Siham Hammid Khatir Naima Omolayo M.Ikumapayi Cheikh Kezrane Abdelkrim Liazid Jihad Asad Mokdad Hayawi rahman farhan Lafta rashid Naseer Ali Hussien Younes Menni Laboratory of Development in Mechanics and Materials(LDMM) Zian Achour UniversityDjelfaPB 3117Algeria Department of Technology University Center Salhi Ahmed Naama(Ctr.Univ.Naama)P.O.Box 66Naama45000Algeria Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti360101Nigeria Departement of Physics Faculty of ScienceAbou Bekr Belkaid UniversityTlemcen13000Algeria Department of Physics Faculty of Applied SciencesPalestine Technical University-KadoorieTulkarmPalestine Aeronautical Technical Engineering Al-Farahidi UniversityBaghdad10011Iraq Mechanical Engineering Department University of KerbalaKarbala56001Iraq Information and Communication Technology research Group Scientific Research CenterAl-Ayen UniversityThi-QarIraq National University of Science and Technology Dhi QarIraq

The objective of this investigation is to assess the effect of obstacles on numerical heat transfer and fluid flow momentum in a rectangular microchannel(MC).Two distinct configurations were studied:one without obstacles and the other with alternating obstacles placed on the upper and lower *** research utilized the thermal lattice Boltzmann method(LBM),which solves the energy and momentum equations of fluids with the BGK approximation,implemented in a Python coding *** jump and slip velocity conditions were utilized in the simulation for the MC and extended to all obstacle *** study aims to analyze the rarefaction effect,with Knudsen numbers(Kn)of 0.012,0.02,and *** outcomes indicate that rarefaction has a significant impact on the velocity and temperature *** presence of nine obstacles led to slower fluid movement inside the microchannel MC,resulting in faster cooling at the *** MCs with obstacles,the rarefaction effect plays a crucial role in decreasing the Nusselt number(Nu)and skin friction coefficient(Cf).Furthermore,the study demonstrated that the obstacles played a crucial role in boosting fluid flow and heat transfer in the *** findings suggest that the examined configurations could have potential applications as cooling technologies in micro-electro-mechanical systems and microdevice applications.

关键词： Microfluid rarefied flow LBM microchannel Knudsen number numerical simulation

In silico identification of antiviral compounds for the treatment of chikungunya virus infection:qsar modelling and md simulation analysis

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Medicine in Novel Technology and Devices 2024年第2期22卷 102-114页

作者： Hayder M Abdulhamza Muthanna S.farhan Sara.S Hassan Hany Aqeel Al-Hussainy Amjad Ibrahim Oriabi Department of Pharmacy Kut University CollegeAlkutWasit52001Iraq Department of Pharmaceutical Chemistry College of PharmacyUniversity of BaghdadBaghdadIraq Al-Manara College for Medical Sciences Department of PharmacyIraq Department of Pharmacy Hilla University CollegeBabylonIraq Dr Hany Akeel Institute Iraqi Medical Research CenterBaghdad10001Iraq

Chikungunya virus(CHIKV),transmitted by arthropods,has gained global recognition for its impact on public *** has expanded globally,including Africa,Asia,and the Indian subcontinent,and has a helicase protein in its genome that is crucial for its ***,the study targeted the helicase protein of CHIKV with 745 antiviral compounds using an ML-based QSAr model and molecular *** binders(5279172,78161839,6474310,and 5330286)were selected for MD simulation based on the control(Silvestrol).All compounds had the highest binding scores,with 78161839 showing the most consistent rMSD and the least conformational variation,indicating high *** also showed the lowest binding free energy(ΔG¼31.31 kcal/mol),indicating energetically favourable *** and FEL also depicted the stable complex confirmation of the protein and 78161839 complex during the 100 ns ***,this study aimed to identify helicase function antiviral binders that could be experimentally tested for treating CHIKV.

关键词： Chikungunya virus Machine learning based QSAr Molecular dynamic solution Helicase

Plant Nitrogen Metabolism: Balancing resilience to Nutritional Stress andAbiotic Challenges

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Phyton-International Journal of Experimental Botany 2024年第3期93卷 581-609页

作者： Muhammad farhan Manda Sathish rafia Kiran Aroosa Mushtaq Alaa Baazeem Ammarah Hasnain Fahad Hakim Syed Atif Hasan Naqvi Mustansar Mubeen Yasir Iftikhar Aqleem Abbas Muhammad Zeeshan Hassan Mahmoud Moustafa Department of Plant Pathology Faculty of Agricultural Sciences&TechnologyBahauddin Zakariya UniversityMultan60800Pakistan Centro de Investigación de Estudios Avanzados del Maule(CIEAM) Vicerrectoría de Investigación y PostgradoUniversidad Católica del MauleTalca3460000Chile College of Food Science and Nutritional Engineering China Agricultural UniversityBeijingChina Department of Biology College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Biotechnology Faculty of Biological SciencesLahore University of Biological and Applied SciencesLahorePakistan Department of Plant Pathology College of AgricultureUniversity of SargodhaSargodha40100Pakistan Department of Agriculture and Food Technology Karakorum International UniversityGilgitPakistan Department of Biology Faculty of ScienceKing Khalid UniversityAbhaSaudi Arabia

Plant growth and resilience to abiotic stresses,such as soil salinity and drought,depend intricately on nitrogen *** review explores nitrogen’s regulatory role in plant responses to these challenges,unveiling a dynamic interplay between nitrogen availability and abiotic *** the context of soil salinity,a nuanced rela-tionship emerges,featuring both antagonistic and synergistic interactions between salinity and nitrogen ***-induced chlorophyll depletion in plants can be alleviated by optimal nitrogen supplementation;however,excessive nitrogen can exacerbate salinity *** delve into the complexities of this interaction and its agri-cultural ***,a vital element within essential plant structures like chloroplasts,elicits diverse responses based on its *** review comprehensively examines manifestations of nitrogen deﬁciency and toxicity across various crop types,including cereals,vegetables,legumes,and ***,we explore the broader consequences of nitrogen products,such as N_(2)O,NO_(2),and ammonia,on human ***-ing the intricate relationship between nitrogen and salinity,especially chloride accumulation in nitrate-fed plants and sodium buildup in ammonium-fed plants,is pivotal for optimizing crop nitrogen ***,prudent nitrogen use is essential,as overapplication can exacerbate nitrogen-related *** Use Efﬁ-ciency(NUE)is of paramount importance in addressing salinity challenges and enhancing sustainable crop *** this goal requires advancements in crop varieties with efﬁcient nitrogen utilization,pre-cise timing and placement of nitrogen fertilizer application,and thoughtful nitrogen source selection to mitigate losses,particularly urea-based fertilizer *** review article delves into the multifaceted world of plant nitrogen metabolism and its pivotal role in enabling plant resilience to nutritional stress and abiotic chall

关键词： Synthetic nitrogen nitrogen signaling sustainable agriculture eutrophication ammonium nitrate