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Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

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Energy and AI 2024年第2期16卷 306-315页

作者： Waqar muhammad Ashraf Vivek Dua The Sargent Centre for Process Systems Engineering Department of Chemical EngineeringUniversity College LondonTorrington PlaceLondon WC1E 7JEUK

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

关键词： Explainable AI Model interpretation Scientific machine learning Artificial neural network Loss function

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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)

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A Hybrid SIR-Fuzzy Model for Epidemic Dynamics:A Numerical Study

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3417-3434页

作者： muhammad Shoaib Arif Kamaleldin Abodayeh Yasir Nawaz Department of Mathematics and Sciences College of Humanities and SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Mathematics Air UniversityIslamabad44000Pakistan

This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIR-Fuzzy *** integrating the traditional Susceptible-Infectious-Recovered(SIR)modelwith fuzzy logic,ourmethod effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model *** main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical *** main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership *** enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical *** investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic *** order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical *** convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler *** numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease ***,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological ***,there is a direct relationship between higher Holling type rate values and a decrease in the number of individuals who are infected,as well as an increase in the number of susceptible *** correlation offers a significant understanding of how many elements affect the con

关键词： Fuzzy-based model sensitivity equilibriumpoints proposed numerical scheme convergence and stability analysis

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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq Salman Afsar Awan Fahad Ahmad muhammad Azam Zia muhammad Zafar Iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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Current and further trajectories in designing functional materials for solid oxide electrochemical cells:A review of other reviews

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Journal of Energy Chemistry 2024年第7期94卷 302-331页

作者： Stanislav Baratov Elena Filonova Anastasiya Ivanova muhammad Bilal Hanif Muneeb Irshad muhammad Zubair Khan Martin Motola Sajid Rauf Dmitry Medvedev Hydrogen Energy Laboratory Ural Federal University620002 EkaterinburgRussia Institute of Natural Sciences and Mathematics Ural Federal University620002 EkaterinburgRussia Department of Inorganic Chemistry Faculty of Natural SciencesComenius University BratislavaIlkovicova 6Mlynska Dolina84215 BratislavaSlovakia State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and EngineeringXi'an Jiaotong UniversityXi'an 710049ShaanxiChina Department of Physics University of Engineering and TechnologyLahore 54890Pakistan Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology Mang Haripur 22621Khyber PakhtunkhwaPakistan College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518000GuangdongChina Laboratory of Electrochemical Devices Based on Solid Oxide Proton Electrolytes Institute of High Temperature Electrochemistry620066 EkaterinburgRussia

Complex oxides are an important class of materials with enormous potential for electrochemical *** on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting *** properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas *** literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since *** manual analysis of such a large volume of material is ***,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining *** perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical ***,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.

关键词： SOFCs SOECs PCFCS Electrochemistry Energy conversion Hydrogen energy,Carbon neutrality

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Negative Poisson’s ratio locally resonant seismic metamaterials vibration isolation barrier

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Acta Mechanica Sinica 2024年第8期40卷 239-252页

作者： Haibin Ding Nianyong Huang muhammad Changjie Xu Lihong Tonog State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Nanchang330013China Institute of Geotechnical Engineering School of Civil Engineering and ArchitectureEast China Jiaotong UniversityNanchang330013China Department of Mechanical Manufacturing and Biomedical EngineeringTrinity College DublinDublinD02PN40Ireland Department of Mechanical Engineering University of GalwayUniversity RoadGalwayH91 HX31Ireland

In recent decades,the application of seismic metamaterials to protect civil infrastructures being free of the damage of earthquakes has been attracting extensive ***,the proposed locally resonant seismic metamaterials provide the probability of isolating the low-frequency seismic wave using a small-size isolation ***,in previous studies,the energy absorption properties of locally resonant seismic metamaterials remain one of the least understood aspects of *** from the fascinating energy absorption characteristic of negative Poisson ratio(NPR)metamaterial,we creatively design a new seismic metamaterial structure by assembling the locally resonant seismic metamaterial and NPR metamaterial,to isolate seismic *** sound cone technique combining the transmission spectrum is employed to identify the surface wave from the hybrid *** generation mechanism of frequency bandgap and the isolation effectiveness of the proposed seismic metamaterial are discussed in *** results indicate that the generation of ultra-low and ultra-wide frequency bandgap with the range of 0.65 Hz–18.9 Hz is attributed to the locally resonant and energy absorption of the proposed seismic metamaterial structure and the excellent isolation effect is achieved by transforming the surface wave into the bulk *** frequency bandgap narrows as the distance increases between each *** addition,the mechanical properties of the NPR bearing,such as the Poisson ratio,mass density,and elastic modulus,have remarkable impact on the frequency bandgap,especially on the upper bound *** practical engineering,the NPR bearing with a low Poisson ratio,small mass density,and high elastic modulus is suggested for the design of the NPR locally resonant seismic metamaterial *** domain analysis for the practical seismic wave verifies that the proposed seismic metamaterial has a promising application in isolating ultra-low and ultra-wi

关键词： Seismic metamaterials Locally resonant Negative Poisson ratio(NPR) Seismic waves Frequency bandgap Vibration isolation barrier

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Seasonal variations in dissolved organic matter concentration and composition in an outdoor system for bank filtration simulation

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Journal of Environmental Sciences 2024年第1期135卷 252-261页

作者： muhammad Zeeshan Omamah Ali Shamas Tabraiz Aki Sebastian Ruhl German Environment Agency Section II 3.3Schichauweg 5812307BerlinGermany Technische Universitat Berlin Water TreatmentKF4Str.des 17.Juni 13510623BerlinGermany Natural and Applied Sciences Section School of Psychology and Life SciencesCanterbury Christ Church UniversityCanterburyCT11QUUK

Dissolved organic matter(DOM)in surface waters can vary markedly in character depending on seasonal variations such as rainfall intensity,UV radiations and *** in DOM as well as temperature and rainfall intensity over the year can affect the biochemical processes occurring in bank filtration(BF).Identification and characterization of DOM in the surface water could help to optimize the water treatment and provide stable and safe drinking *** study investigated year-long variations of DOM concentrations and compositions in a surface water of a circulated outdoor pond(research facility)connected to a BF *** was dominated by humic substances and a changing pattern of DOM in surface water was observed throughout the year.A significant increase of DOM(~38%)in surface water was noted in August compared to *** fluorescent DOM showed that DOM in summer was enriched with the degradable fraction whilst non-degradable fraction was dominated in winter.A constant(1.7±0.1 mg/L)effluent DOM was recirculated in the system throughout the *** removal through BF varied between 4%to 39%and was achieved within a few meters after infiltration and significantly correlated with influent DOM concentration(R^(2)=0.82,p0.05)change in the removal of DOM was observed in two subsurface layers(upper and lower).This study highlights the presence of a constant non-degradable DOM in the bank filtrate,which was not affected by temperature,redox conditions and UV radiations.

关键词： Bank filtration Dissolved organic matter Dissolved oxygen Fluorescence spectroscopy Excitation-emission matrix

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Study of bending angle and shadow in a new Schwarzschild-like black hole affected by plasma and non-plasma mediums

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Chinese Physics C 2024年第5期48卷 309-318页

作者： Riasat Ali Tiecheng Xia muhammad Awais Rimsha Babar Department of Mathematics Newtouch Center for Mathematics of Shanghai UniversityShanghai 200444China Department of Mathematics University of Engineering and Technology Lahore-54890Pakistan Department of Mathematics GC University Faisalabad Layyah CampusLayyah-31200Pakistan

In this study,we analyze the models of the deflection angle of a new Schwarzschild-like black hole(BH)and employ the optical metric of the *** achieve this,we use the Gaussian curvature of the optical metric and the Gauss-Bonnet theorem,known as the Gibbons-Werner technique,to determine the deflection ***,we examine the deflection angle in the presence of a plasma medium and the effect of the plasma medium on the deflection *** deflection angle of the BH solution in the gauged super-gravity is computed using the Keeton-Petters *** the ray-tracing technique,we investigate the shadow of the corresponding BH and analyze the plots of the deflection angle and shadow to verify the influence of the plasma and algebraic thermodynamic parameters on the deflection angle and shadow.

关键词： new Schwarzschild-like black hole Gibbons-Werner and Keeton-Petters techniques deflection angle ray-tracing approach shadow

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif muhammad Ali Mughal muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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