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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

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

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

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

Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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Computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan muhammad Ramzan Mudassar Raza muhammad Attique Khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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Computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： muhammad Ayoub Kamal muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

Phonon Thermal Transport at Interfaces of a Graphene/Vertically Aligned Carbon Nanotubes/Hexagonal Boron Nitride Sandwiched Heterostructure

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Chinese Physics Letters 2024年第1期41卷 57-62页

作者：李檬璘 muhammad Asif Shakoori 王瑞鹏李海鹏 School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China

Molecular dynamics simulation is used to calculate the interfacial thermal resistance of a graphene/carbon nanotubes/hexagonal boron nitride(Gr/CNTs/hBN)sandwiched heterostructure,in which vertically aligned carbon nanotube(VACNT)arrays are covalently bonded to graphene and hexagonal boron nitride *** find that the interfacial thermal resistance(ITR)of the Gr/VACNT/hBN sandwiched heterostructure is one to two orders of magnitude smaller than the ITR of a Gr/hBN van der Waals heterostructure with the same plane *** is observed that covalent bonding effectively enhances the phonon coupling between Gr and hBN layers,resulting in an increase in the overlap factor of phonon density of states between Gr and hBN,thus reducing the ITR of Gr and *** addition,the chirality,size(diameter and length),and packing density of sandwich-layer VACNTs have an important influence on the ITR of the *** the same CNT diameter and length,the ITR of the sandwiched heterostructure with armchair-shaped VACNTs is higher than that of the sandwiched heterostructure with zigzag-shaped VACNTs due to the different chemical bonding of chiral CNTs with Gr and *** the armchair-shaped CNT diameter increases or the length decreases,the ITR of the sandwiched heterostructure tends to ***,the increase in the VACNT packing density also leads to a continuous decrease in the ITR of the sandwiched heterostructure,attributed to the extremely high intrinsic thermal conductivity of CNTs and the increase of out-of-plane heat transfer *** work may be helpful for understanding the mechanism for ITR in multilayer vertical heterostructures,and provides theoretical guidance for a new strategy to regulate the interlayer thermal resistance of heterostructures by optimizing the design of sandwich layer thermal interface materials.

关键词： sandwich shaped packing

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 RAljohani Ibrahim AHameed 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

Recent Advances in Deuteration Reactions

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Chinese Journal of Chemistry 2024年第10期42卷 1145-1156页

作者： Hao Li muhammad Shabbir Wu Li Aiwen Lei Institutefor Advanced Studies(IAS) Collegeof Chemistry and Molecular SciencesWuhan UniversityWuhanHubei 430072China

The deuteration of organic compounds has attracted more attentions in recent years for the potential applications in new drug discovery and synthetic *** this purpose,many efficient deuterium labeling methodologies have been developed,including hydrogen isotope exchange(HIE),reductive deuteration,and dehalogenative deuteration that allow for the synthesis of selectively deuterated *** the last few years,great breakthroughs in selective isotope labeling have been achieved and the interest in new methodologies for the deuteration of organic molecules is *** this review,we summarized the recent developments in the selective deuteration of organic molecules since *** types of key processes in deuterium incorporation reactions,including H/D exchange,reductive deuteration and dehalogenative deuteration,are introduced and discussed.

关键词： Deuterated compounds H/D exchange Reductive deuterations Dehalogenative deuterations Deuterium C-Hactivation Synthetic methods