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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar pradip kumar sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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Computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu pradip kumar sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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Computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba pradip kumar sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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Computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang pradip kumar sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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Computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja pradip kumar sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

Appraisal of pollution and health risks associated with coal mine contaminated soil using multimodal statistical and Fuzzy-TOPSIS approaches

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 101-115页

作者： Sumit kumar Sonali Banerjee Saibal Ghosh Santanu Majumder Jajati Mandal Pankaj kumar Roy pradip Bhattacharyya Agricultural and Ecological Research Unit Indian Statistical InstituteGiridih815301India Department of Life and Environmental Sciences Bournemouth University(Talbot Campus)Fern BarrowPooleBH12 5BBUK School of Sciences Engineering & EnvironmentUniversity of SalfordSalfordM5 4QJUK School of Water Resource Engineering Jadavpur UniversityKolkata700032India School of Environmental Studies Jadavpur UniversityKolkata700032India

The present study assesses the concentration,probabilistic risk,source classification,and dietary risk arising from heavy metal(HMs)pollution in agricultural soils affected by coal mining in eastern part of *** of soil and rice plant indicated significantly elevated levels of HMs beyond the permissible limit in the contaminated zones(zone 1:PbSoil:108.24±72.97,CuSoil:57.26±23.91,CdSoil:8.44±2.76,CrSoil:180.05±46.90,NiSoil:70.79±25.06 mg/kg;PbGrain:0.96±0.8,CuGrain:8.6±5.1,CdGrain:0.65±0.42,CrGrain:4.78±1.89,NiGrain:11.74±4.38 mg/*** 2:PbSoil:139.56±69.46,CuSoil:69.89±19.86,CdSoil:8.95±2.57,CrSoil:245.46±70.66,NiSoil:95.46±22.89 mg/kg;PbGrain:1.27±0.84,CuGrain:7.9±4.57,CdGrain:0.76±0.43,CrGrain:8.6±1.58,NiGrain:11.50±2.46 mg/kg)compared to the uncontaminated zone(zone 3).Carcinogenic and non-carcinogenic health risks were computed based on the HMs concentration in the soil and rice grain,with Pb,Cr,and Ni identified as posing a high risk to human *** Carlo simulation,the solubility-free ion activity model(FIAM),and severity adjusted margin of exposure(SAMOE)were employed to predict health *** hazard quotient(HQ)values for Ni,Cr,Cd,and Pb were>1,indicating a significant non-carcinogenic ***(risk thermometer)results for contaminated zones ranged from low to moderate risk(CrSAMOE:0.05,and NiSAMOE:0.03).Fuzzy-TOPSIS and variable importance plots(from random forest)showed that Ni and Cr were mostly responsible for the toxicity in the rice plant,respectively.A self-organizing map for source classification revealed common origin for the studied HMs with zone 2 exhibiting the highest *** positive matrix factorization model for the source apportionment identified coal mining and transportation as the predominant sources of *** distribution analysis indicated higher contamination near mining sites as compared to distant sampling ***,this study will aid environmental scientists and policymakers controllin

关键词： Coal mine Free ion activity model Monto Carlo Simulation Pollution and Health risk Fuzzy-TOPSIS

Effect of cooling media on bead geometry,microstructure,and mechanical properties of wire arc additive manufactured IN718 alloy

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Advances in Manufacturing 2024年第1期12卷 124-149页

作者： Parveen kumar Satish kumar sharma Ratnesh kumar Raj Singh Department of Mechanical Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147004India

This work aims to present and explore thermal management techniques for the wire arc additive manufacturing(WAAM)of IN718 *** heat can be mitigated via air or water *** this study,the material was deposited under four different heat-input conditions with air or water *** air cooling,the layer is deposited in a normal atmospheric air environment,whereas with water cooling,the material is deposited inside a water tank by varying the water *** validate the air and water cooling thermal management techniques,IN718 single-pass and multilayer linear walls were deposited using the bidirectional gas metal arc welding based WAAM setup under four different heat input *** the deposition of single layers,the temperature profiles were recorded,and the geometric and microstructural features were *** multilayer wall structures,the mechanical properties(hardness,tensile strength,and elongation)were determined and assessed using the corresponding microstructural features explored through scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and electron backscatter diffraction(EBSD)*** microstructure observed through SEM analysis in the building direction was found to be nonhomogenous compared with that in the deposition ***,water cooling was found to govern bead characteristics,such as wall width and *** grain size and anisotropy of the mechanical properties also decreased in the water-cooled ***,water cooling is an economical and efficient method to mitigate excessive heat accumulation in WAAM-deposited IN718.

关键词： Air cooling Water cooling IN718 Bead characteristics Microstructure Electron backscatter diffraction(EBSD)

Wharton’s jelly mesenchymal stem cells: Future regenerative medicine for clinical applications in mitigation of radiation injury

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World Journal of Stem Cells 2024年第7期16卷 742-759页

作者： Prashasti sharma Dharmendra kumar Maurya Life Sciences Homi Bhabha National InstituteMumbai 400094MaharashtraIndia Radiation Biology&Health Sciences Division Bhabha Atomic Research CentreMumbai 400085MaharashtraIndia

Wharton’s jelly mesenchymal stem cells(WJ-MSCs)are gaining significant attention in regenerative medicine for their potential to treat degenerative diseases and mitigate radiation ***-MSCs are more naïve and have a better safety profile,making them suitable for both autologous and allogeneic *** review highlights the regenerative potential of WJ-MSCs and their clinical applications in mitigating various types of radiation *** this review,we will also describe why WJ-MSCs will become one of the most probable stem cells for future regenerative medicine along with a balanced view on their strengths and ***,the most updated literature related to both preclinical and clinical usage of WJ-MSCs for their potential application in the regeneration of tissues and organs will also be compiled.

关键词： Stem cells Wharton’s jelly mesenchymal stem cells Radiotherapy Xerostomia Lung fibrosis

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

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Cybersecurity 2024年第2期7卷 81-95页

作者： Vaishnavi Sivagaminathan Manmohan sharma Santosh kumar Henge Lovely Professional University PhagwaraIndia

Network Intrusion Detection Systems(NIDS)are utilized to find hostile network *** can be accom-plished by looking at traffic network activity,but it takes a lot of *** NIDS heavily utilizes approaches for data extraction and machine learning to find *** terms of feature selection,NIDS is far more *** is accurate since anomaly identification uses a number of time-consuming *** of this,the feature selec-tion method influences how long it takes to analyze movement patterns and how clear it *** goal of the study is to provide NIDS with an attribute selection *** has been used for that *** Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network,allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being *** the research,datasets were produced utilising both a network infrastructure and a simulation *** is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated ***,a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot,has been built and communication packets are being recorded using the Wireshark *** train several machine learning models,all the datasets that were gatheredcre-ated datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle-were ***,PsO,which is an optimization method,has been used with these ML algorithms for feature *** the research,KNN,decision trees,and ANN have all been combined with PSO for a specific case *** it was found demonstrated the classification methods PSO+ANN outperformed PSO+KNN and PSO+DT in this case study.

关键词： Network intrusion detection systems(NIDS) Cisco packet tracer Wireshark tool Machine learning PSO Cybersecurity Optimization