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A hybrid system to predict brain stroke using a combined feature selection and classifier

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Intelligent Medicine 2024年第2期4卷 75-82页

作者： Priyanka Bathla Rajneesh kumar Department of Computer Science&Engineering Maharishi Markandeshwar UniversityMullanaAmbalaHaryanaIndia

Background Brain stroke is a serious health issue that requires timely and accurate prediction for effective treatment and *** study described a hybrid system that used the best feature selection method and classifier to predict brain *** The Stroke Prediction Dataset from Kaggle was used for this *** minority over-sampling technique(SMOTE)analysis was used to accomplish class ***,sensitivity,specificity,precision,and the F-Measure were the main performance parameters considered for *** determine the best combination for predicting brain stroke,the performance of five classifiers,Naïve Bayes(NB),support vector machine(SVM),random forest(RF),adaptive boosting(Adaboost),and extreme gradient boosting(XGBoost),was compared along with three feature selection techniques,mutual information(MI),Pearson correlation(PC),and feature importance(FI).The performance parameters were assessed using k-fold *** The hybrid system proposed in this study identified a reduced set of features that were able to effectively predict brain *** provided a feature reduction ratio of 36.3%.The most successful hybrid system for predicting brain stroke used FI as the feature selection technique and RF as the classifier,achieving an accuracy of 97.17%.Conclusion The proposed system predicted brain stroke with high *** findings could be used to inform the early detection and prevention of brain stroke,allowing healthcare professionals to provide timely and targeted care to at-risk patients.

关键词： Machine learning Naive Bayes Extreme gradient boosting Support vector machine Adaptive boosting Random forest

Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

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Computers, Materials & Continua 2024年第7期80卷 695-713页

作者： G.Sunil Santhosh kumar M.Rudra kumar Department of CSE Jawaharlal Nehru Technological UniversityAnantapur515002India Department of CSE Marri Laxman Reddy Institute of Technology and ManagementHyderabad500043India Department of Information Technology Mahatma Gandhi Institute of TechnologyHyderabad500075India

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and *** framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern *** goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied ***,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the *** sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant *** core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature *** article presents a detailed overview of the mathematical model and algorithmic steps behind this ***,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data *** employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation *** research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.

关键词： XGBoost business rule engine machine learning categorical query language humanitarian computing environment

DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of Computer Science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav kumar Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan kumar Neeraj kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

Highly Efficient Back‑End‑of‑Line Compatible Flexible Si‑Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

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Nano-Micro Letters 2024年第11期16卷 323-339页

作者： Dayanand kumar Hanrui Li Dhananjay D.Kumbhar Manoj kumar Rajbhar Uttam kumar Das Abdul Momin Syed Georgian Melinte Nazek El‑Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer EngineeringComputer Electrical Mathematical Science and EngineeringKing Abdullah University of Science and Technology(KAUST)23955‑6900 ThuwalSaudi Arabia

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced *** 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy ***,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia *** experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,*** optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing ***,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision *** display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory *** proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

关键词： Neuromorphic computing Electrophysiological signal Artificial vision system Image recognition Memristor

Application of Response Surface Methodology for Analysing and Optimizing the Finned Solar Air Heater

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Journal of Thermal Science 2024年第3期33卷 985-1009页

作者： Vineet SINGH Vinod Singh YADAV Vaibhav TRIVEDI Manoj kumar Niraj kumar Department of Mechanical Engineering School of Engineering&TechnologyIFTM UniversityMoradabadUttar PradeshIndia Department of Mechanical Engineering NITUttarakhandPauriUttarakhandIndia Department of Electronics and Communication Engineering Jaypee Institute of Information TechnologyNoidaUttar Pradesh201309India

In this research paper,a solar air heater with triangular fins has been experimentally analysed and ***,an experimental set-up of a solar air heater having triangular fins has been developed at the location of 28.10°N,78.23°*** heat transfer rate through fins and fins efficiency has been determined by the Finite Difference Method model *** experimental data and modeled data of response parameters have been optimized in MINITAB-17 software by the Response Surface Methodology *** creating the response surface design,three input parameters have been selected namely solar intensity,Reynolds number,and fin base-to-height *** range of solar intensity,Reynolds number,and fin base-to-height ratio is 600 to 1000W/m^(2),4000 to 6000,and 0.4 to 0.8 *** response surface design has been analyzed by calculating the outlet temperature,friction factor,Nusselt number,fin efficiency,thermal performance factor,and exergy *** optimum settings of input parameters:solar intensity is 1000 W/m^(2);Reynolds number is 4969.7,and the fin base to height ratio is 0.6060,on which these response:namely outlet temperature of 92.531℃,friction factor of 0.2350,Nusselt number of 127.761,thermal efficiency of 50.836%,thermal performance factor of 1.4947,and exergy efficiency of 8.762%.

关键词： solar air heater optimization fins exergy response surface methodology air

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

Traffic Engineering and Quality of Service in Hybrid Software Defined Networks

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China Communications 2024年第2期21卷 96-121页

作者： Samiullah Mehraban Rajesh kumar Yadav IOT Lab Department of Computer Science&EngineeringDelhi Technological UniversityDelhi 110042India

For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.

关键词： DRL hSDN QoE QoS SDN TE

Assessment of scale interactions associated with wake meandering using bispectral analysis methodologies

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 65-75页

作者： Dinesh kumar Kinjangi Daniel Foti Department of Mechanical Engineering University of MemphisMemphisTennesseeUSA

Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon,a coherent,dynamic,turbine-scale oscillation of the far *** interactions,the mechanism of energy transfers between scales,manifest as triples of wavenumbers or frequencies and can be characterized through bispectral *** bispectrum,which correlates the two frequencies to their sum,is calculated by two recently developed multi-dimensional modal decomposition methods:scale-specific energy transfer method and bispectral mode ***-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity *** bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering *** coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.

关键词： Wind turbine wake Mode decomposition Bispectral analysis