检索结果-南通市图书馆

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

Computer Systems Science & Engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar g.shanthi L.Raja gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Enhancing Wildlife Monitoring Through Infrared Imaging: A Deep Learning Approach for Improved Animal Region Segmentation and Species Identification

引用

Journal of Harbin Institute of Technology(New series) 2024年

作者： V Janani C shanthi Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies

Infrared imaging has proven to be a powerful tool in various fields， with particular significance in wildlife monitoring. In this paper， we present an extension of our previous research， focusing on advancing the segmentation of animal regions in enhanced infrared images and expanding the scope to include species identification. Our proposed methodology builds upon the success of the R-CNN (Region-based Convolutional Neural Network) object detection to improve the accuracy and robustness of animal region segmentation， while simultaneously extending our model’s capabilities to identify and classify the species within those regions. By fine-tuning the R-CNN model on a larger dataset that includes annotated infrared images and species labels， we enhance its capacity to not only accurately segment animal regions but also classify them into specific species categories. To assess the performance of our extended model， we employ a comprehensive set of evaluation metrics， including pixel-based metrics like Intersection over Union (IoU)， as well as species classification accuracy. Our results demonstrate significant improvements in both region segmentation accuracy and species identification compared to our previous work and existing methods. This research showcases the potential of deep learning techniques， combined with transfer learning， to advance wildlife monitoring applications using infrared imaging.

关键词：

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Contraceptive behaviors and awareness in an urban slum community of south India

引用

Asian pacific Journal of Reproduction 2024年第4期13卷 160-168页

作者： Monica Albert Sekhar shanthi Edward Angeline grace Sushmitha g Shirley Esther Priscilla Department of Community Medicine Sree Balaji Medical College and Hospital(SBMCH)ChennaiIndia

Objective:To assess the factors associated with knowledge,attitude and behavior of contraception use among urban slum population in ***:A cross-sectional study was conducted in the urban slum of Anakaputhur,*** couples between ages of 19-49 years and living in slum areas were taken up for the study by simple random *** women,postnatal and postmenopausal women were excluded from the *** collection was done using a pretested structured questionnaire focusing on details regarding contraception knowledge,attitude,and *** analysis was done using IBM SPSS version *** test like Chi square and odds ratio(OR)were used to identify association between knowledge,attitude,and behavior of contraception with the associated variables and enter method of logistic regression analysis was ***:Out of the 360 respondents,228 were females,with a mean age of(34±5)*** 43%demonstrated adequate knowledge of contraception use,and 91%were aware of at least one contraceptive ***,72%exhibited a positive attitude towards contraceptive ***,43%(155 individuals)did not utilize any contraceptive *** contraceptive users,intrauterine devices were the most commonly adopted,followed by barrier methods and female *** was significant association between knowledge of contraception and education(aOR 7.29,95%CI 2.93-18.10,Pge(aOR 2.04,95%CI 1.19-3.50,Pgarding attitude towards contraception use,education(aOR 8.54,95%CI 2.35-31.03,Pge(aOR 0.53,95%CI 0.28-0.99,Pgnificant ***:These findings underscore the imperative for enhanced d

关键词： Family planning Unmet need Sterilization Knowledge Practice Urban slum

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

引用

Computers, Materials & Continua 2023年第11期77卷 2341-2357页

作者： shanthi Perumalsamy Venkatesh Kaliyamurthy Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathurTamil Nadu603203India

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients *** pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient ***(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake *** designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)*** this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical ***,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical *** addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and *** spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN *** design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the *** presented model is tested on the UCI repository’s open-access drug reviews database.

关键词： Drug supply chain pharmaceutical industry deep learning blockchain hyper ledger fabric security drug recommendation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

引用

Computer Systems Science & Engineering 2023年第6期45卷 3231-3246页

作者： E.Susi A.P.shanthi Department of Computer Science&Engineering Anna UniversityChennai600025Tamil NaduIndia

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to *** growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real *** work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real *** framework also works on static data by converting them to data streams using the Kafka *** experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,*** the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

关键词： Sentiment drift sentiment classification big data BERT real time data streams twitter

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intrusion Detection System for Big Data Analytics in IoT Environment

引用

Computer Systems Science & Engineering 2022年第10期43卷 381-396页

作者： M.Anuradha g.Mani T.shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 957-969页

作者： T.shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6g improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

引用

Computer Systems Science & Engineering 2023年第3期44卷 2489-2500页

作者： Saranya Veerapaulraj M.Karthikeyan S.Sasipriya A.S.shanthi Tamilnadu College of Engineering CoimbatoreIndia Krishna College of Engineering CoimbatoreIndia

Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime *** WSN is a challenging issue faced by *** systems are very helpful in detecting interfering nodes in *** have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization *** metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their *** considers two herding behaviors to solve and enhance optimization *** on Elephant Herd Optimization,a trust-based security method is built in this *** proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting *** results have demonstrated the effectiveness of the proposed EHO based *** Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes *** the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s *** is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

关键词： Wireless sensor network low energy adaptive clustering hierarchy trust systems elephant herding optimizations algorithm trust-based elephant herd optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

引用

Computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam S.shanthi K.Kalirajan Sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar Sri Sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional glowworm Swarm Optimization(OgSO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OgSO algorithm for SqueezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Using Umbilical Cord Tissue to Identify Prenatal Exposure to Fentanyl and Other Commonly Abused Drugs

引用

Open Journal of Obstetrics and gynecology 2022年第5期12卷 434-442页

作者： shanthi Hariharan Donna Coy Joseph Jones United States Drug Testing Laboratories Des Plaines Illinois USA

Background: Prenatal exposure to fentanyl may lead to Neonatal Abstinence Syndrome (NAS), a constellation of symptoms observed when newborns begin withdrawing from addictive substances such as opioids. The use of umbilical cord tissue segments (UC) for newborn toxicology has been increasing due to its apparent long detection window, sensitivity, and ease of collection. However, very little has been reported in the literature concerning the prevalence of in utero exposure to fentanyl and co-exposure with other commonly abused substances. Specific aim: The specific aims of this retrospective study are twofold. We will report prevalence of neonatal exposure to fentanyl for a nationwide high-risk population using UC submitted to a national reference laboratory for routine forensic toxicology analysis and the co-exposure patterns observed for these fentanyl-exposed neonates. Methods: A secondary analysis was performed using historical data for UC received between January 1, 2020 and December 31, 2020 for routine forensic toxicology analysis. Results: During the study period, our laboratory received 23,104 UC for analysis and 9667 (41.8%) of those UC were positive for at least one drug. The prevalence of fentanyl detection was 1.9% (n = 429). Of these 429 specimens there were 407 UC where both fentanyl and norfentanyl were detected. There were 14 UC where only fentanyl was detected and 8 UC where only norfentanyl was detected. When detected, the median concentrations of fentanyl and norfentanyl were 4029 pg/g (IQR: 1696, 9230 pg/g) and 10,756 pg/mg (IQR: 3925, 25,288 pg/g), respectively. Of the 429 positive fentanyl and/or norfentanyl UC, 33 (7.7%) were only positive for fentanyl and/or norfentanyl. Of the 396 polypositive UC, morphine was the highest co-exposure with 243 UC (56.6%) being positive for both fentanyls and morphine. The second most prevalent co-exposure observed was methamphetamine/amphetamine (n = 173;40.3%) followed by cannabinoids (n = 113;26.3%) and benz

关键词： Fentanyl Norfentanyl Umbilical Cord Neonatal Abstinence Syndrome NAS Newborn Toxicology Prenatal Drug Exposure Polysubstance Abuse

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：