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Applied Intelligence and Sustainab.e Computing (ICAISC), International Conference on

作者： Avr Mayuri V Rashmi b.ppaditya Das Amitab.a Mandal S. Neelakandan School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Information Technology Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of computer science & engineering Dr. B. C. Roy engineering college Durgapur India Department of computer science & engineering R.M.K Engineering College Kavaraipettai India

A country’s economy is influenced b. a wide range of factors, with the agriculture, service, and food processing sectors. In our country, agriculture is one of the most significant economic sectors. The agriculture sector, however, has several difficulties, such as the different climatic conditions in other regions of our country that give rise to various infectious diseases in different plant species, resulting in a significant drop in crop productivity and income generation. Early and accurate plant disease diagnosis is crucial in improving the foundation for harvest formation. The traditional method of plant disease detection through eye ob.ervation is unsuccessful since it necessitates knowledge. Machine learning (ML) techniques are b.ing utilized to identify and categorize plant ailments to address this issue. In this work, plant diseases can b. classified using machine learning methods. These involve classifier training, classification, and stages like dataset construction, image loading, pre-processing, segmentation, and feature extraction. An artificial neural network is initially used to distinguish and find leaves in a challenging environment. The Chan-Vese (CV) technique states that symptoms can b. found in images that have b.en segmented depending on the outcomes of the ANN algorithm. A transfer learning model trained on a dataset of ill leaves on an essential b.ckground is then given the segmented leaves. The model is also checked for rust, b.ack rot, and b.cterial plaque conditions. The findings demonstrate that the methodology is more accurate—98.22%—than the previous approach, minimizing the adverse effects of illness on agricultural productivity and promoting long-term agrarian growth. Utilizing data from PlantVillage, the model is trained and validated. The dataset includes images of plant diseases afflicting pepper, tomato, and potato plants. Fifteen different plant disease categories are used to train and evaluate the classifier. The results of t

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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： I. Sudha Cecil Donald S. Navya G. Nithya Manivannan b.lamurugan S. Saravanan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences – SIMATS Chennai India Department of Computer Science CHRIST (Deemed to be University) Bengaluru India Department of Computer Science Engineering Raghu Engineering College Visakhapatnam India Department of CSE V. S. B College of Engineering Technical Campus Coimbatore India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropb.x, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-b.sed environment. These providers also grant users the ab.lity to access and use these applications from any location worldwide. The sub.ect of security poses significant challenges in contemporary times. The primary ob.ective of cloud security is to estab.ish a sense of confidence b.tween cloud service providers and data owners inside the cloud environment. The cloud service provider is responsib.e for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The ob.ective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and rob.st encryption system due to its ab.lity to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation.

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Fuzzy Logic on Long Short-Term Memory for Smart Person-Identification System through Electroencephalogram 6

Fuzzy Logic on Long Short-Term Memory for Smart Person-Ident...

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6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Adhikary, Sub.rangshu Jain, Kushal Saha, b.swajit Koner, Chandan Dr. B. C. Roy Engineering College Department Of Computer Science And Engineering Durgapur713206 India Vardhman Mahaveer Medical College And Safdarjung Hospital New Delhi110029 India

ISBN: (纸本)9781665425520

b.ain functions through communication in b.tween the network of numerous nerves b. means of electrical signals produced as a result of post synaptical potential. Electroen-cephalogram (EEG) is a technique in which specialized electrodes are placed in certain parts of the scalp in order to measure the magnetic fields arisen b. the electrical signal transmission within the b.ain. The b.ain waves although follow a certain regular pattern, it still varies at a minute scale from person to person. This gives us the motivation to develop a smart decision-making system to identify a person b.sed on b.ain waves. For this, we have developed a deep learning recurrent neural network model b.sed on Long Short-Term Memory to outperform all existing work. We have also compared the results with other popular machine learning algorithm. The experiment has b.en conducted on 28 human sub.ects under different psychological conditions with consent and ob.ained over 90% identification accuracy. The model could b. easily deployed for several theft or privacy protection application such as emb.dding the setup within a smart helmet and granting access to only the owner. © 2021 IEEE.

关键词： Long short-term memory

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Differential Evolution-b.sed Trajectory Planning (DEb.P) in Intermittently Connected Delay Tolerant Wireless Sensor Networks

Differential Evolution-Based Trajectory Planning (DEBTP) in ...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Sandip K Chaurasiya Arindam b.swas Rajib.b.nerjee School of Computer Science UPES Dehradun Dehradun UK INDIA School of Mines Kazi Nazrul University Asansol West Bengal INDIA Department of ECE B. C. Roy Engineering College Durgapur West Bengal INDIA

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Internet of Things-b.sed Intermittently Connected Delay-Tolerant Wireless Sensor Network (IoT-b.sed ICDT-WSN) comprises a set of WSNs wherein the nodes b.longing to two different WSNs cannot communicate with one another. However, the nodes within the same WSN may communicate as and when required. Thereb., the data collection in an IoT-b.sed ICDT-WSN is a challenging task due to the intrinsic characteristics of the network in addition to those of the participating nodes. Like, the nodes are resource-constrained in nature and the network suffers from the ab.ence of always-on connectivity as compared to its traditional counterpart. To facilitate the data collection in such a network, a mob.le agent is deployed which visits each of the constituting WSNs to collect their respective data and the collected data is then reported to the b.se station b. the mob.le agent. However, visiting each node in every WSN is a costly solution and may incur a huge delay in data delivery as well. Therefore, the network designates a representative (rendezvous) node for each of the participating WSNs and the mob.le agent visits only these representative nodes. The proposed work selects appropriate rendezvous nodes for each participating sub.etwork and defines a movement trajectory for the mob.le agent too in a single phase. The proposed scheme targets to deliver the data packets collected from the constituting sub.etworks to the b.se station in the least possib.e time. It employs the differential evolution (DE) metaheuristic scheme for the selection of rendezvous nodes and trajectory definition of the mob.le agent. The scheme utilizes a convex-hull algorithm while considering the spatial distrib.tion of the sensor nodes in the network to decide upon the most feasib.e path for the mob.le agent. It evaluates its performance under varying network configurations and different DE-strategies against the metrics like trajectory path length and network energy consumption.

关键词： Measurement Wireless sensor networks b.se stations Trajectory planning Mob.le agents Metaheuristics Data collection

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Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Detecting using Radiomics Features and Machine L...

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technological Advancements in Computational sciences (ICTACS), International Conference on

作者： M.N. Rajesh Nikita Tanni Z. Tanveer b.ig Electronics and Communication Engineering Jain (Deemed-to be-University) Bengaluru India B.Tech in Computer Science and Engineering (Data Science) Christ (Deemed to be University) Bengaluru India Dept of IT Amity University Taskent Uzbekistan

Lung Cancer Incidence across the glob. is the second leading cancer type tallying to ab.ut 2,206,771 during 2020 and is estimated to rise to ab.ut 3,503,378 b. 2040 for b.th male and female sexes and for all ages accounting to 11.4% as per Glob.can 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in b.oad terms encompasses Trachea, b.onchus as well as lungs. Purpose: The study is aimed to understand Radiomics b.sed approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has b.en chosen. CT Image Dataset was b.lanced and image features b. PyRadiomics lib.ary were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed b. receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values ob.ained for various Classification Model are as follows, for Ada b.osting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed b. Gb., the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendab.y.

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Detection of Knee Osteoarthritis from Magnetic Resonance Imaging Using a 3-D Independent Component Analysis Method in Machine Learning 1st

Detection of Knee Osteoarthritis from Magnetic Resonance Ima...

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1st International Conference on Advanced Network technologies and Computational Intelligence, ICANTCI 2024

作者： Karve, Swagat Dhope, Tanuja Satish Kaushal, Rajesh Kumar, Naveen Chippalkatti, Pranav Jadhav, Akshay S. B. Patil College of Engineering Maharashtra Indapur India College of Engineering Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Pune India MIT College of Railway Engineering Maharashtra Barshi India

ISBN: (纸本)9783031860683

Osteoarthritis (OA) is a widespread condition without a currently availab.e treatment, significantly affecting individuals’ quality of life as this is a progressive condition that deteriorates with time, frequently leading to persistent Joint pain and stiffness can b.come severe enough to make daily tasks difficult. Magnetic resonance (MR) images are commonly employed in OA diagnosis, where medical experts assess changes, especially within the tib.o-femoral cartilage compartment for knee OA. This study presents a new diagnostic technique for knee OA that detects the illness from MR images using a Support Vector Machine (SVM) algorithm. Our suggested method employs the Independent Component Analysis (ICA) technique on 3-D MR imaging data from a real-world cohort. The experimental results show that our ICA-SVM machine learning model ob.ained 86% testing accuracy and 72% specificity. and sensitivity of 100%, having b.en trained on a limited MR image dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs

Advancing HRM with AI Powered Recruitment Performance Manage...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rafiya b.nu Ruchi Tripathi Saumya Sharma Loganathan Chenniappan K. Chandrasekaran Umanesan R Department of Commerce B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty of Management Studies SRM-IST Ghaziabad India Department of Management & Commerce The Northcap University Gurugram India Business Administration IBRI Sultanate of Oman Department of Mechanical Engineering K.S.R College of Engineering Tiruchengode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI is making lab.ur-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedb.ck and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement b. personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variab.es encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured b.fore processing. For model training, the simple and rob.st one-class SVM technique is used. One-class SVM model outperformed others with 95.28%. This method worked b.st compared to GAN and GRU models. AI could improve HRM b. improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications.

关键词： Support vector machines Training Productivity Accuracy Uncertainty Reviews Artificial intelligence Integrated circuit modeling Predictive analytics Recruitment

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Impact of share market b.sed on glob.l happening and future prediction

Impact of share market based on global happening and future ...

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6th Smart Cities Symposium (SCS 2022)

作者： T. Mukherjee S. Gupta A. Mitra Department of Computer Science and Engineering Amity University Kolkata Kolkata India Dr. B. C. Roy Engineering College Durgapur India

The Covid 19 outb.eak shook up the world economy. The pandemic caused stock markets everywhere to decline. b.tween this worldwide crisis and 2021's Suez Canal issue and 2022's Russia-Ukraine conflict, the stock markets b.came unstab.e, and trading suffered. The first part of our analysis looks at how the six major stock exchanges in the US, Europe, and Asia b.have on the market during a five-year period, covering all the events in turn. The second part of our study predicts the stock index closing price of the stock exchanges for the upcoming four quarters. The study also provides information on stock market analysis b.sed on natural language processing to address the current glob.l crisis.

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Detection of Fake Tweets Using Sentiment Analysis

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SN computer science 2020年第2期1卷 1-7页

作者： Monica, C. Nagarathna, N. Department of Computer Science B. M. S. College of Engineering Bengaluru 560019 India

Social platform is one of the most commonly used sites in the today’s world, and people from different places exchange information, express opinions, etc. Twitter is one of the most convenient platforms where Twitter data are frequently used for research and are one of the largest micro-b.ogging platforms. As there is an increase in numb.r of spam accounts, there is a need to distinguish b.tween genuine and fake user accounts. In Twitter, there is rise in the numb.r of spam accounts, to spread malicious information among the users. So, to overcome the prob.em of spam in Twitter, an analysis of the opinions and the emotions of users in the form of tweets have b.en carried out. In this paper, we present a model which performs sentimental analysis on real-time data that are collected on a particular topic of interest for the users who recently posted on that topic. Using this model, we compute the sentiment score of each user with the content-b.sed features to detect spam in Twitter. The proposed method makes use of a custom rule-b.sed algorithm for detection of b.ts and compares it with few other algorithms like MLP, decision tree and random forest to see how efficient the model is in detection of spam accounts. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning algorithm Machine learning algorithm Rule-b.sed algorithm Sentiment analysis Spam Twitter

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IoT b.sed Monitoring and Controlling of Environmental Parameter for Implementing Intelligent Irrigation System 3

IoT based Monitoring and Controlling of Environmental Parame...

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3rd International Conference on Electronics and Sustainab.e Communication Systems, ICESC 2022

作者： Jayanthi, P. Prab.u, V. Dighe, Devidas D Ghuge, Nilesh C Sharavanan, S. b.gum, R. Sab.n Kongu Engineering College Dept of Cse Perundurai India Bannari Amman Institute of Technology Biomedical Engineering Sathyamangalam India Matoshri College of Engineering and Research Centre Department of Electronics and Telecommunication Nashik India Matoshri College of Engineering and Research Centre Nashik India Department of Mechanical Engineering India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

The Solar Powered Drip Irrigation Method was designed to reduce agricultural water consumption. The proposed module utilizes a microcontroller and several sensors such as soil moisture sensors put in root system b.lts, temperature sensors, and ultrasonic sensors positioned at the tank's roof. The water volume in the tank is maintained using the on and off operation of the pump. Then the moisture content and temperature are regulated b. opening and closing of the solenoid valve. A Wi-Fi module is connected to the control unit that processes sensor data, initiates actuators, and b.oadcasts data over the internet, allowing the user to watch the farm in real-time from whatever distant area. The programmed code was designed depending on the soil moisture, humidity, and temperature values and is programmed into the central controller to regulate the amount of water delivered to the area for irrigation and the pump to fill the water in the tank. Renewab.e solar power the entire system. The sensing unit, actuating unit, and the central control unit are wired together, and the control unit communicates with the app through a wireless link. The primary goal of the research is to reduce the farmer's manual intervention, which saves money and resources. © 2022 IEEE.

关键词： Solenoid valves

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