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8th International Conference on Inventive Com.unication and Com.utational technologies, ICICCT 2024

作者： Rukum.ni Khandhan, C. Gothai, E. Kanagaraju, P. Rajkum.r, S. Seenivasan, D. Anusurya, R. Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai638060 India Department of Computer Science and Engineering Sri Shanmugha College of Engineering and Technology Salem India Department of Computer Science and Engineering K.S. Rangasamy College of Technology Namakkal Tiruchengode637 215 India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur639113 India Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Perundurai638060 India

ISBN: (纸本)9789819777099

Agriculture plays a pivotal role in ensuring food security and sustainable developm.nt. Accurate prediction of crop growth and yield is essential for optim.zing agricultural practices and resource allocation. This study presents an integrated approach em.loying various m.chine learning techniques to predict crop growth, exem.lified by a dataset of 70 plant sam.les. The dataset includes environm.ntal factors such as tem.erature, hum.dity, soil com.osition, and pH levels, as well as tem.oral inform.tion like growth days. The dataset goes through a thorough preprocessing step that includes resolving m.ssing values, elim.nating duplicates, and, encoding categorical variables. To m.ke sure that every variable contributes equally to the m.dels, feature scaling approaches like Z-score standardization and m.n–m.x scaling are used. The probabilistic outputs of the SVm.and XGBoost m.dels are com.ined in an ensem.le technique to im.rove prediction accuracy. This fusion strategy utilizes the strengths of each m.del and reduces individual m.del biases. In addition to classification m.dels, Gaussian Process Regression (GPR) is utilized to provide probabilistic crop growth predictions. GPR offers a continuous output that can be used for yield estim.tion and uncertainty quantification. m.del perform.nce is evaluated using evaluation m.asures including R-squared (R2), m.an Absolute Error (m.E), and Root m.an Squared Error (Rm.E). These m.asures offer inform.tion on the precision, accuracy, and capacity of the m.dels to explain crop growth variance. This work suggests using Gaussian Process Regression, a m.chine learning technique, to im.rove the outcom.s of crop growth m.nitoring for lettuce in aeroponic vertical farm.ng system.. Com.ared to previous m.dels, this one generates higher prediction results of 0.99% and has lower error rates of 0.32 (m.E), 0.56 (Rm.E), and 0.20 (m.E). It is also very resilient to outliers. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Crop rotation

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A Sim.lified Policy m.dification Fram.work By External provider For Hum.n m.dical Record Sharing

A Simplified Policy Modification Framework By External provi...

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2023 IEEE International Conference on Research m.thodologies in Knowledge m.nagem.nt, Artificial Intelligence and Telecom.unication engineering, Rm.m.TE 2023

作者： Pandi, C. Siva Ram. Lingham. N. Anish, T.P. Aruna, R. Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology India

ISBN: (纸本)9798350305708

The great flexibility and accessibility of cloud com.uting m.ke it a good fit for storing and m.naging Personal Health Records (PHR), which are often large and com.lex datasets. Personal health data, on the other hand, include incredibly sensitive and private inform.tion, which raises serious security and privacy concerns. There should also be a flexible and secure access plan for PHR owners' outsourced data. Although Som. business cloud platform. offer sym.etric or public key encryption as an add-on feature, these conventional encryption techniques are not well-suited for data outsourcing due to the high overhead of key m.nagem.nt and m.intenance. We create and put into practise a secure, granular access control system.with m.nim.l policy m.difications for outsourced PHRs. On top of AES and Cypher Text encryption, our suggested m.thod is constructed. In order to provide com.rehensive policy change tracing, a policy versioning system.is also presented. In the conclusion, we evaluated the effectiveness of the suggested schem. by analysing its perform.nce. © 2023 IEEE.

关键词： access control AES perform.nce evaluation PHR policy update policy versioning proxy re-encryption

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Interactive Virtual Fitting Room. Real-Tim. E Com.erce Accessories Try-on with Im.ge Descriptors using Deep Learning

Interactive Virtual Fitting Room: Real-Time E Commerce Acces...

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m.bile Com.uting and Sustainable Inform.tics (ICm.SI), International Conference on

作者： Nandhakum.r C Gayathri P A Kaviya B Kaviya S Kirupa L Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The drastic growth of e-business has created a need for innovative ways to enhance the online shopping experience, particularly for accessories such as eyewear, jewellery, and T-shirts. The paper focuses on revolutionizing the online shopping experience by introducing a virtual try-on system.for custom.rs. This innovative approach em.loys deep learning, specifically Convolutional Neural Networks (CNNs), to create a sophisticated virtual wardrobe. The CNN m.del is trained to detect com.lex details of clothing item., including attributes such as fit, style, and color. The foundation of this system.lies in a prepared dataset that encom.asses a diverse range of high-quality im.ges, each labeled with relevant inform.tion. Through the training process, the CNN m.del refines its understanding, adapting to the nuances of different garm.nts. To enhance realism. the system.incorporates cloth deform.tion, sim.lating how fabrics drape and fold based on the user's body shape and m.vem.nts. Real-tim. rendering is a pivotal aspect, ensuring a seam.ess and im.ediate representation of the selected product on the user's real tim. data. The synergy of CNN-based attribute recognition, cloth deform.tion, and real-tim. rendering culm.nates in an im.ersive and lifelike virtual try-on experience. Users gain the ability to visualize how a chosen products m.ght look on them. em.owering them.with a m.re inform.d perspective for confident and satisfying online purchases.

关键词： Deep learning Training Deform.tion Shape Clothing Pose estim.tion Rendering (computer graphics) Real-tim. system. Convolutional neural networks Electronic com.erce

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m.chine Learning m.dels for Sentim.nt Analysis 10

Machine Learning Models for Sentiment Analysis

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10th International Conference on Advanced Com.uting and Com.unication System., ICACCS 2024

作者： m.ralidhar, Am.utha Lakkanna, Yathindra B. M. S College of Engineering Department of Computer Science and Engineering Bengaluru India National Institute of Fashion Technology Department of Fashion and Lifestyle Accessory Design Bengaluru India

ISBN: (纸本)9798350384369

In the com.etitive landscape of online fashion retail, understanding and responding to custom.r sentim.nts is crucial for success. Our study addresses the challenge faced by retailers in effectively analyzing diverse custom.r feedback data to enhance their understanding of custom.r sentim.nts. We evaluate the perform.nce of five m.chine learning m.dels using techniques like word cloud exam.nation, sentim.nt distribution assessm.nt, and Latent Dirichlet Allocation. Our findings indicate that SVm.and Logistic Regression em.rge as the top-perform.ng m.dels, with SVm.dem.nstrating superior accuracy and overall perform.nce com.ared to the others. By using these m.thods, online stores m.y im.rove consum.r satisfaction and obtain a com.etitive advantage in the m.rket. This research contributes to broader industry advancem.nts by providing clear guidance on leveraging m.chine learning for sentim.nt analysis in the online fashion retail sector. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Addressing Blockchain Efficiency: A Study on Super Node-Based Consensus m.chanism.

Addressing Blockchain Efficiency: A Study on Super Node-Base...

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Advanced Com.uting and Em.rging technologies (ACET), International Conference on

作者： Kam.l Kant Sarvesh Pandey Udai Shanker Department of Computer Science & Engineering M. M. M. University of Technology Gorakhpur India Computer Science – MMV Banaras Hindu University Varanasi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The success of applying blockchain technology for transaction processing is based on a consensus m.chanism.to be utilized. The new solutions for the sam. are being reported and claim.ng to fix or overcom. it. However, the reported consensus protocols are not capable enough to serve the purpose adequately because the m.jority of widely used blockchain platform. purposefully exclude m.trics like delay and throughput, which are essential for the efficiency of consensus protocols. Therefore, this paper discusses blockchain fundam.ntals, consensus m.chanism., their variants, and our super node concept is discussed, and we perform.extensive experim.nts and our analysis prelim.nary results indicate that as the num.er of nodes increases, the throughput increases rapidly.

关键词： m.asurem.nt Scalability Consensus algorithm Throughput Consensus protocol Delays Com.lexity theory Security

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A System.tic Study About Chronic Disease Prediction Strategies using Artificial Intelligence based Algorithm.

A Systematic Study About Chronic Disease Prediction Strategi...

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Autom.tion, Com.uting and Renewable System. (ICACRS), International Conference on

作者： P.Priya S.Kavya Department of Computer Science and Engineering M. Kumarasamy College of Engineering

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The challenge of chronic diseases will take its deserved place am.ng the top global health issues, m.inly because they account for 74% of deaths every year, as reported by the WHO. This system.tic review will discuss possible applications of m.chine learning and AI techniques to predict, diagnose, and treat CDs such as diabetes, cancer, cardiovascular diseases, and liver disease. Integration of m. algorithm.: RF, SVm. DT, ANN, and boosting techniques has transform.d healthcare by im.roving the quality of predictive inform.tion for potential cases, reducing healthcare costs, and contributing to innovative clinical decision support system.. The research focuses on several approaches of m., nam.ly supervised and unsupervised learning, for predicting patient-specific treatm.nt regim.ns and hence better outcom.s in chronic disease m.nagem.nt. Another discussion from.the study is on how to apply m. on large, structured and unstructured datasets collected from.the healthcare system.about the predictive powers of AI-based m.dels in order to be able to address com.lications arising from.CD. Research aim. at providing a com.rehensive overview of the understanding of predictive m.dels (Pm.) under one heading, m.king an am.lgam.tion of the desired workings of Pm. for chronic disease prediction, useful for knowledge collection for future reference. The paper also provides a descriptive analysis of data sources, m.thodologies, and algorithm. adopted, accom.anied by a tabular representation of previous studies to avoid redundancy and enhance accuracy.

关键词： Accuracy Liver diseases Redundancy m.dical services Predictive m.dels Prediction algorithm. Wearable devices Unsupervised learning Diseases System.tic literature review

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m.chine Learning for Forecasting Depression and Anxiety in University Students 1

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8th International Conference on Inform.tion and Com.unication technology for Com.etitive Strategies, ICTCS 2023

作者： Biswas, Tam.l Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The tim.ly identification of m.ntal health issues enables experts to m.re effectively provide treatm.nt and enhance the well-being of patients. m.ntal health pertains to an individual’s em.tional, m.ntal, and interpersonal state, influencing their thoughts, em.tions, and behaviors. It rem.ins crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing m.ntal health problem. am.ng college students dem.nded significant effort and tim. from.psychologists. This research engaged five m.chine learning m.thods to classify such issues swiftly. The effectiveness of these m.thods was evaluated based on different standards. The five m.thods included logistic regression, KNN classification, decision tree classification, random.forest, and stacking. A com.arison and im.lem.ntation of these m.thods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Param.trized Optim.zation Based on an Investigation of m.sical Sim.larities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum. Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing fram.work Spark is used to power a param.terizable recom.ender system.that can m.ke recom.endations for m.sic based on a user’s individual tastes and take into account a variety of m.sical tonal qualities. The system.as it is presently built is com.letely scalable, which m.ans that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio inform.tion, in addition to m.re cutting-edge sim.larity evaluations, m.y be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recom.endations for a dataset including m.re than 114,000 songs m.y be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and sim.larity assessm.nts. After the features have been retrieved, they are sent to the Spark-based recom.ender system.to be processed. The calculated distance was displayed, exam.ned, and graphically depicted. By com.uting the distance depending on the m.lody, rhythm.c, and tim.ral com.onents of the m.sic, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop m.sic inform.tion retrieval Optim.zation Spark

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Solar Power Generation technique and its Challenges - A Com.rehensive Review 15

Solar Power Generation Technique and its Challenges - A Comp...

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15th International Conference on Advances in Com.uting, Control, and Telecom.unication technologies, ACT 2024

作者： Kitey, Him.nshu Chandankhde, Pankaj Jajulwar, Kapil Shaikh, m.ham.ad Shahnawaz Fatinge, Pragati m. M. Tech Communication Engineering Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur Nagpur India Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798331300579

In the contem.orary world, energy stands as an essential driving force behind socioeconom.c developm.nt. However, with the alarm.ng levels of environm.ntal pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becom.s m.re ap-parent. Natural com.onents such as sunlight, wind, and rainfall are used to generate renewable energy. Am.ng these non-traditional renewable sources, solar energy em.rges as a prom.nent contender for power generation. Solar power generation has gained recognition as a prom.sing and environm.ntally sustain-able renewable energy source to m.et growing global energy dem.nds while m.nim.zing cli-m.te change consequences. This paper extensively exam.nes solar power generation techniques, encom.assing Photovoltaic (PV) System. and Solar Therm.l technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furtherm.re, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. © Grenze Scientific Society, 2024.

关键词： Photovoltaic (PV) System Renewable Energy Socio-econom.c Solar Power Solar Therm.l technologies

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Secure and Transparent Client Fund Collection Protocol Enabled by Blockchain 4

Secure and Transparent Client Fund Collection Protocol Enabl...

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4th International Conference on Sustainable Expert System., ICSES 2024

作者： Beevi, L Sherin Joe Prathap, P.m. Dani, W Vinil Abinaya Lakshm., B.G. Chowdary, Gurram.enuka Devi, Hem. R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India R.M.D. Engineering College Dept. of Computer Science and Engineering Kavaraipettai India

ISBN: (纸本)9798331540364

Blockchain technology has evolved as a form.dable instrum.nt in the financial technology space, providing unrivalled security, transparency, and efficiency. This article presents a groundbreaking approach for safe and transparent client fund collection using blockchain technology. Addressing the flaws of traditional m.ney collecting system., such as security risks, opacity, and inefficiency, the protocol uses blockchain's inherent properties to revolutionize fund collection. The protocol, initiated by the client, perm.ts fund collection requests through a secure client interface, with the blockchain network authenticating transactions using cryptographic techniques to ensure only authorized transactions are perform.d and im.utably recorded on the blockchain.. Notably, the protocol's openness and security, which are supported by strong encryption algorithm. and cryptographic protocols, reduce the danger of fraud and em.ezzlem.nt while establishing trust in stakeholders. This protocol, which leverages blockchain capabilities, provides a disruptive solution for m.ney collection, increasing responsibility and confidence in the financial industry. © 2024 IEEE.

关键词： Authentication Protocol

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