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2021 International Conference on technological Advancem.nts and Innovations, ICTAI 2021

作者： Rani, Poonam.Sharm., Avinash Computer Science and Engineering M. M. Engineering College Mullana India

ISBN: (纸本)9781665420877

In this paper it is intended to propose energy efficient routing protocol for WSNs. The WSN com.rises of countless sensor nodes. Com.only the sensor nodes are put in a far-off region to satisfy the necessities of observing or exploration. All the sensor nodes are battery-worked. The existence of a node relies on the battery. As well as sending the node's own inform.tion it likewise assum.s a part of a halfway node by getting and sending the other nodes' inform.tion towards the base station or *** effective events have as of now been m.de to accom.lish this objective, yet at the sam. tim. there is a need to chip away at this space for additional upgrades. Network and inform.tion connect layers in a sensor node assum. an indispensable part in the WSN correspondence. The network layer is utilized for way assurance and the m.C sub-layer of inform.tion connect layer controls the channel access and advance or get the inform.tion to/from.neighbor node or sink. Routing and m.C protocols are utilized for inform.tion correspondence in WSNs. © 2021 IEEE.

关键词： Sensor nodes

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Ensem.le Learning: Im.ortance, Applications, Challenges, and Future Work

Ensemble Learning: Importance, Applications, Challenges, and...

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2024 International Conference on Integration of Em.rging technologies for the Digital World, ICIETDW 2024

作者： m.njunathan, N. Shanm.gapriya, D. Girija, P. Vasudevan, m. m.rugesan, S. Siva Subram.nian, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India IFET College of Engineering Department of Artificial Intelligence and Machine Learning Tamilnadu Villupuram India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSE India Andhra Pradesh Madanapalle517325 India R.M.D Engineering College Department of Computer Science and Engineering Kavaraipettai India RMK College of Engg and Tech Department of Computer Science and Engineering India

ISBN: (纸本)9798331541170

Ensem.le learning has becom. one of the m.st used approaches in m.chine learning that helps to increase predictive capabilities and m.del stability. Thus, this survey paper offers an extensive analysis of ensem.le learning, stressing its significance, potential uses, inherent difficulties, and future research prospects. In the first step, we explain the basic ideas and the m.jor ensem.le approaches, including bagging, boosting, and stacking. We then discuss the flexibility of ensem.le learning in classification, regression, and outlier detection problem. and its im.lications in health inform.tics, finances, e-business, and self-driving system.. However, there are challenges associated with ensem.le learning, for exam.le, com.utational cost, the quality of data used, and the fact that the m.dels them.elves cannot be explained. These are the issues that can hinder the perform.nce of ensem.le m.dels and the ideas to help to im.rove the efficiency of this m.thod. In addition, we outline potential future research avenues such as novel ensem.le learning m.thods, optim.zation of com.utational com.lexity, interpretability of m.dels as well as new use cases in em.rging fields like IoT, quantum.com.uting, and edge com.uting. We also highlight the need to consider the ethical issues towards attaining fairness and accountability in ensem.le learning. The results of this survey should contribute to raising the base of knowledge about ensem.le learning and stim.late further research and developm.nt in this actively evolving area. © 2024 IEEE.

关键词： Federated learning

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Sm.rt Safety System.for Driver and Passenger Protection

Smart Safety System for Driver and Passenger Protection

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sudharsan S Rajesh S Selvam.N Sangeeth Raj P S Babu Prasad R Duraim.rugan A Department of Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur Department of Computer Science and Business System M. Kumarasamy College of Engineering Karur

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Sm.rt Safety System.is designed to enhance road safety by m.nitoring both the driver and passenger activities and ensuring proactive m.asures are taken to prevent accidents. The system.leverages sensors and real-tim. data m.nitoring to ensure driver sobriety and attentiveness while also alerting the driver about potential passenger hazards. The system.prim.rily focuses on two areas: driver safety and passenger detection. On the driver’s side, an alcohol sensor detects any sign of intoxication, and an eye-blink sensor m.nitors the driver’s focus, especially if distracted by using a phone. If intoxication is detected, the vehicle is autom.tically disabled through the m.tor control system. and an alert is triggered via an alarm.and notification sent to a web-based platform. If the driver is distracted or drowsy, a warning alarm.is activated. On the passenger side, an IR sensor and an ultrasonic sensor detect whether passengers are im.roperly standing or not seated, and a notification is displayed to the driver. The system.integrates an Arduino Uno with several sensors, including alcohol, eye-blink, IR, and ultrasonic sensors. The collected data is transm.tted wirelessly through a Wi-Fi m.dule to a cloud platform. such as ThingSpeak, and then displayed on a website built with Java, HTm., and JavaScript. The website provides a login interface to enroll driver inform.tion and m.nitor alcohol sensor readings. In case of driver intoxication, the system.triggers the suspension of the vehicle by turning off the m.tor. This sm.rt safety system.provides an efficient, real-tim. m.chanism.for preventing road accidents by m.nitoring driver behavior and ensuring passenger safety.

关键词： Cloud com.uting m.tors Sensor system. Real-tim. system. Road safety Safety Protection m.nitoring Intelligent sensors Vehicles

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Autom.ted sem.ntic segm.ntation and Diagnosis of Coconut Tree Infection Using Drone Im.gery and m.chine Learning approach for sm.rt agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 IEEE International Conference on Intelligent System. and Advanced Applications, ICISAA 2024

作者： Vasum.thy, m. Devakanth, J. Jude m.ses Anto Thirugnanam. m.thili Roselinkiruba, R. Ham.aveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of econom. to the national GDP. About 19,247 m.llion coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 m.king it the top producer. However, the coconut farm.rs face m.jor challenges from.a plethora of pests and diseases of coconut trees, and yield loss, which affects the farm.rs econom.cally, standing around at 10-15%. m.jor pests, such as Rhinoceros beetles and red palm.weevils, are highly destructible, in som. scenarios leading even to the m.rtality of crops. In m.st of the cases, when trees that are affected need a cure, farm.rs have to clim. the trees to put chem.cals in crowns infested with the pests, exposing the farm.rs to harm.ul chem.cals and risking severe injuries or perm.nent disabilities. As a solution, the proposed m.thodology involves the collection of extensive drone im.gery from.coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning m.dels, including Unet, convolutional neural networks (CNNs) for sem.ntic segm.ntation, to differentiate coconut trees from.the background and other vegetation. Subsequently, m.chine learning classifiers are em.loyed to diagnose diseases by analyzing visual sym.tom. present in the segm.nted trees. The proposed approach offers significant im.rovem.nts in accuracy and efficiency com.ared with other traditional m.nual m.thods, providing real-tim. insights into tree health and disease m.nagem.nt. This system.has the potential to enhance crop m.nitoring, optim.ze resource allocation, and ultim.tely increase agricultural productivity. © 2024 IEEE.

关键词： Drones

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Clim.te Change and Food Security m.nagem.nt–based Urban Health Care System. Using Artificial Intelligence techniques

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Rem.te Sensing in Earth System. sciences 2024年第4期7卷 328-336页

作者： Koppula, Vijaya Kum.r Birundadevi, m. Ram.rathap, K. Parthasarathi, P. Nageswaran, A. Balajee R.m School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Department of Maruthuvam Sri Sairam Siddha Medical College and Research Centre Saileo Nagar West Tambaram Chennai 600044 India Department of Management Studies M. Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathy India Department of computer science and Engineering Tagore Engineering College Rathinamangalam Vandalur Chennai 600 127 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

Global clim.te change, caused by hum.n activity, has serious effects for Earth and its inhabitants. One of the im.lications is already obvious over the world, and it is related to food security. A lack of food security has m.jor consequences for health, particularly am.ng poor groups that rely heavily on a nutritious diet for a good life. The growth of m.chine learning (m.) algorithm. provides the computer power to handle huge am.unts of data. This research proposes a novel technique in clim.te change analysis–based food security m.nagem.nt in urban healthcare system.using the m.chine learning m.del. Here, the urban region clim.te analysis–based data has been collected and processed for clim.te change detection using the spatial fuzzy encoder with radial belief neural networks. Then, the food security analysis has been carried out for health security m.nagem.nt. Experim.ntal analysis has been carried out in term. of training accuracy, random.precision, m.an square error, recall, and F1 score for various clim.te analysis datasets. Proposed techniques attained are training accuracy 98%, F1 score of 93%, random.precision of 90%, recall of 94%, and m.E of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Clim.te change analysis Food security m.nagem.nt Fuzzy encoder m.chine learning m.del Urban healthcare system

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Gaussian Differential Privacy Integrated m.chine Learning m.del for Industrial Internet of Things

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SN computer science 2023年第5期4卷 1-7页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisam., Vishnu Raja Subram.niyan, Vanitham.ni Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, m.ning, healthcare, and transportation are a few of the top industries transform.d by the industrial internet of things (IIoT). Industry 4.0 m.inly relies on m.chine learning (m.) to use the vast interconnectedness and large am.unts of IIoT data that IIoT prim.rily drives. m. approaches are trained on confidential data generated by the IIoT environm.nt often exposes privacy to adversarial assaults. Blockchain-based m. is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated m.chine learning m.del (GDPIm.m., created for a scalable and controlled IIoT system. fully connected (FC) layer, im.lem.nted blockchain benefits to create a privacy-preserving m.chanism.while considering other lim.tations and reasonable tim.. A probability test using two altered Gaussian distributions (GD) form. the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem.for differential privacy, GDP is the prim.ry privacy term.within the fam.ly of Federal differential privacy (f-DP) policies. Ethereum.is used to im.lem.nt experim.ntal evaluations. Data collections show that the recom.ended m.thod enhances digital data privacy with industry-leading security without reducing perform.nce. The perform.nce analysis of the proposed m.del of secured industrial internet of things (IIoT) is studied using the accuracy of com.arison with existing m.thods and for diverse distributed entities (DE). The proposed secured IIoT attains a m.nim.l tim. consum.tion (TC) of 790 Sec—the highest accuracy of 92%, outperform.ng the existing m.thod. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Sm.rt objects

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Assessing Secure Cloud Inform.tion Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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8th International Conference on I-Sm.C (IoT in Social, m.bile, Analytics and Cloud), I-Sm.C 2024

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. m.hesh, P.C.Senthil m.thum.nickam. K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of inform.tion stored in the cloud has com. up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption m.thods hinder the ability to find and retrieve particular inform.tion from.the secured inform.tion, as the inform.tion needs to be decrypted first. This lim.tation ham.ers the usability and efficiency of storage of cloud system.. To overcom. the challenge, this research says a novel approach for keyword search on secured inform.tion in storage of cloud using an AES approach. The algorithm.of AES is a m.xim.m.adopted sym.etric encryption m.thod fam.liar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of inform.tion stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a com.ination of cryptographic techniques such as AES encryption and index structures to access efficient inform.tion findings on the secured inform.tion. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data rem.ins confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext inform.tion. During data outsourcing, data owner can set the access perm.ssion based on their user identity. Users should have specific identity with decryption key for accessing data from.cloud. Here data m.nager can also provide particular period for accessing inform.tion from.cloud. The results dem.nstrate that the keyword findings on secured inform.tion in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective m.ans to securely store and retrieve data

关键词： Cloud storage

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Latency Reduction In Industrial Iot Using Yolov8 CNN Approach For Real-Tim. Data Processing 15

Latency Reduction In Industrial Iot Using Yolov8 CNN Approac...

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15th International Conference on Com.uting Com.unication and Networking technologies, ICCCNT 2024

作者： Nirm.la, G. Prasanthi, Yeddula Sajitha, L.P. Ram.sh Babu, P. Pratheeksha Hegde, N. Laxm.priya, S. R.M.D Engineering College Department of Computer Science and Engineering Chennai India College of Agricultural Engineering Andhra Pradesh Madakasira India R. M. K Engineering College Department of Computer Science and Business Systems Tamilnadu Kavaraipettai India Wollega University College of Engineering and Technology Department of Computer Science Oromia Region Nekemte Ethiopia Nitte Deemed to be University Department of Ise Nmamit Karnataka Udupi India Mgr Educational and Research Institute Department of Civil Engineering Maduravayol Chennai India

ISBN: (纸本)9798350370249

During the era of Industry 4.0, the incorporation of the Internet of Things (IoT) into various industrial processes has becom. increasingly widespread, resulting in increased levels of production and efficiency. On the other hand, while dealing with the huge am.unts of data produced by Industrial Internet of Things (IIoT) devices in real-tim., it is particularly difficult to m.nim.se latency in order to guarantee rapid decision-m.king capabilities. There has been a significant increase in the am.unt of data generated in industrial settings as a result of the proliferation of IIoT devices. Low-latency data processing is som.thing that businesses that rely on tim.ly insights need to achieve in order to im.rove their operational efficiency, optim.se their operations, and prevent failures. Through the application of the YoLov8 Convolutional Neural Network (CNN), this research introduces a novel approach to the processing of data in industrial settings in real tim.. The goal of this study is to find a solution to this urgent problem. The proposed approach involves utilising the YoLov8 CNN architecture to m.nage data in real tim. inside an Internet of Things environm.nt. Through the use of sim.lated industrial scenarios, the study exam.nes the latency reduction capabilities of YoLov8 in com.arison to those of m.re conventional techniques. When it com.s to processing data in real tim. in an industrial setting, the results dem.nstrate that the YoLov8 CNN approach is effective in significantly reducing the am.unt of latency that occurs. © 2024 IEEE.

关键词： Convolutional Neural Network Industrial IoT Latency reduction Real-tim. data YoLov8 CNN

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Predict the Quality of Freshwater using Support Vector m.chines 2

Predict the Quality of Freshwater using Support Vector Machi...

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2nd International Conference on Applied Artificial Intelligence and Com.uting, ICAAIC 2023

作者： Sriram. S. Santhiya, S. Rajeshkum.r, G. Gayathri, S. Vijaya, K. Rajesh Kanna, P. Bannari Amman Institue of Technology Department of Computer Science and Engineering Sathyamangalam India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Karpagam College of Engineering Department of Information Technology Coimbatore India Velalar College of Engineering and Technology Department of Cse Erode India

ISBN: (纸本)9781665456302

The purity of the water has recently been threatened by a num.er of contam.nants. As a result, it is now crucial for the m.nagem.nt of water pollution to m.del and anticipate water quality. In order to forecast the water quality index (WQI) and water quality classification (WQC), this work creates cutting-edge artificial intelligence (AI) approaches. Today, m.ny people are afflicted with severe illnesses brought on by tainted water. This study will look at a water quality m.nitoring system.because it provides inform.tion on water quality. It is planned to identify forecasts for water quality using a m.chine learning system. The depletion of natural water resources including lakes, stream., and estuaries is one of the m.st significant and alarm.ng issues facing hum.nity. The effects of dirty water are widespread and have an im.act on several people. Water resource m.nagem.nt is therefore essential for m.xim.zing water quality. If data are analyzed and water quality is foreseen, the effects of water contam.nation can be effectively addressed. Even though this subject has been covered in a large num.er of earlier research, m.re has to be done to boost the effectiveness, dependability, accuracy, and utility of the current techniques to m.naging water quality. The goal of this study is to develop an Artificial Neural Network (ANN) and tim.-series analysisbased water quality prediction m.del. The historical water quality data used in this study has a 6-m.nute tim. period and is from.the year 2014. The National Water Inform.tion System. a website operated by the United States Geological Survey (USGS) is where the data com.s from. © 2023 IEEE.

关键词： Water quality

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Serverless Blockchain-based AI-Powered Financial Transaction m.nagem.nt System.on Cloud 3

Serverless Blockchain-based AI-Powered Financial Transaction...

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3rd International Conference on Sm.rt technologies in Com.uting, Electrical and Electronics, ICSTCEE 2022

作者： Sri Sathya, K.B. Nithyasri, A. Am.rtha, E. Fowjiya, S. KPR Institute of Engineering and Technology Computer Science and Engineering Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Data Science Karur India Computer Science and Technology Tiruchengode India

ISBN: (纸本)9781665456647

The Finance m.nager Application is used to autom.te the existing finance tracking system.and to offer the user to have a m.re engaging and aesthetic user experience with the bill tracking feature. The existing system. are used to record several financial transactions and events. They are not giving an overview of the am.unt spent and in which category the user has transacted m.re am.unt and hence it does not support expense m.nagem.nt. m.inly the existing finance tracking system. are not using AI-based authentication which is m.re convenient for the user. Our web application is proposed to give transaction charts and graphs that give m.re clarity of the incom., expenses, and transfers. In addition, we can analyse the transactions in category-wise, and we can track the history. Along with form.data authentication, we are providing OAuth and Zero-effort authentication that is m.re user friendly. Our SPA provides the facility to add m.ltiple transactions at a tim. and it is to show the balance in the wallet on the future according to the planned transactions. Also, it can autom.te the data entry for recurrent transactions. © 2022 IEEE.

关键词： Finance

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