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2nd International Conference on disruptive Technologies, ICdT 2024

作者： Banu, M.Sheerin Elanangai, V. Chakraborty, Uddyalok Sharmiladevi, d. Ramesh Babu, P. Rajeswari, P. R.M.K. Engineering College Department of Information Technology India St. Peter's Institute of Higher Education and Research Department of Eee Avadi Tamilnadu Chennai India Upl University of Sustainable Technology Department of Computer Engineering Gujarat Ankleshwar India Sona College of Technology Tamilnadu Salem India College of Engineering and Technology Wollega University Department of Computer Science Oromia Region Nekemte Ethiopia R.M.D Engineering College India

ISBN: (纸本)9798350371055

Skin cancer is very common and can be fatal. To improve patient results, it is essential to find the disease early. It can be hard to tell the difference between benign and malignant skin lesions when using standard picture classification methods. The complicated features and intricate patterns that can be seen in dermatoscopic images make it very hard to use the current methods for classifying skin cancer signs. If a diagnosis is delayed or not dealt with properly, the effects can be very bad. The study goal is to make skin cancer classification more accurate with the help of TasNet v2 in order to meet a need that appears in the current methods. This study suggests that TasNet v2, a state-of-the-art deep learning framework, be used to sort skin cancer into different groups. In order to solve the problem of the need for more reliable and advanced diagnostic tools, this would work. The suggested method uses transfer learning on a very large set of labelled dermatoscopic images to teach TasNet v2 how to find signs of skin cancer. The trained model is checked and tried on several different sets of data. Using TasNet v2 unique design, which improves feature extraction, lets the model find smaller details that could be signs of skin cancer. The fact that TasNet v2 was able to do picture classification tasks that were not in its original area of expertise shows how flexible and useful it is. These results make it more likely that a new development will happen that will help doctors make more accurate skin cancer diagnoses. © 2024 IEEE.

关键词： deep learning dermatoscopic Images Image Classification Skin Cancer TasNet v2

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Emergency Vehicle detection Using deep ConvNet2d and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements deep ConvNet2d (Convolutional Network 2d) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses deep ConvNet2d Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Multi-Task distillation learning for Coffee Corticium Salmonicolor Pink Berry disease for Real-Time Prediction 5

Multi-Task Distillation Learning for Coffee Corticium Salmon...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Raveena, S. Surendran, R. Sangeetha, M. Kartheesan, l. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala engineering college Avadi Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Coffee Corticium Salmonicolor Pink Berry disease (CCSPBd) is a substantial risk to coffee cultivation. Precise and prompt identification is essential for efficient disease treatment. This research introduces a multi-task distillation learning (MTdl) method for the real-time prediction of CCSPBd. A more extensive teacher model is trained on a comprehensive dataset of coffee plant diseases, and its knowledge is conveyed to a smaller student model tailored for CCSPBd prediction. Integrating supplementary activities about coffee plant health into the teacher model enhances the student model's comprehension of plant disease patterns. The resultant student model can precisely forecast CCSPBd in real-time, allowing farmers to implement prompt interventions to avert crop loss. Experimental findings indicate the efficacy of the suggested MTdl method in attaining high accuracy and efficiency for CCSPBd prediction. © 2024 IEEE.

关键词： Fruits

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Algorithm-driven Optimization of Cloud Computing Architectures for Superior data Security and Efficiency 2

Algorithm-Driven Optimization of Cloud Computing Architectur...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pillai, N. Muthuvairavan Jesu Jayarin, P. david, d. Beulah Jeeva, K. Kumari, V Sheeja R.M.D Engineering College Department of Computer Science and Business Systems KavaraipettaiTamil Nadu Thiruvallur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Information Technology Tamil NaduThandalam Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil NaduThandalam Chennai India

ISBN: (纸本)9798350384277

This paper examines how algorithm-driven optimization transforms cloud computing systems, focusing on data security and efficiency. Following a thorough cloud environment study, algorithms were picked and adapted to solve specific difficulties. After integration into the architecture, these algorithms were tested in a controlled simulation environment. CPU utilization rose from 70% to 90%, memory from 65% to 85%, storage from 60% to 80%, and network bandwidth from 75% to 95%. These improvements improve computational and storage efficiency. Intrusion detection became AI-based, and encryption increased from 128-bit to 256-bit. data breaches dropped from 5 to 1 per year, while security standards compliance increased from partial to full. These improvements strengthened cloud security. The system became more responsive from 200 to 100 ms. Server load distribution evened out, peak load handling improved, and user experience rating rose from 3.5/5 to 4.5/5. These enhancements highlighted optimized load *** consumption dropped 30%, lowering operational costs. High cooling requirements became moderate due to energy-efficient infrastructure. Sustainability goals were met by reducing annual carbon footprint by 30%. © 2024 IEEE.

关键词： Cloud security

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AI algorithms for Autonomous Robotics Navigation 2

AI algorithms for Autonomous Robotics Navigation

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Komala, C.R. Raja, J. Marotrao, Sarange Shreepad Neelima, Nalla Sujatha, R. Chidambarathanu, K. East Point College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru560049 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering School of Computing Tamil Nadu Chennai600062 India Ajeenkya D Y Patil School of Engineering Department of Mechanical Engineering Maharashtra Pune412105 India R.V.R & J.C. College of Engineering Department of Information Technology Andhra Pradesh Chowdavaram522019 India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai625022 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai601206 India

ISBN: (纸本)9798350364699

Robotic systems depend much on autonomous navigation since it allows them to operate on their own in environments constantly changing and lacking clarity. The most evolved artificial intelligence (AI) techniques applied in self-driving robot navigation are briefly reviewed in this work. These approaches call for classical methodologies, machine learning, and deep learning as well. We examine the advantages and drawbacks of numerous approaches applied in route planning, location finding, map creation, obstacle avoidance. We investigate how neural networks, sensor fusion, and reinforcement learning may be applied to provide increased dependability and flexibility for guiding systems. Case examples illustrating the advantages and drawbacks of applying artificial intelligence (AI) in practical contexts including service robots, drones, and self-driving cars abound. Combining modern AI algorithms with conventional approaches indicates that it is feasible to create self-driving robots more dependable and efficient based on the increased tracking performance. © 2024 IEEE.

关键词： Reinforcement learning

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Energy-Efficient Green Information Centric Networking for Future Wireless Communications

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 149-158页

作者： Girirajan, S. Vinoth Kumar, S. Karthick, M. Saju Raj, T. Ponnaian, Geetha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Nandha College of Technology Tamil Nadu Erode India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content. © 2024 by the authors of this article.

关键词： energy efficiency (EE) future wireless communications green information-centric networking information-centric networking (ICN) wireless network

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Multimedia Wireless Sensor Network Platform data Encryption Algorithm based on Blockchain Technology 2

Multimedia Wireless Sensor Network Platform Data Encryption ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Uma Maheswari, G. Amutha, S. Rajeshkumar, C. Vargheese, M. Nallasivan, G. Selvarani, J. Hilda RMK College of Engineering and Technology Department of Computer Science and Engineering Pudhuvayal Tamil Nadu Chennai India R&D Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Tamil Nadu Chennai India Sri Krishna College of Technology Department of Information Technology Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

This research presents a data encryption technique based on blockchain technology to address issues of inadequate encryption efficacy and information loss in multimedia platforms' data encryption algorithms. The study explores the dES encryption method, its symmetrical key algorithm and public key algorithm, electronic envelopes, and message authentication. It also investigates asymmetric encryption techniques using neural networks with chaotic attractors and sequences. The goal is to improve the efficiency of data encryption and ensure data correctness, requiring a blockchain-based data encryption system. The empirical evaluation shows that the proposed method can successfully encrypt multimedia data, providing excellent encryption results and ensuring data integrity. The findings provide a theoretical foundation for further research in this area. © 2024 IEEE.

关键词： Cryptography

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data integrity aware system for executing dynamic operations on outsourced cloud data

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Measurement: Sensors 2024年 31卷

作者： Roslin dayana, K. Shobha Rani, P. Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai India

The cloud computing platform of today handles a greater amount of real-time data, which requires dynamic data operations. The majority of the currently available cloud techniques, however, do not concentrate on dynamic data operations. Hence, this work provides a system that enables dynamic data operations to be carried out on a cloud computing platform, such as the insertion, deletion, and updating of data. The performance of the work is tested in terms of time consumption with respect to data storage, retrieval, data block insertion and deletion operations. The analysis is carried out by altering the data volume and the performance of the work is satisfactory with reasonable time consumption. © 2023

关键词： Cloud storage

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Predictive Analytics for Stress Management in the digital Workplace Using IoT

Predictive Analytics for Stress Management in the Digital Wo...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Shanthi, K.G. Rukmani devi, S. Sesha Vidhya, S. Santhi, S. Kalaiselvi, A. Harinee, G.A. R.M.K College of Engineering and Technology Dept of Electronics and Communication Engineering Chennai India R.M.D Engineering College Dept of Electronics and Communication Engineering Chennai India Kalaignarkarunanidhi Institute of Technology Dept of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Dept of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350313987

It is a well-known fact that Work from Home (WFH) had put people under more stress in the recent times. In general, the human body's natural response to both internal and external stimuli is stress. despite the fact that stress is a usual part of human life, working online for an extended period of time without taking any steps to reduce, it can have serious detrimental impact on one's health. The most important factors that are associated with the level of stress felt by Work from Home employees have been carefully examined. The main objective of this research work is to give online workers a precise measure of their stress level in real-time and the best solutions for enjoying healthy lives. Human stress signals are collected by the Galvanic Skin Response (GSR) sensor and analyzed digitally. The obtained information is then utilized by the proposed Internet of Things (IoT) architecture to calculate the stress level. Recommendations for stress management include meditation, yoga, music, or exercise. © 2023 IEEE.

关键词： GSR sensor Internet of Things (IoT) liquid-Crystal display (lCd) Microcontroller Speaker Voice IC

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Smart Web Application for Efficient Management of Hospital Appointments

Smart Web Application for Efficient Management of Hospital A...

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2022 IEEE International Conference on Current development in engineering and Technology, CCET 2022

作者： Patil, Siddheshwar V. Patil, Shobha B. Terdalkar, Omarsharif A. Yelure, Bhushan S. D. Y. Patil College of Engineering and Technology Dept. of Computer Science and Engineering Kolhapur India Government College of Engineering Dept. of Computer Science and Engineering Karad India

ISBN: (纸本)9781665454155

The interest in medical care administrations is expanding as the populace develops, and the number of patients looking for care at clinics, clinical foundations, all-encompassing gatherings, and doctors' workplaces has significantly expanded. These present new hindrances for the office's laborers and directors. The most pervasive trouble that clinics, their administration, and their staff face are bringing down functional costs and accomplishing productivity objectives. In the interim, most clinics are working on a careful financial plan. Patients have generally been not able to move openly all through their visits and return because of their endeavors to smooth out the association. due to the shortcomings of existing appointment monitoring and management systems, this paper proposes an alternative and efficient approach for appointment monitoring. In this work, a smart-appointment booking system for patients to book appointments with storage facilities for medical reports and emergency service is proposed. A Health-Buddy BooknMeet system with online taking appointments to a specific hospital enables patients to check directly in without interacting with staff. © 2022 IEEE.

关键词： Emergency services

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