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3rd IEEE International Conference on Automation and Computation, AUTOCOm 2025

作者： Sumithra, m. Sahebzathi, S. Parveen, Nikhat Arun, K.K. Dahane, Ganesh madhukar Nagabalan, U. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Velammal College of Engineering and Technology Department of Cse Madurai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Kumaraguru College of Technology Department of Mechanical Engineering TamilNadu Coimbatore India Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmedhnagar India R.M.D. Engineering College Department of Science and Humanities - General Engineering Chennai India

ISBN: (纸本)9798331542375

Artificial intelligence approach is now able to anticipate fatigue failure of machined components based on elementary key numbers without the need for professional knowledge of evaluation processes. In this paper, we develop a meta-heuristic approach for the prediction of defects using Bees Swarm Optimisation (BSO) in machined components. The study mainly predicts the fatigue failure by training the BSO experimentally with the training datasets. The three most essential input variables are the load stress, the hardness of the material, and the size of the fatal flaw (KD). In the context of fatigue testing, the only outcomes that may be determined are failure or non-failure. In this investigation, a single data set is generated for each specimen that is examined. When a full fracture has occurred at a given stress level, fracture surface analysis estimates the size of the deadly flaw that has formed. The architecture and hyperparameters of the neural network are cross-validated using a K-fold technique in order to get the highest possible prediction accuracy. After all is said and done, a cautious mean fatigue failure prediction accuracy of 92% is achieved. © 2025 IEEE.

关键词： Fatigue testing

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Experimental Evaluation of Internet of Things Assisted Industrial Automation System with Smart Emergency Alert mechanism

Experimental Evaluation of Internet of Things Assisted Indus...

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2023 IEEE Global Conference on Information technologies and Communications, GCITC 2023

作者： Yamini, B. Sreeraj, S. Supriya, K. Vinithra Banu, T. Ragini, P.Y. Kurada, Phaneendra Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamil Nadu Chennai India Sri Krishna College of Engineering and Technology Department of M. Tech Cse Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350308167

Environmental sustainability, energy and raw material availability, and globalization are just a few of the major societal concerns that are generating new demands on a wide range of societal players. Efficiency, adaptability, sustainability, and competitiveness are some of the new criteria. These have been around for a while, and a variety of systems have been tuned locally in terms of a few of these requirements, but recent developments with regard to communication and computational characteristics have made them much more useful to guarantee new possibilities for combining various parts of society, allowing for optimum performance on a scale that is far greater. The impact of IoT on several sectors of the economy has been substantial. The Internet of Things (IoT) is a technology that allows us to manage physical things over the internet in order to save time and energy. There are several factories in India because of the country's rapid economic growth. However, industrial accidents were a severe problem in many sectors. However, industrial accidents were a big problem because they resulted in casualties and financial losses for businesses. This initiative aims to reduce the devastating effects of fires in high-risk industries including the oil, chemical and petrochemical sectors, where many lives and fortunes have been lost. It is crucial to have a system in place that can ensure the safety of the factory grounds and promptly alert the proper authorities should an emergency arise. In the past, a single bottle at a time would be loaded onto a conveyor and filled. This strategy is time-consuming and costly. Internet of Things (IoT) in Industries has been implemented to monitor and notify the individual in charge to take suitable measures in order to reduce manual overhead and therefore partially meet our needs. The manufacturing sector might benefit from AI-based management and oversight. 'Ecosystem having manufacturing Relevant Implication' better describes the I

关键词： Accident prevention

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： R.C. Ilambirai Barkathulla. A G. mahalakshmi J. Jagan Babu Prabu K C. Srinivasan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity.

关键词： Training Cloud computing Accuracy Crops Robot sensing systems Real-time systems Sensor systems Convolutional neural networks Robots Farming

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

关键词： Robotic arms

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Bees Swarm Optimization Based Prediction of machined Fatigue Failure

Bees Swarm Optimization Based Prediction of Machined Fatigue...

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Automation and Computation (AUTOCOm), International Conference on

作者： Sumithra m S. Sahebzathi Nikhat Parveen Arun K. K Ganesh madhukar Dahane U Nagabalan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Dr. Vithalrao Vikhe Patil College of Engineering Ahmedhnagar Maharashtra India Department of Science and Humanities – General Engineering R.M.D. Engineering College Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

关键词： Training Accuracy machine learning Fatigue Production facilities Wind turbines Surface cracks Particle swarm optimization Stress Testing

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Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling

arXiv

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arXiv 2023年

作者： Shisher, md Kamran Chowdhury Ji, Bo Hou, I-Hong Sun, Yin The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States The Department of Computer Science Virginia Tech BlacksburgVA24061 United States The Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

In this paper, we consider a remote inference system, where a neural network is used to infer a time-varying target (e.g., robot movement), based on features (e.g., video clips) that are progressively received from a sensing node (e.g., a camera). Each feature is a temporal sequence of sensory data. The inference error is determined by (i) the timeliness and (ii) the sequence length of the feature, where we use Age of Information (AoI) as a metric for timeliness. While a longer feature can typically provide better inference performance, it often requires more channel resources for sending the feature. To minimize the time-averaged inference error, we study a learning and communication co-design problem that jointly optimizes feature length selection and transmission scheduling. When there is a single sensor-predictor pair and a single channel, we develop low-complexity optimal co-designs for both the cases of time-invariant and time-variant feature length. When there are multiple sensor-predictor pairs and multiple channels, the co-design problem becomes a restless multi-arm multi-action bandit problem that is PSPACE-hard. For this setting, we design a low-complexity algorithm to solve the problem. Trace-driven evaluations demonstrate the potential of these co-designs to reduce inference error by up to 10000 times. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Optimizing Supply Chain Security Using ACO and Blockchain: Integrating Root of Trust, Unclonable Functions, and SBOm for Cybersecurity

Optimizing Supply Chain Security Using ACO and Blockchain: I...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： L Sherin Beevi Ponsy R K Sathia Bhama J Albert Vijayan Joe Prathap P. m. W Vinil Dani J. Premalatha Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of Computer Technology Madras Institute of Technology Anna University Chromepet Amaala Utilities PPP Larsen & Toubro Limited Soudi Arabia Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engg. and Tech. Mamallapuram

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Proposed work describes an optimal framework for improving supply chain security and delivery efficiency by combining Ant Colony Optimization (ACO) and blockchain technology. Inconvenient path design is a primary trouble in last-mile delivery, causing delays and wasting resources. This study seeks to address the above issue by optimizing delivery schedules, guaranteeing that many purchasers in the same geographic area are handled by a single delivery person, hence eliminating redundant trips and delivery times. Proposed technique is driven by real-world inefficiencies seen in e-commerce systems, such as the case of two independent things bought to the same address but delivered at different times by different couriers on the same day. ACO is used to find the best delivery route, guaranteeing that all consumers in a particular area receive their items simultaneously. The framework incorporates blockchain technology to improve security, transparency, and traceability during the delivery process. Furthermore, advanced cybersecurity measures such as Root of Trust (RoT), Physically Unclonable Functions (PUFs), and Software Bill of materials (SBOm) are used to secure data integrity and enable third-party monitoring. By combining ACO for route optimization with blockchain for safe transaction processing, this system not only improves shipping time as well as asset efficiency, but it also protects supply chain cybersecurity. The suggested approach provides a scalable and reliable framework for future smart logistics and e-commerce applications, assuring operational efficiency and security.

关键词： Ant colony optimization Visual analytics Supply chains Bills of materials Physical unclonable function Software Real-time systems Blockchains Electronic commerce Software measurement

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Soil mineral Prediction of Crops Using machine Learning 3

Soil Mineral Prediction of Crops Using Machine Learning

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3rd International Conference on Intelligent Computing, Instrumentation and Control technologies, ICICICT 2022

作者： Akkasaligar, Prema T. Biradar, Sunanda Gudgeri, manjula C. mulla, Sana mohammadi A BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur586103 India Karnataka Vijayapur586103 India Kle Dr M S Sheshgiri College of Engg and Tech Department of Engg Science and Humanities Mathematics Karnataka Belagavi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In India, agriculture is the primary source of income. Agriculture employs the majority of the people. Because India's financial system is so reliant on agriculture, there is a pressing need to boost agricultural output generally. Soil is the most important natural resource in agriculture. The most important requirement is good soil quality. The pH of the soil is crucial in this regard. The acidity of the soil is measured by its pH. The total yield is influenced by the pH level. Nitrogen, Potassium, and Phosphorus all influence pH of soil. Farmers can then begin cultivating crops as a result of this. pH sensors are commonly used to determine the pH of soil. However, because they increase the cost of manufacture, they are not always practicable. To address this problem, the proposed method determines the pH value while simultaneously recommending a crop. The crop recommendation method takes into account factors such as soil pH, season, and temperature. © 2022 IEEE.

关键词： Nitrogen

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An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep Learning techniques

An Effective Approach for Image Denoising Using Wavelet Tran...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Abhay Shukla K. Seethalakshmi P Hema Jitendra Chandrakant musale Department of Computer Science and Engineering Axis Institute of Technology and Management Kanpur Uttar Pradesh India Department Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 62 Tamilnadu India Department of Mathematics R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Computer Science and Engineering Shri Jagdishprasad Jhabarmal Tibrewala University University in Jhunjhunu Rajasthan India

In contemporary years, image denoising has developed as an important investigation topic in the arena of image processing due to the increasing demand for high-quality images. Deep learning approaches have recently demonstrated outstanding results in a variety of image processing applications. In this research, a wavelet transform and deep learning-based method is suggested for image denoising. To successfully remove noise from an image, the suggested method is based on a hybrid network that combines wavelet transform with convolutional neural networks (CNNs). The suggested method is divided into two steps, one based on wavelet transforms and the other on deep learning. The input image is divided into wavelet coefficients at various scales and orientations in the wavelet transform-based stage. The noise is subsequently filtered out of the wavelet coefficients using a thresholding method. A CNN is taught to acquire the plotting among the noisy image and the denoised image during the deep learning-based step. The experimental results show that, in terms of both quantitative metrics and visual quality of the denoised images, the suggested strategy outperforms a number of state-of-the-art image denoising methods. The suggested method is also independent of the prior knowledge of the noise statistics and resilient to various types of noise. Overall, the suggested method successfully tackles image denoising by fusing the compensations of deep learning and wavelet transform. The suggested method could be used for a variety of computer vision applications that call for high-quality image denoising.

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Towards Precision Oncology: A Study of machine Learning models for Breast Cancer Prediction

Towards Precision Oncology: A Study of Machine Learning Mode...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： L.A. Anto Gracious P. J. Beslin Pajila G. Johncy T. P. Anish K. Prasanth Siva Subramanian R Dept of CSE R.M.K. College of Engineering and Technology Tiruvallur India Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept of CSE St. Xaviers Catholic College of Engineering Nagercoil India Computer Science and Engineering S.A.Engineering College Chennai Dept of CSE R.M.K. College of Engineering and Technology Puduvoyal

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The rapid growth in Breast Cancer (BC) related death is frightening, making it the most prominent cancer type and a cause of death of women worldwide. It is most important to have advancements in predicting and diagnosing cancer for promoting a healthier life. To increase the advancement in treatment and to increase the survivability of patients it is important to achieve a high level of accuracy in cancer prediction. The mL techniques have given a huge contribution in the prediction process and diagnosing BC, gathering noticeable attention in research, and demonstrating the desired result. The mL algorithms like SVm, RF, LR, C4.5, KNN are applied to the BC Wisconsin Diagnostic dataset. After getting the results, performance evaluation and comparisons were conducted among the various classifiers. The aim of the research paper is to find out and diagnose the BC with the help of the applications of mL algorithms, and with the most effective factors like recall, F-measure accuracy, and precision. SVm gave a good performance comparing to the other classifies by achieving the accuracy of 97.2%. The research describes the role of mL in improving the BC prediction and diagnosis, and providing a clear understanding about the effectiveness of different algorithms to improve the health conditions.

关键词： Support vector machines Radio frequency Accuracy machine learning algorithms Computational modeling Predictive models Prediction algorithms Classification algorithms Reliability Python

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