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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Kalaiyarasi M N venkata sireesha s. saravanan D s Naga Malleswara Rao Karthi s K. Karunanithi Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences SIMATS Chennai Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of EEE B V Raju Institute of Technology Narsapur Telangana India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of CSE V.S.B Engineering College Karur India Department of EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

COvID-19, caused by the sARs-Cov-2 virus, has impacted over 250 countries worldwide. Early recognition of COvID-19 infections is essential to effectively manage this global outbreak. While chest X-rays are widely available and cost-effective imaging techniques, their accurate interpretation necessitates the expertise of knowledgeable professionals in assessing the severity of infectious. Even though many COvID-19 pneumonia virus recognition techniques are available, each method has advantages and disadvantages. This paper proposes an efficient automated detection system for pneumonia-affected X-ray images utilising a bilateral filter and RF classifier. The proposed method presented in this study was trained and evaluated using a publicly available X-ray dataset specifically designed for classifying tertiary and normal cases. Furthermore, the proposed method demonstrated reliable accuracy of 94% and recall of 87% in binary classification tasks.

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Machine Learning Approach for Diabetes Prediction using Genetic Algorithm based Feature selection

Machine Learning Approach for Diabetes Prediction using Gene...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： T.s. Ravi Kiran A. srisaila G. siva shankar bodasingi sowjanya A. Lakshmanarao Department of Computer Science P.B. Siddhartha College of Arts & Science Technology Vijayawada A.P India Department of Information Technology V.R Siddhartha Engineering College Vijayawada A.P India Department of AI&ML Aditya Engineering College Surampalem India Department of CSE Centurion University of Technology and Management Vizianagaram Andhra Pradesh Department of IT Aditya Engineering College Surampalem India

Diabetes, a prevalent and complex medical condition, demands accurate predictive models for early detection and effective management. This paper introduces a novel approach for diabetes prediction by combining genetic algorithm-based feature selection with ML classification. by combining the genetic algorithm's capability to optimize feature selection and the predictive power of ML classifier, this work offers a promising avenue for improving diabetes prediction accuracy. Two datasets from Kaggle were collected. Initially, RF applied on both datasets. Later, datasets ae balanced using oversampling technique "ADAsYN". Later, genetic algorithm is employed to optimize feature selection, with the fitness function minimizing the negative accuracy of the model. The selected features are then used to train a final model, and the accuracy is evaluated on the test set. The results showcase the effectiveness of the proposed approach in enhancing diabetes prediction accuracy when compared to base model. Results from both datasets shown accuracy enhancement with GA feature selection. The selected features provide valuable insights into the influential factors contributing to diabetes outcomes.

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Cognitive Fire Alert Framework for Realizing Fire in Unfriendly Environment using Wireless sensor Network

Cognitive Fire Alert Framework for Realizing Fire in Unfrien...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： A. sathiya A. K. velmurugan P. santhuja G. Indra Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science And Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering R.M.K College of Engineering and Technology Chennai Tamil Nadu India

Nowadays, Wireless sensor Networks (WsNs) are extensively distributed in environmental monitoring, operational health monitoring, and industrial monitoring. It is an inexpensive application. It contains small, less energy-consuming, lower-cost devices combined with restricted computation, observation, and radio transmission capacities. People nowadays use fire alarms with sensors to detect fires. Fire sensors are unreliable. With the fast growth of WsNs, people can now remotely watch their surroundings to ascertain the current status. A standard fire alarm system is only able to detect one of the environmental parameters at a time. It is possible that there is a fire if the detection value is greater than the threshold; this would indicate that there is a fire. Copper, insulated, or cables are used to make the connections between detectors and alarms in a traditional fire alarm system. Due to the presence of a large number of elements at the site of the fire, the smoke alarm system will be unable to make an accurate determination as to the location of the flames. This article proposes a cognitive fire alert framework (CFAF) for realizing fire in unfriendly environments using WsN. It has a phase for wireless transmission, a phase for cognitive fire recognition, and a phase for the Random Forest Method (RFM). This approach utilizes the RFM to classify the fire by data classification. The results of the experiments show that the suggested technique minimizes the number of false alarms by evaluating the delay value by utilizing data samples and comparing the temperature, humidity, pressure, and dust level.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Temperature distribution Humidity Forestry

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Autonomous vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Jayant shekhar Rama Prabha K. P s. Prashanth Umamaheswari K Rahul s G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (Av) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (vWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help Avs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mobile communication smart cities Heuristic algorithms Real-time systems Artificial intelligence vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Enhancing Fruit and vegetable Preservation with support vector Machine and IoT Connectivity

Enhancing Fruit and Vegetable Preservation with Support Vect...

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Automation and Computation (AUTOCOM), International Conference on

作者： D. satyaraj T. Prabakaran Y. M. blessy L. N. Jayanthi s. K. Mouleeswaran s. velmurugan Department of Electronics and Communication Engineering Arjun College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Computer Science and Engineering Dayananda Sagar University Innovation Campus Bangalore Karnataka India Department of Electronics and Communication Engineering T.J.S. Engineering College Channai Tamil Nadu India

This paper presents a new method for extending the shelf life of perishable food items by combining the use of support vector Machine (svM) algorithms with Internet of Things (IoT) connection. Problems with early spoiling detection and inefficient storage condition control plague conventional approaches to fruit and vegetable preservation. Using svM it analyze environmental variables including humidity, temperature, and gas concentrations that impact commodities with a near-term expiration date in real time via this research. sensors, actuators, and a command centre can all communicate to one other without a hitch thanks to the IoT. With this integration, storage conditions may be dynamically adjusted according to svM predictions, guaranteeing the best possible preservation. In light of the ever-changing nature of farming, the suggested system provides an adaptable and scalable answer to the growing need for effective and environmentally friendly food storage solutions. The experimental findings show that the suggested method may prolong the freshness and quality of produce while reducing the amount of resources wasted. The findings of this study will help bring data-driven technology into the food business more widely and improve smart agriculture practices. It highlights the efficacy of combining IoT connection with svM algorithms for vegetables and fruits storage. Agricultural operations and customers may both profit from the results, which show a considerable decrease in waste, an increase in freshness, and better quality control.

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Autonomous vehicle Traffic Flow Optimization Using AI and 5G Communication Networks 3

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： shekhar, Jayant Rama Prabha, K.P. Prashanth, s. Umamaheswari, K. Rahul, s.G. Gayathri, K. Sharda School of Engineering and Technology Sharda University Department of Cse UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Excel Engineering College Department of Aeronautical Engineering Tamil Nadu Namakkal India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

关键词： Traffic congestion

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

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International Conference on Futuristic Technologies (INCOFT)

作者： shifa Aiman bagban Ravi Hosur sahebgouda Patil Dept. of Computer Science & Engineering BLDEA's V.P.Dr. P. G Halakatti College of Engineering & Technology Vijaypura Karnataka (INIDA) Dept. of CSE(AI & ML) BLDEA's V.P.Dr. P. G Halakatti College of Engineering & Technology Vijaypura Karnataka (INIDA)

Agricultural productivity is the factor on which many countries' economies heavily rely on. Identifying plant diseases is extremely crucial in the agricultural sector as they can hamper the plant's robustness and health which is important in agricultural productivity. These obstacles are regular in plant production ,and if adequate procedures of prevention are not followed it may inversely affect the cultivation. Farmers are confronting financial loss due to various crop diseases. The traditional method of disease detection is achieved by the opinion of an expert and physical inspection, which takes much time and is expensive in the practical life. And when the cultivated area is big in acres, it becomes very tough for the cultivators to track the crops periodically. That's why an automated computer based disease detection system has become essential. The system developed in this project involves using image processing techniques such as opencv2 that are designed using python to segment the disease part from the leaf while using machine learning techniques like svM and CNN to categorize the leaves of the plant as healthy or infected. For classification purposes, morphological characteristics and properties such as colour, strength and measurements of the plant leaves are taken into account.

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Enhanced Rendering-based Approach for Improved Quality of Instance segmentation in Detecting Green Gram (vigna Rediata) Pods

SSRN

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ssRN 2023年

作者： Dharwadkar, Nagaraj v. Math, RajinderKumar M. Department of Computer Science and Engineering Rajarambapu Institute of Technology Maharashtra Islampur India Department of Electronics and Communication Engineering B.L.D.E. Association’s V.P Dr. P.G. Halakatti College of Engineering and Technology Ashram Road Vijayapur India

The emergence of Artificial Intelligence, deep learning, and current computer vision algorithms are the main contributors to innovations in the agricultural domain. The most recent detection algorithms capable of giving real-time detections at the edge nodes tackle most agricultural problems, such as disease, pest or insect detections, and maturity level detection of crops (fruits and vegetables). Modern harvesters and fruit-picking robots rely heavily on the detection capability of the algorithms used. various detection algorithms have been proposed and used in literature, having good performance in terms of mean average precision. still, the current agricultural systems require not only high mean average accuracy but also algorithms should have high inference speeds. The research proposes a Detectron2-based framework with PointRend (Point-based Rendering), capable of providing enhanced, high-quality pixel-level instance segmentation in identifying and detecting green gram pods or Mung bean (vigna Radiata) in natural field conditions rendering crisp and smooth boundaries for accurately locating the green gram pods. The results indicate that the proposed framework outperforms the famous Mask R-CNN model to obtain higher mean average precision and improved quality of detections. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Experimental validation of the Aging Model of Lithium-Ion batteries Regardless of Deterioration Conditions

Experimental Validation of the Aging Model of Lithium-Ion Ba...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Nitin A. Kharche Praveen singh N.b. soni Daxa vekariya Harshal Patil Ramya Maranan Department of Mechanical Engineering Padm. Dr. V. B. Kolte College of Engineering Malkapur Maharashtra India Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electrical Engineering Maharshi Parshuram College of Engineering Velneshwar Maharashtra India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

This work validates, experimentally, the conformity of the electrical aging model for LiFeP04 and NMC type lithium cells by taking into account the effects of temperature, current and internal resistance on its yield. If necessary, modifications will be made to the aging model based on the experimental results. To carry out this work, the following structure was used. First, a literature review outlining the chemical principles and aging mechanisms of lithium batteries. Next, the battery aging model and everything that makes it up will be analyzed. In order to collect experimental data, lithium batteries and a test protocol will be put to the test bench. validation will be done by comparing the simulation results of the proposed model with the experimental data obtained. This model will then be compared to another aging simulation technique. To conclude, the differences between the two simulation methods and the experiment will be analyzed to establish the validity and advantages of the proposed model.

关键词： Lithium-ion batteries Analytical models Chemistry voltage Aging Predictive models Lithium batteries

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Optimizing English Lexical Databases with bERT and Reinforcement Learning

Optimizing English Lexical Databases with BERT and Reinforce...

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Advances in Electrical, Computing, Communication and sustainable Technologies (ICAECT), International Conference on

作者： v. saranya G.R.K. Murthy Purnachandra Rao Alapati M. Mythili K. swarnamughi b Kiran bala Department of English Panimalar Engineering college Chennai India Department of B.S&H (English) Narasaraopeta Engineering College Narasaraopet India Department of English Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh India Department of English Nandha Engineering College(Autonomous) Erode India Department of English Sathyabama Institute of Science And Technology Chennai India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging bERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates bERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics.

关键词： Databases scalability semantics Reinforcement learning bidirectional control Linguistics Transformers Natural language processing Encoding Usability

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