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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoliA-Net (Forest lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoliA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoliA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine learning Rare Trees Simulation Sustainability

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Hybrid GCN-lSTM Framework for Enhanced Schizophrenia diagnosis: Integrating Spatial and Temporal Insights from Brain Connectivity data 1

Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnos...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Anitha, R. Hemalatha Prathyusha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai601206 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

A new hybrid approach for diagnosing schizophrenia combines graph convolutional networks (GCNs) with long short-term memory (lSTM) networks, leveraging GCNs for spatial connectivity and lSTMs for temporal modeling. This integration allows a simultaneous examination of spatial and sequential brain connectivity patterns, offering a comprehensive view of schizophrenia's complex dynamics. By extracting critical features from brain connectivity data, the hybrid GCN-lSTM model enhances research insights and improves clinical interpretability. Testing and validation with real-world data demonstrate the model's superior performance over existing methods, advancing clinical decision support in psychiatry. Additionally, this approach not only improves our understanding of schizophrenia's mechanisms but also shows potential for broader applications, such as early and accurate dementia diagnosis. The study marks significant progress in applying advanced machine learning to clinical research. © 2024 IEEE.

关键词： Clinical research

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Multispectral image processing using Ml based classification approaches in satellite images

Multispectral image processing using ML based classification...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tallapragada, V. V. Satyanarayana Venkatesan, G. Manisha, G. Sivakumar, N. Kumar, Ashok Karthika, J. Department of Electronics and Communication Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Civil Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9781032669663

Many academics now choose to use intelligent algorithms over traditional ones when dealing with huge amounts of data, thanks to the introduction of technologies that resemble nature. Among these algorithms, the generative adversarial network (GAN) is one of the most important. The purpose of this research is to investigate whether the GAN can be used to classify multispectral images captured by satellites in the research area. Agriculture is represented by the research area. In addition to researching the GAN’s capability to classify multispectral images, an architecture for this domain is being developed. When the GAN was used to classify data, it showed higher accuracy compared to Maximum likelihood, where the GAN had an overall accuracy of (84.14%), while Maximum likelihood had an accuracy of (81.66%). © 2024 The Author(s).

关键词： Generative adversarial networks

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Autism detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： diseases

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Elevating Medical Training: A Synergistic Fusion of AI and VR for Immersive Anatomy learning and Practical Procedure Mastery

Elevating Medical Training: A Synergistic Fusion of AI and V...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Harinee, S. Vimal Raja, R. Mugila, E. Govindharaj, I. Sanjaykumar, V. Ragavendhiran, T. Computer Science and Engineering C.K. College of Engineering and Technology Cuddalore India Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Virtual reality with its 3d visualization have brought an overwhelming change in the face of medical education, especially for courses like human anatomy. The proposed virtual reality system to bring massive improvements in the education received by a medical student studying for their degree courses. The project puts forward the text-to-speech and speech-to-text aligned system that simplifies the usage of a chatbot empowered by OpenAI GPT-4 and allows pupils to vocally speak with Avatar, the set virtual assistant. Contrary to the current methodologies, the setup of virtual reality is powered by avatars and thus covers an enhanced virtual assistant environment. Avatars offer students the set of repeated practicing of medical procedures on it, and the real uniqueness in the proposed product. The developed virtual reality environment is enhanced over other current training techniques where a student should interact and immerse in three-dimensional human organs for visualization in three dimensions and hence get better knowledge of the subjects in greater depth. A virtual assistant guides the whole process, giving insights and support to help the student bridge the gap from theory to practice. Then, the system is essentially Knowledge based and Analysis based approach. The combination of generative AI along with embodied virtual agents has great potential when it comes to customized virtual conversation assistant for much wider range of applications. The study brings out the value of acquiring hands-on skills through simulated medical procedures and opens new frontiers of research and development in AI, VR, and medical education. In addition to assessing the effectiveness of such novel functionalities, the study also explores user experience related dimensions such as usability, task loading, and the sense of presence in proposed virtual medical environment. © 2024 IEEE.

关键词： Virtual environments

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CNN-Based Pneumonia detection and Severity Analysis in Chest X-Ray Images for Enhanced diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, d. Surendiran Chowdary, Bommineni Sreekar Kumar, d. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PdF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Transfer learning Model for lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.l. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCd lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： lung cancer

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Enhancing mechanical performance of kenaf fiber reinforced polymer composites: influence of fiber characteristics and processing techniques

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Interactions 2024年第1期245卷 1-20页

作者： Chanderasekaran, J. H. Rakini Naidu, B. Vishnu Vardhana Palanisamy, Satishkumar Vallinayagam, Sugumari Saminathan, Rajasekaran Arignar Anna Government Arts College Tamilnadu Musiri India Department of Mechanical Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Biotechnology Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jizan Saudi Arabia

The inherent fiber strength of kenaf is decent because of the high cellulose content and low micro fibrillar angle of the fiber. during the hand layup process of creating the epoxy matrix composite, it will be used as reinforcement in the proposed project. Composite testing has looked at how different fiber types, orientations, and content affect mechanical properties. different types of fibers (short length and long length fibers), orientations (0°, 90°, and 60°), and fiber contents (6, 12, and 18) were altered to prepare the samples. At 18 wt% fiber content, the mechanical characterization results show that the short fiber composite achieves its maximum tensile strength of 24.90 MPa and its ultimate fiber strength of 45.94 MPa. Nevertheless, the highest tensile and fracture strengths of long fiber reinforced composites containing 5% fiber are 38.96 MPa and 59.43 MPa, respectively. The composite showed an impact strength of 25.30 J m− 2 and a highest flexural strength of 56.32 MPa with fibres orientated at 0°. A maximum water absorption value of 1.25% was recorded for long fiber reinforced composites and 1.74% for short fiber reinforced composites. At last, the morphological analysis was performed to identify the most common causes of composite failure: debonding, splitting, and fiber pull-out. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Kenaf fibers

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Approach to Identifying Counterfeit Products with QR Codes and Computational Algorithms 5

Approach to Identifying Counterfeit Products with QR Codes a...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Prabhu Shankar, B. Rajkumar, N. Viji, C. dinesh Kumar, K. Saravanakumar, S. Mohanraj, A. Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Alliance University Department of Computer Science and Engineering Karnataka India Paavai Engineering College Department of Information Technology Tamil Nadu India Sri Eshwar College of Engineering Department of Computer science and engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331504403

The biggest challenge in the global market is Counterfeiting, it has undesirably affected consumer perception and damaged the brand reputation of the product. Recently few techniques used in the identification of counterfeit products include block chaining, RFId tags, and unique code techniques among others but they do not entirely control the existence of counterfeits in the market as well as losing their dependability and scalability. Consequently, these techniques are expensive and technologically advanced. This research presents a new approach based on reliable product authentication using QR code technology powered by mathematical computational algorithms. Employing implementing this system;however, customers can recognize fake goods and sustain the brand's integrity. The proposed approach entails creating Unique QR codes for every product whose coded data contains the encrypted product information. These include product numbers, manufacturing dates as well as batch numbers of the products which are hashed and then encrypted with sophisticated computational algorithms;these two algorithms secure the product, one generating QR codes for a product containing information thereof and the other that confirms if the consumer is holding a genuine item using a mobile application. Through experimental results, it was evident that the system is highly accurate, reliable, and speedy since verification times averaged below 1 second. This research also outlines some of the strengths of the proposed system including enhanced product originality as well as potential limitations such as internet connectivity and initial set-up costs. In future work, we will optimize our encryption algorithm and expand to other products within the system's coverage area. © 2024 IEEE.

关键词： Commerce

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Evaluation of CatBoost for diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Gifta Jerith, G. Jagadeesh, S. Ponmathi Jeba Kiruba, P. Mahesh, C. Anantha Babu, S. Samsudeen Shaffi, S. Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India GITAM University GITAM School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522988

diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes;to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach. © 2024 IEEE.

关键词： Prediction models

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