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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Keerthana, E. Rekha, K.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

Advanced Multimodal Conversational Bot powered by the LLama 2 model, which has 13 billion parameters. The bot can provide personalized song recommendations that are flexible across languages. It uses text, and audio inputs to understand users from different linguistic backgrounds. It accurately assesses the user's mood in multiple languages through sentiment analysis and refines emotional context using image and video analysis. This allows it to offer song recommendations considering the user's mood, preferences, and realtime context, regardless of their chosen language. The bot also handles incomplete or unclear queries in multiple languages, ensuring precise and relevant song suggestions. A custom dataset tailored for music recommendations is used for finetuning, enhancing the bot's ability to provide personalized and contextually relevant song suggestions. © 2024 IEEE.

关键词： Bot (Internet)

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Gesture-Driven Virtual Mouse: Empowering Accessibility through Hand Movements 5

Gesture-Driven Virtual Mouse: Empowering Accessibility throu...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Suresh, Danush Ramyadevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The mouse has been a vital tool for human-computer interaction, with wired, wireless, and Bluetooth variations requiring power to connect a dongle to a PC. The proposed work uses the latest technology in computer vision and machine learning to recognize hand movements without needing extra equipment. Compatible with CNN models through mediapipe implementation, it enables various actions via various hand movements, all on a single computer. Utilizing a camera as the input device, the system relies on Python and OpenCV. The camera's output is displayed on the system's screen, allowing users to fine-tune their interactions. © 2024 IEEE.

关键词： computer vision gesture recognition hand gestures interaction Virtual mouse

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Detection of Neurocognitive Impairment in the Elderly Using TabNet Algorithm

Detection of Neurocognitive Impairment in the Elderly Using ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ananthajothi, K. Oviya, K. Balasundaram, Navya Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

As the global population ages, neurocognitive disorders like the Alzheimer's disease which is a form of dementia, and several other forms of dementia are becoming more commonplace. These conditions significantly impair the quality of life for patients and present a growing challenge for healthcare systems worldwide. Early detection is critical to improving patient outcomes, yet traditional diagnostic methods often struggle to catch on to the very early and subtle signs of decline in cognitive function. In this paper, a deep learning algorithm, TabNet, is used on clinical data to detect neurocognitive impairment. Before applying the algorithm, Exploratory Data Analysis is also performed to get a better understanding of the dataset. This research is mainly focused on people with family history. The TabNet model, designed especially for tabular data easily and effectively learns the importance of each feature and their inter-dependencies. In future work, it is aimed to make this research more holistic by using Vision Transformer models to analyze MRI images. This study currently focuses on clinical data, which includes demographic, lifestyle, and cognitive assessments, and demonstrates how TabNet can be used to identify signs of cognitive impairment. Working with critical features, this study also focuses on data pre-processing and feature selection. This project's main aim is to utilize AI in healthcare to help with diagnoses that are otherwise somewhat difficult to make. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Certificateless Multi -Replica Data Integrity Auditing for Shared Data in Cloud-Based Disaster Resilience Systems 1

Certificateless Multi -Replica Data Integrity Auditing for S...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Kumar, Deepansh Giri, Aaryaman Srivastava, Akhilesh Kumar Abes Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350384246

In the context of Disaster Resilience Systems(DRS), where data integrity and availability are paramount, cloud storage plays a crucial role. However, security concerns persist, necessitating innovative approaches. In the context of the DRS framework, this paper presents a novel Certificateless Multi-Replica Public Auditing Scheme designed for shared data in cloud storage. The suggested solution handles security concerns without adding to the workload associated with key escrow problems or certificate administration. This technique allows for secure user withdrawal from user group. It is shown that this scheme is secure against three various adversaries, defined in this paper, under common hardness assumptions by a thorough security study. Performance evaluations shows that this scheme is efficient and is also practical, positioning it as a viable solution for ensuring data integrity and availability in DRS. © 2024 IEEE.

关键词： Cloud storage

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Early Diagnosis of Lung Cancer using Hyperparameter-Tuned Machine Learning Models 2

Early Diagnosis of Lung Cancer using Hyperparameter-Tuned Ma...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Basha, Shaik Khaja Mohiddin Rizwana, Syed Chandra, Gottimukkala Sasank Manikanta, Gudi Naik, Bukya Venkata Sai Durga Dodda, Venkata Reddy Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopet India

ISBN: (纸本)9798331504960

Globally, lung cancer is the leading cause of cancer-related deaths. Improving survival depends on early and precise identification, which is still mostly accomplished using antiquated techniques that are frequently ineffective and erroneous. In this study, we used patient demographic and clinical data to create a machine learning-based risk prediction model that aims to diagnose lung cancer early and accurately. To verify the performance on our lung cancer data set, we employed important machine learning algorithms such as logistic regression, random forest, and support vector machines (SVM). PCA for feature selection, resampling to balance classes, and handling missing values were all examples of preprocessing. Among models. Random Forest shown excellent efficiency in risk detection, while SVM ultimately. © 2024 IEEE.

关键词： Lung cancer

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An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

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Multimedia Tools and Applications 2025年 1-35页

作者： Alqahtani, Abdullah Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University KSA Al-Kharj Saudi Arabia

In this study, we propose a novel framework for detecting abnormal events in surveillance videos, a critical yet challenging task in security applications. This research introduces a robust and efficient solution for video anomaly detection, offering substantial improvements in surveillance systems' ability to detect abnormal events, thereby contributing to enhanced security measures in public spaces. The proposed framework utilizes a Multiscale Convolutional Autoencoder (MSCAE) that processes inputs from RGB, depth, and optical flow video clips, enhancing the detection accuracy in complex scenes characterized by varying object scales, aspect ratios, and occlusions. To address the challenge of noise and preserve edges in video data, we implement a two-pass bilateral smooth filtering method, which is effective for noise-invariant, edge-preserving image smoothing. For object detection within these complex scenes, an enhanced Faster R-CNN model is employed. This model's performance is further refined through transfer learning on a dataset specifically composed of abnormal event videos. We also introduce significant improvements to the region proposal network (RPN) of the Faster R-CNN, particularly in non-maximum suppression (NMS) and anchor generation techniques, to better detect anomalies in diverse and complex environments. Furthermore, the MSCAE is integrated with Long Short-Term Memory (LSTM) neural networks to classify the detected anomalies, creating an end-to-end solution for video anomaly detection. Hyperparameter optimization for our deep learning models is performed using the Chameleon Swarm Algorithm, ensuring optimal model performance. Our framework was rigorously tested on the CUHK Avenue dataset, where it achieved a remarkable 99.5% accuracy, significantly outperforming existing methods and demonstrating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Optical flows

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ResNet-CNN Model for Plant Disease Classification for E-Agriculture Applications

ResNet-CNN Model for Plant Disease Classification for E-Agri...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, Arava Vamsi Sreenivasu, S.V.N. Narasimha Reddy, Kandi Venkata Narasaraopeta Engineering College Department of Computer Science & Engineering Narasaraopet India

ISBN: (纸本)9798350360660

The advancement of an economy's innovation capacity is closely tied to the progress in agriculture. Effective plant disease management is crucial for optimizing crop yields and ensuring agricultural sustainability. This project focuses on developing a Plant Disease Detection and Classification Network (PDDC-Net) by integrating deep learning models. The preprocessing stage standardized the dataset images by eliminating various types of interference. The PDDC-Net employs a Residual Network (ResNet)-Convolution Neural Network (CNN), featuring residual networks, to enhance feature extraction and classification accuracy. The PDDC-Net model demonstrated exceptional performance in detecting and classifying plant leaf diseases. Specifically, it achieved an accuracy rate of 99.84% across various crop diseased leaves, including those from potato, tomato, and pepper plants. Further, the suitable pesticide also suggested for the recognised disease. The proposed PDDC-Net effectively addresses the challenge of plant disease identification with high accuracy, underscoring its potential as a robust tool for agricultural disease management and innovation. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Hyperparameter Tuning Strategies for Optimizing Model Performance 2

Exploring Hyperparameter Tuning Strategies for Optimizing Mo...

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

作者： Julian, Anitha Devipriya, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The proposed work explores different machine learning hyperparameter tuning techniques to maximize model performance. By systematically adjusting hyperparameters, such as learning rates, regularization strengths, and network architectures, models can better capture underlying patterns in data and improve predictive accuracy. The study looks on commonly utilized hyperparameter tuning techniques. The benefits, drawbacks, and suitability for various situations of each technique are examined. Furthermore, the efficiency and scalability of automated frameworks for hyperparameter tuning are assessed to maximize model performance. The information gathered from this study provides practitioners and researchers with helpful guidance for optimizing their machine learning models through hyperparameter adjustments. © 2024 IEEE.

关键词： Adversarial machine learning

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Heart disease prediction using machine learning

Heart disease prediction using machine learning

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

作者： Julian, Anitha Deepika, R. Geetha, B. Jeslin Sweety, V. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Anticipating and diagnosing coronary illness is the greatest test in the clinical business and depends on variables like the actual assessment, side effects and indications of the patient. Factors that impact coronary illness are body cholesterol levels, smoking propensity and corpulence, family background of diseases, pulse, and workplace. Coronary illness can be anticipated in light of different side effects, for example, age, orientation, pulse, and so on and lessens the demise pace of heart patients. In this task, we will be intently working with the coronary illness expectation. The coronary illness dataset from that dataset we will determine different experiences that assist us with knowing the weightage of each element and how they are interrelated to one another. Our only point is to recognize the likelihood of individual that will be impacted by a heart issue or not. We are utilizing K-Closest Neighbor (KNN) to foresee the exactness. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Machine learning

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Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance

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SN computer science 2025年第3期6卷 1-14页

作者： Shankar, G. Geetha, M. Kalaiselvi Ezhumalai, P. Department of Computer Science and Engineering FEAT Annamalai University Chidambaram 608002 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai 601206 India

Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach utilizing the recent YOLOv9 (You Only Look Once, version 9) deep learning model to detect and monitor river water flow levels effectively. By leveraging YOLOv9’s robust object detection capabilities, the proposed system achieves precise segmentation and real-time analysis of river water regions. The method not only identifies river water areas with high accuracy but also quantifies flow levels, providing critical data for flood prediction and management. Comprehensive testing was conducted using diverse datasets representing various river conditions and flow scenarios. The results demonstrate that YOLOv9 significantly outperforms traditional methods in terms of detection speed and accuracy, making it a valuable tool for enhancing flood disaster response and management strategies. This work contributes to the development of more resilient and proactive flood disaster management systems through the integration of state-of-the-art machine learning techniques, offering significant improvements in predictive capabilities and response effectiveness. The proposed system was trained and evaluated using real-time river flood datasets, achieving a notable accuracy rate of 98%, a mean average precision (mAP) of 97.5%, precision of 98%, and recall of 98% in predicting flood level characteristics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Flood disaster management Machine learning Object detection River water flow detection Water level segmentation YOLOv9

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