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Continuous Sign Language Recognition Using 3D Convolutional Neural Networks 3

Continuous Sign Language Recognition Using 3D Convolutional ...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Monish Shivamma, D. Srinivas, K. Vinuth Gowda, S. Sheetal, P. Nandhini, S. Bengaluru India

ISBN: (纸本)9798331515911

This work proposes a novel computer vision approach for recognizing American Sign Language (ASL) gestures, utilizing OpenCV for image capture and manipulation, and MediaPipe for image labeling and preprocessing. With a diverse dataset of ASL gestures, two models are trained: a Convolutional Neural Network (CNN) and a Random Forest Classifier. The CNN model automatically extracts intricate spatial patterns from the ASL gesture images, achieving high accuracy through backpropagation and gradient descent optimization. Simultaneously, the Random Forest Classifier provides an efficient and accurate alternative approach by combining multiple decision trees based on features extracted from the images. Experimental results showcase the effectiveness of both models, offering promising prospects for improving communication and inclusivity among individuals with hearing impairments. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

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Human Activity Recognition with EfficientNetB3: A Model-Driven Approach to Complex Activity Classification 3

Human Activity Recognition with EfficientNetB3: A Model-Driv...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kaur, Jashanpreet Rani, Shalli Singh, Gurpreet Archana Reddy, R. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

Human Activity Recognition (HAR) is an application that encompasses surveillance to health care monitoring. The paper propounds deep learning (DL) in the classification of 15 different activities including calling, cycling, dancing, and running etc. through the use of EfficientNetB3 for the convolutional neural network. The dataset comprises more than 12,000 images with one specific category per image. In the experiment, different forms of data augmentation were used to avoid overfitting and enhance generalization. EfficientNetB3 has excellent balance between accuracy and computational efficiency in handling intricate complex patterns with fewer parameters and thus adopted for this experiment. The model was trained from scratch for 50 epochs, resulting in an accuracy of 90.10%, which was higher than that of traditional and hybrid methods of HAR. From a comparative analysis, the proposed model outperforms all competing approaches, such as YOLOv5 with LDA and SVM, as well as hierarchical classification models. The results show that DL based models work very well in discrimination of complex human activities. In an attempt to further make the system more versatile, future work will concentrate on optimization of the model for real-time HAR, fusion with sensor data, and extension to larger datasets with more classes of activities. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

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CNOL: A Convoluted Neural optimization Logic to Predict Leptospirosis Disease Detection based on Medical Data Evaluation Scheme 3

CNOL: A Convoluted Neural Optimization Logic to Predict Lept...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sajiv, G. Meenakshisundaram, N. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Leptospirosis is a widespread zoonotic disease that can cause severe health issues if not detected and treated early. This study presents a Convoluted Neural optimization Logic (CNOL) framework designed to predict Leptospirosis Disease using both structured and unstructured medical data. The proposed model integrates convolutional layers for feature extraction from complex datasets, including clinical information and medical images, and utilizes advanced optimization techniques like Adam to enhance learning. The CNOL framework tested on a Leptospirosis Disease Dataset, and results show a significant improvement in predictive accuracy. The model achieved an accuracy of 95.67%, outperforming traditional machine learning models such as Logistic Regression (85.45%) and Random Forest (89.56%), and deep learning models like Deep Neural Networks (93.10%). Additionally, the CNOL framework exhibited superior precision (94.85%) and recall (96.42%), highlighting its effectiveness in both identifying true positive cases and minimizing false negatives. This study demonstrates the potential of combining convolutional layers and advanced optimization techniques for enhancing disease prediction, providing a robust tool for early diagnosis and intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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A Hybrid Deep learning Model for Violence Detection 3

A Hybrid Deep Learning Model for Violence Detection

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chandira Prabha, S. Kaviyadharshini, S. Annie Micheal, A. Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence Dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios. © 2025 IEEE.

关键词： Deep learning

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DemoCraft: Using In-Context learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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Cyber Threat Attack Level Detection using Machine learning 3

Cyber Threat Attack Level Detection using Machine Learning

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rahul, R. Mythili, S. Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

This study focuses on creating a machine learning-based system for identifying cyber-attacks in real time using network data, system logs, and attack history. The work presents a thorough methodology that includes data collection, preprocessing, feature selection, model training, and real-time monitoring. The system uses a variety of machine learning techniques to classify attack types, including supervised learning methods such as Random Forest, Support Vector Machines (SVM), and Neural Networks, as well as unsupervised learning methods such as clustering to detect anomalies that indicate potential threats. The model was trained and evaluated on a dataset of 477 items, reaching an accuracy of 83.33%, with 470 entries identified as carrying threats. The study effectively proved the ability to detect numerous attack types, such as SQL injection, cross-site scripting (XSS), and DDoS attacks, as well as accurately classify and prioritize threat levels. Furthermore, the system was successfully deployed in a live environment, providing real-time threat detection, automatic response capabilities, and detailed alerts to security personnel. This research resulted in the creation of an efficient and scalable cyber threat detection framework capable of both detecting existing attacks and discovering novel, previously unknown threats. © 2025 IEEE.

关键词： Cyber attacks

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IoT-based Women’s Safety Monitoring in Educational Campuses using WSN and Deep learning 3

IoT-based Women’s Safety Monitoring in Educational Campuses...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pallavi Sinha, Raj Department of Electronics & Communication Jayoti Vidyapeeth Women’s University Rajasthan Jaipur India School of Computer Applications Lovely Professional University Punjab India

ISBN: (纸本)9798331527549

The recognition of human activity is significant in such domains as smart environments, security, and health. A novel approach to IoT-based women's safety monitoring at educational campuses using a hybrid deep learning model that combines CNNs and LSTMs has been proposed in this study. Using wearable inertial sensors, the system classifies six predetermined activities - walking, walking upstairs, walking downstairs, sitting, standing, and laying. The Human Activity Recognition dataset preprocessed with noise filtering and feature extraction was utilized in training and testing. Spatial features are extracted from CNNs while the LSTMs capture temporal dependencies, giving a 95.38% accuracy that is augmented with F1-score, precision, and recall exceeding 95%. This approach offers a robust, scalable safety solution for monitoring of activities that would be accomplished in real-world educational settings. © 2025 IEEE.

关键词： Wearable sensors

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Diagnose Kidney Cancers Using Machine learning Classification Algorithm From Transcriptome Profiles of Patients 3

Diagnose Kidney Cancers Using Machine Learning Classificatio...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Keerthika, V. Rakshitha, B. Poojashree, H.G. Alliance University Bangalore India

ISBN: (纸本)9798331515911

Kidney cancer is the ninth most diagnosed kidney cancer in men and the fourth most common in women according to the world health organization Report. Worldwide, kidney cancer causes 144,000 accidental deaths and 338,000 fresh cases of the disease each year. It is very important to diagnose it in the early stage to save one life. So that they can get treatment at the early stage. It is very hard to diagnose manually from clinical data and images by the doctors in the medical field. The recent technologies like artificial intelligence using image processing will help them to diagnose it *** have used TCGA Kidney Cancers Dataset, is a bulk RNA-seq dataset that contains transcriptome profiles of patients to identify kidney cancer. In this research we used MRMR and regression. In terms of accuracy, our results indicate that the regression model achieves 97% accuracy, this Machine learning algorithms to create a reliable and accurate diagnostic tool for diagnose the kidney cancer. The potential for improved clinical decision-making will be demonstrated by the combination of transcription profiles and machine learning approaches © 2025 IEEE.

关键词： Diseases

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Smartphone Price Prediction and Category Classification using Machine learning 3

Smartphone Price Prediction and Category Classification usin...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital Image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer intelligent System Segmentation U-Net

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