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共 11874 条记录，以下是131-140 订阅

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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

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intelligent Classification and Sorting optimization of Transported Items Combined with BiLSTM Model 3

Intelligent Classification and Sorting Optimization of Trans...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Fu, Guodong Hou, Huijuan Shi, Sansheng Song, Shanshan Marketing Service Center State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798331508456

In view of the low efficiency and large error of the traditional method of transport item classification and sorting, this paper proposes an optimization method based on the bidirectional long short-term memory network (BiLSTM) model. First, the multi-dimensional feature data of the items, including size, weight, shape, material, and historical location information, are collected to construct a comprehensive feature vector. Then, the BiLSTM model is used to train the time series data, and the time dependency is captured through forward and backward propagation. Finally, the proximal policy optimization (PPO) strategy is combined to optimize the transportation path and sequence to improve the system adaptability. This method can significantly reduce the transportation time (such as from 38.87 minutes to 18.58 minutes in Experiment 1) and the path error (from 7.16 meters to 1.13 meters in Experiment 3). This study provides an efficient and reliable optimization solution for intelligent warehousing systems. © 2025 IEEE.

关键词： Travel time

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Fusion learning Framework for Malignant and Benign Classification in Histopathology Images 3

Fusion Learning Framework for Malignant and Benign Classific...

引用

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

作者： Subramaniyam, P. Hari Krishnan, G. Department of ECE School of Engineering Mohan Babu University Tirupati India Department of EEE School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798331527549

This work presents a robust ensemble-based deep learning approach for classifying breast cancer histopathology images from the BreakHis dataset into Malignant and Benign categories. This study implements three ensemble strategies efficientNetB0 and ResNet50, EfficientNetB0 and VGG16, and VGG16 and ResNet50—the study highlights the potential of combining complementary deep learning architectures for improved classification performance. Each ensemble incorporates modifications to optimize feature extraction and generalization for the specific dataset, including dense layers, dropout, and feature concatenation techniques. The proposed methodology achieves high accuracy, precision, recall, and F1-scores across all methods, with the EfficientNetB0 and VGG16 ensemble demonstrating superior performance. Comparative analysis with state-of-the-art methods with robustness and adaptability of the proposed approach, offering a scalable and efficient framework for medical image classification. This work provides valuable insights into the application of ensemble learning for reliable cancer diagnosis. © 2025 IEEE.

关键词： Contrastive learning

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Performance optimization in Communication Systems Using Deep Reinforcement learning with Elite Reverse learning Strategy - Arctic Puffin optimization 3

Performance Optimization in Communication Systems Using Deep...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Zhang, Jiaxiao Xidian University Xi'an China

ISBN: (纸本)9798331508456

The communication system involved in the various networking it becomes decentralized and automatic, network articles is to make decisions for increasing network performance in uncertainty network environment. The challenges of handle the large-scale network it making complex, state and action spaces are generally huge and reinforcement learning unable to identify optimum policy in reasonable time. In this research, Deep Reinforcement learning with Elite Reverse learning Strategy - Arctic Puffin optimization (DRL with ERL-APO) is developed for the effective performance optimization in communication. The strategy of reverse learning strategy is incorporated with traditional APO algorithm which improves the convergence rate of traditional APO algorithm for efficient communication performance optimization. For ensuring the high rate for less channel gained the users who needs much energy in terms of deprived energy share. Therefore, outcomes on substantial minimize in sum rate. The proposed DRL-APO with ELS obtained execution time of 8 on 10 SNR, 9 of 20 SNR, 10 of 30 SNR, 11 of 40 SNR and 12 of 50 SNR which is better while compared with existing algorithms. © 2025 IEEE.

关键词： Signal systems

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Design of a Real-Time Crime Monitoring System using Machine learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

引用

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

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 IEEE.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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ProctorGuard: AI-Powered Examination Monitoring with Face and Device Detection 3

ProctorGuard: AI-Powered Examination Monitoring with Face an...

引用

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

作者： Harshitha, G.M. Kotian, Rahul V. Shetty, Rakshith Nitte Karkala India

ISBN: (纸本)9798331527549

This study addresses the challenges of cheating and illegal access in online exams, maintaining academic integrity with an AI-powered web-based monitoring *** we uses COCO-SSD to detect misconduct such as many faces, no face, and phone usage, notifying the user and recording instances for teachers. It is built with *** and *** to deliver a safe, scalable, and user-friendly platform. Future additions will incorporate advanced models such as Vision Transformers, super-resolution techniques, and language assistance to improve detection and accessibility. This solution establishes a standard for secure, inclusive, and dependable online exams. © 2025 IEEE.

关键词： Academic honesty AI-powered surveillance anti-plagiarism tools automated exam grading digital exam platform distance learning internet-based assessments live monitoring Node.js server-side

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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

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Iterative Skill optimization based Recurrent Network for Air Quality Forecasting 3

Iterative Skill Optimization based Recurrent Network for Air...

引用

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

作者： Sannasi, Periasamy Subramanian, P. Surendran, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509675

This research introduces a novel air quality prediction model using the optimized deep learning model for enhancing the forecasting accuracy by solving the issue of over-fitting. For this, Iterative Skill optimization based Recurrent Network (Iter-SkO-RecurNet) model is proposed. The Iter-SkO algorithm is used to optimize the algorithm's hyper-parameters in order to minimize the loss function in the developed model, which uses LSTM for air quality forecasting. The loss function optimization is carried out using the suggested Iter-SkO algorithm, which incorporates the iterative chaotic mapping into the Skill optimization process. The iterative chaos introduces the randomness in the algorithm and assists to acquire global best solution. The analysis of the proposed model based on R2, RMSE, and MAPE and acquired the values of 0.9775, 0.92714775, and 8.174332 respectively. © 2025 IEEE.

关键词： Prediction models

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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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Efficient Brain Tumour Classification using Pretrained Xception Model 3

Efficient Brain Tumour Classification using Pretrained Xcept...

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

作者： Raghava Kumari, D. Rajendra Prasad, Ch. Samala, Srinivas Dept. of ECE SR University Warangal506371 India Department of ECE Sumathi Reddy Institute of Technology for Women Telangana Warangal506371 India

ISBN: (纸本)9798331527549

A brain tumour is among the most lethal tumours worldwide. It exhibits the lowest survival rate and presents various varieties based on location, texture, and form. Thus, accurately determining the type and grade in the early stages is crucial to selecting an appropriate treatment strategy. The substantial volume of data and the diverse forms of brain tumors render the manual procedure time consuming and prone to human error. This research study presents a brain tumour classification using the Xception transfer learning model. The proposed Xception model is trained and tested using the Brain Tumour Image Dataset collected from Kaggle. The raw data are preprocessed before applying to transfer learning model to match its parameters. The proposed model provides a testing accuracy of 0.98 and a loss of 0.02 and shows exceptional efficiency contrasted to the existing models. In real-time, this model assists the doctor in identifying the particular brain tumor. © 2025 IEEE.

关键词： Contrastive learning

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