检索结果-内蒙古大学图书馆

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

作者： Snigdha, D. Tejasree, S. Rithvik, N. Shanmuagasundaram, Hariharan Motikar, Siddharth Reddy, S. Srinivas Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331509828

This project integrates Mediapipe with Python to implement a yoga posture analysis system for evaluating and providing efficacious feedback on human poses. The system establishes ideal joint landmarks using reference images, which are compared with uploaded analysis images over multiple simulated epochs. Angles of key joints, such as the elbow, shoulder, hip and knee are calculated and annotated onto the analyzed images for visual feedback. Deviations of analyzed images are identified and corrective suggestions are provided. Comprehensive graphs of accuracy and loss for each epoch are displayed to visualize the trends in accuracy and pose improvement. Accuracy reflects how well the model predicts the correct output, while loss indicates areas for improvement. In addition, for easy reference, all epoch data are tabulated to summarize overall performance, and a final model accuracy percentage is computed. The project also gives visual presentations of accuracy and loss over each epoch. The system serves as an effective tool for posture correction, making it suitable for fitness applications, physiotherapy, and ergonomic evaluations, with its detailed visual and numerical feedback. © 2025 IEEE.

关键词： Joints (anatomy)

来源：评论

学校读者我要写书评

暂无评论

NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

引用

SN Computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

来源：评论

学校读者我要写书评

暂无评论

PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

引用

International Journal of Advanced Computer science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (science and Information Organization). All rights reserved.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

RagaMoodSync: a deep learning framework for Hindustani Raga recommendations driven by facial emotions

引用

International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India Department of Computer Science & Engineering (Data Science) Vidyavardhini’s College of Engineering & Technology University of Mumbai Maharashtra Mumbai India

RagaMoodSync presents an innovative framework for personalized music therapy through Hindustani Raga recommendations based on facial emotion recognition. Utilizing a Fusion-DenseNet (FusDenseNet) architecture with advanced multi-pooling for emotion detection, the system achieves high precision in identifying user moods. Integrated with a Focus Deep Collaborative Filtering (FDCF) recommendation system, RagaMoodSync delivers tailored raga suggestions that address emotional well-being and stress management. Validated with the FER2013 dataset, the framework demonstrates superior performance over existing approaches. By merging AI-driven emotion recognition with the therapeutic potential of Indian classical music, this system pioneers a novel intersection of technology and mental health care, offering a promising pathway for non-invasive emotional healing solutions. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Focus Deep Collaborative Filtering FusDenseNet RagaMoodSync

来源：评论

学校读者我要写书评

暂无评论

Peer Collaboration in DBLP Using Graph Convolutional Network

引用

SN Computer science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP Graph convolutional network Graph mining Social networks

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Identification and Diagnosis Using YOLOv10

Brain Tumor Identification and Diagnosis Using YOLOv10

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nikhade, Aishwarya Barde, Dewanshu Murme, Harsh Agrawal, Rahul Dhule, Chetan Morris, Nekita Chavhan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India

ISBN: (纸本)9798331512965

The field of medical imaging has been greatly impacted in recent years by developments in deep learning and artificial intelligence, especially in the detection and diagnosis of brain tumours. In order to improve the accuracy and efficiency of brain tumour diagnosis on MRI scans, this research explores the possibilities of YOLOv10 (You Only Look Once), an inventive object detection algorithm. YOLOv10 differs from conventional segmentation techniques in that it incorporates state-of-the-art neural network architectures to offer real-time cancer region detection and classification. We investigate how well the model performs in relation to the most advanced methods currently in use across a range of parameters, including false positive/negative rates, processing speed, and detection accuracy. Our results show that YOLOv10 performs better in localising tumour boundaries while undergoing rigorous testing on a sizable dataset of annotated MRI images. Brain tumors present a major challenge to patients and the medical community, requiring novel strategies for accurate diagnosis and early identification. This journal article examines the state-of-the-art discoveries at the nexus of neuroscience and medicine that have transformed the area of diagnosing and detecting brain tumors. © 2025 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Secure AI based Chatbot Web Application with IBM Watson 3

Secure AI based Chatbot Web Application with IBM Watson

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvi, A. Abishak, D. Harish Sriraj, N. Navaneeth, S. Prasanna, R. Department of Artificial Intelligence and Data Science M Kumarasamy College of Engineering Karur India

ISBN: (纸本)9798331527549

This project focuses on developing a secure, AI-powered chatbot for retail chains, integrated within a web application to efficiently handle Frequently Asked Questions (FAQs). Utilizing IBM Watson’s advanced Natural Language Processing (NLP), the chatbot delivers precise, context-aware responses, enhancing the customer experience across retail environments. The platform offers a user-friendly interface for seamless interactions, a robust backend powered by IBM Watson’s AI model, and a database optimized for managing customer queries. A dedicated security module ensures data protection through encryption, authentication, and access control. This chatbot manages high volumes of inquiries with real-time responses, boosting customer engagement and operational efficiency while generating valuable insights for retail decision-making. Designed to be scalable and secure, the system upholds high standards of accessibility and data integrity, meeting the specific demands of the retail sector. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated Learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：