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共 31068 条记录，以下是31-40 订阅

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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Edge Node Load Balancing Using Game Theoretic Approaches 5

Edge Node Load Balancing Using Game Theoretic Approaches

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Haldar, Rajdeep Rooj, Saptadip Sharma, Neeraj Das, Ritambhar Mandal, Arnab Bandyopadhyay, Anjan Dept. of Information Technology Bhubaneswar India Lovely Professional University School of Computer Science and Engineering Jalandhar India School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computing systems. Focusing on a non-cooperative game model, we aim to enhance system efficiency by strategically optimizing resource allocation and load distribution among autonomous edge nodes. Initial simulations demonstrate that our approach not only addresses the inherent complexities of edge environments but also outperforms traditional load balancing mechanisms in terms of both operational efficiency and system responsiveness. © 2025 IEEE.

关键词： Edge computing

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Pathology Image Classification for Colorectal Cancer Prediction Using Experience Replay

Pathology Image Classification for Colorectal Cancer Predict...

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2025 IEEE International Conference on Computational, Communication and information technology, ICCCIT 2025

作者： Kumar, Penumacha Naveen Krishna, Ch. Nanda Lukka, Sai Lasya Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331512965

Pathology image classification helps diagnosis and monitoring as well as treatment of a disease. Thus, it can help the decision-making of a health worker. Still, conventional machine learning models struggle with medical data that often changes, as they typically need retraining on the full dataset to learn new information on a frequent basis. This retraining requires a lot of computations and sometimes loses what it 'knew' previously. This is called catastrophic forgetting. To solve this problem, we propose a continual learning framework, using Experience Replay to gradually add new pathology image data, while retaining knowledge of older tasks. Using Experience Replay, the model replays a sample of past data while training so that it does not forget any new information. The proposed framework is validated on the PathMNIST dataset, where it shows better classification accuracy and robustness than the classical retraining. This indicates that continual learning has the potential to be used for pathology image classification, and as a result, it can be scalable and adaptive to the needs of large-scale medical data. Through such advancements diagnostic accuracy will increase, resource use will become more efficient, and patient outcomes will improve in medicine. © 2025 IEEE.

关键词： Contrastive Learning

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Deepfake Detection of Singing Voices With Whisper Encodings

Deepfake Detection of Singing Voices With Whisper Encodings

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharma, Falguni Gupta, Priyanka Centre for Artificial Intelligence Banasthali Vidyapith Jaipur India Dept. of Communication and Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9798350368741

The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI's Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be noise-robust, the encodings are rich in non-speech information, and are noise-variant. This leads us to evaluate Whisper encodings as feature representations for the SVDD task. Therefore, in this work, the SVDD task is performed on vocals and mixtures, and the performance is evaluated in %EER over varying Whisper model sizes and two classifiers-CNN and ResNet34, under different testing conditions. © 2025 IEEE.

关键词： anti-spoofing Deepfake Detection Singing voice deepfake detection Transfer Learning Whisper

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Machine Learning-based Soil Analysis for Crop Forecasting

Machine Learning-based Soil Analysis for Crop Forecasting

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Raji Singh, Shiv Prakash Gupta, Rishabh Saurabh Agarwal, Shikha Ajay Kumar Garg Engineering College Dept. of Information Technology Ghaziabad India

ISBN: (纸本)9798331508685

Agriculture is a crucial part of India's economy, contributing over 17% to the nation's GDP (Gross Domestic Product), engaging over 60% of the workforce but still the effort-to-yield ratio of the sector remains significantly low. Analyzing soil properties, such as temperature, pH, humidity, water retention capacity, etc., helps determine soil quality, which is crucial for optimal agricultural productivity. This assessment later recommended crops that align with soil conditions and external environments, maximizing yield potential, and sustainable land management thereby preventing degradation. Similar to most studies aimed at advancing agriculture, the main objective of this research is to drive development by implementing ML-based models for crop classification. This paper presents a comparative analysis of various machine learning including KNN, SVC, Random Forest, etc on a single dataset. The comparative analysis highlights the advantages of each algorithm over others, helping to select the best-suited model to be implemented for real life applications. Our findings prove that the Random Forest algorithm outperforms the others, achieving the highest accuracy of 99.24%. © 2025 IEEE.

关键词： agriculture crop nutrient prediction soil analysis

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Bird Repeller Using Opencv 4

Bird Repeller Using Opencv

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Upendra, M. Anuradha, T. Prakash, L. Dept. of Information Technology Siddhartha Engineering College Velagapudi Ramakrishna Vijayawada India

This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person's presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used;YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds. © 2025 The Authors. Published by Elsevier B.V.

关键词： Bird Repellent Cascade Classifier computer Vision Distress Call image detection Open CV pi camera Raspberry pi YOLO

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Wrist Fracture Detection Using Deep Learning

Wrist Fracture Detection Using Deep Learning

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Rafi, Shaik Mahmamad Anuradha, T. Srinivas, Kothapalli S. V. V. R. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331529833

Effective treatment of wrist bone fractures depends on early and precise identification. The finding shows that disparities in accessing timely emergency services translate into adverse patient outcomes, especially in regions with fewer healthcare facilities and extended distances, mostly rural areas. Distal radial fractures or wrist fractures are among the commonly encountered musculoskeletal injuries and the treatment and subsequent management depend on timely and precise identification of the fracture pattern. This paper discusses a wrist fracture detection scheme using a deep learning approach for diagnosing wrist bone fractures from X-ray images which can close the health disparities due to the lack of adequate early diagnosis. and his team developed the system using DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-net and Inception V3 architecture because each was selected for a certain use effect patient outcome. Wrist fractures, among the most common musculoskeletal injuries, require early and accurate diagnosis to optimize treatment and rehabilitation. This project presents a deep learning-based system to detect wrist fractures in X-ray images, designed to bridge healthcare inequalities by facilitating early and automated diagnosis. The system integrates DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-Net, and Inception V3 models, each chosen for specific roles. DenseNet-201, MobileNetV2, and ResNet-50 are classification networks for fracture detection while U-Net is segmentation network to segment fractured areas. Furthermore, the results using EfficientNetV2 and Inception V3 improve diagnostic capability and image *** work compares the pros and cons of each model in terms of accuracy, computational time, and loss and presents recommendations for the most appropriate models for clinical applications in practice. © 2025 IEEE.

关键词： Deep learning

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Inspection of Ethical and Privacy Concerns in Online Social Networks

Inspection of Ethical and Privacy Concerns in Online Social ...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chandana, D.M. Sankalp, M.R. Kathavate, Sheela Aruna, S. Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

As the prevalence of Online Social Networks (OSNs) are created, it is apparent that questions related to ethics and privacy become more ambiguous and this paper aims at giving an overview on privacy concomitants and ethical concerns surrounding OSNs, with especial attention to the susceptibility of the user data, ethical dilemmas with regard to content regulating and the impact of policies and regulations. The proposed system provides the entry to establish the enhanced, sound, and fairly protected user privacy and ethical policies for OSNs with the help of boosted privacy-related settings, decentralised identity management, the utilisation of homomorphic encryption, and federated learning. It also underlines the need to have adaptive privacy dashboards, information accessibility real-time data, and other ethical measures keeping risks like cyberbullying and unauthorized usage of data checked. The study also examines new trends which have been occasioned by artificial intelligence in content moderation and the legal development aspect of it. As much as this survey focuses on current practices and asks questions in support of proactive measures, it seeks to enlighten policy makers on the best practices to engage in and create awareness to positive handling of user data in OSNs for the safety of users, as well as promoting ethical usage of the social networking systems. © 2025 IEEE.

关键词： Tweets

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A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exploiting Harmonic Optimization Technique for mmWave Applications

A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exp...

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2025 International Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits, INMMiC 2025

作者： Khanehbeygi, Mehran Tatangelo, Giorgio Pantoli, Leonardo Leuzzi, Giorgio University of L'Aquila Dept. of Industrial and Information Engineering & Economics L'Aquila Italy University of L'Aquila Computer Science and Mathematics Dept. of Information Engineering L'Aquila Italy

ISBN: (纸本)9798331519605

This brief presents a Voltage-Controlled Oscillator (VCO) for operation in the mmWave frequency range. The VCO is based on the Colpitts topology, utilising SiGe:C 130 nm bipolar devices. Unlike traditional configurations, the proposed VCO simultaneously provides both a differential output at the fundamental frequency (200 GHz) and a push-push output at the second harmonic (400 GHz). The overall performance is significantly enhanced through the implementation of an innovative Second Harmonic Extraction Network (SHEN), which efficiently captures and utilizes the second harmonic for improved output power and efficiency. © 2025 IEEE.

关键词： Colpitts MMIC mmWave Oscillator SiGe

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