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共 20255 条记录，以下是281-290 订阅

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Melanoma Detection with DenseNet201: A Deep learning Approach for Early Skin Cancer Diagnosis 3

Melanoma Detection with DenseNet201: A Deep Learning Approac...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sumalatha, V. Bhuria, Ruchika School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Melanoma is a serious and sometimes fatal disease, thus early diagnosis is crucial. In the present research work, a DenseNet201 deep learning model fine-tuned to classify skin lesions as either benign or malignant is used. The used dataset list is composed of 13,900 images of dermoscopy images that belong to the annotated dataset for feeding deep learning frameworks accordingly to the basic requirements of the platforms like TensorFlow and PyTorch by resizing the images with the 224×224 resolution of RGB format. Benign lesions have smooth margins, a uniform color and shape while malignant lesions for instance Melanoma often have irregular shape and color so as to merge seamlessly with the normal tissue. Preprocessing of the received data involved scaling where the pixel values were normalized to a range of between 0 and *** minimize the problem of overfitting, data was augmented through such operations as rotation, flipping and zooming. To overcome class imbalance, weighting of the loss function was used. Specificity: 0.96 for malignant lesion, sensitivity: 0.86 of the model, and F1-score of 0.90 was obtained by the model for diagnosing malignant lesion. The DenseNet201 model proved for assessing melanoma appropriately and this research showed it as a reliable technique for its early identification. Further work shall build upon the intervention using enhanced augmentation and clinical metadata integration to enhance the classification outcome. © 2025 IEEE.

关键词： Oncology

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Real-Time Global Parcel Tracking System in the Supply Chain Using RFID and GPS 3

Real-Time Global Parcel Tracking System in the Supply Chain ...

引用

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

作者： Vyshak, T.M. Varun, S.N. Unnathi, G.D. Sujaya, B.L. Munavalli, Jyoti R. Bnm Institute of Technology Affiliated to Vtu Dept. of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331515911

This paper presents an in-depth exploration of a Real-Time Global Parcel Tracking System in the Supply Chain Using RFID and GPS, designed to revolutionize logistics and parcel management. By integrating RFID technology for automated identification, GPS for precise real-time tracking, and data analytics for intelligent routing, the system addresses inefficiencies in traditional supply chain operations. A comprehensive review of literature on RFID-based tracking, GPS integration, clustering techniques, and IoT applications in logistics is presented, highlighting their capabilities, limitations, and future potential. As part of the project's initial implementation, a prototype was developed using an Arduino-based RFID system that links unique order IDs to RFID tags. This low-cost and scalable solution demonstrates how emerging technologies can be harnessed to automate parcel identification and streamline tracking processes. The study underscores the transformative potential of such a system to reduce delays, enhance resource optimization, and provide unparalleled real-time visibility, setting the stage for a further efficient and reliable global supply chain. © 2025 IEEE.

关键词： Global positioning system

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3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

引用

3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819725076

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient data Dissemination in VANETs;a Reactive Approach for High-Accuracy and data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect Detection;CNN Based Real Time Detection of Words from Lip Movements and automated into Text;application of Artificial Intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud Computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech Detection on Twitter: A Comparative Evaluation of Different Machine learning Techniques;detection of Heart Disease Using Machine learning;SVM-Based Framework for Breast Cancer Detection;hand Gesture Recognition System Using Machine learning;deep learning Method for Plant Disease Recognition and Prediction;Lung Cancer Detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various Machine learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep learning for Big Earth data;tackling Misinformation Through Tweets: A Comparative Study of Various Machine learning Approaches;feature-Based Anomaly Detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using Machine learning;evaluation of Load Forecasting in intelligent Grid Systems Through Machine learning Techniques.

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Research on Anomaly Detection in Time-Series Streaming data 3

Research on Anomaly Detection in Time-Series Streaming Data

引用

3rd international conference on Computer Science, Electronic Information engineering and intelligent Control Technology, CEI 2023

作者： Gong, Luyang School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9798350372052

The detection of anomalies in streaming data is crucial in enterprise operations, employing statistical and machine learning methods to identify irregularities. This enhances service stability and reduces operational complexity. However, existing monitoring platforms face challenges such as complex rule configurations and a lack of unified standards. To address these issues, this paper proposes a container performance anomaly detection module that combines statistical and machine learning approaches. This module, through steps including streaming data collection, data preprocessing, anomaly detection models, and monitoring alerts, achieves rapid detection and accurate identification of container performance anomalies. In comparison to traditional monitoring platforms, this module not only saves manpower but also demonstrates higher accuracy. © 2023 IEEE.

关键词： Time series

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Application of Deep learning-Based Malware Detection in Video Conferencing Systems 24

Application of Deep Learning-Based Malware Detection in Vide...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Wang, Weitao Lei, Lei Shao, Lisong Peng, Wang Chang, Jiale Beijing KeDong Electric Power Control System Co. Ltd Beijing China China Electric Power Research Institute Beijing China Nari Group Corporation Beijing China State Grid Electric Power Research Institute Beijing China

ISBN: (纸本)9798400718120

This paper explores the application of deep learning-based malware detection models in video conferencing systems. By constructing a large-scale dataset of malware and training deep neural network models such as CNN, RNN, and LSTM, high-precision detection of malware is achieved, outperforming traditional machine learning models. Integrating this deep learning model into the video conferencing system, a comprehensive malware defense system is built, effectively blocking a large number of attacks through real-time monitoring and intelligent analysis, significantly improving the security of video conferencing. The research results demonstrate that deep learning technology has broad prospects for application in this field, providing strong support for building a more secure video conferencing environment. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research on a Bridge Health Monitoring System Based on Machine learning 24

Research on a Bridge Health Monitoring System Based on Machi...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Zhang, DaYong Changjiang Survey Planning Design and Research Limited Company Chengdu China

ISBN: (纸本)9798400718120

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector Machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management. © 2024 Copyright held by the owner/author(s).

关键词： Damage detection

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Efficient Brain Tumour Classification using Pretrained Xception Model 3

Efficient Brain Tumour Classification using Pretrained Xcept...

引用

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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Deploying and Managing Web Application using Kubernetes 3

Deploying and Managing Web Application using Kubernetes

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhanwai, Aditya Bhagwat, Shivam Supekar, Kunal Deshmukh, Saurabh More, Amar School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

This project examines the deployment and management of web applications using Kubernetes, an open-source container orchestration platform. It provides an analysis of the core features of Kubernetes, including pods, services, and deployments, to simplify and optimize the deployment process of web-based systems. The study investigates various deployment strategies, such as rolling updates, to showcase Kubernetes’ adaptability and efficiency in maintaining continuous service availability during updates. By providing practical insights and guidelines, this project offers a comprehensive understanding of Kubernetes and its transformative potential in web application management. The findings are particularly valuable for developers, IT professionals, and anyone involved in the complex process of web application deployment. © 2025 IEEE.

关键词： Open source software

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A Study on Hardware-based Implementation of Homomorphic Encryption 3

A Study on Hardware-based Implementation of Homomorphic Encr...

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

作者： Singh, Gurdeep Mittal, Sonam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

Homomorphic encryption is an innovative cryptographic mechanism that allows performing cryptographic operations even on encoded data without intermediate decoding. As a result, sensitive information cannot be compromised in case of dealing with outsourcing services. This article firstly provides the general background of HE and most notably discusses the types of HE such as partial homomorphic encryption, somewhat homomorphic encryption, and fully homomorphic encryption Each of these types has its advantages on drawbacks depending on the type of operations and the level of computation efficiency. The paper also considers research undertaken recently oriented towards increasing the feasibility of HE by all means, especially the lowering of the costs in computation. A comparative analysis matrix contains distinctive features of various approaches in HE and defines their complexity, security, and usability concerns. This and further surveys present the HE history and its above- and below-the-line progress and suggest paths for HE-morphed developmental works for applications such as cloud computing and machine learning that are inherently privacy-preserving. © 2025 IEEE.

关键词： Differential privacy

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Research on the Classification and Planning of Functional Areas in Smart Cities Based on Artificial Intelligence 3

Research on the Classification and Planning of Functional Ar...

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3rd international conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Yi, Jiachen Liu, Qian Jia, Yuanyuan Wu, Junshuo Zhou, Chutong Wang, Yuchen Xian Univ Architecture & Technol Xian Peoples R China Univ South Australia Adelaide SA Australia

ISBN: (纸本)9798350352634;9798350352627

This study presents an innovative approach for urban functional zoning and planning based on artificial intelligence (AI) techniques. Utilizing multi-modal remote sensing data and geographic information, this research proposes a deep learning framework to accurately classify and segment urban functional areas. The proposed method leverages a Transformer-based Spatio-Temporal Fusion Network (TBST) that integrates high-resolution satellite imagery and time-series population data. The TBST model comprises two feature extraction branches, ResMixer and PDNet, which respectively handle spatial and temporal features, followed by a Transformer-based adaptive fusion layer to enhance the extraction of informal settlements in urban areas. Experimental results demonstrate that the multi-modal data fusion significantly improves the accuracy of urban functional area classification, achieving superior performance metrics compared to single-modal approaches. This method holds significant potential for advancing smart city planning by providing precise and up-to-date spatial distribution information, thus supporting urban policy-making and sustainable development.

关键词： component Smart City Planning Urban Functional Zoning Multi-modal data Fusion Deep learning

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