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检索条件"任意字段=3rd IEEE International Conference on Software Engineering and Formal Methods"

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Proceedings - 3rd ieee international conference on software engineering and formal methods, SEFM 2005

Proceedings - 3rd IEEE International Conference on Software ...

引用

3rd ieee international conference on software engineering and formal methods, SEFM 2005

ISBN: (纸本)0769524354

The proceedings contain 44 papers. The topics discussed include: specifying urgency in timed I/O automata;putting detectors in their place;precise analysis of memory consumption using program logics;timed automata with data structures for distributed systems design and analysis;operational semantics for real-time processes with action refinement;using dominators to extract observable protocol contexts;experimental evaluation of FSM-based testing methods;counting interface automata and their application in static analysis of actor models;workflow enactment based on a chemical metaphor;generating relational database transactions from recursive functions defined on EB 3 traces;safe concurrency for aggregate objects with invariants;omnibus verification policies: a flexible, configurable approach to assertion-based software verification;customised induction rules for proving correctness of imperative programs;and object oriented verification kernels for secure Java applications.

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Botnet Detection methods: A Review and Classification 3

Botnet Detection Methods: A Review and Classification

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mathew, Seena Elizebeth Sharmasth Vali, Y. Shakkeera, L. Presidency University Bangalore India

ISBN: (纸本)9798331527549

In today's communication landscape, ensuring network security is crucial. Despite the availability of various modern techniques, cyber threats, particularly botnets, continue to pose significant risks. Botnets are a potent weapon in the cybercriminal's toolkit and their deployment in cyber-attacks highlights the need for robust cybersecurity measures. Devices become part of a botnet when they are infected with malware, which can spread through methods such as email attachments, downloads, or software vulnerabilities. Once infected, these devices can connect to the botmaster's command-and-control (C&C) server. Botnets are commonly used for Distributed Denial of Service (DDoS) attacks, spam distribution, credential theft, and click fraud. To counteract these threats, measures such as network monitoring, regular software updates, anti-malware tools, firewalls, and intrusion detection systems are essential. This paper provides a classification of different botnet detection methods. © 2025 ieee.

关键词： Bot (Internet)

来源：评论

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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

来源：评论

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Colorectal Cancer Detection based on Integrated Graph Convolutional Networks and Shift Window Transformer 3

Colorectal Cancer Detection based on Integrated Graph Convol...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Gayathri, M. Thamarai Selvi, D. Kalaiselvi, S. Santhi, S. Department of computer science and engineering National Engineering College Tamil Nadu Kovilpatti India Department of information technology National Engineering College Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331527549

Advanced predictive analytics is required to improve early identification and patient outcomes for colorectal cancer (CRC), which is still a major worldwide health concern. Using deep learning and graph-based methods, this study suggests a new prediction framework that makes use of Graph Convolutional Networks (GCNs) and a Shifted Window Transformer architecture by means of thorough pre-processing, which includes graph creation and normalization, we establish a strong input structure for our model. To improve feature extraction from histopathology pictures, the Shifted Window Transformer—which is well-known for its effectiveness in capturing long-range dependencies—is incorporated. The model is able to utilize both individual cellular properties and their interrelations thanks to GCNs that learn from the graph structure. By automatically identifying colorectal cancer in its early stages, this prediction system seeks to do away with the requirement for human identification by medical professionals and allow for prompt treatment without awaiting a formal diagnosis. This work aims to improve early-stage (stage 1) detection using our suggested automated models because it is challenging to detect CRC in stage 0. This innovative strategy promises prompt interventions that will enhance patient care and clinical results. © 2025 ieee.

关键词： Diseases

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 ieee.

关键词： Application programs

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A Comparison of Deep Learning and Machine Learning methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 ieee.

关键词： Support vector machines

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Knowledge-based Retrieval methods for Enhancing Aerospace Model software Documentation 24

Knowledge-based Retrieval Methods for Enhancing Aerospace Mo...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Duan, Jinjian Wei, Deming Wang, Meiqing Chen, Gaohui Zhang, Yang Gao, Yanhua School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798400707308

Addressing the critical challenges of poor structure, weak relevance, and limited reusability in aerospace model software development documents, we propose a knowledge-based retrieval method. This method constructs a knowledge base by analyzing key document features, including storage methods, file formats, content structure, inter-document associations, and naming conventions. The system implements structured document management and associates documents based on software configuration information. By partitioning the retrieval domain, constructing semantic vectors, and applying multi-dimensional weight configurations, the method enables efficient retrieval in domains characterized by dense information and frequent term repetition. An aerospace model software document retrieval system has been developed and preliminarily implemented in an aerospace enterprise, demonstrating its effectiveness in improving retrieval efficiency and enhancing the reuse of development knowledge. © 2024 Copyright held by the owner/author(s).

关键词： Content based retrieval

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Simulation of Differential Protection of Transmission Line using Matlab 3

Simulation of Differential Protection of Transmission Line u...

引用

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

作者： Dhamane, Mohit Joshi, Namra Patil, Ghanshyam SVKM's Institute of Technology Department of Electrical Engineering Maharashtra Dhule India

ISBN: (纸本)9798331509675

The transmission Line is a crucial part of the Power System. The power system is subjected to several faults and abnormal conditions. A competent methodology is required for keeping the power system in a normal and efficient working state. Differential protection is one of the most important methods of protection. This paper focuses on the implementation of a differential protection scheme on a transmission line with the help of Matlab. In this method, the current entering & leaving the transmission line is compared, and the trip signal is given to the circuit breaker. The comparative analysis of the transmission line voltage is presented. © 2025 ieee.

关键词： Power transmission lines

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A Comparative Study of Anomaly Detection: Integrating Joint Detection and Tracking Techniques 3

A Comparative Study of Anomaly Detection: Integrating Joint ...

引用

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

作者： Lekha, T. R. Jaya Chandra Aravind, T. Ramesh, R. Lourdes Mount College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Tagore Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331509675

As video data becomes increasingly pervasive in various domains, the need for robust and efficient anomaly detection methods has grown exponentially. This comparative analysis explores and evaluates diverse methods for video anomaly detection, ranging from traditional techniques to advanced deep learning methodologies. The study aims to unravel the complexities inherent in identifying anomalies within dynamic environments and offers insights into each method's strengths, limitations, and adaptability. The research investigates the impact of these anomaly detection methods across different datasets, assessing their generalizability and performance consistency. The study further addresses the computational aspects, comparing the efficiency and resource requirements of the various models under consideration. This comparative analysis contributes to the ongoing discourse on enhancing surveillance and security measures in diverse real-world applications by delving into the nuances of video anomaly detection. © 2025 ieee.

关键词： Generative adversarial networks

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Exploring Hand Gesture Recognition: Trends, Technologies, and Application 3

Exploring Hand Gesture Recognition: Trends, Technologies, an...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pohekar, Ayesha Pundge, Ajitkumar Shirsath, Vishal School of Engineering Ajeenkya DY Patil University Maharashtra Pune India

ISBN: (纸本)9798331527549

Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, and user-friendly HGR solutions remains a challenge due to issues such as environmental variability and user adaptability. This survey explores state-of-the-art HGR techniques, encompassing traditional methods and advanced deep learning-based methodologies. Classification techniques are analyzed based on sensory-based, vision-based, and hybrid input approaches, highlighting their respective strengths and limitations. The impact of feature extraction methods, dataset quality, and real-time processing on the performance of classification algorithms is discussed. By addressing these challenges, this work identifies potential pathways for advancing HGR systems to enhance their usability and reliability across diverse applications. © 2025 ieee.

关键词： Palmprint recognition

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