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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Alagh, Arjun Wason, Ritika Arora, Parul Dept. of Computer Science and Applications Delhi India

ISBN: (纸本)9798331518592

The Internet Service Providers (ISP), and their structures are at high risk with the growing quantity and complexity of cyber threats. The growth of quantum computing further elevates the risks and challenges especially for the conventional cryptographic standards. This manuscript proposes a multi-layered suite of quantum-resistant cryptographic algorithms that can be proficiently integrated into ISP networks to secure against conventional cyber threats as well as prevent the impending quantum-enabled cyberattacks. This work emphasizes the importance of transitioning to quantum-resistant cryptography to ensure the longevity of ISP cybersecurity measures in the face of emerging quantum threats. © 2024 IEEE.

关键词： Encryption algorithms

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TEST-Web: An Automated Web Application Testing Tool for Enhanced Efficiency and Consistency 3

TEST-Web: An Automated Web Application Testing Tool for Enha...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Verma, Ishaan Wason, Ritika Arora, Parul Dept. of Computer Science and Applications Delhi India

ISBN: (纸本)9798331518592

This manuscript specifies the development of TEST-Web an automated testing tool projected to evolve the efficacy and reliability of web application testing process. Using the Selenium WebDriver and the TestNG framework, TEST-Web restructures the testing process by automating repetitive and comprehensive testing in large organizations. The tool generates detailed reports, providing valued visions into application performance and functionality. TEST-Web development follows an iterative approach, beginning with prototype development and then expanding functionality through user feedback. This work aims to achieve a user-friendly and robust solution for enhancing the accuracy, speed, and reliability of web application testing processes. © 2024 IEEE.

关键词： Automated Testing Tool Software Testing TestNG Web Application Testing

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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Performance Evaluation of Multi-Disease Classification and Recommendation Models 3

Performance Evaluation of Multi-Disease Classification and R...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Singh, Lakhvinder Dalip Dept. of Computer Science & Applications Ambala India

ISBN: (纸本)9798331518592

This paper aims to review the current advances in healthcare uses of machine learning (ML) for the diagnosis of numerous diseases using different classification models. It proposes an efficient mechanism for multi-disease diagnosis by implementing the Bee-Featured Convolutional Neural Network (BFCNN), coupled with a Hybrid Statistical Fuzzy (Hybrid S-Fuzzy) scheme, to improve classification efficiency. The primary goal is to develop an accurate diagnostic system for heart, kidney, and liver diseases at early stages. The methodology presented compares several common ML models-Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Fast Condition Mutual Information (FCMIM-SVM)-using datasets from various sources with the BFCNN model. According to the simulated results, the proposed BFCNN model achieved a 96.7% accuracy rate and outperformed existing models in terms of sensitivity and specificity. This study suggests that using the BFCNN together with the Hybrid S-Fuzzy model enhances diagnosis for multiple diseases, offering higher accuracy and earlier identification compared to individual models, with up to 78% potential benefits in healthcare. Further studies on this model may lead to the application of deeper methods to increase diagnostic accuracy and improve treatment recommendations. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine Learning in Metropolitan Cities-A Review 4

Forecasting Traffic Air Pollution Increases from Urban Trans...

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

作者： Bhaskar, Hari Naga Vijaya Mayil, V. Valli KL University Dept. of Computer Science and Applications India

ISBN: (纸本)9798331523923

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5-10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements. © 2025 IEEE.

关键词： Air Pollution Forecasting Environmental Impact Machine Learning Metropolitan Cities Public Health Urban Transportation

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 IEEE.

关键词： Resource allocation

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Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation 7

Enhancing Quantum Operations: Random Forests for Anomaly Det...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Anoop, M. Nithya, B. SRM Institute of Science and Technology Dept. of Computer Science and Applications Chennai India Hindustan Institute of Technology and Science Dept. of Computer Applications Chennai India

ISBN: (纸本)9798350372816

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential. © 2024 IEEE.

关键词： Quantum communication

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NFT Marketplace: What are NFTs, and how does OpenSea succeed in acquiring the most of the NFT Space 1

NFT Marketplace: What are NFTs, and how does OpenSea succeed...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Thakur, Anil Divya, K. Verma, Sahil Kaur, Maninder Chandigarh University Dept. of Computer Science & Engineering Mohali India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Guru Gobind Singh College for Women Dept. of Computer Science & Applications Chandigarh India

ISBN: (纸本)9798350373042

Nowadays, the world is becoming smarter, so as the technology, the new era is on the boom, which is blockchain because of its different use cases and benefits. The blockchain is a growing list of records called blocks that are linked together with the use of cryptography. Because all servers on a network are corresponding and a significant concern in the types of network style, its data sources are managed through a peer-to-peer (p2p) network. The servers engage in negotiation and communication through the agreement method. The dispersed network is a network system in which information, software application, and computer system programs are spread out on greater than one node (computer systems). These nodes also depend on each other. It is only a p2p network that gets rid of a solitary factor of failing. It is a decentralized, dispersed, and unalterable journal to show electronic purchases. One of the concepts of blockchain is NFTs. These are digital assets with a unique identity and cannot be replicated. They can be used for a variety of purposes, such as in-game items, digital collectibles, or even to represent real-world assets like cars or houses. They can represent any digital asset or physical goods. NFTs can represent ownership and authenticity of any type of digital asset or physical goods. They can also represent a stake in the game, such as ownership in a casino or a share in a company. © 2024 IEEE.

关键词： Smart contract

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Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict 3

Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Nain, Vishal Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

The 2010 discovery of the Stuxnet malware signaled a sea change in the field of cyberwarfare, and the 21st century has seen an unparalleled spread of digital technology and their incorporation into vital infrastructure systems. Given this, Stuxnet is a sobering reminder of the weaknesses present in our globalized society. This paper aims to investigate the intricacies of the Stuxnet attack, including its origins, development, objectives, and significant implications for cybersecurity and international relations. By carefully analyzing this sophisticated cyberweapon, this study sheds light on how cyberthreats are evolving in the twenty-first century. © 2024 IEEE.

关键词： Critical infrastructures

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