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Artificial intelligence (AI)-Enhanced Security Monitoring and Threat Detection in Cloud Infrastructures 1

Artificial Intelligence (AI)-Enhanced Security Monitoring an...

引用

1st international conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Bishukarma, Ramesh Mathur, Sumeet Gupta, Sandeep University of Waikato Nz - Joint Institute at Zhejiang University Hangzhou China Sati Department of Computer Science Engineering Vidisha India

ISBN: (纸本)9798331529246

Threat identity and counteraction measures are critical to data security and system integrity in the rapidly evolving environment of cloud computing. The current paper proposes a method to improve security monitoring and threat detection in cloud environments with ML algorithms. The approach involves data pre-processing on a data set obtained from KDD Cup'99 comprising of data imputation for missing values, normalisation using min-max scaling respectively, and feature selection by employing RFE for the efficient execution of the model. The data is divided into training and testing datasets and the proportion used is 80% training and 20% test. Classification is done with Isolated Heuristic Neural Networks (IHNN), as well as with GRU and Random Forest methods. As seen from the result analysis, the IHNN model established a high performance with a 99.35% accuracy, 96.70% precision, 91.20% recall, and a 96.32% F1 score. The efficiency of the model is then measured with parameters such as accuracy and error rate, precision, recall rate and F-score and confusion matrix in order to study errors committed. IHNN outperforms other comparable models, such as GRU and RF, in the context of threat detection in cloud environments, thus providing assured security analysis. © 2025 ieee.

关键词： Cloud security

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Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

引用

2025 international conference on Pervasive computational Technologies, ICPCT 2025

作者： Chhabra, Gurpreet Kaur Mishra, Ashutosh Kumar Tyagi, Varsha Mani, Chandra Tyagi, Vani Sagar, Pramod Kumar Management Education and Research Institute Department of Management Delhi India Srm Business School Department of Management UP Lucknow India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Mangalmay Institute of Engineering & Technology Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management. © 2025 ieee.

关键词： Decentralized finance

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Evaluating Generative Models: A Review of Challenges and Opportunities in Assessment of Generative Adversarial Networks 2

Evaluating Generative Models: A Review of Challenges and Opp...

引用

2nd international conference on computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Shah, Akash Rakesh, Nitin Gulhane, Monali Agrawal, Pratik Kaur, Mandeep Dixit, Saurav Pune India Nagpur Campus Nagpur India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798331530389

Generative adversarial networks (GANs) have achieved remarkable success in image synthesis but evaluating their quality remains an open challenge. Existing automated metrics like FID are unable to fully capture generation performance across both objective fidelity and subjective aesthetic attributes important to human perception. This paper conducts an extensive review of literature on GAN evaluation methodology. Key limitations of current approaches are analyzed through a discussion of popular metrics and their inability to predict human judgment comprehensively. To address this, a novel hybrid evaluation model is proposed that combines automated scores with human ratings to provide a more balanced assessment. A pilot study demonstrates the hybrid metric can predict human response more accurately. However, additional validation is required. The review emphasizes the need for evaluation systems integrating richer human input to better optimize models according to user priorities. Interactive frameworks and techniques from computational creativity may help advance assessment towards comprehensively benchmarking generative capacity and artistic exploration. © 2025 ieee.

关键词： Generative adversarial networks

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Healthcare Data Protection and Biometric Access on Cloud Platforms 6

Healthcare Data Protection and Biometric Access on Cloud Pla...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Gayathri, S. Devaraj, S. Muthupandi, B. Vishwaraj, S. Department of Artificial Intelligence and Data Science K S Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

This paper discusses about various fingerprint recognition methods, each founded on different principles, in search of a robust and efficient technique. The research concentrates on designing an easy yet efficient alternative for fingerprint recognition. The technique is divided into two main stages: the first is the acquisition of human fingerprint samples, and the second is the development and implementation of an advanced fingerprint recognition system. One of the features of the method is feature extraction using multiple iterations of the two-dimensional discrete cosine transform (DCT) to extract robust features. This method combines thumbprints from both hands for better recognition. The experiments show that the proposed technique is capable of yielding a good recognition accuracy. © 2025 ieee.

关键词： Cloud platforms

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Analyzing Sentiments in Twitter Data on Russia-Ukraine Conflict Using Machine Learning Methods 2

Analyzing Sentiments in Twitter Data on Russia-Ukraine Confl...

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2nd international conference on computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Chaudhry, Meeta Kumar, Rakesh Patel, Harvendra Kumar Prakash, Navin Ashish Mishra, D.K. Department of CSIT KIET Group of Institutions Ghaziabad India GNIOT Greater Noida India Noida International University India Department of CSE IIMT College of Engineering Greater Noida India Department of CSE RIMT Bareilly India Department of CSE ABES Ghaziabad India

ISBN: (纸本)9798331530389

As the conflict between Russia and Ukraine has deepened and more international countries have been engaged, it has become critical to ascertain if the universal message is one of support for or opposition to military activities. This goal is supported by research on how public opinion and foreign affairs are critically connected. As a result, researchers looked at how the global community felt about Russia's invasion of Ukraine. In total, 27,894 tweets with the hashtag #UkraineRussia that were published on the first day were examined. The findings indicate that some of the most often recurring words in the tweets were "war," "people," "world," and "people." Sadness was the most prominent emotion, and negative sentiments were higher than positive sentiments. It was discovered that nearly 89% of conflicts involved the word "world" when a term was supervised in context concordance analysis. This finding underlines the rising worry that the situation between Russia and Ukraine may worsen. © 2025 ieee.

关键词： Tweets

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Hybrid Machine Learning for Feature-Based Spam Detection 2

Hybrid Machine Learning for Feature-Based Spam Detection

引用

2nd international conference on computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Gupta, Sandeep Barigidad, Susmith Hussain, Shadab Dubey, Santosh Kanaujia, Sandeep Uttarakhand Dehradun India Computer Science Santa Clara University United States MathCo DallasTX United States Department of Electronics and Communication Engineering United College of Engineering & Research Naini Prayagraj India Naini Prayagraj India

ISBN: (纸本)9798331530389

researchers have been paying significant attention to the increasing need for and prevalence of unwanted spam on networks. Multiple studies have extensively examined the fundamental origins of spam in networks. Previous research has shown that spam-related problems are one of the main dangers to network systems, affecting both ongoing operations and past data. To tackle and reduce the increase in spam, researchers have put forward different methodologies. Although certain techniques have received approval from experts, every method has its own set of constraints. Furthermore, the persistent issue of inadequate precision in identifying spam poses a significant obstacle for numerous current predictive models. Therefore, the task of finding a reliable prediction method continues to be a challenging and extensively studied field, attracting significant attention from researchers in recent decades. This paper introduces a machine learning approach for detecting spam in emails. The proposed model demonstrates improved accuracy compared to previous studies. © 2025 ieee.

关键词： Adversarial machine learning

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Yu Zhang, Sheng Wu, Jie School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China State Key Laboratory for Novel Software Technology Nanjing University China Center for Networked Computing Temple University United States

ISBN: (纸本)9798350368741

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach. © 2025 ieee.

关键词： online optimization resource allocation Video analytics

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Intelligent Platform for international Talent Cultivation using Multiverse Optimization Algorithm based Support Vector Machine 1

Intelligent Platform for International Talent Cultivation us...

引用

1st international conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Dai, Xiaojia Liu, Chao Hunan Sany Polytechnic College International Trade School Changsha China

ISBN: (纸本)9798331529246

The fats growth of Artificial intelligence (AI) advancement demands greater necessities for the engagement and talent training and talent cultivation. The combination of education and enterprises is a significant approach for addressing the mismatch among the industrial demand as well as talent supply. An aim of this article is to identify the new necessities for developing the AI-based approach for solving the talent management problems. Thus, this research proposes the Multiverse Optimization with Support Vector Machine (MVO-SVM) approach to the intelligent platform for international talent cultivation. The combination of MVO with SVM supports in fine-tuning hyperparameters, enhanced the model precision for talent selection and cultivation. The exploration and exploitation capabilities of the MVO approach allowed the SVM to maintain the large dataset based on the talent cultivation, learning procedure as well as career development, resulted in effective model performance. The experimental outcomes signifies that the introduced MVO-SVM approach attains the better accuracy of 91.63%, precision of 90.24%, recall of 89.33% and F1-score of 89.78% correspondingly as compared to the existing methods like conventional SVM. © 2025 ieee.

关键词： Adversarial machine learning

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Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs 31

Empirical Study on Data Attributes Insufficiency of Evaluati...

引用

31st international conference on computational Linguistics, COLING 2025

作者： Liu, Chuang Jin, Renren Yao, Zheng Li, Tianyi Cheng, Liang Steedman, Mark Xiong, Deyi College of Intelligence and Computing Tianjin University China School of Electrical Engineering and Computer Science University of Queensland Australia School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9798891761964

Previous benchmarks for evaluating large language models (LLMs) have primarily emphasized quantitative metrics, such as data volume. However, this focus may neglect key qualitative data attributes that can significantly impact the final rankings of LLMs, resulting in unreliable leaderboards. In this paper, we investigate whether current LLM benchmarks adequately consider these data attributes. We specifically examine three attributes: diversity, redundancy, and difficulty. To explore these attributes, we propose a framework with three separate modules, each designed to assess one of the attributes. Using a method that progressively incorporates these attributes, we analyze their influence on the benchmark. Our experimental results reveal a meaningful correlation between LLM rankings on the revised benchmark and the original benchmark when these attributes are accounted for. These findings indicate that existing benchmarks often fail to meet all three criteria, highlighting a lack of consideration for multifaceted data attributes in current evaluation datasets. © 2025 Association for computational Linguistics.

关键词： computational linguistics

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Integrating research on AI-Driven Hyper-Personalization: A Review and Framework for Scalable Social Media Campaigns

Integrating Research on AI-Driven Hyper-Personalization: A R...

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2025 international conference on Pervasive computational Technologies, ICPCT 2025

作者： Patil, Shalmali Bhat, Aparna Jain, Nilesh Javalkar, Vishal University of Texas at Dallas RichardsonTX United States DallasTX United States Oracle America Inc SterlingVA United States Independent Researcher FriscoTX United States

ISBN: (纸本)9798331508685

This paper introduces a novel theoretical framework for AI-driven hyper-personalization in social media marketing, addressing the critical gap in existing research that overlooks the integration of technological, strategic, and ethical dimensions at scale. The framework uniquely combines advanced user profiling, dynamic content personalization, and iterative feedback loops to optimize engagement, loyalty, and ROI. It further tackles scalability challenges through innovations like cloud computing and federated learning, while embedding ethical principles of fairness, transparency, and privacy into its design. Unlike prior studies, this paper provides a holistic roadmap for implementing scalable, cross-platform AI systems and explores the integration of emerging technologies such as AR, VR, and blockchain. By synthesizing actionable insights and proposing future research directions, it redefines the potential of hyper-personalized, user-centric marketing strategies in the digital era. © 2025 ieee.

关键词： Federated learning

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