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

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer N

关键词： Molecules

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Optimizing cloud-based intrusion detection systems through hybrid data sampling and feature selection for enhanced anomaly detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 74-84页

作者： Viharika, Sadargari Balaji, N. Alangudi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

To enhance detection accuracy in network intrusion scenarios, this study proposes an optimized intrusion detection system (IDS) framework that integrates advanced data sampling, feature selection, and anomaly detection techniques. Leveraging random forest (RF) and genetic algorithm, the framework optimizes sampling ratios and identifies critical features. In contrast, the isolation forest algorithm detects and excludes outliers, refining dataset quality and classification performance. Evaluated on the UNSW-NB15 dataset, comprising over 2.5 million records and 42 diverse features, the proposed framework demonstrates significant improvements in anomaly detection, including reduced false alarm rates and enhanced identification of rare threats, such as shellcode, worms, and backdoors. Experimental results reveal that the RF-based model achieves an F1 score of 91.8% and an area under the curve (AUC) of 96%, outperforming traditional machine learning models and standalone RF classifiers. The integration of extreme gradient boosting (XGB) and its optimized variant, XGBGA, further enhances the framework, with XGBGA achieving the highest performance metrics, including an F1 score of 92.8% and an AUC of 97%. These findings underscore the importance of data optimization strategies in improving the accuracy and reliability of IDSs, particularly in handling imbalanced datasets and diverse network traffic. Future work will focus on real-time processing capabilities to handle streaming data and expanding the framework's applicability to domains such as fraud detection and cybersecurity, where precise anomaly detection is essential. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Anomaly Detection Data Optimization Intrusion Detection System Machine Learning

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Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-16页

作者： Ren, Siyu Hou, Junhui Chen, Xiaodong Xiong, Hongkai Wang, Wenping City University of Hong Kong Department of Computer Science Hong Kong Tianjin University School of Precision Instruments and OptoElectronic Engineering Tianjin300072 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Texas A& M University Department of Computer Science & Engineering United States

Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing methods mainly focus on directly establishing the correspondence between two models and then aggregating point-wise distance between corresponding points, resulting in them being either inefficient or ineffective. In this paper, we propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry data. Specifically, we construct DirDist based on the proposed implicit representation of 3D models, namely directional distance field (DDF), which defines the directional distances of 3D points to a model to capture its local surface geometry. We then transfer the discrepancy between two 3D geometric models as the discrepancy between their DDFs defined on an identical domain, naturally establishing model correspondence. To demonstrate the advantage of our DirDist, we explore various distance metric-driven 3D geometric modeling tasks, including template surface fitting, rigid registration, non-rigid registration, scene flow estimation and human pose optimization. Extensive experiments show that our DirDist achieves significantly higher accuracy under all tasks. As a generic distance metric, DirDist has the potential to advance the field of 3D geometric modeling. © 1979-2012 IEEE.

关键词： Mesh generation

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Video forgery localization using inter-frame denoising and intra-frame segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Banerjee, Debanik Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Department of Information Science and Engineering Siddaganaga Institute of Technology Karnataka Tumkur572103 India

Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake videos by creating an ensemble network, based on statistical and deep learning methods to detect interframe forgery and intraframe forgery in forged videos separately. In this paper, Noise signature extraction of a particular image capturing sensor and an Autoencoder-based Convolutional Neural Network model (CNN) are used to localize the forged regions. We have trained the model to localize Deepfake video forgeries as well as copy-paste forgeries with effective results in the test data. The proposed fake video detector can be applied at the back-end of on-line video aggregating services and check their authenticity to verify the genuineness of videos. The results achieved have shown better performances in detecting fake videos compared to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Constructing a Hierarchical Model Based on the Blockchain in Health Care 5th

Constructing a Hierarchical Model Based on the Blockchain in...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Ravendra Singh, Anupam Singh, Raj Pal Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9789819773701

Building a hierarchical conceptual structure for smart health care, identifying the effects of distributed ledger technology on clever medical treatment, and then developing a system of applications for smart health care via blockchain that utilizes the theory of involvement of stakeholders are the goals of the research. However, besides the essential characteristics and the connections between different features and characteristics, such a hierarchy conceptual framework should also consider the involvement of several stakeholders. The study offers decision-making including analytical lab (DEMATEL) to control the intricate relationships between various features and attributes. Finally, a hierarchical conceptual framework is created by splitting the hierarchy employing Interpretive Structural Modeling (ISM). The results show that the top-level organization, medical records leadership, and general doctor management are among the system's core reasons. A specific application of blockchain in the field of healthcare automation requires a decentralized agreement that depends on how health information is handled within system constraints. The secret to updating a system is application optimization. Environmental control, health insurance, and internal and external regulation all contribute to the system's development and the successful protection of stakeholder interests. The financing layer, data layer, as well as stakeholder layer must form the basis of a blockchain-based application structure for smart health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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ADNeuroNet: a neuroevolution-based neural network algorithm for the diagnosis of neurodegenerative diseases

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Neural Computing and Applications 2025年第14期37卷 8593-8627页

作者： Khan, Afreen Zubair, Swaleha Ali, Irfan Department of Computer Application Faculty of Engineering & IT Integral University Lucknow226026 India Department of Statistics and Operations Research Aligarh Muslim University Aligarh India Department of Computer Science Aligarh Muslim University Aligarh India

Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Disease control

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An IoT-Driven Performance Evaluation Framework for Secure Healthcare Monitoring Systems 4

An IoT-Driven Performance Evaluation Framework for Secure He...

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

作者： Aswathy, R.H. Suresh, P. Mani, C. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai Erode India

ISBN: (纸本)9798331523923

Today, all countries have a socio-economic concern about the healthcare services. Advanced healthcare systems leverage technology, such as the Internet of Things (IoT), to enhance quality and reduce costs. IoT integrates smart devices, medical professionals, and patients into a connected network. The advanced healthcare trend extends the possibilities for doctors to monitor the patient remotely and give medication during emergencies. Since it is a critical application security plays a vital role as the data transferred through the wireless environment and it is maintained digitally as a digital patient record. A major challenge in IoT healthcare includes security, privacy and power consumption. In this proposed work the secure cryptographic based MIAS server is applied for establishing the data transmission and tested over the known security attacks such as DDoS, replay attack, password guessing, sensor node spoofing etc. In contrast to alternative approaches, the proposed scheme is better suited to ensuring security and minimizing power consumption within the limited confines of IoT environments. © 2025 IEEE.

关键词： Electronic health record

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Enhancing SDN Traffic Analysis Through Machine Learning on Preprocessed Controller Flow Statistics and Packet Analysis Data

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SN computer science 2025年第5期6卷 1-22页

作者： Vadivu, D. Sendil Santhosh, Ashwin Rajagopalan, Narendran Pavagada, Kaustub Valsaraj, Aswin Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

As an emerging technology, Software Defined Networks (SDN) has led to several vulnerabilities and risks, making it adoption challenging. Cyber threats in SDN include a wide range of malicious activities intended to exploit the architecture's centralized control and programming ability vulnerabilities. Adequate safety precautions for maintaining the stability of networks are required. Establishing an Intrusion Detection System (IDS) is vital in solving such issues, enabling real-time monitoring and classification of suspicious activity. Very few SDN-based datasets are available for attack detection. Hence, this study aims to generate two SDN-specific datasets, CSOR_SDN (Controller Statistics with Oftcl_Rest application for SDN) and WSCF_SDN (Wireshark Statistics with CicFlowmeter for SDN), from the network environment emulated using the Mininet and RYU controller by utilizing controller statistics and packet analysis data. The dataset generated with normal, DDoS (Distributed Denial of Service), DoS (Denial of Service), and probe attack flows is evaluated with multiple feature selection methods and Machine Learning (ML) classifiers to assess the complexity of the dataset. Correlation Coefficient-based feature selection (CFS) of filter methods is selected out of others with a Decision Tree (DT) classifier, capable of producing 100% results for the CSOR_SDN dataset with the least execution time in attack detection. For the proposed IDS, tenfold stratified cross-validation is performed with confidence interval assessment for the test dataset created on an enterprise network use case to highlight the scalability support of the model. A comparative analysis of both the dataset and its resource utilization is conducted to evaluate its feasibility. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber threat Dataset OpenFlow Ryu SDN Security Traffic analysis

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Sentiment Analysis of Movie Reviews Using Machine Learning

Sentiment Analysis of Movie Reviews Using Machine Learning

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Yadav, Avantika Garg, Payal Jain, Tarun Manipal University Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

This paper delves into the critical role that machine learning plays in the accurate classification of sentiments expressed in movie reviews. Throughout the study we research and apply NLP using deep learning and machine learning algorithms to train and test our IMDb 50K movie reviews dataset on different models and feature embedding to compare their accuracies and choose the most optimal model for sentimental analysis. Ultimately, using deep neural network model BERT neural gives us the best accuracy of 94.2% compared to lexicon-based approaches due to the limitations in feature extractions like Bag of Words and TF-IDF. © 2025 IEEE.

关键词： Bag of Words BERT Deep Learning IMDb Dataset Machine Learning Natural Language Processin Sentiment Analysis TF-IDF Training and Testing

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