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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Gayathri, G.N. Channakrishnaraju Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumkur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Nowadays, cloud storage is one of the popular application models in various fields, where the security and access permission of storage data are widely considered. De-duplication algorithms are designed to mitigate redundant storage of data in the cloud. However, the main problem was data duplication which occupied the storage in many cloud storage systems that require an appropriate data security model. To overcome this problem, Rivest-Shamir-Adleman (RSA) - BLAKE3 based Optimal Cache Oblivious (OCO) demonstrates ensured access and privacy while removing the duplicates in cloud computing. The combination of RSA and BLAKE-3 is used to maintain efficient attribute exchange and prevent data leakage and privacy violations. In addition, the OCO technique is used to hide the access pattern in the cloud storage and copy the data by matching the random pattern. This process ensures data privacy and secure data sharing in cloud storage by avoiding data corruption. The result analysis clearly shows that the proposed RSA-BLAKE3-OCO attains a communication overhead of 11MB, a data integrity rate of 93% and a data confidentiality rate of 95% from 50 files which is better when compared with the existing methods. © 2025 ieee.

关键词： data integrity

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Occlusion-aware data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments 6

Occlusion-aware Data Augmentation and Reconstruction for Imp...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Pham, Vincent Singh, Sonit University of New South Wales School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798331506520

The demand for adaptive learning in education is an important application for Computer Vision (CV)-based detection models which extract learners' faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners' faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection. © 2025 ieee.

关键词： Contrastive Learning

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Reimagining the data Landscape: A Multi-Agent Paradigm for data Interfacing 8

Reimagining the Data Landscape: A Multi-Agent Paradigm for D...

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8th international conference on data science and Machine Learning Applications, CDMA 2025

作者： Renney, Harri Nethercott, Max Williams, Olivia Evetts, Jayden Lang, James Kaze Consulting Kaze Technologies Bath United Kingdom University of Bath Department of Computer Science Bath United Kingdom

ISBN: (纸本)9798331539696

The concept of using multi-agent systems is an area of contemporary artificial intelligence (AI) that dates back as far as the 1970s. At the time, these concepts were discussed in a primarily theoretical capacity, with the idea that once AI agents with sufficient reasoning abilities were achieved, novel multi-agent systems could be tried and tested. With the emergence of a combination of key enabling technologies, including the transformer, large language models (LLMs), and access to considerable compute power, conceptual multi-agent designs are now possible to investigate in practice. Reflecting on these observations, a multi-agent approach for effectively navigating domain-specific landscapes is formalised. The paradigm draws from the observations seen in research, industry developments and community support for creating multi-agent networks composed of specialist agents to target domain-specific problems. The paradigm focuses particularly on data interfacing across multiple distinct datasets at once. Using the proposed paradigm, an example is developed for a Cyber Intelligence use case along with suggestive results from a small preliminary user experience study. The paper concludes by discussing this paradigm's strengths and weaknesses and speculates where future developments may progress. © 2025 ieee.

关键词： Multi agent systems

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A Modified Incremental SVM for data Stream Classification

A Modified Incremental SVM for Data Stream Classification

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Vidhya, M. Aleena, V.S. University of Kerala Department of Computer Science Kerala India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Traditional machine learning models struggle in dynamic environments where data distribution changes over time, making continuous learning crucial for real-time applications. In this work, we propose a Modified Incremental Support Vector Machine (MISVM) to enhance data stream classification by selectively updating only key support and boundary vectors, reducing computational complexity. Unlike conventional models requiring full retraining, MISVM iteratively updates as new data arrives, improving adaptability and efficiency in evolving datasets. This approach achieves high accuracy, computational efficiency, and reduced training and testing time, as demonstrated on the UCI Breast Cancer (Accuracy: 96%, 10.07 seconds for training, 0.0002 seconds for testing) and SMS Spam Detection (Accuracy: 89%, 0.46 seconds for training, 0.00099 seconds for testing) datasets, and shows promise in complex, evolving applications such as financial forecasting and medical data analysis. © 2025 ieee.

关键词： Adversarial machine learning

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Streamlined data Pipeline for Real-Time Threat Detection and Model Inference 17

Streamlined Data Pipeline for Real-Time Threat Detection and...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Singh, Rajkanwar Aravindan, V. Mishra, Sanket Singh, Sunil Kumar VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798331531195

Real-time threat detection in streaming data is crucial yet challenging due to varying data volumes and speeds. This paper presents an architecture designed to manage large-scale, high-speed data streams using deep learning and machine learning models. The system utilizes Apache Kafka for high-throughput data transfer and a publish-subscribe model to facilitate continuous threat detection. Various machine learning techniques, including XGBoost, Random Forest, and LightGBM, are evaluated to identify the best model for classification. The ExtraTrees model achieves exceptional performance with accuracy, precision, recall, and F1 score all reaching 99% using the SensorNetGuard dataset within this architecture. The PyFlink framework, with its parallel processing capabilities, supports real-time training and adaptation of these models. The system calculates prediction metrics every 2, 000 data points, ensuring efficient and accurate real-time threat detection. © 2025 ieee.

关键词： Adversarial machine learning

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data-Driven Feedforward Control for Trade-Off Optimization in Machine Stand Vibration systems

Data-Driven Feedforward Control for Trade-Off Optimization i...

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2025 ieee international conference on Mechatronics, ICM 2025

作者： Yamaguchi, Daigo Teramoto, Shota Shiohara, Takuya Maeda, Yoshihiro Nagoya Institute of Technology Dept. of Engineering Nagoya Japan

ISBN: (纸本)9798331533892

In various industrial mechatronic equipment for processing and assembling electronic components, high-acceleration drives in the internal positioning mechanisms cause machine stand vibrations, degrading their production accuracy and throughput. Generally, a trade-off exists between the fast and precise control of positioning mechanisms and suppressing machine stand vibrations, which complicates control design. This study presents an extended data-driven vibration suppression feedforward (FF) control method that addresses both positioning performance and machine stand vibrations. The proposed method designs FF controllers to pursue the trade-off through a multi-objective optimization problem using predicted responses of both the positioning device and the machine stand. Experiments with a laboratory table positioning system demonstrate the effectiveness of the proposed method, compared to an existing data-driven vibration suppression FF control method. © 2025 ieee.

关键词： Multiobjective optimization

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RF Challenge: The data-Driven Radio Frequency Signal Separation Challenge

IEEE Open Journal of the Communications Society

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ieee Open Journal of the Communications Society 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C.F Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Universidad Carlos III de Madrid Leganés28911 Spain Gregorio Marañón Health Research Institute Madrid28007 Spain Bar-Ilan University Ramat Gan Israel Institute for Infocomm Research 138632 Singapore Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP’24). © 2020 ieee.

关键词： Matched filters

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Mapping Seasonal Potential Fishing Zone of Skipjack Tuna in Biak Numfor using Remote Sensing data

Mapping Seasonal Potential Fishing Zone of Skipjack Tuna in ...

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2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Arta, Jordy Wira Miranda, Eka Bina Nusantara University Master of Information Systems Management Information Systems and Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Overfishing has been a problem in worldwide aquaculture. Indonesia is one of the countries that has this problem. We found Biak Numfor has lots of fishing potential and not overfished yet. Therefore, this study used remote sensing data to map the skipjack tuna's Potential Fishing Zone (PFZ) surrounding Biak Numfor and determine the best fishing months. To determine regions ideal for skipjack tuna, the study looked at sea surface temperature (SST) and chlorophyll-a concentration using Python libraries and Geographical Information System (GIS) techniques using publicly available Ocean Color data within the Google Colab environment. The findings show that March, June, July, August, and September of 2023 saw the highest PFZ occurrences, while October had the lowest. By pinpointing peak fishing seasons and reducing the risk of overfishing, these findings give Indonesian authorities important information to maximize fishing operations in Biak Numfor and ultimately support the region's status as a national fish barn. © 2025 ieee.

关键词： Fisheries

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 ieee.

关键词： Differential privacy

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain 3

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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

作者： Deepa, R. Jayaraman, Venkatesh Srinivasulu, Senduru Umamageswari, B. Surendran, R. Srm Institute of Science & Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Srm Institute of Science & Technology School of Computing Department of Computing Technologies Chennai600119 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology - a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models. © 2025 ieee.

关键词： data assimilation

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