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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6589 条记录，以下是11-20 订阅

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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Optimizing Fog computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum Computers 1

Standardized Post-Quantum Cryptography and Recent Developmen...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Chib, Shiney Devarajan, Harshitha Raghavan Chundru, Swathi Pulivarthy, Padmaja Isaac, R. Augustian Oku, KrishnaMurthy Meghe Institute of Management Studies Department of Management Datta Maharashtra Nagpur India New York University Department of Computer Science United States Motivity labs INC United States Samsung Korea Republic of Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India AT&T Services Inc Tech Lead Infrastructure/Cloud Architecture Department of Automation platform Development India

ISBN: (纸本)9798331518578

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's 'quantum supremacy' assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper. © 2025 IEEE.

关键词： Quantum cryptography

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Predicting System Performance and Resource Exhaustion with Machine Learning: Tackling Class Imbalance 1

Predicting System Performance and Resource Exhaustion with M...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Swati, Shipra Kumar, Naween Dubey, Ankit Saini, Vaibhav Sharma, Subham Pranav, Ayushman Bennett University SCSET U.P. Greater Noida India

ISBN: (纸本)9798331518578

This study introduces a robust machine learning pipeline for event classification, utilizing a large-scale dataset from eight Google Borg compute clusters (May 2019) available via Google Big- Query. The dataset includes detailed job submissions, scheduling decisions, and resource usage data, with enhancements such as 5-minute CPU usage histograms, shared resource reservations, and job-parent relationships critical for MapReduce task analysis. data preprocessing involves imputation for missing values, label encoding for categorical data, and SMOTE to address the class imbalance. Feature scaling using StandardScaler and dimensionality reduction via Principal Component Analysis (PCA) retained 95% of the variance. The dataset was divided into training and testing sets, preserving class distribution. Seven machine learning models were evaluated, spanning linear, non-linear, ensemble, and neural network approaches. The Support Vector Machine (SVM) achieved exceptional performance, with 98.0% accuracy, 97.5% cross-validation score, 98.2% precision, 97.8% recall, and 98.0% F1-score. This study highlights the evolution of workloads and resource usage patterns, contributing to developing scalable and efficient event classification frameworks in cloud computing. © 2025 IEEE.

关键词： Cloud computing Event classification Google Borg clusters Machine learning Principal component analysis (PCA) Resource usage patterns Synthetic minority oversampling technique (SMOTE)

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices.. © 2025 IEEE.

关键词： data quality

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AI-Driven Test Automation for Big data Systems in Cloud Environments 1

AI-Driven Test Automation for Big Data Systems in Cloud Envi...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Gupta, Ankit Kumar Govindankutty, Sreeprasad Nandyala, Avinash Reddy Maria Thason, Justin Rajakumar Pippal, Sanjeev Gupta, Gopal Kumar Uttar Pradesh Technical University Uttar Pradesh Lucknow India Rochester Institute of Technology RochesterNY14623 United States Texas State University San MarcosTX United States Maniapl University Sikkim Gangtok737102 India Greater Noida201306 India Nagpur Campus Pune440008 India

ISBN: (纸本)9798331518578

AI-based Test Automation for Cloud-Based Big Advanced data Systems this method used AI and other new data science methods such as big data, cloud computing, etc. Current testing methodologies do not suit the environments of modern big data systems in a cloud-based environment. AI based test automation approaches: Automated test scenario preparation, examination of test events to identify defects and generation of testing issues. Big data systems, on the other hand, can periodically scan for maximum performance with an all-AI approach, carry out A/B testing, and fine-tune performance. This is particularly important for cloud environments when the architecture of the system and data processing continually changes at a high rate. Additionally, AI in test automation is capable of detecting anomalies and predicting possible system breakdown, providing early warning of issues to ensure mitigation and thereby avoiding costly downtime. This way various scenarios and data sets can be tested and covered to ensure that the best results are covered. © 2025 IEEE.

关键词： data centers

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Prediction of Liver Diseases using Radiomics Features and Convolutional Neural Networks 5

Prediction of Liver Diseases using Radiomics Features and Co...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Parimala, A. Bathsheba Shanmugasundaram, R.S. Department of Computer Science and Engineering Tamil Nadu Salem636308 India

ISBN: (纸本)9798350356236

Liver disease is a serious health condition that affects millions of individuals worldwide. Radiomics and deep learning (DL) are two AI-based methods that are being actively researched in the field of liver imaging. The analysis of liver related problems is a tedious tasks and it requires models to classify and predict. Radiomics is an effective method for extracting an extensive amount of distinctive characteristics from medical images using data-characterization techniques. Radiomic features possess the ability to reveal tumoral patterns and traits that the human eye alone cannot detect. Convolutional Neural Networks (CNN) is well suited method to analyze visual data and it is most useful to diagnose the liver diseases with extracted radiomics features. This study suggests an extraction of radiomics features and classifies it using CNN for liver diseases. The combination of Radiomics and CNN improves the prediction performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Privacy Preservation of Cluster Integrity on Web-Scraped Hospital data 5

Privacy Preservation of Cluster Integrity on Web-Scraped Hos...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Bojnal, Hrishik Sai Dharmisht, S.V.K. Kaarthik, J. Krishna Kotian, Dhyaan Virupaksha, Shashidhar Rv Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798350356236

This paper proposes a method for anonymizing geospatial healthcare data that preserves the natural clustering of locations. A dataset is built with each healthcare facility's location, ratings, and review count by scraping it off an online maps platform. This enables the calculation of each facility's regional influence. The approach retains spatial clusters by restricting the displacements on each facility's geospatial coordinates to at most the minimum radius of the cluster while also factoring for the prominence of the facility in the region. This ensures that the shape and magnitude distribution of clusters is maintained, thereby preserving dataset quality. Impactful insights can be drawn from this anonymized dataset without having to identify any individual facility. © 2025 IEEE.

关键词： Nutrition

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Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional Networks 5

Analysis for Predicting English Premier League Matches Using...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Mohideen AbdulKader, M. Vaithieswaran, S. Advay Rao, Tanuj School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798350356236

The most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). The data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while The Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. The rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%. © 2025 IEEE.

关键词： Image reconstruction

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Color Image Encryption Using Novel 5D Piecewise Map 5

Color Image Encryption Using Novel 5D Piecewise Map

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Salunke, Bharti Salunke, Sharad Parul University Department of Computer Science & Engineering Vadodara India

ISBN: (纸本)9798350356236

This paper presents a novel color image encryption technique using a 5D piecewise chaotic map. In recent years, image encryption has become crucial for securing multimedia data in various applications, especially in communication over the internet. The proposed 5D piecewise map introduces complex chaotic behavior, ensuring higher security for color images. We extensively evaluate the performance of the encryption scheme using metrics such as correlation coefficient, information entropy, and keyspace. The results indicate that the proposed method achieves excellent security properties, and provides a substantial keyspace to prevent unauthorized access. Additionally, the comparison with recent methods demonstrates the superiority of our proposed encryption approach in terms of encryption speed, randomness, and robustness. © 2025 IEEE.

关键词： Authentication

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