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Implementation of Machine Learning and Ensemble Learning Techniques to Proactively Predict Students' academic Success in the Realm of Higher Education 4

Implementation of Machine Learning and Ensemble Learning Tec...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Devkishan, Taneja Sanjay Singh, Sanjay Kumar Bharti, Ajay Kumar Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India United College of Engineering and Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350387490

Predicting and analyzing student performance is a huge boon for both students and teachers. A number of analytical methodologies have been utilized in recent studies to forecast students' success;the CGPA is the end product of these analyses, which have been conducted for both the internal assessment as well as for the external assessment. Few well known machine learning algorithms have been suggested. Ensemble Learning along with Decision Trees, Support Vector Machine and Random Forest -obtained perfect 100s for Precision Value, Recall Value, as well as in F1 Scores in the evaluation of performances. Naive Bayes has worse performance in comparison, with F1 Scores of 66, Recall Value of 65, and Precision of 68. When contrasted with decision tree models, Support Vector Machines, Multi-Layer Perceptron, Ensemble Enabled Learning, and Random Forest, Naive Bayes, AdaBoost, and K-Nearest Neighbors all perform worse in terms of accuracy. The Naive Bayes classifier models the input distributions across classes and is very good in text categorization because it is a generative method. Since it fails to identify critical features for class differentiation, it cannot be distinguished from discriminative classifiers. © 2024 IEEE.

关键词： Adversarial machine learning

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Fault-Tolerant ST-Diameter Oracles 50

Fault-Tolerant ST-Diameter Oracles

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50th International Colloquium on Automata, Languages, and Programming, ICALP 2023

作者： Bilò, Davide Choudhary, Keerti Cohen, Sarel Friedrich, Tobias Krogmann, Simon Schirneck, Martin Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India School of Computer Science Tel-Aviv-Yaffo Academic College Israel Hasso Plattner Institute Universität Potsdam Germany Faculty of Computer Science Universität Wien Austria

ISBN: (纸本)9783959772785

We study the problem of estimating the ST-diameter of a graph that is subject to a bounded number of edge failures. An f-edge fault-tolerant ST-diameter oracle (f-FDO-ST) is a data structure that preprocesses a given graph G, two sets of vertices S, T, and positive integer f. When queried with a set F of at most f edges, the oracle returns an estimate Db of the ST-diameter diam(G−F, S, T), the maximum distance between vertices in S and T in G− F. The oracle has stretch σ ≥ 1 if diam(G−F, S, T) ≤ Db ≤ σ diam(G−F, S, T). If S and T both contain all vertices, the data structure is called an f-edge fault-tolerant diameter oracle (f-FDO). An f-edge fault-tolerant distance sensitivity oracles (f-DSO) estimates the pairwise graph distances under up to f failures. We design new f-FDOs and f-FDO-STs by reducing their construction to that of all-pairs and single-source f-DSOs. We obtain several new tradeoffs between the size of the data structure, stretch guarantee, query and preprocessing times for diameter oracles by combining our black-box reductions with known results from the literature. We also provide an information-theoretic lower bound on the space requirement of approximate f-FDOs. We show that there exists a family of graphs for which any f-FDO with sensitivity f ≥ 2 and stretch less than 5/3 requires Ω(n3/2) bits of space, regardless of the query time. © Davide Bilò, Keerti Choudhary, Sarel Cohen, Tobias Friedrich, Simon Krogmann, and Martin Schirneck.

关键词： Data structures

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Explainable Emotion Recognition in Social Networks with Transformers

Explainable Emotion Recognition in Social Networks with Tran...

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International Conference on computer and Information Science (ACIS)

作者： Isidoros Perikos Chara Pylarinou Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press “Diophantus” Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences.

关键词： Human computer interaction Emotion recognition Analytical models Sentiment analysis Social networking (online) Computational modeling Predictive models Transformers Data models Artificial intelligence

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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A Structured Analysis on IPL 2022 matches by approaching various Data Visualization and Analytics

A Structured Analysis on IPL 2022 matches by approaching var...

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2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： Data visualization

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