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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Automated Subject Allotment to Faculty in an academic institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

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Advances in Computing research on Science engineering and technology (ACROSET), International Conference on

作者： Neha Sehta Naman Shah Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty.

关键词： Productivity Measurement Heuristic algorithms Scalability Manuals Organizations Resource management Personnel research and development Testing

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks 19

Comparing the Effectiveness of Generative Adversarial Networ...

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19th IEEE International Conference on computer Science and Information Technologies, CSIT 2024

作者： Lytvynenko, Volodymyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution 'Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine' Kyiv Ukraine

ISBN: (纸本)9798331542627

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets. © 2024 IEEE.

关键词： adjusted mutual information Davies-Bouldin index generative adversarial networks adjusted rand index mutual information silhouette index

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A Patient-Centered Secured Approach to E-Health 12

A Patient-Centered Secured Approach to E-Health

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12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Sheeba, Adlin Maheswari, B. Uma Sam, Dahlia Jenipher, V. Nisha Rajarajeswari, S. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India Dr. Mgr Educational and Research Institute Department of Information Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9781665480352

Electronic health records (EHRs) have largely supplanted paper-based systems in order to improve the accuracy, reliability, and accessibility of patient data. Furthermore, EHR security is a major concern that puts patient privacy at risk. User privacy and data security are issues with the majority of third-party hosting solutions. As a result, access control regulations must be restricted and effective procedures for cloud-based EHR data must be developed. Under the suggested system, only authorised individuals will be allowed to update or examine some of the patient's data, ensuring the data's confidentiality. Each EHR record is encrypted by the controlling authority. The requesting user may receive dynamically changing permissions based on authentication and context factors. This paper is to provide security and patient-centric approach to e-health solutions. The patient can have control over their medical records. The potential of data theft grows when users' data is outsourced, and attackers can compromise data. So, the authority verifies the request with the database and then generates the secret key. Then with the key provided, files are shared with the authorized user. © 2022 IEEE.

关键词： Hospital data processing

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Unleashing the Power of Big Data: A Comprehensive Analysis and Future Directions 6

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： Sharma, Annu Kumar, Gaurav Farooq, Mansoor Gupta, Manish Kumar Raj, Saranya Rai, Ashok Kumar RajaRajeshwari College of Engineering Department of Computer Science & Application Karnataka Bengaluru India Indian Institute of Science Education and Research Bhopal Department of Computer Science & Engineering Madhya Pradesh Bhopal India University of Kashmir Department of Management Studies Jammu & Kashmir India Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Dronacharya Group of Institutions Department of Data Science & Engineering Uttar Pradesh Greater Noida India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India

ISBN: (纸本)9798350304480

Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains. © 2023 IEEE.

关键词： Analytics artificial intelligence Big data Internet of Things Processing

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Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators 16

Measurement of Synthetic-Dimension Band Structures in Silico...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Zhang, Ruming Li, Liucun Kamata, Mikiya Baba, Toshihiko Ozawa, Tomoki Ota, Yasutomo Iwamoto, Satoshi Research Center for Advanced Science and Technology The University of Tokyo Tokyo153-8904 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8533 Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (纸本)9798350372076

关键词： Silicon photonics

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Optimal stochastic gradient descent with multilayer perceptron based student's academic performance prediction model

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Recent Advances in computer Science and Communications 2021年第6期14卷 1728-1741页

作者： Ranjeeth, S. Latchoumi, T.P. Paul, P. Victer Department of Computer Science and Engineering Vignan's Foundation for Science Technology & Research Guntur AP India Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India

Introduction: Educational Data Mining and Machine Learning Models gained more interest among researchers and academicians in recent years. These are used to extract meaningful data from the educational database can be applied to predict the student academic performance Objective: The main objective of this research shows the capability to predict the student's performance and is highly beneficial to take remedial actions in the present educational system. It also offers suitable student assistance, allows an educational institution or teacher to help the students in gaining extra marks. Methods: The presented model operates in two stages, namely classification and outlier detection. Initially, a multilayer perceptron is employed for data classification. Later, a stochastic gradient descent classifier and multi-layer perceptron are integrated to effectively classify the data. For further improvement in the classification, Student academic performance, outlier detection method namely radial basis function is applied to remove the misclassified instances Results: The proposed model achieves superior classification performance with the maximum precision of 79.30, recall of 79.30, and accuracy of 79.30, F-score of 79.30 and kappa value of 52.40 respectively. The simulation outcome exhibited that the multilayer perceptron and stochastic gradient descentmodel offers better results over the other classifiers. However, the usage of outlier detection using the radial basis function model takes the classifier results to the next level. Conclusion: Using classification techniques a new student academic performance model is proposed by addressing some issues in the existing model which is used to predict the student academic performance assist inproper time. © 2021 Bentham Science Publishers.

关键词： Students

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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