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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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Efficient Cache Performance Equivalent 2D-Texel to Memory Mapping Identification for Embedded GPUs

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IEEE Access 2025年 13卷 95094-95121页

作者： Ahmed El-Mahdy Marwa K. Elteir Kholoud Shata School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Department of Computer Science and Engineering Egypt-Japan University of Science and Technology New Borg El-Arab City Egypt Department of Computer and Systems Engineering Alexandria University Alexandria Egypt Informatics Research Institute (IRI) City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Egypt

With the increasing trend of utilizing smart devices, exploiting embedded GPUs becomes plausible to provide for intense computations that approach workstation performance, opening the way for complex, server-grade applications on such devices. Although general-purpose development frameworks exist, they generally abstract implementation details making it difficult for further performance exploitation. One such critical aspect is the texture memory hierarchy design parameters, which are primarily trade secrets. Unfortunately, standard cache hierarchy identification methods are not applicable, due to utilizing logical 2D-texel-to-memory mapping to exploit the 2D locality of access prior to physical cache access. This paper presents, for the first time, a parameterized model capable of describing the underlying multidimensional tiling layouts governing this mapping. Although it can be shown that reverse-engineering the model to obtain the exact parameters is an undecidable problem, we strive to obtain a corresponding set of parameters that results in the same cache behavior. In particular, such parameters define the texture order resulting in a linear order that traverses the caches in memory contiguous blocks. This study proposes a reverse-engineering observation method that exploits the contiguity of tiled cached regions and cache set conflicts to efficiently reveal such parameters. The complexity of the method is $O(n^{2})$ , where n is the number of bits in one of the texture buffer dimensions, ensuring practical applicability. Furthermore, optimization of a benchmark workload—via input data and memory access pattern alignment with the inferred multidimensional tiling layout—yields up to a $2.22\times $ speedup.

关键词： Layout Graphics processing units Performance evaluation Electronic mail Urban areas Systems architecture Object recognition Three-dimensional displays Standards Smart devices

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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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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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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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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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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning 3

Predictive Design for Quality Assessment Employing Cloud Com...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Jindal, Gaurav Tiwari, Vidhika Mahomad, Riyaz Gehlot, Anita Jindal, Mimoh Bordoloi, Dibyhash G L Bajaj College of Technology and Management Department of Master of Computer Applications Gr. Noida India DPGITM Mechanical Engineering Department Gurugram India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India National Informatics Centre Delhi India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure. © 2023 IEEE.

关键词： Cloud computing

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