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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Aslam, Waseem Raza, Asim University of Central Punjab Department of Software Engineering Lahore Pakistan University of Central Punjab Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Key distribution as a core feature of any security system is one of the challenging tasks in an online transaction. Pairing is used to share the key between the users as an answer to the underlying security problem. Diffie-Hellman key exchange protocol computes pairing in two rounds whereas Eta-T is the most efficient and widely used pairing algorithm in the field of elliptic curve cryptosystems. After careful analysis of existing Eta-T pairing techniques, this work proposes a modification from serial computation scheme to parallel for better overall performance. Parallel Karatsuba multiplier and Most Significant reduction algorithm is used to speed up and optimize the pairing algorithm. Consequently, proposed pairing accelerator calculates Eta-T pairing in just 36.4μs and takes only 3597 clock cycles with maximum clock frequency of 98.7 MHz on Virtex-II. Further, by increasing the number of coefficients processed per cycle up to 6, this work shows reduction in the calculation time and clock cycles of Eta-T pairing up to 26.16μs and 1997 respectively. It is also shown that calculation time starts increasing after reaching D = 14. Similarly, it is shown that the fastest calculation of Eta-T pairing in characteristics three over the field F397 uses significantly less clock cycles than previous known implementations at the cost of less clock frequency. © 2024 IEEE.

关键词： Electric clocks

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Liquid Neural Networks - Classification and Time Series Forecasting Use Case 8

Liquid Neural Networks - Classification and Time Series Fore...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Kavakli, Mehmet Ucar, Ferhat Gazi Vocational and Technical Collage Computer Science Department Elazig Turkey Firat University Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798331531492

This study aims to comprehensively examine the potential of Liquid Neural Networks (LNNs) in machine learning field and various application areas. LNNs offer significant advantages over traditional neural networks due to their adaptive learning capacity and dynamic structures. The study involves visual classification using the MNIST dataset and time series analysis on the Yahoo Finance dataset. The performance of the LNN model on these two different datasets has been thoroughly evaluated through the training and testing processes, with results rigorously analyzed. This comprehensive analysis aims to provide an important discussion of the potential uses and advantages of the LNN model. © 2024 IEEE.

关键词： Time series

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Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors 19

Green Computing Adoption: Understanding the Role of Individu...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Anni, Fahima Akter Islam, Muhammad Rezaul Sadia, Farzana Hasan, Mahady Rokonuzzaman, M. Department of Software Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9789897586965

This study investigates the factors influencing the attitudes of software developers and IT professionals towards Green Information Technology (GIT) in Bangladeshi IT/software firms and examines their impact on engagement in green computing practices. Data was collected from 130 participants. A thorough literature review was conducted. The findings highlight key individual factors that influence employees’ attitudes, including awareness, knowledge, and perception of environmental issues. The study demonstrates the connection with GIT attitudes and behavior modification, especially through stated usage of green computing methods. Data analysis confirms 5 out of 8 hypotheses and reveals the complexity of the relationships between the constructs. The report promotes the adoption of Green IT technology by software companies in Bangladesh and highlights the significance of an organized office system integrating Green IT. Limitations include a relatively small sample size and the multidimensional nature of the relationships between the constructs. The findings can assist software companies in addressing customer concerns about the performance and functionality of green computing practices, ultimately promoting sustainable computing practices in the IT sector of Bangladesh. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Green computing

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Diabetes Prediction in Healthcare Utilizing Machine Learning Algorithms

Diabetes Prediction in Healthcare Utilizing Machine Learning...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Tripathy, Nrusingha Hota, Sarbeswara Satapathy, Pranati Nayak, Subrat Kumar Nayak, Debasish Swapnesh Kumar Nayak, Purnya Prava Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Siksha 'O' Anusandhan Deemed to be University Department of Computer Application Odisha Bhubaneswar India Utkal University Department of Imca Odisha Bhubaneswar India Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Giet Department of Computer Science and Engineering Baniatangi Odisha Bhubaneswar India

ISBN: (纸本)9798331530259

Diabetes mellitus is among the most dangerous conditions that many people suffer from. Age, obesity, poor food, heredity, high blood pressure, and inactivity are some of the variables that might lead to diabetes mellitus. Though there are additional forms of diabetes, such as gestational diabetes, which appears during pregnancy, the two most common types are type 1 and type 2. In data science, machine learning is a relatively new field of study that looks at how machines pick up knowledge via experience. The goal of this work is to build a system that can more accurately forecast a patient's risk of developing diabetes in their early years by utilizing a combination of research data and machine learning techniques. Interpretable findings using logistic regression (LR) show how various risk variables affect the chance of acquiring diabetes. The most crucial characteristics for diabetes prediction may be automatically discovered by neural networks (NN) from unprocessed input data, eliminating the requirement for human feature selection. While compared to previous models, both the logistic regression and the neural network in this study provide a respectable accuracy score following a 10-fold classification. © 2025 IEEE.

关键词： Nutrition

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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A Blockchain Framework for Securing Examination Results in Higher Institutions of Learning

A Blockchain Framework for Securing Examination Results in H...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Charles, Taiwo LYemi Azeta, Ambrose A. Crawford University Department of Computer and Mathematical Sciences Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350358155

With data security and transparency guaranteed, stakeholders' confidence and trust in certificates and degrees from our universities and colleges will improve, third party investments in education will also increase and government effort at building a strong and high quality education for all will be adequately complemented. Existing traditional approaches to execution of academic activities lacks capabilities in the area of preventing alterations to academic records after being recorded, preventing single points of failure, providing enhanced data security, encouraging the use of decentralized storage system and most importantly, ensuring distributive transparency. This study provides a blockchain framework for securing examination results in higher institutions of learning. Some of the methodology engaged includes Blockchain technology, Cryptography, schematic diagram, and Unified Modelling Language (UML). Java was used to implement the blockchain component services. Generic flat file was used to implement the database. Having adopted a measurement scale of 1-5 in this study and based on the mean ratings of the usability average rating of 4.18 findings, it can be concluded that the blockchain framework has a "Good Usability"rating. Using blockchain platform for storing students' examination results ensures transparency and provides enhance data security by locking the finalized scores to prevent any alteration. © 2024 IEEE.

关键词： Decentralized finance

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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Automatic Anomaly Monitoring in Public Surveillance Areas

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Intelligent Automation & Soft Computing 2023年第3期35卷 2655-2671页

作者： Mohammed Alarfaj Mahwish Pervaiz Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classiﬁcation also *** it has multiple applications,the problem is very *** this paper,a novel approach for detecting nor-mal/abnormal activity has been *** used the Gaussian Mixture Model(GMM)and Kalmanﬁlter to detect and track the objects,*** that,we performed shadow removal to segment an object and its *** object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded *** c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalﬂow are extracted for each identiﬁed *** Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classiﬁcation that is performed using the XG-Boost classiﬁ*** system is applicable in any surveillance appli-cation used for event detection or anomaly *** of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of *** mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%*** results are more accurate as compared to other existing methods.

关键词： Abnormal event classiﬁcation gray wolf optimizer region shrinking xg-boost classiﬁer

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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