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2022 IEEE International Conference on Agents, ICA 2022

作者： Ali, Muhammad Shahroze Azam, Farooque Safdar, Aon Anwar, Muhammad Waseem Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (数字)9781665469364

ISBN: (纸本)9781665469364

Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease. © 2022 IEEE.

关键词： Natural language processing systems

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Recently emerging trends in big data analytic methods for modeling and combating climate change effects

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Energy Informatics 2024年第1期7卷 6页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Mkpojiogu, Emmanuel Anikwe, Chioma Virginia Igwe, Sylvester Agbo Alo, Uzoma Rita Software Engineering Department Veritas University Abuja Nigeria Computer Science Department David Umahi Federal University of Health Sciences Ebonyi State Uburu Nigeria Computer Science Department Alex Ekwueme Federal University Ebonyi Ndufu-Alike Nigeria Computer Science Department Ebonyi State University Ebonyi State Abakaliki Nigeria

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. © The Author(s) 2024.

关键词： Data Analytics

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The Impact of Blockchain Technology for Evaluation of Electronic Medical Certification

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SN computer Science 2025年第5期6卷 1-14页

作者： Azam Zia, M. Anwar, Munazza Mehmood, Faisal Razzaq, Abdul Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Punjab Faisalabad 38000 Pakistan Department of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Computer Science Muhammad Nawaz Shareef University of Agriculture Punjab Multan 59300 Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Lahore Pakistan

Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy issues are standard in electronic medical certification. Incompatible systems make sharing data across healthcare providers difficult, which may lead to data breaches and a lack of patient authority over their medical data. Blockchain has become essential for real-world corporate applications where confidentiality of information is paramount. The handling of security in medical records through a decentralized peer-to-peer network might be revolutionized by blockchain technology and improving the protection of the interchange of healthcare records. This study proposes a lightweight flask framework-based blockchain strategy to store and display patient status and transaction log information about their disease diagnosis. In a potential Blockchain system, the diseased patients act as assets, and their records contain any prior illnesses as blocks. The report analyzes a lot of national and international EHR standards. It identifies which of these interoperability needs to be addressed, discusses the interoperability challenges in but also outlines the standards. Current blockchain-based EHR frameworks. Without the suggested has the requirement for centralized systems. By using this framework, the healthcare industry can be provided safer ways to while also providing the exchange of health information. It offers features of immutability, security, and user control over stored records. This work contributes to an understanding of what potential Blockchain technology has offered an interoperable application in the EHR frameworks. Meet the blockchain-based EHR framework. Multiple national and international EHR standards. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cryptography Decentralized Electronic medical certification (EMC) Transaction

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New tunable phase oscillator structure

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International Journal of Electronics Letters 2024年第1期12卷 41-48页

作者： Elwakil, A.S. Psychalinos, Costas Maundy, Brent Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Electronics Laboratory Department of Physics University of Patras Rio Patras Greece

In this Letter, we introduce and experimentally verify a very simple tunable-phase oscillator structure based on cascaded standard filter sections. In particular, a standard inverting second-order low-pass filter is cascaded with a first-order bi-linear filter in a closed loop resulting in a third-order structure that can be made marginally stable. The phase difference between the outputs of the two filter sections is a design parameter that is tunable independent of the oscillation frequency or start-up gain. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： all-pass filters low-pass filters multi-phase oscillators Sinusoidal oscillators tunable phase

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks 9

A Trust-based Framework for the Detection and Avoidance of B...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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Future Intelligent Autonomous Robots, Ethical by Design. Lessons Learned from Autonomous Cars Ethics 14

Future Intelligent Autonomous Robots, Ethical by Design. Les...

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14th International Conference on Society and Information Technologies, ICSIT 2023

作者： Dodig-Crnkovic, Gordana Holstein, Tobias Pelliccione, Patrizio Thavarasa, Jathoosh Division of Computer Science and Software Engineering Mälardalen University Västerås Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Information Engineering Computer Science and Mathematics University of l'Aquila L'Aquila Italy Software Engineering Efrei Paris Villejuif France

ISBN: (纸本)9781950492701

The "ethical by design" approach involves examining all stages of a lifecycle of technology to ensure that they are ethically justifiable and socially sustainable. Building on our work on the ethics of autonomous intelligent robocars, and studies of the literature on the ethics of robotics, we propose for robot applications a set of values and ethical principles including safety, security, privacy, transparency, and explainability, accountability, fairness, human control, well-being, autonomy and freedom, and sustainability. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Most ethical considerations we identified in our work on autonomous cars are relevant to all AI-powered robots, but robots require additional examination depending on their application domain, such as social robots (care robots, personal companions, robots used in education, health care, elderly care, education, entertainment, chat-bots), industrial robots, etcetera. Thus, existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need for continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders. This implies designing the socio-technical system as an intelligent learning ecology. © 2023 Proceedings ICSIT, International Conference on Society and Information Technologies. All rights reserved.

关键词： Intelligent robots

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Human Pose Estimation and Object Interaction for Sports Behaviour

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computers, Materials & Continua 2022年第7期72卷 1-18页

作者： Ayesha Arif Yazeed Yasin Ghadi Mohammed Alarfaj Ahmad Jalal Shaharyar Kamal Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-AhsaSaudi Arabia Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and *** understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose *** understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been *** existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized *** existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the *** estimation depends on appearance features and spatial ***,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the ***,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using *** predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction *** interactions are linked with the human and object to predict the *** experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.

关键词： Human object interaction human pose estimation object detection sports estimation sports prediction

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A Novel Comparative Method for Evaluating Compensation Networks in IPT Systems 49

A Novel Comparative Method for Evaluating Compensation Netwo...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Dong, Weihao Madawala, Udaya Baguley, Craig The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Department of Engineering Computer & Mathematical Sciences Auckland New Zealand

ISBN: (纸本)9798350331820

A novel method for evaluating compensation networks in IPT systems is proposed for a fair comparison. An optimal control strategy is adopted in the comparison to ensure operation under optimal conditions. Results indicate efficiency boundary can be used as a powerful tool for intuitive evaluation of the compensation networks. © 2023 IEEE.

关键词： Efficiency

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer Science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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Optimization of Decentralized Caching with Improved Coded Delivery Under Heterogeneous File Popularity 22

Optimization of Decentralized Caching with Improved Coded De...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Zhang, Siyu Liang, Jiasheng Deng, Yong Lakehead University Department of Software Engineering ON Canada Binghamton University Department of Computer Science New York United States

ISBN: (纸本)9798331508050

This paper considers the design and optimization of decentralized coded caching under heterogeneous file popularity. We propose a decentralized nested coded caching scheme (D-NCCS) that implements an improved nested coded delivery strategy to avoid using zero-padding, a common sub-optimal approach for simplifying the coded delivery under heterogeneous file popularity. We formulate a joint optimization problem to jointly optimize the cache placement and nested coded delivery strategies of the D-NCCS, and develop a successive approximation algorithm to solve the problem. Numerical results demonstrate the close to optimal performance of the optimized D-NCCS. © 2025 IEEE.

关键词： Approximation algorithms

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