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Information Technology for Education and Development (ITED)

作者： Ugochukwu. E. Orji Elochukwu Ukwandu Ezugwu. A. Obianuju Modesta. E. Ezema Chikaodili. H. Ugwuishiwu Malachi. C. Egbugha Dept. of Computer Science University of Nigeria Nsukka Enugu Nigeria Dept. of Applied Computing & Engineering Cardiff School of Technologies Cardiff Metropolitan University UK Dept of Computer Engineering Ahmadu Bello University Zaria

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far.

关键词： COVID-19 Visualization Technological innovation Data analysis Pandemics Data visualization Big Data

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Federated Learning for Distributed Optimal Power Flow Solutions in Smart Grids

Federated Learning for Distributed Optimal Power Flow Soluti...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Aishwarya, B.K. Shekar, K. Revathi, V. Singh, Jay Singh, Navdeep Abdulaali, Hayder Saadoon Institute of Aeronautical Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

The rapid evolution of smart grids has necessitated the development of advanced computational techniques to ensure efficient and reliable power distribution. This paper introduces a novel approach to solving the Optimal Power Flow (OPF) problem by leveraging Federated Learning (FL) - a decentralized machine learning paradigm. Traditional centralized methods for OPF solutions often face scalability issues and data privacy concerns when applied to large-scale, distributed smart grids. The proposed FL-based method addresses these challenges by allowing individual grid nodes to locally compute model updates without sharing raw data. This not only preserves data privacy but also reduces the communication overhead. The paper presents a comprehensive mathematical formulation of the FL-based OPF problem, followed by a detailed analysis of its convergence properties and computational efficiency. Comparative studies with conventional methods demonstrate the superiority of the proposed approach in terms of accuracy, speed, and scalability. The findings suggest that FL can serve as a robust framework for distributed OPF solutions, paving the way for more resilient and efficient smart grid operations. © 2023 IEEE.

关键词： Data Privacy. Top of Form Distributed Computing Federated Learning Optimal Power Flow Smart Grids

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Automated Bell Inequality Violation using Uncalibrated Fiber Squeezing Polarization Controllers

Automated Bell Inequality Violation using Uncalibrated Fiber...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Dowling, Evan Morris, Mark Baumgartner, Gerald Roy, Rajarshi Murphy, Thomas E. Inst. for Research in Electronics & Applied Physics University of Maryland College ParkMD20740 United States Dept. of Physics University of Maryland College ParkMD20740 United States Laboratory for Telecommunication Sciences College ParkMD20740 United States Inst. for Physical Science & Technology University of Maryland College ParkMD20740 United States Dept. of Electrical & Computer Engineering University of Maryland College ParkMD20740 United States

We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po...

ISBN: (纸本)9781957171258

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Investigating Academia-Industry Gap for Data science Jobs and Curriculum

Investigating Academia-Industry Gap for Data Science Jobs an...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Suryan, Komal Gupta, Rajan University of Delhi Deen Dayal Upadhyaya College Computer Science Dept. Delhi India University of Nova Gorica Centre for Information Technologies Applied Mathematics Vipava Slovenia

ISBN: (纸本)9781665416566

As big data analytics present an array of applications;its importance is growing for contemporary organisations. The vast amount of data and the competitive advantage of analysing triggers the digital transformations throughout many industries. Despite the towering demands of data science, there seem to be a lack of skilled professionals. Analysing this pressing issue, the purpose of this paper is to investigate the academia-industry interlude and reviewing the status-quo of technical and managerial skills in diverse industries. To study this gap, we collected job descriptions of over 200 companies and then comparing them with the curriculum findings corresponding to 100 institutional courses and professional certification trainings and closely studying the composition of technical and managerial skills required by different industries. Our research summarises the findings and inferences regarding gaps and potential amends that exist in in the domain of data science as well as suggestions on how to bridge the gap indicating industry-specific skillset that are of highlighted significance. © 2021 IEEE.

关键词： Big data

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A New Clustering Approach based on Trust and Rat Swarm Algorithm for WSN Applications

A New Clustering Approach based on Trust and Rat Swarm Algor...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Walid Osamy Ahmed Salim Amel Al Ali Ahmed M. Khedr Computer Science Department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Math. Department Faculty of Science Zagazig University Zagazig Egypt Department of CS College of Sci. and Arts Al-Methnab Qassim University KSA Dept. of Information Systems Sharjah University Sharjah UAE Dept. of Computer Science Sharjah University Sharjah UAE

In this paper, a clustering approach called CATRSO is proposed. The selection of cluster heads (CH) is performed by considering the trust value of the nodes in order to select the most trustworthy nodes as CH and Rat Swarm Optimizer is employed for CH selection process. The trust value of the nodes and remaining energy are taken into account while designing the fitness function. In addition, a chain routing approach is employed between CHs for energy savings. The results demonstrate that the CATRSO technique is successful in selecting the most trustworthy nodes as CH and outperforms earlier efforts in the literature in terms of energy efficiency, average network lifetime, and trustworthiness of selected CHs.

关键词： Wireless communication Measurement Wireless sensor networks Simulation Clustering algorithms Rats Routing

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High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle Communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Preeti Sharma Prabhakar Singh Rakesh N. Tiwari Pradeep Kumar Physics Division School of Basic Sciences Galgotias University Greater Noida India Dept. of Applied Science Greater Noida Institute of Technology Greater Noida India Dept. of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India Dept. of Electrical Electronic and Computer Engineering University of KwaZulu-Natal Durban South Africa

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of $14\times 10\times 0.58\mathrm{m}\mathrm{m}^{3}$. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) <0.025 and diversity gain (DG) is found >9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation.

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Asynchronous Traffic Shaping and Redundancy: Avoiding Unbounded Latencies in In-Car Networks

arXiv

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arXiv 2025年

作者： Lübeck, Teresa Meyer, Philipp Häckel, Timo Korf, Franz Schmidt, Thomas C. Dept. Computer Science Hamburg University of Applied Sciences Germany

Time-Sensitive Networking (TSN) enhances Ethernet-based In-Vehicle Networks (IVNs) with real-time capabilities. Different traffic shaping algorithms have been proposed for time-critical communication, of which the Asynchronous Traffic Shaper (ATS) is an upcoming candidate. However, recent research has shown that ATS can introduce unbounded latencies when shaping traffic from non-FIFO systems. This impacts the applicability of ATS in IVNs, as these networks often use redundancy mechanisms that can cause non-FIFO behaviour. In this paper, we approach the problem of accumulated delays from ATS by analyzing the scenarios that generate latency and by devising placement and configurations of ATS schedulers to prevent this behavior. Our solution successfully mitigates problematic preconditions that lead to unbounded delays, which we evaluate in simulations. Through a realistic IVN simulation case study, we demonstrate the occurrence of unbounded latencies and validate the effectiveness of our approach in avoiding them. Copyright © 2025, The Authors. All rights reserved.

关键词： Time division multiple access

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Semirings for Probabilistic and Neuro-Symbolic Logic Programming

arXiv

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arXiv 2024年

作者： Derkinderen, Vincent Manhaeve, Robin Dos Martires, Pedro Zuidberg De Raedt, Luc DTAI Dept. of Computer Science KU Leuven LeuvenB-3000 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Center for Applied Autonomous Systems Örebro University ÖrebroSE-70182 Sweden

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neural-symbolic methods. We provide a unified algebraic perspective on PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting. Copyright © 2024, The Authors. All rights reserved.

关键词： Logic programming

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An Improved Hybrid Approach for Coronary Heart Disease Diagnosis Using PSO and GA

An Improved Hybrid Approach for Coronary Heart Disease Diagn...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagmohan Kaur Baljit S. Khehra Amarinder Singh IKG Punjab Technical University Jalandhar India Dept. of Computer Science & Engg. BBSB Engg. College Fatehgarh Sahib India Department of Applied Sciences BBSB Engg. College Fatehgarh Sahib India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

The most deadly disease in the world is coronary heart disease. Early detection of cardiac disease is essential as it is the only way to reduce the overall death rate caused by the disease. The majority of methods for diagnosing heart disease are clinical and expert dependent, which renders them expensive and out of reach for the general public. In this era of digital health, numerous researchers and medical professionals have created a variety of computer-aided diagnosis systems by using various machine learning techniques: K-nearest neighbour, random forest, linear regression, naive bayes, neural network, support vector machine, and decision tree are a few examples. This paper proposes a highly automated, less expensive and timesaving diagnosis model of coronary heart disease using fuzzy logic system optimized by particle swarm optimization and genetic algorithm. The model has been applied to the heart patients dataset extracted from Kaggle with thirteen clinical factors. In the initial stage, the system makes use of fuzzy logic with mamdani type-1. Further, the study tunes and optimizes the rules and membership functions using two evolutionary algorithms and compare the results of these techniques through the experimental records, RMSE and cross-validation method. The experimental findings yield 0.3437 and 0.3547 RMSE along with validation of testing set having RMSE 0.367 and 0.369 using particle swarm optimization and genetic algorithm respectively.

关键词： Heart Fuzzy logic Support vector machines Mortality Particle swarm optimization Random forests Medical diagnostic imaging Diseases Genetic algorithms Testing

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Envisaging an Intelligent Blockchain Network by Intelligence Sharing

Envisaging an Intelligent Blockchain Network by Intelligence...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Nayak, Arijit De, Sourav Bhattacharyya, Siddhartha Mukhopadhyay, Debarka Muhammad, Khan Gorbachev, Sergey Alumnus Software Limited Kolkata India Cooch Behar Govt. Engineering College Dept. of Computer Science Engineering Cooch Behar India Rajnagar Mahavidyalaya Birbhum India Department of Computer Science and Engineering Bangalore India Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of National Research Tomsk State University Department of Innovative Technologies Tomsk Russia

ISBN: (纸本)9781665425773

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. © 2022 IEEE.

关键词： Blockchain

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