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2023 International Technical Conference on Circuits/systems, computers, and Communications, ITC-CSCC 2023

作者： Charernmool, Piyanuch Tawornying, Chonlasit Prapakornwanichakun, Theerasak Vanijkachorn, Pavarit Visutsak, Porawat Pensiri, Fuangfar Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Bangkok Thailand School of Computer Science and Technology Beijing Institute Technology Beijing100081 China Kasetsart University Faculty of Science at Sriracha Chonburi Thailand

ISBN: (纸本)9798350326413

In developing countries, the well-being of the elderly is not given enough attention, especially in terms of nutrition. This is a vital point since the meal which the elderly eat typically reflects their health. This paper aims to research and develop a web-based system with the capacity to design appropriate and healthy meal plans for the elderly by nutrition specialist's recommendation. Eating healthy meal plans for a person helps to have good health. It was found that the elderly with diabetic mellitus and kidney failure had the better health together with good mental health. Moreover, the system can accurately calculate the optimal number of calories which the elderly should have each day. A supporting website was developed using HTML and *** for expert evaluation © 2023 IEEE.

关键词： Expert systems

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models 25

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Nahar, Khalid M.O. Ebrahim Megdad, Mohammad Al Mamlook, Rabia Emhamed Aljaidi, Mohammad Nahar, Besan K.M. Nahar, Mohammad K. M. Anbarkhan, Samar Hussni Khafajeh, Hayel Amer, Waleed Arab Open University Faculty of Computer Studies P.O. Box 84901 Riyadh11681 Saudi Arabia Yarmouk University Computer Science Department Irbid21163 Jordan University of Zawia Dept. of Mechanical and Industrial Engineering Zawia Libya Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Faculty of Medicine Irbid Jordan Jordan University of Science and Technology Faculty of Applied Medical Science Irbid21163 Jordan Northern Border University Information Systems Department Rafha76322 Saudi Arabia Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa13110 Jordan Zarqa University Faculty of Information Technology Department of Cybersecurity Zarqa13110 Jordan

ISBN: (纸本)9798331540012

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses. © 2024 IEEE.

关键词： COVID-19

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Comparative Analysis of Compromise Approaches in Multiple Rankings Decision-Making: A Case Study on Performance of Electric Cars 28th

Comparative Analysis of Compromise Approaches in Multiple Ra...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Paradowski, Bartosz Gajewski, Mariusz Wilczynski, Andrzej Salabun, Wojciech National Institute of Telecommunications National Research Institute ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

As civilization advances, decision-making problems become increasingly complex, posing significant challenges in deriving optimal solutions. The field of multi-criteria decision analysis (MCDA) has evolved to address these complexities;however, single-solution approaches are often inadequate in multi-decision-maker scenarios, necessitating group decision-making methods. This study proposes an alternative compromise solution for evaluating electric car performance, examining various compromise approaches, including voting methods like Borda and Copeland counts, and more advanced techniques such as Iterative Compromise Ranking Analysis (ICRA), Improved Borda, HQ compromise, Dominance-directed graph, and Rank position methods. These methods were assessed using two ranking correlation coefficients to evaluate their effectiveness in multi-decision-maker scenarios. Results indicate that while minor differences in rankings were observed based on the chosen compromise method, these differences could influence decision-making outcomes. The analysis highlights the importance of selecting methods that align with specific needs and expectations. Future research should focus on simulation-based studies to better understand the characteristics of each method and apply these methods to real-world case studies to determine their suitability in various contexts. © 2024 The Authors.

关键词： Optimal systems

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A Depth-Based DES Model for Enhanced Confidentiality in Datasets

A Depth-Based DES Model for Enhanced Confidentiality in Data...

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International Multidisciplinary information Technology and Engineering Conference (IMITEC)

作者： Oratile Sebithing Lucky Makhoere Nthabiseng Modiba Mnoneleli Nogwina Phumezo Ntlatywa Colin Chibaya Faculty of Natural and Applied Science Computer Science & information Tech Kimberly South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Data encryption is a fundamental aspect of ensuring the confidentiality and security of sensitive information in various applications. In this paper, we present an implementation of the Data Encryption Standard (DES) model for encrypting datasets, aiming to enhance data security through multilevel depth encryption with dynamic key transformation. The DES algorithm, known for its robustness and efficiency, forms the basis of our encryption framework. We explore novel encryption techniques by applying the DES algorithm at multiple levels of depth, leveraging dynamic key transformation strategies to enhance security. Specifically, we introduce multiple levels of depth encryptions through the use of random numbers that determine the number of key transformations. The key transformation process dynamically modifies the encryption key to provide additional layers of security. Our initial experimental results in particular CPU time/speed utilization and RAM demands demonstrate the effectiveness of our approach in enhancing data security. By combining the robustness of the DES algorithm with innovative encryption techniques, we achieve a higher level of protection for sensitive information, ensuring its confidentiality.

关键词： Codes Heuristic algorithms Random access memory Robustness Data models Encryption Protection information technology Standards

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Flipped Teaching in Software Engineering Education 5

Flipped Teaching in Software Engineering Education

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5th European Conference on Software Engineering Education, ECSEE 2023

作者： Bartel, Paula Bartel, Alexander Hagel, Georg Allgäu Research Centre Kempten University of Applied Sciences Kempten Germany Faculty Information Management Neu-Ulm University of Applied Sciences Neu-Ulm Germany Faculty Computer Science Kempten Kempten University of Applied Sciences Kempten Germany

ISBN: (纸本)9781450399562

Flipped teaching is an approach which is becoming increasingly popular. There are already various field reports and scientific findings in different educational institutions and subjects. The approach of flipped teaching also appears to be promising in the university subject of software engineering. This paper explores the question of how this approach can be evaluated in teaching areas such as software engineering in comparison to traditional teaching approaches. A long-term study is presented in which courses with flipped teaching and courses without flipped teaching are compared. In total, 11 courses were analyzed over a period of six years (N=157). The results show that flipped teaching in software engineering has great potential and is rated better by students than traditional teaching in many areas. At the same time, the study also raises questions, for example, it is not clear from the results that active involvement is higher in flipped teaching than in traditional teaching. © 2023 ACM.

关键词： Teaching

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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Correction to: Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19254-0)

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Multimedia Tools and Applications 2024年 1-2页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The original publication of this article contains an error in the affiliation of authors Fadwa Alrowais and Hanen Karamti. Incorrect: Department of information systems, College of computer and information sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Correct: Department of computer sciences, College of computer and information sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia The original article has been corrected. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

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