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21st International Learning and Technology Conference, L and T 2024

作者： Mustapha, Jawahir Che Ghazali, Muhammad Azmin Mohamed Abdullah, Munaisyah Sulaiman, Nor Intan Saniah Rahman, Hezlin Aryani Abd Universiti Kuala Lumpur Malaysian Institute of Information Technology Informatics & Analytics Section Kuala Lumpur Malaysia Universiti Kuala Lumpur Malaysian Institute of Information Technology Computer Engineering Tech. Section Kuala Lumpur Malaysia Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Utara Malaysia College of Arts and Science School of Quantitative Sciences Kedah Malaysia Universiti Teknologi Mara Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Selangor Malaysia

ISBN: (纸本)9798350393569

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools. © 2024 IEEE.

关键词： Social networking (online)

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An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

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computers, Materials & Continua 2021年第4期67卷 1191-1206页

作者： Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulKorea Department of Computer Science&Engineering Soonchunhyang UniversityAsan36538Korea

Stock market forecasting is an important research area,especially for better business decision *** stock predictions continue to be significant for business *** short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily ***,major events’news also contains significant information regarding market *** effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock *** research proposes an efficient model for stock market *** current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and *** use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock *** LSTM has the advantage of analyzing relationship between time-series data through memory *** performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

关键词： Business intelligence decision making stock prediction long short-term memory COVID-19 event sentiment

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DESIGNING A SKILLED SOCCER TEAM FOR ROBOCUP: EXPLORING SKILL-SET-PRIMITIVES THROUGH REINFORCEMENT LEARNING

arXiv

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arXiv 2023年

作者： Abreu, Miguel Reis, Luis Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D Soccer Simulation League serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance is based on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the Proximal Policy Optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast sprint-kick developed in 2021 and progress to the most recent skill set, which includes a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push tackles both low-level collision-prone scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the codebase of our team to the RoboCup community, enabling other teams to transition to Python more easily and providing new teams with a robust and modern foundation upon which they can build new features. © 2023, CC BY.

关键词： High level languages

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An investigation of novel features for predicting student happiness in hybrid learning platforms – An exploration using experiments on trace data

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International Journal of Information Management Data Insights 2024年第1期4卷

作者： Verma, Chaman Illés, Zoltán Kumar, Deepak Eötvös Loránd University Department of Media and Educational Informatics Faculty of Informatics Budapest 1053 Hungary Apex Insitute of Technology Department of Computer Science and Engineering Mohali Chandigarh 140413 India

The current study investigated the novel factors influencing informatics students' happiness and satisfaction using statistical and machine learning algorithms. A statistical t–test proved that the students in the younger age group (<=20) who were enrolled in bachelor's programs (***.) were more confident than those in the older age group (>=21) who had enrolled in master's programs (***.) that hybrid learning was a secure mode of education during the pandemic (p < 0.05). The happiness of students has had a significant influence on the challenges and prospects for online education that involve safety (p < 0.05). It was determined that factors such as time and cost utilization with management, university initiatives and assistance, and challenging group tasks substantially affected student satisfaction (p < 0.05). The paper's findings recommended some vital issues such as Internet disconnection during the lesson, Less support during technical glitches or errors, Less competitive environment, Possibility of cheating in exams due to lack of surveillance, Less focused and interactive during hybrid learning, important assistance and support, and safety modes for higher educational institutions during a pandemic. Using a maximum accuracy of 88%, Random Forest (RF) outperformed Logistic Regression (LR), Xtream Gradient Boosting (XGBoost), and other classifiers to predict student happiness based on hybrid learning features. The RF algorithm's superior performance was validated and confirmed by all performance metrics, such as F1-score, precision, recall, and specificity. © 2024 The Author(s)

关键词： Classification Factor analysis Happiness Hybrid learning Machine learning Prediction Satisfaction

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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NCC: Neural concept compression for multilingual document recommendation[Formula presented]

NCC: Neural concept compression for multilingual document re...

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作者： Tashu, Tsegaye Misikir Lenz, Marc Horváth, Tomáš ELTE – Eötvös Loránd University Faculty of Informatics Institute of Academia-Industry Cooperation Department of Data Science and Engineering Pázmány Péter sétány 1/C 1117 Budapest Hungary Pavol Jozef Šafárik University in Košice Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia Department of Artificial Intelligence Bernoulli Institute of Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Wollo University Kombolcha Institute of Technology College of Informatics Kombocha Ethiopia

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://***/Tsegaye-misikir/NCC. © 2023

关键词： Information retrieval systems

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A Survey of Lung Nodules Detection and Classification from CT Scan Images

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computer Systems Science & engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed Fazli Subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： Lung nodules computed tomography scans lung cancer deep learning

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Modified Flower Pollination Algorithm for Energy Forecasting and Demand Management Coupled with Improved Battery Life for Smart Building Micro-Grid

Modified Flower Pollination Algorithm for Energy Forecasting...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Ali Hamza Zunaib Ali Sandra Dudley Muhammad Uneeb Sultan M Alghamdi Nicholas Christofides School of Engineering London South Bank University (LSBU) London UK National University of Sciences and Technology(NUST) Islamabad Pakistan Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Frederick University Cyprus

This paper presents the Modified Flower Pollination Algorithm-based Multi-Layer Perceptron Neural Network (MFPA-MLPNN) as an optimization technique for efficient power flow management in a Smart Building Microgrid (SBMG) in-tegrated with solar and wind generation, and Electric Vehicle Batteries (EVBs) within grid connected structure while concurrently reducing optimization processing time. To achieve both technical and economic superiority, two optimization objectives are addressed. Firstly, a Demand Response (DR) framework is harnessed to accommodate the stochastic behavior and forecasting errors associated with intermittent sources. Secondly, the degradation of EVBs is considered, ensuring an economically viable power flow proposed strategy for both EV owners and mi-crogrid (MG) authorities. Power generation of Variable Renew-able Energy Sources (VRES) has been forecasted using MLPNN. Battery degradation and system stability under the action of the proposed topology have been evaluated using a simulation-based environment. Results show a significant decrease in battery degradation and processing time using the proposed MFPA-MLPNN optimization architecture.

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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

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computers, Materials & Continua 2021年第9期68卷 2961-2977页

作者： Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Capability Systems Centre School of Engineering and ITUNSW CanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the ***,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels ***,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal *** improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive ***-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed *** develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local *** two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR *** compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and *** was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with

关键词： Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method

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