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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Advancing Online Education: An Interactive Framework for Aligning Teaching Methods with Learning Styles

Advancing Online Education: An Interactive Framework for Ali...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： D. I. De Silva K. S. N. Athukorala Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This research develops an innovative method for enhancing existing distance education by adjusting the teaching process to students' learning styles. The study investigates the relationship between teachers' teaching styles and learners' preferences, refining Grasha's teaching style model to better suit students' needs. As a result, an online system was created that enabled teachers to modify their strategies during lessons, thereby improving student participation and performance. Furthermore, a mobile solution was also implemented to address the need for portable and easy-to-use options. To evaluate the adoption of the system, university students were administered questionnaires aimed primarily at determining their main learning styles. Based on the analysis, lecturers were trained to modify their teaching methods accordingly. The findings revealed significant improvements in student satisfaction and academic performance after the alignment of teaching methods with learning preferences. Cronbach's Alpha values confirmed the high reliability of the results, highlighting increased engagement and satisfaction among students. The contribution of this study lies in its practical approach to improving online education through personalized teaching methods. The platform provides educators with a real-time feedback loop, enabling dynamic adjustment of teaching strategies to optimize student engagement. The research demonstrates technical soundness, supported by rigorous data analysis, and has the potential to revolutionize online learning environments globally.

关键词： Feedback loop Data analysis Education Refining Electric variables measurement Real-time systems Time measurement Mobile applications Reliability Artificial intelligence

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Exploring Computational Linguistics Techniques for Enhanced Outing Planning: A Comprehensive Review

Exploring Computational Linguistics Techniques for Enhanced ...

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Advanced Research in Computing (ICARC), International Conference on

作者： Faizan Muthaliff T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

In this day and age where convenience is king a new competitor in the form of chat-bots has appeared which requires users to only have prior knowledge on the language the chat-bot converses in. When it comes to planning outings there are a multitude of criteria to be wary of to determine the ideal solution. This review paper will address the common issues with extracting meaningful information from the user utilizing modern NLP techniques, the required criteria in addition to the user input such as, real time weather information and communicable diseases pertaining to the outing location, to determine an ideal suggestion and finally the method in which said ideal suggestion is determined after prioritizing criteria. The conclusion underscores that while NLP applications might not entirely replace existing menu-based systems for outing planning, they excel in certain scenarios. Notably, NLP's innate capacity to comprehend user preferences and context allows it to offer tailored outing suggestions. Furthermore, the application's unique feature of dynamically adapting to user requests without necessitating a return to the home page adds a distinctive advantage to its usability.

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A Projected Subgradient Method for the Computation of Adapted Metrics for Dynamical Systems

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SIAM Journal on Applied Dynamical Systems 2022年第4期21卷 2610-2641页

作者： Louzeiro, Mauricio Kawan, Christoph Hafstein, Sigurdur Giesl, Peter Yuan, Jinyun Dongguan University of Technology School of Computer Science and Technology Guangdong Dongguan523808 China Institute of Informatics LMU Munich München80538 Germany The Science Institute University of Iceland Dunhagi 5 Reykjavik107 Iceland Department of Mathematics University of Sussex FalmerBN1 9QH United Kingdom

In this paper, we extend a recently established subgradient method for the computation of Riemannian metrics that optimizes certain singular value functions associated with dynamical systems. This extension is threefold. First, we introduce a projected subgradient method which results in Riemannian metrics whose parameters are confined to a compact convex set and we can thus prove that a minimizer exists;second, we allow inexact subgradients and study the effect of the errors on the computed metrics;and third, we analyze the subgradient algorithm for three different choices of step sizes: constant, exogenous, and Polyak. The new methods are illustrated by application to dimension and entropy estimation of the Hénon map. © 2022 Society for Industrial and Applied Mathematics.

关键词： Dynamical systems

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children

Advancing Color Perception: An IoT-driven Pedagogical System...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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Classification of Tuna Meat Grade Quality Based on Color Space Using Wavelet and k-Nearest Neighbor Algorithm

Classification of Tuna Meat Grade Quality Based on Color Spa...

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International Conference on Smart Green technology in Electrical and Information Systems (ICSGTEIS)

作者： I Gede Sujana Eka Putra I Ketut Gede Darma Putra Made Sudarma Anak Agung Kompiang Oka Sudana Department of Computer Informatics Institute of Business and Technology Indonesia Denpasar Indonesia Department of Information Technology Udayana University Denpasar Indonesia Department of Electrical and Computer Engineering Udayana University Denpasar Indonesia

Tuna products are one of Indonesia’s leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to Robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from RGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better accuracy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%.

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Exploiting Direct Link in RIS-Aided Two-Way Communications with Traffic Asymmetry

Exploiting Direct Link in RIS-Aided Two-Way Communications w...

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International Conference on Radioelektronika

作者： Suneel Yadav Radhika Gour Devendra S. Gurjar Juraj Gazda Department of ECE Indian Institute of Information Technology Allahabad Prayagraj India Department of ECE National Institute of Technology Silchar Assam India Department of Computer and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

In this paper, we examine a two-way communication system empowered by a reconfigurable intelligent surface (RIS), where two half-duplex nodes communicate to each other through an RIS with $N$ reflective elements and a direct link. To exploit the advantage of direct link, the overall information exchange between two end nodes takes two time phases. By considering different target rates at two-end nodes in view of traffic asymmetry, and different powers at two-end nodes, we present the overall outage probability (OOP) analysis for the considered system in a Rayleigh fading environment. To explore further insights into the system's performance, we perform an asymptotic outage analysis in the high signal-to-noise ratio regime. This analysis reveals that the system's diversity order does not depend on the target rates of two end nodes, and only varies with the number of RIS's reflecting elements. Lastly, the derived expressions are verified via simulation studies, and reveal the impact of various parameters on the OOP performance.

关键词： Communication systems System performance Half-duplex system Rayleigh channels Reconfigurable intelligent surfaces Probability Power system reliability Information exchange Signal to noise ratio

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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