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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Noorlela Marcheta Iik Muhamad Malik Matin Rifqi Choiru Nazruli Iwan Sonjaya Mira Rosalina Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

Diarrhea is one of the biggest diseases in the world that can cause death. Diarrhea can be caused by poor environmental sanitation or poor lifestyle habits. Prevention can be done early, such as education at the primary education level to reduce the high number of diarrheal diseases. However, the education provided is still minimal. This is caused by the educational media which still uses books and blackboards, thus affecting students' understanding of diarrheal disease so that it is less than optimal in preventing diarrheal disease. Animation technology is one example of learning media development. Through animation, students become more interested and teachers become more helpful in communicating diarrheal disease material. In this paper, animation development is carried out. We develop 2D video animation using computer Generated Imagery techniques. The results of beta testing via a questionnaire administered to respondents showed that 98.43% of respondents agreed that animated videos could be used as a learning medium for diarrheal disease material.

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Stock Price Forecasting in Finance with Artificial Neural Networks 5

Stock Price Forecasting in Finance with Artificial Neural Ne...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Daloglu, Turgut Osi, Garibe Aslan, Murat Beskirli, Mehmet Cizre District Directorate of National Education Şirnak University Şirnak Turkey Şirnak University Department of Computer Engineering Şirnak Turkey Karamanoǧlu Mehmetbey University Faculty of Medicine Departmen of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies. © 2025 IEEE.

关键词： Neural network models

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Hardware-in-the-Loop Simulation for Satellite System Verification

Hardware-in-the-Loop Simulation for Satellite System Verific...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Saroj, Tanin Sreesawet, Suwat Channumsin, Sittiporn Kaewkhuntod, Nisarat Tandayya, Pichaya Geo-Informatics and Space Technology Chonburi20230 Thailand Prince of Songkhla University Faculty of Engineering Department of Computer Engineering Songkhla90110 Thailand

ISBN: (纸本)9781665402064

The satellite simulator platform is necessary for the satellite development. It is an essential tool to verify the operations of flight software. The Controller-in-the-Loop simulation with Simulation Third Generation (SimTG) existing at Thailand's Geo-informatics and Space Technology Development Agency (GISTDA) can help verify the flight software along with the on-board computer (OBC). However, it is rather rigid and cannot adjust other devices arbitrarily. This paper presents the requirements of the Hardware-in-the-Loop Simulation (HILS) platform along with the proposed solution to solve this problem. The interface module is developed by using the Python EtherSpaceLink-API along with socket programming. The essential tool in the development is the Parsl, which is a promising Python parallel library. The experimental testing of the proposed HILS is done by using a cloned simulator working as physical devices. Then, the response times are measured adjusting the frequency of the flight software. The proposed design achieved the expected results, respecting to the Controller-in-the-Loop simulator. © 2022 IEEE.

关键词： Python

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Eye-Tracking Based Automatic Summarization of Lecture Slides

Eye-Tracking Based Automatic Summarization of Lecture Slides

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International Conference on computer and Applications (ICCA)

作者： Stylianos Vazaios Andreas Mallas Michalis Xenos Computer Engineering and Informatics Department University of Patras Patras Greece

This paper presents a novel approach to automatically summarize lecture slides using eye-tracking data. The tool generates personalized summaries by analyzing learners’ visual attention patterns, with the aim of reducing information overload and enhancing comprehension. While existing text summarization methods often overlook individual preferences and attention patterns, our proposed approach considers all slide elements, including text and visuals. It dynamically selects areas of interest based on learners’ fixation points. The tool is developed as a web application, enabling users to access presentations, receive personalized summaries, and save them for optimized studying. It incorporates features such as administration and settings, slides uploading, and summary viewing. To evaluate the tool, we conducted an experiment involving 62 participants to assess usability and satisfaction with the automated summaries. The evaluation results indicated positive feedback in terms of ease of use and visual appeal. Participants expressed satisfaction with the generated summaries and were given the option to select their preferred summarization percentage. Our analysis revealed that summarization preferences are not related to prior knowledge. Overall, this tool demonstrates the potential of personalized summarization based on eye-tracking data, thereby enhancing learning experiences.

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Synthesis of Batik Motifs using a Diffusion - Generative Adversarial Network

arXiv

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arXiv 2023年

作者： Octadion, One Yudistira, Novanto Kurnianingtyas, Diva Informatics Engineering Faculty of Computer Science Universitas Brawijaya Indonesia

Batik, a unique blend of art and craftsmanship, is a distinct artistic and technological creation for Indonesian society. Research on batik motifs is primarily focused on classification. However, further studies may extend to the synthesis of batik patterns. Generative Adversarial Networks (GANs) have been an important deep learning model for generating synthetic data, but often face challenges in the stability and consistency of results. This research focuses on the use of StyleGAN2-Ada and Diffusion techniques to produce realistic and high-quality synthetic batik patterns. StyleGAN2-Ada is a variation of the GAN model that separates the style and content aspects in an image, whereas diffusion techniques introduce random noise into the data. In the context of batik, StyleGAN2-Ada and Diffusion are used to produce realistic synthetic batik patterns. This study also made adjustments to the model architecture and used a well-curated batik dataset. The main goal is to assist batik designers or craftsmen in producing unique and quality batik motifs with efficient production time and costs. Based on qualitative and quantitative evaluations, the results show that the model tested is capable of producing authentic and quality batik patterns, with finer details and rich artistic variations. The use of the Wasserstein loss function tends to produce batik motifs that are relatively new but less neat than the use of the StyleGAN2-Ada loss. The quality of the dataset also has a positive impact on the quality of the resulting batik patterns. Overall, this research contributes to the integration of Diffusion-GAN technology with traditional arts and culture, especially in the synthesis of batik motifs. However, there is still room for further development in increasing skill and accuracy in producing more detailed batik motifs. The dataset and code can be accessed here: https://***/octadion/diffusion-stylegan2-ada-pytorch © 2023, CC BY-NC-SA.

关键词： Diffusion

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WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

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Sensors 2023年第23期23卷 9486-9486页

作者： Balatsouras, Christos-Panagiotis Karras, Aristeidis Karras, Christos Karydis, Ioannis Sioutas, Spyros Computer Engineering and Informatics Department University of Patras Patras26504 Greece Department of Informatics Ionian University Kerkira49100 Greece

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications. © 2023 by the authors.

关键词： Internet of things

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Ransomware Extraction Using Static Portable Executable (PE) Feature-Based Approach

Ransomware Extraction Using Static Portable Executable (PE) ...

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International Conference of computer and informatics engineering (IC2IE)

作者： Iik Muhamad Malik Matin Computer and Informatics Engineering Politeknik Negeri Jakarta Jakarta Indonesia

Currently cyberspace has often been used for crime. Ransomware is a type of malware that takes the victim's data hostage by asking for a ransom accompanied by threats. This attack is more aimed at Windows users. Preventive measures are needed to prevent ransomware attacks, one of which is a learning-based approach. The challenge to the learning-based approach is data. We propose a static non-signature approach for extracting PE-based ransomware. The main focus of this research is the development of a non-signature static analysis approach that can identify and extract ransomware code from PE files without being affected by signature obfuscation or mutation. We extracted the 3 main headers namely dos_header, file_header, and optional_header plus the derived feature. The results show that from 132 samples, 71 of them were successfully extracted with PE file ransomware. This research contributes to the development of ransomware detection methodologies by offering a non-signature static analysis approach that improves resilience against evolving ransomware threats.

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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