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5th International Conference on Advancements in Computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Synthetic Data Generator for an E-Learning Platform in a Big Data Environment

Synthetic Data Generator for an E-Learning Platform in a...

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International Conference in Information Technology and Education, ICITED 23

作者： Moreno, Yaneth Montero, Anthony Hidrobo, Francisco Infante, Saba CEMISID Faculty of Engineering University of Los Andes Mérida Venezuela Department of Computer Science Faculty of Engineering University of Los Andes Mérida Venezuela School of Mathematical and Computational Sciences Yachay Tech University Imbabura San Miguel de Urcuquí Ecuador

ISBN: (纸本)9789819954131

In this paper, we present the implementation of a big data environment to extract, filter, and classify data to use tools that anticipate its growth and that allow scaling resources in order for results to be used as a basis for future analysis or, as in this case study, for the generation of synthetic data. Specifically, we focus on the information available in a learning management system, concretely Moodle, where numerous interactions occur between users and the learning platform. All actions of users, mainly students, are recorded and can be used to reproduce behaviors. Then, we create a request simulator to emulate the behavioral pattern of students, using data from a database taken from an operative learning management system for training. Results were quite similar concerning student behavior, as reflected in the statistics on the operational learning management system database and the data synthetically generated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Big data

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Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic Data Analysis

Enhancing the Efficiency of the Grouping-Scoring-Modeling Fr...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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Intelligent Headlights for Adapting Beam Patterns with Raspberry Pi and Convolutional Neural Networks 2

Intelligent Headlights for Adapting Beam Patterns with Raspb...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Maheswari, P. Gowriswari, S. Balasubramani, S. Ramesh Babu, A. Nk, Jijith Murugan, S. Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India Dxn Holdings Bhd Maharashtra Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

This study uses intelligent headlights to improve car safety and economy. The suggested system uses Raspberry Pi and Convolutional Neural Networks (CNNs) to dynamically modify beam patterns to real-time ambient circumstances and context. The Raspberry Pi is a flexible computer platform, and CNNs automatically analyze sensor and video data to modify headlight beam patterns. A trained CNN model recognizes impending vehicles, pedestrians, and road conditions. By analyzing this information, the headlights dynamically optimize the beam pattern to maximize visibility and minimize road user glare. This flexibility improves road safety by optimizing lighting when required, minimizing driver fatigue, and enhancing response times. Raspberry Pi allows for a cost-effective and easy retrofitting of current vehicles with intelligent headlights. The system's scalability and adaptability make it suitable for many automotive applications, helping to construct smart and linked transportation systems. This discovery is a major step towards intelligent and adaptable lighting systems that improve vehicle safety and economy. © 2024 IEEE.

关键词： Vehicle safety

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

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Design of Slotted Substrate Integrated Waveguide Antenna for Ka-band Automotive Wireless Communication

Design of Slotted Substrate Integrated Waveguide Antenna for...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Ashish Singh, Gurprince Sahoo, Bikash Chandra Kumar, Ashwini Singh, Gurmeet Chitkara University Chitkara University Institute of Engineering and Technology Punjab India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Indira Gandhi Institute of Technology Department of ETC Engg. Odisha Sarang India Poornima University Faculty of Engineering and Technology Rajasthan Jaipur India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

In this paper, a slotted multi-input-multi-output (MIMO) substrate integrated waveguide (SIW) antenna is presented for mm wave frequency bands and 5G wireless applications. The proposed design resonates in n257 frequency band dedicated for 5G communication according to 3rd generation partnership project (3GPP). The presented antenna design exhibits excellent impedance matching of -29 dB with the gain of about 8.5 dB. Also, the antenna covers required impedance bandwidth of 500 MHz with mutual coupling of about -35 dB. Further, in the context of beamforming feature, the proposed design achieves the ±15° with the progressive phase shift of feeds of the antenna structure and also the design demonstrates the excellent performance in terms of diversity parameters. The proposed design is suitable for various 5G automotive use cases with the calculated data rate according to link budget calculations. © 2024 IEEE.

关键词： Budget control

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Spatiotemporal Assessment of Land Use Land Cover Change Detection and Normalized Difference Vegetation Index in Desert Area

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Rangeland Ecology and Management 2025年 101卷 126-139页

作者： Tunio, Imran Aziz Memon, Sheeraz Ahmed Abbasi, Habibullah Abro, Abdul Ahad Kumar, Love Tunio, Muhammad Abdullah Tunio, Abdul Rehman Kumar, Jaten Institute of Environmental Engineering and Management Mehran University of Engineering and Technology Sindh Jamshoro Pakistan Project Management Office Sindh Barrages Improvement Project Irrigation Department Government of Sindh Sindh Karachi Pakistan Institute of Environmental Sciences University of Sindh Sindh Jamshoro Pakistan Department of Computer Science Faculty of Engineering Sciences and Technology Iqra University Sindh Karachi Pakistan Illinois State Water Survey Prairie Research Institute University of Illinois at Urbana-Champaign 2204 Griffith Drive Champaign 61820 IL United States Department of Civil Engineering NED University of Engineering and Technology Sindh Karachi Pakistan Department of Civil Engineering Mehran University of Engineering and Technology Sindh Jamshoro Pakistan Institute of Business Management Sindh Karachi Pakistan

This study examines the spatiotemporal changes in land use land cover (LULC) from 1990 to 2023 in the Islamkot subdistrict of Tharparkar, Pakistan, an area known for its vast coal reserves and environmental challenges. A change detection analysis was performed using the maximum likelihood supervised classification method, and a Normalized Difference Vegetation Index (NDVI) analysis was conducted to evaluate vegetation variations. The findings indicate significant changes in land cover patterns: settlement, vegetation, water bodies, and mining areas increased by approximately 3.02%, 20.2%, 3.8%, and 0.9%, respectively, while bare land decreased by 27.9%. The primary drivers of these changes were identified as mining activities and precipitation variations. These results provide a foundation for informed land use planning, conservation efforts, and regional sustainable development strategies. © 2025 The Society for Range Management

关键词： Change detection LULC NDVI RS & GIS Thar coal open-pit mining

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Image-Based Extraction of Prescription Information using OCR-Tesseract 2

Image-Based Extraction of Prescription Information using OCR...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ponnuru, Mahesh Ponmalar, Sridevi P. Likhitha, A. Sree, Tanu B. Chaitanya, Guna G. School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India School of Computer Science Vellore Institute of Technology Amaravathi522237 India

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. © 2024 Elsevier B.V.. All rights reserved.

关键词： Optical character recognition

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Towards New Frontiers in Mobile Manipulation: Team CTU-UPenn-NYU at MBZIRC 2020

Field Robotics

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Field Robotics 2025年 2卷 75-106页

作者： Petr Štibinger George Broughton Filip Majer Zdeněk Rozsypálek Anthony Wang Kshitij Jindal Alex Zhou Dinesh Thakur Giuseppe Loianno Tomáš Krajník Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Department of MAE Tandon School of Engineering New York University GRASP Laboratory University of Pennsylvania Philadelphia PA USA Department of ECE and MAE Tandon School of Engineering New York University

In this paper we present an autonomous robotic system for picking, transporting, and precisely placing magnetically graspable objects. Such a system would be especially beneficial for construction tasks where human presence is not possible, e.g. due to chemical or radioactive pollution. The system comprises of two primary components — a wheeled, mobile platform and a manipulator arm. Both are interconnected through an onboard computer and utilize various onboard sensors for estimating the state of the robot and its surroundings. By using efficient processing algorithms, data from the onboard sensors can be used in a feedback loop during all critical operational sections, resulting in a robust system capable of operating on uneven terrain and in environments without access to satellite navigation. System functionality has been proven in Challenge II of the MBZIRC 2020 competition. The Challenge required a ground robot to build an L-shaped structure of colored bricks laid in a predefined pattern. Such a mission incorporates several demanding subchallenges, spanning multiple branches of computer science, cybernetics, and robotics. Moreover, all the subchallenges had to be performed flawlessly in rapid succession, in order to complete the Challenge successfully. The extreme difficulty of the task was highlighted in the MBZIRC 2020 finals, where our system was among the only two competitors (out of 32) that managed to complete the task in autonomous mode.

关键词： Robots Manipulators Robot sensing systems Reliability Autonomous aerial vehicles Service robots Multi-robot systems Assembly Valves Safety

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