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arXiv 2024年

作者： Kurniati, Florentina Tatrin Sembiring, Irwan Setiawan, Adi Setyawan, Iwan Huizen, Roy Rudolf Faculty of Information Technology Universitas Kristen Satya Wacana Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Indonesia Jl. Raya Puputan No.86 Denpasar Bali80234 Indonesia Faculty of Science and Mathematics Universitas Kristen Satya Wacana Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Faculty of Electronics and Computer Engineering Universitas Kristen Satya Wacana Salatiga Jl. Diponegoro No.52-60 Jawa Tengah Salatiga50711 Indonesia Department of Magister Information System Institut Teknologi dan Bisnis STIKOM Bali Indonesia Jl. Raya Puputan No.86 Denpasar Bali80234 Indonesia

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. High computing time delays can cause overall system failure. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that KNN outperforms SVM in terms of computational complexity. Specifically, KNN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Research contribution to improving computational efficiency in object detection using the GLCM method and KNN and SVM classification models to achieve high accuracy with low complexity. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness. © 2024, CC BY.

关键词： Support vector machines

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Evaluation university ranking system using quacquarelli Symonds and integrated performance measurement system approach 9

Evaluation university ranking system using quacquarelli Symo...

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9th International Seminar on New Paradigm and Innovation of Natural sciences and Its Application, ISNPINSA 2019

作者： Prisyanti, A. Nurhayati, O.D. Widodo, A.P. Department of Magister Information System Postgraduate School Diponegoro University Semarang Indonesia Departement of Computer Systems Faculty of Engineering Diponegoro University Semarang Indonesia Departement of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Institutional assessment in the current era is indispensable because of increasing global competition and economic and information growth. Assessment efforts were also carried out on various factors including improving the quality of lecturers, meeting study programs that were in line with national development needs, developing educational curricula, improving the quality of community services and research, and adding internal and external collaboration. It aims to strengthen management and governance so that it has good performance and makes the university a World Class University (WCU). Fulfillment of WCU criteria in faculties or departments is one of the steps to determine the strategy for achieving a tertiary institution by giving more weight to faculty excellence to encourage the use of relevant new criteria. Also, in the ranking process, there are different criteria or indicators in several ranking techniques in the world as a manifestation of performance appraisal of the overall implementation of the organization. Quacquarelli Symonds (QS) is a rating agency that evaluates university weaknesses and strengths and produces assessments that show achievement ratings. The QS ranking also has six indicators that are believed to illustrate the value of the university. © Published under licence by IOP Publishing Ltd.

关键词： Curricula

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Performance of Machine Learning and Deep Learning on Arabic Handwritten Text Recognition

Performance of Machine Learning and Deep Learning on Arabic ...

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2020 International Conference on Emerging Technology in Computing, Communication and Electronics, ETCCE 2020

作者： Alkhateeb, Jawad H. Turani, Aiman A. Alsewari, AbdulRahman A. Taibah University Computer Science Department AlMadinah Almunawarah Saudi Arabia Saudi Arabia Taibah University Information System Department AlMadinah Almunawarah Saudi Arabia Faculty of Computing Software Engineering Department Pekan Pahang Malaysia

ISBN: (纸本)9781665419628

This paper proposes machine learning and deep learning techniques for recognizing Arabic handwritten text. Finally, this paper introduces a comparative study between them in term of their performance. Actually, the classification of Arabic handwritten text plays a vital role in the computer vision domain, where traditional machine learning techniques and deep learning techniques are commonly used by researchers. In this paper, both machine learning and deep learning techniques are proposed and evaluated for recognizing Arabic handwritten text. Several experiments were carried out using both machine learning and deep learning on two different databases the AHCR and ADBase. The AHCR contains 28000 images of handwritten Arabic alphabet letters written by 100 writers. While the ADBase contains 70,000 images of handwritten Arabic digits written by 700 writers. The experimental results on both databases have demonstrated that the performance of the deep learning outperforms machine learning. © 2020 IEEE.

关键词： Database systems

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Hien's Framework for Examining information system Quality of Mobile-based Smart Regency Service in Madura Island Districts 4

Hien's Framework for Examining Information System Quality of...

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4th International Conference on Informatics and Computational sciences, ICICoS 2020

作者： Darmawan, Aang Kisnu Siahaan, Daniel Susanto, Tony Dwi Hoiriyah, Hoiriyah Umam, Busro Hidayanto, Achmad Nizar Subiyakto, A'Ang Walid, Miftahul Santosa, Iwan Universitas Islam Madura Department of Information System Pamekasan Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia UIN Syarif Hidayatullah Jakarta Department of Information System Jakarta Indonesia Universitas Trunojoyo Madura Department of Informatics Bangkalan Indonesia

ISBN: (纸本)9781728195261

Today, almost every nation strives to incorporate city governance with the idea of smart cities. Several previous studies have developed various models to assess the efficiency of ISS. During the past, numerous studies evaluated the quality of service and experience of smart city applications. But very few research also explores the Smart District model's performance of services, which differs substantially from the general concept of a smart city. This study aims to examine the quality of service through the usage of the region or district with mobile devices. The model and approach used is the Hien's Framework, a development framework of the Technology Acceptance Model and the Delone McLean IS Success Model to measure the quality of information system services. The online survey form, data processing using AMOS 24.0 software, was used to collect data from two hundred and seventeen interviewees. The research findings show that the variables introduced by the Hien Framework have a significant and positive impact on the quality assessment of smart district services. This work helps to explain the performance model of a smart district information system based on mobile devices. This research recommends that local governments and politicians take better concern for critical problems affecting the quality of mobile services © 2020 IEEE.

关键词： information systems

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural science and Engineering 2025年第2期 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Exploration of user experience approach in using the digital commerce platform on millennial user in Indonesia 6

Exploration of user experience approach in using the digital...

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6th information Technology International Seminar, ITIS 2020

作者： Suryanto, Tri Lathif Mardi Faroqi, Asif Ridwandono, Doddy Saftri, Nadia Diah Ithriah, Syurfah Ayu Pratama, Arista University of Pembangunan Nasional Veteran Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia Universitat Ramon Llull UI/UX Design Escola Superior de Disseny Barcelona Spain

ISBN: (纸本)9781728177267

Product marketing makes many researchers conduct causality research from various aspects;This phenomenon is related to the development of digital ecommerce platforms in the era of digital information, especially in Indonesia, where the millennial generation is above 50% of Indonesia population. In this paper, research was carried out quantitatively on the millennial generation in Indonesia to measure how user experience can Affect Trust in technology and Purchase Intention in the social aspects of the information Technology system. The findings of this study confirm that the experience of using and the user interface is one of the important keys to use a digital e-commerce platform. © 2020 IEEE.

关键词： Social aspects

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Efficient Compression Sensing Mechanism Based WBAN system Using Blockchain

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Security and Communication Networks 2023年第1期2023卷

作者： Pathak, Vinay Singh, Karan Chandan, Radha Raman Gupta, Sachin Kumar Kumar, Manoj Bhushan, Shashi Jayaprakash, Sujith School of Computer and System Sciences Jawaharlal Nehru University New Delhi110067 India UP Varanasi221011 India School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Jammu and Kashmir Katra India Faculty of Engineering and Information Sciences University of Wollongong Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan Department of Computer Science & Engineering Amity University Punjab Punjab Mohali India BlueCrest University College Accra Ghana

The hybrid wireless sensor network is made up of Wireless Body Area Network (WBAN). Generally, many hospitals use cellular networks to support telemedicine. To provide the treatment to the patient on time, for this, an early diagnosis is required, for treatment. With the help of WBANs, collections and transmissions of essential biomedical data to monitor human health becomes easy. Compressor Sensing (CS) is an emerging signal compression/acquisition methodology that offers a protruding alternative to traditional signal acquisition. The proposed mechanism reduces message exchange overhead and enhances trust value estimation via response time and computational resources. It reduces cost and makes the system affordable to the patient. According to the results, the proposed scheme in terms of Compression Ratio (CR) is 18.18% to 88.11% better as compared to existing schemes. Also in terms of Percentage Root-Mean-Squared Difference (PRD) value, the proposed scheme is 18.18% to 34.21% better than with respect to existing schemes. The consensus for any new block is achieved in 24% less time than the Proof-of-Work (PoW) approach. The shallow CPU usage is required for the leader election mechanism. CPU utilization while the experiment lies in the range of 0.9% and 14%. While simulating a one-hour duration, the peak CPU utilization is 21%. © 2023 Vinay Pathak et al.

关键词： Patient treatment

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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