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Computer science and Emerging Technologies (CSET), International Conference on

作者： Mohammmadnour Mosbah Aljarrah Farah H. Zawaideh Murad Magableh Hassan Al Wahshat Rajina R. Mohamed Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of BI and Data Analysi Faculty of Financial Sciences and Business Irbid National University Irbid Jordan Department of Management Information System Faculty of Business Ajloun National University Ajloun Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Jain Deemed To Be University Bangalore India

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancement

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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Information Technology (ACIT)

作者： Raed Alazaidah Mohammad Hassan Lara Al-Rbabah Ghassan Samara Marina Yusof Ala'a Saeb Al-Sherideh Department of Data Science AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Computer Informatics and Media Universiti Teknologi MARA (UiTM) Selanger Malaysia Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection.

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Learning Inter-Lingual Document Representations via Concept Compression 22nd

Learning Inter-Lingual Document Representations via Concept ...

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22nd International Conference on Intelligent data Engineering and Automated Learning, IDEAL 2021

作者： Lenz, Marc Tashu, Tsegaye Misikir Horváth, Tomáš Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories ELTE - Eötvös Loránd University Pázmány Péter Sétány Budapest1117 Hungary College of Informatics Wollo University Kombolcha Institute of Technology Kombolcha208 Ethiopia Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9783030916077

In this work, we proposed a novel approach to derive inter-lingual document representations. The introduced methods aim to enhance the quality of content-based Multilingual Document Recommendation and information retrieval Systems. The main idea centers around creating inter-lingual representations by using mappings to align monolingual representation spaces. According to the experimental results carried out on JRC-Acquis and EU bookshop multilingual corpora, the proposed concept compression approach has outperformed the traditional cross-lingual retrieval and recommendations methods. © 2021, Springer Nature Switzerland AG.

关键词： Information retrieval systems

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Extension of GIS Functionality for Integration of Surface Water Bodies Assessment

Extension of GIS Functionality for Integration of Surface Wa...

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Joint International Baltic Conference on Digital Business and Intelligent Systems 2022 Doctoral Consortium and Forum, Baltic-DB and IS-DC-Forum 2022

作者： Dzemydiene, Dale Maskeliunas, Saulius Dzemydaite, Giedre Institute of Data Science and Digital Technologies Faculty of Mathematics and Informatics Vilnius University Akademijos str. 4 Vilnius08412 Lithuania Institute of Regional Development Šiauliai Academy Vilnius University P. Višinskio str. 38 Šiauliai76352 Lithuania Department of Economic Policy Faculty of Economics and Business Administration Vilnius University Sauletekio av. 9 Vilnius10222 Lithuania

This research is devoted to solving the main problem of representation of the assessment results of different types of surface water bodies in the geographical information system (GIS). For the development of such type of infrastructure, the problems arise in a few directions: (a) how to express the complexity, differentiation and variety of surface water bodies, (b) how to extract knowledge for qualitative assessment, and (c) how to integrate new kinds of visualisation possibilities into the GIS. The surface water bodies are of a large spectrum of types with quite dynamically changing properties. The main problem in the realisation of such type of infrastructure is in finding the right relationships between the analysed objects of water resources and visualisation of them in GIS. The main question in this research is in revealing possibilities of integration of functions of digital maps with assessment processes of water bodies. Our approach is based on the development of a deeper knowledge base for defining the required domain properties and representing them in the computer-based ontology in connection with functions of GIS working online. The ontology is used here to design data warehouses' structures, define the relationship functions for expression of the dynamic assessment results in GIS, and map required inputs in user interfaces for online decisionmakers. The algorithms for the main integrating components between the Water Resource Management Information System and GIS have been developed as wrappers, while service provision scenarios enable maintenance of the information with complex features. It becomes easier to realise the decision support process by having the visualisation of the monitoring data assessment and the representation of it in the online working GIS. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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A Review of the Effectiveness of Management Information System in Decision Making

A Review of the Effectiveness of Management Information Syst...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Hazem Mohammad Zeidan Al-kaseasbeh Firas Rashed Wahsheh Fatma Susilawati Mohamad Julaily Aida Jusoh Mazen Mohammed Farea Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of Management Information Systems Faculty of Business Ajloun National University Ajloun Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Jain Deemed To Be University Bangalore India

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information Systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information Systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information Systems, including Executive Information Systems (EIS), Decision Support Systems (DSS), and Transaction Processing Systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information Systems and decision-making effectiveness

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Efficient Surrogate Models for Materials science Simulations: Machine Learning-Based Prediction of Microstructure Properties

SSRN

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SSRN 2023年

作者： Nguyen, Binh Duong Potapenko, Pavlo Dermici, Aytekin Govind, Kishan Sandfeld, Stefan Forschungszentrum Jülich GmbH Jülich52425 Germany Research Centre Jülich Germany Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this. © 2023, The Authors. All rights reserved.

关键词： Magnetic domains

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Multiple lung diseases detection using advanced deep learning model with attention mechanisms and upsampling features

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhu, Jie Al-qaness, Mohammed A.A. AL-Alimi, Dalal Tao, Heng Zhi Alsamhi, Saeed Hamood College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Faculty of Engineering Sana'a University Sana'a12544 Yemen Insight Centre for Data Analytics University of Galway Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Lung diseases represent a major health challenge, necessitating accurate and timely diagnostic methods. X-ray imaging plays a critical role in diagnosing various lung conditions, with physicians traditionally relying on these images for disease identification. However, with rapid advancements in artificial intelligence and imaging technologies, the volume of medical image data has grown significantly, spurring the development of machine learning techniques, particularly deep learning, to improve diagnostic accuracy. Unlike previous studies that focused on binary or three-class classification tasks, this paper presents an efficient deep learning model capable of classifying six types of lung X-ray images. The model employs an efficient feature extraction approach by combining the Visual Geometry Group Network (VGG16) with a Convolutional Neural Network (CNN) feature fusion, further enhanced by a Squeeze-and-Excitation (SE) block and self-attention modules. This comprehensive framework enables effective classification of tuberculosis, pneumonia, COVID-19, normal lungs, lung opacity, and pneumothorax. To validate the model, we constructed a robust dataset consisting of 45,241 X-ray images sourced from multiple databases. The proposed model was evaluated in various conditions to verify its performance. It achieves a remarkable validation accuracy of 93% in detecting the six lung diseases from X-ray images. © 2025 Elsevier Ltd

关键词： Lung cancer

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Smart Surveillance for COVID-19 Health Protocol Compliance in Vehicles 21

Smart Surveillance for COVID-19 Health Protocol Compliance i...

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Proceedings of the 2021 International Conference on Computer, Control, informatics and Its Applications

作者： Rachmat Hidayat Aniati Murni Arymurthy Heru Susanto Faculty of Computer Science University of Indonesia Indonesia and Data and Information Center National Research and Innovation Agency Indonesia Faculty of Computer Science University of Indonesia Indonesia Research Center for Informatics National Research and Innovation Agency Indonesia and Tunghai University Taiwan

ISBN: (纸本)9781450385244

Nowadays, the Corona Virus outbreak in 2019 (COVID-19) has become a global pandemic. The public must implement health protocols to reduce the spread of COVID-19. Trends show that the number of COVID-19 is increasing over time. This study proposes and develops a smart model to detect COVID-19 Health protocol violators in vehicles. This model can detect violations of the use of masks and social distancing in vehicles. The proposed model is a combination of the YOLO object detection method and the Hourglass architecture. The experimental results of the proposed model can detect violations with a high success rate. Here, the standard YOLOv4 detection model as baseline yields an mAP of 0.87 for validation and 0.74 for test data. On the other hand, the proposed method produces an mAP of 0.92 on the validation data, 0.78 on the test data. From these results, this smart model is quite promising to help reduce the spread of COVID-19.

关键词： YOLO Deep Learning Vehicles Object Detection COVID-19 Health Protocol Hourglass

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

arXiv

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arXiv 2024年

作者： Takayama, Masayuki Okuda, Tadahisa Pham, Thong Ikenoue, Tatsuyoshi Fukuma, Shingo Shimizu, Shohei Sannai, Akiyoshi Data Science and AI Innovation Research Promotion Center Shiga University Japan Department of Health Data Science Tokyo Medical University Graduate School of Medicine Human Health Sciences Kyoto University Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Japan Graduate School of Medicine Human Health Sciences Kyoto University Department of Epidemiology Infectious Disease Control and Prevention Hiroshima University Graduate school of Biomedical and Health Sciences Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Institute for the Advanced Study of Human Biology Kyoto University Center for Advanced Intelligence Project RIKEN Japan Department of Physics Kyoto University Data Science and AI Innovation Research Promotion Center Shiga University Graduate School of Engineering The University of Tokyo Center for Advanced Intelligence Project RIKEN Research Development Center for Large Language Models National Institute of Informatics Japan

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, by using an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. © 2024, CC BY.

关键词： Computational linguistics

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An ontology-aided, natural language-based approach for multi-constraint BIM model querying

arXiv

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arXiv 2023年

作者： Yin, Mengtian Tang, Llewellyn Webster, Chris Xu, Shen Li, Xiongyi Ying, Huaquan Department of Real Estate and Construction The University of Hong Kong Hong Kong Faculty of Architecture The University of Hong Kong Hong Kong Informatics Data Science Function Royal Berkshire NHS Trust Foundation United Kingdom Faculty of Civil and Environmental Engineering Technion-Israel Institute of Technology Haifa Israel

Being able to efficiently retrieve the required building information is critical for construction project stakeholders to carry out their engineering and management activities. Natural language interface (NLI) systems are emerging as a time and cost-effective way to query Building Information Models (BIMs). However, the existing methods cannot logically combine different constraints to perform fine-grained queries, dampening the usability of natural language (NL)based BIM queries. This paper presents a novel ontology-aided semantic parser to automatically map natural language queries (NLQs) that contain different attribute and relational constraints into computer-readable codes for querying complex BIM models. First, a modular ontology was developed to represent NL expressions of Industry Foundation Classes (IFC) concepts and relationships, and was then populated with entities from target BIM models to assimilate project-specific information. Hereafter, the ontology-aided semantic parser progressively extracts concepts, relationships, and value restrictions from NLQs to fully identify constraint conditions, resulting in standard SPARQL queries with reasoning rules to successfully retrieve IFC-based BIM models. The approach was evaluated based on 225 NLQs collected from BIM users, with a 91% accuracy rate. Finally, a case study about the design-checking of a real-world residential building demonstrates the practical value of the proposed approach in the construction industry. © 2023, CC BY-NC-ND.

关键词： Construction industry

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