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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Abdulrahman, Hajar Taha, Ranyia Al Haj, Duaa Al-Ammal, Hesham College of Science University of Bahrain Sakhir Big Data Science and Analytics Bahrain College of Information Technology University of Bahrain Sakhir Department of Computer Science Bahrain

ISBN: (纸本)9781665416566

Driver drowsiness is one of the leading causes of deadly car accidents, and therefore has become of great interest in the field of Artificial Intelligence. In this paper, convolutional neural networks (CNNs) are used to determine the state of the driver (i.e. Drowsy or not), based on 4 observed features: open eyes, closed eyes, yawn and no yawn. The data was collected from two open source website, which was severely imbalanced between the open/closed eyes images and yawn/no yawn images, the former two classes being the majority class. Augmentation techniques were applied, and three different model structures were investigated, where the best architecture yielded an accuracy rate of 95%, and a loss of 13%. The paper discusses the success and limitations of these models and possible future work on approaching this problem. © 2021 IEEE.

关键词： Machine learning

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Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Exploring the Intersection of IoT and Blockchain: An Analysi...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Harishma Ashok Swathi Ganesan Nalinda Somasiri Maters in Data Analytics PSG Institute of Advanced Studies Coimbatore India Department of Computer Science York St John University London UK

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer’s data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message.

关键词： Privacy Strips Public key Time measurement Blockchains Safety Internet of Things

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Autonomous Multi-Robot Collaboration in Virtual Environments to Perform Tasks in Industry 4.0 2

Autonomous Multi-Robot Collaboration in Virtual Environments...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Alsamhi, Saeed Hamood Saif, A.A.F. Curry, Edward Kumar, Santosh Hawbani, Ammar University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen Netstars Co Ltd Tokyo Japan IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Science and Technology of China School of Computer Science and Technology Hefei230027 China

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

In Industry 4.0, multi-robot efficient coordination is a critical enabler for many commercial and industrial automation activities. This paper describes how a group of robots may dynamically produce actions from a common. We focus on examining the performance of autonomous multi-robot collaboration effectively and efficiently to achieve a common task and plan for the action of future task allocation dynamically, autonomously, and in a decentralized fashion. The common goal for multi-robots here is to bring the ball from the current position to predefine the desk target. Unreal Engine (UE4) is used for designing and implementing the platform of robots. The results show significant achievement in accurate trajectories for the ball taken via multi-robot collaboration in an autonomous and decentralized fashion. As a result, multi-robot collaboration can be available for numerous applications in Industry 4.0, warehouse automation, transport, risky environments, search and rescue (SAR) teams, and disaster recovery. © 2022 IEEE.

关键词： Industry 4.0

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QUALYST: data Quality Assessment System for Thailand Open Government data

QUALYST: Data Quality Assessment System for Thailand Open Go...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Tanapat Samakit Chutiporn Anutariya Marut Buranarach ICT Department Computer Science & Information Management School of Engineering and Technology Asian Institute of Technology Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Thailand

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all; thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format.

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

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2024 IEEE International Conference on Big data and Machine Learning, ICBDML 2024

作者： Jayaprakash, M. Hency Juliet, A. Jaya Mabel Rani, A. Velusudha, N.T. Lakshmisridevi, S. Samuthira Pandi, V. R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India BS Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Integrated Technology Chennai Institute of Technology Centre for Advanced Wireless Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all - billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of. © 2024 IEEE.

关键词： Internet of things

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Image Processing: Unlocking the Secrets of Visual Information

Image Processing: Unlocking the Secrets of Visual Informatio...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： D. Lavanya Kavi Bhushan Gur Sharan Kant A C Sountharraj Akanksha Srivastav M. Sharmila Begum Computer Science and Engineering Department Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Department of Computer Science Sir Chhotu Ram Institute of Engg. & Technology CCS University Meerut U.P India Department Of Computer Science With Data Analytics Dr. Ngp Art's and Science College Coimbatore Department of Computer Science and Engineering Tula's Institute Dehradun Department of Computer Science Maharaja Institute of Technology Mysore

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out; in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector.

关键词： Visualization Image recognition Systematics Quantum computing Navigation Image processing Noise

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Cardiovascular Disease Prediction Using Machine Learning Approaches

Cardiovascular Disease Prediction Using Machine Learning App...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Taminul Islam Adifa Vuyia Mahadi Hasan Md Masum Rana Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Analytics University of Tennessee Chattanooga Tennessee United States Department of Computer Science University of South Dakota Vermillion SD United States

As of the release of COVID-19, cardiovascular disease has surpassed all other causes of mortality among both sexes. In most cases, this condition is associated with atherosclerosis and the formation of blood clots. Heart disease, stroke, and other CVDs are major causes of mortality worldwide. Because even a small inaccuracy might lead to exhaustion or death, increased precision, perfection, and accuracy are required for diagnosing and predicting heart-related disorders. In this paper, data was collected from 1189 patients with some attributes related to heart disease and kept 80% data for training, and 20% data for testing and determining their accuracy using different models. In this study, enhanced preprocessing steps were used to increase the accuracy of cardiovascular disease prediction. It aids in determining whether a patient has heart disease and helps a doctor to determine whether or not a patient has cardiovascular disease. The applied models are Extreme Gradient Boosting, Random Forest, CART, Extra Tree Classifier, and Gradient Boosting Machine. This work compared the performance of several Machine Learning algorithms that make use of the accuracy of the metrics, $F_1$ -score, recall, and precision to demonstrate the validity of our findings. The Extreme Gradient Boosting model has achieved the best 91.9% accuracy in this research.

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Accelerated Malaria Diagnosis with Deep Convolutional Neural Network

Accelerated Malaria Diagnosis with Deep Convolutional Neural...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： V. Krishna Kumar M.S. Geetha Devasana B Saravana Balaji Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Business Data Analytics De Montfort University Kazakhstan Almaty Kazakhstan

The disease known as malaria is a communicable illness that is spread by the bites of mosquitoes. There are already diagnostic approaches that include manually counting the amount of red blood cells (RBC) that are contaminated. This is accomplished by doing a microscopic examination of the stained blood cells of the individual who is suffering from the disease. However, this is a hard job that requires careful attention both visually and intellectually. In order to accomplish this, a comprehensive manual examination must be undertaken by an expert who is informed about the subject matter. It is possible that the implementation of deep learning algorithms will simplify the process of disease diagnosis and make this analysis more straightforward. Furthermore, because they are diagnosing malaria, they are expected to manage a vast quantity of data, which includes photographs of microscopic blood smears. This is because they are responsible for finding the disease. In order to get a better level of accuracy, it is necessary to train models that are more in-depth. This requires fast computing since it requires a substantial amount of computer resources. An accelerated convolutional neural network technique that is based on a graphics processing unit (GPU) is utilized by the Accelerated Malaria Diagnosis System (AMDS) that has been created in order to diagnose malaria.

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Deep Learning for Automated Quantification of Irradiation Defects in TEM data: Relating Pixel-level Errors to Defect Properties

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1559-1560页

作者： Sainju, Rajat Roberts, Graham Chen, Wei-Ying Hutchinson, Brian Yang, Qian Ding, Caiwen Edwards, Danny J. Li, Meimei Zhu, Yuanyuan Department of Materials Science and Engineering University of Connecticut Storrs CT United States Department of Computer Science and Engineering University of Connecticut Storrs CT United States Nuclear Science and Engineering Division Argonne National Laboratory Lemont IL United States Computer Science Department Western Washington University Bellingham WA United States National Security Directorate AI and Data Analytics Division Pacific Northwest National Laboratory Richland WA United States Energy and Environment Directorate Nuclear Sciences Division Pacific Northwest National Laboratory Richland WA United States

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Private, Efficient and Scalable Kernel Learning for Medical Image Analysis 19th

Private, Efficient and Scalable Kernel Learning for Medica...

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19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024

作者： Hannemann, Anika Swaminathan, Arjhun Ünal, Ali Burak Akgün, Mete Department of Computer Science Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML) University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Tübingen Germany

ISBN: (纸本)9783031897030

Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized therapies, machine learning techniques like kernel methods have been widely used. Nevertheless, there are multiple challenges for implementing kernel methods. Medical image data often originates from various hospitals and cannot be combined due to privacy concerns, and the high dimensionality of image data presents another significant obstacle. While randomised encoding offers a promising direction, existing methods often struggle with a trade-off between accuracy and efficiency. Addressing the need for efficient privacy-preserving methods on distributed image data, we introduce OKRA (Orthonormal K-fRAmes), a novel randomized encoding-based approach for kernel-based machine learning. This technique, tailored for widely used kernel functions, significantly enhances scalability and speed compared to current state-of-the-art solutions. Through experiments conducted on various clinical image datasets, we evaluated model quality, computational performance, and resource overhead. Additionally, our method outperforms comparable approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed Learning Kernel Methods Machine Learning Medical Images Privacy

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