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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier. © 2023 IEEE.

关键词： Support vector machines

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LHU-NET: A LIGHT HYBRID U-NET FOR COST-EFFICIENT, HIGH-PERFORMANCE VOLUMETRIC MEDICAL IMAGE SEGMENTATION

arXiv

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arXiv 2024年

作者： Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen52062 Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

The rise of Transformer architectures has revolutionized medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers for enhanced accuracy. However, these models often suffer from increased complexity and overlook the interplay between spatial and channel features, which is vital for segmentation precision. We introduce LHU-Net, a streamlined Hybrid U-Net for volumetric medical image segmentation, designed to first analyze spatial and then channel features for effective feature extraction. Tested on five benchmark datasets (Synapse, LA, Pancreas, ACDC, BRaTS 2018), LHU-Net demonstrated superior efficiency and accuracy, notably achieving a 92.66 Dice score on ACDC with 85% fewer parameters and a quarter of the computational demand compared to leading models. This performance, achieved without pre-training, extra data, or model ensembles, sets new benchmarks for computational efficiency and accuracy in segmentation, using under 11 million parameters. This achievement highlights that balancing computational efficiency with high accuracy in medical image segmentation is feasible. Our implementation of LHU-Net is freely accessible to the research community on GitHub. © 2024, CC BY.

关键词： Convolutional neural networks

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

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JHEP Reports 2025年第1期7卷 101229-101229页

作者： Wang, Tingyan Campbell, Cori Stockdale, Alexander J. Todd, Stacy McIntyre, Karl Frankland, Andrew Jaworski, Jakub Glampson, Ben Papadimitriou, Dimitri Mercuri, Luca Mayer, Erik Jones, Christopher R. Salih, Hizni Roadknight, Gail Little, Stephanie Noble, Theresa Várnai, Kinga A. Davis, Cai Heinson, Ashley I. George, Michael Borca, Florina English, Louise Romão, Luis Ramlakhan, David Barnes, Eleanor Matthews, Philippa C. Gelson, William Cooke, Graham S. Khakoo, Salim I. Nastouli, Eleni Davies, Jim Woods, Kerrie Ryder, Stephen Elsharkawy, Ahmed Easom, Nicholas Bernal, William Ahmad, Shazaad Macdonald, Douglas Stanworth, Simon Maynard, Suzanne MacNaughton, Heidi Kim, Yun Meneses, Josune Olza Roberts, Timothy Rogers, Heather Roberts, Lara Higgins, Finola Vilar, Javier Norris, Ruth Tilston, George Serafimova, Ilina Montague, Sarah Verheyden, Juliette Juurlink, Irene Jack, Kathryn Waldren-Glenn, Alex Poole, Lizzie Day, Victoria Reglar, Berit NIHR Oxford Biomedical Research Centre Oxford United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Clinical Infection Microbiology and Immunology Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Tropical Infectious Diseases Unit Royal Liverpool Hospital Liverpool University Hospitals NHS Trust Liverpool United Kingdom Liverpool Clinical Laboratories Liverpool University Hospitals NHS Trust Liverpool United Kingdom Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom iCARE Secure Data Environment Digital Collaboration Space Imperial College Healthcare NHS Trust London United Kingdom Department of Surgery and Cancer Imperial College London London United Kingdom Department of Infectious Disease Imperial College London London United Kingdom NIHR Health Informatics Collaborative Oxford University Hospitals NHS Foundation Trust Oxford United Kingdom Southampton Emerging Therapies and Technologies Centre University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom NIHR University College London Hospitals Biomedical Research Centre London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Infection Immunity and Inflammation UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Virology UCLH London United Kingdom School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton United Kingdom Cambridge Liver Unit Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom Faculty of Medicine Department of Infectious Disease Imperial College London United Kingdom The Francis Crick Institute London United Kingdom Division of Infection and Immunity University College Lon

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7–9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5–17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n = 827, 60.5%), class 2 ‘timely virological suppression’ (n = 254, 18.6%), class 3 ‘persistent moderate viraemia’ (n = 140, 10.2%), class 4 ‘persistent high-level viraemia’ (n = 44, 3.2%), and class 5 ‘slow virological suppression’ (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5;all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00;95% CI, 1.33–3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. Impact and implications: Treatment recommendations f

关键词： Antiviral treatment Cirrhosis HBV Health informatics Collaborative (HIC) Latent class mixed models Liver fibrosis Longitudinal Nucleotide analogue Viral load

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A Curious New Result of Resolution Strategies in Negation-Limited Inverters Problem

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2022年第4期49卷 1212页

作者： Ando, Ruo Takefuji, Yoshiyasu National Institute of Informatics 2-1-2 HitotsubashiTokyo Nasrudeen city101-8430 Japan Faculty of Data Science Musashino University 3-3-3 AriakeTokyo Koto-Ku135-8181 Japan

Generally, the negation-limited inverters problem is a puzzle of constructing an inverter with AND gates and OR gates, and a few inverters. This paper introduces a curious new result about the effectiveness of two powerful ATP (Automated Theorem Proving) strategies in tackling negation-limited inverter problems. Two resolution strategies are UR (Unit Resulting) resolution and Hyper-Resolution. In the experiment, we cope with two kinds of automated circuit construction: three input/output inverters and four input/output BCD Counter Circuit. Both circuits are constructed with a few limited inverters. Curiously, it has been turned out that UR resolution is drastically faster than Hyper-Resolution in the measurement of the size of SoS (Set of Support). Besides, we discuss the syntactic and semantic criteria, which might cause a considerable difference in computation cost between UR resolution and Hyper-Resolution. © 2022,IAENG International Journal of Computer science. All Rights Reserved.

关键词： Semantics

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Quantifying the Benefits of Implementing Big data Analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Wei, Justin Ang Kang Mansor, Mahayaudin M. Derasit, Zuraidah Bakar, Mohd Aftar Abu Ul-Saufie, Ahmad Zia Hamzah, Firdaus Mohamad Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331530303

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth. ©2024 IEEE.

关键词： Marketplaces

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Regression Analysis of Humidity and Temperature on Chicken Lifespan

Regression Analysis of Humidity and Temperature on Chicken L...

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Agrosystem Engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Sittilin Salleh Razak Mohd Ali Lee Nurul Rawaida Ain Burhani Kennedy Juani Farah Hasanah Ab Jaid Electrical and Electronics Engineering Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Mechanical Engineering Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Veterinary Services Sabah Malaysia Computer Science (Data Science) Faculty of Computing & Informatics Universiti Malaysia Sabah

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

This study presents a comprehensive analysis of the effects of humidity and temperature variations on the lifespan of Ross broiler chickens at two different locations over two 35-day experimental periods. The study initially included a group of 20 chickens, followed by another group of 41 chickens. Through rigorous regression analysis, the research evaluates the correlation between environmental variables and chicken lifespan, using a multi-sensor system integrated with IoT technology to monitor temperature and humidity values in poultry farms. The results presented in significant MSE and R 2 values for Batch 2 using correlation matrix and polynomial regression provide valuable insights for optimizing poultry management practices.

关键词： Temperature sensors Temperature measurement Correlation Humidity Polynomials Regression analysis Monitoring

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Blockchain for Internet of Things: A Survey

arXiv

引用

arXiv 2019年

作者： Dai, Hong-Ning Zheng, Zibin Zhang, Yan Faculty of Information Technology Macau University of Science and Technology China School of Data and Computer Science Sun Yat-sen University China Department of Informatics University of Oslo Norway Simula Metropolitan Center for Digital Engineering Norway

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area. Copyright © 2019, The Authors. All rights reserved.

关键词： Smart contract

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Using Synthetic Training data in Neural Networks for the Estimation of Fiber Orientation Distribution Functions from Single Shell data

Using Synthetic Training Data in Neural Networks for the Est...

引用

IEEE International Symposium on Biomedical Imaging

作者： Amelie Rauland Dorit Merhof Institute of Imaging and Computer Vision RWTH Aachen University Aachen German Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Several studies have investigated the possibility of predicting the fiber orientation distribution function (fODF), which is obtained using the very accurate multi-shell multi-tissue constrained spherical deconvolution (MT-CSD) from single-shell or low angular resolution multi-shell diffusion magnetic resonance imaging (dMRI) data using deep *** all these approaches show promising results, the vast majority have in common that they require multi-shell high angular resolution diffusion imaging (HARDI) data to calculate the ground truth fODF using the MT-CSD for training their networks. This data, however, is difficult to acquire in a clinical context and it is yet unclear how well networks trained on data acquired on a certain scanner with a certain protocol would generalize to different *** this work, we address these shortcomings and present a method that can estimate an accurate fODF from single-shell diffusion data without the need for multi-shell data for training. This is achieved by generating patient-, acquisition-and scanner-specific synthetic single voxel diffusion signals with a known ground truth fODF from single shell data that can be used to train the neural network. The trained network will then be applied to the real patient data to predict the fODF with a quality standard close to that of an MT-CSD and the ability to determine if white matter (WM) is present in the underlying *** approach is evaluated on 20 subjects from the Human Connectome Project (HCP) for all three shells (b=1000, 2000, 3000 s/mm 2 ). When comparing both this approach and a single shell constrained spherical deconvolution (CSD) to the results of the MT-CSD, this work outperforms the single shell CSD in terms of the angular correlation coefficient and root mean squared error on all three shells.

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ReviewRanker: A Semi-supervised Learning-Based Approach for Code Review Quality Estimation 6th

ReviewRanker: A Semi-supervised Learning-Based Approach for ...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Mahbub, Saifullah Easin Arafat, Md. Rahman, Chowdhury Rafeed Ferdows, Zannatul Hasan, Masum Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh School of Computing National University of Singapore Singapore Singapore Department of Computer Science University of Rochester RochesterNY United States

ISBN: (纸本)9789819734412

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning-based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and well-defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection by reducing human bias and the effort required for such a task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://***/saifarnab/code_review. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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