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TechRxiv 2020年

作者： Shah, Saloni Mulahuwaish, Aos Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Department of Computer Science and Information Systems Saginaw Valley State University MI United States Department of Software Engineering Salahaddin University Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq

Since the initial reports of the Coronavirus surfacing in Wuhan, China;the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent;catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology;the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease;while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19. © 2020, CC BY.

关键词： Viruses

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Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Study of Block Chain and its Impact on Internet of Health Th...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Aslam Muhammad Waseem Iqbal Syed Khuram Shahzad Marium Malik Muhammad Usman Tahir Department of Computer Science The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

Blockchain has been making some vital progress in these days because of its secure and amiable structure. It is a distributed and decentralized database that deals with transactions. The blockchain is also emerging in the healthcare sector rapidly. Healthcare has now become a prestigious part of our life as it is making life-changing innovations day by day whether it is about artificial intelligence influence in the diagnostic system, expert system, patient's data storage, retrieval, security, and any other healthcare smart services. Smart health data is the most important ingredient to help us in solving our healthcare needs. Many researchers are now emerging blockchain in smart health because of its secure, flexible, and reliable architecture. In this paper, the major progress made in implementing the blockchain and its vulnerabilities are discussed in the area of smart health. The major focus is on the issues of patient's electronic health record, retrieval, security, and interoperability in Internet of Health Things (IoHT).

关键词： Interoperability Contracts computer science

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Feature importance in mobile malware detection

arXiv

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arXiv 2020年

作者： Kouliaridis, Vasileios Kambourakis, Georgios Peng, Tao Department of Information & Communication Systems Engineering University of the Aegean Greece School of Computer Science and Educational Software Guangzhou University Guangzhou China

The topic of mobile malware detection on the Android platform has attracted significant attention over the last several years. However, while much research has been conducted toward mobile malware detection techniques, little attention has been devoted to feature selection and feature importance. That is, which app feature matters more when it comes to machine learning classification. After succinctly surveying all major, dated from 2012 to 2020, datasets used by state-of-the-art malware detection works in the literature, we analyse a critical mass of apps from the most contemporary and prevailing datasets, namely Drebin, VirusShare, and AndroZoo. Next, we rank the importance of app classification features pertaining to permissions and intents using the Information Gain algorithm for all the three above-mentioned datasets. © 2020, CC BY.

关键词： Feature extraction

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Importance of Big Data in Precision and Personalized Medicine

Importance of Big Data in Precision and Personalized Medicin...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Arfan Jaffar Muhammad Aslam Syed Khuram Shahzad Muhammad Waseem Iqbal Amjad Farooq Department of Computer Science The Superior College Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

The rapidly increasing adaptation of Big data technologies in biomedicine, has introduced a revolution in and medical research practice. Trending high-throughput data analysis techniques, have converted the appearance of the biological system to acquire idolization methods for complicated diseases. Majority of the acquired Big-data models govern the materialization of illustrating medicine. This transformation aims at quantification of the period of P4 medicine that will then progressively be more predictive, personalized, pre-emptive, and participatory. It layouts a track to modernize antiseptic methods for the patient's concern center. P4 medicine besides being a scientific face of systems medicine has two highlighted purposes first of which is to evaluate wellness, while the other is, to identify and expose disease. Patients are major operators in the cognizance of P4 medicine as they directly get engaged with a medically familiar network that helps them boost their health. This article will discuss the maturity in big data planning and correlated challenges in biomedicine.

关键词： Big Data Genomics Bioinformatics Cancer Medical diagnostic imaging

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A Policy Recommendations Framework to Resolve Global software Development Issues 3

A Policy Recommendations Framework to Resolve Global Softwar...

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3rd International Conference on Innovative Computing, ICIC 2019

作者： Hassan, Mujtaba Hussain, Muzammil Ayubi, Salah-U-Din Irfan, Maham School of Systems and Technology University of Management and Technology Department of Software Engineering Lahore Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781728146829

Global software development (GSD) is basically a development which is done through low cost in given time frame by sitting in remote areas within cities, countries and around the globe. The global software development is facing major challenges such as, time zone differences, language barrier, cultural differences, geographical distance, communication gap, coordination problems, lack of project guidance, lack of experienced software testers, customer dissatisfaction, poor project management and so on. These challenges has major impact on software quality in GSD and decreases the acceptability among its users. In recent years, some strategies had been applied to reduce quality related issues that exist in GSD, however there is still a need to develop effective and efficient methods, techniques and best practices that can lead to enhance the quality of software development. The main objective of this article is to highlight the existing issues of GSD and to provide the policy recommendations to mitigate them. Therefore, a quantitative analysis have been performed that provides detailed statistical analysis on data by analyzing and applying nonparametric test (rejected the null hypothesis with less than 0.5 significance) and one sample T-test (with 2-tailed significance difference 0.00) through SPSS. Hence, this work contributes by making strong policy recommendations which is explained how GSD project should be handled by selecting best process, and how to achieve maximum quality of software product for customers to satisfy them, improve the acceptance among its users, bring their trust back to gain more business, and overall development in (software) industry. © 2019 IEEE.

关键词： Project management

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A Raspberry Pi-based Traumatic Brain Injury detection system for single-channel Electroencephalogram

arXiv

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arXiv 2021年

作者： Dhillon, Navjodh Singh Sutandi, Agustinus Vishwanath, Manoj Lim, Miranda M. Cao, Hung Si, Dong Computing and Software Systems University of Washington BothellWA United States Department of Electrical Engineering and Computer Science University of California IrvineCA United States VA Portland Health Care System PortlandOR United States Department of Neurology Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering University of California IrvineCA United States

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems. © 2021, CC BY.

关键词： Brain

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A Unified Graph-Theoretic Framework for Free-Fermion Solvability

arXiv

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arXiv 2023年

作者： Chapman, Adrian Elman, Samuel J. Mann, Ryan L. Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW 2007 Australia Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Centre for Quantum Computation and Communication Technology Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We show that a quantum spin system has an exact description by non-interacting fermions if its frustration graph is claw-free and contains a simplicial clique. The frustration graph of a spin model captures the pairwise anticommutation relations between Pauli terms of its Hamiltonian in a given basis. This result captures a vast family of known free-fermion solutions. In previous work, it was shown that a free-fermion solution exists if the frustration graph is either a line graph, or (even-hole, claw)-free. The former case generalizes the celebrated Jordan-Wigner transformation and includes the exact solution to the Kitaev honeycomb model. The latter case generalizes a non-local solution to the four-fermion model given by Fendley. Our characterization unifies these two approaches, extending generalized Jordan-Wigner solutions to the non-local setting and generalizing the four-fermion solution to models of arbitrary spatial dimension. Our key technical insight is the identification of a class of cycle symmetries for all models with claw-free frustration graphs. We prove that these symmetries commute, and this allows us to apply Fendley's solution method to each symmetric subspace independently. Finally, we give a physical description of the fermion modes in terms of operators generated by repeated commutation with the Hamiltonian. This connects our framework to the developing body of work on operator Krylov subspaces. Our results deepen the connection between many-body physics and the mathematical theory of claw-free graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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2020 Annual Conference of the Prognostics and Health Management Society, PHM 2020

作者： Hendrickx, Kilian Meert, Wannes Cornelis, Bram Gryllias, Konstantinos Davis, Jesse Siemens Digital Industries Software Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Computer Science Celestijnenlaan 200A box 2402 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 Leuven3001 Belgium Dynamics of Mechanical and Mechatronic Systems Flanders Make Belgium

ISBN: (纸本)9781936263059

Monitoring machines and early fault detection reduces production downtime, repair costs, and human casualties. Traditionally, machine condition monitoring requires performing a time-consuming manual analysis to find indicators for each potential fault. Moreover, these approaches often only focus on a single machine, while many industrial applications involve monitoring a fleet of machines. In such applications, it is often safe to assume that the majority of machines are in a healthy state. In comparable operating states, the behavior of these healthy machines is similar and any deviating machine is thus likely to be faulty. Previously, we proposed a fleet-based anomaly framework that can assess the health status of each machine in the fleet by detecting these deviations. It groups together similarly behaving machines and assumes that the healthy ones form the largest group and assigns an anomaly score to each machine based on the size of the group it belongs to. In this work, we propose a similarity-based anomaly score that offers multiple benefits over the cluster-based anomaly score. First, this score better represents the severity of a machine fault. Second, it allows to assess the health status of individual machines instead of machine groups. Finally, using similarities provides more nuanced insights in a machine's health status, especially for gradual degrading machines. Experiments show that the similarity-based anomaly score is superior to the cluster-based approach. © 2020 Prognostics and Health Management Society. All rights reserved.

关键词： Condition monitoring

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Corrigendum to “Remote sensing and machine learning approach for zoning of wastewater drainage system” [Desalin Water Treat 319 (2024) 100549]

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Desalination and Water Treatment 2025年 321卷

作者： A. Saranya Alanoud Al Mazroa Mashael Maashi T.M. Nithya V. Priya Department of Civil Engineering VOC College of Engineering Anna University Thoothukudi Campus Thoothukudi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University PO Box 103786 Riyadh 11543 Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Civil Engineering GMR Institute of Technology Razam India

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