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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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C3BAM-XAI: Convolutional Block Attention Module Enhanced Explainable Artificial Intelligence-Based Parkinson’s Disease Stage Classification

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Cognitive Computation 2025年第3期17卷 1-19页

作者： Abbas, Muhammad John Khan, Muhammad Attique Hamza, Ameer Alsenan, Shrooq Rehman, Aleesha Baili, Jamel Zhang, Yudong Department of Computer Science HITEC University Taxila47080 Pakistan Center of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas51368 Lithuania Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Mang Pakistan Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Informatics University of Leicester Leicester United Kingdom

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes significant impairment in neurons, physiological structures, and behavior of people. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome such challenges, we proposed a novel convolutional neural network (CNN) architecture called C3BAM-XAI that is based on the convolutional block attention module (CBAM) enhanced CNN and explainable artificial intelligence (XAI). In the proposed architecture, the data imbalance issue was resolved at the initial stage by employing augmentation techniques for the training data. After that, the proposed CNN architecture is designed with CBAM. The CBAM architecture is based on two sub-modules—the channel attention module and the spatial attention module. The input of the initial convolutional layer is passed to the channel attention module, which is further passed to the spatial attention module for more critical information extraction. For smooth training, hyperparameters are selected using Nadam Optimization (NO) instead of manual assignment. In the testing phase, features are extracted from the dense layer and passed to several neural network classifiers. In addition to this, the designed model is interpreted using the explainable AI technique to see the predicted label and insight decision features. The experimental process was performed on the publically available PD Kaggle dataset and obtained an improved accuracy of 93.33%. In addition, several ablation studies were conducted, and it is concluded that the CBAM not only improved accuracy but also made this model a reliable tool for clinical diagnosis, both in terms of accuracy and interpretability. The results thus open new avenues toward applying deep learning models practically for applications in the medical domain and early-stage detection of Parkinson’s disease, thereby helping to improve patient outcomes. © The Author(s), under

关键词： Convolutional neural networks

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Security Analysis for Threats to Patient Data in the Medical Internet of Things

Security Analysis for Threats to Patient Data in the Medical...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anand Singh rajawat S.B. Goyal Pradeep Bedi Abhudaya Shrivastava Neagu Bogdan Constantin Maria Simona raboaca Chaman Verma School of Computer Science and engineering Sandip University Nashik India Faculty of Information technology City University Petaling Jaya Malaysia Galgotais University Greater Noida India Department of Computer Science The State University of New Jersey Camden NJ United States Neagu Bogdan Constantin Department of Power Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eötvös Loránd Budapest Hungary

ISBN: (纸本)9781665487351

The current model of the healthcare system, which is being changed, is based on hospitals and specialists. The new model will be less centralised and will put more attention on each patient. The Medical Internet of Things (MIoT) makes it possible for machines to talk to each other and for humans to intervene in real time. These changes will soon make medical care much easier to get, cheaper, and more reliable. The promise of the Medical Internet of Things (MIoT) is that it will make patients happier with their healthcare overall by making it easier for them to get personalized e-health services no matter where they are or what time it is. The Medical Internet of Things (MIoT), also called the healthcare Internet of Things, is made up of medical devices and software that are all connected to each other. Many Internet of Things (IoT) applications are based on the management of personally identifiable information, such as a patient's name, address, medical history, and health conditions. This is one reason why IoT applications have a special place in the field of healthcare services. When it comes to the Internet of Things (IoT), protecting the privacy and safety of patients without compromising data security is one of the hardest things to do. The privacy and safety of the information that IoT devices collect is still a big concern, both when the information is being sent to the cloud for storage and when it is being sent there. This is because there is a lot of data and it is important and sensitive. In this paper, we look at the technical flaws and research challenges for future work in security and privacy, as well as the current challenges and needs related to the flow of data in the MIoT. To proposed approach for Security Analysis for Threats to Patient Data in the Medical Internet of Things

关键词： Industries Data privacy Privacy Medical services Software Real-time systems Safety

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Forecasting water level of Glacial fed perennial river using a genetically optimized hybrid Machine learning model

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Materials Today: Proceedings 2021年 46卷 11113-11119页

作者： Mirza Imran P. Sheikh Abdul Khader Mohammd rafiq Kishan Singh rawat Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science And Technology Vandalur Ch 41 India National Atmospheric Research Laboratory Department of Space Government of India Gadanki 517112 India Geo-Informatics Civil Engineering Department Graphic Era (Deemed to be University) Dehradun 248002 Uttrakhand India

The floods are the significant damaging disasters in the world. Either the causes of floods can be natural or anthropogenic but in both cases people have to bear the burden of devastation. The forecasting of water levels of a flood prone river is must in order to minimize the life and property damages. In this paper, we propose a hybrid machine learning model of Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic algorithm, which is trained on the temperature, precipitation data of two Meteorological stations and water level of the river. Different ANFIS models with different membership functions and optimization techniques are tested and the best performing model is picked for further modification. The parameters of selected ANFIS model are then optimized using the genetic algorithm to get better results. The ANFIS optimized with genetic algorithm showed better results as compared to the conventional ANFIS.

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Gamma entrainment frequency affects mood, memory and cognition: an exploratory pilot study

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Brain informatics 2020年第1期7卷 1-12页

作者： Sharpe, Ryan L. S. Mahmud, Mufti Kaiser, M. Shamim Chen, Jianhui Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom Medical Technology Innovation Facility Nottingham Trent University NottinghamNG11 8NS United Kingdom Institute of Information Engineering Jahangirnagar University Savar Dhaka1342 Bangladesh Beijing Key Laboratory of MRI and Brain Informatics Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China

Here we provide evidence with an exploratory pilot study that through the use of a Gamma 40 Hz entrainment frequency, mood, memory and cognition can be improved with respect to a 9-participant cohort. Participants constituted towards three binaural entrainment frequency groups: the 40 Hz, 25 Hz and 100 Hz. Participants attended a total of eight entrainment frequency sessions twice over the duration of a 4-week period. Additionally, participants were assessed based on their cognitive abilities, mood as well as memory, where the cognitive and memory assessments occurred before and after a 5-min binaural beat stimulation. The mood assessment scores were collected from sessions 1, 4 and 8, respectively. With respect to the Gamma 40 Hz entrainment frequency population, we observed a mean improvement in cognitive scores, elevating from 75% average to 85% average upon conclusion of the experimentation at weak statistical significance (α = 0.10, p = 0.076). Similarly, memory score improvements at a greater significance (α = 0.05, p = 0.0027) were noted, elevating from an average of 87% to 95%. In pertinence to the mood scores, a negative correlation across all populations were noted, inferring an overall increase in mood due to lower scores correlating with elevated mood. Finally, correlation analysis revealed a stronger R2 value (0.9838) within the 40 Hz group between sessions as well as mood score when compared across the entire frequency group cohort. © 2020, The Author(s).

关键词： Population statistics

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Deep Learning Architecture for Automatic Essay Scoring

arXiv

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arXiv 2022年

作者： Tashu, Tsegaye Misikir Maurya, Chandresh Kumar Horváth, Tomáš ELTE Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories Pázmány Péter sétány 1/C Budapest1117 Hungary Pavol Jozef Šafárik University in Košice Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia Wollo University Kombolcha Institute of Technology College of Informatics Kombolcha 208 Ethiopia

Automatic evaluation of essay (AES) and also called automatic essay scoring has become a severe problem due to the rise of online learning and evaluation platforms such as Coursera, Udemy, Khan academy, and so on. Researchers have recently proposed many techniques for automatic evaluation. However, many of these techniques use hand-crafted features and thus are limited from the feature representation point of view. Deep learning has emerged as a new paradigm in machine learning which can exploit the vast data and identify the features useful for essay evaluation. To this end, we propose a novel architecture based on recurrent networks (RNN) and convolution neural network (CNN). In the proposed architecture, the multichannel convolutional layer learns and captures the contextual features of the word n-gram from the word embedding vectors and the essential semantic concepts to form the feature vector at essay level using max-pooling operation. A variant of RNN called Bi-gated recurrent unit (BGRU) is used to access both previous and subsequent contextual representations. The experiment was carried out on eight data sets available on Kaggle for the task of AES. The experimental results show that our proposed system achieves significantly higher grading accuracy than other deep learning-based AES systems and also other state-of-the-art AES systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Middle East and North African Health informatics Association (MENAHIA): Health informatics Response to COVID-19 Crisis

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Yearbook of Medical informatics 2021年第1期30卷 328-334页

作者： Tara, Mahmood Taweel, Adel Alhuwail, Dari Al Thani, Dena A. Al-Jafar, Eiman Hamra, Eman Abu Ghazal, Hassan Wahba, Hoda Bahaadinbeigy, Kambiz AlKhalifa, Khalid Joseph, Leena Zeroug, Mohamed Odeh, Mohammad Bani Alhefzi, Mohammed I. Hamdi, Mounir Househ, Mowafa Al-Shorbaji, Najeeb Hassan, Osama El Al-Salmi, Salim Abdullah Amara, Tamer Sari, Zoubir International Academy of Health Sciences Informatics Member of the Iranian Medical Informatics Association Department of Medical Informatics Mashhad University of Medical Sciences Mashhad Iran Department of Computer Science Birzeit University Birzeit Palestine Department of Information Science College of Life Sciences Kuwait University Kuwait Health Informatics Association Kuwait Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Kuwait Health Informatics Association Kuwait Master of Public Health - Al Quds University Palestine Moroccan Society for Telemedicine and eHealth Morocco & National Center for Scientific and Technical Research Rabat Morocco Ain Shams University Virtual Hospital Cairo Egypt The Iranian Medical Informatics Association Health Information Technology Department Kerman University of Medical Sciences Kerman Iran National Health Informatics Committee Supreme Council of Health Manama Bahrain Oman College of Health Sciences Ministry of Health Muscat Oman Société Algérienne de Télémédecine et de eSanté SATeS Algeria Palestine National Institute of Public Health Ramallah Palestine Saudi Association for Health Informatics Saudi Arabia College of Science and Engineering Hamad Bin Khalifa University Doha Qatar International Academy of Health Sciences Informatics Hamad Bin Khalifa University Doha Qatar International Academy of Health Sciences Informatics President of the eHealth Development Association Amman Jordan Health Informatics & Smart Health Department Dubai Health Authority Dubai United Arab Emirates Ain Sham University Virtual Hospital Cairo Egypt

The unforeseen pandemic of COVID-19 forced all countries worldwide to appreciate digital solutions and their potential contributions in managing such outbreaks. Countries began to share their knowledge and ongoing experiences on how to employ the latest technologies to trace infected cases, warn people on potential danger, increase social and population awareness, and how to provide effective and efficient telehealth services. MENAHIA (The Middle East and North Africa Health informatics Association), established in 2018, as a new IMIA regional chapter which has started regional work in several areas of collaboration and knowledge sharing, particularly focused on the common basis of MENA countries’ needs towards health informatics solutions regarding COVID-19 crisis management. This article briefly presents health informatics activities and prospects by countries of the Region with a non-exclusive focus on the COVID-19 pandemic. Needless to say, not all the countries have reported using this platform, and some may have reported through other venues. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Algeria Bahrain COVID-19 digital health Egypt health informatics IMIA International Medical informatics Association Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco National eHealth societies Oman Palestine Qatar Saudi Arabia United Arab Emirates

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Hard hat wearing detection based on head keypoint localization

arXiv

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arXiv 2021年

作者： Wójcik, Bartosz Zarski, Mateusz Ksiązek, Kamil Miszczak, Jaroslaw A. Skibniewski, Miroslaw J. Faculty of Civil Engineering Silesian University of Technology Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Civil & Environmental Engineering University of Maryland College Park United States

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems. However, still there is no reliable way to establish the relationship between supervised construction workers and their basic personal protective equipment, like hard hats. To address this problem, a novel deep learning method combining object detection, keypoint localization, and simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the above-mentioned approaches, with class specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that the conjunction of deep learning methods with humanly-interpretable rule-based algorithm is better suited for detection of hard hat non-wearers. Code is available at barwojcik/hard-hats. © 2021, CC BY.

关键词： Object detection

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ADvoCATE: A consent management platform for personal data processing in the IoT using blockchain technology 11th

ADvoCATE: A consent management platform for personal data pr...

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11th International Conference on Security for Information technology and Communications, SecITC 2018

作者： rantos, Konstantinos Drosatos, George Demertzis, Konstantinos Ilioudis, Christos Papanikolaou, Alexandros Kritsas, Antonios Department of Computer and Informatics Engineering Eastern Macedonia and Thrace Institute of Technology Kavala Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Information Technology Alexander Technological Educational Institute of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783030129415

The value of personal data generated and managed by smart devices which comprise the Internet of Things (IoT) is unquestionable. The EU General Data Protection Regulation (GDPR) that has been recently put in force, sets the cornerstones regarding the collection and processing of personal data, for the benefit of Data Subjects and Controllers. However, applying this regulation to the IoT ecosystem is not a trivial task. This paper proposes ADvoCATE, a user-centric solution that allows data subjects to easily control consents regarding access to their personal data in the IoT ecosystem and exercise their rights defined by GDPR. It also assists Data Controllers and Processors to meet GDPR requirements. A blockchain infrastructure ensures the integrity of personal data processing consents, while the quality thereof is evaluated by an intelligence service. Finally, we present some preliminary details of a partial implementation of the proposed framework. © Springer Nature Switzerland AG 2019.

关键词： Data privacy

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SensOrch: QoS-Aware Resource Orchestration for Provisioning Sensors-as-a-Service

SensOrch: QoS-Aware Resource Orchestration for Provisioning ...

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IEEE International Conference on Communications (ICC)

作者： Aishwariya Chakraborty Sudip Misra Ayan Mondal Mohammad S. Obaidat Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India College of Computing and Informatics University of Sharjah UAE

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In this work, we address the problem of efficient utilization of resource-constrained wireless sensor nodes for provisioning Sensors-as-a-Service (Se-aaS) with high quality. In sensor-cloud, the sensor-owners provide their respective sensor nodes to the sensor-cloud service provider (SCSP) on rent. The SCSP utilizes these nodes to create virtual sensors and provisions them as Se-aaS for serving their WSN-dependent applications of the end-users and earns revenue in exchange. To ascertain high quality-of-service (QoS) of Se-aaS while simultaneously ensuring profits for itself and the sensor-owners, the SCSP needs to optimally allocate physical sensor nodes to serve the virtual sensors, while considering their limited capacity and the fair distribution of service load among different sensor-owners. Although a few existing works focused on resource allocation problem in sensor-cloud, none of them considered the possibility of sharing the same physical sensor node among multiple virtual sensors. Hence, in this work, we propose a resource orchestration scheme for sensor-cloud, named SensOrch, which is based on coalition formation game with transferable utility. Using SensOrch, the SCSP ensures the optimal allocation of sensor nodes to form virtual sensors while maintaining high QoS and profitability of Se-aaS. Through simulations, we yield that, using SensOrch, the network lifetime increases by 25.31 - 59.6% along with a simultaneous increase in the profit of the SCSP by 23.64 - 29.49%, compared to the existing schemes. Additionally, SensOrch ensures fair revenue distribution among the sensor-owners.

关键词： Quality of service Resource management Wireless sensor networks Cloud computing Games Memory management Pricing

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