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Deep neural network-based secure healthcare framework

Neural Computing and Applications 2024年第28期36卷 17467-17482页

作者： Aldaej, Abdulaziz Ahanger, Tariq Ahamed Ullah, Imdad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Management Information Systems Department CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW2006 Australia

Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as IoT applications expand. Addressing security challenges is paramount for a successful IoT healthcare framework, with blockchain technology offering a decentralized structure for robust data protection and secure data exchange within multi-node IoT networks. The research introduces a secure IoT healthcare diagnostic model empowered by deep neural networks, emphasizing encryption, safe transactions, and healthcare diagnostics as key components. Notably, the model incorporates innovative techniques like the orthogonal particle swarm optimization algorithm for sharing medical images and a neighborhood indexing sequence method for hash value encryption. The development of an optimized deep neural network-based classification model for illnesses, validated through extensive trials, demonstrates superior performance metrics compared to existing decision-making techniques, with significant improvements in f-Measure (96.25%), sensitivity (93.26%), specificity (94.26%), and accuracy (93.26%). This study’s scientific contribution lies in its innovative approach to securing IoT-healthcare diagnosis models, validated performance enhancements using real-world datasets, and insightful recommendations for future research directions, fostering advancements in healthcare technology for enhanced patient care and system efficiency. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets

Diversity Over Size: On the Effect of Sample and Topic Sizes...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Schiller, Benjamin Daxenberger, Johannes Waldis, Andreas Gurevych, Iryna summetix GmbH Germany Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Information Systems Research Lab Lucerne University of Applied Sciences and Arts Switzerland

ISBN: (纸本)9798891761643

Topic-Dependent Argument Mining (TDAM), that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large TDAM datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. In this work, we investigate the effect of TDAM dataset composition in few- and zero-shot settings. Our findings show that, while fine-tuning is mandatory to achieve acceptable model performance, using carefully composed training samples and reducing the training sample size by up to almost 90% can still yield 95% of the maximum performance. This gain is consistent across three TDAM tasks on three different datasets. We also publish a new dataset and code for future benchmarking. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Boosting white shark optimizer for global optimization and cloud scheduling problem

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Neural Computing and Applications 2024年第18期36卷 10853-10879页

作者： Mostafa, Reham R. Chhabra, Amit Khedr, Ahmed M. Hashim, Fatma A. University of Sharjah Sharjah27272 United Arab Emirates Information Systems Department Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Computer Science Department University of Sharjah Sharjah27272 United Arab Emirates Faculty of Engineering Helwan University Helwan Egypt MEU Research Unit Middle East University Amman11831 Jordan

With the growing adoption of cloud computing in both public and private sector enterprises, the industry has experienced rapid expansion. To fully unlock the potential of cloud computing, efficient task scheduling becomes crucial. In cloud computing, task scheduling involves optimizing the allocation of tasks to a diverse range of resources, such as virtual machines, with the goals of reducing makespan, maximizing resource utilization, and minimizing response times. This challenge becomes even more pronounced for large-scale tasks due to the NP-hard nature of the problem. Consequently, the integration of metaheuristic algorithms into task scheduling has emerged as a solution to equitably distribute complex and diverse tasks across limited resources within acceptable timeframes. To enhance the quality of cloud computing services, this research introduces the modified white shark optimizer (mWSO) as an alternative task scheduling technique. The improved variant mWSO boosts the performance of the original WSO by introducing the following three enhancement steps: (1) introduce memory-based WSO to boost the exploitation phase, (2) propose an exploration-exploitation balance phase to enhance the exploration phase, and (3) introduce a control randomization parameter to balance exploration and exploitation properly. The mWSO is subjected to testing on both the global optimization problems from CEC2020 and cloud task scheduling problems. The experimental results of mWSO demonstrate high performance for CEC2020 competition benchmarks compared to other state-of-the-art and recent metaheuristic algorithms. In the case of the task scheduling problem, the mWSO achieved − 0.01 to 13.53% and 0.62–10.42% makespan and energy consumption reduction, respectively, for CEA-Curie workloads. For HPC2N workloads, mWSO achieved 7.27–29.53% makespan reduction and 3.52–26.08% energy savings over the compared metaheuristics. The statistical validity of the performance is also verified using Wil

关键词： Global optimization

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Efficient Video Object Detection of Indoor Furniture and Home Appliances

Efficient Video Object Detection of Indoor Furniture and Hom...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Fuangfar Pensiri Phasuwut Chunnapiya Wanida Khamprapai Porawat Visutsak Department of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Chonburi Thailand Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

Video object detection extends the principles of object detection in still images to the realm of moving pictures. It involves identifying and localizing objects of interest within each frame of a video sequence. In this work, we propose an efficient method for video object detection of indoor furniture and home appliances using YOLO, data augmentation, and a novel frame sampling technique. We tested our proposed method in a real-world environment, and performance measurements demonstrate a 24.73% increase in mean average precision (mAP50-95) and an 89.77% reduction in processing time compared to the standard YOLOv7 baseline.

关键词： YOLO Measurement Home appliances Runtime Video sequences Predictive models Motion pictures

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Digital Transformation and its Impact on Operational Efficiency and Competitive Advantage in Islamic Banks 2

Digital Transformation and its Impact on Operational Efficie...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Shehadeh, Maha Almajali, Dmaithan Abu-Alsondos, Ibrahim A. Alkhwaldi, Abeer F. Al-Gasaymeh, Anwar S. Applied Science Private University Finance and Banking Science Department Amman Jordan Applied Science Private University Management Information System Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Mutah University Department of Management Information Systems Karak Jordan

ISBN: (纸本)9798350335644

This study aimed to identify the impact of digital transformation on Islamic banks operating in Jordan by using the descriptive analytical method. Primary data were obtained through a questionnaire and distributed to the study sample, which consisted of 68 employees of 4 Islamic banks operating in Jordan. The structural equation model was used in the process of testing the study hypotheses. We obtained a number of results, the most important of which is the presence of a statistically significant effect at the significance level (α ≤ 0.05) of digital transformation in Islamic banks operating in Jordan in relation to operational efficiency and competitive advantage. We recommend that Islamic banks need to develop a strategy for digital transformation aimed at innovation and competition, without prejudice to the controls of Islamic finance. There is a need to focus on managing the risks associated with digital transformation, as well as treating the risks associated with modern technologies, especially those that threaten Islamic banks and financial stability. © 2023 IEEE.

关键词： Competition

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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HeadweAR: Development of Augmented Reality Based on Hat Tester to Support Product Testing 5

HeadweAR: Development of Augmented Reality Based on Hat Test...

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5th International Conference on Advancement in Data science, E-Learning and information systems, ICADEIS 2023

作者： Juma'At, Saiful Anwar Fudzee, Mohd Farhan Md Nugraha, Ryan Adhitya Syamsuar, Dedy Kontagora, Ibrahim Umar Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and IT Parit Raja86400 Malaysia Telkom University Information System Department Bandung Indonesia Bina Nusantara University School of Information Systems Information Systems Department Indonesia Niger State Polytechnic Department of Computer Science Niger State Zungeru Nigeria

ISBN: (纸本)9798350303414

Augmented reality technology application in variety of sectors is becoming more common in line with progress of technological advances towards the digital future. However, the experience of online shopping that lacks in virtual dressing room deter consumers to make the purchase decisions. Existing application for virtual dressing room is yet to include headwear products in software applications. Therefore, this project is proposed to develop an augmented reality mobile application that helps user to visualize virtual hat onto user's head. The targeted users for this project are adults aged 20 to 30 years old who are the customers of The Cap City. Multimedia Mobile Content Development (MMCD) model is implemented to develop the headweAR application. Based on user testing results, more than 80% of the respondents left positive feedback on the proposed application. Development with additional 3D models and multiple face tracking are some of the future works of this markerless AR-based application. © 2023 IEEE.

关键词： Augmented reality

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