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Information Technology (ACIT)

作者： Suhail Odeh Anas Samara Nicola Zreineh Seif Obeid Maen Ibrieghieth Mahmoud Obaid Software Engineering Dept. Bethlehem University Palestine Computer System Engineering Arab American University Jenin Palestine

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care.

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare

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Procedia computer Science 2024年 241卷 570-575页

作者： Ammerha Naz Muhammad Ali Sehrish Munawar Cheema Ivan Miguel Pires Dept. of Computer Science University of Sialkot Sialkot Punjab Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries.

关键词： Healthcare Hospital information systems (HIS) Electronic health records (EHRs) Cloud computing. Data transmission

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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Advancements in Computing (ICAC), International Conference on

作者： K. C. Nirasha Jayasiri Sanjeevi Chandrasiri Shalini Rupasinghe K.O. Rivindu Karunanayake Abineshh Uthayachandran M.I. Fathima Zihara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns.

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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Cyberattack Classification in Smart Grid Distribution Substations using a Novel Ensemble Bagging Learning Technique

Cyberattack Classification in Smart Grid Distribution Substa...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Victor O. Ijeh Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Smart grid incorporates the communication networking that enables the exchange of information among the monitoring and controlling devices. Such incorporation of the communication networking into the electricity grid infrastructure poses the risk of cyberattacks that target the critical assets within such an infrastructure. Most of the existing research focuses on the detection of such cyberattacks but without identifying the type of the attacks. This can result in overlooked threats and misdirected the necessary countermeasures. Recognizing the attack’s type is essential for timely responses and strategic planning against future threats, thereby enhancing the resilience of the smart grid. In this paper, a Fine Tree Bagging-based Ensemble Learning (FTBE) technique is proposed to detect and classify the different types of cyberattacks and power quality disturbances. The salient features of the attacks’ types are highlighted, which helps in identifying the types of the attack following the detection process.

关键词： Training Accuracy Substation automation Uncertainty Scalability Smart grids Ensemble learning

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Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

arXiv

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arXiv 2023年

作者： Zhang, Meiying Zhao, Huan Ebron, Sheldon Xie, Ruitao Yang, Kan Dept. of Computer Science University of Memphis United States College of Computer Science and Software Engineering Shenzhen University China

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the ‘integrated’ dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid. © 2023, CC BY.

关键词： Heuristic algorithms

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G

Work in Progress: Congestion Control in mmWave Fluctuating S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jorge Ignacio Sandoval Sandra Céspedes Department of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng Concordia University Montreal Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times.

关键词： Measurement Machine learning algorithms Millimeter wave technology Bandwidth Machine learning Radio links Millimeter wave propagation Millimeter wave communication Next generation networking Convergence

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An Automated Wavelet Generation Tool for Cyberattack Detection in Substation Automation Systems

An Automated Wavelet Generation Tool for Cyberattack Detecti...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Matthew Oinonen Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This paper presents a novel method, the Automated Custom Wavelet Generation-Deep Learning-based classifier (ACWGDL), for cyberattack detection in smart grids. The proposed method generates custom wavelets for enhanced feature extraction of cyberattacks on substation automation systems (SASs). Physical and network data from a substation is converted into time-frequency spectrograms using the Discrete Wavelet Transform (DWT) with the custom generated wavelets. Deep learning is implemented with a convolutional neural network to classify the generated spectrograms as cyberattacks from normal cases and power quality disturbance cases. The proposed method has been tested on two publicly available datasets from two different substation automation systems. The results have shown that the use of the custom wavelets was able to enhance the detection accuracy of the cyberattacks by up to 11% compared to the existing benchmark wavelets. The proposed method is versatile and has demonstrated its effectiveness in enhancing the detection accuracy of the cyberattacks irrespective of the choice of the datasets.

关键词： Training Time-frequency analysis Substation automation Accuracy Benchmark testing Feature extraction Discrete wavelet transforms

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A New Random-Forest-Based Approach for Cyberattack Detection in Digital Substations

A New Random-Forest-Based Approach for Cyberattack Detection...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Kripa M. Jose Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Digital substation automation systems rely heavily on information and communication technologies, which are prone to cyberattack threats. Traditionally, the focus has been geared toward the detection and classification of power quality disturbances from the normal operation. This paper presents a novel approach that uses random forests to detect and classify both cyberattacks and power quality disturbances from normal operation. The proposed approach was successful in identifying the salient features that significantly influence the detection of such cyberattacks. In order to demonstrate the effectiveness of the proposed approach, it has been tested on two different publicly available datasets. Unlike in previous work, the results have shown that the uncertainty associated with the choice of the training and testing sets has insignificant effect on the classification accuracies of the proposed approach. Furthermore, the proposed approach was successful in overcoming the issues of data imbalance and overfitting, which are common in cybersecurity problems.

关键词： Training Accuracy Sensitivity Uncertainty Substation automation Power quality Forestry

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Spatial Mini Golf: Game-Based Spatial Intelligence Testing and Training Using VR and Non-VR Platforms

Spatial Mini Golf: Game-Based Spatial Intelligence Testing a...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Patrick Oliver Andy Fong Zixuan Wang Burkhard C. Wünsche Dept. of Software Engineering University of Auckland Auckland New Zealand School of Computer Science University of Auckland Auckland New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Spatial intelligence is a key determinant of students' motivation and success in pursuing a STEM career. To improve students' participation in STEM subjects it is important to test and train spatial skills in an enjoyable and unobtrusive manner. We present Spatial Mini Golf, a game designed to test and enhance spatial intelligence, targeting four sub-skills: visual perception, mental rotation, spatial visualization, and visuospatial working memory. Two versions-VR and desktop-were compared. While VR increases engagement, it also presents control challenges that raise cognitive load and extend task completion times. Performance correlates with spatial intelligence, particularly in desktop environments, but VR reduces performance gaps among lower-ability participants. Our research suggest that both VR and desktop games are suitable for testing and training spatial reasoning skills. VR tools might be more accessible for users with low spatial skills if intuitive and easy-to-use interfaces are provided, but results might get affected by users' hand-eye coordination.

关键词： Training Visualization engineering profession Games Cognitive load Testing Visual perception

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