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9th International Conference on Computing and Data engineering, ICCDE 2023

作者： Rasool, Ehtsham Anwar, Muhammad Junaid Shaker, Bilawal Hashmi, Muhammad Harris Rehman, Khalil Ur Seed, Yousaf Department of Computer Science The University of Alabama Birmingham United States School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781450398022

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women's at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies. © 2023 ACM.

关键词： Diseases

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Impact of SNR on Sensorless Control of PMSMs with PHF Signal Injection 13

Impact of SNR on Sensorless Control of PMSMs with PHF Signal...

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13th International Conference on Power and Energy Systems, ICPES 2023

作者： Soysa, Gayani De Madawala, Udaya Bevilaqua, Matheus Patel, Nitish University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Fisher and Paykel Technologies Auckland New Zealand

ISBN: (纸本)9798350345063

Pulsating High Frequency (PHF) signal injection is a popular sensorless technique used to estimate the rotor position at starting and low speeds of Permanent Magnet Synchronous Motors (PMSMs). However, with the absence of a specific criteria for selecting the frequency and amplitude of the injected High Frequency (HF) signal, implementation of PHF is still a challenge for any given application. Therefore, this paper investigates the impact of Signal to Noise Ratio (SNR) on the error between the estimated and actual rotor position when using the PHF signal injection technique. As SNR depends on the frequency and amplitude of the injected HF signal, an analysis is carried out by varying the injected frequency and the amplitude to determine the minimum SNR to achieve minimum rotor position error is determined. The results of an S-pole, 1 kW PMSM are presented to demonstrate the relationship between the SNR and the rotor position error for different injected HF signals. © 2023 IEEE.

关键词： Sensorless control

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Work-in-Progress: Merkle Tree-Based Secure Routing for Digital Twin-Assisted Aircraft Network in 6G Wireless

Work-in-Progress: Merkle Tree-Based Secure Routing for Digit...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Bilen, Tugce Erel-Ozcevik, Muge Bozkaya, Elif Ozcevik, Yusuf Turk Telekom Istanbul Turkey Manisa Celal Bayar University Department of Software Engineering Manisa Turkey National Defence University Turkish Naval Academy Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350337822

Providing Internet access above-the-clouds has made the development of aircraft networks more important than ever. However, new and emerging Internet applications have increased the challenge of providing seamless and real-time connectivity with traditional routing algorithms for aircraft networks in the upcoming 6G due to the highly-dynamic and unstable topology. Moreover, traditional routing mechanisms are prone to routing attacks, which can increase the packet transfer delay. To this end, in this paper, we present Merkle Tree-based secure routing mechanism with the assistance of digital twin. First, we construct a blockchain-based system with decentralized and distributed characteristics for the clustering of aircraft. Then, we propose a novel Merkle-Tree-based secure routing algorithm by combining real-time and historical data with digital twins. A Merkle tree is a data structure that contains gathered data from sender and receiver aircraft. Merkle root in an aircraft cluster forwards the gathered data to a satellite or a ground station in a secure manner. Accordingly, Secure Hash Algorithm (SHA)-256 is used for data integrity and a tree-based structure is built to reduce the number of transactions so that it is aimed to prevent malicious aircraft attacks. Finally, we show through a simulation environment, how blockchain processing time and the number of transactions can be reduced by meeting the strict Quality of Service (QoS) requirements for aircraft networks in 6G. Furthermore, we also use Proof of Stake (PoS) to reduce the computational time for mining a block as well as reducing the packet delivery ratio and packet transfer delay. © 2023 IEEE.

关键词： Blockchain

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A Data-Driven Analysis of software Testing Automation Challenges Using Structural Equation Modeling (SEM) Approach

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IEEE Access 2025年 13卷 96159-96181页

作者： Abrar, Muhammad Faisal Majeed, Muhammad Faran Saqib, Muhammad Alferaidi, Ali Uddin, Raza University of ha'Il College of Computer Science and Engineering 55476 Saudi Arabia Kohsar University Department of Computer Science Punjab Muree Pakistan University of Engineering & Technology Department of Software Engineering Peshawar25000 Pakistan

The adoption of automation in software testing presents challenges that can hinder its effectiveness and scalability. This study systematically investigates these challenges using a multi-phase research approach. First, a Systematic Literature Review (SLR) was conducted to identify 14 Critical Challenges (CCs) in automation adoption. Second, a questionnaire survey of 50 industry experts validated these challenges and examined their interrelationships. Finally, Structural Equation Modeling (SEM) a mathematical and statistical approach was employed to analyze correlations and uncover structural dependencies among the challenges. The SEM analysis identified five latent variables: Human Resource Constraints (HRC), Technological & Process Challenges (TPC), Financial & Resource Constraints (FRC), Security & Reliability Issues (SRI), and Future Adaptability & Scalability (FAS) that significantly influence automation adoption. Hypothesis testing revealed that HRC (-0.32), TPC (-0.45), FRC (-0.41), and SRI (-0.38) negatively impact automation success, whereas FAS (+0.51) plays a pivotal role in enabling successful adoption. Model validation through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) confirmed strong construct reliability and fit indices (RMSEA = 0.043, CFI = 0.95, TLI = 0.92, SRMR = 0.037). The study highlights the need for workforce training, standardized automation processes, cost-effective solutions, and security enhancements. By providing an empirically validated framework, this research contributes to both academia and industry, guiding decision-makers in optimizing automation strategies and improving software testing efficiency. © 2013 IEEE.

关键词： software testing

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： Yazeed Yasin Ghadi Nida Khalid Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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Energy-Aware Task Scheduling for Digital Twin Edge Networks in 6G

Energy-Aware Task Scheduling for Digital Twin Edge Networks ...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Bozkaya, Elif Bilen, Tugce Erel-Ozcevik, Muge Ozcevik, Yusuf National Defence University Turkish Naval Academy Department of Computer Engineering Istanbul Turkey Turk Telekom Istanbul Turkey Manisa Celal Bayar University Department of Software Engineering Manisa Turkey

ISBN: (纸本)9798350302523

With the recent surge in the Internet of Things (IoT) devices and applications, computation offloading services in Mobile Edge Computing (MEC) have provided the significant potential to upcoming 6G networks for a better Quality of Service (QoS). However, IoT devices are typically resource and energy-constrained, so this challenge can be compensated by incorporating energy-efficient approaches into the solution. Digital Twin is a candidate technology to reshape the future of the industry and energy-efficiently manage tremendous growth in data traffic at the network edge. Thus, we propose a Digital Twin Edge Network (DTEN) architecture for energy-aware task scheduling. More specifically, we formulate an energy optimization problem and identify a set of computation strategies to minimize both the task processing time and energy consumption. Due to being NP-hard, we compare it by Warehouse Location Problem (WLP) and solve it with the genetic algorithm-based approach in an energy and time-efficient manner. To achieve these, we present our digital twin-assisted energy-aware task scheduling algorithm by using both real-time and historical data in virtualization and service layers. After this, IoT devices can compute their tasks locally or offload to the edge/cloud server with the assistance of digital twins of the physical assets. Simulations are carried out to show the superiority of the proposed energy-aware task scheduling algorithm in terms of the task processing time and consumed energy in DTEN. © 2023 IEEE.

关键词： Genetic algorithms

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Design of Flexure Robotic Hand for Teleoperation 6th

Design of Flexure Robotic Hand for Teleoperation

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6th Joint International Conference on Multibody System Dynamics and 10th Asian Conference on Multibody Systems, IMSD-ACMD 2020

作者： Zubair, Mohammad Bhandari, Shubham Chatterjee, Indranath Department of Mechanical Engineering Adamas University Kolkata India Software Development Engineer-I HashedIn by Deloitte Bengaluru India Department of Computer Science Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9789819775248

Robots are getting deployed at home and in industries, becoming an integral part of our daily lives. Sophisticated mechanisms are required to make the joints of the robots compact and simple. In this paper, we have developed a flexure joint for the fingers of the robotic hand. The flexure joint is arranged in series to make a three-DOF underactuated finger. Five fingers are arranged to make a robotic hand. Castigliano’s theorem is used to develop the deformation related to the applied loads, using the equation the flexure mechanism was designed. Pseudo-rigid model is developed to determine the stiffness of the flexure mechanism. A virtual model of the flexure mechanism is developed in a CAD environment. Finite Element (FE) simulation is conducted to the different loading conditions to evaluate the performance of the flexure mechanism. It was seen that the flexure mechanism is flexible about the rotational axis and stiff about the other axes. The stiffness model developed using the pseudo-rigid model has a good correlation with the FE simulation model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Convolutional Neural Network Based Thermal Image Classification 2

Convolutional Neural Network Based Thermal Image Classificat...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ashfaq, Qirat Usman Akram, Muhammad National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Classification of Thermal Images has been extensively used for its significant applications in many fields. There are many problems with the visible spectrum like object shadows, clothes or the body of a human being matches the background and different lighting conditions. These limitations are overcome by using thermal imaging. Each and every object emits heat (Infrared energy) according to its temperature. Normally the hotter object emits more radiation than the colder one. As all objects have a mostly different temperature so thermal camera detects them and these objects get appear as distinct objects. In the start, thermal imaging was used by the military for detection, recognition, and identification of enemy personnel and equipment. Nowadays it is extensively used in the detection of face, self-driving car, detection of pedestrians and it also has application in the field of environmental work that is monitoring for energy conservation and pollution control. This research presents a novel study for the classification of thermal images using convolutional neural networks (CNN). The research focused on developing a framework that detects multiple thermal objects using CNN. Developed a framework based on deep learning Inception v3 model;work with thermal images that are captured by Seek Thermal and FLIR. For training and testing of the model two datasets are used that include three classes' cat, car, and man. For the FLIR dataset the highest accuracy achieved is 98.91% and for Seek thermal dataset highest accuracy achieved is 100%. A comparison of the proposed framework with some other CNN models (DenseNet, MobileNet, and YOLOv4), with a customized CNN model and with a conventional model is also presented. The results of the proposed framework and comparison with other models prove that the proposed framework is effective for the classification of thermal images. © 2022 IEEE.

关键词： Convolutional neural networks

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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