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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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WIP: An Engaging Undergraduate Intro to Model Checking in software engineering Using TLA+

arXiv

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arXiv 2024年

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Software and Systems Laboratory Department of Computer Science Loyola University Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one’s undergraduate studies, (3) demonstrate how to address successive "failures" through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

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computers, Materials & Continua 2021年第6期67卷 3505-3521页

作者： Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem N.Z.Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing ***-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and *** this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy *** paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network *** scheme is presented in six phases to explore ow and *** propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation *** scheme consumes less energy and balances the load by clustering the smart *** work is validated through extensive simulation using *** elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead *** the future,we shall consider edge computing to analyze the performance of robust clustering.

关键词： Energy efciency routing load balancing cluster selection

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A Survey of Edge Computing Privacy and Security Threats and Their Countermeasures

A Survey of Edge Computing Privacy and Security Threats and ...

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IEEE computer Society Annual Symposium on VLSI

作者： Ahmed Shafee Tasneem A. Awaad Ahmed Moro Department of Engineering and Computer Science Adams State University Alamosa CO USA Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Lifecycle Collaboration Software Segment Cairo Egypt

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Recent advancements in Artificial Intelligence (AI) and the proliferation of the Internet of Things (loT) and mobile devices have generated enormous data volumes, necessitating advanced data processing closer to data sources through edge computing to enhance efficiency and reduce latency. Nonetheless, privacy concerns in cloud storage and data transmission, alongside the inadequacy of traditional privacy protections, have highlighted the vulnerability to sophisticated data breaches. This paper offers a survey on edge computing, emphasizing privacy and security challenges, evaluating threats, and exploring solutions to promote the development of secure, robust and privacy-preserving edge computing frameworks.

关键词： Surveys Data privacy Cloud computing Privacy Very large scale integration Data processing Security

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks

A Trust-based Framework for the Detection and Avoidance of B...

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International Conference on Signal Processing and Communication (ICSC)

作者： Protyasha Kundu Swarnadeep Saha Smritikona Barai Anindita Kundu Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node’s nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant.

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A Study of Two Types of Introductory Exercise Problems for Network Programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

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computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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Advances in Data engineering and Intelligent Computing systems (ADICS), International Conference on

作者： P. Ananthi K. Nirmala Devi Gopinath D P. Shanmugapriya Gowtham S Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu Department of Computer Science (PG) Kristu Jayanti College Bangaluru Department of Computer Science Kristu Jayanti College Bangaluru MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight ‘Region Convolutional Neural Network (R-CNN)‘ head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision.

关键词： Deep learning Plant diseases Computational modeling Neural networks Predictive models Reliability engineering Prediction algorithms

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Deep Learning Based Detection of Cardiovascular Diseases Using ECG Images

Deep Learning Based Detection of Cardiovascular Diseases Usi...

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International Conference on Communication and Electronics systems (ICCES)

作者： P Ananthi K Nirmala Devi Pradisa. T Department of Computer Technology Kongu Engineering College Perundurai TamilNadu Department of Computer Science(PG) Kristu Jayanti College Bangaluru MSc Software Systems Kongu Engineering College Perundurai TamilNadu

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

In today's world, cardiovascular disease is still a major concern. The electrocardiogram (ECG) is one of the most trustworthy non-invasive tools for identifying heart problems. ECG interpretation, however, takes a lot of effort and specialized knowledge. The quality and effectiveness of medical care are enhanced by the development of a revolutionary technique for early disease detection. Using a public dataset of ECG scans from cardiac patients, this study leveraged deep learning algorithms to predict two primary cardiac abnormalities: abnormal heartbeat and normal person classes. The low-scale pre-trained deep neural Alex Net was used to study the transfer learning process. To forecast cardiac issues, a novel Convolutional Neural Network (CNN) architecture was put out. We used the conventional deep learning methods, VGG-16, Resnet50, RNN, and LSTM, to feature-extracted data utilizing the previously mentioned pre-trained models and our suggested LSTM model. The experimental results show that the LSTM model outperforms the previous attempts in terms of performance measures.

关键词： Deep learning Transfer learning Medical services Electrocardiography Prediction algorithms Data models Convolutional neural networks Cardiovascular diseases Long short term memory Residual neural networks

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