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Optimal Resource Allocation in Fog Computing for Healthcare Applications

computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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Object Recognition and Tracking System to Assist Visually Impaired: A Neural Network-Based Deep RBM Technique 1

Object Recognition and Tracking System to Assist Visually Im...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 IEEE.

关键词： Object detection

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Diagnosis Of Cassava Leaf Diseases and Classification Using Deep Learning Techniques 16

Diagnosis Of Cassava Leaf Diseases and Classification Using ...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Riaz, Syed Mursleen Ahsan, Muhammad Akram, Muhammad Usman Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665464772

The plants disease diagnosis is very challenging research in the field of agriculture. Cassava is a second most provider of carbohydrates in Africa. It is a key food for people of Africa in very harsh conditions. According to United Nations (FAO) almost eighty percent farmers of sub Saharan Africa are growing cassava roots, but due to a variety of viral diseases the production of cassava is very low from last two years. With the help of data science, it is possible to diagnose and classify these types of viral diseases. Existing methods of disease detection require farmers to solicit the help of government-funded agricultural experts to visually inspect and diagnose the plants. Moreover, this process is labor-intensive, time taken, costly and impacting the production and supply cycle. As an added challenge, effective solutions for farmers must perform well under significant constraints since African farmers may only have access to mobile-quality cameras with low-bandwidth. The dataset which we use in this research is taken from Kaggle competition 2020. Dataset contains 21397 images of cassava plants which belongs to five different classes i.e., Cassava Bacterial Blight, Cassava Brown Streak Disease, Cassava Green Mottle, Cassava Mosaic Disease and Healthy leaves. In this work we have used augmentation technique to increase the samples for classification and balancing the uneven distribution of data for all classes and used deep learning model efficiennetB3 for identification classification of diseases and got 83.03% overall accuracy on test dataset with more than 90% individual accuracy of each class. We have developed a graphical user interface for using the model in more efficient way with the aim to help the industry for prediction of diseases during its initial stages. © 2022 IEEE.

关键词： Classification (of information)

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A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection 7

A Convolutional Neural Network Based Classification Approach...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Or Rashid, Md Harun Shahriyar, S.M. Shamrat, F M Javed Mehedi Mahbub, Tanzil Tasnim, Zarrin Ahmed, Md Zunayed Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350397284

Thousands of women worldwide are diagnosed with breast cancer yearly, which may be fatal if not treated. The diagnosis of the condition may take years, by which time the patient has little choice except to have the affected breast removed. Early diagnosis and treatments are the best ways to stop this disease's spread. In this study, the authors presented a computer Aided Diagnosis (CAD) system to assist in breast cancer diagnosis. The study uses the Wisconsin breast cancer dataset to classify benign and malignant data. For the classification, three pre-trained Deep learning algorithms: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), were used. A novel CNN model that exceeds the performance efficiency of three pre-trained models and requires minimal compilation time is proposed. A number of evaluation matrices are used to analyze the models' classification abilities. Upon closer inspection, it has been established that the proposed CNN model outperforms CNN, LSTM, and MLP models with validation accuracy of 97.85%. CNN and LSTM performed with accuracies of 94.12% with the Adagrad optimizer and 93.5% with the Adam optimizer, respectively. Furthermore, MLP performance with 92.44% accuracy using the Adam optimizer. The proposed CNN model achieves the lowest Loss value and compilation time. In addition, the models' recall value, precision, and f1-score are computed to pick out the most effective model for diagnosing breast cancer on numeric data. © 2023 IEEE.

关键词： Neural network models

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Efficient Detection of Selfish Mining Attacks on Large-Scale Blockchain Networks 24

Efficient Detection of Selfish Mining Attacks on Large-Scale...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Erfan, Fatemeh Bellaiche, Martine Halabi, Talal Polytechnique Montréal Department of Computer and Software Engineering MontréalQC Canada Université Laval Department of Computer Science QuébecQC Canada

ISBN: (纸本)9798350365658

Selfish mining attacks pose a significant and ongoing security threat to blockchain networks, including major platforms like Bitcoin and Ethereum. Understanding and effectively countering these attacks is crucial for maintaining the stability and integrity of these networks. This attack strategy involves a miner or a group of miners seeking to gain an advantage by delaying the immediate broadcasting of their mined blocks to the network. The selfish miners potentially mine more blocks in secret, increasing their rewards at the expense of other miners and the stability of the blockchain. Research has mainly focused on detecting this attack by analyzing data related to blocks and forks. This paper presents a new approach to efficiently detect selfish mining attacks in large-scale networks by analyzing various network indicators. To achieve this, we simulate a Bitcoin network and generate a dataset consisting of several features of individual miners and the overall network. We select the most reliable indicators by analyzing network features and reducing feature dimensions. Then, we employ random forest classification (RFC) to classify benign and selfish network behaviors. This approach not only achieves enhanced accuracy (99.96%), surpassing state-of-the-art methods utilizing deep learning, but also significantly reduces computational, storage, and temporal complexities. In doing so, it fortifies blockchain security more efficiently and accurately. © 2024 IEEE.

关键词： Blockchain

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A Lecture Centric Automated Distractor Generation for Post-Graduate software engineering Courses

A Lecture Centric Automated Distractor Generation for Post-G...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Chughtai, Rudeema Azam, Farooque Anwar, Muhammad Waseem Haider But, Wasi Farooq, Muhammad Umar Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350345933

Critical circumstances, natural disasters or pandemics like COVID 19 gave rise to the wide applicability of E-learning into education system. Efficient and fair online assessment is very important to utilize the inevitable benefits of E-learning.. In order to make it efficient, the trend of assessment has shifted from the subjective type to the objective type assessments which is mainly based on Multiple Choice Questions (MCQs), generation of MCQs is a tedious, tiresome and time consuming task. To cater this dire need, this study proposes an automated Multiple Choice Question (MCQ) generation by utilizing state of the art transformer based model T5 for the task of question generation and a lexicon based approach Sense2vec for the task of distractor generation. It also presented a domain specific lecture text based test data for performing evaluation on the task of domain specific lecture text based MCQ generation. © 2022 IEEE.

关键词： E-learning

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Implementing and Evaluating a P4-based Access Gateway Function on a Tofino Switch 6

Implementing and Evaluating a P4-based Access Gateway Functi...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Makhroute, El-Mehdi Elharti, Mohammed-Ayoub Brouillard, Vincent Savaria, Yvon Ould-Bachir, Tarek Polytechnique Montréal Department of Computer and Software Engineering Montreal Canada Polytechnique Montréal Department of Electrical Engineering Montreal Canada

ISBN: (纸本)9798350329391

This paper introduces an Access Gate Function (AGF) that has been implemented using the P4 language and evaluates its performance when running on a Tofino switch. Through the process of translating specifications from the Broadband Forum (BBF) for Fixed Mobile Convergence in the 5G Core into a P4 code and by conducting data plane simulations on software switch models, we provide evidence that the AGF solution conforms to industry standards and exhibits efficient and scalable performance characteristics. Subsequently, we assess the prototype's implementation on a Tofino switch, analyzing its capabilities and limitations. The reported results demonstrate that the programmable P4 Tofino switch efficiently handles a significant number of sessions, further reinforcing the potential and practicality of the proposed solution for industrial deployment in fixed-mobile convergence scenarios. © 2023 IEEE.

关键词： 5G mobile communication systems

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Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL 16

Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Anwaar, Zaeem Khan, Farasat Ullah Naz, Mehwish Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665464772

Science and health care systems are working hand in hand to cater and support each other in the current era. The liver is one of the important body parts that need to work appropriately for a human body. But sometimes the most hazardous reasons for liver problems are infections or diseases like Hepatitis because they remain for an extended time and direct to dangerous difficulties (liver swelling, liver cancer, etc.). Liver hepatitis contains a diversity of different types of viruses: hepatitis A, B, C, and D. This study presents the design and implementation of liver hepatitis ontology. Protege-Owl and WEB-V-OWL is used for implementing and its visualization. The proposed ontology is portable and can be edited for further addition of concepts in the future. Web ontology language is used to implement this proposed ontology. By developing the Hepatitis ontology, both Intelligent health care systems and physicians can share, reason, and exploit this knowledge in different ways. © 2022 IEEE.

关键词： Ontology

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Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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