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DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

Multimedia Tools and Applications 2023年第28期82卷 44387-44430页

作者： Alsadoon, Abeer Al-Naymat, Ghazi Osman, Ahmed Hamza Alsinglawi, Belal Maabreh, Majdi Islam, Md Rafiqul Bathurst Australia Sydney Australia Sydney Australia College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Computing and Information Technology Rabigh King Abdulaziz University Rabigh Saudi Arabia Department of Information Technology The Hashemite University Zarqa Jordan

The deep learning (DL) classification technique is extensively researched and considered for early lung cancer diagnosis. Despite the encouraging performance reported in the literature, DL models face several challenges to be deployed in real-life systems. These include the DL-Models' stability, the nodule structure's complexity, the lack of proper lung segmentation technique, high false-positive results, and the availability of publically shared medical imaging data. This paper investigates, identifies, and intensively studies DL approaches that yield high performance in the classification of Lung Cancer. We reviewed 338 articles, of which 37 met the inclusion criteria we have set for the proposed framework. In addition, we propose and evaluate a framework to govern the DL model selection and deployment process in real-world systems. The framework consists of four main components;Data, Feature Selection, Classification Technique, and View (DFCV). We discuss the efficiency and the importance of the proposed DFCV framework on 37 state-of-the-art research papers in the field of deep learning-based lung cancer classification systems. The DFCV framework could represent a guide for DL-based systems selection and deployment in medical centers for lung cancer. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Diseases

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Ensemble methods for prediction of Parkinson disease

Ensemble methods for prediction of Parkinson disease

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13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012

作者： Halawani, Sami M. Ahmad, Amir Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

ISBN: (纸本)9783642326387

Parkinson disease is a degenerative disorder of the central nervous system. In the present paper, we study the effectiveness of regression tree ensembles to predict the presence and severity of symptoms from speech datasets. This is a regression problem. Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to convert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we also study a recently developed RvC ensemble method for the prediction of Parkinson disease. Experimental results suggest that the RvC ensembles perform better than a single regression tree. Experiments also suggest that regression tree ensembles created using bagging procedure can be a useful tool for predicting Parkinson disease. The RvC ensembles and regression tree ensembles performed similarly on the dataset. © 2012 Springer-Verlag.

关键词： Decision trees

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Fractional Analysis of Viscous Fluid Flow with Heat and Mass Transfer Over a Flexible Rotating Disk

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Computer Modeling in Engineering & Sciences 2020年第4期123卷 377-400页

作者： Muhammad Shuaib Muhammad Bilal Muhammad Altaf Khan Sharaf J.Malebary Department of Mathematics City University of Science and Information TechnologyPeshawarPakistan Institute for Groundwater Studies Faculty of Natural and Agricultural SciencesUniversity of the Free StateBloemfonteinSouth Africa Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabighSaudia Arabia

An unsteady viscous fluid flow with Dufour and Soret effect,which results in heat and mass transfer due to upward and downward motion of flexible rotating disk,has been *** upward or downward motion of non rotating disk results in two dimensional flow,while the vertical action and rotation of the disk results in three dimensional *** using an appropriate transformation the governing equations are transformed into the system of ordinary differential *** system of ordinary differential equations is further converted into first order differential equation by selecting suitable ***,we generalize the model by using the Caputo *** numerical result for the fractional model is presented and validated with Runge Kutta order 4 method for classical *** compared results are presented in Table and *** is concluded that the fractional model is more realistic than that of classical one,because it simulates the fluid behavior at each fractional value rather than the integral values.

关键词： Caputo derivatives similarity transformation RK4 soret effect Dufour effect

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An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using attention induced deep convolution neural networks

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Scientific Reports 2025年第1期15卷 1-26页

作者： Maghrabi, Louai A. Subahi, Alanoud Alghanmi, Nouf Atiahallah Althaqafi, Turki Abid, Nahla J. Albogami, Nasser N. Ragab, Mahmoud Department of Software Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Rabigh 25732 Saudi Arabia Computer Science Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science College of Computer Science and Engineering Taibah University Madinah Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats’ growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices. Furthermore, combining FL into cybersecurity structures can strengthen attack intelligence models by permitting real upgrades and adaptive learning mechanisms. Recently, ML and Deep Learning (DL) approaches have drawn the study community to advance security solutions for cyberattack defence mechanism models. Conventional ML and DL techniques that function with data kept on a federal server increase the main privacy issues for user information. This manuscript presents a Cyberattack Defence Mechanism System for Federated Learning Framework using Attention Induced Deep Convolution Neural Networks (CDMFL-AIDCNN) technique. The CDMFL-AIDCNN model presents an improved structure incorporating self-guided FL with attack intelligence to improve defence mechanisms across varied cybersecurity applications in distributed systems. Initially, the data preprocessing stage utilizes Z-score normalization to transform input data into a beneficial format. The Dung Beetle Optimization (DBO) technique is used in the feature selection process to identify the most relevant and non-redundant features. Furthermore, the fusion of convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A) models are employed to classify cyberattack defence mechanisms. Finally, the parameter tuning of the CBLG-A appro

关键词： Cyberattack defence Cybersecurity Deep convolution neural network Federated learning Growth optimizer

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Quality-Aware swarm-based optimal multi-user video streaming rate capacity in congested wireless networks 17

Quality-Aware swarm-based optimal multi-user video streaming...

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2017 International Conference on Telecommunications and Communication Engineering, ICTCE 2017

作者： Baguda, Yakubu S Information System Department Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

ISBN: (纸本)9781450353151

Due to alarming increase in video traffic over wireless network, there is a dramatic need to optimally support real-Time video applications in order to provide the quality-of-service (QoS) needed from different users and applications with different users requirement. A low complexity quality-Aware swarm-based mathematical model has been formulated and developed which is aware of the time-varying channel condition to support video streaming. In this paper, a quality-Aware swarm-based scheme is proposed to solve the video streaming rate optimization problem in a congested wireless network and it primarily uses the fundamental principle of particle swarm optimization (PSO). The optimal streaming rate scheme is used to dynamically adjust the resources based on the quality-Aware algorithm to provide the necessary QoS among users. The proposed swarm-based optimal video streaming rate controlled algorithm outperforms rate distortion optimization (RDO) and traditional (conventional) approaches in terms of perceived video quality and ultimately it has been constantly maintained. The performance of the developed scheme has been verified using experimental simulation and its highly effective for streaming rate control application in congested wireless networks. © 2017 Association for computing Machinery.

关键词： Video streaming

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Measuring business entities in multi-ontologies

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International Journal of Electronic Business 2017年第4期13卷 342-358页

作者： Bramantoro, Arif Alshammari, Reem K. Almagrabi, Alaa Omran Faculty of Computing and Information Technology King Abdulaziz University Rabigh21911 Saudi Arabia National Satellite Technology Center King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

One of the most important tasks for business entity is to make its service available for users' requests through the web. The semantic description of business entity needs to be developed to enhance the matching process between users' requests and the best advertised services. The modern business entities typically have semantic relations represented in multi-inheritance ontologies. This paper argues that the relation between business entities in multi-ontologies can be measured to provide better matching results based on the similarity between the requested and advertised business entities within all *** propose a business entity distance measure that represents a quantitative result. During the evaluation, a matching engine was developed based on web service modelling ontology technologies and validated by using business *** established that business entity distance measure is suitable for finding various degrees of matching between requested and advertised business entities. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Ontology

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Allocation and Migration of Virtual Machines Using Machine Learning

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Computers, Materials & Continua 2022年第2期70卷 3349-3364页

作者： Suruchi Talwani Khaled Alhazmi Jimmy Singla Hasan JAlyamani Ali Kashif Bashir School of Computer Science&Engg. Lovely Professional UniversityPunjabIndia National Center for Robotics and Internet of Things Technology Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computing and Mathematics ManchesterMetropolitan UniversityManchesterM156BHUK

Cloud computing promises the advent of a new era of service boosted by means of virtualization *** process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application *** extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to *** whole process is very challenging not only in terms of computation but also in terms of energy and *** research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data *** Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial *** most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto *** simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

关键词： Cloud computing VM allocation VM migration machine learning

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Machine repositioning for DMG analysis based on the maintenance cost

Machine repositioning for DMG analysis based on the maintena...

引用

International Conference on Computational Intelligence, Modelling and Simulation

作者： Halawani, Sami M. Burhanuddin, M.A. Faculty of Computing and Information Technology King Abdul Aziz University Rabigh Saudi Arabia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Malaysia

ISBN: (纸本)9780769542621

Recent economic depression worldwide demand every department in industries to develop better control on their production cost, hence increasing company profit margin. Therefore, it is vital for maintenance department as a support team in the industries to acquire all maintenance data, to process information instantaneously, and subsequently to transform it into a useful decision. This study presents a methodology to utilize available information from failure-based maintenance. The goal of the study is to show the importance of costing analysis by describing some observation in small and medium industries. This paper demonstrates how costing analysis is conducted before fitting other variables in Decision Making Grid model. This model gives better result to categorize the machines, reduce cost and boost the earning for the manufacturing plant. The case study and experimental result shows the cost analysis in Decision Making Grid model give more promising result for real production floor in SMI. The improvement of Decision Making Grid model for decision analysis with costing analysis is recommended as useful function in Computerized Maintenance Management System. © 2010 IEEE.

关键词： Decision making

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QH2O: Energy Efficient Task Scheduling Using Quasi Reflected Harris Hawks Algorithm in Fog Environment 9th

QH2O: Energy Efficient Task Scheduling Using Quasi Reflected...

引用

9th International Conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Barik, Lalbihari Patra, Sudhansu Shekhar Mukherjee, Pratyusa Mohanty, Jnyana Ranjan Barik, Rabindra Kumar Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9789811666155

The volume of the data is increasing exponentially due to the enourmous growth of Industrial Internet of Things (IIoT), digitization with advancement of wireless technologies. Cloud computing was evolved as a technological solution for handling and processing voluminous data. With cloud computing, the next technological advancement known as fog computing is an emerging technology that helps manage a massive volume of IoTs data by delivering their services as well as applications toward nearer to the network edge. There is a rise concern for improving the Quality of Services (QoS) provided to the users all through cloud computing infrastructure. Transferring and communicating all the desired data to the cloud layer and moving back from the cloud layer affect the high latency as well as necessitate high amount of network bandwidth. To consider this situation, we considered the fog computing strategies which are assisting the cloud infrastructure for IIoT. In specific IIoT applications/services, there are sufficient amount of energy needed in the fog computing layer, which is one of the urging area for handling energy minimization scheme by tmany cloud computing service providers. Apart from energy minimization schme, Task scheduling is also crucial factor that adds toward the energy consumptions in fog computing servers. The present research paper proposed an energy-saving task scheduling algorithm based on a meta-heuristic named quasi reflected Harris Hawks optimization technique to achieve energy conservation by attaining the QoS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Modified Pixel Value Ordering-based Predictor for Reversible Data Hiding on Video 9

Modified Pixel Value Ordering-based Predictor for Reversible...

引用

9th International Conference on information and Communication technology, ICoICT 2021

作者： Ahmad, Tohari Fatman, Alek Nur Hoirul Basori, Ahmad Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Makkah Rabigh Saudi Arabia

ISBN: (纸本)9781665404471

Communication security has become an issue where massive data exchanges take place. Hiding personal data in a particular file during transmission can be a possible way to protect those data. Nevertheless, some parameters, such as stego file quality and the data capacity, are still challenging. In this paper, a method that modified Pixel-based Pixel Value Ordering (PPVO) Predictor is proposed for reversible data hiding on video. PPVO can embed the payload into a pixel whose value equals the maximum or minimum context pixel. To increase the number of payloads embedded, we modify the PPVO method by using only both right and bottom pixels to develop the predictors. The embedding process is carried out if the considered pixel has the same value as that in the right or bottom. The experimental result shows that this method has a significant increase in the embedding capacity and quality. Nevertheless, the overall maximum capacity of each cover is less than some other methods, while its corresponding quality is still the highest. © 2021 IEEE.

关键词： Pixels

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