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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Temporal Preferences-Based Utility control for Smart Homes

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Intelligent Automation & Soft computing 2023年第5期36卷 1699-1714页

作者： Salman Naseer Raheela Saleem Muhammad Mudasar Ghafoor Shahzada Khurram Shafiq Ahmad Abdelaty Edrees Sayed Muhammad Shafiq Jin-Ghoo Choi Department of Information Technology University of the PunjabGujranwala Campus52250Pakistan Department of Computer Science Gift UniversityGujranwalaPunjab52250 Pakistan University of the Punjab Jehlum CampusJehlum49600Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The residential sector contributes a large part of the energy to the global energy *** date,housing demand has mostly been uncontrollable and inelastic to grid *** the performance of a home energy manage-ment system requires the creation of various proﬁles of real-world residential demand,as residential demand is complex and includes multiple factors such as occupancy,climate,user preferences,and appliance *** Peak Ratio(A2P)is one of the most important parameters when managing an efﬁcient and cost-effective energy *** the household level,the larger relative magni-tudes of certain energy devices make managing this ratio critical,albeit difﬁ*** Demand Response(DR)and Demand Side Management(DSM)systems have been proposed to reduce this ratio to *** main ways to achieve this are economic incentives,user comfort modeling and control,or *** this study,we propose a unique opportunistic social time approach called the Time Utility Based control Feature(TUBCF),which uses the concept of a utility function from economics to model and control consumer *** propose a DR model for residential customers to reduce Peak-to-Average Ratio(PAR)and improve customer satisfaction by eliminating Appliance Wait Time(WTA)during peak *** PAR reduction and WTA,we propose a system architecture and mathematical *** proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational *** results show that using this strategy can reduce A2P by 80%and improve user comfort during peak hours.

关键词： Temporal preferences utility control ﬁxed priority demand shaping

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer Systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

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computers, Materials & Continua 2022年第2期70卷 2525-2542页

作者： Kamred Udham Singh Hatem Salem Abu-Hamatta Abhishek Kumar Achintya Singhal Mamoon Rashid A.K.Bashir Department of Computer Science and Information Engineering National Cheng Kung UniversityTainanTaiwan Department of Applied Sciences Aqaba University CollegeAl Balqa Applied UniversityAqabaJordan School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia Department of Computer Science Banaras Hindu UniversityVaranasiIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPune411048India Department of Computing and Mathematics ManchesterMetropolitan UniversityUK

Teleradiology plays a vital role in the medical field,which permits transmitting medical and imaging data over a communication *** ensures data reliability and provides convenient communication for clinical interpretation and diagnostic *** transmission of this medical data over a network raises the problems of legal,ethical issues,privacy,and copyright *** copyright protection of medical images is a significant issue in the medical *** schemes are used to address these issues.A gray-level or binary image is used as a watermark frequently in color image watermarking *** this paper,the authors propose a novel non-blind medical image watermarking scheme based on 2-D LiftingWavelet Transform(LWT),Multiresolution Singular Value Decomposition(MSVD),and LU factorization to improve the robustness and authenticity of medical *** this scheme,multiple color watermarks are embedded into the colored DICOM(Digital Imaging and Communications inMedicine)images obtained from Color Doppler images(DICOM format),and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio(PSNR),37.46 db for Signal-to-Noise Ratio(SNR),0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing *** results make our watermarking technique an ideal candidate for medical image watermarking.

关键词： Digital imaging medicine multiresolution medical image watermarking

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Design of adaptive controllers by means of PPO algorithm using MATLAB

Design of adaptive controllers by means of PPO algorithm usi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Veljko Radojičić Milan R. Rapaić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper investigates reinforcement learning (RL) as a practical framework for achieving optimal adaptive control across several simple dynamical system models. All experiments were conducted using the Proximal Policy Optimization (PPO) algorithm, implemented within the MATLAB Reinforcement Learning Toolbox. The primary focus of this study is to explore how the learning process can be empirically designed to sufficiently excite the system dynamics and obtain a sufficiently robust controller.

关键词： Adaptation models System dynamics Heuristic algorithms Process control Reinforcement learning MATLAB Dynamical systems Adaptive control Optimization

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Optimal control of Systems with Fractional Dynamics and Algebraic Constraints

Optimal Control of Systems with Fractional Dynamics and Alge...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Nikolina Živanović Zoran D. Jeličić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study investigates optimal control problems described by fractional differential equations, with the control vector components subject to algebraic constraints. Two case studies are analyzed: an illustrative example designed to test the algorithm’s effectiveness and a biologically significant model of acute myeloid leukemia (AML), which applies optimal control theory to design therapeutic strategies under medical constraints. The methodology transforms fractional differential equations into equivalent ordinary differential equations using the framework of Atanacković and Stanković. L.S. Pontryagin’s maximum principle is then applied to derive control functions that satisfy the algebraic constraints, followed by numerical optimization of the Hamiltonian function. We formally parsed the problem of dynamic optimization into a static optimization problem using the Forward-Backward Sweep Method (FBSM). The approach is validated through comprehensive numerical simulations, demonstrating its robustness and applicability to real-world problems.

关键词： Systematics Refining Optimal control Leukemia Transforms Mathematical models Robustness Vectors Reproducibility of results Optimization

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Desertification Detection in Makkah Region based on Aerial Images Classification

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computer Systems Science & engineering 2022年第2期40卷 607-618页

作者： Yahia Said Mohammad Barr Taoufik Saidani Mohamed Atri Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia Department of Computer Sciences Faculty of Computing&Information TechnologyNorthern Border UniversityRafhaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi *** is one of the most important cities in Saudi Arabia that needs to be protected from *** vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate *** damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation *** this paper,we propose an automatic desertification detection system based on Deep Learning *** images are classified using Convolutional Neural Networks(CNN)to detect land state variation in *** have been widely used for computer vision applications,such as image classification,image segmentation,and quality *** proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded *** has achieved high efficiency with 96.47% *** light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

关键词： Desertification detection deep learning convolutional neural networks(CNN) aerial images classification Makkah region

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Evaluating Extended Kalman Filter and Unscented Kalman Filter for accurate trajectory estimation

Evaluating Extended Kalman Filter and Unscented Kalman Filte...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Anastasija Golić Mirna Radović Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper evaluates two common methods for trajectory estimation: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF). The EKF and UKF are well-established recursive filtering techniques commonly used for nonlinear state *** performance of these methods is compared through their implementation on a standard trajectory estimation problem, with a focus on assessing their accuracy.

关键词： Accuracy Linear approximation Trajectory Computational efficiency Kalman filters Reliability Nonlinear systems State estimation Tuning Standards

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Modification of the PSO Algorithm: Variable Population Size and Hybridization with the Nelder-Mead Method

Modification of the PSO Algorithm: Variable Population Size ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Stefan Topalov Mirna Radović Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm’s speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources.

关键词： Heuristic algorithms Stochastic processes Switches Linear programming Robustness Computational efficiency Optimization Standards Tuning Convergence

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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