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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer al Duhayyim Mohammed Maray Ayman Qahmash Fatma S.alrayes Nuha alshuqayran Jaber S.alzahrani Mohammed alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

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computers, Materials & Continua 2021年第11期69卷 1927-1944页

作者： Marriam Nawaz Zahid Mehmood Tahira Nazir Momina Masood Usman Tariq Asmaa Mahdi Munshi Awais Mehmood Muhammad Rashid Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia College of Computer Science and Engineering University of JeddahJeddah21577Saudi Arabia Department of Computer Engineering Umm Al-Qura UniversityMakkah21421Saudi Arabia

Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge *** manipulations are performed to hide the desired ***,forgery detection methods are required to identify forged *** have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the *** proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing *** also uses discrete wavelet transform(DWT)for dimension *** obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its ***,Jeffreys and Matusita distance is used for similarity *** the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and *** the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.

关键词： Copy-move forgery discrete wavelet transform LTrP features image forensic circular blocks

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Exploring AI Innovations in Automated software Source Code Generation: Progress, Hurdles, and Future Paths

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第8期48卷 125-136页

作者： Odeh, Ayman Odeh, Nada Software Engineering and Computer Science Department College of Engineering Al Ain University Al Jimi Al Ain United Arab Emirates

In today's dynamic world of software development, the demand for efficient and rapid creation of high-quality code has never been more pronounced. Automated software source code generation (ASSCG) emerges as a compelling solution to meet this demand, offering significant advantages in terms of speed, accuracy, and scalability. This paper aims to explore the critical role of automated software source code generation and its profound significance in modern software development practices. By navigating through the intersection of ASSCG, AI innovations, and the challenges therein, this paper endeavors to provide a comprehensive understanding of this transformative field and pave the way for informed decision-making and advancements in software development practices. This paper delves into the critical role of ASSCG and its transformative impact on modern software development. In this work, we endeavor to delve into the multifaceted landscape of automated code generation, assessing its significance, the transformative potential of AI innovations, and the challenges and objectives inherent in this evolving domain. © 2024 Slovene Society Informatika. all rights reserved.

关键词： Deep learning

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Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization algorithm

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computers, Materials & Continua 2023年第6期75卷 6145-6160页

作者： Fadwa alrowais Jaber S.alzahrani Radwa Marzouk Abdullah Mohamed Gouse Pasha Mohammed Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel *** disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex *** have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black ***,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)*** ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation ***,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality ***,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)*** last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED *** experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

关键词： Economic and emission dispatch multi-objective optimization metaheuristics fuel cost minimization sand cat optimization

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Nonlinear algebraic Equations Solved by an Optimal Splitting-Linearizing Iterative Method

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computer Modeling in engineering & sciences 2023年第5期135卷 1111-1130页

作者： Chein-Shan Liu Essam REl-Zahar Yung-Wei Chen Center of Excellence for Ocean Engineering Center of Excellence for the OceansTaiwan Ocean UniversityKeelung202301Taiwan Department of Mathematics College of Sciences and Humanities in Al-KharjPrince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Basic Engineering Science Faculty of EngineeringMenofia UniversityShebin El-Kom32511Egypt Department of Marine Engineering Taiwan Ocean UniversityKeelung202301Taiwan

How to accelerate the convergence speed and avoid computing the inversion of a Jacobian matrix is important in the solution of nonlinear algebraic equations(NAEs).This paper develops an approach with a splitting-linearizing technique based on the nonlinear term to reduce the effect of the nonlinear *** decompose the nonlinear terms in the NAEs through a splitting parameter and then linearize the NAEs around the values at the previous step to a linear *** the maximal orthogonal projection concept,to minimize a merit function within a selected interval of splitting parameters,the optimal parameters can be quickly *** each step,a linear system is solved by the Gaussian elimination method,and the whole iteration procedure is convergent very *** numerical tests show the high performance of the optimal split-linearization iterative method(OSLIM).

关键词： Nonlinear algebraic equations novel splitting-linearizing technique iterative method maximal projection optimal splitting parameter

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Low-Power Envelope Detector for WSN Wake-up Receiver Applications 1

Low-Power Envelope Detector for WSN Wake-up Receiver Applica...

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1st IEEE International Conference on Design, Test and Technology of Integrated Systems, DTTIS 2023

作者： Bouraoui, Mariem Neifar, Amel Barraj, Imen Masmoudi, Mohamed National Engineers School of Sfax University of Sfax Systems Integration and Emerging Energies Laboratory Electrical Engineering Department Sfax3038 Tunisia University of Gabes National Engineers School of Gabes Tunisia Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Engineering Al-Kharj11942 Saudi Arabia

ISBN: (纸本)9798350316957

This paper highlights the importance of the wake-up receiver in applications using WSN while offering a brief description of this element. In this work, the design of a low-power envelope detector for wake-up receiver applications is presented. By using RF-TSMC CMOS 0.18μm, the proposed envelope circuit is designed for 52 GHz frequency. The forward body biasing method is employed to decrease the circuit energy consumption. The proposed circuit can achieve 152MHz cut-off frequency over the operating frequency range and has power dissipation of 87.5μW under 1V. © 2023 IEEE.

关键词： CMOS integrated circuits

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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Qader Ismail alnajem, Ahmed Abdel Ibrahim, Amer Abdulrahman, Mayasa M. Abdulrazak al-Kubaisi, Abdulrazak A. Tawfeq, Jamal Fadhil Khishe, Mohammad Saeed, ali Baghdad College of Economic Sciences Business Administration Department Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Medical Technical College Department of Medical Instrumentation Technical Engineering Al-Farahidi University Baghdad00965 Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara University Jadara Research Center Jordan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage. © 2024 IEEE.

关键词： Competition

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A Convolutional Neural Network Model for Wheat Crop Disease Prediction

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computers, Materials & Continua 2023年第5期75卷 3867-3882页

作者： Mahmood Ashraf Mohammad Abrar Nauman Qadeer Abdulrahman A.alshdadi Thabit Sabbah Muhammad Attique Khan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Department of Computer Science Bacha Khan University CharsaddaKhyber Pakhtunkhwa24461Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Faculty of Technology and Applied Sciences Al-Quds Open UniversityAl-Bireh1804Palestine Department of Computer Science HITEC UniversityTaxila47080Pakistan

Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major factors that result in low production and negatively affects the national ***,timely detection of wheat diseases is necessary for improving *** CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop ***,these models are computationally expensive and need a large amount of training *** this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases *** high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human *** convolutional layers use 16,32,and 64 *** filter uses a 3×3 kernel *** strides for all convolutional layers are set to *** this research,three different variants of datasets are *** variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed *** extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%*** experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

关键词： Machine learning wheat crop disease prediction convolutional neural network artificial intelligence

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Recent trends in crowd management using deep learning techniques: a systematic literature review

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Journal of Umm al-Qura University for engineering and Architecture 2024年第4期15卷 355-383页

作者： alasmari, Aisha M. Farooqi, Norah S. alotaibi, Youseef A. Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia Dar Al-Hekma University Jeddah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia

Crowd management has become an integral part of urban planning in abnormality in the crowd and predict its future issues. Big data in social media is a rich source for researchers in crowd data analysis. In this systematic literature review (SLR), modern societies. It can organize the flow of the crowd, perform counting, recognize the related works are analyzed, which includes crowd management from both global and local sides (Hajj events—Saudi Arabia) based on deep learning (DL) methods. This survey concerns crowd management research published from 2010 to 2023. It has specified 45 primary studies that accomplish the objectives of the research questions (RQs), namely, investigation of the taxonomies, approaches, and comprehensive studies of crowd management both globally and locally and focusing on the most commonly used techniques of DL. We found both supervised and unsupervised DL techniques have achieved high accuracy, with different strengths and weaknesses for each approach. A lot of these studies discuss aspects of scene analysis of crowds, that are captured by installed cameras in the place. However, there is a dilemma regarding exploiting data provided on social media to use in the crowd analysis domain. Which we believe that the analysis of big data may raise crowd management to the upper level of enhancement. To this end, motivated by the findings of this SLR. The primary purpose of this review is strived to illustrate obstacles and dilemmas in crowd analysis fields to provide a road map for future researchers. Furthermore, it aims to find research gaps existing to focus on it in the future studies. The results indicate that the lack of Hajj research, especially in sentiment analysis and the study of the pilgrims' behavior. © The Author(s) 2024.

关键词： Crowd analysis Crowd behaviors analysis Crowed management Hajj Social media

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