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Light field depth estimation:A comprehensive survey from principles to future

High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Steven, Alvin Suharnawi Alzami, Farrikh Saputra, Filmada Ocky Megantara, Rama Aria Andono, Pulung Nurtantio Universitas Dian Nuswantoro Dept. of Information Systems Indonesia Universitas Dian Nuswantoro Dept. of Information Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Indonesia

ISBN: (纸本)9798350339215

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools & automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance. © 2023 IEEE.

关键词： Crops

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The Effect of Buyer Churn Factors on Buyer’s Loyalty Through Telecommunication Service Providers in Egypt 3rd

The Effect of Buyer Churn Factors on Buyer’s Loyalty Throug...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Mohamed, Mohamed Hegazy Magdi, Dalia Ahmed Business Information Systems Department Helwan University Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences School of Computer Science Canadian International College Cairo Egypt

ISBN: (纸本)9789819947638

This study looked into how loyal Egyptian telecoms customers are affected by factors that contribute to customer churn. Descriptive language is used to describe this. The emails used were selected at random from 1500 different telecom service users who had previously used the services. The surveys were disseminated and self-administered for data collection, with a response rate of 25.6%. The responses were subjected to linear regression analyses. The most recent findings suggest that in order to gain a foothold in Egypt’s telecom sector, service providers ought to focus on improving the management of customer connections. The discoveries uncovered a genuinely critical connection between client stir counteraction components and client devotion to support degrees of consistency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Sales

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Fundamental Components of Microlearning for Sustainable Quality Education: A Systematic Literature Review 8

Fundamental Components of Microlearning for Sustainable Qual...

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8th International Conference on information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Hiererra, Siti Elda Arifin, Yulyani Suprayogi, Muhamad Nanang Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Psychology Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

E-learning was disrupted by a new concept of education named microlearning that carried various digital learning materials in convenient learning paths that were customizable according to users' necessity by using digital application. The competitive advantage of microlearning has been published but caused ubiquitous bias definition. This study aims to identify and analyze fundamental components of existing microlearning research and implementations in e-learning platforms. 42 research articles from SCOPUS database were collected from 2018 to 2022. They were reviewed to discover fundamental components of microlearning that must be considered in its implementations. The result of this study uncovered 'Content's variety' the most fundamental components. In contrast, this study finds that advance technology is the least discussed but required in supporting specific usage of microlearning. Limitation of this study lies on the determination of database and keyword usage and the methodology. This study recommends to consider combination of microlearning components to concept a specialized microlearning for sustainable quality education. © 2023 IEEE.

关键词： E-learning

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Physician Scheduling using Constraint Satisfaction Programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

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computers, Materials & Continua 2022年第8期72卷 2581-2596页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Ibrahim Abunadi Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia

Data mining in the educational field can be used to optimize the teaching and learning performance among the *** recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data *** study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational *** proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification ***,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of ***,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification *** showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning *** simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

关键词： Data mining pattern recognition feature selection data classification SSAE model

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Pseudo Random Number Generators Employing Three Numerical Solvers of Chaotic Generators 11

Pseudo Random Number Generators Employing Three Numerical So...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Yacoub, Mohammed H. Sharobim, Bishoy K. Fetteha, Marwan A. Said, Lobna A. Aslan, Heba K. Nile University Nanoelectronics Integrated Systems Center Giza12588 Egypt Nile University Centre of Informatics Science Faculty of Information Technology and Computer Science Giza12588 Egypt

ISBN: (纸本)9798350369328

Pseudo-Random Number Generator (PRNG) is required for various applications, especially cryptography. PRNGs are employed in symmetric-key algorithms, where a single key is used as a seed to the PRNG to generate a sequence of random numbers that are employed to encrypt and decrypt certain data. This work proposes a PRN G system that employs the time series generated from the numerical solution of systems of chaotic-generators Differential Equations (DEs) utilizing three different DEs solvers;Euler, Runge-Kutta 4th order, and Runge-Kutta 5th order. Various systems were solved using each of the three methods and their randomness was evaluated by several tests. The PRNGs passed the NIST test and reached an entropy of 7.9998, very close to 8 which is the optimal value, proving the robustness of this method for encryption applications. © 2023 IEEE.

关键词： Cryptography

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Proposed Method of Seeding and Constraint in One-Parameter-At-a-Time Approach for t-way Testing

Proposed Method of Seeding and Constraint in One-Parameter-A...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Muazu, Aminu Aminu Hashim, Ahmad Sobri Sarlan, Aliza Maiwada, Umar Danjuma Universiti Teknologi Petronas Faculty of Science and Information Technology Computer & Information Sciences Department Malaysia Umaru Musa Yar'adua University Katsina Faculty of Natural and Applied Science Computer Sciences Department Nigeria

ISBN: (纸本)9798350397000

T-way testing has become a popular way to software testing since it can often deliver good error detection at a low cost, and the test case generation process is one of its biggest drawbacks. One difficulty in this field is the combinatorial explosion problem, which aims to discover the best final test suite that covers all possible combinations of interaction strength. Looking at this limitation, nowadays researchers are focusing more on t-way based strategies. As such, this research examines the present state-of-the-art and proposed an effective combinatorial t-way test case generation strategy that support seeding and constraint support based on One-Parameter-At-a-Time approach, as part of an effort to build an optimized strategy for paired testing. The performance of the proposed approach to achieve a minimal test suite size has been evaluated. The results have undergone statistical analysis using the Wilcoxon Rank and Friedman tests. The suggested approach, however, consistently produces outcomes that are competitive. © 2022 IEEE.

关键词： Software testing

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