Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- China Simulation Federation (3880)
- TÜBİTAK (3106)
- Wright State University (1960)
- Old Dominion University (937)
- Singapore Management University (884)
-
- Washington University in St. Louis (730)
- Embry-Riddle Aeronautical University (590)
- Air Force Institute of Technology (443)
- Missouri University of Science and Technology (440)
- Neutrosophic Systems with Applications (375)
- University of Nebraska - Lincoln (274)
- Chulalongkorn University (235)
- University of Central Florida (188)
- Portland State University (158)
- University of Nevada, Las Vegas (152)
- University of Arkansas, Fayetteville (149)
- University of South Carolina (125)
- Purdue University (118)
- Chapman University (114)
- University for Business and Technology in Kosovo (114)
- University of Kentucky (111)
- University of South Florida (104)
- Technological University Dublin (100)
- California Polytechnic State University, San Luis Obispo (79)
- University of New Haven (77)
- University of Dar es Salaam (74)
- New Jersey Institute of Technology (63)
- Michigan Technological University (61)
- University of Texas at El Paso (59)
- University of Malaya (56)
- Keyword
-
- Machine learning (392)
- Computer Science (351)
- Deep learning (301)
- Department of Computer Science and Engineering (285)
- Engineering (253)
-
- Simulation (216)
- Machine Learning (186)
- Artificial intelligence (169)
- Optimization (164)
- Technical writing (158)
- College of Engineering and Computer Science (157)
- Newsletters (157)
- Science news (157)
- Classification (130)
- Genetic algorithm (120)
- Cybersecurity (115)
- Security (108)
- Deep Learning (102)
- Reinforcement learning (102)
- Computer Engineering (101)
- Computer vision (101)
- Neural networks (99)
- Artificial Intelligence (92)
- Path planning (91)
- Particle swarm optimization (88)
- Algorithms (85)
- Clustering (79)
- Image processing (78)
- Robotics (78)
- Virtual reality (77)
- Publication Year
- Publication
-
- Journal of System Simulation (3880)
- Turkish Journal of Electrical Engineering and Computer Sciences (3106)
- Computer Science & Engineering Syllabi (1312)
- Research Collection School Of Computing and Information Systems (857)
- All Computer Science and Engineering Research (683)
-
- Theses and Dissertations (503)
- Neutrosophic Systems with Applications (375)
- Browse all Theses and Dissertations (308)
- Journal of Digital Forensics, Security and Law (300)
- Electrical and Computer Engineering Faculty Research & Creative Works (282)
- Electronic Theses and Dissertations (249)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (235)
- Electrical & Computer Engineering Theses & Dissertations (217)
- Computer Science and Engineering Faculty Publications (184)
- Annual ADFSL Conference on Digital Forensics, Security and Law (174)
- BITs and PCs Newsletter (157)
- School of Computing: Dissertations, Theses, and Student Research (154)
- Faculty Publications (150)
- Electrical & Computer Engineering Faculty Publications (137)
- Dissertations (121)
- Computer Science Faculty Publications (107)
- USF Tampa Graduate Theses and Dissertations (94)
- Computer Science Faculty Publications and Presentations (83)
- Engineering Faculty Articles and Research (81)
- UBT International Conference (73)
- Electrical & Computer Engineering and Computer Science Faculty Publications (71)
- Graduate Theses and Dissertations (69)
- Tanzania Journal of Engineering and Technology (TJET) (69)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (64)
- Doctoral Dissertations (59)
- Publication Type
Articles 1921 - 1950 of 17309
Full-Text Articles in Computer Sciences
Design And Application Of Hardware-In-The-Loop Simulation System For Infrared Imaging Guide Missile Test And Evaluation, Jianbin Dou, Xiaobing Wang, Hongjian Yang, Yulong Gao
Design And Application Of Hardware-In-The-Loop Simulation System For Infrared Imaging Guide Missile Test And Evaluation, Jianbin Dou, Xiaobing Wang, Hongjian Yang, Yulong Gao
Journal of System Simulation
Abstract: The characteristics of the army's conventional infrared imaging guide missile hardware-in-theloop simulation system used for test and evaluation is analyzed, and a system that can meet the test and evaluation requirement of infrared imaging guide missile is designed. The key technologies such as universal system design, rapid iterative development design of heterogeneous communication data, high radiation infrared interference dual channel coupling simulation, high-precision timing and synchronization, generation of complex battlefield environment are solved. The application of the system is verified on the typical anti-tank missile and helicopter borne missile tests. The system can be used for infrared imaging guide …
Design Of 3d Visualization Monitoring System For Oil Field Pumping Unit Based On Unity3d, Liqiang Liu, Wenlei Sun, Yi Wang, Bingkai Wang
Design Of 3d Visualization Monitoring System For Oil Field Pumping Unit Based On Unity3d, Liqiang Liu, Wenlei Sun, Yi Wang, Bingkai Wang
Journal of System Simulation
Abstract: Aiming at the defects of single monitoring form, low degree of 3D visualization and poor linkage of pumping unit, the information-physical real-time mapping is introduced to develop the 3D visualization monitoring system for oil field pumping unit. The digital space of pumping unit are designed and the design architecture of virtual-real interaction layer is built. The composition and relation framework of 3D visualization system are built. Combined with the inverse kinematics, the mathematical model is built, and the overall architecture of the pumping unit 3D visualization system is designed based on the five-dimensional model and Unity3D platform. The beam …
Using Natural Language Processing To Identify Mental Health Indicators In Aviation Voluntary Safety Reports, Michael Sawyer, Katherine Berry, Amelia Kinsella, R Jordan Hinson, Edward Bynum
Using Natural Language Processing To Identify Mental Health Indicators In Aviation Voluntary Safety Reports, Michael Sawyer, Katherine Berry, Amelia Kinsella, R Jordan Hinson, Edward Bynum
National Training Aircraft Symposium (NTAS)
Voluntary Safety Reporting Programs (VSRPs) are a critical tool in the aviation industry for monitoring safety issues observed by the frontline workforce. While VSRPs primarily focus on operational safety, report narratives often describe factors such as fatigue, workload, culture, staffing, and health, directly or indirectly impacting mental health. These reports can provide individual and organizational insights into aviation personnel's physical and psychological well-being. This poster introduces the AVIation Analytic Neural network for Safety events (AVIAN-S) model as a potential tool to extract and monitor these insights. AVIAN-S is a novel machine-learning model that leverages natural language processing (NLP) to analyze …
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Theses and Dissertations
Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …
Mmwave Rat Optimization: Mac Layer Initial Access Design And Transport Layer Integration, Suresh Srinivasan
Mmwave Rat Optimization: Mac Layer Initial Access Design And Transport Layer Integration, Suresh Srinivasan
Dissertations and Theses
MmWave Radio Access Technology (RAT) is a promising technology for wireless communication due its large bandwidth and is already being deployed in 5G cellular and emerging WiFi technologies. MmWave systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. A mmWave radio can be used either in a standalone mode (where all radios use the same technology) or simultaneously with other technologies such as LTE and low frequency WiFi in a communication mode commonly referred to as integrated mode. This thesis proposes two methods to optimize mmWave RAT performance in both …
Fractional Delay-Dependent Load Frequency Controller Design For A Single-Area Power System With Communication Delay, Erhan Yumuk
Fractional Delay-Dependent Load Frequency Controller Design For A Single-Area Power System With Communication Delay, Erhan Yumuk
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a fractional delay-dependent load frequency control design approach for a single-area power system with communication delay based on gain and phase margin specifications. In this approach, the closed-loop reference transfer function relies on the delayed Bode’s transfer function. The gain and phase margin specifications are established in order to optimize the reference model based on three time-domain performance indices. Here, a category of fractional-order model is employed to describe the single-area power system incorporating communication delay. The controller parameters are determined using the fractional-order system model and optimal closed-loop reference model. Then, a delay-dependent control mechanism is …
Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu
Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation …
Artificial Intelligence-Based Evaluation Of The Factors Affecting The Sales Of An Iron And Steel Company, Mehmet Pekkaya, Zafer Uysal, Aytaç Altan, Seçkin Karasu
Artificial Intelligence-Based Evaluation Of The Factors Affecting The Sales Of An Iron And Steel Company, Mehmet Pekkaya, Zafer Uysal, Aytaç Altan, Seçkin Karasu
Turkish Journal of Electrical Engineering and Computer Sciences
It is important to predict the sales of an iron and steel company and to identify the variables that influence these sales for future planning. The aim in this study was to identify and model the key factors that influence the sales volume of an iron and steel company using artificial neural networks (ANNs). We attempted to obtain an integrated result from the performance/sales levels of 5 models, to use the ANN approach with hybrid algorithms, and also to present an exemplary application in the base metals industry, where there is a limited number of studies. This study contributes to …
Machine Learning Approaches In Comparative Studies For Alzheimer’S Diagnosis Using 2d Mri Slices, Zhen Zhao, Joon Huang Chuah, Chee-Onn Chow, Kaijian Xia, Yee Kai Tee, Yan Chai Hum, Khin Wee Lai
Machine Learning Approaches In Comparative Studies For Alzheimer’S Diagnosis Using 2d Mri Slices, Zhen Zhao, Joon Huang Chuah, Chee-Onn Chow, Kaijian Xia, Yee Kai Tee, Yan Chai Hum, Khin Wee Lai
Turkish Journal of Electrical Engineering and Computer Sciences
Alzheimer’s disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented …
A Novel Extended Reaction Force/Torque Observer With Impedance Control, İlkay Turaç Özçeli̇k, Abdurrahman Eray Baran
A Novel Extended Reaction Force/Torque Observer With Impedance Control, İlkay Turaç Özçeli̇k, Abdurrahman Eray Baran
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new extended version of the reaction force observer (RFOB) for high-precision motion control systems. The RFOB has been proven to be useful for many applications in the literature. However, because of the low-pass filter present inside of the RFOB, it has certain limitations. In this study, a new algorithm is proposed to compensate for filtering-based errors in the classical RFOB structure. The algorithm includes the differentiation of the observed force and scaling with a proper value. However, since the force has a noisy nature, differentiation also affects the signal’s stability and performance. To resolve this issue, …
Longitudinal Attacks Against Iterative Data Collection With Local Differential Privacy, Mehmet Emre Gürsoy
Longitudinal Attacks Against Iterative Data Collection With Local Differential Privacy, Mehmet Emre Gürsoy
Turkish Journal of Electrical Engineering and Computer Sciences
Local differential privacy (LDP) has recently emerged as an accepted standard for privacy-preserving collection of users’ data from smartphones and IoT devices. In many practical scenarios, users’ data needs to be collected repeatedly across multiple iterations. In such cases, although each collection satisfies LDP individually by itself, a longitudinal collection of multiple responses from the same user degrades that user’s privacy. To demonstrate this claim, in this paper, we propose longitudinal attacks against iterative data collection with LDP. We formulate a general Bayesian adversary model, and then individually show the application of this adversary model on six popular LDP protocols: …
Traffic Signal Optimization Using Multiobjective Linear Programming For Oversaturated Traffic Conditions, Mustafa Murat Coşkun, Cevat Şener, İsmail Hakkı Toroslu
Traffic Signal Optimization Using Multiobjective Linear Programming For Oversaturated Traffic Conditions, Mustafa Murat Coşkun, Cevat Şener, İsmail Hakkı Toroslu
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we present a framework designed to optimize signals at intersections experiencing oversaturated traffic conditions, utilizing mixed-integer linear programming (MILP) techniques. The proposed MILP solutions were developed with different objective functions, namely a reduction in the total remaining queue and fair distribution of the remaining queue after each signal cycle. Our framework contains two distinct stages. The initial stage applies two distinct MILP methodologies, while the subsequent stage employs a neighborhood search method to further reduce the delays associated with the green signal timings derived from the first stage. Ultimately, to evaluate their effectiveness across various intersections, we …
Error Performance Enhancement And Complexity Reduction In Ofdm Systems Via Coordinate Interleaving Under Practical Impairments, Mustafa Anıl Reşat, Armed Tusha, Seda Doğan Tusha, Serdar Özyurt, Hüseyin Arslan
Error Performance Enhancement And Complexity Reduction In Ofdm Systems Via Coordinate Interleaving Under Practical Impairments, Mustafa Anıl Reşat, Armed Tusha, Seda Doğan Tusha, Serdar Özyurt, Hüseyin Arslan
Turkish Journal of Electrical Engineering and Computer Sciences
In this work, subcarrier coordinate interleaving (CI) is implemented to orthogonal frequency division multiplexing (OFDM) systems with the aim of both enhancing the error performance and reducing the implementation complexity. To this end, the modulated symbols are independently chosen from a modified M-ary amplitude-shift keying signal constellation under a specific CI strategy. In addition to doubling the diversity level of the original OFDM scheme, the adopted CI approach also drastically reduces the inverse fast Fourier transform (IFFT) size at the transmit side by guaranteeing the first half of the input vector to be identical with the second half at the …
Fast Grid Search: A Grid Search-Inspired Algorithm For Optimizing Hyperparameters Of Support Vector Regression, Mustafa Açikkar
Fast Grid Search: A Grid Search-Inspired Algorithm For Optimizing Hyperparameters Of Support Vector Regression, Mustafa Açikkar
Turkish Journal of Electrical Engineering and Computer Sciences
This study presents a fast hyperparameter optimization algorithm based on the benefits and shortcomings of the standard grid search (GS) algorithm for support vector regression (SVR). This presented GS-inspired algorithm, called fast grid search (FGS), was tested on benchmark datasets, and the impact of FGS on prediction accuracy was primarily compared with the GS algorithm on which it is based. To validate the efficacy of the proposed algorithm and conduct a comprehensive comparison, two additional hyperparameter optimization techniques, namely particle swarm optimization and Bayesian optimization, were also employed in the development of models on the given datasets. The evaluation of …
Grey Wolf Optimization Algorithm-Based Robust Neural Learning Control Of Passive Torque Simulators With Predetermined Performance, Seyyed Amirhossein Saadat, Mohammad Mehdi Fateh, Javad Keighobadi
Grey Wolf Optimization Algorithm-Based Robust Neural Learning Control Of Passive Torque Simulators With Predetermined Performance, Seyyed Amirhossein Saadat, Mohammad Mehdi Fateh, Javad Keighobadi
Turkish Journal of Electrical Engineering and Computer Sciences
In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is efficient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown …
Milp Modeling Of Matrix Multiplication: Cryptanalysis Of Klein And Prince, Murat Burhan İlter, Ali Aydın Selçuk
Milp Modeling Of Matrix Multiplication: Cryptanalysis Of Klein And Prince, Murat Burhan İlter, Ali Aydın Selçuk
Turkish Journal of Electrical Engineering and Computer Sciences
Mixed-integer linear programming (MILP) techniques are widely used in cryptanalysis, aiding in the discovery of optimal linear and differential characteristics. This paper delves into the analysis of block ciphers KLEIN and PRINCE using MILP, specifically calculating the best linear and differential characteristics for reduced-round versions. Both ciphers employ matrix multiplication in their diffusion layers, which we model using multiple XOR operations. To this end, we propose two novel MILP models for multiple XOR operations, which use fewer variables and constraints, proving to be more efficient than standard methods for XOR modeling. For differential cryptanalysis, we identify characteristics with a probability …
Differentially Private Online Bayesian Estimation With Adaptive Truncation, Sinan Yildirim
Differentially Private Online Bayesian Estimation With Adaptive Truncation, Sinan Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel online and adaptive truncation method is proposed for differentially private Bayesian online estimation of a static parameter regarding a population. A local differential privacy setting is assumed where sensitive information from individuals is collected on an individual level and sequentially. The inferential aim is to estimate, on the fly, a static parameter regarding the population to which those individuals belong. We propose sequential Monte Carlo to perform online Bayesian estimation. When individuals provide sensitive information in response to a query, it is necessary to corrupt it with privacy-preserving noise to ensure the privacy of those …
Automated Identification Of Vehicles In Very High-Resolution Uav Orthomosaics Using Yolov7 Deep Learning Model, Esra Yildirim, Umut Güneş Seferci̇k, Taşkın Kavzoğlu
Automated Identification Of Vehicles In Very High-Resolution Uav Orthomosaics Using Yolov7 Deep Learning Model, Esra Yildirim, Umut Güneş Seferci̇k, Taşkın Kavzoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the …
Broadening Participation Of Teachers In Computing: Examining Postsecondary Educational Experiences And Prospective Educators’ Cs Teaching Interests, Robert Schwarzhaupt, Alexsandra Galanis, Joanna Goode, Kate Blanchard, Jill Bowdon, Joseph P. Wilson
Broadening Participation Of Teachers In Computing: Examining Postsecondary Educational Experiences And Prospective Educators’ Cs Teaching Interests, Robert Schwarzhaupt, Alexsandra Galanis, Joanna Goode, Kate Blanchard, Jill Bowdon, Joseph P. Wilson
Journal of Computer Science Integration
Teacher shortages in K–12 computer science (CS) education negatively impact students’ access to CS courses, exposure to CS concepts, and interest in CS-related careers. To address CS teacher shortages, this study seeks to understand factors related to expressing a preference to teach CS among prospective teachers. The study team analyzed data from 27,700 prospective teacher applications accepted into the 2016–2020 Teach For America (TFA) corps (cohorts). The TFA corps is an alternative teacher development program that recruits and prepares participants to obtain their teaching certification while they work for at least two years in underserved communities on a temporary teaching …
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Research Collection School Of Computing and Information Systems
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problems. The deep reinforcement learning (DRL) based methods that learn useful patterns from massive data demonstrate notable advantages. However, their decision space will become prohibitively huge as the problem scales up, thus deteriorating the computation efficiency. To alleviate this issue, we propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework …
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Research Collection School Of Computing and Information Systems
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …
The Aim To Decentralize Economic Systems With Blockchains And Crypto, Mary Lacity
The Aim To Decentralize Economic Systems With Blockchains And Crypto, Mary Lacity
Arkansas Law Review
As an information systems (“IS”) professor, I wrote this Article for legal professionals new to blockchains and crypto. This target audience likely is most interested in crypto for its legal implications—depending on whether it functions as currencies, securities, commodities, or properties; however, legal professionals also need to understand crypto’s origin, how transactions work, and how they are governed.
Natural Language Processing Analysis Of Online Reviews For Small Business: Extracting Insight From Small Corpora, Benjamin J. Mccloskey, Phillip M. Lacasse, Bruce A. Cox
Natural Language Processing Analysis Of Online Reviews For Small Business: Extracting Insight From Small Corpora, Benjamin J. Mccloskey, Phillip M. Lacasse, Bruce A. Cox
Faculty Publications
Receiving and acting on customer input is essential to sustaining and growing any service organization, particularly a small family business whose livelihood depends on strong relationships with its customers. The competitive advantage offered by advanced analytical approaches for supporting decisions is not trivial, and enterprises across virtually all domains of society are investing heavily in this emerging discipline. Natural Language Processing (NLP) is a subset of computer science that employs computational approaches to analyze human language; it is effective at extracting insight from text data but frequently requires large corpora to train its models, in the scale of thousands or …
A Multi-Criteria Decision Making Model For Sustainable And Resilient Supplier Selection And Management, Samah Ibrahim Abdel Aal
A Multi-Criteria Decision Making Model For Sustainable And Resilient Supplier Selection And Management, Samah Ibrahim Abdel Aal
Neutrosophic Systems with Applications
Sustainable and resilient supplier selection and management are essential to building environmentally responsible and resilient supply chains. Selecting sustainable and resilient suppliers enables organizations to ensure the long-term viability of the supply chain. To do that, there is a need to identify suitable suppliers that align with sustainability and resilience goals. This study aims to provide an overview of the requirements and criteria for selecting and managing sustainable and resilient suppliers and emphasizes the significance of integrating sustainability and resilience principles throughout the supply chain. Multi-criteria decision-making (MCDM) is used to deal with various criteria. The double normalization-based multi-aggregation (DNMA) …
Foundation Of Appurtenance And Inclusion Equations For Constructing The Operations Of Neutrosophic Numbers Needed In Neutrosophic Statistics, Florentin Smarandache
Foundation Of Appurtenance And Inclusion Equations For Constructing The Operations Of Neutrosophic Numbers Needed In Neutrosophic Statistics, Florentin Smarandache
Neutrosophic Systems with Applications
We introduce for the first time the appurtenance equation and inclusion equation, which help in understanding the operations with neutrosophic numbers within the frame of neutrosophic statistics. The way of solving them resembles the equations whose coefficients are sets (not single numbers).
A Multi-Criteria Decision Making Model For Sustainable And Resilient Supplier Selection And Management, Samah Ibrahim Abdel Aal
A Multi-Criteria Decision Making Model For Sustainable And Resilient Supplier Selection And Management, Samah Ibrahim Abdel Aal
Neutrosophic Systems with Applications
Sustainable and resilient supplier selection and management are essential to building environmentally responsible and resilient supply chains. Selecting sustainable and resilient suppliers enables organizations to ensure the long-term viability of the supply chain. To do that, there is a need to identify suitable suppliers that align with sustainability and resilience goals. This study aims to provide an overview of the requirements and criteria for selecting and managing sustainable and resilient suppliers and emphasizes the significance of integrating sustainability and resilience principles throughout the supply chain. Multi-criteria decision-making (MCDM) is used to deal with various criteria. The double normalization-based multi-aggregation (DNMA) …
Foundation Of Appurtenance And Inclusion Equations For Constructing The Operations Of Neutrosophic Numbers Needed In Neutrosophic Statistics, Florentin Smarandache
Foundation Of Appurtenance And Inclusion Equations For Constructing The Operations Of Neutrosophic Numbers Needed In Neutrosophic Statistics, Florentin Smarandache
Neutrosophic Systems with Applications
We introduce for the first time the appurtenance equation and inclusion equation, which help in understanding the operations with neutrosophic numbers within the frame of neutrosophic statistics. The way of solving them resembles the equations whose coefficients are sets (not single numbers).
Unveiling Efficiency: Investigating Distance Measures In Wastewater Treatment Using Interval-Valued Neutrosophic Fuzzy Soft Set, Muhammad Saeed, Kinza Kareem, Fatima Razzaq, Muhammad Saqlain
Unveiling Efficiency: Investigating Distance Measures In Wastewater Treatment Using Interval-Valued Neutrosophic Fuzzy Soft Set, Muhammad Saeed, Kinza Kareem, Fatima Razzaq, Muhammad Saqlain
Neutrosophic Systems with Applications
To reduce the threats that wastewater poses to human health and the environment, water treatment techniques must be improved. The use of a procedure that includes preparation, testing, primary and secondary treatments, filtration, disinfection, and continuous monitoring is therefore required. The objective of this research is to create a hybrid notion that extends the idea of an interval-valued neutrosophic fuzzy soft set (IVNFSS) to an interval-valued neutrosophic fuzzy set. Operations like complement, union, and integration are included in the idea. To improve decision-making accuracy, a quality-assessment distance measure is incorporated, offering a numerical representation of the disparity between various factors. …
Unveiling Efficiency: Investigating Distance Measures In Wastewater Treatment Using Interval-Valued Neutrosophic Fuzzy Soft Set, Muhammad Saeed, Kinza Kareem, Fatima Razzaq, Muhammad Saqlain
Unveiling Efficiency: Investigating Distance Measures In Wastewater Treatment Using Interval-Valued Neutrosophic Fuzzy Soft Set, Muhammad Saeed, Kinza Kareem, Fatima Razzaq, Muhammad Saqlain
Neutrosophic Systems with Applications
To reduce the threats that wastewater poses to human health and the environment, water treatment techniques must be improved. The use of a procedure that includes preparation, testing, primary and secondary treatments, filtration, disinfection, and continuous monitoring is therefore required. The objective of this research is to create a hybrid notion that extends the idea of an interval-valued neutrosophic fuzzy soft set (IVNFSS) to an interval-valued neutrosophic fuzzy set. Operations like complement, union, and integration are included in the idea. To improve decision-making accuracy, a quality-assessment distance measure is incorporated, offering a numerical representation of the disparity between various factors. …
Spatio-Temporal Association Rule Mining Of Traffic Congestion In A Large-Scale Road Network Based On Trajectory Data, Qifan Zhou, Haixu Liu, Zhipeng Dong, Yin Xu
Spatio-Temporal Association Rule Mining Of Traffic Congestion In A Large-Scale Road Network Based On Trajectory Data, Qifan Zhou, Haixu Liu, Zhipeng Dong, Yin Xu
Journal of System Simulation
Abstract: A K neighbor-RElim (KNR) algorithm and a sequential KNbr-RElim (SKNR) algorithm are proposed to mine traffic congestion association rules and congestion propagation spatio-temporal association rules by vehicle trajectory data in a large-scale road network. The KNR algorithm extends the spatial topology constraint based on the RElim algorithm. The KNR can be used to mine the road links prone to congestion from the large-scale trajectory dataset in a large-scale road network and quantify the strength of association for congested road links. The SKNR algorithm expands the time dimension in the form of sliding window and can be applied for mining …