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Articles 4231 - 4260 of 27385
Full-Text Articles in Engineering
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm, Jian Li, Huankun Li, Pengbo He, Huabei Wang, Liping Xu, Kui He
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm, Jian Li, Huankun Li, Pengbo He, Huabei Wang, Liping Xu, Kui He
Journal of System Simulation
Abstract: To enhance the efficiency of flexible job shop scheduling, this paper develops a Markov decision process with specific constraints tailored to the scheduling problem. A cooperative agent reinforcement learning method is proposed to solve the problem of concurrent selection of workpieces and machines. During the construction of the Markov decision process, a disjunctive graph is introduced to represent the state characteristics. Two agents are introduced to select the workpieces and machines. The reward parameters governing the entire scheduling process are established by predicting variations in the minimum-maximum completion time across different time points. A GIN(graph isomorphic network) graph neural …
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components, Yufang Wang, Dianqing Zhang, Xiaolin Hua, Binbin Yao, Fan Chen
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components, Yufang Wang, Dianqing Zhang, Xiaolin Hua, Binbin Yao, Fan Chen
Journal of System Simulation
Abstract: A dual-resource constrained distributed flexible job-shop scheduling model was established by taking into account the worker constraints of the finishing process and the requirements of distributed multi-factory collaboration in the production of aerospace structural components. A hybrid grey wolf optimization algorithm based on the critical factory was proposed to solve this problem. The model contained four subproblems: factory selection, operation sequencing, machine selection, and worker selection. In view of these four sub-problems, a four-layer coding and a new decoding method were designed to avoid the use conflict of machines and workers. In addition, a new mechanism for hunting and …
Automatic Detection Algorithm For Typical Defects Of Substation Based On Improved Yolov5, Zhongkai Xu, Yanling Liu, Xiaojuan Sheng, Chao Wang, Wenjun Ke
Automatic Detection Algorithm For Typical Defects Of Substation Based On Improved Yolov5, Zhongkai Xu, Yanling Liu, Xiaojuan Sheng, Chao Wang, Wenjun Ke
Journal of System Simulation
Abstract: In response to the challenges present in the context of defect recognition in substations, such as complex substation defects and sample imbalance, an improved YOLOv5 algorithm was proposed. The Transformer model was introduced into the YOLOv5 network structure, leveraging the self-attention mechanism to capture long-range dependencies among features. A focal loss-based optimization was employed to improve the loss function, as well as the detection accuracy and robustness of defects of small sample substations. To meet the requirements of substation defect recognition, a dedicated dataset was constructed. A clustering algorithm was applied to the real annotation boxes to generate more …
Object Detection Of Lightweight Transformer Based On Knowledge Distillation, Gaihua Wang, Kehong Li, Qian Long, Jingxuan Yao, Bolun Zhu, Zhengshu Zhou, Xuran Pan
Object Detection Of Lightweight Transformer Based On Knowledge Distillation, Gaihua Wang, Kehong Li, Qian Long, Jingxuan Yao, Bolun Zhu, Zhengshu Zhou, Xuran Pan
Journal of System Simulation
Abstract: In autonomous driving, the efficiency and accuracy of object detection are significant. Object detection based on Transformer structure has gradually become the mainstream method, eliminating the complex anchor generation and non-maximum suppression (NMS). It has problems of high computing cost and slow convergence. An object detection model of the based lightweight pooling transformer (LPT) is designed, which contains a pooling backbone network and dual pooling attention mechanism. A general knowledge distillation method is intended for the DETR (detection transformer) model, which transfers prediction results, query vector, and features extracted by the teacher as knowledge to the LPT model to …
A Hybrid Genetic Search Algorithm For Capacitated Electric Vehicle Routing Problem, Dongyao Jin, Mi Liu, Yena Zhu, Yijiang Zhao
A Hybrid Genetic Search Algorithm For Capacitated Electric Vehicle Routing Problem, Dongyao Jin, Mi Liu, Yena Zhu, Yijiang Zhao
Journal of System Simulation
Abstract: The capacitated electric vehicle routing problem (CEVRP) is an NP-hard combinatorial optimization problem in logistics distribution, aiming to minimize the total delivery distance of electric vehicles while satisfying carrying capacity and battery charge constraints. A hybrid genetic search algorithm is proposed to solve CEVRP by decomposing it into two subproblems: capacitated vehicle routing problem (CVRP) and fixed-route vehicle charging problem (FRVCP). A coding scheme with a two-layer chromosome structure is designed to represent the decision variables of these two subproblems. A Split operation is employed to generate vehicle routes for solving CVRP, and five neighborhood search operators, including Relocate, …
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui
Journal of System Simulation
Abstract: Accurate recognition of traffic signs plays an important role in the field of intelligent driving. Traffic sign training datasets with long-tail distribution increase the difficulty of traffic sign recognition. A traffic sign recognition model with long-tail distribution based on YOLOX-Tiny was proposed to improve the poor performance of the model trained on long-tail distribution datasets. A long-tail traffic sign dataset was created based on the TT100K_2021 (tsinghua-tencent 100K 2021) dataset. YOLOX-Tiny was chosen as the underlying model by considering picture numbers in datasets, sample distribution, and model size. Equalization loss v2 (EQL v2) was used as classification loss to …
Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration, Weigang Li, Chuxiang Yu, Yongqiang Wang, Shaofeng Zou
Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration, Weigang Li, Chuxiang Yu, Yongqiang Wang, Shaofeng Zou
Journal of System Simulation
Abstract: When scanning the surrounding environment, a lidar will generate some cluttered and sparse point cloud, which will cause excessive distribution fitting errors and correlation distances in the registration process, thus affecting the accuracy of the registration algorithm and the effect of simultaneous localization and mapping (SLAM). To address this problem, a real-time lidar SLAM algorithm based on distribution optimal registration is proposed. An eigenspectrum filter is designed, which takes the normalized minimum eigenvalue as the filtering object to filter out the points that do not match the set distribution in order to reduce the distribution fitting error. Secondly, a …
Research On Green Job Shop Scheduling Based On Herd Immunity Optimizer, Xunde Ma, Li Bi, Junjie Wang
Research On Green Job Shop Scheduling Based On Herd Immunity Optimizer, Xunde Ma, Li Bi, Junjie Wang
Journal of System Simulation
Abstract: In view of the green flexible job shop scheduling problem where machines have multiple speeds, a green flexible job shop scheduling model under multiple speeds was constructed to minimize the makespan and total energy consumption under different speeds. A discrete coronavirus herd immunity optimizer (DCHIO) was proposed for a solution. A discrete individual updating method was introduced for the relatively large solution space of the multi-speed problem, based on which a population updating mechanism with multi-scale joint search was proposed to search the solution space quickly and uniformly. A dynamic mutation operation was designed to enhance the population diversity …
Agv Scheduling Problem At Automated Terminals Based On Improved Dqn Algorithm, Chengji Liang, Shidong Zhang, Yu Wang, Bin Lu
Agv Scheduling Problem At Automated Terminals Based On Improved Dqn Algorithm, Chengji Liang, Shidong Zhang, Yu Wang, Bin Lu
Journal of System Simulation
Abstract: A future tasks considering deep Q-network (F-DQN) algorithm was proposed to output realtime scheduling results of automated guided vehicles (AGVs) at automated terminals. This algorithm combined the advantages of real-time scheduling and static scheduling, improving the system status by considering static future task information when making real-time decisions, so as to obtain a better scheduling solution. In this study, the actual layout and equipment conditions of the Yangshan phase IV automated terminal were considered, and a series of simulation experiments were conducted using the Plant Simulation software. The experimental results show that the F-DQN algorithm can effectively solve the …
Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang
Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang
Journal of System Simulation
Abstract: As the 3D object detection based on point clouds shows an incapacity of feature extraction and incongruity between classification and regression, this research introduces a novel ResCST architecture based on the SECOND network. It incorporates residual connections into the 3D sparse convolutional layer, with the advantages of capturing long-distance dependent relation by SwinTransformer and obtaining local features by convolutional neural network integrated, proposing the CNN-SwinTransformer hybrid model for enhanced feature extraction. It introduces the RCIoU method for the joint optimization of classification and regression tasks. The experimental results show that the model achieves a 3D detection accuracy of 91.21%, …
Optimization Of Crucial Targets For Air Defense Based On Combined Weighting-Topsis Model, Peng Zhang, Ke Feng
Optimization Of Crucial Targets For Air Defense Based On Combined Weighting-Topsis Model, Peng Zhang, Ke Feng
Journal of System Simulation
Abstract: To optimize the selection of crucial defended targets in regional air defense operations and improve the selection accuracy, this paper constructs a factor model considering the importance of air defense targets from the perspectives of target value, defense urgency, target vulnerability, and target recovery. Under the optimization of the TOPSIS method through a combination of the ANP and entropy weight methods, tendentious opinions of commanders and the excessive reliance on objective data can be overcome to ensure the factor weighting is more reasonable and accurate; Super Decisions is used to calculate the weights of the ANP method, accelerating data …
Eecbs Multi-Robot Path Planning Based On Variable Suboptimal Factors Of Prioritizing Conflicts, Xingyu Yan, Niya Wang, Jianlin Mao, Zhigang He, Dayan Li
Eecbs Multi-Robot Path Planning Based On Variable Suboptimal Factors Of Prioritizing Conflicts, Xingyu Yan, Niya Wang, Jianlin Mao, Zhigang He, Dayan Li
Journal of System Simulation
Abstract: In the process of multi-robot path planning (MRPP), the unavoidable key conflicts between the optimal paths have a significant impact on the efficiency of path solving. To address this issue, an MRPP method based on variable suboptimal factors of prioritizing conflicts (PC) was proposed. Path search was performed at the lower layer of the explicit estimation conflict-based search (EECBS) algorithm; in the upper layer of the EECBS algorithm framework, the PC was determined, and the suboptimal factor of the robot with key conflicts was adaptively increased; by analyzing the distribution of obstacles in the surrounding neighborhoods of key conflicts, …
Slam Dynamic Algorithm Based On Improved Feature Description, Qiang Fu, Xianyun Teng, Yuanfa Ji, Fenghua Ren
Slam Dynamic Algorithm Based On Improved Feature Description, Qiang Fu, Xianyun Teng, Yuanfa Ji, Fenghua Ren
Journal of System Simulation
Abstract: The original ORB descriptor algorithm has a low matching accuracy and long matching time, the positioning accuracy and robustness of the SLAM(simultaneous localization and mapping) system are severely disturbed by moving objects in dynamic scenes, and the ORB-SLAM3 system is incapable of constructing dense maps. To address the above problems, this paper proposes an improved ORB-SLAM3 based on the BEBLID descriptor and object detection. A lightweight YOLOv5s dynamic object detection network and dynamic feature removal module are fused with the tracking thread to improve the system's positioning accuracy. Replacing the original feature description algorithm, an improved local image descriptor …
Design And Implementation Of Maritime Unmanned Cross-Domain Collaborative Effectiveness Evaluation System Based On Mlp, Hongyu Hu, Tianzhu Gao, Haitao Gu
Design And Implementation Of Maritime Unmanned Cross-Domain Collaborative Effectiveness Evaluation System Based On Mlp, Hongyu Hu, Tianzhu Gao, Haitao Gu
Journal of System Simulation
Abstract: In the face of the problem of evaluating the detection capability effectiveness of the maritime unmanned cross-domain collaborative system, it is necessary to study the evaluation indexes and evaluation algorithm. In this paper, the robot's own parameters and environmental parameters are combined to build a calculation model for evaluation indexes, such as detection coverage rate, repeated detection rate, the number of pixels per unit area, and energy as well as an evaluation system for detection capability of the maritime unmanned cross-domain collaborative system. The subjectivity in the evaluation process is reduced, and training data is generated by the availability …
Improvement Of A* Algorithm In Path Planning Of Mobile Robot, Dexin Yao, Hongjun San, Yaru Wang, Haijie Sun, Jiupeng Chen, Xiaoyuan Yang
Improvement Of A* Algorithm In Path Planning Of Mobile Robot, Dexin Yao, Hongjun San, Yaru Wang, Haijie Sun, Jiupeng Chen, Xiaoyuan Yang
Journal of System Simulation
Abstract: To solve the problems of excessively redundant nodes, low search efficiency, and excessive path turning angle in the path search of the traditional A* algorithm, an improved A* algorithm is proposed to plan the optimal path. First, the amount of search neighborhood of the A* algorithm is increased to 24 to obtain a more accurate and comprehensive search field. Second, the angle search algorithm is introduced, eliminating the unnecessary nodes in the path search and making the search more target-oriented. Third, the heuristic function is weighted by the exponential attenuation through the relative position of the current point and …
Biomechanical Insights Of Head Trauma Based On Computational Simulation, Ghaidaa Abdulrahman Khalid
Biomechanical Insights Of Head Trauma Based On Computational Simulation, Ghaidaa Abdulrahman Khalid
AUIQ Technical Engineering Science
Head injuries from falls are a major cause of illness and death in children due to trauma. Despite their prevalence and impact, there is limited understanding of how children's heads react during impact events. Infant Post-Mortem-Human-Surrogate (PMHS) testing, a reasonable method for studying impact biomechanics, faces significant restrictions due to emotional, moral, and ethical issues. Computer modeling, though holding significant promise for creating alternative pediatric head surrogates, encounters numerous challenges because of the intricacies of child growth and development. A finite-element (FE) model of an infant head was created from high-resolution CT scans, utilizing published data on tissue material properties. …
Large Dams Establishment Impacts On Different Environmental Aspects: A Review, Anas Ahmad
Large Dams Establishment Impacts On Different Environmental Aspects: A Review, Anas Ahmad
AUIQ Technical Engineering Science
The construction of large dams has been an essential component of the development of global infrastructure, offering significant advantages such as ensuring water storage, reducing the risk of flooding, and producing about 20% of the world's electricity through hydropower. On the other hand, these structures have significant and varied environmental effects. The environmental effects of large dams are critically examined in this analysis, with an emphasis on four main areas: fish biodiversity, riparian vegetation, sediment deposition, and water quality. The study estimates the degree of these consequences by examining global case studies and evaluating data from over 145 significant rivers …
If Androids Dream, Are They More Than Sheep?: Westworld, Robots And Legal Rights, Amanda J. Dipaolo
If Androids Dream, Are They More Than Sheep?: Westworld, Robots And Legal Rights, Amanda J. Dipaolo
Dialogue: The Interdisciplinary Journal of Popular Culture and Pedagogy
The robot protagonists in HBO’s Westworld open the door to several philosophical and ethical questions, perhaps the most complex being: should androids be granted similar legal protections as people? Westworld offers its own exploration of what it means to be a person and places emphasis on one’s ability to feel and understand pain. With scientists and corporations actively working toward a future that includes robots that can display emotion in a way that can convincingly pass as that of a person’s, what happens when androids pass the Turing test, feel empathy, gain consciousness, are sentient, or develop free will? The …
Rad-Quasi-Prime Submodules, Rana Noorimajeed, Ghaleb Ahmed Hammood, Mahmood S. Fiadh, Lemya Abd Alameer Hadi
Rad-Quasi-Prime Submodules, Rana Noorimajeed, Ghaleb Ahmed Hammood, Mahmood S. Fiadh, Lemya Abd Alameer Hadi
Iraqi Journal for Computer Science and Mathematics
Consider a left J-module I. The present study introduces the conception of rad-Quasi- Prime submodule, that serves as a dual popularization of both Quasi-Prime submodules and primary submodules. An apposite submodule A of an J-module named as rad- Quasi Prime if for all and with implies that either or . Numerous facts and characterizations that concerning are acquired.
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
Neutrosophic Systems with Applications
In 2017, Professor F. Smarandache introduced the Neutrosophic Theory of Evolution, Involution, and Indeterminacy (or Neutrality) (NToEIaI). He concluded that every theory of evolution is characterized by a certain degree of truth, indeterminacy, and untruth, as in neutrosophic logic. In this perspective, he raised several open questions on evolution, neutrality, and involution that required further research effort. Very recently, in 2024, Smarandache conducted research, from a soft sciences/philosophical viewpoint, on identifying and studying common parts in uncommon things and uncommon parts in common things emphasizing the complexity and interconnectedness of concepts within the context of neutrosophy. In this article, we …
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Systems with Applications
Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …
Product Perspective From Fuzzy To Neutrosophic Graph Extension- A Review Of Literature, G. Vetrivel, M. Mullai, G. Rajchakit
Product Perspective From Fuzzy To Neutrosophic Graph Extension- A Review Of Literature, G. Vetrivel, M. Mullai, G. Rajchakit
Neutrosophic Systems with Applications
This review article consolidates and tabulates research on the product operations done in fuzzy, intuitionistic fuzzy, and neutrosophic graphs. This article encompasses the previous product discussions on fuzzy graphs and their extensions. This article aims to list the origin, structural properties, applications, etc. done by the researchers and academicians using the product behavior of two graphs on the fuzzified environment. This review provides a clear understanding of enhancements of product approach on graphs from fuzzy to neutrosophic kind.
Some Similarity Measures On Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets And Their Applications In Medical Diagnosis, Afshan Qayyum, Noreen Mushtaq, Tanzeela Shaheen, Wajid Ali, Florentin Smarandache
Some Similarity Measures On Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets And Their Applications In Medical Diagnosis, Afshan Qayyum, Noreen Mushtaq, Tanzeela Shaheen, Wajid Ali, Florentin Smarandache
Neutrosophic Systems with Applications
In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the …
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Neutrosophic Systems with Applications
The authors explore the innovative application of the Neutrosophic series, particularly the Neutrosophic Poisson Distribution Series (NPDS), to investigate various indeterminacy or uncertainties inherent in the classical univalent harmonic function class. The Neutrosophic Poisson Distribution Series is equipped with a Salangean derivative operator and convoluted with analytic univalent harmonic function class to derive new properties, such as inclusion relation, and coefficient inequalities for star-likeness. The results obtained demonstrate the effectiveness of this approach in capturing the inherent uncertainties and complexities associated with harmonic functions. There are several other areas of importance of our results that can be unlocked by computer …
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Neutrosophic Systems with Applications
The authors explore the innovative application of the Neutrosophic series, particularly the Neutrosophic Poisson Distribution Series (NPDS), to investigate various indeterminacy or uncertainties inherent in the classical univalent harmonic function class. The Neutrosophic Poisson Distribution Series is equipped with a Salangean derivative operator and convoluted with analytic univalent harmonic function class to derive new properties, such as inclusion relation, and coefficient inequalities for star-likeness. The results obtained demonstrate the effectiveness of this approach in capturing the inherent uncertainties and complexities associated with harmonic functions. There are several other areas of importance of our results that can be unlocked by computer …
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
Neutrosophic Systems with Applications
In 2017, Professor F. Smarandache introduced the Neutrosophic Theory of Evolution, Involution, and Indeterminacy (or Neutrality) (NToEIaI). He concluded that every theory of evolution is characterized by a certain degree of truth, indeterminacy, and untruth, as in neutrosophic logic. In this perspective, he raised several open questions on evolution, neutrality, and involution that required further research effort. Very recently, in 2024, Smarandache conducted research, from a soft sciences/philosophical viewpoint, on identifying and studying common parts in uncommon things and uncommon parts in common things emphasizing the complexity and interconnectedness of concepts within the context of neutrosophy. In this article, we …
Book Review: Speed, Safety, And Comfort: The Origins Of Delta Airlines, Dana Johnson
Book Review: Speed, Safety, And Comfort: The Origins Of Delta Airlines, Dana Johnson
Georgia Library Quarterly
No abstract provided.
Some Similarity Measures On Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets And Their Applications In Medical Diagnosis, Afshan Qayyum, Noreen Mushtaq, Tanzeela Shaheen, Wajid Ali, Florentin Smarandache
Some Similarity Measures On Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets And Their Applications In Medical Diagnosis, Afshan Qayyum, Noreen Mushtaq, Tanzeela Shaheen, Wajid Ali, Florentin Smarandache
Neutrosophic Systems with Applications
In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the …
Building Services Engineering November/December 2024
Building Services Engineering November/December 2024
Building Services Engineering
No abstract provided.
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Systems with Applications
Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …