Pub Date : 2023-03-25DOI: 10.32604/iasc.2022.021559
Abu Taher Tamim, H. Begum, Sumaiya Ashfaque Shachcho, Mohammad Monirujjaman Khan, Bright Yeboah-Akowuah, Mehedi Masud, Jehad F. Al-Amri
Aquaculture mainly refers to cultivating aquatic organisms providing suitable environments for various purposes, including commercial, recreational, public purposes. This paper aims to enhance the production of fish and maintain the aquatic environment of aquaculture in Bangladesh. This paper presents the way of using Internet of Things (IoT) based devices to monitor aquaculture’s basic needs and help provide things needed for the fisheries. Using these devices, various parameters of water will be monitored for a better living environment for fish. These devices consist of some sensors that will detect the Potential of Hydrogen (pH) level, the water temperature, and there will be two extra sections where the measurement of dissolved oxygen level and ammonia level using the testing kits can be determined which are needed for proper fish farming in the right water. An android-based mobile application has also been developed. In this system, farmers, fishermen, and people related to aquaculture will be the users of an android application. Via that application and with the help of a device, users will be notified about the amount of dissolved oxygen, ammonia level, pH level, and water body temperature. This monitoring system will help fish farmers to take the necessary steps to prevent any disturbance in an aquatic environment. Though Bangladesh is a riverine country and fish farming has a huge impact on this country’s economy, it is necessary to keep in good health to produce more and more fish. But the fisheries of this country are not expert enough to understand how to provide necessary elements to fish and what to do. They might get help from this system and measure the parameters they can give necessary things to grow more fish.
{"title":"Development of IoT Based Fish Monitoring System for Aquaculture","authors":"Abu Taher Tamim, H. Begum, Sumaiya Ashfaque Shachcho, Mohammad Monirujjaman Khan, Bright Yeboah-Akowuah, Mehedi Masud, Jehad F. Al-Amri","doi":"10.32604/iasc.2022.021559","DOIUrl":"https://doi.org/10.32604/iasc.2022.021559","url":null,"abstract":"Aquaculture mainly refers to cultivating aquatic organisms providing suitable environments for various purposes, including commercial, recreational, public purposes. This paper aims to enhance the production of fish and maintain the aquatic environment of aquaculture in Bangladesh. This paper presents the way of using Internet of Things (IoT) based devices to monitor aquaculture’s basic needs and help provide things needed for the fisheries. Using these devices, various parameters of water will be monitored for a better living environment for fish. These devices consist of some sensors that will detect the Potential of Hydrogen (pH) level, the water temperature, and there will be two extra sections where the measurement of dissolved oxygen level and ammonia level using the testing kits can be determined which are needed for proper fish farming in the right water. An android-based mobile application has also been developed. In this system, farmers, fishermen, and people related to aquaculture will be the users of an android application. Via that application and with the help of a device, users will be notified about the amount of dissolved oxygen, ammonia level, pH level, and water body temperature. This monitoring system will help fish farmers to take the necessary steps to prevent any disturbance in an aquatic environment. Though Bangladesh is a riverine country and fish farming has a huge impact on this country’s economy, it is necessary to keep in good health to produce more and more fish. But the fisheries of this country are not expert enough to understand how to provide necessary elements to fish and what to do. They might get help from this system and measure the parameters they can give necessary things to grow more fish.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"21 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87318949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/iasc.2023.045930
Fangyu Ye, Xiaoshu Xu, Yunfeng Zhang, Yan Ye, Jingyu Dai
{"title":"Marketing Model Analysis of Fashion Communication Based on the Visual Analysis of Neutrosophic Systems","authors":"Fangyu Ye, Xiaoshu Xu, Yunfeng Zhang, Yan Ye, Jingyu Dai","doi":"10.32604/iasc.2023.045930","DOIUrl":"https://doi.org/10.32604/iasc.2023.045930","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135443344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/iasc.2023.047463
Abdus Saboor, Arif Hussain, Bless Lord Y. Agbley, Amin ul Haq, Jian Ping Li, Rajesh Kumar
{"title":"Correction: Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques","authors":"Abdus Saboor, Arif Hussain, Bless Lord Y. Agbley, Amin ul Haq, Jian Ping Li, Rajesh Kumar","doi":"10.32604/iasc.2023.047463","DOIUrl":"https://doi.org/10.32604/iasc.2023.047463","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135704807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/iasc.2023.047522
Yaojin Sun, Nan Jiang, Min Zhu, Hao Hua
{"title":"Retraction: Precise Rehabilitation Strategies for Functional Impairment in Children with Cerebral Palsy","authors":"Yaojin Sun, Nan Jiang, Min Zhu, Hao Hua","doi":"10.32604/iasc.2023.047522","DOIUrl":"https://doi.org/10.32604/iasc.2023.047522","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135704816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retraction: Fluid Flow and Mixed Heat Transfer in a Horizontal Channel with an Open Cavity and Wavy Wall","authors":"Tohid Adibi, Shams Forruque Ahmed, Omid Adibi, Hassan Athari, Irfan Anjum Badruddin, Syed Javed","doi":"10.32604/iasc.2023.047521","DOIUrl":"https://doi.org/10.32604/iasc.2023.047521","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135704808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/iasc.2023.033869
Jin Wang, Yongsong Zou, Se-Jung Lim
Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t − 2 to be directly transferred to time t without the nonlinear transformation at time t − 1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.
{"title":"An Improved Time Feedforward Connections Recurrent Neural Networks","authors":"Jin Wang, Yongsong Zou, Se-Jung Lim","doi":"10.32604/iasc.2023.033869","DOIUrl":"https://doi.org/10.32604/iasc.2023.033869","url":null,"abstract":"Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t − 2 to be directly transferred to time t without the nonlinear transformation at time t − 1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135584270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.016075
M. Bozorgi, Morteza Mohammadi Zanjireh, Mahdi Bahaghighat, Qin Xin
Today, resource waste is considered as one of the most important challenges in different industries. In this regard, the Rectangle Packing Problem (RPP) can affect noticeably both time and design issues in businesses. In this study, the main objective is to create a set of non-overlapping rectangles so that they have specific dimensions within a rectangular plate with a specified width and an unlimited height. The ensued challenge is an NP-complete problem. NP-complete problem, any of a class of computational problems that still there are no efficient solution for them. Most substantial computer-science problems such as the traveling salesman problem, satisfiability problems (sometimes called propositional satisfiability problem and abbreviated SAT or B-SAT), and graph-covering problems are belong to this class. Essentially, it is complicated to spot the best arrangement with the highest rate of resource utilization by emphasizing the linear computation time. This study introduces a time-efficient and exploratory algorithm for the RPP, including the lowest front-line strategy and a Best-Fit algorithm. The obtained results confirmed that the proposed algorithm can lead to a good performance with simplicity and time efficiency. Our evaluation shows that the proposed model with utilization rate about 94.37% outperforms others with 87.75%, 50.54%, and 87.17% utilization rate, respectively. Consequently, the proposed method is capable to of achieving much better utilization rate in comparison with other mentioned algorithms in just 0.023 s running-time, which is much faster than others.
{"title":"A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem","authors":"M. Bozorgi, Morteza Mohammadi Zanjireh, Mahdi Bahaghighat, Qin Xin","doi":"10.32604/iasc.2022.016075","DOIUrl":"https://doi.org/10.32604/iasc.2022.016075","url":null,"abstract":"Today, resource waste is considered as one of the most important challenges in different industries. In this regard, the Rectangle Packing Problem (RPP) can affect noticeably both time and design issues in businesses. In this study, the main objective is to create a set of non-overlapping rectangles so that they have specific dimensions within a rectangular plate with a specified width and an unlimited height. The ensued challenge is an NP-complete problem. NP-complete problem, any of a class of computational problems that still there are no efficient solution for them. Most substantial computer-science problems such as the traveling salesman problem, satisfiability problems (sometimes called propositional satisfiability problem and abbreviated SAT or B-SAT), and graph-covering problems are belong to this class. Essentially, it is complicated to spot the best arrangement with the highest rate of resource utilization by emphasizing the linear computation time. This study introduces a time-efficient and exploratory algorithm for the RPP, including the lowest front-line strategy and a Best-Fit algorithm. The obtained results confirmed that the proposed algorithm can lead to a good performance with simplicity and time efficiency. Our evaluation shows that the proposed model with utilization rate about 94.37% outperforms others with 87.75%, 50.54%, and 87.17% utilization rate, respectively. Consequently, the proposed method is capable to of achieving much better utilization rate in comparison with other mentioned algorithms in just 0.023 s running-time, which is much faster than others.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"9 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88011912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.021696
M. Rajalakshmi, V. Saravanan, V. Arunprasad, C. A. T. Romero, O. I. Khalaf, C. Karthik
In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry’s clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition Algorithm are used to evaluate the most accurate model generated by the algorithms. By capturing the principal dynamic features of the process, the clarifier model produced from State Transition Algorithm (STA) acts more like the actual clarifier process. According to the findings, the controllers provided in this paper may be used to achieve greater performance than the standard controller design during the control of any nonlinear procedure, and STA is extremely helpful in modeling a nonlinear process.
{"title":"Machine Learning for Modeling and Control of Industrial Clarifier Process","authors":"M. Rajalakshmi, V. Saravanan, V. Arunprasad, C. A. T. Romero, O. I. Khalaf, C. Karthik","doi":"10.32604/iasc.2022.021696","DOIUrl":"https://doi.org/10.32604/iasc.2022.021696","url":null,"abstract":"In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry’s clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition Algorithm are used to evaluate the most accurate model generated by the algorithms. By capturing the principal dynamic features of the process, the clarifier model produced from State Transition Algorithm (STA) acts more like the actual clarifier process. According to the findings, the controllers provided in this paper may be used to achieve greater performance than the standard controller design during the control of any nonlinear procedure, and STA is extremely helpful in modeling a nonlinear process.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"45 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88480199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.023570
Betania Hern醤dez-Oca馻, Adrian Garc韆-L髉ez, Jos�Hern醤dez-Torruco, Oscar Ch醰ez-Bosquez
{"title":"Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems","authors":"Betania Hern醤dez-Oca馻, Adrian Garc韆-L髉ez, Jos�Hern醤dez-Torruco, Oscar Ch醰ez-Bosquez","doi":"10.32604/iasc.2022.023570","DOIUrl":"https://doi.org/10.32604/iasc.2022.023570","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"43 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90747791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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