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.2022.019669
N. Reda, A. Hamdy, E. Rashed
Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multiobjective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MOBPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.
{"title":"Multi-Objective Adapted Binary Bat for Test Suite Reduction","authors":"N. Reda, A. Hamdy, E. Rashed","doi":"10.32604/iasc.2022.019669","DOIUrl":"https://doi.org/10.32604/iasc.2022.019669","url":null,"abstract":"Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multiobjective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MOBPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"84 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73559264","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.2022.019778
A. Dutta
{"title":"Detecting Lung Cancer Using Machine Learning Techniques","authors":"A. Dutta","doi":"10.32604/IASC.2022.019778","DOIUrl":"https://doi.org/10.32604/IASC.2022.019778","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"31 1","pages":"1007-1023"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69778414","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.2022.020662
H. Mahmoud, Amal H. Alharbi, N. Alghamdi
{"title":"Breast Cancer Detection Through Feature Clustering and Deep Learning","authors":"H. Mahmoud, Amal H. Alharbi, N. Alghamdi","doi":"10.32604/IASC.2022.020662","DOIUrl":"https://doi.org/10.32604/IASC.2022.020662","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"31 1","pages":"1273-1286"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69779510","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：