Intelligent Automation and Soft Computing最新文献

{"title":"Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks","authors":"A. Jothi, P. L. K. Priyadarsini","doi":"10.32604/iasc.2022.020974","DOIUrl":"https://doi.org/10.32604/iasc.2022.020974","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are in use for surveillance services in the geographic areas, that are very hard and sometimes not reachable by humans. Nowadays, UAVs are being used as substitutions to manned operations in various applications. The intensive utilization of autonomous UAVs has given rise to many new challenges. One of the vital problems that arise while deploying UAVs in surveillance applications is the Coverage Path Planning(CPP) problem. Given a geographic area, the problem is to find an optimal path/tour for the UAV such that it covers the entire area of interest with minimal tour length. A graph can be constructed from the map of the area under surveillance, using computational geometric techniques. In this work, the Coverage Path Planning problem is posed as a Travelling Salesperson Problem(TSP) on these graphs. The graphs obtained are large in number of vertices and edges and the real-time applications require good computation speed. Hence a model is built using Graph Convolution Network (GCN). The model is effectively trained with different problem instances such as TSP20, TSP50, and TSP100. Results obtained from the Concorde Benchmark Dataset were used to analyze the optimality of the predicted tour length by the GCN. The model is also evaluated against the performance of evolutionary algorithms on several self-constructed graphs. Particle Swarm Optimization, Ant Colony Optimization, and Firefly Algorithm are used to find optimal tours and are compared with GCN. It is found that the proposed GCN framework outperforms these evolutionary algorithms in optimal tour length and also the computation time.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"25 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88797084","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":"Optimized Control of Single Phase Reboost Luo Converter Fed Grid-Connected PV System","authors":"S. Baskaran, Raghuraman Sivalingam","doi":"10.32604/iasc.2022.023093","DOIUrl":"https://doi.org/10.32604/iasc.2022.023093","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":"88979317","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":"User Interaction Based Recommender System Using Machine Learning","authors":"R. Sabitha, S. Vaishnavi, S. Karthik, R. M. Bhavadharini","doi":"10.32604/iasc.2022.018985","DOIUrl":"https://doi.org/10.32604/iasc.2022.018985","url":null,"abstract":"In the present scenario of electronic commerce (E-Commerce), the indepth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS) that utilizes the data from multiple sources and opinion-based analysis for sensing the consumer needs and interests. For that, Content-Based Filtering (CBF) analyses various products and determines the likeliness of products based on User Interaction to recommend that to consumers. Then, the product information from multiple sources is combined with DempsterShafer (D-S) evidence theory, and then, decision making for product recommendation is performed with CBF. Moreover, the modified Radial Basis Function Neural Networks (RBFNN) technique has been incorporated for measuring product recommendations. The results show that the proposed model produces better results in providing accurate recommendations to Consumers with a higher rate of coverage and precision, thereby enhancing significant growth in E-Commerce.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"11 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89133178","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":"Requirement Design for Software Configuration and System Modeling","authors":"W. Mehmood, A. Waheed Khan, W. Aslam, Shafiq Ahmad, Ahmed M. El-Sherbeeny, M. Shafiq","doi":"10.32604/iasc.2022.016116","DOIUrl":"https://doi.org/10.32604/iasc.2022.016116","url":null,"abstract":"Software Configuration Management (SCM) aims to control the development of complex software systems. Traditional SCM systems treat text files as central artifacts, so they are mainly developed for source code. Such a system is not suitable for model-based software development with model-centric artifacts. When applying traditional systems to model-based software development, new challenges such as model mapping, differentiation, and merging arise. Many existing methods mainly use UML or domain-specific languages to determine model differences. However, as far as we know, there is no such technology for System Modeling Language (SysML) models. SysML covers the entire development life cycle of various complex systems, covering information, processes, hardware and software. SysML contains nine types of diagrams for system modeling. One of them is the SysML requirement diagram, which is used to capture the functional requirements of the system. We propose a differentiation method for the SysML demand model. We recommend to create a SysML requirement model in the CASE tool first, and then export the SysML model in the form of XMI. Then, we parse the XMI representation through difference calculations. Finally, we summarize the results in annotated form. We implemented our method in a satellite system case study and demonstrated the experimental use of the method.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"33 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89442040","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":"A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning","authors":"Venkateswari Pichaimani, K. R. Manjula","doi":"10.32604/iasc.2022.023105","DOIUrl":"https://doi.org/10.32604/iasc.2022.023105","url":null,"abstract":"The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset are through two processing stages dimensionality reduction and position estimation. Initially, the essential elements selection using the Gaussian Distributive Feature Embedding technique is the novel framework. The feature reduction process aims to reduce the time consumption and overhead for estimating the location of various devices. In the next stage, employ Deep Recurrent multilayer Perceptive Neural Learning to evaluate the device position with dimensionality reduced features. The proposed Deep-learning approach accurately learns the quality and the signal strength data with multiple layers by applying Deming Regressive Trilateral Positioning Model. As a result, the GDFE-DRPNL framework increases the positioning accuracy and minimizes the error rate. The experimental assessments with various factors such as positioning accuracy minimized by 70% and 60%, computation time minimized by 45% and 55% as well as overhead by 11% and 23% compared with PFRL and two-dimensional localization algorithm. Through the experiment and after analyzing the data, verify that the proposed GDFEDRPNL algorithm in this paper is better than the previous methods.","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":"89592789","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":"Big Data Analytics with OENN Based Clinical Decision Support System","authors":"T. Murari, L. Prathiba, Kranthi Kumar Singamaneni, D. Venu, Vinay Kumar Nassa, R. Kohar, Satyajit Sidheshwar Uparkar","doi":"10.32604/iasc.2022.020203","DOIUrl":"https://doi.org/10.32604/iasc.2022.020203","url":null,"abstract":"In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision support system. The focus of the BDA-OENN model is to design a diagnostic tool for Autism Spectral Disorder (ASD), which is a neurological illness related to communication, social skills and repetitive behaviors. The presented BDA-OENN model involves different stages of operations such as data preprocessing, synthetic data generation, classification and parameter optimization. For the generation of synthetic data, Synthetic Minority Over-sampling Technique (SMOTE) is used. Hadoop Ecosystem tool is employed to manage big data. Besides, the OENN model is used for classification process in which the optimal parameter setting of the ENN model by using Binary Grey Wolf Optimization (BGWO) algorithm. A detailed set of simulations were performed to highlight the improved performance of the BDA-OENN model. The resultant experimental values report the betterment of the BDA-OENN model over the other methods in terms of distinct performance measures. Ligent healthcare systems assists to make better decision, which further enables the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Early, skin lesion segmentation and classification play a vital role in the precise diagnosis of skin cancer by intelligent system. But the automated diagnosis of skin lesions in dermoscopic images is a challenging process because of the problems such as artifacts (hair, gel bubble, ruler marker), This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.020203 Article ech T Press Science","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"30 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83024744","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":"IIoT Framework Based ML Model to Improve Automobile Industry Product","authors":"S. Gopalakrishnan, M. Senthil Kumaran","doi":"10.32604/iasc.2022.020660","DOIUrl":"https://doi.org/10.32604/iasc.2022.020660","url":null,"abstract":"In the automotive industry, multiple predictive maintenance units run behind the scenes in every production process to support significant product development, particularly among Accessories Manufacturers (AMs). As a result, they wish to maintain a positive relationship with vehicle manufacturers by providing 100 percent quality assurances for accessories. This is only achievable if they implement an effective anticipatory strategy that prioritizes quality control before and after product development. To do this, many sensors devices are interconnected in the production area to collect operational data (humanity, viscosity, and force) continuously received from machines and sent to backend computers for control operations and predictive analysis. As a result, there is a vast volume of data that may be processed further to obtain accurate information on equipment processing speed and production efficiency. However, extracting details in the essential format for data-driven decision support for predictive maintenance is problematic. As a result, an effective predictive maintenance approach based on Machine Learning (ML) methods is established. It has an impact on the Hybrid Machine Learning (HML) model, which blends supervised and unsupervised learning. It helps to forecast breakdowns and production line deviations ahead of time, preventing the manufacturing unit from shutting down. The proposed predictive methodology has been tested in terms of earlier anomaly detection, production line accuracy & machinery efficiency and compared with other existing ML based predictive maintenance approaches.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"60 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83379200","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":"Improved Homomorphic Encryption with Optimal Key Generation Technique for VANETs","authors":"G. Tamilarasi, K. Rajiv Gandhi, V. Palanisamy","doi":"10.32604/iasc.2022.024687","DOIUrl":"https://doi.org/10.32604/iasc.2022.024687","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"62 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90998692","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":"Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction","authors":"C. Thaventhiran, K. R. Sekar","doi":"10.32604/iasc.2022.019546","DOIUrl":"https://doi.org/10.32604/iasc.2022.019546","url":null,"abstract":"Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANNLSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given input patient data. Different processes are carried out at each layer to predict lung cancer at an earlier stage. The input layer of the deep neural network receives the data and associated features and sends them to the hidden layer. The first hidden layer performs the feature selection process using Target Projection matching to identify the relevant features for accurate disease prediction with minimum time consumption. Hidden layer 2 performs the patient Data Classification based on Czekanowski's dice similarity coefficient with the selected relevant features from the previous layer to predict lung cancer. The factors considered for performance evaluation of the proposed technique with the existing state of the art approaches include prediction accuracy, false-positive rate and prediction time. Lunar 16 Lung Cancer dataset consisting of patient data is used for evaluation. The obtained results show that the proposed TPFMDANN-LSTM technique achieves higher prediction accuracy with minimum time consumption and less false positive rate than the state-of-the-art methods. The experimental results reveal that the TPFMDANN-LSTM technique performs better with a 6% improvement in prediction accuracy, 36% reduction of false positives, and 16% faster prediction time for lung cancer detection compared to existing works.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"61 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90999917","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":"Process Optimization Method for Day Ward Based on Bayesian Decision-Tree","authors":"Ting Chen, Kai Pu, Lanzheng Bian, M. Rao, Jing Hu, Rugang Lu, Jinyue Xia","doi":"10.32604/iasc.2022.022510","DOIUrl":"https://doi.org/10.32604/iasc.2022.022510","url":null,"abstract":"The day surgery management mode is mainly decentralized management, with clinical departments as the unit, and with reference to the experience of inter project operation management in benchmark hospitals, the empirical management is implemented. With the development of day surgery, the extensive decentralized management mode has been unable to meet the needs of the current day surgery development situation. At first, the paper carefully analyzes the existing problems in the day surgery process in the day ward of the Children’s Hospital of Nanjing Medical University. And then, the concerns of doctors, nurses, anesthesiologists and other hospital staff in day ward, children and their parents are considered. Based on Bayesian decision-making theory, this paper optimizes the pre-admission evaluation of sick children and hospitalization process for day surgery in Nanjing Children’s Hospital. Moreover, the specific optimization process is designed. After the optimizations, it can be seen that the time consumption of each step of the hospitalization process in day surgery is reduced. Thus, the hospital stay of sick children are significantly reduced, and the operation cost is reduced. In addition, the first preoperative preparation time and the average time of receiving children were reduced in day ward. The satisfaction of children’s parents was significantly improved.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"8 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87089081","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}