Computational Intelligence and Machine Learning最新文献

{"title":"Design of Self Adaptive Fuzzy Sliding Mode Controller for Robot Manipulators","authors":"Sunil Kalshetti, D. S. Dixit","doi":"10.36647/ciml/03.02.a004","DOIUrl":"https://doi.org/10.36647/ciml/03.02.a004","url":null,"abstract":"This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models. Index Terms : Sliding Mode Control, Robot manipulators, Controller, Noise.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"609 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115104909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Anatomization Of An Iot Based Aquaponic Farm Model Towards The Agronomic Resilience In The Indian Subcontinental Region","authors":"Pritam Dutta, Roshni Shome, D. Chowdhury","doi":"10.36647/ciml/03.01.a001","DOIUrl":"https://doi.org/10.36647/ciml/03.01.a001","url":null,"abstract":"The paper describes most advanced ways of farming in agronomy. The key motive of this paper is to discuss the creation and monitoring of an IoT based aquaponic model on Indian crops such as paddy and its analysis. Aquaponics integrates both aquaculture as well as hydroponics, the planting of plants in groundwater within a circulation system. The result is beneficial products such as fish and crops as well as reducing nutrient pollution in aquatic habitats This paper includes automating the PH concentration and maintaining likewise for crops management for hence better results such as more yield and more income.The primary mechanism is to control the flow of water between the fish pond and the plant beds. The water usage will be 90% lower than the conventional way of farming. More growth will be seen because plants will face ample amounts of nutrients every time. Keyword : Aquaponic; IoT; Agriculture.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132361527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land Use Land Cover Study of Sentinel-2A and Landsat-5 Images Using NDVI and Supervised Classification Techniques","authors":"Dr. Aziz Makandar, Shilpa Kaman","doi":"10.36647/ciml/02.02.a003","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a003","url":null,"abstract":"Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy. Keyword : Land Use Land Cover, Supervised Classification, Remote Sensing, Maximum Likelihood Classification, Normalized Difference Vegetation Index, High Resolution, Multitemporal, Satellite Images.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"75 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114112622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faster-RCNN Based Deep Learning Model for Pomegranate Diseases Detection and Classification","authors":"A. Makandar, Syeda Bibi Javeriya","doi":"10.36647/ciml/02.02.a002","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a002","url":null,"abstract":"India is the largest producer of pomegranates in the world which earns a high profit. However, due to atmospheric conditions such as temperature variations, climate, and heavy rains, pomegranate fruits become infected with various diseases, resulting in agricultural losses. The two most common diseases seen in the Karnataka region are bacterial blight and anthracnose, both of which cause a significant production loss. This paper has detected and classified these two diseases by extracting knowledge from custom trained models using Deep Learning. To overcome the traditional methods, Faster-RCNN helps us to do better object detection. Keyword : Deep Learning, Faster-RCNN, Tensorflow Bacterial blight, Anthracnose, Object detection.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning based robotic End Effector System for Monitoring and Control of External Bleeding of Vehicle Accident Victims, Review Paper","authors":"D. Simango, T. Mushiri, A. Yahya, Madhurima Majumder","doi":"10.36647/ciml/02.02.a004","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a004","url":null,"abstract":"Vehicle accidents are on the rise in the roads due to over speeding, poor roads, and misjudgment during driving, not forgetting overloading and lack of proper vehicle maintenance. Private transport has increased rapidly, thereby resulting in many accidents on the roads. Whenever there is an accident, it has been realized that some accident victims who would have survived the accident end up dead due to continuous bleeding. As a result, many lives are lost because of a lack of emergency measures to avoid that constant loss of blood through external bleeding. In this regard, there is a need to design a robotic system based on machine learning (ML) to monitor and control the external bleeding from vehicle accident victims in the shortest possible time through the utilization of software such as SolidWorks, Matlab, and proteus. The nanotechnology-based system interfaced with the robotic end-effector shall be used to apply to stop gel through the utilization of Comsol software. The robotic system shall be integrated with a monitoring system for precise and useful quantification of the bleeding wound, thus the importance of machine learning to achieve accurate information. Keyword : Robotic end effector, Machine Learning, nanotechnology, monitoring of external bleeding.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123019219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early & Accurate Forecasting of Mid Term Wind Energy Based on PCA Empowered Supervised Regression Model","authors":"P. Dutta, Neha Shaw, K. Das, Luna Ghosh","doi":"10.36647/ciml/02.02.a006","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a006","url":null,"abstract":"In the Development stage, information about the environment should be accessed. This data is pre-processed with the help of Principal Component Analysis (PCA) to reduce the irrelevant attributes. After that MLA’s such as Decision Tree (DT), Random Forest (RF), KNN, Linear regression (LR) & multilayer Neural Network model (MLP-ANN) models are utilized in testing datasets to predict the wind energy. The Power which is determined necessities to check and separate from the first ability to refresh the framework until and except if the necessary dependability is assembled from learning. In the assessment stage, the prepared expectation framework is then utilized to anticipate the Power for the test samples. In this research four statistical performance indicators & training time used to identify the best-fitted model. In the analysis section, it is seen that PCA Based DT outperformed the others algorithm by means of MAE, MSE, RMSE, Regression & Training time. Keyword : Wind speed prediction, Machine Learning techniques, Regression Analysis, Performance metrics","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121243148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of PCA based AdaBoost Machine Learning Model for Predict Mid-Term Weather Forecasting","authors":"S. Sen, S. Saha, Sudipta Chaki, P. Saha, P. Dutta","doi":"10.36647/ciml/02.02.a005","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a005","url":null,"abstract":"In general, weather forecasting is done with the use of enormously complicated physical science models that use a variety of environmental circumstances over a long period of time. Because of the annoyances of the climatic framework, these criteria are frequently fragile, causing the models to produce inaccurate forecasts. The models are mostly run on multiple hubs in a massive High-Performance Computing (HPC) environment that uses a lot of energy. In this research, we offer a climate expectation approach that uses historical data from various climate stations to create basic AI models that may provide meaningful forecasts for specific climatic conditions in the not-too-distant future within a given time frame. In this paper, we offer a climate expectation approach that uses historical data from several climate stations to create basic AI models that can anticipate certain climatic conditions in the not-too-distant future within a given time frame. Overall research performed into two stages; in first stage Principle component Analysis has been used to extract the irrelevant attributes from the datasets. In second stage five different machines learning algorithm used to predict temperature condition for midterm span & finally four performance indicators along with training time used to identify the best fitted model. From the result analysis it is seen that PCA based AdaBoost model is the fittest model with acquired the best outcome of R2, RMSE, MAE & MSE are 0.992, 0.539, 0.398 & 0.209 respectively. Beside of this present model also outperformed than the other state of art model proposed for midterm weather forecasting purpose. Keyword : Weather Forecasting, PCA, Machine learning, Performance indicator","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130416232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Customer Churn in Telecom Industry: A Machine Learning Perspective","authors":"Lopamudra Hota, P. Dash","doi":"10.36647/ciml/02.02.a001","DOIUrl":"https://doi.org/10.36647/ciml/02.02.a001","url":null,"abstract":"The business world is becoming increasingly saturated in today's competitive environment. There is a great deal of competition in the telecommunications industry, especially due to various vibrant service providers. As a result, they have had difficulty retaining their existing customers. As attracting new customers is much more costly than retaining current ones, now is the time to ensure the telecom industry maintains value by retaining customers over acquiring new ones. Numerous machine learning and data mining methods have been proposed in the literature to predict customer churners using heterogeneous customer records over the past decade. This research gives a brief idea on the Customer Churn problem, and explores how various machine learning techniques can be used to predict customer churn via models such as XGBoost, GradientBoost, AdaBoost, ANN, Logistic Regression and Random Forest, and also compare the effectiveness of the models in term of accuracy. Keyword : Machine Learning, Customer Churn, Prediction Model, Random Forest, XGBoost, AdaBoost, GBoost","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128674632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Ontology Based Semantic Search System in Ayurvedic Medicine","authors":"Gayathri M, Dr. Jagadeesk Kannan R.","doi":"10.36647/ciml/02.01.a002","DOIUrl":"https://doi.org/10.36647/ciml/02.01.a002","url":null,"abstract":"India is known for its traditional medicinal system such as Ayurveda, Yoga, Unani, Siddha and Homeopathy. Ayurveda plays a significant role in curing the diseases without any side effects. Medicinal plants or herbs are considered as a major resource in meeting the need of people health care. Information about this medicinal knowledge must be preserved and digitized. There have been a massive number of publications and large number of articles on ayurvedic research in the form of unstructured textual data. Text mining approach is used to provide the solution to handle such voluminous of unstructured data. With the exponential growth of text based data, navigating the relevant information needed is the challenging task. Semantic understanding of document content forms the vital requirement for ensuring the quality of content retrieval. However, the current approaches are finding variation in textual classification in bringing the classification accuracy which may fail to understand the data during classification. Hence, an efficient model is required to search, classify and retrieve the most relevant data. The main objective of this research is to develop an effective and efficient framework and algorithm to search and retrieve the most relevant facts by including the application of ontology-based text mining approach. The current status of research is analyzed and reviewed in the area of semantic web retrieval, ontology-based approaches and various classification technique for building the framework. Text mining with the special emphasis on understanding the semantic meaning of content is achieved by using domain ontology called medicinal plant ontology construction. The challenges in finding the semantically related content for the given query are achieved through semantic web and ontology which enriched the data on web for structured representation thereby providing the strong semantics in knowledge representation. The methodology of information extraction is implemented by using medicinal plant ontology with semantic knowledge representation, an algorithm called OCEC (Ontology based Concept Extraction and Classification) was developed where each term is described semantically by mapping the terms and its related terms in the medicinal plant ontology. The web language called Web Ontology Language (OWL) is used for knowledge representation and is considered as richer semantic description language for describing unstructured and semi-structured content on the web thereby extracting the exact and relevant data and to offer a strong semantic search. To evaluate the performance of the proposed method, less relevant and most relevant documents were collected from online sources and digital libraries. Comparative study has been performed with various classification techniques. The experimental results show that the proposed method out performed. To further prove the efficiency of the model, experiments were conducted by giving different queries and the","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130541921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scope and Integration of Computational Intelligence in Traditional Power Sector","authors":"Ayan Banik","doi":"10.36647/ciml/02.01.a005","DOIUrl":"https://doi.org/10.36647/ciml/02.01.a005","url":null,"abstract":"An electrical power system is an infinite vast complex network of sophisticated equipment and confederate control to ensure a sustainable energy supply. Ever-increasing power demand in the recent decade has made it difficult to maintain its viability. A rapid transformation in the internal architecture of power system infrastructure is the need of the hour to continue the fictitious lifeline. With the passage of time, electricity has become one of the most crucial elements with almost no substitute. Cutting edge energy-efficient technologies and modern generation computational tools can trigger its growth to maximize its potential, which may entirely transform the power sector scenario and make it future-ready. It is predicted that the adoption of artificial intelligence mutually with data science must have a remarkable outcome to incorporate and develop automation and move towards a smart grid by slashing energy consumption, lowering prices, enhance transparency, gear up efficiency and boost clean green renewable sources globally. AI can further improve the planning, operation, and intelligent control of power systems. Data-intensive technologies can be introduced in diverse dimensions of the electricity value chain following an authentic road map which may considerably reduce the conventional challenges and create significant value. In this work, the authors have attempted to study, investigate, and explore the Power System's present outline in context to India and summarize future possibilities, difficulties, and specific outcome in a systematic, logical manner. This novel work shall benefit distinct researchers and dynamic academicians to get a fundamental idea and strengthen their existing knowledge over the subject. Keyword : Energy, power system, AI, data science, cloud computing, automation, management","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128086490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}