{"title":"An Application of the Filter Feature Selection Method in a Machine Learning Model for the Prognostic of Parkinson's Disease","authors":"D. Stalin David","doi":"10.46253/j.mr.v5i2.a4","DOIUrl":"https://doi.org/10.46253/j.mr.v5i2.a4","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668271","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":"Review and Analysis of Deep Learning-based Approach on Artificial Emotional Intelligence","authors":"Harikrushnareddyvangala","doi":"10.46253/j.mr.v5i3.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v5i3.a1","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126994257","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}
: In the research community field, query optimization plays an important role to retrieve the important and the appropriate documents on the basis of query indexing. In the documents, using the query retrieval process the information is retrieved on the basis of the distance measured. Although several methods are present in the query processing scheme as well as indexing, extracting the matched as well as appropriate documents still outcomes in numerous confronts in the research community. Hence, to retrieve the appropriate documents competently an effective cluster-based inverted indexing model is adopted. By exploiting stop word removal and stemming approaches, unnecessary and redundant words are removed. By cluster-based inverted indexing approach, document indexing is carried out that is the integration of Possibilistic fuzzy c-means (PFCM) clustering approach to index the documents. For user queries, such as multigram queries or semantic queries, on basis of Bhattacharyya distance to generate an enhanced query outcome, query matching is processed. By exploiting the Pearson correlation coefficient, the query optimization is carried out and the appropriate documents are retrieved efficiently. The achievement of a developed cluster-based indexing approach is carried out in this paper. The developed cluster-based indexing approach performance is calculated by exploiting measures, namely precision, recall, as well as F-measure. exploiting the Bhattacharyya distance. On the basis of the least distance measure or Bhattacharya distance, the enhanced query matching outcomes were obtained. The Pearson correlation coefficient was used by the query optimization on the basis of the interactive query optimization and retrieves appropriate documents competently. The developed cluster-based inverted indexing approach obtains enhanced performance with the measures, such as recall, precision, as well as F-measure values.
{"title":"Query Indexing and Cluster-based Indexing Model for the Document Retrieval","authors":"Sathish Vuyyala","doi":"10.46253/j.mr.v4i4.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v4i4.a2","url":null,"abstract":": In the research community field, query optimization plays an important role to retrieve the important and the appropriate documents on the basis of query indexing. In the documents, using the query retrieval process the information is retrieved on the basis of the distance measured. Although several methods are present in the query processing scheme as well as indexing, extracting the matched as well as appropriate documents still outcomes in numerous confronts in the research community. Hence, to retrieve the appropriate documents competently an effective cluster-based inverted indexing model is adopted. By exploiting stop word removal and stemming approaches, unnecessary and redundant words are removed. By cluster-based inverted indexing approach, document indexing is carried out that is the integration of Possibilistic fuzzy c-means (PFCM) clustering approach to index the documents. For user queries, such as multigram queries or semantic queries, on basis of Bhattacharyya distance to generate an enhanced query outcome, query matching is processed. By exploiting the Pearson correlation coefficient, the query optimization is carried out and the appropriate documents are retrieved efficiently. The achievement of a developed cluster-based indexing approach is carried out in this paper. The developed cluster-based indexing approach performance is calculated by exploiting measures, namely precision, recall, as well as F-measure. exploiting the Bhattacharyya distance. On the basis of the least distance measure or Bhattacharya distance, the enhanced query matching outcomes were obtained. The Pearson correlation coefficient was used by the query optimization on the basis of the interactive query optimization and retrieves appropriate documents competently. The developed cluster-based inverted indexing approach obtains enhanced performance with the measures, such as recall, precision, as well as F-measure values.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127661097","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":"Forest Change Detection and Prediction using the Crow Chicken Swarm Optimization based Deep LSTM Classifier","authors":"M Naveen Kumar","doi":"10.46253/j.mr.v6i1.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v6i1.a2","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729593","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":"Study and Analysis of Various Pan-Sharpening Techniques: A Challenging Overview","authors":"Preeti Singh","doi":"10.46253/j.mr.v5i1.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v5i1.a2","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117215125","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}
: The increase in population leads to an increase in the need for food production. A healthy, pest-free plant can providea considered amount of yield in time. However, many conditions affect crop production.Identifying crop disease accurately, fast, and intelligently, plays an important role in agriculture informatization development. Most existing methods are performed manually, which affects the identifyingresult. Careful monitoring and diagnosis of crops for a large area manually is a tedious process. To address these issues, we proposed an improved crop disease identification based on the convolutional neural network (CNN) architecture.The first operation is to resize crop images and to be normalizedthem.Here, we built a neural network toload data samples for training and dividedthe verification set and training set. Furthermore, to adjust the learning rate dynamically, we use Adam algorithms which combinedthe RMSprop algorithm and momentum algorithm to improve the training learning rate.Finally, we used PlantVillage dataset to carry out the validations, this dataset contains 38 different types of crops. The experimentalresult showed the test accuracy and validation accuracy are100% and 97.50% respectively. Compared with state-of-the-art methods, our proposed model has higher detection accuracy. The convolutional neural network proposed in this paper has a high accuracy and fast training speed. The proposed architecture is less time-consuming which can help to improve the development of smart agriculture.
{"title":"An Improved Crop Disease Identification Based on the Convolutional Neural Network","authors":"Hiba Asri","doi":"10.46253/j.mr.v6i3.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v6i3.a2","url":null,"abstract":": The increase in population leads to an increase in the need for food production. A healthy, pest-free plant can providea considered amount of yield in time. However, many conditions affect crop production.Identifying crop disease accurately, fast, and intelligently, plays an important role in agriculture informatization development. Most existing methods are performed manually, which affects the identifyingresult. Careful monitoring and diagnosis of crops for a large area manually is a tedious process. To address these issues, we proposed an improved crop disease identification based on the convolutional neural network (CNN) architecture.The first operation is to resize crop images and to be normalizedthem.Here, we built a neural network toload data samples for training and dividedthe verification set and training set. Furthermore, to adjust the learning rate dynamically, we use Adam algorithms which combinedthe RMSprop algorithm and momentum algorithm to improve the training learning rate.Finally, we used PlantVillage dataset to carry out the validations, this dataset contains 38 different types of crops. The experimentalresult showed the test accuracy and validation accuracy are100% and 97.50% respectively. Compared with state-of-the-art methods, our proposed model has higher detection accuracy. The convolutional neural network proposed in this paper has a high accuracy and fast training speed. The proposed architecture is less time-consuming which can help to improve the development of smart agriculture.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133245492","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":"Controlling the Computer using Hand Gestures","authors":"Pradnya Kedari,Shubhangi Kadam,Rajesh Prasad","doi":"10.46253/j.mr.v5i3.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v5i3.a2","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133533945","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}
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