Pub Date : 2022-04-27DOI: 10.33130/ajct.2022v08i01.09
R. Anuradha
{"title":"An Assessment on Cardiovascular Disease Prediction and Diagnosis using Machine Learning Algorithms","authors":"R. Anuradha","doi":"10.33130/ajct.2022v08i01.09","DOIUrl":"https://doi.org/10.33130/ajct.2022v08i01.09","url":null,"abstract":"","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127488455","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}
Pub Date : 2022-04-27DOI: 10.33130/ajct.2022v08i01.017
Shubham K Makwana, Vinod Patel
{"title":"A Review on Comparative analysis and methods of Early detection of Brain tumor using Deep Learning CNN","authors":"Shubham K Makwana, Vinod Patel","doi":"10.33130/ajct.2022v08i01.017","DOIUrl":"https://doi.org/10.33130/ajct.2022v08i01.017","url":null,"abstract":"","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145055","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}
Pub Date : 2022-04-27DOI: 10.33130/ajct.2022v08i01.013
Sikha Suhani Bhuyan, A. Mishra
{"title":"Machine Learning Algorithms for Heart Disease Prediction","authors":"Sikha Suhani Bhuyan, A. Mishra","doi":"10.33130/ajct.2022v08i01.013","DOIUrl":"https://doi.org/10.33130/ajct.2022v08i01.013","url":null,"abstract":"","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133248987","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":"Image Captioning from Wikipedia for Multi-Language using Deep Learning Models","authors":"Anusha Garlapati, Neeraj Malisetty, Gayathri Narayanan","doi":"10.33130/ajct.2022v08i01.014","DOIUrl":"https://doi.org/10.33130/ajct.2022v08i01.014","url":null,"abstract":"","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862460","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}
Pub Date : 2022-04-27DOI: 10.33130/ajct.2022v08i01.005
Marianne Eleanor, A. Catanyag, L. Edward, T. Michael, Lochinvar Sim Abundo
{"title":"Financial Analysis Of A Hybrid Tidal Stream Energy System For Sustainable Island Electrification In The Philippines","authors":"Marianne Eleanor, A. Catanyag, L. Edward, T. Michael, Lochinvar Sim Abundo","doi":"10.33130/ajct.2022v08i01.005","DOIUrl":"https://doi.org/10.33130/ajct.2022v08i01.005","url":null,"abstract":"","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121777477","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}
Sports Analytics is a growing industry and one of the best real-word applications of Data Science. In this paper, the interest of author and machine learning capabilities were combined to develop a result predictor for football matches. The model proposed is capable of predicting result of any English Premier League Match at the half-time with 75% accuracy. The full-time result predictor is a system based on ensemble of powerful classification algorithms which can predict the odds of winning and draw of both home team and away team on the basis of goals scored at the half time and the current standings in the league. The model learns from the past records of the league and the results of different models are compared in the last section of the paper. Keywords— Data Science, comparative models, result prediction, football analysis
{"title":"Full Time Result Prediction using Ensemble Techniques","authors":"Mrigank Vashist, Vasudha Bahl, Amita Goel, Nidhi Sengar","doi":"10.33130/ajct.2021v07i03.006","DOIUrl":"https://doi.org/10.33130/ajct.2021v07i03.006","url":null,"abstract":"Sports Analytics is a growing industry and one of the best real-word applications of Data Science. In this paper, the interest of author and machine learning capabilities were combined to develop a result predictor for football matches. The model proposed is capable of predicting result of any English Premier League Match at the half-time with 75% accuracy. The full-time result predictor is a system based on ensemble of powerful classification algorithms which can predict the odds of winning and draw of both home team and away team on the basis of goals scored at the half time and the current standings in the league. The model learns from the past records of the league and the results of different models are compared in the last section of the paper. Keywords— Data Science, comparative models, result prediction, football analysis","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431917","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}
Pub Date : 2021-12-20DOI: 10.33130/ajct.2021v07i03.005
Rajeshwari Dharavath, K. Shyamala
Biomedical technology now plays a critical role in the detection and treatment of a wide range of diseases, from minor to life-threatening. One of the most life-threatening disorders is brain tumour, which is defined as a mass development of abnormal cells in the brain. By avoiding the spread of aberrant cells, early discovery and treatment can save a person's life. In the medical field, it is vital to find a certain image categorization strategy based on tumor cell regions. The tumor region is then selected to perform the segmentation process and then classification is performed. The identificationbased method helps to limit the image area and to identify the border area in a reduced time period. Automatic brain tumor classification is a difficult undertaking due to the enormous geographical and structural heterogeneity of the brain tumor's surrounding environment. The use of Deep Neural Networks classification for automatic brain tumor detection is proposed. The proposed a Relatable Pixel Extraction with Magnetic Resonance Imaging (MRI) Image Segmentation for Brain Tumor Cell Detection (RPEIS-BTCD) using Deep Learning Model. The proposed model is compared with the existing models and the results indicate that the proposed model performance the
{"title":"MRI Image Based Relatable Pixel Extraction with Image Segmentation for Brain Tumor Cell Detection Using Deep Learning Model","authors":"Rajeshwari Dharavath, K. Shyamala","doi":"10.33130/ajct.2021v07i03.005","DOIUrl":"https://doi.org/10.33130/ajct.2021v07i03.005","url":null,"abstract":"Biomedical technology now plays a critical role in the detection and treatment of a wide range of diseases, from minor to life-threatening. One of the most life-threatening disorders is brain tumour, which is defined as a mass development of abnormal cells in the brain. By avoiding the spread of aberrant cells, early discovery and treatment can save a person's life. In the medical field, it is vital to find a certain image categorization strategy based on tumor cell regions. The tumor region is then selected to perform the segmentation process and then classification is performed. The identificationbased method helps to limit the image area and to identify the border area in a reduced time period. Automatic brain tumor classification is a difficult undertaking due to the enormous geographical and structural heterogeneity of the brain tumor's surrounding environment. The use of Deep Neural Networks classification for automatic brain tumor detection is proposed. The proposed a Relatable Pixel Extraction with Magnetic Resonance Imaging (MRI) Image Segmentation for Brain Tumor Cell Detection (RPEIS-BTCD) using Deep Learning Model. The proposed model is compared with the existing models and the results indicate that the proposed model performance the","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131094937","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}
Pub Date : 2021-12-20DOI: 10.33130/ajct.2021v07i03.004
Dr. Asawari Dudwadkar, Omkar N. Tulaskar, Mitesh R. Khedekar, Anuja K. Merwade, Shubham P. Sutrakar
This paper deals with the implementation of software tools and the whole framework for IoT Enabled Notice Board. Notice boards can change the way people communicate with each other, providing important information to large people at the right time. Notice boards are used extensively in schools, colleges, hospitals, railway stations, hotels, malls, etc. The developed system includes the notice board being connected to a local server running on Raspberry Pi 24*7. The notice to be displayed can be sent via the developed Android application or from the webpage. The data is then sent onto the server where it is pushed in a backend MySQL database. From the database, the contents of the notice are then displayed on the Monitor. The system provides an authentication layer to avoid any unauthorized activities since the target audience for the prototype developed is mainly schools and colleges. Keywords—Raspberry Pi, Internet of Things, Database, E-notice
{"title":"IoT Enabled Notice Board","authors":"Dr. Asawari Dudwadkar, Omkar N. Tulaskar, Mitesh R. Khedekar, Anuja K. Merwade, Shubham P. Sutrakar","doi":"10.33130/ajct.2021v07i03.004","DOIUrl":"https://doi.org/10.33130/ajct.2021v07i03.004","url":null,"abstract":"This paper deals with the implementation of software tools and the whole framework for IoT Enabled Notice Board. Notice boards can change the way people communicate with each other, providing important information to large people at the right time. Notice boards are used extensively in schools, colleges, hospitals, railway stations, hotels, malls, etc. The developed system includes the notice board being connected to a local server running on Raspberry Pi 24*7. The notice to be displayed can be sent via the developed Android application or from the webpage. The data is then sent onto the server where it is pushed in a backend MySQL database. From the database, the contents of the notice are then displayed on the Monitor. The system provides an authentication layer to avoid any unauthorized activities since the target audience for the prototype developed is mainly schools and colleges. Keywords—Raspberry Pi, Internet of Things, Database, E-notice","PeriodicalId":138101,"journal":{"name":"ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121233759","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}
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：