{"title":"Like, Share and Comment: Adolescent’s Social Media Motivators and Threat During Covid-19 Lockdown","authors":"","doi":"10.37896/pd91.4/91479","DOIUrl":"https://doi.org/10.37896/pd91.4/91479","url":null,"abstract":"","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"12 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73303486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique","authors":"","doi":"10.37896/pd91.4/91477","DOIUrl":"https://doi.org/10.37896/pd91.4/91477","url":null,"abstract":"","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"65 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80614343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim: Examining the success of women's entrepreneurship, during and after Covid-19 pandemic, in South India. Methodology: The study adopts the quantitative method. Data is acquired through 'survey' as the tool. The regression and percentage analysis are used for examining the data with SPSS as software. The targets are the women entrepreneurs (SMEs) in South India. The sample size (n) is 254. Association of the variables is found through hypothesis testing. Findings: The outcome from analyses indicates both internal and external factors impact the success of women entrepreneurs in India amid Covid-19. More than external factors, during Covid-19, the motivation, need-for-achievement, self-confidence and risk-taking were found to be more impactful in a woman entrepreneur's success. Value/Originality: The paper examined and investigated the impact of Covid-19 on women entrepreneurs and found that technological implications in businesses and social networking in entrepreneurship during Covid-19, highly assisted the women entrepreneurs and supported their sales and operations which the traditional business lacked and was limited during Covid-19. Conclusion: Research concluded that internal and external factors indeed impact the small-and-medium entrepreneurs where during the Covid-19, internal factors impacted more than external factors. Though external factors like socio-cultural and economic hindrances impacted the women entrepreneurs, the willingness, risking capability and level-of-confidence to compete and survive was found to be the key drivers that kept the women entrepreneurs to sustain.
{"title":"Empowering Women Entrepreneurship During and After Covid-19 Pandemic- A Study in South India","authors":"B. Chitra, M. Vijaya, S. Yamuna","doi":"10.37896/pd91.4/91476","DOIUrl":"https://doi.org/10.37896/pd91.4/91476","url":null,"abstract":"Aim: Examining the success of women's entrepreneurship, during and after Covid-19 pandemic, in South India. Methodology: The study adopts the quantitative method. Data is acquired through 'survey' as the tool. The regression and percentage analysis are used for examining the data with SPSS as software. The targets are the women entrepreneurs (SMEs) in South India. The sample size (n) is 254. Association of the variables is found through hypothesis testing. Findings: The outcome from analyses indicates both internal and external factors impact the success of women entrepreneurs in India amid Covid-19. More than external factors, during Covid-19, the motivation, need-for-achievement, self-confidence and risk-taking were found to be more impactful in a woman entrepreneur's success. Value/Originality: The paper examined and investigated the impact of Covid-19 on women entrepreneurs and found that technological implications in businesses and social networking in entrepreneurship during Covid-19, highly assisted the women entrepreneurs and supported their sales and operations which the traditional business lacked and was limited during Covid-19. Conclusion: Research concluded that internal and external factors indeed impact the small-and-medium entrepreneurs where during the Covid-19, internal factors impacted more than external factors. Though external factors like socio-cultural and economic hindrances impacted the women entrepreneurs, the willingness, risking capability and level-of-confidence to compete and survive was found to be the key drivers that kept the women entrepreneurs to sustain.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88996148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: The massive growth of abnormal cell development in the brain region is known as a tumor. It is treated as a high prior disease in the modern medical domain, and it is difficult to cure. This type of tumor can be controlled only if it is diagnosed at an earlier stage. For making the accurate analysis and diagnosis process, the MR imaging tool is used by the radiologist. The exact portion of the tumor can be addressed by an MR image from the brain region. A deep convolutional neural network-based (DCNN) on Visual Geometry Group (VGG-19) architecture is proposed to detect the malignant portion in the brain region from the brain magnetic resonance imaging (MRI) dataset. The publically available BraTS dataset is used in our experimental study. The proposed DCNN uses a layer-based automatic segmentation and classification technique, and the hierarchy of the system is followed by, preprocessing, segmentation, feature extraction, and classification. A softmax classifier is used alongside the classification process, in order to classify the brain MR images efficiently. All together, obtained training and testing accuracy outcome of the proposed system is 99.2%, and the training and testing loss outcomes are 0.158 and 0.138 respectively.
{"title":"Detection and Classification of Brain Tumor on MR Imaging using Deep Neural Network based VGG-19 Architecture","authors":"G. Saranya, H. Venkateswaran","doi":"10.37896/pd91.4/91444","DOIUrl":"https://doi.org/10.37896/pd91.4/91444","url":null,"abstract":": The massive growth of abnormal cell development in the brain region is known as a tumor. It is treated as a high prior disease in the modern medical domain, and it is difficult to cure. This type of tumor can be controlled only if it is diagnosed at an earlier stage. For making the accurate analysis and diagnosis process, the MR imaging tool is used by the radiologist. The exact portion of the tumor can be addressed by an MR image from the brain region. A deep convolutional neural network-based (DCNN) on Visual Geometry Group (VGG-19) architecture is proposed to detect the malignant portion in the brain region from the brain magnetic resonance imaging (MRI) dataset. The publically available BraTS dataset is used in our experimental study. The proposed DCNN uses a layer-based automatic segmentation and classification technique, and the hierarchy of the system is followed by, preprocessing, segmentation, feature extraction, and classification. A softmax classifier is used alongside the classification process, in order to classify the brain MR images efficiently. All together, obtained training and testing accuracy outcome of the proposed system is 99.2%, and the training and testing loss outcomes are 0.158 and 0.138 respectively.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"408 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75753990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Novel methods for the analysis of functional magnetic resonance imaging (fMRI) data are being reported lately. It is necessary to validate these methods for reliability as the interpretations of the results are subjective as the ground truth in the fMRI data is not known. Validation of analysis methods requires knowledge of the ground truth of the data. Simulation studies are necessary to assess the quality of the statistical technique/analysis methods. The simulated fMRI dataset provided by various research institutions and researchers are mostly event/task-related. Resting-state fMRI analysis has been gaining importance recently for its ability to be used as a biomarker for various psychopathological conditions. Hence there is a need to generate simulated data for evaluation of the resting-state fMRI data analysis methods. In this paper, a method is proposed to simulate a complete 4D resting-state fMRI data using MATLAB. The fMRI data is simulated for normal and ADHD subject and the results are compared with real time data.
{"title":"Resting State Analysis: Simulation and validation of RS-fMRI dataset for ADHD Subjects","authors":"P. Prasanth, O. Maheswari","doi":"10.37896/pd91.4/91475","DOIUrl":"https://doi.org/10.37896/pd91.4/91475","url":null,"abstract":"Novel methods for the analysis of functional magnetic resonance imaging (fMRI) data are being reported lately. It is necessary to validate these methods for reliability as the interpretations of the results are subjective as the ground truth in the fMRI data is not known. Validation of analysis methods requires knowledge of the ground truth of the data. Simulation studies are necessary to assess the quality of the statistical technique/analysis methods. The simulated fMRI dataset provided by various research institutions and researchers are mostly event/task-related. Resting-state fMRI analysis has been gaining importance recently for its ability to be used as a biomarker for various psychopathological conditions. Hence there is a need to generate simulated data for evaluation of the resting-state fMRI data analysis methods. In this paper, a method is proposed to simulate a complete 4D resting-state fMRI data using MATLAB. The fMRI data is simulated for normal and ADHD subject and the results are compared with real time data.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"42 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81288102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Noval Method of Levenberg-Marquardt Based Solar Mppt for Single Phase Grid Connected System","authors":"","doi":"10.37896/pd91.4/91474","DOIUrl":"https://doi.org/10.37896/pd91.4/91474","url":null,"abstract":"","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"33 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83155704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human activity recognition (HAR) is made to identify actions and goals of persons one or more from the images which contain sequence of actions related on environments and actions. However, different issues and challenges are increased in the applications of human activity recognition for improving detection accuracy with different activities. Hence, Optimized Human Action Recognition Paradigm (OHAR) is developed. In the paper, optimized type 2 fuzzy classifier is designed to classify human actions from the image database. The input video is transformed into multiple region in the initial stage. The collected frames are sent to the pre-processing stage for removing noise from frames. After that, the key frame is selected from the image frames by using the Structural Similarity Index (SSIM). Once key frames are selected, the three feature extraction methods are utilized like Space-Time Interest (STI) points, grid shape feature, and coverage factor. Finally, the proposed classifier is proceeding to human activity recognition with selected features set. Here, an optimized type 2 neuro-fuzzy classifier is used for detecting human action. The proposed classifier is enhanced Rider optimization algorithm (ROA). The presentation of proposed method is evaluated based on statistical computations such as accuracy, sensitivity, specificity, recall, and F_Score.
{"title":"OHAR: Optimized Human Action Recognition Paradigm using Optimized Type 2 Neuro-Fuzzy Classifier","authors":"DR. J. A. Smitha, R. Ramamoorthy, A. Naidu","doi":"10.37896/pd91.4/91445","DOIUrl":"https://doi.org/10.37896/pd91.4/91445","url":null,"abstract":"Human activity recognition (HAR) is made to identify actions and goals of persons one or more from the images which contain sequence of actions related on environments and actions. However, different issues and challenges are increased in the applications of human activity recognition for improving detection accuracy with different activities. Hence, Optimized Human Action Recognition Paradigm (OHAR) is developed. In the paper, optimized type 2 fuzzy classifier is designed to classify human actions from the image database. The input video is transformed into multiple region in the initial stage. The collected frames are sent to the pre-processing stage for removing noise from frames. After that, the key frame is selected from the image frames by using the Structural Similarity Index (SSIM). Once key frames are selected, the three feature extraction methods are utilized like Space-Time Interest (STI) points, grid shape feature, and coverage factor. Finally, the proposed classifier is proceeding to human activity recognition with selected features set. Here, an optimized type 2 neuro-fuzzy classifier is used for detecting human action. The proposed classifier is enhanced Rider optimization algorithm (ROA). The presentation of proposed method is evaluated based on statistical computations such as accuracy, sensitivity, specificity, recall, and F_Score.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"2 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81070963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Priyadharsini, R. Kavitha, A. Kaliappan, D. Chitra
of pattern matching in developed a hybrid deep learning based on a pre-trained Convolution Neural Network and One-class SVM is trained with spatial features for robust classification of abnormal shapes. the experimental the proposed anomaly detection techniques existing techniques in of within a continuous learning setup. Multi cue learning approach presents rule based event detection and multiple feature tracking.
{"title":"Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment","authors":"N. Priyadharsini, R. Kavitha, A. Kaliappan, D. Chitra","doi":"10.37896/pd91.4/91442","DOIUrl":"https://doi.org/10.37896/pd91.4/91442","url":null,"abstract":"of pattern matching in developed a hybrid deep learning based on a pre-trained Convolution Neural Network and One-class SVM is trained with spatial features for robust classification of abnormal shapes. the experimental the proposed anomaly detection techniques existing techniques in of within a continuous learning setup. Multi cue learning approach presents rule based event detection and multiple feature tracking.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"63 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83678994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With big data widening in healthcare groups, precise investigation of medical data conveniences early disease detection. However, the analysis accuracy is reduced when the parallel processing of medical data is not performed. Moreover, with curse of dimensionality as several regions discloses distinctive facets and if not properly filtered, relevant information’s are also discarded which may reduce the early prediction of disease outbreaks. To address these issues, in this work, a method using machine learning technique called, Polynomial Tikhonov Entropy and Kullback Vuong Logistic Classifier (PTE-KVLC) is presented. First, Inverse Polynomial Map Reduce Pre-processing is applied to the input data that both minimizes the signal to noise ratio and obtains computationally efficient features via parallel processing. This is turn provides a mean for early detection of epileptic seizures. Second, the feature extraction model is based on Entropy Tikhonov Regularization and is applied to the pre-processed features to identify the features pertinent to seizures. These features are then selected and fed into a Kullback–Leibler Vuong and Logistic Regressive Machine Learning Classifier for early epileptic seizure recognition. Experimental results demonstrate that the proposed method significantly classifies the epileptic seizure classes by means of specificity, sensitivity, and accuracy.
{"title":"Tikhonov Kullback Leibler Vuong Logistic Machine Learning Classifier for Early Disease Diagnosis Over Big Data","authors":"","doi":"10.37896/pd91.4/91443","DOIUrl":"https://doi.org/10.37896/pd91.4/91443","url":null,"abstract":"With big data widening in healthcare groups, precise investigation of medical data conveniences early disease detection. However, the analysis accuracy is reduced when the parallel processing of medical data is not performed. Moreover, with curse of dimensionality as several regions discloses distinctive facets and if not properly filtered, relevant information’s are also discarded which may reduce the early prediction of disease outbreaks. To address these issues, in this work, a method using machine learning technique called, Polynomial Tikhonov Entropy and Kullback Vuong Logistic Classifier (PTE-KVLC) is presented. First, Inverse Polynomial Map Reduce Pre-processing is applied to the input data that both minimizes the signal to noise ratio and obtains computationally efficient features via parallel processing. This is turn provides a mean for early detection of epileptic seizures. Second, the feature extraction model is based on Entropy Tikhonov Regularization and is applied to the pre-processed features to identify the features pertinent to seizures. These features are then selected and fed into a Kullback–Leibler Vuong and Logistic Regressive Machine Learning Classifier for early epileptic seizure recognition. Experimental results demonstrate that the proposed method significantly classifies the epileptic seizure classes by means of specificity, sensitivity, and accuracy.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"119 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82468056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are six brand-new dual-pin FSW tools that are displayed, each with a unique combination of Dual Circle, Dual Triangle, Dual rectangle, and Triangle-Rectangle profiles. Combination pins in the shapes of circles, triangles and rectangles were developed. The samples with the welded joints underwent quasi-static testing, and information on stress-strain was gathered. The use of dual pin welding equipment resulted in expanded SZ and improved plastic flow, among other micro-structural changes. The highest tensile strength and ductility were found in the weld connections made using Dual Triangle and Rectangle-Triangle tools. This investigation looks at the effects of tool shapes on the tensile characteristics and micro-structural components in the stir zone and heat-affected zone of friction stir-welded Al 6061 joints.
{"title":"A Productive Friction Stir-Welded Aa 6061 Joints Using Different Pin Tool Profiles Configuration to Grow Mechanical and Microstructural Characteristics","authors":"Pandy Madhan Kumar, V. Anbumalar","doi":"10.37896/pd91.4/91439","DOIUrl":"https://doi.org/10.37896/pd91.4/91439","url":null,"abstract":"There are six brand-new dual-pin FSW tools that are displayed, each with a unique combination of Dual Circle, Dual Triangle, Dual rectangle, and Triangle-Rectangle profiles. Combination pins in the shapes of circles, triangles and rectangles were developed. The samples with the welded joints underwent quasi-static testing, and information on stress-strain was gathered. The use of dual pin welding equipment resulted in expanded SZ and improved plastic flow, among other micro-structural changes. The highest tensile strength and ductility were found in the weld connections made using Dual Triangle and Rectangle-Triangle tools. This investigation looks at the effects of tool shapes on the tensile characteristics and micro-structural components in the stir zone and heat-affected zone of friction stir-welded Al 6061 joints.","PeriodicalId":20006,"journal":{"name":"Periodico Di Mineralogia","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83465680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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