Periodico Di Mineralogia最新文献_第2页

Like, Share and Comment: Adolescent’s Social Media Motivators and Threat During Covid-19 Lockdown 点赞、分享和评论:青少年在Covid-19封锁期间的社交媒体动机和威胁

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-21 DOI: 10.37896/pd91.4/91479

引用次数: 0

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique 使用深度CNN-SLSTM技术的Twitter COVID推文分类

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91477

引用次数: 0

Empowering Women Entrepreneurship During and After Covid-19 Pandemic- A Study in South India 在Covid-19大流行期间和之后赋予女性创业能力——一项在南印度的研究

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91476

B. Chitra, M. Vijaya, S. Yamuna

{"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}

引用次数: 0

Detection and Classification of Brain Tumor on MR Imaging using Deep Neural Network based VGG-19 Architecture 基于VGG-19结构的深度神经网络对脑肿瘤磁共振成像的检测与分类

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91444

G. Saranya, H. Venkateswaran

{"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}

引用次数: 1

Resting State Analysis: Simulation and validation of RS-fMRI dataset for ADHD Subjects 静息状态分析:ADHD受试者RS-fMRI数据集的模拟与验证

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91475

P. Prasanth, O. Maheswari

引用次数: 0

A Noval Method of Levenberg-Marquardt Based Solar Mppt for Single Phase Grid Connected System 一种基于Levenberg-Marquardt的单相并网系统最大功率分析新方法

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91474

引用次数: 0

OHAR: Optimized Human Action Recognition Paradigm using Optimized Type 2 Neuro-Fuzzy Classifier 使用优化的2型神经模糊分类器的优化人类动作识别范式

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-20 DOI: 10.37896/pd91.4/91445

DR. J. A. Smitha, R. Ramamoorthy, A. Naidu

{"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}

引用次数: 1

Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment 基于一类支持向量机的混合深度学习技术在拥挤环境下的异常检测

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-18 DOI: 10.37896/pd91.4/91442

N. Priyadharsini, R. Kavitha, A. Kaliappan, D. Chitra

引用次数: 0

Tikhonov Kullback Leibler Vuong Logistic Machine Learning Classifier for Early Disease Diagnosis Over Big Data Tikhonov Kullback Leibler Vuong基于大数据的早期疾病诊断逻辑机器学习分类器

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-18 DOI: 10.37896/pd91.4/91443

{"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}

引用次数: 0

A Productive Friction Stir-Welded Aa 6061 Joints Using Different Pin Tool Profiles Configuration to Grow Mechanical and Microstructural Characteristics 采用不同销形工具配置的a6061多产摩擦搅拌焊接接头提高力学和显微组织特性

IF 0.7 4区地球科学

Periodico Di Mineralogia Pub Date : 2022-04-17 DOI: 10.37896/pd91.4/91439

Pandy Madhan Kumar, V. Anbumalar

引用次数: 0