International Research Journal on Advanced Science Hub最新文献

{"title":"Emotion Analysis Using Speech","authors":"Krupashree M M, Naseeba Begum, N. Priya, N. S, Rashmi Motkur","doi":"10.47392/irjash.2023.s023","DOIUrl":"https://doi.org/10.47392/irjash.2023.s023","url":null,"abstract":"The main goal of our project is to identify the emotions a speaker evokes when speaking. For example, utterances uttered in states of fear, surprise, excite-ment, anger, or joy are loud and fast and have a large and wide pitch range, whereas utterances uttered in states of depression or fatigue are slow and deep. This is us We use deep learning techniques to build models that can identify human emotions through the analysis of speech and language patterns. The main reason for choosing this project is that speech sentiment analysis has become one of the largest commercialization strategies in which client moods and dispositions play a large role. Therefore, there is an increased demand for products or companies to recognize an individual’s emotions and recommend appropriate products or assist him accordingly. It can also be used to monitor status. More recently, speech recognition and analysis have also been applied to medicine and forensics.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128395258","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 Literature Review on Using Machine Learning Algorithm to Predict House Prices","authors":"Tanmoy Dhar, M. P","doi":"10.47392/irjash.2023.s017","DOIUrl":"https://doi.org/10.47392/irjash.2023.s017","url":null,"abstract":"In this study, we use a variety of machine-learning methods to forecast the sale prices of residences. The size, location, building type, age, number of bedrooms, garages, and other characteristics of the property all affect how much it is worth when it is sold. Machine-learning algorithms are employed to develop the prediction model for houses in this article. Using machine learning methods, such as call trees, supply regression, support vector regression, and the Lasso Regression methodology, a prognostic model is developed in this case. Also, we have contrasted supported parameters for these algorithms such as MAE, MSE, RMSE, and accuracy. In this research, machine learning algorithms are used as a hunting tool to create models for predicting housing value.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480339","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":"Intelligent Farming Techniques Using Machine Learning","authors":"Sanush Br, S. Vijay, Soujan Wd, U. Kumar, Yaswanth Velpuri","doi":"10.47392/irjash.2023.s033","DOIUrl":"https://doi.org/10.47392/irjash.2023.s033","url":null,"abstract":".","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125443560","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":"Predict Customer Churn through Customer Behaviour using Machine Learning Algorithms","authors":"H. T, H. T, Sushma G, Charankumar Reddy P, Mohammad Thahir S","doi":"10.47392/irjash.2023.s045","DOIUrl":"https://doi.org/10.47392/irjash.2023.s045","url":null,"abstract":"Customers are becoming more concerned to the quality of service (QoS) offered by organizations in the present. However, the present day shows greater rivalry in offering the clients with technologically innovative QoS. However, an orga-nization may benefit from effective customer relationship management systems in order to increase sales, maintain relationships with existing customers and improve customer retention. The customer retention strategies can benefit greatly by the use of machine learning models like Decision Tree, Na¨ıve-Bayes Classification, Logistic Regression algorithms.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258027","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":"Parkinson Disease Diagnosis and Severity Rating Prediction Based on Gait analysis using Deep Learning","authors":"K. Sai Kumar, Indeti Sirisha, Kalakoti Vathsalya, Kotha Krishna Veera V enkata Vamsi","doi":"10.47392/irjash.2023.s057","DOIUrl":"https://doi.org/10.47392/irjash.2023.s057","url":null,"abstract":"R.V.R","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169623","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":"Soul Beat – The Red Guardian","authors":"A. Deepika, G. A., Selvakanmani S","doi":"10.47392/irjash.2023.s020","DOIUrl":"https://doi.org/10.47392/irjash.2023.s020","url":null,"abstract":"R.M.K","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742473","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 Hybrid Approach of Weather Forecasting using Data Mining","authors":"stutiii i, Shashwat Tandon, Manjula R, Shiv Kumar","doi":"10.47392/irjash.2023.s029","DOIUrl":"https://doi.org/10.47392/irjash.2023.s029","url":null,"abstract":"In the paper, the work focuses on weather prediction by using real time data from day to day. Weather Prediction has proven to be a very important application of Machine Learning since the beginning. Different models were studied and found out ways how prediction could be made more accurate by aban-doning the classical models and adopted a hybrid method of including more than hundred decision trees bagged to form an aggregate total. The aggregate results achieved from each tree was considered to be a random split of data, saving a lot of computation time. Gradient Boosting was used to increase accuracy significantly making it a very efficient model to work with. The boosting helped the weak learner Decision Tree to select a random sample of data, fit it with a model and train it sequentially to compensate for the weakness of its predecessor. To improve the accuracy of a model in boosting, a combination of a convex loss function, which measures the gap between the expected and goal outputs, and a penalty term for the complexity of the model were used to reduce a regularized objective function that included both L1 and L2 regression tree functions. The resulting model achieved a significantly high level of accuracy when tested with new data.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"24 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133387729","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":"Automated Brain Tumor Segmentation Using Attention gate Inception UNet with Guided Decoder","authors":"A. P., Adersh V R","doi":"10.47392/irjash.2023.s062","DOIUrl":"https://doi.org/10.47392/irjash.2023.s062","url":null,"abstract":"Brain tumor segmentation technology is a crucial step for the detection and treatment of MRI brain tumors. Tumors can occur in various locations and can be of any size or form. The use of skip connections in MRI brain tumor segmentation approach based on U-Net architecture helps to incorporate low-level and high-level feature information and has recently gained popularity. By introducing an attention mechanism into the UNet architecture, the performance of local feature expression and medical image segmentation can be enhanced. In this paper, we present an innovative deep learning architecture called Attention gate Inception UNet with Guided Decoder for brain tumor segmentation. The backbone of the model is a popular segmentation method called U-Net architecture. While dealing with small-scale tumors, the U-Net network has low segmentation accuracy. Therefore several modifications are made, which results in the integration of attention gates and inception block together with a guided decoder. A sequence of attention gate modules are introduced to the skip connection, that focus on a selected part of an image while ignoring the others. The inception module used will help us to extract further characteristics at each layer. The proposed architecture has the ability of explicitly guiding each decoder layer’s learning process and it is supervised by using individual loss function, allowing them to produce efficient","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133527874","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":"Diabetic Retinopathy (DR) Detection and Grading Using Federated Learning (FL)","authors":"Priya Vishnu A S, Vijaykumar D, M. R, Suryaprakash P, S. R.","doi":"10.47392/irjash.2023.s054","DOIUrl":"https://doi.org/10.47392/irjash.2023.s054","url":null,"abstract":"Diabetic Retinopathy (DR) is the predominant and leading causes of blindness for people who have affected by diabetes in the world. DR Complication leads to affect the eyes and can lead to vision loss. Early detection and treatment are crucial for preventing or slowing the progression of the disease. In this study, we propose an approach for detecting diabetic retinopathy using federated learning (FL). A distributed machine learning technique called federated learning allows numerous devices to work together to jointly train a deep learning model without sharing their raw data. Each device in federated learning builds a local model on its own data, then aggregates the base model parameters to upgrade a global model. This process is repeated iteratively until convergence is reached. Computer-Aided Diagnosis frameworks are initially using machine learning and deep learning algorithms. DR diagnostic tools have been established in recent years using machine learning and deep learning models. these models need big data for training and testing to validation of model behaviour. The Federated Learning utilizes the collaboration of multiple devices to train a deep learning model without compromising the privacy of individual patient data. Data dimensionality reduction and data cleaning and other exploratory data analysis process are carried as before implementing the model. We show that federated learning can be used to overcome the problems caused by class imbalance when using real-world patient data. The main goal is to create a system that can control several medical facilities while maintaining data privacy. The findings indicate that the federated learning-based strat-egy is very accurate in identifying diabetic retinopathy and offers a potential technique for enhancing the early diagnosis and management of this condition. The proposed model outperforms existing state-of-the-art techniques in detecting DR and grading the severity of penetration levels while employing unseen fundus images, according to an analysis of observing performance metrics and model interpretation with reliability.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133120570","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 of Anomalous Subgraph Detection in Social Networks","authors":"Anagha Ajoykumar, V. M","doi":"10.47392/irjash.2023.s039","DOIUrl":"https://doi.org/10.47392/irjash.2023.s039","url":null,"abstract":"The reliance on the internet has made it possible for a number of internet networks to arise, each with a distinct user base. Intentionally or not, we are all members of a wide range of social networks. Online interpersonal and professional interactions are significantly influenced by social networking. It has a tremendous effect on a global scale and an individual one, affecting a wide range of industries including education, healthcare, entertainment, bank-ing, and telecommunications. As their dependency on social media increases, users are publishing a lot of information about themselves online, leaving their data and themselves vulnerable to the outside world and making them ideal targets for criminals which not only jeopardizes the security of the social network’s data but also make way to a slew of other potentially harmful situations, ranging from identity theft to major cybercrime such as hacking, cyber-bullying cyber threats, and even national security threats such as terrorism. This neces-sitated the development of methods and strategies to detect fraudulent users or abnormalities on social media. A graph framework is the most prominent form of mathematical modeling of a social network, hence deducing methods to identify abnormalities from a graph is critical. This paper gives a thorough review of graph-based anomaly detection methods, with a focus on identifying anomalous subgraphs.","PeriodicalId":244861,"journal":{"name":"International Research Journal on Advanced Science Hub","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115132270","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}