{"title":"A Comprehensive Discourse on Shallow Learning and its Applications","authors":"Bonam Geetha Chitti Jyothi, Manas Kumar Yogi","doi":"10.46610/joidta.2024.v01i01.005","DOIUrl":"https://doi.org/10.46610/joidta.2024.v01i01.005","url":null,"abstract":"Shallow learning, a fundamental approach in machine learning, encompasses a variety of algorithms and techniques aimed at learning patterns and making predictions from labelled data. Unlike deep learning, which involves complex architectures with multiple layers of abstraction, shallow learning focuses on simpler models with limited complexity. This abstract explores the essence of shallow learning, its algorithms, applications, and challenges. Shallow learning algorithms include classic methods such as decision trees, support vector machines, k-nearest neighbours, and logistic regression, among others. These algorithms are typically trained using supervised learning techniques, where the model learns from input-output pairs to make predictions on new, unseen data. Shallow learning models excel in tasks such as classification and regression, where the goal is to assign labels or predict continuous values to input data. Applications of shallow learning span across various domains, including healthcare, finance, marketing, and cyber security. In healthcare, shallow learning models are used for disease diagnosis and prognosis prediction based on patient data. In finance, these models aid in fraud detection, credit scoring, and stock market prediction. Marketing applications involve customer segmentation and churn prediction, while in cyber security; shallow learning is utilized for malware detection and network intrusion detection.","PeriodicalId":516987,"journal":{"name":"Journal of Intelligent Decision Technologies and Applications","volume":"48 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661005","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":"Survey on E-Commerce Product Price Monitoring System","authors":"Yogesh Patil, Rahil Desai, Anand Gudnavar","doi":"10.46610/joidta.2024.v01i01.001","DOIUrl":"https://doi.org/10.46610/joidta.2024.v01i01.001","url":null,"abstract":"In our fast-paced digital era, online shopping has become integral to daily life, prompting consumers to seek the best deals and lowest prices. The E-commerce product price monitoring system addresses this need by offering a sophisticated solution, allowing users to actively track and monitor product prices across diverse E-commerce platforms. The perpetual challenge faced by online shoppers– identifying the optimal time to purchase price fluctuations – is efficiently managed by the system. This innovative tool provides real-timenotifications, alerting users when the prices ofdesired products drop. By eliminating the needfor continuous platform monitoring, it empowers users to capitalize on the most favorable deals effortlessly. In the dynamiclandscape of digital commerce, the E-commerce product price monitoring system serves as a reliable companion, reshaping the online shopping experience. Its integration into the consumer journey introduces unprecedented convenience, ensuring that users are well-informed and equipped to makepurchase decisions at precisely the rightmoment. Ultimately, the system maximizes savings, enhances overall satisfaction, and establishes itself as an indispensable asset in navigating the intricate world of E-commerce.","PeriodicalId":516987,"journal":{"name":"Journal of Intelligent Decision Technologies and Applications","volume":"38 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895257","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}