Mahalakshmi K, Dharish Jaya priyan J, DharshanRaj N, Aravind A
Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations of different ML techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.
准确的房价预测对于房地产市场的利益相关者和经济政策的制定至关重要。本研究探讨了如何应用复杂的机器学习（ML）算法来提高房价预测的准确性。通过分析现有文献，我们探讨了使用 ML 方法预测房价的方法。我们强调精确预测对购房者、卖房者、投资者和政策制定者等不同利益相关者的重要意义。我们的目标是通过严谨的分析提高模型的可预测性，从而通过我们的研究促进住房交易、投资和政策实施方面的明智决策。
{"title":"Enhancing House Price Predictability: A Comprehensive Analysis of Machine Learning Techniques for Real Estate and Policy Decision-Making","authors":"Mahalakshmi K, Dharish Jaya priyan J, DharshanRaj N, Aravind A","doi":"10.46632/daai/4/2/11","DOIUrl":"https://doi.org/10.46632/daai/4/2/11","url":null,"abstract":"Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations of different ML \u0000techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696989","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}
The Know Your Customer (KYC) operations in India need to be done securely and efficiently due to the financial services industry's fast digitalization. The creation of a digital assistant that is customized for the Video KYC framework and prioritizes user experience over compliance requirements is suggested in this abstract. The digital assistant uses artificial intelligence and natural language processing to automate the KYC process and guarantee accuracy, dependability, and compliance with rules like those issued by the Reserve Bank of India (RBI). The assistant improves security protocols by utilizing advanced facial recognition, document verification, and biometric authentication, hence reducing the potential for identity fraud and data breaches.
{"title":"Digital Assistant for Video KYC Framework in India","authors":"Mizpah Queeny R, K. S, A. V, Harini A, Harithaa S","doi":"10.46632/daai/4/2/12","DOIUrl":"https://doi.org/10.46632/daai/4/2/12","url":null,"abstract":"The Know Your Customer (KYC) operations in India need to be done securely and efficiently \u0000due to the financial services industry's fast digitalization. The creation of a digital assistant that is customized for the Video KYC framework and prioritizes user experience over compliance requirements is suggested in this abstract. The digital assistant uses artificial intelligence and natural language processing to automate the KYC process and guarantee accuracy, dependability, and compliance with rules like those issued by the Reserve Bank of India (RBI). The assistant improves security protocols by utilizing advanced facial recognition, document verification, and biometric authentication, hence reducing \u0000the potential for identity fraud and data breaches.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"77 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690756","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}
Soundharya K, K. S, Velmurugan T, Vishwa S, Srikanth S. G. S
The Smart Home Concept responds to the increasing need for integrating smart appliances and systems within residential environments. It includes a growing array of devices, services, and applications designed to simplify daily tasks and enhance the quality of life. Utilizing various technologies and standards, numerous device suppliers offer a wide range of solutions, including meters, actuators, sensors, and micro systems, which are integrated into the home environment. This advanced system incorporates sensors, artificial intelligence, and machine learning algorithms to develop an intelligent, responsive, and personalized living space. Continuous sensor data collection on environmental conditions and user behaviors allows AI to autonomously manage various home functions. The system emphasizes interoperability and standardization to ensure compatibility with a wide range of devices. Improvements in natural language processing and voice recognition further enhance human-machine interactions. This comprehensive approach aims to optimize energy efficiency, bolster security, and streamline daily activities, providing residents with a more intuitive and adaptable smart home experience in the evolving field of home automation.
{"title":"Smart Home Automation","authors":"Soundharya K, K. S, Velmurugan T, Vishwa S, Srikanth S. G. S","doi":"10.46632/daai/4/2/13","DOIUrl":"https://doi.org/10.46632/daai/4/2/13","url":null,"abstract":"The Smart Home Concept responds to the increasing need for integrating smart appliances and systems within residential environments. It includes a growing array of devices, services, and applications designed to simplify daily tasks and enhance the quality of life. Utilizing various technologies and standards, numerous device suppliers offer a wide range of solutions, including meters, actuators, sensors, and micro systems, which are integrated into the home environment. This advanced system incorporates sensors, artificial intelligence, and machine learning algorithms to develop an intelligent, responsive, and personalized living space. Continuous sensor data collection on environmental conditions and user behaviors allows AI to autonomously manage various home functions. The system emphasizes interoperability and standardization to ensure compatibility with a wide range of devices. Improvements in natural language processing and voice recognition further enhance human-machine interactions. This comprehensive approach aims to optimize energy efficiency, bolster security, and streamline daily activities, providing residents with a more intuitive and adaptable smart home experience in the evolving field of home automation.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"20 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689011","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}
We offer a brand-new technique for real-time control over the project's water flow rate and electricity usage. Unlike current systems, which are frequently costly and solely monitor power or water, our method integrates both parameters into a single, reasonably priced platform. Our solution allows users to track their resource consumption in real-time by utilizing Arduino-based sensors and a user-friendly mobile application created with MIT App Inventor. By providing consumers with the knowledge, they need to make educated decisions about how much water and power to use, this promotes more effective and sustainable resource management. It is unique in that it provides an affordable solution that integrates water and power metering capabilities, solving major issues with current systems.
{"title":"Hydro Electrometric Tracking Application","authors":"","doi":"10.46632/daai/4/2/10","DOIUrl":"https://doi.org/10.46632/daai/4/2/10","url":null,"abstract":"We offer a brand-new technique for real-time control over the project's water flow rate and electricity usage. Unlike current systems, which are frequently costly and solely monitor power or water, our method integrates both parameters into a single, reasonably priced platform. Our solution allows users to track their resource consumption in real-time by utilizing Arduino-based sensors and a user-friendly mobile application created with MIT App Inventor. By providing consumers with the knowledge, they need to make educated decisions about how much water and power to use, this promotes more effective and sustainable resource management. It is unique in that it provides an affordable solution that integrates water and power metering capabilities, solving major issues with current systems.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383426","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}
The internet has become a vital platform for people to express their views and beliefs. The users on social media platforms and blogging services are free to publish anything they like. But occasionally, information that targets a particular group of people intending to promote hate or discrimination rises causing trouble in the community. We refer to such material as hate speech. Hate speech has the potential to significantly damage social peace and harmony. Extremism and societal instability have occasionally resulted from hate speech. The several forms of hate speech like racism, sexism, hate speech based on religion, etc.—as well as the approaches put out to combat them are covered. Additionally, we list the problems and provide fixes for issues with hate speech identification on the open internet. Therefore, it is necessary to monitor hate speech on the internet. We analyze relevant research in the field of hate speech detection in this paper. Our proposed system not only identify the Hate Speech on internet but also label them into categories like (Offensive Speech, Hate Speech, fair Speech etc.) The gathered information can be processed to provide Hate speech reports, which will make the internet more user-friendly for anyone using it.
{"title":"Analysis of Machine Learning Models for Hate Speech Detection in Online Content","authors":"","doi":"10.46632/daai/4/2/9","DOIUrl":"https://doi.org/10.46632/daai/4/2/9","url":null,"abstract":"The internet has become a vital platform for people to express their views and beliefs. The users on social media platforms and blogging services are free to publish anything they like. But occasionally, information that targets a particular group of people intending to promote hate or discrimination rises causing trouble in the community. We refer to such material as hate speech. Hate speech has the potential to significantly damage social peace and harmony. Extremism and societal instability have occasionally resulted from hate speech. The several forms of hate speech like racism, sexism, hate speech based on religion, etc.—as well as the approaches put out to combat them are covered. Additionally, we list the problems and provide fixes for issues with hate speech identification on the open internet. Therefore, it is necessary to monitor hate speech on the internet. We analyze relevant research in the field of hate speech detection in this paper. Our proposed system not only identify the Hate Speech on internet but also label them into categories like (Offensive Speech, Hate Speech, fair Speech etc.) The gathered information can be processed to provide Hate speech reports, which will make the internet more user-friendly for anyone using it.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382640","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}
Diabetic Retinopathy (DR) is a medical condition caused by diabetes. The development of retinopathy significantly depends on how long a person has had diabetes. Initially, there may be no symptoms or just a slight vision problem due to impairment of the retinal blood vessels. Later, it may lead to blindness. Recognizing the early clinical signs of DR is very important for intervening in and effectively treating DR. Thus, regular eye check-ups are necessary to direct the person to a doctor for a comprehensive ocular examination and treatment as soon as possible to avoid permanent vision loss. Nevertheless, due to limited resources, it is not feasible for screening. As a result, emerging technologies, such as artificial intelligence, for the automatic detection and classification of DR are alternative screening methodologies and thereby make the system cost-effective. People have been working on artificial- intelligence-based technologies to detect and analyze DR in recent years. This study aimed to investigate different machine learning styles that are chosen for diagnosing retinopathy. Thus, a bibliometric analysis was systematically done to discover different machine learning styles for detecting diabetic retinopathy. The data were exported from popular databases, namely, Web of Science (WoS) and Scopus. These data were analyzed using Biblioshiny and VOS viewer in terms of publications, top countries, sources, subject area, top authors, trend topics, co-occurrences, thematic evolution, factorial map, citation analysis, etc., which form the base for researchers to identify the research gaps in diabetic retinopathy detection and classification
糖尿病视网膜病变（DR）是由糖尿病引起的一种病症。视网膜病变的发展在很大程度上取决于糖尿病的病程。最初，由于视网膜血管受损，可能没有任何症状或仅有轻微的视力问题。后来，它可能会导致失明。识别 DR 的早期临床症状对于干预和有效治疗 DR 非常重要。因此，定期进行眼科检查是非常必要的，这样可以引导患者尽快到医院接受全面的眼科检查和治疗，以避免永久性视力丧失。然而，由于资源有限，进行筛查并不可行。因此，自动检测和分类 DR 的新兴技术（如人工智能）成为筛查的替代方法，从而使该系统具有成本效益。近年来，人们一直在研究基于人工智能的 DR 检测和分析技术。本研究旨在调查用于诊断视网膜病变的不同机器学习方式。因此，我们系统地进行了文献计量分析，以发现用于检测糖尿病视网膜病变的不同机器学习方式。数据从流行的数据库（即 Web of Science (WoS) 和 Scopus）中导出。研究人员使用 Biblioshiny 和 VOS 浏览器对这些数据进行了分析，分析内容包括出版物、热门国家、来源、主题领域、热门作者、趋势主题、共同出现、主题演变、因子图、引文分析等，从而为研究人员确定糖尿病视网膜病变检测和分类方面的研究空白奠定了基础。
{"title":"Detection of Diabetic Retinopathy Using KNN & SVM Algorithm","authors":"","doi":"10.46632/daai/4/2/8","DOIUrl":"https://doi.org/10.46632/daai/4/2/8","url":null,"abstract":"Diabetic Retinopathy (DR) is a medical condition caused by diabetes. The development of retinopathy significantly depends on how long a person has had diabetes. Initially, there may be no symptoms or just a slight vision problem due to impairment of the retinal blood vessels. Later, it may lead to blindness. Recognizing the early clinical signs of DR is very important for intervening in and effectively treating DR. Thus, regular eye check-ups are necessary to direct the person to a doctor for a comprehensive ocular examination and treatment as soon as possible to avoid permanent vision loss. Nevertheless, due to limited resources, it is not feasible for screening. As a result, emerging technologies, such as artificial intelligence, for the automatic detection and classification of DR are alternative screening methodologies and thereby make the system cost-effective. People have been working on artificial- intelligence-based technologies to detect and analyze DR in recent years. This study aimed to investigate different machine learning styles that are chosen for diagnosing retinopathy. Thus, a bibliometric analysis was systematically done to discover different machine learning styles for detecting diabetic retinopathy. The data were exported from popular databases, namely, Web of Science (WoS) and Scopus. These data were analyzed using Biblioshiny and VOS viewer in terms of publications, top countries, sources, subject area, top authors, trend topics, co-occurrences, thematic evolution, factorial map, citation analysis, etc., which form the base for researchers to identify the research gaps in diabetic retinopathy detection and classification","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"62 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382969","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 Study on Network Security Through a Combined Cryptographic Strategy","authors":"","doi":"10.46632/daai/4/2/7","DOIUrl":"https://doi.org/10.46632/daai/4/2/7","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"39 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275205","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 Study On Predictive Modeling for Niche Website Success","authors":"","doi":"10.46632/daai/4/2/4","DOIUrl":"https://doi.org/10.46632/daai/4/2/4","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"57 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275810","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":"Prediction of COVID-19 From Chest X-Ray Images","authors":"","doi":"10.46632/daai/4/2/2","DOIUrl":"https://doi.org/10.46632/daai/4/2/2","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"29 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276154","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":"Video Transcription in to Enhanced Text Summarization","authors":"","doi":"10.46632/daai/4/2/3","DOIUrl":"https://doi.org/10.46632/daai/4/2/3","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276504","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}
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