Pub Date : 2024-07-12DOI: 10.46610/rrmlcc.2024.v03i02.001
Karthikeyan S, Manickam Ramasamy, Mahesh Prabu Arunachalam
Drug abuse remains a pervasive societal issue with far-reaching consequences for individuals and communities. Current drug testing methods often need more speed and accuracy for timely intervention. This proposal introduces an innovative approach to drug detection by integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into rapid saliva drug testing devices. By harnessing AI and ML capabilities, the proposed solution aims to enhance the efficiency and accuracy of drug detection while minimizing false positives and negatives. The device will be portable, user-friendly, and capable of delivering quick results within minutes, making it suitable for deployment in diverse settings such as workplaces, schools, law enforcement, and healthcare facilities. Through collaborative efforts with experts in AI, ML, and drug testing technology, the device will undergo rigorous development, validation, and regulatory approval processes. Upon implementation, it anticipated that the integration of AI and ML into rapid saliva drug testing would lead to improved public health outcomes by enabling early identification and intervention for individuals struggling with drug abuse. This abstract outlines the methodology, key features, implementation plan, expected outcomes, and potential impact of the proposed solution in addressing the challenge of drug abuse.
药物滥用仍然是一个普遍的社会问题，对个人和社区都有深远的影响。目前的毒品检测方法往往需要更快、更准确的速度才能及时干预。本提案通过将人工智能（AI）和机器学习（ML）算法集成到快速唾液毒品检测设备中，引入了一种创新的毒品检测方法。通过利用人工智能和 ML 功能，拟议解决方案旨在提高毒品检测的效率和准确性，同时最大限度地减少假阳性和假阴性。该设备便于携带、使用方便，并能在几分钟内快速出结果，因此适合在工作场所、学校、执法部门和医疗机构等不同场合使用。通过与人工智能、ML 和药物检测技术专家的合作，该设备将经历严格的开发、验证和监管审批流程。在实施过程中，预计将人工智能和 ML 与快速唾液药物检测相结合，可以及早识别和干预药物滥用者，从而改善公共卫生成果。本摘要概述了该拟议解决方案在应对药物滥用挑战方面的方法、主要特点、实施计划、预期成果和潜在影响。
{"title":"Leveraging AI and ML in Rapid Saliva Drug Testing for Efficient Identification of Drug Users","authors":"Karthikeyan S, Manickam Ramasamy, Mahesh Prabu Arunachalam","doi":"10.46610/rrmlcc.2024.v03i02.001","DOIUrl":"https://doi.org/10.46610/rrmlcc.2024.v03i02.001","url":null,"abstract":"Drug abuse remains a pervasive societal issue with far-reaching consequences for individuals and communities. Current drug testing methods often need more speed and accuracy for timely intervention. This proposal introduces an innovative approach to drug detection by integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into rapid saliva drug testing devices. By harnessing AI and ML capabilities, the proposed solution aims to enhance the efficiency and accuracy of drug detection while minimizing false positives and negatives. The device will be portable, user-friendly, and capable of delivering quick results within minutes, making it suitable for deployment in diverse settings such as workplaces, schools, law enforcement, and healthcare facilities. Through collaborative efforts with experts in AI, ML, and drug testing technology, the device will undergo rigorous development, validation, and regulatory approval processes. Upon implementation, it anticipated that the integration of AI and ML into rapid saliva drug testing would lead to improved public health outcomes by enabling early identification and intervention for individuals struggling with drug abuse. This abstract outlines the methodology, key features, implementation plan, expected outcomes, and potential impact of the proposed solution in addressing the challenge of drug abuse.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"40 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652930","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}
Pub Date : 2024-07-12DOI: 10.46610/rrmlcc.2024.v03i01.003
T. A. Sai Srinivas, M. Bharathi
In this comparative analysis, we delve into the disparities between the US Air Quality Index (AQI) and the Indian AQI methodologies, with a specific focus on PM2.5 concentrations. Through the utilization of bar charts, we visually represent AQI values derived from both methodologies, thus elucidating the divergences and convergences in outcomes. This visual depiction serves to highlight how different regions interpret air quality data, shedding light on the complexities inherent in air quality assessment. Furthermore, our study goes beyond mere comparison by offering insights into the AQI calculation process. We emphasize the necessity of tailoring methodologies to specific geographical and environmental contexts, recognizing the importance of regional nuances in accurately assessing air quality conditions. By tending to these varieties, our examination adds to a more profound comprehension of air quality evaluation and illuminates future endeavours in the normalization and variation of AQI techniques around the world. Ultimately, our findings underscore the imperative of considering regional differences in formulating AQI standards to facilitate more effective environmental management strategies on a global scale.
{"title":"Breathing Easy: A Python Dive into Air Quality Analysis","authors":"T. A. Sai Srinivas, M. Bharathi","doi":"10.46610/rrmlcc.2024.v03i01.003","DOIUrl":"https://doi.org/10.46610/rrmlcc.2024.v03i01.003","url":null,"abstract":"In this comparative analysis, we delve into the disparities between the US Air Quality Index (AQI) and the Indian AQI methodologies, with a specific focus on PM2.5 concentrations. Through the utilization of bar charts, we visually represent AQI values derived from both methodologies, thus elucidating the divergences and convergences in outcomes. This visual depiction serves to highlight how different regions interpret air quality data, shedding light on the complexities inherent in air quality assessment. Furthermore, our study goes beyond mere comparison by offering insights into the AQI calculation process. We emphasize the necessity of tailoring methodologies to specific geographical and environmental contexts, recognizing the importance of regional nuances in accurately assessing air quality conditions. By tending to these varieties, our examination adds to a more profound comprehension of air quality evaluation and illuminates future endeavours in the normalization and variation of AQI techniques around the world. Ultimately, our findings underscore the imperative of considering regional differences in formulating AQI standards to facilitate more effective environmental management strategies on a global scale.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652809","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}
Pub Date : 2024-03-26DOI: 10.46610/rtaia.2024.v03i01.001
S. T. Anand Reddy
As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdiscip
{"title":"Human-Computer Interaction Techniques for Explainable Artificial Intelligence Systems","authors":"S. T. Anand Reddy","doi":"10.46610/rtaia.2024.v03i01.001","DOIUrl":"https://doi.org/10.46610/rtaia.2024.v03i01.001","url":null,"abstract":"As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdiscip","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"60 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378336","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}
Pub Date : 2023-05-29DOI: 10.46610/rrmlcc.2023.v02i02.002
Aman Nikhra, Devansh Santuwala, Dev Verma, Anshika Sain, Pawan Kumar Singh
Sometimes, it is very difficult for someone to determine whether a person wants to hear a particular music from the vast array of available choices. So, this paper has proposed a new concept of playing music that is based on the emotion of the user. The primary goal of the music recommendation system which is proposed in this paper is to offer customers recommendations that match their tastes. The most current view of the paper involves manually playing the jukebox, using wearable computers, or classifying based on auditory characteristics. Understanding the user's present emotional or mental state may result from analysing the user's facialexpression and emotions. One area is having a great possibility to provide the audience, with a vast variety of options that are based on their preferences and music and video. In this paper, the primary goal is to show a playlist of songs on any particular music application (YouTube/Spotify) based on each person’s mood. Several images of the user are collected at that precise moment using a camera with the user's consent. To determine a person's mood, these photos go through a thorough testing andtraining process. For this, the deep learning technique called CNN is used to categorize various emotions. After this, based on the trained model, the various emotions are categorized and based on this the music playlist is generated.
{"title":"Facial Emotion Song Recommender System","authors":"Aman Nikhra, Devansh Santuwala, Dev Verma, Anshika Sain, Pawan Kumar Singh","doi":"10.46610/rrmlcc.2023.v02i02.002","DOIUrl":"https://doi.org/10.46610/rrmlcc.2023.v02i02.002","url":null,"abstract":"Sometimes, it is very difficult for someone to determine whether a person wants to hear a particular music from the vast array of available choices. So, this paper has proposed a new concept of playing music that is based on the emotion of the user. The primary goal of the music recommendation system which is proposed in this paper is to offer customers recommendations that match their tastes. The most current view of the paper involves manually playing the jukebox, using wearable computers, or classifying based on auditory characteristics. Understanding the user's present emotional or mental state may result from analysing the user's facialexpression and emotions. One area is having a great possibility to provide the audience, with a vast variety of options that are based on their preferences and music and video. In this paper, the primary goal is to show a playlist of songs on any particular music application (YouTube/Spotify) based on each person’s mood. Several images of the user are collected at that precise moment using a camera with the user's consent. To determine a person's mood, these photos go through a thorough testing andtraining process. For this, the deep learning technique called CNN is used to categorize various emotions. After this, based on the trained model, the various emotions are categorized and based on this the music playlist is generated.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130258580","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}
Pub Date : 2023-02-22DOI: 10.46610/rrmlcc.2023.v02i01.001
Shivam Tiwari, Prem Prakash, Vaishnavi Dixit
Market Basket Analysis (MBA) is a crucial technique used in the field of data mining to understand consumer purchasing patterns. The importance of advertisement in an MBA has been widely acknowledged as a key factor in influencing consumer behavior. With the advent of technology, MBA is undergoing a paradigm shift, with new tools and techniques being developed to improve its accuracy and efficiency. This research paper focuses on the various techniques used in MBA, the role of advertisement in MBA, and the impact of technology on the improvement of MBA. The paper discusses the various algorithms and data mining techniques used in MBA and their advantages and disadvantages. Additionally, it analyzes the importance of advertisement in MBA, its impact on consumer behavior, and the role of technology in enhancing MBA. The paper concludes by highlighting the potential of technology in revolutionizing the MBA field, providing more accurate and efficient results, and ultimately improving business outcomes.
{"title":"Enhancing Market Basket Analysis Through the Interplay of Advertisement and Technology","authors":"Shivam Tiwari, Prem Prakash, Vaishnavi Dixit","doi":"10.46610/rrmlcc.2023.v02i01.001","DOIUrl":"https://doi.org/10.46610/rrmlcc.2023.v02i01.001","url":null,"abstract":"Market Basket Analysis (MBA) is a crucial technique used in the field of data mining to understand consumer purchasing patterns. The importance of advertisement in an MBA has been widely acknowledged as a key factor in influencing consumer behavior. With the advent of technology, MBA is undergoing a paradigm shift, with new tools and techniques being developed to improve its accuracy and efficiency. This research paper focuses on the various techniques used in MBA, the role of advertisement in MBA, and the impact of technology on the improvement of MBA. The paper discusses the various algorithms and data mining techniques used in MBA and their advantages and disadvantages. Additionally, it analyzes the importance of advertisement in MBA, its impact on consumer behavior, and the role of technology in enhancing MBA. The paper concludes by highlighting the potential of technology in revolutionizing the MBA field, providing more accurate and efficient results, and ultimately improving business outcomes.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547411","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}
Pub Date : 2022-12-06DOI: 10.46610/rrmlcc.2022.v01i03.002
Harshal Wangikar, Priya Surana, Prakash Sawant, Napul Labde, A. Shah
Road signs are essential for providing information to drivers. Understanding road signs are essential for ensuring traffic safety because doing so can stop 4484 accidents. The identification of traffic signs has been the focus of research in recent decades. Accurate real-time recognition is the cornerstone of a robust but underdeveloped traffic sign recognition system. This study provides drivers with real-time voice-advice traffic sign recognition technology. This system is composed of two subsystems. Using a trained convolutional neural network, the first recognizes and detects traffic signs (CNN). When the system notices a particular traffic sign, the text-to-speech engine is employed to play a voice message to the driver. An efficient- CNN model is built on the reference data set using deep learning methods for search and real-time search. This system's advantage is that it recognizes traffic signs and guides the car even if the driver overlooks, ignores, or doesn't understand them. Say. These technologies are also necessary for the development of autonomous vehicles.
{"title":"A Review on Real-Time Traffic Sign Recognition with Voice Warnings","authors":"Harshal Wangikar, Priya Surana, Prakash Sawant, Napul Labde, A. Shah","doi":"10.46610/rrmlcc.2022.v01i03.002","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i03.002","url":null,"abstract":"Road signs are essential for providing information to drivers. Understanding road signs are essential for ensuring traffic safety because doing so can stop 4484 accidents. The identification of traffic signs has been the focus of research in recent decades. Accurate real-time recognition is the cornerstone of a robust but underdeveloped traffic sign recognition system. This study provides drivers with real-time voice-advice traffic sign recognition technology. This system is composed of two subsystems. Using a trained convolutional neural network, the first recognizes and detects traffic signs (CNN). When the system notices a particular traffic sign, the text-to-speech engine is employed to play a voice message to the driver. An efficient- CNN model is built on the reference data set using deep learning methods for search and real-time search. This system's advantage is that it recognizes traffic signs and guides the car even if the driver overlooks, ignores, or doesn't understand them. Say. These technologies are also necessary for the development of autonomous vehicles.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474714","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}
Pub Date : 2022-09-13DOI: 10.46610/rrmlcc.2022.v01i03.001
Munesh Meena, Ruchi Sehrawat
Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. For our project, we will take the help of NLP (Natural Language Processing) which will help us to perceive and sort fake/spam comments. Also, we will be using this tool to prevent fake promotion and help people when buying products on E-commerce sites and as well as to avoid fake comments on Social Media Platforms that spread hate among people. This application will create a transparent and safe internet for everyone. Spam-Attack will be using NLP to achieve the goal and create a better ecosystem for browsing the internet.
{"title":"Application of NLP and ML Using a Refined Dataset","authors":"Munesh Meena, Ruchi Sehrawat","doi":"10.46610/rrmlcc.2022.v01i03.001","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i03.001","url":null,"abstract":"Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. For our project, we will take the help of NLP (Natural Language Processing) which will help us to perceive and sort fake/spam comments. Also, we will be using this tool to prevent fake promotion and help people when buying products on E-commerce sites and as well as to avoid fake comments on Social Media Platforms that spread hate among people. This application will create a transparent and safe internet for everyone. Spam-Attack will be using NLP to achieve the goal and create a better ecosystem for browsing the internet.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129272720","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}
Pub Date : 2022-07-01DOI: 10.46610/rrmlcc.2022.v01i02.006
Sharan Shetty, S.Catherine Mary
The phrase "cloud computing" refers to any activities connected with the delivery of hosted services through the Internet. The term "cloud computing" is frequently used to describe data centers that are accessible to many people online. Drops for efficient and secure data dissemination and duplication in the cloud. Technology called Cloud Drop is about cloud data protection, e.g., users have concerns about security when extracting their external sources data on external administrative management. Loss of data can be caused by attacks on other users and nodes in the cloud. Cloud Drops is a ubiquitous awareness platform that closely integrates visual information from Webs have entered the visual contexts that we live in and work. Cloud Drops has a variety of interactive features, including stamp-like advertisements that each displays a small amount of digital data. Numerous screens and their little size enable the user to use the flexible tool, rearrange it reset their information status. We show different forms of forms on stamped screens, bring up the idea of the device and the original use. We suggest light strategies and consultation familiar with small phone form. We to provide ways for tying these parts to the information the user wants to maintain, such as contacts, locations, and websites. To show platform functionality, we present a specific program example. User research provides initial information on the usage of cloud removal by users to give a customized one-information environment advertisements stored throughout the site location of buildings.
{"title":"Cloud Drops Technology Application in Cloud Computing","authors":"Sharan Shetty, S.Catherine Mary","doi":"10.46610/rrmlcc.2022.v01i02.006","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i02.006","url":null,"abstract":"The phrase \"cloud computing\" refers to any activities connected with the delivery of hosted services through the Internet. The term \"cloud computing\" is frequently used to describe data centers that are accessible to many people online. Drops for efficient and secure data dissemination and duplication in the cloud. Technology called Cloud Drop is about cloud data protection, e.g., users have concerns about security when extracting their external sources data on external administrative management. Loss of data can be caused by attacks on other users and nodes in the cloud. Cloud Drops is a ubiquitous awareness platform that closely integrates visual information from Webs have entered the visual contexts that we live in and work. Cloud Drops has a variety of interactive features, including stamp-like advertisements that each displays a small amount of digital data. Numerous screens and their little size enable the user to use the flexible tool, rearrange it reset their information status. We show different forms of forms on stamped screens, bring up the idea of the device and the original use. We suggest light strategies and consultation familiar with small phone form. We to provide ways for tying these parts to the information the user wants to maintain, such as contacts, locations, and websites. To show platform functionality, we present a specific program example. User research provides initial information on the usage of cloud removal by users to give a customized one-information environment advertisements stored throughout the site location of buildings.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122610762","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}
Pub Date : 2022-06-23DOI: 10.46610/rrmlcc.2022.v01i02.005
Aman Kumar, Flavia D Gonsalves
Conventional fire detection system was based mechanical sensor for fire detection. The smoke particles in the surrounding detected by sensors in the traditional fire detection system. However, this can also lead to false alarms. For example, a person smoking in a room of can activate a general fire alarm system. In addition, these systems are expensive and ineffective if the fire is far away from the detector. An alternatives fire detection system such as system based on computer vision and Image/video Processing technology to manage false alarms from conventional fire detection. One of the most cost-effective ways is to use surveillance cameras to detect fires and alert affected parties. In the following proposed system proposes a technique which will be monitor the outburst of a fire anywhere within the camera range using a surveillance camera. In this Paper, fire alarm system will be developed to efficiently detect fires and protect lives and property from fire hazards. This research describes a fire detection system that uses color and motion models derived from video sequences. The proposed approach identifies color changes and mobility in common areas to identify fires and can therefore be used both in real time and in datasets.
{"title":"Computer Vision Based Fire Detection System Using OpenCV - A Case Study","authors":"Aman Kumar, Flavia D Gonsalves","doi":"10.46610/rrmlcc.2022.v01i02.005","DOIUrl":"https://doi.org/10.46610/rrmlcc.2022.v01i02.005","url":null,"abstract":"Conventional fire detection system was based mechanical sensor for fire detection. The smoke particles in the surrounding detected by sensors in the traditional fire detection system. However, this can also lead to false alarms. For example, a person smoking in a room of can activate a general fire alarm system. In addition, these systems are expensive and ineffective if the fire is far away from the detector. An alternatives fire detection system such as system based on computer vision and Image/video Processing technology to manage false alarms from conventional fire detection. One of the most cost-effective ways is to use surveillance cameras to detect fires and alert affected parties. In the following proposed system proposes a technique which will be monitor the outburst of a fire anywhere within the camera range using a surveillance camera. In this Paper, fire alarm system will be developed to efficiently detect fires and protect lives and property from fire hazards. This research describes a fire detection system that uses color and motion models derived from video sequences. The proposed approach identifies color changes and mobility in common areas to identify fires and can therefore be used both in real time and in datasets.","PeriodicalId":149011,"journal":{"name":"Research & Review: Machine Learning and Cloud Computing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131789110","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}
Pub Date : 2022-06-08DOI: 10.46610/rrmlcc.2022.v01i02.004
Sahil M. Kargutkar, Omprakash L. Mandge
An impending decision in front is often searched for a past presence for the purpose of gathering information from the data and to make a decision out of it. Machine Learning, a field which allows systems, a computer to be specific to make a fortunate prediction out of past information or experiences. Machine Learning being a phenomenon widely used throughout the technological advances, is constantly finding itself to be introduced to newer domains. Image processing, medical diagnosis, predictions, speech recognition and many more are among the applications of Machine Learning. Digital Marketing, too, emerges for the need and betterment with aid from the field of Machine Learning. Digital Marketing is a way of maintaining relationships with customers for your business through a way of an online medium. Digital Marketing has made the lives of local businesses a lot easier as a target audience can be reached very easily with just a bare touch of technology. Earlier in the days, Traditional Marketing was the only way for the Businesses to promote their products and services which was done by magazines, newspapers, billboards and with the word of mouth, it was capable of doing the bare minimum publicity. Introduction of Digital marketing has paved a way to reach to a wider, broader and the exact specific target audience. Local businesses are flourishing due to the aid of this type of marketing and machine learning plays a huge role in it.
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