The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.
{"title":"CareerBoost: Revolutionizing the Job Search with Resume Enhancement and Tailored Recommendations","authors":"Asoke Nath, Sunayana Saha, Shrestha Dey Sarkar, Anchita Bose","doi":"10.32628/cseit24103106","DOIUrl":"https://doi.org/10.32628/cseit24103106","url":null,"abstract":"The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123720","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}
This research paper offers an in-depth examination of digital steganography, with a focus on the diverse methodologies utilized for embedding secret data within cover files. Steganography, the practice of concealing information within other non-secret data, ensures the hidden message remains undetectable to unauthorized observers. This study systematically reviews both traditional and modern steganographic techniques, dissecting their fundamental mechanisms, advantages, and weaknesses. Techniques explored include Least Significant Bit (LSB) insertion, discrete cosine transform (DCT), discrete wavelet transform (DWT), and innovative methods leveraging deep learning and adaptive algorithms. Each method is assessed for its imperceptibility, robustness, and data capacity, offering a comparative analysis to underscore their respective practical applications and limitations. Additionally, the paper delves into steganalysis—methods for detecting hidden information—to provide a comprehensive perspective on the field. Through experimental evaluation and theoretical analysis, this study seeks to enhance the understanding of digital steganography, presenting insights that could inform future research and the development of more secure data hiding techniques.
{"title":"Digital Steganography : A Comprehensive Study on Various Methods for Hiding Secret Data in a Cover file","authors":"Asoke Nath, Sankar Das, Rahul Sharma, Subhajit Mandal, Hardick Sadhu","doi":"10.32628/cseit24103107","DOIUrl":"https://doi.org/10.32628/cseit24103107","url":null,"abstract":"This research paper offers an in-depth examination of digital steganography, with a focus on the diverse methodologies utilized for embedding secret data within cover files. Steganography, the practice of concealing information within other non-secret data, ensures the hidden message remains undetectable to unauthorized observers. This study systematically reviews both traditional and modern steganographic techniques, dissecting their fundamental mechanisms, advantages, and weaknesses. Techniques explored include Least Significant Bit (LSB) insertion, discrete cosine transform (DCT), discrete wavelet transform (DWT), and innovative methods leveraging deep learning and adaptive algorithms. Each method is assessed for its imperceptibility, robustness, and data capacity, offering a comparative analysis to underscore their respective practical applications and limitations. Additionally, the paper delves into steganalysis—methods for detecting hidden information—to provide a comprehensive perspective on the field. Through experimental evaluation and theoretical analysis, this study seeks to enhance the understanding of digital steganography, presenting insights that could inform future research and the development of more secure data hiding techniques.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"35 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123527","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}
Asoke Nath, Shreya Maity, Soham Banerjee, Rohit Roy
Classical cryptographic systems are increasingly challenged by advances in computing power and new algorithmic techniques, particularly with the rise of quantum computing, which threatens the security of current encryption methods. This has spurred interest in quantum-resistant cryptography, aimed at creating algorithms that can withstand attacks from quantum computers. Traditionally, secure key transport over alternate channels has been a significant challenge, but quantum mechanics offers a solution. Quantum Key Distribution (QKD) is a revolutionary method for secure communication that leverages quantum principles. Unlike traditional methods, QKD provides unconditional security, with key security ensured by the laws of physics rather than computational difficulty. The BB84 protocol, introduced in 1984 by Bennett and Brassard, is a leading QKD scheme known for its simplicity and effectiveness in generating eavesdropping-resistant cryptographic keys. It facilitates secure key transport over alternate channels. This documentation aims to advance QKD security by practically implementing and analyzing the BB84 protocol. Through detailed theoretical analysis, simulation studies, and experimental validation, the practical impacts, and limitations of BB84-based QKD systems are examined. Additionally, a practical implementation of quantum key distribution using a sudoku key demonstrates the process's simplicity and effectiveness. These findings are expected to pave new paths in the field of cryptanalysis in the emerging Quantum Age.
{"title":"Quantum Key Distribution (QKD) for Symmetric Key Transfer","authors":"Asoke Nath, Shreya Maity, Soham Banerjee, Rohit Roy","doi":"10.32628/cseit24103105","DOIUrl":"https://doi.org/10.32628/cseit24103105","url":null,"abstract":"Classical cryptographic systems are increasingly challenged by advances in computing power and new algorithmic techniques, particularly with the rise of quantum computing, which threatens the security of current encryption methods. This has spurred interest in quantum-resistant cryptography, aimed at creating algorithms that can withstand attacks from quantum computers. Traditionally, secure key transport over alternate channels has been a significant challenge, but quantum mechanics offers a solution. Quantum Key Distribution (QKD) is a revolutionary method for secure communication that leverages quantum principles. Unlike traditional methods, QKD provides unconditional security, with key security ensured by the laws of physics rather than computational difficulty. The BB84 protocol, introduced in 1984 by Bennett and Brassard, is a leading QKD scheme known for its simplicity and effectiveness in generating eavesdropping-resistant cryptographic keys. It facilitates secure key transport over alternate channels. This documentation aims to advance QKD security by practically implementing and analyzing the BB84 protocol. Through detailed theoretical analysis, simulation studies, and experimental validation, the practical impacts, and limitations of BB84-based QKD systems are examined. Additionally, a practical implementation of quantum key distribution using a sudoku key demonstrates the process's simplicity and effectiveness. These findings are expected to pave new paths in the field of cryptanalysis in the emerging Quantum Age.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":" May","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127916","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}
Dr. M. Praneesh, Sai Krishna P K, Febina. N, Ashwanth.V
Malaria remains a significant global health concern, posing formidable challenges to healthcare systems. Conventional diagnostic methods rely on manual examination of blood smears under a microscope, a process prone to inefficiencies and subjectivity. Despite prior attempts to leverage Deep Learning algorithms for malaria diagnosis, practical performance has often fallen short. This paper presents a novel machine learning model centred on Convolutional Neural Networks (CNNs) designed to automate the classification and prediction of infected cells in thin blood smears on standard microscope slides. Through rigorous ten-fold cross-validation with 27,558 single-cell images. This paper reviews various image processing techniques employed for the detection of malaria infection in humans, presenting a comparative analysis of these methods
{"title":"Malaria Parasite Detection in Microscopic Blood Smear Images using Deep Learning Approach","authors":"Dr. M. Praneesh, Sai Krishna P K, Febina. N, Ashwanth.V","doi":"10.32628/cseit2410286","DOIUrl":"https://doi.org/10.32628/cseit2410286","url":null,"abstract":"Malaria remains a significant global health concern, posing formidable challenges to healthcare systems. Conventional diagnostic methods rely on manual examination of blood smears under a microscope, a process prone to inefficiencies and subjectivity. Despite prior attempts to leverage Deep Learning algorithms for malaria diagnosis, practical performance has often fallen short. This paper presents a novel machine learning model centred on Convolutional Neural Networks (CNNs) designed to automate the classification and prediction of infected cells in thin blood smears on standard microscope slides. Through rigorous ten-fold cross-validation with 27,558 single-cell images. This paper reviews various image processing techniques employed for the detection of malaria infection in humans, presenting a comparative analysis of these methods","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"97 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676469","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}
Bharati A. Patil, Prajakta R. Toke, Sharyu S. Naiknavare
Cryptography is utilized to make secure data transmission over networks. The algorithm called for cryptography should meet the conditions of authentication, confidentiality, integrity and non-repudiation. Cryptography is a technique used from decenniums to secure and forfend the information and send the data from one place to another without the trepidation of having been read out by some unauthorized and unauthenticated denotes. Several ways has been developed in this field to make the information more secure and evade trespassing. However these methods may have some loopholes or shortcoming which leads to the leakage of information and thus raising a question of information security. The cryptographic technique is utilized not only to provide the security but additionally it deals with data integrity, confidentiality and non-repudiation issues. To safeguard data during transmission or storage, sundry algorithms and methods have been developed in the field of security. A wide range of cryptography approaches are employed, each with its own set of strengths and inhibitions that are acclimated to provide data security. Cryptography can be defined as techniques that cipher data, depending on categorical algorithms that make the data unreadable to the human ocular perceiver unless decrypted by algorithms that are predefined by the sender. It encrypts data utilizing a set of algorithms such as symmetric and asymmetric algorithms. These encryption methods vary in terms of vitality, celerity, and utilization of resources (CPU utilization, recollection, and power). It is utilized to bulwark personal identifiable information (PII) and other confidential data, authenticate identities, avert document tampering, and build trust between servers. Cryptography is one of the most paramount techniques utilized by digital businesses to safeguard the systems that store their most valuable asset – data – whether it is at rest or in kinetic Customer PII, employee PII, perspicacious property, company strategies, and any other confidential information are examples of data. As a result, cryptography is a vital infrastructure, as the aegis of sensitive data increasingly relies on cryptographic solutions. In this paper I have discussed various cryptographic techniques and the inhibitions of those techniques as well. Some cryptographic algorithms are briefly described and their impact on the information is additionally mentioned.
{"title":"Research on Various Cryptography Techniques","authors":"Bharati A. Patil, Prajakta R. Toke, Sharyu S. Naiknavare","doi":"10.32628/cseit2410290","DOIUrl":"https://doi.org/10.32628/cseit2410290","url":null,"abstract":"Cryptography is utilized to make secure data transmission over networks. The algorithm called for cryptography should meet the conditions of authentication, confidentiality, integrity and non-repudiation. Cryptography is a technique used from decenniums to secure and forfend the information and send the data from one place to another without the trepidation of having been read out by some unauthorized and unauthenticated denotes. Several ways has been developed in this field to make the information more secure and evade trespassing. However these methods may have some loopholes or shortcoming which leads to the leakage of information and thus raising a question of information security. The cryptographic technique is utilized not only to provide the security but additionally it deals with data integrity, confidentiality and non-repudiation issues. To safeguard data during transmission or storage, sundry algorithms and methods have been developed in the field of security. A wide range of cryptography approaches are employed, each with its own set of strengths and inhibitions that are acclimated to provide data security. Cryptography can be defined as techniques that cipher data, depending on categorical algorithms that make the data unreadable to the human ocular perceiver unless decrypted by algorithms that are predefined by the sender. It encrypts data utilizing a set of algorithms such as symmetric and asymmetric algorithms. These encryption methods vary in terms of vitality, celerity, and utilization of resources (CPU utilization, recollection, and power). It is utilized to bulwark personal identifiable information (PII) and other confidential data, authenticate identities, avert document tampering, and build trust between servers. Cryptography is one of the most paramount techniques utilized by digital businesses to safeguard the systems that store their most valuable asset – data – whether it is at rest or in kinetic Customer PII, employee PII, perspicacious property, company strategies, and any other confidential information are examples of data. As a result, cryptography is a vital infrastructure, as the aegis of sensitive data increasingly relies on cryptographic solutions. In this paper I have discussed various cryptographic techniques and the inhibitions of those techniques as well. Some cryptographic algorithms are briefly described and their impact on the information is additionally mentioned.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"79 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676994","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}
This paper delves into the evolving landscape of cybersecurity threats, focusing on the latest attack vectors and techniques employed by malicious actors. With the rapid advancement of technology and increasing connectivity, the cybersecurity landscape is continuously evolving, presenting new challenges and threats to organizations and individuals alike. The analysis covers various modern attack methods, including but not limited to, ransomware, phishing, advanced persistent threats (APTs), and supply chain attacks. Each of these attack vectors is examined in detail, highlighting their characteristics, impact, and potential mitigation strategies. Furthermore, the paper discusses the role of emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) in shaping the cybersecurity threat landscape. While these technologies offer numerous benefits, they also introduce new vulnerabilities that can be exploited by cybercriminals.
{"title":"Emerging Threats in Cybersecurity : A Deep Analysis of Modern Attack","authors":"Ashish Dewakar Pandey, Shakil Saiyad","doi":"10.32628/cseit2410297","DOIUrl":"https://doi.org/10.32628/cseit2410297","url":null,"abstract":"This paper delves into the evolving landscape of cybersecurity threats, focusing on the latest attack vectors and techniques employed by malicious actors. With the rapid advancement of technology and increasing connectivity, the cybersecurity landscape is continuously evolving, presenting new challenges and threats to organizations and individuals alike. The analysis covers various modern attack methods, including but not limited to, ransomware, phishing, advanced persistent threats (APTs), and supply chain attacks. Each of these attack vectors is examined in detail, highlighting their characteristics, impact, and potential mitigation strategies. Furthermore, the paper discusses the role of emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) in shaping the cybersecurity threat landscape. While these technologies offer numerous benefits, they also introduce new vulnerabilities that can be exploited by cybercriminals.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"21 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673965","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}
Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies and theories such as co-creation processes, interactions and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an “open learning” model. The ability to timely assess customer experience feedback represents a pre-requisite for successful co-creation processes in a service environment.
{"title":"Customer Feedback Analysis Using Text Mining","authors":"Kinnari Mishra, Mansi Vegad","doi":"10.32628/cseit2410238","DOIUrl":"https://doi.org/10.32628/cseit2410238","url":null,"abstract":"Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies and theories such as co-creation processes, interactions and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an “open learning” model. The ability to timely assess customer experience feedback represents a pre-requisite for successful co-creation processes in a service environment.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"101 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678787","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}
Electronic torture, Electromagnetic radiation torture, or Psychotronic torture are terms used by individuals who are targeted by this wireless Synthetic telepathic technology or Direct Energy Weapon [Electromagnetic Radiation weapon]. Wireless Synthetic telepathy technology or Super artificial intelligence computers Criminal operators, often actors or often government employees, agents or crime syndicates, use transmitted electromagnetic radiation (Such as microwave listening effects), satellite technology and surveillance techniques. Wireless Remote Neural Monitoring: Bridging the gap between brain and technology In the field of neuroscience and technological advancement, the concept of deep tech satellite wireless neural monitoring technology has emerged as an unprecedented innovation weapon. Which has been developed to read and understand the human brain and to interact with the human brain and understand the human body, it is also a wireless direct energy weapon. These deep tech satellite surveillance criminal operators are using this Neurotechnology and wireless Direct Energy Weapons on a large scale against civilians. This true research has been done to protect citizens from illegal deep tech satellite surveillance weapon technology and cyber terrorism. The researcher has presented his research to the whole world in very simple words and in an easy manner. So that the world can easily understand this research and the governments of the countries of the world can protect their citizens from future man-made diseases and viruses.
{"title":"Artificial Intelligence Computer Weapon Based True Research on Corona Virus [Covid-19]","authors":"Pradeep Hariom Arora Hariom .J. Arora","doi":"10.32628/cseit2410277","DOIUrl":"https://doi.org/10.32628/cseit2410277","url":null,"abstract":"Electronic torture, Electromagnetic radiation torture, or Psychotronic torture are terms used by individuals who are targeted by this wireless Synthetic telepathic technology or Direct Energy Weapon [Electromagnetic Radiation weapon].\u0000Wireless Synthetic telepathy technology or Super artificial intelligence computers Criminal operators, often actors or often government employees, agents or crime syndicates, use transmitted electromagnetic radiation (Such as microwave listening effects), satellite technology and surveillance techniques.\u0000Wireless Remote Neural Monitoring: Bridging the gap between brain and technology In the field of neuroscience and technological advancement, the concept of deep tech satellite wireless neural monitoring technology has emerged as an unprecedented innovation weapon.\u0000Which has been developed to read and understand the human brain and to interact with the human brain and understand the human body, it is also a wireless direct energy weapon.\u0000These deep tech satellite surveillance criminal operators are using this Neurotechnology and wireless Direct Energy Weapons on a large scale against civilians.\u0000This true research has been done to protect citizens from illegal deep tech satellite surveillance weapon technology and cyber terrorism.\u0000The researcher has presented his research to the whole world in very simple words and in an easy manner.\u0000So that the world can easily understand this research and the governments of the countries of the world can protect their citizens from future man-made diseases and viruses.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681015","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}
With the proliferation of Internet of Things (IoT) devices and advancements in machine learning (ML) techniques, there is growing interest in developing intelligent home automation systems. These systems aim to enhance convenience, comfort, and energy efficiency in modern households. In this paper, we present a comprehensive study on the design, implementation, and evaluation of a home automation system leveraging IoT and ML technologies. Our proposed system integrates various IoT devices such as sensors, actuators, and smart appliances to create a networked environment within the home. These devices collect and transmit real-time data about environmental conditions, user preferences, and energy consumption patterns. We employ machine learning algorithms to analyse this data and make informed decisions to automate various aspects of home management and control.Key components of our system include data preprocessing, feature extraction, model training, and decision-making modules. We explore different ML algorithms such as regression, classification, and clustering to address specific tasks such as temperature regulation, lighting control, security monitoring, and energy optimization. Furthermore, we investigate techniques for model deployment, monitoring, and adaptation to ensure the robustness and reliability of the system in dynamic home environments. To evaluate the effectiveness of our approach, we conduct experiments using a prototype implementation deployed in real-world households. We measure performance metrics such as accuracy, responsiveness, energy savings, and user satisfaction to assess the practical viability of the proposed system. Our results demonstrate significant improvements in home automation capabilities compared to traditional rule-based approaches, highlighting the potential of IoT and ML integration in shaping the future of smart homes.
随着物联网（IoT）设备的普及和机器学习（ML）技术的进步，人们对开发智能家庭自动化系统的兴趣与日俱增。这些系统旨在提高现代家庭的便利性、舒适性和能效。在本文中，我们对利用物联网和 ML 技术的家庭自动化系统的设计、实施和评估进行了全面研究。我们提出的系统集成了各种物联网设备，如传感器、执行器和智能电器，以在家庭中创建一个联网环境。这些设备收集并传输有关环境条件、用户偏好和能源消耗模式的实时数据。我们采用机器学习算法来分析这些数据，并做出明智的决策，以实现家庭管理和控制各方面的自动化。我们系统的关键组件包括数据预处理、特征提取、模型训练和决策模块。我们探索了不同的 ML 算法，如回归、分类和聚类，以解决温度调节、照明控制、安全监控和能源优化等具体任务。此外，我们还研究了模型部署、监控和适应技术，以确保系统在动态家庭环境中的稳健性和可靠性。为了评估我们方法的有效性，我们使用部署在真实家庭中的原型实施方案进行了实验。我们测量了准确性、响应速度、节能效果和用户满意度等性能指标，以评估所提议系统的实际可行性。我们的结果表明，与传统的基于规则的方法相比，我们的家庭自动化能力有了显著提高，这凸显了物联网和 ML 集成在塑造未来智能家居方面的潜力。
{"title":"Home Automation System Base on IoT and ML","authors":"Chandani Thakkar, Karan Pandya","doi":"10.32628/cseit2410278","DOIUrl":"https://doi.org/10.32628/cseit2410278","url":null,"abstract":"With the proliferation of Internet of Things (IoT) devices and advancements in machine learning (ML) techniques, there is growing interest in developing intelligent home automation systems. These systems aim to enhance convenience, comfort, and energy efficiency in modern households. In this paper, we present a comprehensive study on the design, implementation, and evaluation of a home automation system leveraging IoT and ML technologies. Our proposed system integrates various IoT devices such as sensors, actuators, and smart appliances to create a networked environment within the home. These devices collect and transmit real-time data about environmental conditions, user preferences, and energy consumption patterns. We employ machine learning algorithms to analyse this data and make informed decisions to automate various aspects of home management and control.Key components of our system include data preprocessing, feature extraction, model training, and decision-making modules. We explore different ML algorithms such as regression, classification, and clustering to address specific tasks such as temperature regulation, lighting control, security monitoring, and energy optimization. Furthermore, we investigate techniques for model deployment, monitoring, and adaptation to ensure the robustness and reliability of the system in dynamic home environments. To evaluate the effectiveness of our approach, we conduct experiments using a prototype implementation deployed in real-world households. We measure performance metrics such as accuracy, responsiveness, energy savings, and user satisfaction to assess the practical viability of the proposed system. Our results demonstrate significant improvements in home automation capabilities compared to traditional rule-based approaches, highlighting the potential of IoT and ML integration in shaping the future of smart homes.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"124 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680181","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}
Develop a platform that empowers students, educators, policymakers, and the public by offering insights into the educational landscape. This website will be user-friendly and informative, providing a comprehensive overview of universities based on statistical metrics, emphasizing data representation. Users can explore academic achievements through metrics such as pass rates, average GPA, and research output percentages. Additionally, the platform allows for easy comparisons between universities to help users assess each institution's strengths and weaknesses.
{"title":"A Portal to Browse Top Universities According to Your Preferences : Result","authors":"Shravani Meshram, Prathmesh Tambakhe, Nidhi Gupta","doi":"10.32628/cseit2410284","DOIUrl":"https://doi.org/10.32628/cseit2410284","url":null,"abstract":"Develop a platform that empowers students, educators, policymakers, and the public by offering insights into the educational landscape. This website will be user-friendly and informative, providing a comprehensive overview of universities based on statistical metrics, emphasizing data representation. Users can explore academic achievements through metrics such as pass rates, average GPA, and research output percentages. Additionally, the platform allows for easy comparisons between universities to help users assess each institution's strengths and weaknesses.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681412","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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