This paper introduces LLM-CloudComplete, a novel cloud-based system for efficient and scalable code completion leveraging large language models (LLMs). We address the challenges of deploying LLMs for real-time code completion by implementing a distributed inference architecture, adaptive resource allocation, and multi-level caching mechanisms. Our system utilizes a pipeline parallelism technique to distribute LLM layers across multiple GPU nodes, achieving near-linear scaling in throughput. We propose an adaptive resource allocation algorithm using reinforcement learning to optimize GPU utilization under varying workloads. A similarity-based retrieval mechanism is implemented within a three-tier caching system to reduce computational load and improve response times. Additionally, we introduce several latency reduction strategies, including predictive prefetching, incremental completion generation, and sparse attention optimization. Extensive evaluations on diverse programming languages demonstrate that LLM-CloudComplete outperforms existing state-of-the-art code completion systems, achieving a 7.4% improvement in Exact Match accuracy while reducing latency by 76.2% and increasing throughput by 320%. Our ablation studies reveal the significant contributions of each system component to overall performance. LLM-CloudComplete represents a substantial advancement in cloud-based AI-assisted software development, paving the way for more efficient and responsive coding tools. We discuss limitations and future research directions, including privacy-preserving techniques and adaptability to diverse programming paradigms.
{"title":"LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion","authors":"Mingxuan Zhang, Bo Yuan, Hanzhe Li, Kangming Xu","doi":"10.60087/jaigs.v5i1.200","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.200","url":null,"abstract":"This paper introduces LLM-CloudComplete, a novel cloud-based system for efficient and scalable code completion leveraging large language models (LLMs). We address the challenges of deploying LLMs for real-time code completion by implementing a distributed inference architecture, adaptive resource allocation, and multi-level caching mechanisms. Our system utilizes a pipeline parallelism technique to distribute LLM layers across multiple GPU nodes, achieving near-linear scaling in throughput. We propose an adaptive resource allocation algorithm using reinforcement learning to optimize GPU utilization under varying workloads. A similarity-based retrieval mechanism is implemented within a three-tier caching system to reduce computational load and improve response times. \u0000Additionally, we introduce several latency reduction strategies, including predictive prefetching, incremental completion generation, and sparse attention optimization. Extensive evaluations on diverse programming languages demonstrate that LLM-CloudComplete outperforms existing state-of-the-art code completion systems, achieving a 7.4% improvement in Exact Match accuracy while reducing latency by 76.2% and increasing throughput by 320%. Our ablation studies reveal the significant contributions of each system component to overall performance. LLM-CloudComplete represents a substantial advancement in cloud-based AI-assisted software development, paving the way for more efficient and responsive coding tools. We discuss limitations and future research directions, including privacy-preserving techniques and adaptability to diverse programming paradigms.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927494","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}
There is a pressing necessity to shift our economy, society, and culture to systems and actions that promote ecological sustainability. This radical transformation necessitates an equally radical transformation of resource utilization and decision-making strategies. Sustainable entrepreneurship (SE) is frequently touted as the solution to the triple-bottom-line challenges that businesses encounter; however, there are tangible constraints on its potential. SE is currently in the first phase of implementing technological frontier tools that provide empirical guidance throughout the entrepreneurial decision-making process. The potential for artificial intelligence (AI) to inform decision-making is advanced by Big Data (BD), which also establishes pathways to attain desired outcomes. The interactions between AI, BD, and SE have been generally under-studied thus far. The absence of work that consolidates and synthesizes this literature is the primary focus of this conceptual paper. We propose that AI and BD are capable of rapidly contributing to the continued sustainable development of the weak form, but they also hold significant potential for attaining the strong sustainability ideal. We present two proposals for the integration of AI and BD to inform and facilitate SE. Finally, we outline potential areas for future research. The core of human cosmology and ethics has always been the definition of his uniqueness. He ceased to be the species situated at the center of the universe, accompanied by the sun and stars, with the arrival of Copernicus and Galileo. He ceased to be the species that was created and specially endowed by God with soul and reason with the arrival of Darwin. With Freud, he ceased to be the species whose behavior could potentially be regulated by the rational mind. He has ceased to be the species that is uniquely capable of complex, intelligent manipulation of his environment as we begin to produce mechanisms that think and learn.
我们的经济、社会和文化迫切需要向促进生态可持续性的系统和行动转变。要实现这一根本性转变，就必须对资源利用和决策战略进行同样彻底的改革。可持续创业（SE）经常被吹捧为解决企业遇到的三重底线挑战的方法；然而，其潜力却受到了切实的限制。可持续创业目前正处于实施技术前沿工具的第一阶段，这些工具可在整个创业决策过程中提供经验指导。人工智能（AI）为决策提供信息的潜力得到了大数据（BD）的推动，而大数据也为实现预期成果确立了路径。迄今为止，人们对人工智能、BD 和 SE 之间的相互作用普遍研究不足。缺乏对这些文献进行整合和归纳的工作是本概念性论文的主要重点。我们提出，人工智能和生物多样性能够迅速促进弱形式的持续可持续发展，但它们也具有实现强可持续性理想的巨大潜力。我们提出了两项整合人工智能和生物多样性的建议，以指导和促进可持续发展。最后，我们概述了未来研究的潜在领域。人类宇宙学和伦理学的核心一直是人类独特性的定义。随着哥白尼和伽利略的到来，人类不再是位于宇宙中心、与太阳和恒星相伴的物种。随着达尔文的到来，他不再是上帝创造并特别赋予灵魂和理性的物种。随着弗洛伊德的出现，他不再是一个其行为有可能受到理性思维调节的物种。随着我们开始制造出能够思考和学习的机制，人类也不再是独一无二的能够复杂、智能地操纵环境的物种。
{"title":"Role of Artificial Intelligence and Big Data in Sustainable Entrepreneurship","authors":"Rula Abu Shanab","doi":"10.60087/jaigs.v5i1.199","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.199","url":null,"abstract":"There is a pressing necessity to shift our economy, society, and culture to systems and actions that promote ecological sustainability. This radical transformation necessitates an equally radical transformation of resource utilization and decision-making strategies. Sustainable entrepreneurship (SE) is frequently touted as the solution to the triple-bottom-line challenges that businesses encounter; however, there are tangible constraints on its potential. SE is currently in the first phase of implementing technological frontier tools that provide empirical guidance throughout the entrepreneurial decision-making process. The potential for artificial intelligence (AI) to inform decision-making is advanced by Big Data (BD), which also establishes pathways to attain desired outcomes. The interactions between AI, BD, and SE have been generally under-studied thus far. The absence of work that consolidates and synthesizes this literature is the primary focus of this conceptual paper. We propose that AI and BD are capable of rapidly contributing to the continued sustainable development of the weak form, but they also hold significant potential for attaining the strong sustainability ideal. We present two proposals for the integration of AI and BD to inform and facilitate SE. Finally, we outline potential areas for future research. \u0000The core of human cosmology and ethics has always been the definition of his uniqueness. He ceased to be the species situated at the center of the universe, accompanied by the sun and stars, with the arrival of Copernicus and Galileo. He ceased to be the species that was created and specially endowed by God with soul and reason with the arrival of Darwin. With Freud, he ceased to be the species whose behavior could potentially be regulated by the rational mind. He has ceased to be the species that is uniquely capable of complex, intelligent manipulation of his environment as we begin to produce mechanisms that think and learn.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"51 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808741","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}
New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge, and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users, and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning, and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance, and efficient supply chain and transportation.
{"title":"Utilizing the Internet of Things (IoT), Artificial Intelligence, Machine Learning, and Vehicle Telematics for Sustainable Growth in Small and Medium Firms (SMEs)","authors":"Abideen Mayowa Abdul-Yekeen, Opeyemi Rasaq, Maryam Adebukola Ayinla, Azeezat Sikiru, Victoria Kujore, Tawakalit Omolabake Agboola","doi":"10.60087/jaigs.v5i1.197","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.197","url":null,"abstract":"New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge, and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users, and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning, and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance, and efficient supply chain and transportation.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807313","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}
Every year, digital technologies appear in every industry. The new, developing technologies offer both advantages and disadvantages. The following are some recent examples of cutting-edge innovations in technology: data science, cybersecurity, block chain technology, artificial intelligence, machine learning, quantum learning, Internet of Things (IoT), 5G and 6G networks, hyper automation, cloud computing, robotics, and natural language processing. AL and ML combined with other cutting-edge, popular technologies have the potential to yield the positive outcomes and contribute to a greener future. Personalized medicine, drug development and predictive diagnostics using large scale data sets are all areas where machine learning might be beneficial to physicians. Students studying mechanical engineering must have a solid understanding of emerging trends such as autonomous vehicles. The potential of AV to create new, improved lifestyle and revolutionize urban planning and transportation has attracted a lot of interest. A research utilized a quantitative technique to further his research. A questionnaire was used to collect data from different participants, and 120 students from different fields in higher education sector were chosen at random. According to research, students who used popular technologies acquired more sophisticated abilities that will increase their output at work. Technology is always changing because it takes ongoing training to keep up with the latest development. The issue of the digital divide will be resolved by ongoing training.
每年，各行各业都会出现数字技术。这些不断发展的新技术既有利也有弊。以下是一些最新的前沿创新技术：数据科学、网络安全、区块链技术、人工智能、机器学习、量子学习、物联网（IoT）、5G 和 6G 网络、超级自动化、云计算、机器人技术和自然语言处理。AL 和 ML 与其他尖端流行技术相结合，有可能产生积极的成果，并有助于创造一个更加绿色的未来。个性化医疗、药物开发和使用大规模数据集的预测性诊断都是机器学习可能对医生有益的领域。学习机械工程的学生必须对自动驾驶汽车等新兴趋势有扎实的了解。自动驾驶汽车在创造新的、更好的生活方式以及彻底改变城市规划和交通方面的潜力引起了广泛关注。 一项研究利用定量技术来推进他的研究。研究采用问卷调查的方式收集不同参与者的数据，随机选取了 120 名来自高等教育领域不同专业的学生。研究结果表明，使用流行技术的学生获得了更复杂的能力，这将提高他们的工作产出。技术总是在不断变化，因为需要不断培训才能跟上最新的发展。数字鸿沟问题将通过持续培训得到解决。
{"title":"Impact of AI on Education: Innovative Tools and Trends","authors":"Doctor P. Z Msekelwa","doi":"10.60087/jaigs.v5i1.198","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.198","url":null,"abstract":"Every year, digital technologies appear in every industry. The new, developing technologies offer both advantages and disadvantages. The following are some recent examples of cutting-edge innovations in technology: data science, cybersecurity, block chain technology, artificial intelligence, machine learning, quantum learning, Internet of Things (IoT), 5G and 6G networks, hyper automation, cloud computing, robotics, and natural language processing. AL and ML combined with other cutting-edge, popular technologies have the potential to yield the positive outcomes and contribute to a greener future. Personalized medicine, drug development and predictive diagnostics using large scale data sets are all areas where machine learning might be beneficial to physicians. Students studying mechanical engineering must have a solid understanding of emerging trends such as autonomous vehicles. The potential of AV to create new, improved lifestyle and revolutionize urban planning and transportation has attracted a lot of interest. A research utilized a quantitative technique to further his research. A questionnaire was used to collect data from different participants, and 120 students from different fields in higher education sector were chosen at random. According to research, students who used popular technologies acquired more sophisticated abilities that will increase their output at work. Technology is always changing because it takes ongoing training to keep up with the latest development. The issue of the digital divide will be resolved by ongoing training.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":" 71","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827157","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 explores the counter argument for Chapter 3 of Marriages, Families, and Relationships by Mary Ann Lamanna, Agnes Riedmann, and Susan Stewart, which deals with the topic of Gender Identities and Families, especially regarding feminism. This paper will provide a general summary, main points, and concepts of the chapter that focuses on feminism. Afterwards, this paper will continue to provide a general social, legal, and cultural climate of the time the book was written versus now (2024), and then reflect on some new information and research that disproves the glorification of modern feminism as done in the book. The critique will demonstrate how modern feminism, under the guise of advocating for gender equality, can sometimes promote racist and sexist agendas. Specifically, this paper will detail the mechanisms through which modern feminism disguises itself, manipulating social perceptions to orient one group as superior over others. This will include an analysis of how certain feminist narratives utilize the concepts of victimhood and social proof to establish a hierarchy of suffering and legitimacy, thereby positioning some groups as more deserving of support and resources than others, based on race, class, or historical experiences.
{"title":"Critique of Modern Feminism","authors":"Arabella Jo","doi":"10.60087/jaigs.v5i1.196","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.196","url":null,"abstract":"This paper explores the counter argument for Chapter 3 of Marriages, Families, and Relationships by Mary Ann Lamanna, Agnes Riedmann, and Susan Stewart, which deals with the topic of Gender Identities and Families, especially regarding feminism. This paper will provide a general summary, main points, and concepts of the chapter that focuses on feminism. Afterwards, this paper will continue to provide a general social, legal, and cultural climate of the time the book was written versus now (2024), and then reflect on some new information and research that disproves the glorification of modern feminism as done in the book. The critique will demonstrate how modern feminism, under the guise of advocating for gender equality, can sometimes promote racist and sexist agendas. Specifically, this paper will detail the mechanisms through which modern feminism disguises itself, manipulating social perceptions to orient one group as superior over others. This will include an analysis of how certain feminist narratives utilize the concepts of victimhood and social proof to establish a hierarchy of suffering and legitimacy, thereby positioning some groups as more deserving of support and resources than others, based on race, class, or historical experiences.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"9 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641368","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 integration of Artificial Intelligence (AI) into financial technology (FinTech) has revolutionized risk management strategies, offering innovative solutions to longstanding challenges. This paper explores the transformative potential of AI-driven risk management in the financial sector, focusing on predictive accuracy, fraud detection, and regulatory compliance. Employing a mixed-methods approach, the study combines quantitative data from surveys and questionnaires with qualitative insights from interviews and case studies. The findings highlight AI's ability to enhance risk assessment, improve fraud prevention, and optimize compliance processes, thereby creating a more secure and efficient financial environment. Despite the significant benefits, the study also identifies challenges, including regulatory adaptation and ethical considerations. The research concludes with recommendations for stakeholders to effectively implement AI-driven risk management strategies, ensuring a balance between innovation and security.
{"title":"AI-Driven Risk Management Strategies in Financial Technology","authors":"Harsh Daiya","doi":"10.60087/jaigs.v5i1.194","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.194","url":null,"abstract":"The integration of Artificial Intelligence (AI) into financial technology (FinTech) has revolutionized risk management strategies, offering innovative solutions to longstanding challenges. This paper explores the transformative potential of AI-driven risk management in the financial sector, focusing on predictive accuracy, fraud detection, and regulatory compliance. Employing a mixed-methods approach, the study combines quantitative data from surveys and questionnaires with qualitative insights from interviews and case studies. The findings highlight AI's ability to enhance risk assessment, improve fraud prevention, and optimize compliance processes, thereby creating a more secure and efficient financial environment. Despite the significant benefits, the study also identifies challenges, including regulatory adaptation and ethical considerations. The research concludes with recommendations for stakeholders to effectively implement AI-driven risk management strategies, ensuring a balance between innovation and security.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"88 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657665","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 purpose of this research was to comprehensively evaluate the effectiveness of Cyber Threat Intelligence (CTI) in enhancing security operations, while simultaneously identifying the various barriers to its adoption. Additionally, the study aimed to provide potential solutions to mitigate the identified barriers, to ensure successful adoption of CTI.A systematic review was undertaken to identify the main factors of enhanced security operations. Relevant questions and statements were then developed from these factors and a questionnaire was created using Google Forms. These questionnaires were then distributed via email to a sample size of 50 information technology professionals. These results were then analyzed using Microsoft Excel and SPSS. Overall, the research revealed that companies who used CTI reported significant gains in threat detection and response, risk management and threat-hunting abilities, which in turn lead to enhanced security operations. According to the research, several factors prevented organizations from adopting CTI. Among these were technological, regulatory, ignorance-related, and lack of executive support. Finally, to tackle these identified barriers the following were proposed adopting comprehensive awareness and education programs, the formation of an Executive CTI Steering Committees, structured CTI training and skills development programs, technology assessment and modernization initiative-based initiatives, proactive compliance, and legal strategies.
{"title":"The Effectiveness of Cyber Threat Intelligence in Improving Security Operations","authors":"Joshua Smallman","doi":"10.60087/jaigs.v5i1.193","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.193","url":null,"abstract":"The purpose of this research was to comprehensively evaluate the effectiveness of Cyber Threat Intelligence (CTI) in enhancing security operations, while simultaneously identifying the various barriers to its adoption. Additionally, the study aimed to provide potential solutions to mitigate the identified barriers, to ensure successful adoption of CTI.A systematic review was undertaken to identify the main factors of enhanced security operations. Relevant questions and statements were then developed from these factors and a questionnaire was created using Google Forms. These questionnaires were then distributed via email to a sample size of 50 information technology professionals. These results were then analyzed using Microsoft Excel and SPSS. Overall, the research revealed that companies who used CTI reported significant gains in threat detection and response, risk management and threat-hunting abilities, which in turn lead to enhanced security operations. According to the research, several factors prevented organizations from adopting CTI. Among these were technological, regulatory, ignorance-related, and lack of executive support. Finally, to tackle these identified barriers the following were proposed adopting comprehensive awareness and education programs, the formation of an Executive CTI Steering Committees, structured CTI training and skills development programs, technology assessment and modernization initiative-based initiatives, proactive compliance, and legal strategies.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"27 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141663541","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 greatest number of fatal traffic accidents occurs on curved roads at nighttime. In most cases, the late recognition of objects in the traffic zone plays an important role. This highlights to the importance of the role of automobile forward lighting systems. This paper developed a proto-type auto adjustable headlamp and mirror tilt to improve cost and reliability. Also, an adaptive mirror is implemented to remove the blind spots while taking turns. The methodology used here adaptive headlamps and mirrors are developed using Raspberry Pi3 as hardware and Python is used as programming language. Machine learning algorithm “Linear regression” is used for computing the output. Machine Learning Linear regression is considered here as it simple and efficient algorithm in terms of implementation and memory usage. Easily available components like Raspberry Pi3, LDR Sensor, ADXL Gyroscope are used and the design is developed to provide the steering mechanism for the headlamps and mirror which are actuated along with the steering of the front wheels. Around 15% increase in the illuminated area on road and 20% increase in the side mirror view is achieved.
{"title":"Adaptive Headlamp and Side Mirror using Linear Regression based on Raspberry Pi3","authors":"Rahul Ekatpure","doi":"10.60087/jaigs.v5i1.171","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.171","url":null,"abstract":"The greatest number of fatal traffic accidents occurs on curved roads at nighttime. In most cases, the late recognition of objects in the traffic zone plays an important role. This highlights to the importance of the role of automobile forward lighting systems. This paper developed a proto-type auto adjustable headlamp and mirror tilt to improve cost and reliability. Also, an adaptive mirror is implemented to remove the blind spots while taking turns. The methodology used here adaptive headlamps and mirrors are developed using Raspberry Pi3 as hardware and Python is used as programming language. Machine learning algorithm “Linear regression” is used for computing the output. Machine Learning Linear regression is considered here as it simple and efficient algorithm in terms of implementation and memory usage. Easily available components like Raspberry Pi3, LDR Sensor, ADXL Gyroscope are used and the design is developed to provide the steering mechanism for the headlamps and mirror which are actuated along with the steering of the front wheels. Around 15% increase in the illuminated area on road and 20% increase in the side mirror view is achieved.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"31 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354448","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}
Cloud computing has become an integral part of modern digital infrastructure, offering scalable resources and convenient access to data and services. However, ensuring robust security within cloud environments remains a critical challenge. In this paper, we propose an Artificial Intelligence-Based Architecture (AIBA) designed to enhance cloud computing security. By leveraging the capabilities of artificial intelligence, including machine learning and deep learning, the proposed architecture aims to detect, prevent, and mitigate various security threats in cloud systems. Through a combination of advanced algorithms, real-time monitoring, and adaptive responses, AIBA offers proactive defense mechanisms against cyber attacks, data breaches, and unauthorized access. We discuss the key components and functionalities of AIBA, as well as its potential applications and benefits in strengthening cloud security infrastructure.
{"title":"Enhancing Cloud Computing Security Through Artificial Intelligence-Based Architecture","authors":"Sundeep Reddy Mamidi","doi":"10.60087/jaigs.v5i1.166","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.166","url":null,"abstract":"Cloud computing has become an integral part of modern digital infrastructure, offering scalable resources and convenient access to data and services. However, ensuring robust security within cloud environments remains a critical challenge. In this paper, we propose an Artificial Intelligence-Based Architecture (AIBA) designed to enhance cloud computing security. By leveraging the capabilities of artificial intelligence, including machine learning and deep learning, the proposed architecture aims to detect, prevent, and mitigate various security threats in cloud systems. Through a combination of advanced algorithms, real-time monitoring, and adaptive responses, AIBA offers proactive defense mechanisms against cyber attacks, data breaches, and unauthorized access. We discuss the key components and functionalities of AIBA, as well as its potential applications and benefits in strengthening cloud security infrastructure.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"62 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353445","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}
Yiyu Lin, Ang Li, Huixiang Li, Yadong Shi, Xiaoan Zhan
In recent years, deep learning has become a core technology in many fields such as computer vision. The parallel processing capability of GPU, greatly accelerates the training and inference of deep learning models, especially in the field of image processing and generation. This paper discusses the cooperation and differences between deep learning and traditional computer vision technology and focuses on the significant advantages of GPU in medical image processing applications such as image reconstruction, filter enhancement, image registration, matching, and fusion. This convergence not only improves the efficiency and quality of image processing, but also promotes the accuracy and speed of medical diagnosis, and looks forward to the future application and development of deep learning and GPU optimization in various industries.
{"title":"GPU-Optimized Image Processing and Generation Based on Deep Learning and Computer Vision","authors":"Yiyu Lin, Ang Li, Huixiang Li, Yadong Shi, Xiaoan Zhan","doi":"10.60087/jaigs.v5i1.162","DOIUrl":"https://doi.org/10.60087/jaigs.v5i1.162","url":null,"abstract":"In recent years, deep learning has become a core technology in many fields such as computer vision. The parallel processing capability of GPU, greatly accelerates the training and inference of deep learning models, especially in the field of image processing and generation. This paper discusses the cooperation and differences between deep learning and traditional computer vision technology and focuses on the significant advantages of GPU in medical image processing applications such as image reconstruction, filter enhancement, image registration, matching, and fusion. This convergence not only improves the efficiency and quality of image processing, but also promotes the accuracy and speed of medical diagnosis, and looks forward to the future application and development of deep learning and GPU optimization in various industries.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356159","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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