Pub Date : 2024-05-16DOI: 10.47992/ijaeml.2581.7000.0228
Sachin Kumar, K. K., P. S. Aithal
Purpose: Utilising cutting-edge technologies, data analytics, and entrepreneurial strategies to promote innovation, develop resilience, and drive sustainable growth are the main goals of Tech-Business projects in the Blue Economy for industries associated to the marine sector. Design/Methodology/Approach: A methodical approach that combines cutting-edge technologies, analytical techniques, and commercial strategies is used to implement Tech-commercial initiatives in the Blue Economy. Findings/Result: The results highlight the revolutionary effect of Tech-Business endeavours in propelling eco-friendly expansion, stimulating creativity, and fortifying the Blue Economy. Stakeholders can open up new possibilities and build a more affluent and sustainable future for coastal communities and marine ecosystems by embracing data-driven initiatives and utilising technology. Originality/Value: The uniqueness of Tech-Business endeavours in the Blue Economy is found in their creative methods of resolving persistent issues and opening up fresh doors in the maritime sector. Paper Type: Exploratory Research on Technology Management.
{"title":"Tech-Business Analytics in Blue Economy","authors":"Sachin Kumar, K. K., P. S. Aithal","doi":"10.47992/ijaeml.2581.7000.0228","DOIUrl":"https://doi.org/10.47992/ijaeml.2581.7000.0228","url":null,"abstract":"Purpose: Utilising cutting-edge technologies, data analytics, and entrepreneurial strategies to promote innovation, develop resilience, and drive sustainable growth are the main goals of Tech-Business projects in the Blue Economy for industries associated to the marine sector.\u0000Design/Methodology/Approach: A methodical approach that combines cutting-edge technologies, analytical techniques, and commercial strategies is used to implement Tech-commercial initiatives in the Blue Economy.\u0000Findings/Result: The results highlight the revolutionary effect of Tech-Business endeavours in propelling eco-friendly expansion, stimulating creativity, and fortifying the Blue Economy. Stakeholders can open up new possibilities and build a more affluent and sustainable future for coastal communities and marine ecosystems by embracing data-driven initiatives and utilising technology.\u0000Originality/Value: The uniqueness of Tech-Business endeavours in the Blue Economy is found in their creative methods of resolving persistent issues and opening up fresh doors in the maritime sector.\u0000Paper Type: Exploratory Research on Technology Management.","PeriodicalId":503007,"journal":{"name":"International Journal of Applied Engineering and Management Letters","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968399","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 : 2024-05-13DOI: 10.47992/ijaeml.2581.7000.0227
Santhosh Kumar K., P. S. Aithal
Purpose: The success of Micro Finance Institutions (MFIs) hinges on effective decision-making amidst challenging economic circumstances, necessitating the ability to anticipate and navigate future business conditions. This research aims to explore the relationship between financial performance and the COVID-19 pandemic's impact on MFIs listed on the National Stock Exchange (NSE) in India. By analyzing financial data and employing statistical techniques, the study seeks to elucidate the resilience of MFIs during the pandemic and the role of financial ratios in predicting earnings per share (EPS). Additionally, the research aims to contribute to a deeper understanding of the interplay between financial indicators and organizational outcomes within the microfinance sector. Design/Methodology/Approach: This research adopts a quantitative approach, utilizing secondary data obtained from quarterly financial reports of selected MFIs listed on the NSE from 2018 to 2022. Statistical analyses such as paired t-tests, correlation, and regression are employed to assess the impact of the COVID-19 pandemic recession on MFIs' profitability and to investigate the relationship between financial ratios and earnings per share (EPS). Findings/Result: Despite the COVID-19 pandemic recession, the profitability of Indian Micro Finance Institutions (MFIs) remained largely unaffected, as indicated by non-significant differences in earnings before interest and taxes (EBIT) before and after the pandemic. Regression analysis highlighted significant influences of financial ratios like the current ratio, total asset ratio, net profit ratio, and gross profit ratio on earnings per share (EPS), underscoring the sector's resilience and the importance of financial analysis in predicting MFI performance. Originality/Value: This research offers novel insights into the resilience of Indian Micro Finance Institutions (MFIs) during the COVID-19 pandemic and underscores the significance of financial analysis in guiding strategic decision-making processes within the microfinance sector. Paper Type: Empirical Research.
{"title":"Navigating the Cascading Ripples: A Study on the Interplay between the COVID-19 Recession and the Financial Fortunes of Micro Finance Institutions (MFIs)","authors":"Santhosh Kumar K., P. S. Aithal","doi":"10.47992/ijaeml.2581.7000.0227","DOIUrl":"https://doi.org/10.47992/ijaeml.2581.7000.0227","url":null,"abstract":"Purpose: The success of Micro Finance Institutions (MFIs) hinges on effective decision-making amidst challenging economic circumstances, necessitating the ability to anticipate and navigate future business conditions. This research aims to explore the relationship between financial performance and the COVID-19 pandemic's impact on MFIs listed on the National Stock Exchange (NSE) in India. By analyzing financial data and employing statistical techniques, the study seeks to elucidate the resilience of MFIs during the pandemic and the role of financial ratios in predicting earnings per share (EPS). Additionally, the research aims to contribute to a deeper understanding of the interplay between financial indicators and organizational outcomes within the microfinance sector.\u0000Design/Methodology/Approach: This research adopts a quantitative approach, utilizing secondary data obtained from quarterly financial reports of selected MFIs listed on the NSE from 2018 to 2022. Statistical analyses such as paired t-tests, correlation, and regression are employed to assess the impact of the COVID-19 pandemic recession on MFIs' profitability and to investigate the relationship between financial ratios and earnings per share (EPS).\u0000Findings/Result: Despite the COVID-19 pandemic recession, the profitability of Indian Micro Finance Institutions (MFIs) remained largely unaffected, as indicated by non-significant differences in earnings before interest and taxes (EBIT) before and after the pandemic. Regression analysis highlighted significant influences of financial ratios like the current ratio, total asset ratio, net profit ratio, and gross profit ratio on earnings per share (EPS), underscoring the sector's resilience and the importance of financial analysis in predicting MFI performance.\u0000Originality/Value: This research offers novel insights into the resilience of Indian Micro Finance Institutions (MFIs) during the COVID-19 pandemic and underscores the significance of financial analysis in guiding strategic decision-making processes within the microfinance sector.\u0000Paper Type: Empirical Research.","PeriodicalId":503007,"journal":{"name":"International Journal of Applied Engineering and Management Letters","volume":"85 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983184","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 : 2024-05-04DOI: 10.47992/ijaeml.2581.7000.0226
S. Aithal, P. S. Aithal
Purpose: The 21st century has seen an unprecedented surge in nanomaterials research, driven by conventional scientific approaches and the advent of potent AI-based tools. This paper focus on comparative analysis, scrutinizing the trajectory of nanomaterial breakthroughs achieved with and without the integration of AI-based Generative Pre-trained Transformers (GPTs). Historically, advances in nanomaterials have occurred during several historical periods, characterized by the discovery of materials like carbon nanotubes, metamaterials, and self-assembling nanostructures. These turning points, which depended on simulations and testing, influenced a variety of fields, including materials science, electronics, and medicine. On the other hand, the age enabled by AI-based GPTs saw a rapid improvement in fields such as artificial intelligence (AI) assisted material design, predictive simulations, automation of synthesis processes, and the development of self-learning nanomaterials and AI-driven nanorobots. Methodology: This paper uses exploratory research methodology to analyse, compare, evaluate, interpret, and create new knowledge to address the use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century by collecting relevant information using appropriate keywords through Google, Google scholar, and AI-driven GPT search engines. Analysis & Discussion: When comparing the timelines, research procedures, and material design were significantly expedited by the inclusion of AI-based GPTs. In addition to accelerating discoveries, automation and AI-driven approaches reduced research expenses, which may democratize access to nanotechnology. These GPTs delved into uncharted chemical territory, discovering new compounds with uses in electronics, energy, and medicine. However, issues with data accessibility, bias in AI models, and moral questions about self-learning nanomaterials continue to be crucial topics that demand close attention in order to make responsible and fair progress. Originality/Value: AI-based GPTs stand as transformative catalysts in nanomaterials research, complementing traditional methodologies. While their integration promises accelerated progress, the responsible and beneficial evolution of AI-powered nanotechnology mandates addressing challenges related to data, bias, and ethical implications for a sustainable future in this burgeoning field.
{"title":"Predictive Analysis of use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century","authors":"S. Aithal, P. S. Aithal","doi":"10.47992/ijaeml.2581.7000.0226","DOIUrl":"https://doi.org/10.47992/ijaeml.2581.7000.0226","url":null,"abstract":"Purpose: The 21st century has seen an unprecedented surge in nanomaterials research, driven by conventional scientific approaches and the advent of potent AI-based tools. This paper focus on comparative analysis, scrutinizing the trajectory of nanomaterial breakthroughs achieved with and without the integration of AI-based Generative Pre-trained Transformers (GPTs). Historically, advances in nanomaterials have occurred during several historical periods, characterized by the discovery of materials like carbon nanotubes, metamaterials, and self-assembling nanostructures. These turning points, which depended on simulations and testing, influenced a variety of fields, including materials science, electronics, and medicine. On the other hand, the age enabled by AI-based GPTs saw a rapid improvement in fields such as artificial intelligence (AI) assisted material design, predictive simulations, automation of synthesis processes, and the development of self-learning nanomaterials and AI-driven nanorobots. \u0000Methodology: This paper uses exploratory research methodology to analyse, compare, evaluate, interpret, and create new knowledge to address the use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century by collecting relevant information using appropriate keywords through Google, Google scholar, and AI-driven GPT search engines. \u0000Analysis & Discussion: When comparing the timelines, research procedures, and material design were significantly expedited by the inclusion of AI-based GPTs. In addition to accelerating discoveries, automation and AI-driven approaches reduced research expenses, which may democratize access to nanotechnology. These GPTs delved into uncharted chemical territory, discovering new compounds with uses in electronics, energy, and medicine. However, issues with data accessibility, bias in AI models, and moral questions about self-learning nanomaterials continue to be crucial topics that demand close attention in order to make responsible and fair progress. \u0000Originality/Value: AI-based GPTs stand as transformative catalysts in nanomaterials research, complementing traditional methodologies. While their integration promises accelerated progress, the responsible and beneficial evolution of AI-powered nanotechnology mandates addressing challenges related to data, bias, and ethical implications for a sustainable future in this burgeoning field.","PeriodicalId":503007,"journal":{"name":"International Journal of Applied Engineering and Management Letters","volume":"209 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141013411","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 : 2024-03-06DOI: 10.47992/ijaeml.2581.7000.0215
Sudipto Chakraborty, P. S. Aithal
Purpose: Now, artificial intelligence (AI) is booming. Day by day, AI is introduced into the new field. We have lots of expectations for the advancement of AI. In our modern busy schedule, we all expect our everyday monotonous homework to be executed by AI. We are introducing more and more smart devices to do our work smartly. But at the end of the day, all our smart home devices are operated manually. We are not fully satisfied with smart devices. Knowing this, the smart device manufacturer is adding AI features inside their devices. Here, we demonstrate how to build an AI bedroom for better living. Design/Methodology/Approach: We install three devices inside the bedroom. The first is a surveillance PTZ camera, the second is the CPU, and the third is the action module. The camera will capture the events and is transferred to the central processing unit or CPU. It will process the image and then detect the event. Once the event is detected, then through the action module, we trigger the electrical or electronic equipment. Findings/Result: the performance of the centralized system is better than that of distributed individually operated smart devices. Here, we account for two types of performance: the event detection and the action module on the specific action. The event detection module takes much more time due to the processing overhead of the image. We get the result within a couple of milliseconds. Due to the dedicated CPU, the processing is faster than on a cloud-based server, which depends on the bandwidth of the internet. Originality/Value/ Novelty: We studied several research documents on smart homes and artificial intelligence-integrated homes. Most AI homes are built using several smart home appliances operated manually. And there is no centralized control. Without central control, the system could not deliver the best performance. Here, the complete system is nicely controlled by a centralized CPU, which makes it a unique approach to this project. Type of Paper: Conceptual Research.
目的：现在，人工智能（AI）正在蓬勃发展。每天，人工智能都被引入新的领域。我们对人工智能的进步充满期待。在繁忙的现代生活中，我们都希望每天单调的作业能由人工智能来完成。我们正在引入越来越多的智能设备来智能地完成我们的工作。但归根结底，我们所有的智能家居设备都是手动操作的。我们对智能设备并不完全满意。有鉴于此，智能设备制造商开始在其设备中添加人工智能功能。在此，我们将演示如何打造一间人工智能卧室，让生活更美好。设计/方法/途径：我们在卧室内安装三个设备。第一个是监控云台摄像机，第二个是中央处理器，第三个是动作模块。摄像头将捕捉事件并传输到中央处理器或 CPU。它将处理图像，然后检测事件。一旦检测到事件，我们就会通过动作模块触发电气或电子设备。研究结果/成果：集中式系统的性能优于分布式单独操作的智能设备。在这里，我们考虑了两类性能：事件检测和具体操作的行动模块。事件检测模块需要更多的时间来处理图像。我们在几毫秒内就能得到结果。原创性/价值/新颖性：我们研究了几篇关于智能家居和人工智能集成家居的研究文献。大多数人工智能家庭都是通过手动操作多个智能家居设备来实现的。而且没有集中控制。没有集中控制，系统就无法实现最佳性能。在这里，整个系统由一个中央 CPU 控制，这使它成为本项目的一种独特方法：概念研究。
{"title":"AI Bedroom","authors":"Sudipto Chakraborty, P. S. Aithal","doi":"10.47992/ijaeml.2581.7000.0215","DOIUrl":"https://doi.org/10.47992/ijaeml.2581.7000.0215","url":null,"abstract":"Purpose: Now, artificial intelligence (AI) is booming. Day by day, AI is introduced into the new field. We have lots of expectations for the advancement of AI. In our modern busy schedule, we all expect our everyday monotonous homework to be executed by AI. We are introducing more and more smart devices to do our work smartly. But at the end of the day, all our smart home devices are operated manually. We are not fully satisfied with smart devices. Knowing this, the smart device manufacturer is adding AI features inside their devices. Here, we demonstrate how to build an AI bedroom for better living. \u0000Design/Methodology/Approach: We install three devices inside the bedroom. The first is a surveillance PTZ camera, the second is the CPU, and the third is the action module. The camera will capture the events and is transferred to the central processing unit or CPU. It will process the image and then detect the event. Once the event is detected, then through the action module, we trigger the electrical or electronic equipment.\u0000Findings/Result: the performance of the centralized system is better than that of distributed individually operated smart devices. Here, we account for two types of performance: the event detection and the action module on the specific action. The event detection module takes much more time due to the processing overhead of the image. We get the result within a couple of milliseconds. Due to the dedicated CPU, the processing is faster than on a cloud-based server, which depends on the bandwidth of the internet.\u0000Originality/Value/ Novelty: We studied several research documents on smart homes and artificial intelligence-integrated homes. Most AI homes are built using several smart home appliances operated manually. And there is no centralized control. Without central control, the system could not deliver the best performance. Here, the complete system is nicely controlled by a centralized CPU, which makes it a unique approach to this project.\u0000Type of Paper: Conceptual Research.","PeriodicalId":503007,"journal":{"name":"International Journal of Applied Engineering and Management Letters","volume":"35 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261029","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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