Pub Date : 2023-10-23DOI: 10.55529/jaimlnn.36.42.44
Mahnoor Zafar
This article explores the interconnectedness of hyperreality with the new era of artificial intelligence (AI). Jean Baudrillard's concept of hyperreality dates back to the nineteenth century when it was considered a provocative and eccentric notion. In the current digital era, this concept gains prominence as media portrays hyperreal images, making AI-powered technologies amplify the world's realism beyond its actuality. Today, reality is a fusion of physical and virtual realities, human existence and artificial intelligence (AI). Amid the numerous challenges the world faces, artificial intelligence has the potential to be more destructive and perilous than any nuclear weaponry.
{"title":"The Dual Nature of Hyperreality in the Age of Artificial Intelligence","authors":"Mahnoor Zafar","doi":"10.55529/jaimlnn.36.42.44","DOIUrl":"https://doi.org/10.55529/jaimlnn.36.42.44","url":null,"abstract":"This article explores the interconnectedness of hyperreality with the new era of artificial intelligence (AI). Jean Baudrillard's concept of hyperreality dates back to the nineteenth century when it was considered a provocative and eccentric notion. In the current digital era, this concept gains prominence as media portrays hyperreal images, making AI-powered technologies amplify the world's realism beyond its actuality. Today, reality is a fusion of physical and virtual realities, human existence and artificial intelligence (AI). Amid the numerous challenges the world faces, artificial intelligence has the potential to be more destructive and perilous than any nuclear weaponry.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.55529/jaimlnn.36.29.41
Sanjid Bin Karim Sezan, Tisha Rahman, Kazi Tanvir, Nishat Tasnim, Al -Jobair Ibna Ataur
Bangladesh faces significant traffic rule violation problems due to chaotic and overcrowded roads, where drivers often ignore traffic signals, switch lanes without warning, and overload vehicles. Pedestrian safety is also a concern, with jaywalking being common. Illegal parking, speeding, and reckless driving contribute to frequent accidents, and there's a lack of awareness and consistent enforcement of traffic rules. In this challenging scenario, YOLOv5 stands out as a practical solution. It's like having a sharp traffic officer who can quickly spot rule violations like running red lights or illegal parking. YOLOv5's abilities help enforce traffic rules more effectively, making the roads safer for everyone in Bangladesh, where road safety is a pressing concern.
{"title":"Detecting Traffic Rule Violations and Promoting Road Safety through Artificial Intelligence","authors":"Sanjid Bin Karim Sezan, Tisha Rahman, Kazi Tanvir, Nishat Tasnim, Al -Jobair Ibna Ataur","doi":"10.55529/jaimlnn.36.29.41","DOIUrl":"https://doi.org/10.55529/jaimlnn.36.29.41","url":null,"abstract":"Bangladesh faces significant traffic rule violation problems due to chaotic and overcrowded roads, where drivers often ignore traffic signals, switch lanes without warning, and overload vehicles. Pedestrian safety is also a concern, with jaywalking being common. Illegal parking, speeding, and reckless driving contribute to frequent accidents, and there's a lack of awareness and consistent enforcement of traffic rules. In this challenging scenario, YOLOv5 stands out as a practical solution. It's like having a sharp traffic officer who can quickly spot rule violations like running red lights or illegal parking. YOLOv5's abilities help enforce traffic rules more effectively, making the roads safer for everyone in Bangladesh, where road safety is a pressing concern.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.55529/jaimlnn.36.23.28
Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena
Adoption of artificial intelligence (AI) for development of Smart education as the future of a sustainable education system is gaining momentum worldwide. AI can transform the way we teach and learn, making education more personalized and efficient. With AI, adaptive learning platforms can analyse students' strengths and weaknesses, tailoring lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance. AI can also assist in content creation and assessment, automating tasks like grading and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for all students, empowering them to thrive in the digital age. AI has the potential to revolutionize education by personalizing learning experiences and making them more efficient. Adaptive learning platforms that use AI can analyse students' strengths and weaknesses, and tailor lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance, enhancing the learning process. AI can also automate tasks like content creation, assessment, grading, and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for students, empowering them to excel in the digital age. This transformative technology is set to shape the future of education worldwide. With AI, the possibilities are endless.
{"title":"Adoption of Artificial Intelligence (AI) For Development of Smart Education as the Future of a Sustainable Education System","authors":"Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena","doi":"10.55529/jaimlnn.36.23.28","DOIUrl":"https://doi.org/10.55529/jaimlnn.36.23.28","url":null,"abstract":"Adoption of artificial intelligence (AI) for development of Smart education as the future of a sustainable education system is gaining momentum worldwide. AI can transform the way we teach and learn, making education more personalized and efficient. With AI, adaptive learning platforms can analyse students' strengths and weaknesses, tailoring lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance. AI can also assist in content creation and assessment, automating tasks like grading and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for all students, empowering them to thrive in the digital age. AI has the potential to revolutionize education by personalizing learning experiences and making them more efficient. Adaptive learning platforms that use AI can analyse students' strengths and weaknesses, and tailor lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance, enhancing the learning process. AI can also automate tasks like content creation, assessment, grading, and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for students, empowering them to excel in the digital age. This transformative technology is set to shape the future of education worldwide. With AI, the possibilities are endless.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.55529/jaimlnn.36.11.22
John Philip Omol Echevarria, Peter John Berces Aranas
The core objective of this study is to showcase the enhanced forecasting capabilities of a hybrid model that combines the strengths of Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) in predicting the Consumer Price Index (CPI). By harnessing the intricate non-linear pattern capturing ability of ANN and the capabilities of ARIMA in modeling linear and autoregressive components, the hybrid model aims to outperform the standalone ARIMA model in accurately forecasting the CPI. Real-world CPI data will be utilized for empirical evaluation and comparison, providing valuable insights into the effectiveness and practical applicability of the hybrid ARIMA-ANN approach in improving CPI forecasting accuracy. The performance of Box Jenkins Models which gives the resulted value of R-squared values for both stationary and non-stationary data are high, indicating that the models explain a significant portion of the variability in the CPI data. The RMSE, MAPE, and MAE values are relatively low, suggesting that the Box-Jenkins models' predictions are close to the actual values. The Ljung-Box Q statistic indicates that all Box-Jenkins models best fit their respective CPI data. The study also employs rigorous statistical methods of machine learning model accuracy assessment, including the Akaike Information Criterion (AIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), to assess the forecasting performance of both models. The results demonstrate that the hybrid ARIMA-ANN model consistently outperforms the standalone ARIMA model, delivering more accurate and reliable forecasts over an extended forecast horizon. The integration of Artificial Neural Networks (ANN) using Multilayer Perceptron (MLP) in the ARIMA models improved the accuracy of the fitted and forecasted values. RMSE and MSE values for the Hybrid ARIMA-ANN models are lower compared to the original Box-Jenkins/ARIMA models, validating the goal of enhancing accuracy through ANN integration.
{"title":"Forecasting the Consumer Price Index in the Regions of the Philippines using Machine Learning for Time Series Models","authors":"John Philip Omol Echevarria, Peter John Berces Aranas","doi":"10.55529/jaimlnn.36.11.22","DOIUrl":"https://doi.org/10.55529/jaimlnn.36.11.22","url":null,"abstract":"The core objective of this study is to showcase the enhanced forecasting capabilities of a hybrid model that combines the strengths of Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) in predicting the Consumer Price Index (CPI). By harnessing the intricate non-linear pattern capturing ability of ANN and the capabilities of ARIMA in modeling linear and autoregressive components, the hybrid model aims to outperform the standalone ARIMA model in accurately forecasting the CPI. Real-world CPI data will be utilized for empirical evaluation and comparison, providing valuable insights into the effectiveness and practical applicability of the hybrid ARIMA-ANN approach in improving CPI forecasting accuracy. The performance of Box Jenkins Models which gives the resulted value of R-squared values for both stationary and non-stationary data are high, indicating that the models explain a significant portion of the variability in the CPI data. The RMSE, MAPE, and MAE values are relatively low, suggesting that the Box-Jenkins models' predictions are close to the actual values. The Ljung-Box Q statistic indicates that all Box-Jenkins models best fit their respective CPI data. The study also employs rigorous statistical methods of machine learning model accuracy assessment, including the Akaike Information Criterion (AIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), to assess the forecasting performance of both models. The results demonstrate that the hybrid ARIMA-ANN model consistently outperforms the standalone ARIMA model, delivering more accurate and reliable forecasts over an extended forecast horizon. The integration of Artificial Neural Networks (ANN) using Multilayer Perceptron (MLP) in the ARIMA models improved the accuracy of the fitted and forecasted values. RMSE and MSE values for the Hybrid ARIMA-ANN models are lower compared to the original Box-Jenkins/ARIMA models, validating the goal of enhancing accuracy through ANN integration.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-05DOI: 10.55529/jaimlnn.35.9.13
Dr. S. Ramesh
Artificial Intelligence (AI) has emerged as a revolutionary technology with profound implications across various industries and aspects of modern life. This article provides a comprehensive overview of the impact of artificial intelligence in the present world. Through an extensive review of literature encompassing twenty scholarly articles, this study examines the transformative role of AI in fields such as healthcare, finance, education, manufacturing, and more. The research highlights the benefits and challenges of AI adoption, the ethical considerations, and the potential for AI to shape the future of humanity. Understanding the current impact of AI is crucial in navigating the complex landscape of this powerful technology and harnessing its potential for the betterment of society.
{"title":"The Impact of Artificial Intelligence in the Present World","authors":"Dr. S. Ramesh","doi":"10.55529/jaimlnn.35.9.13","DOIUrl":"https://doi.org/10.55529/jaimlnn.35.9.13","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a revolutionary technology with profound implications across various industries and aspects of modern life. This article provides a comprehensive overview of the impact of artificial intelligence in the present world. Through an extensive review of literature encompassing twenty scholarly articles, this study examines the transformative role of AI in fields such as healthcare, finance, education, manufacturing, and more. The research highlights the benefits and challenges of AI adoption, the ethical considerations, and the potential for AI to shape the future of humanity. Understanding the current impact of AI is crucial in navigating the complex landscape of this powerful technology and harnessing its potential for the betterment of society.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136044472","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}
High blood glucose levels cause diabetes, and it is characterized as a chronic disease that will disrupt fat and protein metabolism. The blood glucose levels rise because it cannot be burned in the cells due to a shortage of insulin secretion by the pancreas, or the insulin produced by the cell is insufficient. If exact early detection is possible, the hazard and prevalence of diabetes can be decreased considerably. With this, the application of technology has been an essential part of providing accurate and acceptable results in the prevention and early detection of the illness. This research implements artificial neural networks to predict the early stage of diabetes by incorporating methods involving feature selection or dimension reduction using a Relief-Based Filter for testing and training data. The results show 99.3% prediction accuracy and can be essential in contributing to a new way that is highly accurate in determining diabetes among patients.
{"title":"Artificial Neural Networks Based Predictive Model for Detecting the Early-Stage Diabetes","authors":"Shokhjakhon Abdufattokhov, Nodira Normatova, Makhbuba Shermatova","doi":"10.55529/jaimlnn.241.8","DOIUrl":"https://doi.org/10.55529/jaimlnn.241.8","url":null,"abstract":"High blood glucose levels cause diabetes, and it is characterized as a chronic disease that will disrupt fat and protein metabolism. The blood glucose levels rise because it cannot be burned in the cells due to a shortage of insulin secretion by the pancreas, or the insulin produced by the cell is insufficient. If exact early detection is possible, the hazard and prevalence of diabetes can be decreased considerably. With this, the application of technology has been an essential part of providing accurate and acceptable results in the prevention and early detection of the illness. This research implements artificial neural networks to predict the early stage of diabetes by incorporating methods involving feature selection or dimension reduction using a Relief-Based Filter for testing and training data. The results show 99.3% prediction accuracy and can be essential in contributing to a new way that is highly accurate in determining diabetes among patients.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135672197","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}
Krishnagiri Krishnababu, Gururaj S Kulkarni, Yogaraj R, Padmaa M Paarakh
The pharmaceutical industry is one of the most important industries in the world. It provides essential medicines and treatments that help people live longer and healthier lives. The industry is also one of the most regulated and complex, with drugs taking years to develop and billions of dollars in investment. However, the emergence of artificial intelligence (AI) is transforming the way drugs are developed, tested, and brought to market. AI has the potential to revolutionize the pharmaceutical industry by accelerating drug discovery, reducing costs, and improving patient outcomes. In this article, we will explore the ways in which AI is transforming the pharmaceutical industry and how it is changing the way drugs are developed and delivered to patients. AI simplifies labour by analyzing, filtering, sorting, forecasting, scoping, and recognizing massive data volumes to follow the best implementation techniques for coming up with the optimum solution. Artificial intelligence has the potential to lower prices and provide new, effective medicines, but most significantly, it has the potential to save lives. It can be successfully applied to develop a robust, long-lasting pipeline of new medications. We would be able to produce medicines more quickly and affordably by utilizing the power of current technology.
{"title":"Revolutionizing the Pharmaceutical Industry with Artificial Intelligence","authors":"Krishnagiri Krishnababu, Gururaj S Kulkarni, Yogaraj R, Padmaa M Paarakh","doi":"10.55529/jaimlnn.26.37","DOIUrl":"https://doi.org/10.55529/jaimlnn.26.37","url":null,"abstract":"The pharmaceutical industry is one of the most important industries in the world. It provides essential medicines and treatments that help people live longer and healthier lives. The industry is also one of the most regulated and complex, with drugs taking years to develop and billions of dollars in investment. However, the emergence of artificial intelligence (AI) is transforming the way drugs are developed, tested, and brought to market. AI has the potential to revolutionize the pharmaceutical industry by accelerating drug discovery, reducing costs, and improving patient outcomes. In this article, we will explore the ways in which AI is transforming the pharmaceutical industry and how it is changing the way drugs are developed and delivered to patients. AI simplifies labour by analyzing, filtering, sorting, forecasting, scoping, and recognizing massive data volumes to follow the best implementation techniques for coming up with the optimum solution. Artificial intelligence has the potential to lower prices and provide new, effective medicines, but most significantly, it has the potential to save lives. It can be successfully applied to develop a robust, long-lasting pipeline of new medications. We would be able to produce medicines more quickly and affordably by utilizing the power of current technology.","PeriodicalId":495185,"journal":{"name":"Journal of Artificial Intelligence Machine Learning and Neural Network","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136284266","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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