Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169463
Mazi Essoloani Aleza, D. Vetrithangam
Football is one of the most popular sport games played in the world and counts many fans across the globe. During a match, any mistake or bad decision from the referee may turn the entire match and may lead to heartbreak losses. For solving this issue, FIFA (Federation Internationale de Football Association) came up with the idea of VAR (Video Assistant Referee) system to reduce errors that might occur due to referring. Even though the VAR system seems to have some pitfalls, The referee needs to take extra time to check and sometimes it seems to be inefficient. Artificial Intelligence (AI), may play an important role in this scenario. It can be integrated into referee assistance by reducing human errors and can help to enhance the VAR precision for a better decision on the field during a match. This research proposes the creation of an AI assisted referee system that makes judgments during a match using information from an AI agent. The AI agent may detect mistakes such as penalties, card punishments, or any other miscalculations that may occur throughout a game. This finding indicates that Long Short-Term Memory performed the best, with an accuracy rate of 95%.
{"title":"Use of Artificial Intelligence to Avoid Errors in Referring a Football Match","authors":"Mazi Essoloani Aleza, D. Vetrithangam","doi":"10.1109/ICAIA57370.2023.10169463","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169463","url":null,"abstract":"Football is one of the most popular sport games played in the world and counts many fans across the globe. During a match, any mistake or bad decision from the referee may turn the entire match and may lead to heartbreak losses. For solving this issue, FIFA (Federation Internationale de Football Association) came up with the idea of VAR (Video Assistant Referee) system to reduce errors that might occur due to referring. Even though the VAR system seems to have some pitfalls, The referee needs to take extra time to check and sometimes it seems to be inefficient. Artificial Intelligence (AI), may play an important role in this scenario. It can be integrated into referee assistance by reducing human errors and can help to enhance the VAR precision for a better decision on the field during a match. This research proposes the creation of an AI assisted referee system that makes judgments during a match using information from an AI agent. The AI agent may detect mistakes such as penalties, card punishments, or any other miscalculations that may occur throughout a game. This finding indicates that Long Short-Term Memory performed the best, with an accuracy rate of 95%.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114857246","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169555
V. Asha, Arpana Prasad, C.R Vishwanath, K. Madava Raj, A. R. Manoj Kumar, N. Leelavathi
The objective of conduct the current research is to understand the deep relationship between gaming along with how artificial intelligence (AI) is being incorporated into these fields. It is clear that gaming and AI have a natural connection, and the introduction of AI only strengthens this connection. This study aims to highlight the potential benefits of using AI in gaming and simulation, as well as the challenges that may arise in these contexts. The scope of the research includes exploring the various ways in which AI can be applied in gaming. In addition to optimizing programs and tasks, AI can be trained to work independently, reducing the need for human labour and potentially eliminating human error. Overall, this paper focuses on the diverse potential applications of combining AI with gaming and simulation. This research shows how AI can be learn to play independently with the help of Neuro Evolution of Augmenting Topologies (NEAT) a Genetic algorithm.
{"title":"Designing A Popular Game Framework Using Neat A Genetic Algorithms","authors":"V. Asha, Arpana Prasad, C.R Vishwanath, K. Madava Raj, A. R. Manoj Kumar, N. Leelavathi","doi":"10.1109/ICAIA57370.2023.10169555","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169555","url":null,"abstract":"The objective of conduct the current research is to understand the deep relationship between gaming along with how artificial intelligence (AI) is being incorporated into these fields. It is clear that gaming and AI have a natural connection, and the introduction of AI only strengthens this connection. This study aims to highlight the potential benefits of using AI in gaming and simulation, as well as the challenges that may arise in these contexts. The scope of the research includes exploring the various ways in which AI can be applied in gaming. In addition to optimizing programs and tasks, AI can be trained to work independently, reducing the need for human labour and potentially eliminating human error. Overall, this paper focuses on the diverse potential applications of combining AI with gaming and simulation. This research shows how AI can be learn to play independently with the help of Neuro Evolution of Augmenting Topologies (NEAT) a Genetic algorithm.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114878826","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 weather may have a considerable influence on people’s lives. Changes in the weather have the potential to have an impact on a diverse variety of human activities, including agriculture and transportation. The traditional weather and soil monitoring technologies are erroneous and costly, thus accurate and low-cost technologies are still required. The objective of this study is to monitor and review the current weather conditions to keep people up to date with the latest information and allow for the timely implementation of preventative measures if a disaster is anticipated. Arduino is an open-source electronic device creation platform that is based on the amalgamation of hardware and software components that can be freely and flexibly modified to fulfill the needs of the project. The weather forecasting device is used for monitoring weather conditions, more precisely the temperature, humidity, and moisture content of the soil, from which the user gets real-time weather conditions of the place to take necessary actions accordingly, like watering plants, acting against increased LPG or certain gas content in the atmosphere, and many more. After training and testing of results, it follows that the accuracy of weather monitoring does increase and is a better option for increasing datasets. Furthermore, the low cost of the device makes it easily available to farmers.
{"title":"Weather Monitoring and Prediction System based on Machine Learning and IoT","authors":"Narendra Kumar, Swayam Keshari, Ashutosh Singh Rawat, Abhishek Chaubey, Ishaan Dawar","doi":"10.1109/ICAIA57370.2023.10169428","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169428","url":null,"abstract":"The weather may have a considerable influence on people’s lives. Changes in the weather have the potential to have an impact on a diverse variety of human activities, including agriculture and transportation. The traditional weather and soil monitoring technologies are erroneous and costly, thus accurate and low-cost technologies are still required. The objective of this study is to monitor and review the current weather conditions to keep people up to date with the latest information and allow for the timely implementation of preventative measures if a disaster is anticipated. Arduino is an open-source electronic device creation platform that is based on the amalgamation of hardware and software components that can be freely and flexibly modified to fulfill the needs of the project. The weather forecasting device is used for monitoring weather conditions, more precisely the temperature, humidity, and moisture content of the soil, from which the user gets real-time weather conditions of the place to take necessary actions accordingly, like watering plants, acting against increased LPG or certain gas content in the atmosphere, and many more. After training and testing of results, it follows that the accuracy of weather monitoring does increase and is a better option for increasing datasets. Furthermore, the low cost of the device makes it easily available to farmers.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128336192","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169589
Rajlakshmi Ghatkamble, K. Ratish Kumar, S. John Hrithik, J. Harshith Kumar, P. Sujan
Moving from one location to another is one of the biggest issues that visually impaired individuals have. For these folks, walking canes that are readily accessible solely act as obstacle sensors. Long overdue is the requirement for an affordable guiding and navigation system for the blind. This paper’s major goal is to use ultrasonic technology to broaden the electronic mobility aid for blind and visually impaired walkers. The research described in this article involves designing and implementing an ultrasonic navigation system to offer blind pedestrians completely autonomous obstacle avoidance as well as auditory and tactile feedback. A camera will be used to detect objects higher than knee height. This blind steering method is risk-free, accurate, and efficient.
{"title":"Computer Vision and IoT-Based Smart System for Visually Impaired People","authors":"Rajlakshmi Ghatkamble, K. Ratish Kumar, S. John Hrithik, J. Harshith Kumar, P. Sujan","doi":"10.1109/ICAIA57370.2023.10169589","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169589","url":null,"abstract":"Moving from one location to another is one of the biggest issues that visually impaired individuals have. For these folks, walking canes that are readily accessible solely act as obstacle sensors. Long overdue is the requirement for an affordable guiding and navigation system for the blind. This paper’s major goal is to use ultrasonic technology to broaden the electronic mobility aid for blind and visually impaired walkers. The research described in this article involves designing and implementing an ultrasonic navigation system to offer blind pedestrians completely autonomous obstacle avoidance as well as auditory and tactile feedback. A camera will be used to detect objects higher than knee height. This blind steering method is risk-free, accurate, and efficient.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130922244","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169678
Adarsh S. V. Nair, Rathnakar Achary
One of the weakest links in any security system is neither the devices used nor the programs running on them; but the human beings using these devices. Most cyberattacks are initiated by human error. Hackers always use the most accessible and effective social engineering techniques to attack. Simply put, it is the art of manipulating people into sharing sensitive and confidential information. This research proposes a framework with four modules, namely, a source analyzer, a content classifier and analyzer, a link analyzer, and a risk reporting module, as a social engineering defender system for categorizing the risks before the email reaches the inbox of the user. Before it reaches the end user’s inbox, the system blocks the emails the social engineering defender has marked as “very high risk”.
{"title":"Social Engineering Defender (SE.Def): Human Emotion Factor Based Classification and Defense against Social Engineering Attacks","authors":"Adarsh S. V. Nair, Rathnakar Achary","doi":"10.1109/ICAIA57370.2023.10169678","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169678","url":null,"abstract":"One of the weakest links in any security system is neither the devices used nor the programs running on them; but the human beings using these devices. Most cyberattacks are initiated by human error. Hackers always use the most accessible and effective social engineering techniques to attack. Simply put, it is the art of manipulating people into sharing sensitive and confidential information. This research proposes a framework with four modules, namely, a source analyzer, a content classifier and analyzer, a link analyzer, and a risk reporting module, as a social engineering defender system for categorizing the risks before the email reaches the inbox of the user. Before it reaches the end user’s inbox, the system blocks the emails the social engineering defender has marked as “very high risk”.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738386","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169488
Sahil Jaiswal, S. Srivastava, Shelly Garg, Pardeep Singh
This paper demonstrates the use of news headlines of various news articles to see their impact on the gold commodity. The extracted information is not only limited to the value of the asset but also takes into account various other factors that may help make decisions. Gold prices affect everyone and everyone wants to know how the commodity will behave based on global market sentiment, and statements made by large firms which could affect the market. Although many operations have been performed in this field, it has focused only on one aspect and is related to stocks and not commodities like gold. In this paper, we have a data set of news headlines from the period 2000-2019, and the proposed model is used to extract various information such as previous movements, asset comparisons, price guidance, and various other information that news contains. The experiment is done to check the relationship between gold news and gold prices and the model created produces information that has a significant relationship with the gold price.
{"title":"Effect of News Headlines on Gold Price Prediction using NLP and Deep Learning","authors":"Sahil Jaiswal, S. Srivastava, Shelly Garg, Pardeep Singh","doi":"10.1109/ICAIA57370.2023.10169488","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169488","url":null,"abstract":"This paper demonstrates the use of news headlines of various news articles to see their impact on the gold commodity. The extracted information is not only limited to the value of the asset but also takes into account various other factors that may help make decisions. Gold prices affect everyone and everyone wants to know how the commodity will behave based on global market sentiment, and statements made by large firms which could affect the market. Although many operations have been performed in this field, it has focused only on one aspect and is related to stocks and not commodities like gold. In this paper, we have a data set of news headlines from the period 2000-2019, and the proposed model is used to extract various information such as previous movements, asset comparisons, price guidance, and various other information that news contains. The experiment is done to check the relationship between gold news and gold prices and the model created produces information that has a significant relationship with the gold price.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131222936","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169512
Sundari Tribhuvanam, H. Nagaraj, V. Naidu
The cardiac well-being of humans can be monitored by non-invasive electrocardiogram (ECG) to a greater extent. Subtle changes in ECG waveform can be identified by computer-assisted tools. Machine learning algorithms play an important role in arrhythmia classification. This paper presents a comparative analysis of various classifiers to support ECG classification. The classification model detects seven arrhythmia types from the generated dataset derived from arrhythmia database of MIT-BIH. The proposed technique considers ECG beat features in time domain based on ECG morphology and statistics. Arrhythmia classification is carried out for seven classes. Performance evaluation is carried out for different classifiers with accuracy, sensitivity, specificity, and F1-score as the evaluation metrics. Classification accuracy up to 97%, Recall up to 92%, F1-score up to 91% and precision up to 91% is achieved with specific classifiers across various arrhythmia classes under consideration.
{"title":"Performance Evaluation of Classifiers for ECG Signal Analysis","authors":"Sundari Tribhuvanam, H. Nagaraj, V. Naidu","doi":"10.1109/ICAIA57370.2023.10169512","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169512","url":null,"abstract":"The cardiac well-being of humans can be monitored by non-invasive electrocardiogram (ECG) to a greater extent. Subtle changes in ECG waveform can be identified by computer-assisted tools. Machine learning algorithms play an important role in arrhythmia classification. This paper presents a comparative analysis of various classifiers to support ECG classification. The classification model detects seven arrhythmia types from the generated dataset derived from arrhythmia database of MIT-BIH. The proposed technique considers ECG beat features in time domain based on ECG morphology and statistics. Arrhythmia classification is carried out for seven classes. Performance evaluation is carried out for different classifiers with accuracy, sensitivity, specificity, and F1-score as the evaluation metrics. Classification accuracy up to 97%, Recall up to 92%, F1-score up to 91% and precision up to 91% is achieved with specific classifiers across various arrhythmia classes under consideration.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439977","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169618
Khalid Been Md. Badruzzaman Biplob, Md. Hasan Imam Bijoy, Abu Kowshir Bitto, Aka Das, Amit Chowdhury, Sayed Md. Minhaz Hossain
Suicide is a global health issue with significant negative effects. Individuals at risk of suicide often avoid seeking help due to stigma or fear of forced treatment, and those with mental illnesses, who make up the majority of suicide victims, may not be aware of their condition or risk. Detecting those at risk of suicide is a challenge for healthcare providers. However, advances in artificial intelligence (AI) may lead to the development of new suicide prediction technologies. This study used machine learning to predict suicide rates across different continents using six common classification algorithms: Stochastic Gradient Descent Classifier (SGDC), Random Forest Classifier (RFC), Gaussian Naive Bayes Classifier (GNBC), K-Neighbors Classifier (KNNC), Logistic Regression Classifier (LRC), and Linear Support Vector Classifier (LSVC). The KNNC algorithm had the highest training accuracy at 100%, and a 97% test accuracy. The RFC algorithm achieved the highest test accuracy at 99%, with a corresponding training accuracy of 99%.
{"title":"Suicidal Ratio Prediction Among the Continent of World: A Machine Learning Approach","authors":"Khalid Been Md. Badruzzaman Biplob, Md. Hasan Imam Bijoy, Abu Kowshir Bitto, Aka Das, Amit Chowdhury, Sayed Md. Minhaz Hossain","doi":"10.1109/ICAIA57370.2023.10169618","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169618","url":null,"abstract":"Suicide is a global health issue with significant negative effects. Individuals at risk of suicide often avoid seeking help due to stigma or fear of forced treatment, and those with mental illnesses, who make up the majority of suicide victims, may not be aware of their condition or risk. Detecting those at risk of suicide is a challenge for healthcare providers. However, advances in artificial intelligence (AI) may lead to the development of new suicide prediction technologies. This study used machine learning to predict suicide rates across different continents using six common classification algorithms: Stochastic Gradient Descent Classifier (SGDC), Random Forest Classifier (RFC), Gaussian Naive Bayes Classifier (GNBC), K-Neighbors Classifier (KNNC), Logistic Regression Classifier (LRC), and Linear Support Vector Classifier (LSVC). The KNNC algorithm had the highest training accuracy at 100%, and a 97% test accuracy. The RFC algorithm achieved the highest test accuracy at 99%, with a corresponding training accuracy of 99%.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"41 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116336096","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：