Adel A. Bahaddad, Khalid Almarhabi, Ahmed Alghamdi
Communicating with visitors in tourist areas is one of the best means of conveying tourist information to them and introducing and presenting these areas to end users. Therefore, the use and activation of a new technical and digital service will help to deliver appropriate and reliable information to end users even if they speak different languages. With the current rapid pace of the industrial revolution, there is an increasing need to create a space to deal consistently with tourism in general. Therefore, innovation is gaining importance when it comes to the creation and utilisation of emerging technologies to promote tourism goals. Augmented reality (AR) has revitalised many areas by delivering immersive experiences in the digital world and bringing them to life in the real world. This proposed study sought to enrich the experience of users by displaying various tourist spots in the Makkah region to them with the relevant multimedia information to enable them to build a better connection with the archaeological areas and sites in the city of Makkah, which is the religious capital of the Kingdom of Saudi Arabia (KSA) and is considered as the cradle of Islam. This was where the Islamic civilisation was launched and the call of the prophet, peace and blessings be upon him, began, and there are many areas that are rich in ancient history, where diverse situations and information can be presented in a beautiful and attractive way. This study proposed the use of electronic glasses linked to a smart device application based on the use of AR to review archaeological areas using deep learning (DL) and multimedia information that support visitors through a database that was previously fed by databases dedicated to this matter, as well as by using some websites and online videos for the same purpose. A convolutional neural network (CNN) was used by sensors attached to the glasses to correctly identify artifacts and thus, display information associated with the sites in question. To increase the level of accuracy, feedback was obtained through a questionnaire that carefully evaluated the presented information using relevant evaluation models through a place experience scale (PES) as well as the experience of using the triple interaction of the AR. The results of the study were discussed and evaluated comprehensively for its future development using statistical methods. The results of the study will serve to enhance competitiveness by showing the archaeological monuments in the Makkah region and providing visitors with reliable information about them through multiple media that will automatically identify what is presented to them according to the different languages of the visitors.
在旅游区与游客交流是向他们传递旅游信息以及向最终用户介绍和展示这些旅游区的最佳手段之一。因此，使用和激活新的技术和数字服务将有助于向终端用户提供适当和可靠的信息，即使他们说的是不同的语言。随着当前工业革命的迅猛发展，越来越有必要创造一个空间来持续处理整个旅游业。因此，在创造和利用新兴技术促进实现旅游业目标方面，创新正变得越来越重要。增强现实技术（AR）通过在数字世界中提供身临其境的体验，并将其带入现实世界，使许多领域焕发出新的活力。麦加市是沙特阿拉伯王国（KSA）的宗教之都，被视为伊斯兰教的摇篮，是伊斯兰文明的发源地。这里是伊斯兰文明的发源地，也是先知（愿主赐福之，并使其平安）号召的发源地，有许多地区都蕴含着丰富的古代历史，在这里，各种情况和信息都可以以一种美丽而有吸引力的方式展现出来。本研究建议使用与基于 AR 的智能设备应用程序相连的电子眼镜，利用深度学习（DL）和多媒体信息来回顾考古区域，这些信息可通过以前专门用于此问题的数据库提供给游客，也可通过一些网站和在线视频用于相同目的。眼镜上的传感器使用卷积神经网络（CNN）来正确识别文物，从而显示与相关遗址有关的信息。为了提高准确度，还通过问卷调查获得了反馈意见，该问卷通过场所体验量表（PES）以及 AR 三重交互的使用体验，使用相关评估模型对所显示的信息进行了仔细评估。使用统计方法对研究结果进行了讨论和全面评估，以促进其未来发展。研究结果将通过多种媒体展示麦加地区的考古遗迹，并为游客提供可靠的相关信息，根据游客的不同语言自动识别所展示的内容，从而提高竞争力。
{"title":"Using augmented reality and deep learning to enhance tourist experiences at landmarks in Makkah","authors":"Adel A. Bahaddad, Khalid Almarhabi, Ahmed Alghamdi","doi":"10.32629/jai.v7i4.1502","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1502","url":null,"abstract":"Communicating with visitors in tourist areas is one of the best means of conveying tourist information to them and introducing and presenting these areas to end users. Therefore, the use and activation of a new technical and digital service will help to deliver appropriate and reliable information to end users even if they speak different languages. With the current rapid pace of the industrial revolution, there is an increasing need to create a space to deal consistently with tourism in general. Therefore, innovation is gaining importance when it comes to the creation and utilisation of emerging technologies to promote tourism goals. Augmented reality (AR) has revitalised many areas by delivering immersive experiences in the digital world and bringing them to life in the real world. This proposed study sought to enrich the experience of users by displaying various tourist spots in the Makkah region to them with the relevant multimedia information to enable them to build a better connection with the archaeological areas and sites in the city of Makkah, which is the religious capital of the Kingdom of Saudi Arabia (KSA) and is considered as the cradle of Islam. This was where the Islamic civilisation was launched and the call of the prophet, peace and blessings be upon him, began, and there are many areas that are rich in ancient history, where diverse situations and information can be presented in a beautiful and attractive way. This study proposed the use of electronic glasses linked to a smart device application based on the use of AR to review archaeological areas using deep learning (DL) and multimedia information that support visitors through a database that was previously fed by databases dedicated to this matter, as well as by using some websites and online videos for the same purpose. A convolutional neural network (CNN) was used by sensors attached to the glasses to correctly identify artifacts and thus, display information associated with the sites in question. To increase the level of accuracy, feedback was obtained through a questionnaire that carefully evaluated the presented information using relevant evaluation models through a place experience scale (PES) as well as the experience of using the triple interaction of the AR. The results of the study were discussed and evaluated comprehensively for its future development using statistical methods. The results of the study will serve to enhance competitiveness by showing the archaeological monuments in the Makkah region and providing visitors with reliable information about them through multiple media that will automatically identify what is presented to them according to the different languages of the visitors.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266686","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}
V. Asha, Kalyan S. Kasturi, N. Selvamuthukumaran, Amit Kumar Sahu, R. J. Anandhi
The increasing risk of forest fires demands sophisticated detection systems in order to mitigate the environment effectively. The technology under consideration enhances real-time monitoring and reaction by functioning inside an Internet of Things (IoT) architecture. Even though Artificial Intelligence (AI) algorithms have improved fire detection systems, they are quite expensive and energy-intensive due to their high computing needs. With the use of creative methods for data augmentation and optimization as well as a shared feature extraction module, this research study offers a thorough fire detection model using an improved EfficientNet that tackles these issues. Three technical components are creatively combined in the realm of forest fire detection by this study. The first stage is the use of diagonal swap of random (DSRO) data annotation, which makes use of spatial connections in the data to improve the model’s understanding of complex aspects that are essential for precisely identifying possible fire breakouts. By adding a shared feature extraction module across three functions, the second stage solves difficulties in feature extraction and target identification. This greatly increases the model’s performance in complicated forest scenes while reducing false positives and false negatives. The third and final stage focuses on improving the EfficientNet model’s capacity for accurate forest fire categorization. When taken as a whole, these technical components upon creative combination improve the existing technology in forest fire detection and provide a thorough and practical strategy for reducing environmental hazards. For the purpose of hyperparameter tuning in the EfficientNet for the classification of forest fires, an improved Harris Hawks optimization (HHO) is used. By using the Cauchy mutation approach with adaptive weight, HHO expands the search space, boosts population diversity, and improves overall exploration. By including the sine-cosine algorithm (SCA) into the optimization process, the likelihood of local extremum occurrences is decreased. The proposed strategy is successful compared to other existing models, as shown by the experimental findings that show an improvement of 5% in accuracy compared to the standard existing model, and an improvement of 2% compared to EfficientNet model in detecting forest fire.
{"title":"DSRO based data annotation with improved EfficientNet for forest fire detection using image processing in IoT environment","authors":"V. Asha, Kalyan S. Kasturi, N. Selvamuthukumaran, Amit Kumar Sahu, R. J. Anandhi","doi":"10.32629/jai.v7i4.1088","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1088","url":null,"abstract":"The increasing risk of forest fires demands sophisticated detection systems in order to mitigate the environment effectively. The technology under consideration enhances real-time monitoring and reaction by functioning inside an Internet of Things (IoT) architecture. Even though Artificial Intelligence (AI) algorithms have improved fire detection systems, they are quite expensive and energy-intensive due to their high computing needs. With the use of creative methods for data augmentation and optimization as well as a shared feature extraction module, this research study offers a thorough fire detection model using an improved EfficientNet that tackles these issues. Three technical components are creatively combined in the realm of forest fire detection by this study. The first stage is the use of diagonal swap of random (DSRO) data annotation, which makes use of spatial connections in the data to improve the model’s understanding of complex aspects that are essential for precisely identifying possible fire breakouts. By adding a shared feature extraction module across three functions, the second stage solves difficulties in feature extraction and target identification. This greatly increases the model’s performance in complicated forest scenes while reducing false positives and false negatives. The third and final stage focuses on improving the EfficientNet model’s capacity for accurate forest fire categorization. When taken as a whole, these technical components upon creative combination improve the existing technology in forest fire detection and provide a thorough and practical strategy for reducing environmental hazards. For the purpose of hyperparameter tuning in the EfficientNet for the classification of forest fires, an improved Harris Hawks optimization (HHO) is used. By using the Cauchy mutation approach with adaptive weight, HHO expands the search space, boosts population diversity, and improves overall exploration. By including the sine-cosine algorithm (SCA) into the optimization process, the likelihood of local extremum occurrences is decreased. The proposed strategy is successful compared to other existing models, as shown by the experimental findings that show an improvement of 5% in accuracy compared to the standard existing model, and an improvement of 2% compared to EfficientNet model in detecting forest fire.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"111 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089574","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}
AdaBoost_wear is a Python software that implements the AdaBoost algorithm to predict the coefficient of friction (COF) of B83 babbitt alloy as a function of time. The software uses data from pin-on-disk tests with different loads to train and test the model. The software also provides performance metrics, such as R2 score, mean squared error, and mean absolute error, to evaluate the accuracy of the predictions. The software also generates plots of the actual and predicted COF values, as well as histograms and boxplots of the COF distribution. The software is open source and released under the MIT license.
{"title":"AdaBoost_wear: Adaboost model-based Python software for predicting the coefficient of friction of babbitt alloy","authors":"Mihail Kolev","doi":"10.32629/jai.v7i4.1206","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1206","url":null,"abstract":"AdaBoost_wear is a Python software that implements the AdaBoost algorithm to predict the coefficient of friction (COF) of B83 babbitt alloy as a function of time. The software uses data from pin-on-disk tests with different loads to train and test the model. The software also provides performance metrics, such as R2 score, mean squared error, and mean absolute error, to evaluate the accuracy of the predictions. The software also generates plots of the actual and predicted COF values, as well as histograms and boxplots of the COF distribution. The software is open source and released under the MIT license.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"18 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083567","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 global changes caused by the health crisis of the COVID-19 coronavirus pandemic have significantly impacted education all over the world. Schools today have had to adopt distance learning models using digital tools to ensure the continuity of educational systems in different circumstances. For this reason and to ensure the continuity of education even in the event of future disruptions in Morocco (war, pandemic, natural disaster...). The Minister of National Education for Preschool and Sport has unveiled an initiative to establish digital classrooms within the educational institutions of the Kingdom. This innovative pedagogical approach, grounded in the utilization of digital tools, is specifically designed to bolster the instruction of science subjects, (Mathematics, Physics, and Life and Earth Sciences). This digital educational transformation has emerged as a highly suitable mode of learning, catering to a diverse array of social groups, including individuals with disabilities and refugees. The primary objective of this research is to assess the influence of digital classrooms on the performance of science educators operating within the Rabat Sale Kenitra region. The intention is to gauge how this technological implementation has affected their teaching methods and overall effectiveness. Furthermore, this study seeks to gauge the progression of this educational transformation and advocate for the wider adoption of digital pedagogy, extending its incorporation into the instructional strategies of other subjects. The ultimate goal is to promote inclusivity and level the playing field for all learners, ensuring equal educational opportunities for every student.
{"title":"Intelligent solutions in education: How inclusive the Moroccan Digital Classrooms project is for different social groups","authors":"Hommane Boudine, Meriem Bentaleb, Abdelmajid Soulaymani, Driss El Karfa, Mohamed Tayebi","doi":"10.32629/jai.v7i4.1312","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1312","url":null,"abstract":"The global changes caused by the health crisis of the COVID-19 coronavirus pandemic have significantly impacted education all over the world. Schools today have had to adopt distance learning models using digital tools to ensure the continuity of educational systems in different circumstances. For this reason and to ensure the continuity of education even in the event of future disruptions in Morocco (war, pandemic, natural disaster...). The Minister of National Education for Preschool and Sport has unveiled an initiative to establish digital classrooms within the educational institutions of the Kingdom. This innovative pedagogical approach, grounded in the utilization of digital tools, is specifically designed to bolster the instruction of science subjects, (Mathematics, Physics, and Life and Earth Sciences). This digital educational transformation has emerged as a highly suitable mode of learning, catering to a diverse array of social groups, including individuals with disabilities and refugees. The primary objective of this research is to assess the influence of digital classrooms on the performance of science educators operating within the Rabat Sale Kenitra region. The intention is to gauge how this technological implementation has affected their teaching methods and overall effectiveness. Furthermore, this study seeks to gauge the progression of this educational transformation and advocate for the wider adoption of digital pedagogy, extending its incorporation into the instructional strategies of other subjects. The ultimate goal is to promote inclusivity and level the playing field for all learners, ensuring equal educational opportunities for every student.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084886","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}
R. Boddu, Radha Raman Chandan, M. Thamizharasi, Riyaj Shaikh, Adheer A. Goyal, Pragya Prashant Gupta, Shashi Kant Gupta
The lives of people are at risk from security and safety risks with Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles. In contrast to manual vehicles, the Security of an AV’s computer and communications components may be penetrated using sophisticated hacking methods, preventing us from employing AVs in our daily lives. The Internet of Vehicles, which connects manual automobiles to the Internet, is vulnerable to cyber-attacks such as lack of service, spoofing, sniffer, widespread denial of service and repeat attacks. This paper presents a unique intrusion detection system for ITS, using Enhanced Cuttle Fish Optimized Multiscale Convolution Neural Network (ECFO-MCNN), that uses vehicles to identify networks and infrastructure and detects careful network activity of in-vehicle networks. The primary goal of the suggested strategy is to identify forward events emanating through AVs’ central network gateways. Two benchmark datasets, namely the UNSWNB15 dataset for external network communications and the car hacking dataset for in-vehicle communications, are used to assess the proposed IDS. The evaluation’s findings showed that the performance of our suggested system is superior to that of traditional intrusion detection methods.
{"title":"Using deep learning to address the security issue in intelligent transportation systems","authors":"R. Boddu, Radha Raman Chandan, M. Thamizharasi, Riyaj Shaikh, Adheer A. Goyal, Pragya Prashant Gupta, Shashi Kant Gupta","doi":"10.32629/jai.v7i4.1220","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1220","url":null,"abstract":"The lives of people are at risk from security and safety risks with Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles. In contrast to manual vehicles, the Security of an AV’s computer and communications components may be penetrated using sophisticated hacking methods, preventing us from employing AVs in our daily lives. The Internet of Vehicles, which connects manual automobiles to the Internet, is vulnerable to cyber-attacks such as lack of service, spoofing, sniffer, widespread denial of service and repeat attacks. This paper presents a unique intrusion detection system for ITS, using Enhanced Cuttle Fish Optimized Multiscale Convolution Neural Network (ECFO-MCNN), that uses vehicles to identify networks and infrastructure and detects careful network activity of in-vehicle networks. The primary goal of the suggested strategy is to identify forward events emanating through AVs’ central network gateways. Two benchmark datasets, namely the UNSWNB15 dataset for external network communications and the car hacking dataset for in-vehicle communications, are used to assess the proposed IDS. The evaluation’s findings showed that the performance of our suggested system is superior to that of traditional intrusion detection methods.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":" January","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rise of machine translation systems, it has become essential to evaluate the quality of translations produced by these systems. However, the existing evaluation metrics designed for English and other European languages may not always be suitable or apply to other Indic languages due to their complex morphology and syntax. Machine translation evaluation (MTE) is a process of assessing the quality and accuracy of the machine-translated text. MTE involves comparing the machine-translated output with the reference translation to calculate the level of similarity and correctness. Therefore, this study evaluates different metrics, namely, BLEU, METEOR, and TER to identify the most suitable evaluation metric for Indic languages. The study uses datasets for Indic languages and evaluates the metrics on various translation systems. The study contributes to the field of MT by providing insights into suitable evaluation metrics for Indic languages. This research paper aims to study and compare several lexical automatic machine translation evaluation metrics for Indic languages. For this research analysis, we have selected five language pairs of parallel corpora from the low-resource domain, such as English–Hindi, English-Punjabi, English-Gujarati, English-Marathi, and English-Bengali. All these languages belong to the Indo-Aryan language family and are resource-poor. A comparison of the state of art MT is presented and shows which translator works better on these language pairs. For this research work, the natural language toolkit tokenizers are used to assess the analysis of the experimental results. These results have been performed by taking two different datasets for all these language pairs using fully automatic MT evaluation metrics. The research study explores the effectiveness of these metrics in assessing the quality of machine translations between various Indic languages. Additionally, this dataset and analysis will make it easier to do future research in Indian MT evaluation.
{"title":"A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs","authors":"Kiranjeet Kaur, S. Chauhan","doi":"10.32629/jai.v7i4.1393","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1393","url":null,"abstract":"With the rise of machine translation systems, it has become essential to evaluate the quality of translations produced by these systems. However, the existing evaluation metrics designed for English and other European languages may not always be suitable or apply to other Indic languages due to their complex morphology and syntax. Machine translation evaluation (MTE) is a process of assessing the quality and accuracy of the machine-translated text. MTE involves comparing the machine-translated output with the reference translation to calculate the level of similarity and correctness. Therefore, this study evaluates different metrics, namely, BLEU, METEOR, and TER to identify the most suitable evaluation metric for Indic languages. The study uses datasets for Indic languages and evaluates the metrics on various translation systems. The study contributes to the field of MT by providing insights into suitable evaluation metrics for Indic languages. This research paper aims to study and compare several lexical automatic machine translation evaluation metrics for Indic languages. For this research analysis, we have selected five language pairs of parallel corpora from the low-resource domain, such as English–Hindi, English-Punjabi, English-Gujarati, English-Marathi, and English-Bengali. All these languages belong to the Indo-Aryan language family and are resource-poor. A comparison of the state of art MT is presented and shows which translator works better on these language pairs. For this research work, the natural language toolkit tokenizers are used to assess the analysis of the experimental results. These results have been performed by taking two different datasets for all these language pairs using fully automatic MT evaluation metrics. The research study explores the effectiveness of these metrics in assessing the quality of machine translations between various Indic languages. Additionally, this dataset and analysis will make it easier to do future research in Indian MT evaluation.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"3 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683381","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}
Background: In today's constantly changing business world, the role of Business Intelligence (BI) in organizational decision-making is increasingly critical. This literature review aims to provide a comprehensive understanding of BI’s multi-dimensional nature and its immense potential in enhancing organizational performance. Methods: This study employs a systematic literature review methodology, analyzing peer-reviewed articles, case studies, and seminal works in the field of Business Intelligence. The review focuses on key themes such as BI’s components, its role in strategic decision-making, operational efficiency, and metrics and KPIs influenced by BI. Results: The review synthesizes findings from various studies, revealing that BI significantly influences strategic decision-making, improves operational efficiency, and impacts various metrics and KPIs across sectors. Sample sizes for the analyzed studies range from smaller focus groups to large organizational surveys. Conclusions: The study concludes that Business Intelligence is an indispensable tool for modern organizations, offering various functionalities that enhance decision-making and operational efficiency. Its application spans multiple sectors, providing a competitive advantage and contributing to business success.
{"title":"Business intelligence and its pivotal role in organizational performance: An exhaustive literature review","authors":"Kawtar Moussas, Jihane Hafiane, Allal Achaba","doi":"10.32629/jai.v7i4.1286","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1286","url":null,"abstract":"Background: In today's constantly changing business world, the role of Business Intelligence (BI) in organizational decision-making is increasingly critical. This literature review aims to provide a comprehensive understanding of BI’s multi-dimensional nature and its immense potential in enhancing organizational performance. Methods: This study employs a systematic literature review methodology, analyzing peer-reviewed articles, case studies, and seminal works in the field of Business Intelligence. The review focuses on key themes such as BI’s components, its role in strategic decision-making, operational efficiency, and metrics and KPIs influenced by BI. Results: The review synthesizes findings from various studies, revealing that BI significantly influences strategic decision-making, improves operational efficiency, and impacts various metrics and KPIs across sectors. Sample sizes for the analyzed studies range from smaller focus groups to large organizational surveys. Conclusions: The study concludes that Business Intelligence is an indispensable tool for modern organizations, offering various functionalities that enhance decision-making and operational efficiency. Its application spans multiple sectors, providing a competitive advantage and contributing to business success.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"319 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472257","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}
Shirin Abdallah Alimour, Emad Alnono, Shaima Aljasmi, Hani El Farran, A. Alqawasmi, Mohamed Mahmoud Alrabeei, Fanar Shwedeh, Ahmad Aburayya
As advancements in healthcare technologies continue to emerge, the integration of AI-Technology has brought about significant transformations in various healthcare sectors. While substantial advancements have been made in applying AI to enhance physical health, its implementation in the field of mental health is still in its early stages. This descriptive study aims to address this gap by exploring the perspectives of mental health professionals (MHPs) on the acceptance and utilization of AI technology. Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized to assess MHPs’ attitudes and beliefs towards AI implementation in psychotherapeutic practices. The sample was compromised of 349 MHPs. The findings reveal the task characteristic (TC) domain as the most influential domain, followed by Performance expectancy (PE), Behavioural intentions (BI), Personal innovativeness in IT (PT), Social influence (SI), Effort expectancy (EE), Perceived substitution crisis (PSC), Technology characteristic (TECH), and Initial trust (IT). The study also identifies statistically significant differences in AI usage based on gender variable, with females demonstrating a higher level of AI usage in comparison to males. Furthermore, the study highlights diverse applications of AI in the field of mental health, including AI-assisted assessments (AAA), chatbots for psychotherapy support (CPS), and data analytics for personalized treatment recommendations (DAPTR). By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy. The findings offer valuable insights into MHPs’ perceptions, concerns, and perceived advantages associated with integrating AI technology within clinical settings in the field of mental health.
{"title":"The quality traits of artificial intelligence operations in predicting mental healthcare professionals’ perceptions: A case study in the psychotherapy division","authors":"Shirin Abdallah Alimour, Emad Alnono, Shaima Aljasmi, Hani El Farran, A. Alqawasmi, Mohamed Mahmoud Alrabeei, Fanar Shwedeh, Ahmad Aburayya","doi":"10.32629/jai.v7i4.1438","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1438","url":null,"abstract":"As advancements in healthcare technologies continue to emerge, the integration of AI-Technology has brought about significant transformations in various healthcare sectors. While substantial advancements have been made in applying AI to enhance physical health, its implementation in the field of mental health is still in its early stages. This descriptive study aims to address this gap by exploring the perspectives of mental health professionals (MHPs) on the acceptance and utilization of AI technology. Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized to assess MHPs’ attitudes and beliefs towards AI implementation in psychotherapeutic practices. The sample was compromised of 349 MHPs. The findings reveal the task characteristic (TC) domain as the most influential domain, followed by Performance expectancy (PE), Behavioural intentions (BI), Personal innovativeness in IT (PT), Social influence (SI), Effort expectancy (EE), Perceived substitution crisis (PSC), Technology characteristic (TECH), and Initial trust (IT). The study also identifies statistically significant differences in AI usage based on gender variable, with females demonstrating a higher level of AI usage in comparison to males. Furthermore, the study highlights diverse applications of AI in the field of mental health, including AI-assisted assessments (AAA), chatbots for psychotherapy support (CPS), and data analytics for personalized treatment recommendations (DAPTR). By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy. The findings offer valuable insights into MHPs’ perceptions, concerns, and perceived advantages associated with integrating AI technology within clinical settings in the field of mental health.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"27 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487970","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}
B. Rajalakshmi, Santosh Kumar B., B. S. K. Devi, Balasubramanian Prabhu Kavin, Gan Hong Seng
Diseases of crop plants pose a serious danger to agricultural output and progress. Predicting the onset of a disease outbreak in advance can help public health officials better manage the pandemic. Precision agriculture (PA) applications rely heavily on current information and communication technologies (ICTs) for their contribution to long-term sustainability. Preventative measures against plant diseases require accurate early disease prediction in order to be effective. The current computer vision-based illness detection technology can only detect the disease after it has already manifested. This research intends to provide a deep learning (DL) method for early disease attack prediction using Internet of Things (IoT) directly sensed environmental factors from crop fields. There is a robust relationship between environmental factors and the life cycles of plant diseases. Disease incidence in plants can be forecast based on environmental variables in the crop field. In order to solve these issues, the research presented here suggests using a gated recurrent multi-attention neural network (GRMA-Net). The study uses multilevel modules to zero down on informative areas in order to extract additional discriminative features, as informative characteristics tend to appear at various levels in a network. In order to capture long-range dependence and contextual interaction, these characteristics are first organised as spatial sequences and then input into a deep-gated recurrent unit (GRU). Finally, an enhanced version of the Tunicate swarm optimisation model (ITSO) is used to pick the best values for the model’s hyper-parameters. Four public datasets representing a wide range of crop types are used to assess the model’s efficacy. Some of these databases cover numerous crop species, like PlantVillage (38 categories), while others focus on a single crop, such as Apple (4), Maize (4), or Rice (5). The experimental findings show that the system achieves 99.16% accuracy in identifying agricultural diseases, which is higher than the accuracy of other current deep-learning approaches.
{"title":"Single and multi-crop species disease detection using ITSO based gated recurrent multi-attention neural network","authors":"B. Rajalakshmi, Santosh Kumar B., B. S. K. Devi, Balasubramanian Prabhu Kavin, Gan Hong Seng","doi":"10.32629/jai.v7i4.1126","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1126","url":null,"abstract":"Diseases of crop plants pose a serious danger to agricultural output and progress. Predicting the onset of a disease outbreak in advance can help public health officials better manage the pandemic. Precision agriculture (PA) applications rely heavily on current information and communication technologies (ICTs) for their contribution to long-term sustainability. Preventative measures against plant diseases require accurate early disease prediction in order to be effective. The current computer vision-based illness detection technology can only detect the disease after it has already manifested. This research intends to provide a deep learning (DL) method for early disease attack prediction using Internet of Things (IoT) directly sensed environmental factors from crop fields. There is a robust relationship between environmental factors and the life cycles of plant diseases. Disease incidence in plants can be forecast based on environmental variables in the crop field. In order to solve these issues, the research presented here suggests using a gated recurrent multi-attention neural network (GRMA-Net). The study uses multilevel modules to zero down on informative areas in order to extract additional discriminative features, as informative characteristics tend to appear at various levels in a network. In order to capture long-range dependence and contextual interaction, these characteristics are first organised as spatial sequences and then input into a deep-gated recurrent unit (GRU). Finally, an enhanced version of the Tunicate swarm optimisation model (ITSO) is used to pick the best values for the model’s hyper-parameters. Four public datasets representing a wide range of crop types are used to assess the model’s efficacy. Some of these databases cover numerous crop species, like PlantVillage (38 categories), while others focus on a single crop, such as Apple (4), Maize (4), or Rice (5). The experimental findings show that the system achieves 99.16% accuracy in identifying agricultural diseases, which is higher than the accuracy of other current deep-learning approaches.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"197 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488410","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}
Stock trading is a popular and important profession that requires near-to-perfect data analytical skills, mathematical and statistical knowledge, and a broad understanding of buying and selling stocks. Often, due to the number of factors to consider and the intervention of human bias, traders and investors make wrong decisions that cost them millions of dollars. Therefore, automated algorithmic trading has gained traction in the marketplace due to its ability to process huge amounts of data, perform mathematical calculations and make quick and effective decisions. Most algorithmic trading strategies rely on a single technical indicator; however, it has been found that combining two or more indicators makes a trading strategy profitable. Therefore, this paper proposes a custom algorithmic trading strategy that combines important technical indicators such as the Exponential Moving Average and Relative Strength Index and utilizes sentiment analysis of financial news as well. This combination of technical indicators and sentiment analysis is not prevalent in existing research. The performance of the strategy was tested on fifteen stocks from different sectors of the US market using Python’s VectorBt library. The results showed that most of the stocks produced a higher win rate with the custom strategy as compared to other strategies, with the highest win rate of 88% for the S&P 500 index. To carry out sentiment analysis, a NLP model using BERT was developed which achieved an accuracy of 84%. Finally, to test the strategy on real-time data, paper trading was carried out on the Alpaca API and after six months the portfolio’s ROI is 6.26%.
{"title":"Developing and testing a custom algorithmic trading strategy using exponential moving average, relative strength index, and sentiment analysis","authors":"Sstuti D. Mehra, S. Shetty","doi":"10.32629/jai.v7i4.1328","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1328","url":null,"abstract":"Stock trading is a popular and important profession that requires near-to-perfect data analytical skills, mathematical and statistical knowledge, and a broad understanding of buying and selling stocks. Often, due to the number of factors to consider and the intervention of human bias, traders and investors make wrong decisions that cost them millions of dollars. Therefore, automated algorithmic trading has gained traction in the marketplace due to its ability to process huge amounts of data, perform mathematical calculations and make quick and effective decisions. Most algorithmic trading strategies rely on a single technical indicator; however, it has been found that combining two or more indicators makes a trading strategy profitable. Therefore, this paper proposes a custom algorithmic trading strategy that combines important technical indicators such as the Exponential Moving Average and Relative Strength Index and utilizes sentiment analysis of financial news as well. This combination of technical indicators and sentiment analysis is not prevalent in existing research. The performance of the strategy was tested on fifteen stocks from different sectors of the US market using Python’s VectorBt library. The results showed that most of the stocks produced a higher win rate with the custom strategy as compared to other strategies, with the highest win rate of 88% for the S&P 500 index. To carry out sentiment analysis, a NLP model using BERT was developed which achieved an accuracy of 84%. Finally, to test the strategy on real-time data, paper trading was carried out on the Alpaca API and after six months the portfolio’s ROI is 6.26%.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"21 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490300","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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