Pub Date : 2024-07-15DOI: 10.24018/ejai.2024.3.3.40
Karimullah Ahmadi
Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.
{"title":"Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan","authors":"Karimullah Ahmadi","doi":"10.24018/ejai.2024.3.3.40","DOIUrl":"https://doi.org/10.24018/ejai.2024.3.3.40","url":null,"abstract":"\u0000\u0000\u0000\u0000Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.\u0000\u0000\u0000\u0000","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"34 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.24018/ejai.2024.3.1.36
M. Rabi, Mustapha Amrouche
Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.
{"title":"Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks","authors":"M. Rabi, Mustapha Amrouche","doi":"10.24018/ejai.2024.3.1.36","DOIUrl":"https://doi.org/10.24018/ejai.2024.3.1.36","url":null,"abstract":"\u0000\u0000\u0000\u0000Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.\u0000\u0000\u0000\u0000","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"48 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.24018/ejai.2024.3.1.34
Zahra Karimi, S. Savant, A. Zeid, S. Kamarthi
Creating a 2D geometry model from an image poses challenges for CAD users due to factors such as noise, segmentation difficulties, complex geometric structures, scale and perspective variations, and the need for CAD system compatibility. In this paper, we propose a novel deep learning approach utilizing Long-Short Term Memory (LSTM) to address these challenges. Our approach decomposes the shapes in the images into line and curve segments and accurately locates their intersection points. To enhance the model’s performance, we introduce two distinct types of features (angle and curvature features) and optimize the model through hyperparameter tuning. The resulting model exhibits robustness against noise, varying image sizes, and can effectively locate different types of intersection points. To evaluate the proposed model, we have developed a Python-based software and conducted experiments on a dataset comprising of 200 shapes with seven different resolutions. Comparative analysis against a state-of-the- art method (TCVD) from the literature demonstrates that our approach achieves higher accuracy in terms of line, curve, and intersection point detection.
{"title":"Shape Recognition and Corner Points Detection in 2D Drawings Using a Machine Learning Long Short-Term Memory (LSTM) Approach","authors":"Zahra Karimi, S. Savant, A. Zeid, S. Kamarthi","doi":"10.24018/ejai.2024.3.1.34","DOIUrl":"https://doi.org/10.24018/ejai.2024.3.1.34","url":null,"abstract":"\u0000\u0000\u0000\u0000Creating a 2D geometry model from an image poses challenges for CAD users due to factors such as noise, segmentation difficulties, complex geometric structures, scale and perspective variations, and the need for CAD system compatibility. In this paper, we propose a novel deep learning approach utilizing Long-Short Term Memory (LSTM) to address these challenges. Our approach decomposes the shapes in the images into line and curve segments and accurately locates their intersection points. To enhance the model’s performance, we introduce two distinct types of features (angle and curvature features) and optimize the model through hyperparameter tuning. The resulting model exhibits robustness against noise, varying image sizes, and can effectively locate different types of intersection points. To evaluate the proposed model, we have developed a Python-based software and conducted experiments on a dataset comprising of 200 shapes with seven different resolutions. Comparative analysis against a state-of-the- art method (TCVD) from the literature demonstrates that our approach achieves higher accuracy in terms of line, curve, and intersection point detection.\u0000\u0000\u0000\u0000","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"28 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263961","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: 10.24018/ejai.2023.2.4.29
Ugochi A. Okengwu, Hillard A. Akpughe, Eyinanabo Odogu, Taiye Ojetunmibi
Tomato leaf virus diseases present a significant risk to tomato cultivation, leading to substantial financial losses worldwide. Implementing appropriate control measures depends on these diseases being accurately and quickly identified and classified. This article provides an insight into the analysis of the various technologies used to classify tomato leaf virus diseases as well as some similar plant leaf virus disease. The review encompasses both traditional and modern techniques, including image processing, machine learning, and deep learning methods. It explores the use of different imaging techniques, such as visible light RGB, infrared, and hyperspectral imaging, for capturing leaf disease symptoms. Additionally, it emphasizes the growing significance of deep learning models, such as convolutional neural networks, in identifying diseases with extreme precision. Overall, this study offers insightful information on the technological developments for the categorization of tomato leaf viral illnesses, promoting the creation of efficient disease management techniques.
{"title":"Review on Technologies Applied to Classification of Tomato Leaf Virus Diseases","authors":"Ugochi A. Okengwu, Hillard A. Akpughe, Eyinanabo Odogu, Taiye Ojetunmibi","doi":"10.24018/ejai.2023.2.4.29","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.4.29","url":null,"abstract":"Tomato leaf virus diseases present a significant risk to tomato cultivation, leading to substantial financial losses worldwide. Implementing appropriate control measures depends on these diseases being accurately and quickly identified and classified. This article provides an insight into the analysis of the various technologies used to classify tomato leaf virus diseases as well as some similar plant leaf virus disease. The review encompasses both traditional and modern techniques, including image processing, machine learning, and deep learning methods. It explores the use of different imaging techniques, such as visible light RGB, infrared, and hyperspectral imaging, for capturing leaf disease symptoms. Additionally, it emphasizes the growing significance of deep learning models, such as convolutional neural networks, in identifying diseases with extreme precision. Overall, this study offers insightful information on the technological developments for the categorization of tomato leaf viral illnesses, promoting the creation of efficient disease management techniques.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034621","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 : 2023-10-03DOI: 10.24018/ejai.2023.2.4.32
Yao Cheng, Yu Si Liang
Artificial intelligence (AI) is currently exerting a significant impact on the development of career guidance education, facilitating personalized guidance and data-driven decision-making for students. The historical and evolutionary trajectory of AI-driven career guidance education can be traced back to its early stages as assistive functionalities, which have now advanced to encompass robust learning applications, such as multimedia and interactive features, machine learning, and natural language processing. Notably, AI has transcended its conventional role in vocational development and expanded into the realms of social and emotional learning. The complexity of AI research in international contexts necessitates consideration of various factors, including cognitive development, parental involvement and supervision, and cultural backgrounds. Despite certain limitations in utilizing AI for career exploration, it has brought numerous impacts and insights. These primarily manifest in the areas of data-driven decision-making and the outlook for career exploration, the demand for cultural sensitivity in AI-driven career guidance, and the provision of personalized career guidance through artificial intelligence in education.
{"title":"The Development of Artificial Intelligence in Career Initiation Education and Implications for China","authors":"Yao Cheng, Yu Si Liang","doi":"10.24018/ejai.2023.2.4.32","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.4.32","url":null,"abstract":"Artificial intelligence (AI) is currently exerting a significant impact on the development of career guidance education, facilitating personalized guidance and data-driven decision-making for students. The historical and evolutionary trajectory of AI-driven career guidance education can be traced back to its early stages as assistive functionalities, which have now advanced to encompass robust learning applications, such as multimedia and interactive features, machine learning, and natural language processing. Notably, AI has transcended its conventional role in vocational development and expanded into the realms of social and emotional learning. The complexity of AI research in international contexts necessitates consideration of various factors, including cognitive development, parental involvement and supervision, and cultural backgrounds. Despite certain limitations in utilizing AI for career exploration, it has brought numerous impacts and insights. These primarily manifest in the areas of data-driven decision-making and the outlook for career exploration, the demand for cultural sensitivity in AI-driven career guidance, and the provision of personalized career guidance through artificial intelligence in education.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740390","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 : 2023-09-18DOI: 10.24018/ejai.2023.2.4.31
Yaroslava Pushkarova, Paul Kholodniuk
Classification methods have become one of the main tools for extracting essential information from multivariate data. New classification algorithms are continuously being proposed and created. This paper presents a classification procedure based on a combination of Kohonen and probabilistic neural networks. Its applicability and efficiency are estimated using model data sets (iris flowers data set, wine data set, data with a two-hierarchical structure), then compared with the traditional clustering algorithms (hierarchical clustering, k-means clustering, fuzzy k-means clustering). The algorithm was designed as M-script in Matlab 7.11b software. It was shown that the proposed classification procedure has a great advantage over traditional clustering methods.
{"title":"A New Procedure for Unsupervised Clustering Based on Combination of Artificial Neural Networks","authors":"Yaroslava Pushkarova, Paul Kholodniuk","doi":"10.24018/ejai.2023.2.4.31","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.4.31","url":null,"abstract":"Classification methods have become one of the main tools for extracting essential information from multivariate data. New classification algorithms are continuously being proposed and created. This paper presents a classification procedure based on a combination of Kohonen and probabilistic neural networks. Its applicability and efficiency are estimated using model data sets (iris flowers data set, wine data set, data with a two-hierarchical structure), then compared with the traditional clustering algorithms (hierarchical clustering, k-means clustering, fuzzy k-means clustering). The algorithm was designed as M-script in Matlab 7.11b software. It was shown that the proposed classification procedure has a great advantage over traditional clustering methods.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135202435","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 : 2023-08-02DOI: 10.24018/ejai.2023.2.3.19
Xing Yee Leong, N. Jajo, S. Peiris, M. Khadra
Long wait times for elective surgery have not only caused patients to continue to live with inconvenience or pain but also creates frustrations and dissatisfaction with the local hospitals and healthcare systems. To deal with the increasing demand, hospitals need to be able to accurately predict the future demand to properly equip their facilities and the number of staff. In this paper, we propose various ARIMA-Machine Learning hybrid models to predict future elective surgery wait list demand. The goal of this paper is to improve the future demand predictions for hospital elective surgeries. We also compare our hybrid model to ARIMA and various Machine Learning/Deep Learning models, such as ANN, LSTM, and Random Forest. We found that ARIMA-ANN performed best with MAE of 0.26-0.76 and MSE of 0.13-1.05 with two-week-forward Urology, Orthopaedics and Gynecology elective surgery data.
{"title":"Forecasting Elective Surgery Demand Using ARIMA-Machine Learning Hybrid Model","authors":"Xing Yee Leong, N. Jajo, S. Peiris, M. Khadra","doi":"10.24018/ejai.2023.2.3.19","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.3.19","url":null,"abstract":"Long wait times for elective surgery have not only caused patients to continue to live with inconvenience or pain but also creates frustrations and dissatisfaction with the local hospitals and healthcare systems. To deal with the increasing demand, hospitals need to be able to accurately predict the future demand to properly equip their facilities and the number of staff. In this paper, we propose various ARIMA-Machine Learning hybrid models to predict future elective surgery wait list demand. The goal of this paper is to improve the future demand predictions for hospital elective surgeries. We also compare our hybrid model to ARIMA and various Machine Learning/Deep Learning models, such as ANN, LSTM, and Random Forest. We found that ARIMA-ANN performed best with MAE of 0.26-0.76 and MSE of 0.13-1.05 with two-week-forward Urology, Orthopaedics and Gynecology elective surgery data.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816113","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 : 2023-04-20DOI: 10.24018/ejai.2023.2.2.23
Zhang Youheng
Artificial intelligence (AI) is a field that has undergone significant changes and challenges over time. This paper reviews the historical development of AI and representative philosophical thinking, and also considers the methodology and applications of AI, and anticipates its continued advancement. It discusses two main paradigms: symbolism and connectionism, which differ in how they explain and implement intelligence through symbols or artificial neural networks. However, neither paradigm is the final answer to AI research but rather reflects the best answer at a given time. The paper also analyzes the shortcomings of both paradigms from a philosophical perspective and argues that the most fundamental philosophical issue therein is understanding the difference between biological and artificial intelligence.
{"title":"A Historical Review and Philosophical Examination of the two Paradigms in Artificial Intelligence Research","authors":"Zhang Youheng","doi":"10.24018/ejai.2023.2.2.23","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.2.23","url":null,"abstract":"Artificial intelligence (AI) is a field that has undergone significant changes and challenges over time. This paper reviews the historical development of AI and representative philosophical thinking, and also considers the methodology and applications of AI, and anticipates its continued advancement. It discusses two main paradigms: symbolism and connectionism, which differ in how they explain and implement intelligence through symbols or artificial neural networks. However, neither paradigm is the final answer to AI research but rather reflects the best answer at a given time. The paper also analyzes the shortcomings of both paradigms from a philosophical perspective and argues that the most fundamental philosophical issue therein is understanding the difference between biological and artificial intelligence.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128098649","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 : 2023-04-11DOI: 10.24018/ejai.2023.2.2.22
All universities in and around the globe have senate members whose responsibility is to deliberate on matters that affect the smooth running of the university in senate meetings, such matters include, personnel, management, and student matters. Reports are generated at the end of each senate meeting on these matters and are printed on paper or stored in the system without proper grouping of the matters as a result of lack of efficient classification model. This paper proposes hybrid machine learning and deep learning models for the development of efficient classification model for textual documents and tested with reports from senate deliberations from university of Port Harcourt. The dataset for over ten years was collected and pre-processed, noise and other non-alphanumeric values removed by tokenization. Principal component analysis algorithm which is a machine learning approach was used extensively for feature selection and LSTM a deep learning architecture was used to build the model which has the capacity of retaining the content in its memory for a long time which solves the challenges of memory retention in other models. The model built depicts classification accuracy of 99% and the classification application was able to classify decisions made by the senate into different categories which will assist to eliminate conflicting decisions on the floor of any university senate.
{"title":"Improved Hybrid Model for Classification of Text Documents","authors":"","doi":"10.24018/ejai.2023.2.2.22","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.2.22","url":null,"abstract":"All universities in and around the globe have senate members whose responsibility is to deliberate on matters that affect the smooth running of the university in senate meetings, such matters include, personnel, management, and student matters. Reports are generated at the end of each senate meeting on these matters and are printed on paper or stored in the system without proper grouping of the matters as a result of lack of efficient classification model. This paper proposes hybrid machine learning and deep learning models for the development of efficient classification model for textual documents and tested with reports from senate deliberations from university of Port Harcourt. The dataset for over ten years was collected and pre-processed, noise and other non-alphanumeric values removed by tokenization. Principal component analysis algorithm which is a machine learning approach was used extensively for feature selection and LSTM a deep learning architecture was used to build the model which has the capacity of retaining the content in its memory for a long time which solves the challenges of memory retention in other models. The model built depicts classification accuracy of 99% and the classification application was able to classify decisions made by the senate into different categories which will assist to eliminate conflicting decisions on the floor of any university senate.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123772382","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 : 2023-03-16DOI: 10.24018/ejai.2023.2.2.15
Atif Aziz
The journey of copyright protection that started with the printing press in the 16th century entered a new era of challenges with the technological advancement of the 21st century. Copyright has rights and enforcement that are grounded in legislative enactments. This paper advocates that A. I.-produced work is original and deserves copyright protection. Artificial Intelligence (A. I.) has emerged as a powerful technology that has enabled the creation and assimilation of new and unique authorship. The amount of work that A. I. is producing in the fields of science, medicine, art, law, and literature is increasing dramatically. This paper addresses the question of why A. I. generated work deserves copyright protection and how it correlates with its ownership. A comparative analysis of the existing copyright laws in various jurisdictions is examined. A rundown of current challenges of digital copyright and future developments are discussed. The paper presents the idea of legal personhood and how it correlates with copyright work ownership. Five traditional ownership options are compared and considered. A hybrid ownership model that gives legal personality to the artificial intelligence (AI) system, its programmer, user, and the company under the umbrella of a legal entity like artificial personality (AiLE) is proposed. In most jurisdictions, legislative changes are required to address and provide a new foundation for copyright protection and ownership of AI. -produced original work. Hence, the need to address the current challenges of digital copyright and its rightful owner is essential in unleashing the true potential and further development of A. I.
{"title":"Artificial Intelligence Produced Original Work: A New Approach to Copyright Protection and Ownership","authors":"Atif Aziz","doi":"10.24018/ejai.2023.2.2.15","DOIUrl":"https://doi.org/10.24018/ejai.2023.2.2.15","url":null,"abstract":"The journey of copyright protection that started with the printing press in the 16th century entered a new era of challenges with the technological advancement of the 21st century. Copyright has rights and enforcement that are grounded in legislative enactments. This paper advocates that A. I.-produced work is original and deserves copyright protection. Artificial Intelligence (A. I.) has emerged as a powerful technology that has enabled the creation and assimilation of new and unique authorship. The amount of work that A. I. is producing in the fields of science, medicine, art, law, and literature is increasing dramatically. This paper addresses the question of why A. I. generated work deserves copyright protection and how it correlates with its ownership. A comparative analysis of the existing copyright laws in various jurisdictions is examined. A rundown of current challenges of digital copyright and future developments are discussed. The paper presents the idea of legal personhood and how it correlates with copyright work ownership. Five traditional ownership options are compared and considered. A hybrid ownership model that gives legal personality to the artificial intelligence (AI) system, its programmer, user, and the company under the umbrella of a legal entity like artificial personality (AiLE) is proposed. In most jurisdictions, legislative changes are required to address and provide a new foundation for copyright protection and ownership of AI. -produced original work. Hence, the need to address the current challenges of digital copyright and its rightful owner is essential in unleashing the true potential and further development of A. I.","PeriodicalId":360205,"journal":{"name":"European Journal of Artificial Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301081","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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