Pub Date : 2023-01-01DOI: 10.47852/bonviewaia320526
Maryam Asadzadehkaljahi, Arnab Halder, U. Pal, P. Shivakumara
Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera are inconsistent and unstable while for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of frames with static objects and arbitrary movement objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.
{"title":"Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes","authors":"Maryam Asadzadehkaljahi, Arnab Halder, U. Pal, P. Shivakumara","doi":"10.47852/bonviewaia320526","DOIUrl":"https://doi.org/10.47852/bonviewaia320526","url":null,"abstract":"Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera are inconsistent and unstable while for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of frames with static objects and arbitrary movement objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82801898","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 : 2023-01-01DOI: 10.47852/bonviewaia3202795
Samuel Iorhemen Ayua, Yusuf Musa Malgwi, James Afrifa
The idea of regression has increased rapidly and significantly in the machine-learning domain. This paper builds a salary prediction model to predict a justifiable salary of an employee commensurate to the increase or decrease in exchange rate using polynomial regression techniques of degree 2 in Jupyter Notebook on Annaconda Navigator tool. Predicting a feasible salary for an employee by the employer is a challenging task since every employee has a high goal and hope as the standard of leaving increases without a corresponding increase in salary. This model uses a salary dataset from Taraba State University Jalingo, Nigeria in building and training the model and exchange rate dataset for the prediction of employee salary. The result of the research shows that since the distribution of the dataset was non-linear and the major feature significant in determining employee’s salary from the in-salary dataset was grade level and exchange rate, this fully confirmed the use of polynomial regression algorithm. The research has immensely contributed to the knowledge and understanding of regression techniques. The researcher recommended other machine learning algorithms explored with various salary datasets and the potential applicability of machine learning fully incorporated in the financial department on the large dataset for better performance. The model performance was evaluated using R2 scores accuracy and the value of 97.2% realized, indicating how well the data points fit the line of regression and unseen dataset in the developed model.
{"title":"Salary Prediction Model for Non-Academic Staff Using Polynomial Regression Technique","authors":"Samuel Iorhemen Ayua, Yusuf Musa Malgwi, James Afrifa","doi":"10.47852/bonviewaia3202795","DOIUrl":"https://doi.org/10.47852/bonviewaia3202795","url":null,"abstract":"The idea of regression has increased rapidly and significantly in the machine-learning domain. This paper builds a salary prediction model to predict a justifiable salary of an employee commensurate to the increase or decrease in exchange rate using polynomial regression techniques of degree 2 in Jupyter Notebook on Annaconda Navigator tool. Predicting a feasible salary for an employee by the employer is a challenging task since every employee has a high goal and hope as the standard of leaving increases without a corresponding increase in salary. This model uses a salary dataset from Taraba State University Jalingo, Nigeria in building and training the model and exchange rate dataset for the prediction of employee salary. The result of the research shows that since the distribution of the dataset was non-linear and the major feature significant in determining employee’s salary from the in-salary dataset was grade level and exchange rate, this fully confirmed the use of polynomial regression algorithm. The research has immensely contributed to the knowledge and understanding of regression techniques. The researcher recommended other machine learning algorithms explored with various salary datasets and the potential applicability of machine learning fully incorporated in the financial department on the large dataset for better performance. The model performance was evaluated using R2 scores accuracy and the value of 97.2% realized, indicating how well the data points fit the line of regression and unseen dataset in the developed model.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89103600","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 : 2023-01-01DOI: 10.47852/bonviewaia3202631
Harshita Mangotra, Vibhuti Dabas, Bhanu Khetharpal, Abhigya Verma, S. Singhal, A. K. Mohapatra
When new students enter college, they often have similar questions - ”Where to study for this subject?”, ”How to prepare Data Struc- tures and Algorithms?”, ”How to connect with seniors?” and so on. The use of chatbots can help them get answers to their questions quickly and efficiently. This study proposes a Deep Learning (DL) chatbot for addressing common doubts of university students, pro- viding efficient and accurate responses to college-specific questions. A self-curated dataset is used for the purpose of building the chat- bot and natural language processing techniques (NLP) are utilized for the pre-processing of raw data gathered. The study compares two deep learning models - a bidirectional Long Short Term Memory (LSTM) network and a simple feed-forward neural network model.
{"title":"University Auto Reply FAQ Chatbot Using NLP and Neural Networks","authors":"Harshita Mangotra, Vibhuti Dabas, Bhanu Khetharpal, Abhigya Verma, S. Singhal, A. K. Mohapatra","doi":"10.47852/bonviewaia3202631","DOIUrl":"https://doi.org/10.47852/bonviewaia3202631","url":null,"abstract":"When new students enter college, they often have similar questions - ”Where to study for this subject?”, ”How to prepare Data Struc- tures and Algorithms?”, ”How to connect with seniors?” and so on. The use of chatbots can help them get answers to their questions quickly and efficiently. This study proposes a Deep Learning (DL) chatbot for addressing common doubts of university students, pro- viding efficient and accurate responses to college-specific questions. A self-curated dataset is used for the purpose of building the chat- bot and natural language processing techniques (NLP) are utilized for the pre-processing of raw data gathered. The study compares two deep learning models - a bidirectional Long Short Term Memory (LSTM) network and a simple feed-forward neural network model.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77479739","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 : 2023-01-01DOI: 10.47852/bonviewaia32021224
Lama Alkhaled, Taha Khamis
The objective of this study is to explore the process of developing Artificial Intelligence (AI) and machine learning (ML) applications to establish an optimal support environment. The primary stages of ML include problem understanding, data management, model building, model deployment, and maintenance. This paper specifically focuses on examining the data management stage of ML development and the challenges it presents, as it is crucial for achieving accurate end models. During this stage, the major obstacle encountered was the scarcity of adequate data for model training, particularly in domains where data confidentiality is a concern. The work aimed to construct and enhance a framework that would assist researchers and developers in addressing the insufficiency of data during the data management stage. The framework incorporates various data augmentation techniques, enabling the generation of new data from the original dataset along with all the required files for detection challenges. This augmentation process improves the overall performance of ML applications by increasing both the quantity and quality of available data, thereby providing the model with the best possible input. The tool can be accessed using the following link https://github.com/TahaKh99/Image_Augmentor.
{"title":"Supportive Environment for Better Data Management Stage in the Cycle of ML Process","authors":"Lama Alkhaled, Taha Khamis","doi":"10.47852/bonviewaia32021224","DOIUrl":"https://doi.org/10.47852/bonviewaia32021224","url":null,"abstract":"The objective of this study is to explore the process of developing Artificial Intelligence (AI) and machine learning (ML) applications to establish an optimal support environment. The primary stages of ML include problem understanding, data management, model building, model deployment, and maintenance. This paper specifically focuses on examining the data management stage of ML development and the challenges it presents, as it is crucial for achieving accurate end models. During this stage, the major obstacle encountered was the scarcity of adequate data for model training, particularly in domains where data confidentiality is a concern. The work aimed to construct and enhance a framework that would assist researchers and developers in addressing the insufficiency of data during the data management stage. The framework incorporates various data augmentation techniques, enabling the generation of new data from the original dataset along with all the required files for detection challenges. This augmentation process improves the overall performance of ML applications by increasing both the quantity and quality of available data, thereby providing the model with the best possible input. The tool can be accessed using the following link https://github.com/TahaKh99/Image_Augmentor.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87299780","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 : 2023-01-01DOI: 10.47852/bonviewaia3202689
P. Groumpos
Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a "catastrophic Winter" for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.
{"title":"A Critical Historic Overview of Artificial Intelligence: Issues, Challenges, Opportunities and Threats","authors":"P. Groumpos","doi":"10.47852/bonviewaia3202689","DOIUrl":"https://doi.org/10.47852/bonviewaia3202689","url":null,"abstract":"Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a \"catastrophic Winter\" for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88185811","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 : 2023-01-01DOI: 10.47852/bonviewaia32021018
Hongqing Yu
Few-shot computer vision algorithms have enormous potential to produce promised results for innovative applications which only have a small volume of example data for training. Currently, the few-shot algorithm research focuses on applying transfer learning on deep neural networks that are pre-trained on big datasets. However, adapting the transformers requires highly cost computation resources. In addition, the overfitting or underfitting problems and low accuracy on large classes in the face validation domain are identified in our research. Thus, this paper proposed an alternative enhancement solution by adding contrasted attention to the negative face pairs and positive pairs to the training process. Extra attention is created through clustering-based face pair creation algorithms. The evaluation results show that the proposed approach sufficiently addressed the problems without requiring high-cost resources.
{"title":"Attention Enhanced Siamese Neural Network for Face Validation","authors":"Hongqing Yu","doi":"10.47852/bonviewaia32021018","DOIUrl":"https://doi.org/10.47852/bonviewaia32021018","url":null,"abstract":"Few-shot computer vision algorithms have enormous potential to produce promised results for innovative applications which only have a small volume of example data for training. Currently, the few-shot algorithm research focuses on applying transfer learning on deep neural networks that are pre-trained on big datasets. However, adapting the transformers requires highly cost computation resources. In addition, the overfitting or underfitting problems and low accuracy on large classes in the face validation domain are identified in our research. Thus, this paper proposed an alternative enhancement solution by adding contrasted attention to the negative face pairs and positive pairs to the training process. Extra attention is created through clustering-based face pair creation algorithms. The evaluation results show that the proposed approach sufficiently addressed the problems without requiring high-cost resources.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86291560","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 : 2023-01-01DOI: 10.47852/bonviewaia3202820
Shadi AlZu'bi, Ala Mughaid, Fatima Quiam, Samar Hendawi
ChatGPT, an AI-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT’s responses are not always rooted in reality and are instead generated by a GAN. This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as Naive Bayes, Random Forest, Extra Trees, and Decision Trees. The results show that the Extra Trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative’s performance. The work here, concludes with a discussion of the future directions of large language models and their impact on society and technology.
{"title":"Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models","authors":"Shadi AlZu'bi, Ala Mughaid, Fatima Quiam, Samar Hendawi","doi":"10.47852/bonviewaia3202820","DOIUrl":"https://doi.org/10.47852/bonviewaia3202820","url":null,"abstract":"ChatGPT, an AI-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT’s responses are not always rooted in reality and are instead generated by a GAN. This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as Naive Bayes, Random Forest, Extra Trees, and Decision Trees. The results show that the Extra Trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative’s performance. The work here, concludes with a discussion of the future directions of large language models and their impact on society and technology.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136297927","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 : 2023-01-01DOI: 10.47852/bonviewaia3202434
Gangadhar Bandewad, Kunal P. Datta, Bharti W. Gawali, Sunil N. Pawar
Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Researchers are concentrating on smart sensors because they can detect and forecast the presence of gas in real-time, provide correct information about gas concentration, and detect a target gas from a mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.
{"title":"Review on Discrimination of Hazardous Gases by Smart Sensing Technology","authors":"Gangadhar Bandewad, Kunal P. Datta, Bharti W. Gawali, Sunil N. Pawar","doi":"10.47852/bonviewaia3202434","DOIUrl":"https://doi.org/10.47852/bonviewaia3202434","url":null,"abstract":"Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Researchers are concentrating on smart sensors because they can detect and forecast the presence of gas in real-time, provide correct information about gas concentration, and detect a target gas from a mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135534328","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 : 2023-01-01DOI: 10.47852/bonviewaia22021378
Daniyal Baig, Waseem Akram, H. Burhan ul Haq, Muhammad Asif
Computer science and programming subjects can be overwhelming for new students, presenting them with significant challenges. As programming is considered one of the most important and complex subjects to grasp, it necessitates a fresh teaching methodology that can make the learning process more enjoyable and accessible. One approach that has gained attraction is the integration of gaming elements, which not only makes programming more engaging but also enhances understanding and retention. In our research, we adopted an innovative educational strategy that utilized a Role-Playing Game (RPG) centered on programming concepts. The aim of the research is to create an interactive and enjoyable learning experience for students by leveraging the immersive nature of gaming. The RPG provided a platform for students to actively participate in programming challenges, where they will apply their knowledge and skills to complete tasks and advance through the game. Our teaching methodology focuses on embedding programming concepts within the game's missions and quests. Additionally, we considered students' overall experience and engagement throughout the research study. Capturing both objective and subjective measures, we gained insights into the impact of our teaching methodology on student learning outcomes and their overall perception of the educational experience. In the RPG, each student is required to complete a series of tasks within the game in order to advance to the next mission. The sequential nature of the tasks ensured a structured learning process, gradually introducing new concepts and challenges to the students. The game mechanics provides an immersive environment for students to play different missions and answer the questions and learn programming. Through our research, we aim to present a compelling teaching methodology that effectively addresses the challenges facing new students in learning computer science and programming subjects. Harnessing the power of gaming, we strive to make programming more accessible, enjoyable, and engaging, ultimately empowering students to become proficient programmers. The evaluation of student performance, task accomplishment, and overall experience will provide valuable insights into the effectiveness and potential impact of this innovative approach.
{"title":"Cloud Gaming Approach To Learn Programming Concepts","authors":"Daniyal Baig, Waseem Akram, H. Burhan ul Haq, Muhammad Asif","doi":"10.47852/bonviewaia22021378","DOIUrl":"https://doi.org/10.47852/bonviewaia22021378","url":null,"abstract":"Computer science and programming subjects can be overwhelming for new students, presenting them with significant challenges. As programming is considered one of the most important and complex subjects to grasp, it necessitates a fresh teaching methodology that can make the learning process more enjoyable and accessible. One approach that has gained attraction is the integration of gaming elements, which not only makes programming more engaging but also enhances understanding and retention. In our research, we adopted an innovative educational strategy that utilized a Role-Playing Game (RPG) centered on programming concepts. The aim of the research is to create an interactive and enjoyable learning experience for students by leveraging the immersive nature of gaming. The RPG provided a platform for students to actively participate in programming challenges, where they will apply their knowledge and skills to complete tasks and advance through the game. Our teaching methodology focuses on embedding programming concepts within the game's missions and quests. Additionally, we considered students' overall experience and engagement throughout the research study. Capturing both objective and subjective measures, we gained insights into the impact of our teaching methodology on student learning outcomes and their overall perception of the educational experience. In the RPG, each student is required to complete a series of tasks within the game in order to advance to the next mission. The sequential nature of the tasks ensured a structured learning process, gradually introducing new concepts and challenges to the students. The game mechanics provides an immersive environment for students to play different missions and answer the questions and learn programming. Through our research, we aim to present a compelling teaching methodology that effectively addresses the challenges facing new students in learning computer science and programming subjects. Harnessing the power of gaming, we strive to make programming more accessible, enjoyable, and engaging, ultimately empowering students to become proficient programmers. The evaluation of student performance, task accomplishment, and overall experience will provide valuable insights into the effectiveness and potential impact of this innovative approach.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135557386","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 : 2023-01-01DOI: 10.47852/bonviewaia32021743
Weisong Chen, Boting Liu, Weili Guan
Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.
{"title":"ERNIE and Multi-Feature Fusion for News Topic Classification","authors":"Weisong Chen, Boting Liu, Weili Guan","doi":"10.47852/bonviewaia32021743","DOIUrl":"https://doi.org/10.47852/bonviewaia32021743","url":null,"abstract":"Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134884965","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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