Journal of Artificial Intelligence and Technology最新文献_第2页

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence 人工智能背景下基于强化学习的篆刻机器人仿真

Journal of Artificial Intelligence and Technology Pub Date : 2024-03-21 DOI: 10.37965/jait.2024.0453

Ran Tan, Khayril Anwar, Bin Khairudin

{"title":"Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence","authors":"Ran Tan, Khayril Anwar, Bin Khairudin","doi":"10.37965/jait.2024.0453","DOIUrl":"https://doi.org/10.37965/jait.2024.0453","url":null,"abstract":"The rapid development of robotics technology has made people's lives and work more convenient and efficient. The research and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in various fields of robot applications. In view of this, this study is based on deep reinforcement learning convolutional neural networks, combined with point cloud models, proximal strategy optimization algorithms, and flexible action evaluation algorithms. A seal cutting robot based on deep reinforcement learning has been proposed. The final results show that the descent speed of the seal cutting robot with the root mean square difference as the performance standard is about 1% faster than the flexible action evaluation algorithm. About 2% faster than the proximal strategy optimization algorithm. It is about 4% faster than the deep deterministic strategy gradient algorithm. This indicates that the research model has certain advantages in terms of actual accuracy after cutting. The fluctuation of this model is about 10% smaller than the evaluation of flexible actions and about 60% smaller than the gradient of deep deterministic strategies. Therefore, the research model has the highest overall stability without falling into local optima. In addition, compared to the near end strategy optimization algorithm, it falls into local optima, resulting in a low coincidence degree of about 17%. The deep deterministic strategy gradient algorithm has a large fluctuation amplitude during the seal cutting process, and the overall curve is relatively slow, with a final overlap of about 70%. The overlap degree of flexible action evaluation is slightly higher by about 83%. The maximum stability of the model's overlap is best around 90%. Through experiments, it can be found that the seal cutting robot proposed in the study based on deep reinforcement learning maintains certain advantages in performance indicators in various types of tests.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"10 4p2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140222311","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}

引用次数: 0

Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care 个性化慢性疼痛管理框架：利用人工智能和个性洞察力实现有效护理

Journal of Artificial Intelligence and Technology Pub Date : 2024-03-18 DOI: 10.37965/jait.2024.0457

Akshi Kumar, Rahul Seewal, Dipika Jain, Ravleen Kaur

{"title":"Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care","authors":"Akshi Kumar, Rahul Seewal, Dipika Jain, Ravleen Kaur","doi":"10.37965/jait.2024.0457","DOIUrl":"https://doi.org/10.37965/jait.2024.0457","url":null,"abstract":"This paper introduces a cutting-edge framework for personalized chronic pain management, leveraging the power of artificial intelligence (AI) and personality insights. It explores the intricate relationship between personality traits and pain perception, expression, and management, identifying key correlations that influence an individual's experience of pain. By integrating personality psychology with AI-driven personality assessment, this framework offers a novel approach to tailoring chronic pain management strategies for each patient's unique personality profile. It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator (MBTI) in shaping personalized pain management plans. Additionally, the paper introduces multimodal AI-driven personality assessment, emphasizing the ethical considerations and data collection processes necessary for its implementation. Through illustrative case studies, the paper exemplifies how this framework can lead to more effective and patient-centered pain relief, ultimately enhancing overall well-being. In conclusion, the paper positions the need of an \"AI-Powered Holistic Pain Management Initiative\" which has the potential to transform chronic pain management by providing personalized, data-driven solutions and create a multifaceted research impact influencing clinical practice, patient outcomes, healthcare policy, and the broader scientific community's understanding of personalized medicine and AI-driven interventions.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"66 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234168","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}

引用次数: 0

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm 通过整合 3D PC 重建和 GAN 算法实现历史建筑的数字化修复

Journal of Artificial Intelligence and Technology Pub Date : 2024-03-15 DOI: 10.37965/jait.2024.0514

Tianke Fang, Zhenxing Hui, William P. Rey, Aihua Yang, Bin Liu, Zhiying Xie

{"title":"Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm","authors":"Tianke Fang, Zhenxing Hui, William P. Rey, Aihua Yang, Bin Liu, Zhiying Xie","doi":"10.37965/jait.2024.0514","DOIUrl":"https://doi.org/10.37965/jait.2024.0514","url":null,"abstract":"Historical architecture is an important carrier of cultural and historical heritage in a country and region, and its protection and restoration work plays a crucial role in the inheritance of cultural heritage. However, the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors. Therefore, an artificial intelligence repair technology based on three-dimensional (3D) point cloud reconstruction and generative adversarial networks was proposed to improve the precision and efficiency of repair work. Firstly, in-depth research on the principles and algorithms of 3D point cloud data processing and generative adversarial networks should be conducted. Secondly, a digital restoration framework was constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes. The experimental results showed that the errors in the restoration of palace buildings, defense walls, pagodas, altars, temples, and mausoleums were 0.17, 0.12, 0.13, 0.11, and 0.09, respectively. The technique can significantly reduce the error while maintaining the high precision repair effect. This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration. It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"121 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238044","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}

引用次数: 0

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image 基于深度学习的手写文本图像无分割识别算法

Journal of Artificial Intelligence and Technology Pub Date : 2024-03-05 DOI: 10.37965/jait.2024.0473

Ge Peng

引用次数: 0

DAVSS: Development of Mobile Application for Documenting and Assessing Domestic Violence DAVSS：开发用于记录和评估家庭暴力的移动应用程序

Journal of Artificial Intelligence and Technology Pub Date : 2024-03-03 DOI: 10.37965/jait.2024.0357

Faiqah Hafidzah, Halim, Nor Aimuni, Mohd Rashid, Noor Azhana, Abdul Majid

{"title":"DAVSS: Development of Mobile Application for Documenting and Assessing Domestic Violence","authors":"Faiqah Hafidzah, Halim, Nor Aimuni, Mohd Rashid, Noor Azhana, Abdul Majid","doi":"10.37965/jait.2024.0357","DOIUrl":"https://doi.org/10.37965/jait.2024.0357","url":null,"abstract":"This paper presents the development and evaluation of the Domestic Abuse and Violence Support System (DAVSS), a mobile application designed to address domestic violence issues in Malaysia. DAVSS integrates the essential 27-DASH (Danger Assessment for Separation and Harassment) questionnaire to assess the severity of domestic violence situations. The project follows the phased approach of the Waterfall Model, encompassing requirements, analysis, design, development, and testing stages.Usability testing was conducted to evaluate the DAVSS application, revealing overall user satisfaction. However, this assessment relied primarily on frequency analysis, prompting consideration for enhanced reliability through alternative measurement methods such as median and standard deviation approaches.The DAVSS application's Danger Assessment Test (DAT) algorithm, built upon the 27-DASH questions, represents a significant aspect of the system. Future work includes the validation of this rule-based algorithm using expert evaluation, potentially through Delphi techniques.Given the absence of dedicated domestic abuse mobile applications in Malaysia, DAVSS promises several advantages. It empowers victims to report incidents, locate shelters for protection, and facilitates shelter access through QR code integration. In conclusion, as domestic violence remains a pressing concern requiring increased support, the DAVSS mobile application emerges as a vital system that offers assistance to victims while breaking down barriers that may silence their voices.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"30 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080915","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}

引用次数: 0

Enhancement of Prediction Model for Students’ Performance in Intelligent Tutoring System 智能辅导系统中学生成绩预测模型的改进

Journal of Artificial Intelligence and Technology Pub Date : 2024-02-23 DOI: 10.37965/jait.2024.0319

Hana Fakhira Almarzuki, Khyrina Airin Fariza Abu Samah, Siti Khatijah Nor Abdul Rahim, Shafaf Ibrahim, Lala Septem Riza

{"title":"Enhancement of Prediction Model for Students’ Performance in Intelligent Tutoring System","authors":"Hana Fakhira Almarzuki, Khyrina Airin Fariza Abu Samah, Siti Khatijah Nor Abdul Rahim, Shafaf Ibrahim, Lala Septem Riza","doi":"10.37965/jait.2024.0319","DOIUrl":"https://doi.org/10.37965/jait.2024.0319","url":null,"abstract":"Adapting Artificial Intelligence to Intelligent Tutoring System (ITS) has made teaching and learning more effective. Prediction of students’ performance has gained more interest among researchers to know whether the students master their learning before moving to another topic. For the research scope, we have analyzed numerous Bayesian Knowledge Tracing (BKT) variations in methodology and found that the most precise way to forecast students’ success is through Individualized Bayesian Knowledge Tracing (iBKT). Although iBKT makes a good prediction, iBKT does not consider other knowledge-related elements, such as learning and guess rate, and only uses students’ prior knowledge as the parameters. Due to issues concerning uncertainties in students’ interactions, this study proposes to enhance the prediction function of the iBKT using a feature relating to students’ confidence levels. Thus, this new confidence parameter is defined as P(C), assumed to improve prediction accuracy when forecasting student achievement. The prediction accuracy is tested using the attributes of the ASSISTment and Knowledge Discovery and Data Mining (KDD) datasets as input. In addition, Root Mean Square Error (RMSE) is applied to calculate the performance accuracy of iBKT and enhanced iBKT with the confidence parameter. As a result, the RMSE performance accuracy of iBKT with the confidence parameter shows a low RMSE score for both datasets. The ASSISTment dataset provides a higher prediction when applying the confidence parameter, 0.21190. Therefore, it is concluded that enhancing the confidence parameter is an effective method with accuracy improvement for predicting students’ success in ITS.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436941","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}

引用次数: 0

Deep Learning for Automatic Diagnosis of Skin Cancer Using Dermoscopic Images 利用皮肤镜图像进行皮肤癌自动诊断的深度学习

Journal of Artificial Intelligence and Technology Pub Date : 2024-02-23 DOI: 10.37965/jait.2024.0392

S. Rama Krishna, Karthik Raj V, Anand Gudur, Siddharth Jain, Shanmugavel Deivasigamani, Mohit Tiwari, K. G. S. Venkatesan

{"title":"Deep Learning for Automatic Diagnosis of Skin Cancer Using Dermoscopic Images","authors":"S. Rama Krishna, Karthik Raj V, Anand Gudur, Siddharth Jain, Shanmugavel Deivasigamani, Mohit Tiwari, K. G. S. Venkatesan","doi":"10.37965/jait.2024.0392","DOIUrl":"https://doi.org/10.37965/jait.2024.0392","url":null,"abstract":"Over the past few years, the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by AI technology. These developments are made to better the standard of medical decision making and the standard of treatment given to patients. Fuzzy boundaries, shifting sizes, and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images, slowing down the otherwise efficient process of diagnosing skin cancer. However, these difficulties may be conquered by employing image processing software. To address these issues, the authors of this paper provide a novel IMLT-DL model for intelligent dermoscopic image processing. Multi-level thresholding and deep learning are brought together in this model. Top hat filtering and inpainting have been included into IMLT-DL for use in image processing. In addition, Mayfly Optimization has been used in tandem with multilayer Kapur's thresholding to identify specific trouble spots. For further investigation, it uses an Inception v3-based feature extractor, and for data classification, it makes use of gradient boosting trees (GBTs). On the ISIC dataset, this model was shown to outperform state-of-the-art alternatives by a margin of 0.992% over the duration of trial iterations. These advances are a major step forwards in the quest for faster and more accurate skin lesion detection.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"192 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437780","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}

引用次数: 0

Matching Game Genre with Lesson Content – A Development of Blood Circulation Racing Game 将游戏类型与课程内容相匹配 - 开发血液循环赛车游戏

Journal of Artificial Intelligence and Technology Pub Date : 2024-02-21 DOI: 10.37965/jait.2024.0421

F. Ahmadon, Muhammad Zikri Selahuddeen, Elin Eliana Abdul Rahim, Hazlifah Mohd Rusli

{"title":"Matching Game Genre with Lesson Content – A Development of Blood Circulation Racing Game","authors":"F. Ahmadon, Muhammad Zikri Selahuddeen, Elin Eliana Abdul Rahim, Hazlifah Mohd Rusli","doi":"10.37965/jait.2024.0421","DOIUrl":"https://doi.org/10.37965/jait.2024.0421","url":null,"abstract":"It has been demonstrated that students pay more attention during game-based learning, which in turn leads to greater levels of learning among students. In addition, the success of students in their academic endeavours is increased when the learning styles or learning objectives of the children are matched with the appropriate game type. This paper describes the development of a digital game for learning blood circulation that maps to a ‘racing’ game genre. The lesson’s objective was for the students to be able to understand how blood flows, and a racing game genre was chosen to match the lesson content. Racing game assets such as racetrack, race path, race car, and obstacles are mapped to lesson-embedded designs such as simplified blood circuit, blood direction, blood cells, and cholesterol lumps. Multiple cues are inserted into the game to help players with content recollections. Common challenges for racing games, such as time limits, energy meters, and obstacles, are tailored to the theme of blood circulation. Usability testing was conducted to measure the ease of use of this game using System Usability Scale (SUS). Five fifteen-year-old participants took part in the testing at a secondary school in Melaka. Participants were chosen using convenience sampling, and none of the participants had ever played the game before. A SUS standard score of 78 was obtained, which is considered 'Good' under the Adjective Ratings when measured against the SUS Score Graph.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"6 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957935","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}

引用次数: 0

Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation 利用生物启发计算从语音中检测呼吸系统疾病的智能健康信息学级联 PFLANN 模型

Journal of Artificial Intelligence and Technology Pub Date : 2024-02-21 DOI: 10.37965/jait.2023.0435

Jagannath Dayal Pradhan, L V Narasimha Prasad, Tusar Kanti Dash, Manisha Guduri, Ganapati Panda

{"title":"Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation","authors":"Jagannath Dayal Pradhan, L V Narasimha Prasad, Tusar Kanti Dash, Manisha Guduri, Ganapati Panda","doi":"10.37965/jait.2023.0435","DOIUrl":"https://doi.org/10.37965/jait.2023.0435","url":null,"abstract":"Due to the recent developments in communications technology, cognitive computations have been used in smart healthcare techniques that can combine massive medical data, Artificial intelligence, federated learning, Bio-inspired computation, and the Internet of Medical Things. It has helped in knowledge sharing and scaling ability between patients, doctors, and clinics for effective treatment of patients. Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results. Since the subject’s speech can be remotely recorded and submitted for further examination, it offers a quick, economical, dependable, and non-invasive prospective alternative detection approach. However, the two main requirements of this are higher accuracy and lower computational complexity and, in many cases, these two requirements do not correlate with each other. This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy. A Cascaded Perceptual functional link artificial neural network (PFLANN) is used to capture the non-linearity in the data for better classification performance with low computational complexity. The proposed model is being tested for multiple respiratory diseases and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.\u0000 ","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958028","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}

引用次数: 0

Motion Capture Algorithm for Students' Physical Activity Recognition in Physical Education Curriculum 体育课程中学生体育活动识别的运动捕捉算法

Journal of Artificial Intelligence and Technology Pub Date : 2023-12-17 DOI: 10.37965/jait.2023.0447

Yinfu Lu, Haitao Long

{"title":"Motion Capture Algorithm for Students' Physical Activity Recognition in Physical Education Curriculum","authors":"Yinfu Lu, Haitao Long","doi":"10.37965/jait.2023.0447","DOIUrl":"https://doi.org/10.37965/jait.2023.0447","url":null,"abstract":"Physical training learning is one of the important ways to raise the national physical quality and health level. However, there are many problems in traditional physical education, such as the difficulty to identifying the effectiveness of physical education curriculum and low level of repetitive exercise content. In order to solve this problem and improve the curriculum quality of current middle school physical education courses, a motion capture algorithm based on convolutional neural network and long-term memory network is proposed, and a student physical activity capture model is constructed based on the fusion algorithm. In the performance comparison test of the fusion algorithm proposed in this study, the loss value and accuracy of this fusion algorithm are 0.045 and 0.921, respectively, significantly superior to the comparison algorithm. Then in the empirical analysis, the accuracy rate of this motion capture algorithm model proposed in this study for students' walking posture recognition in physical education courses is 91.5%, which is better than the comparative capture method. This motion capture algorithm can accurately capture the physical activities of students in physical education courses, which has practical application significance.","PeriodicalId":135863,"journal":{"name":"Journal of Artificial Intelligence and Technology","volume":"26 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966076","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}

引用次数: 0