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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

computers, Materials & Continua 2023年第5期75卷 3413-3429页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

关键词： Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet

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Understanding Behavioral Intention to Use Social Media Technology: Two Comparing Model, TAM and UTAUT 6

Understanding Behavioral Intention to Use Social Media Techn...

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6th International Conference on information Technology, InCIT 2022

作者： Harnadi, Bernardinus Prasetya, Fx Hendra Widiantoro, Albertus Dwiyoga Soegijapranata Catholic University Computer Science Faculty Information Systems Department Semarang Indonesia

ISBN: (纸本)9781665489126

This study has purpose to investigate behavioral intention to use social media technology using two model TAM and UTAUT. The proposed models were examined by 326 sample data from university students and reveal that all variables on TAM and UTAUT show their significantly direct effect among variables in the models. Hedonic motivation as main adding variable in the two model also proves the significantly direct effect on behavioral intention. By way of investigating the strong variables on previous related studies and employing the variables on the proposed model, the study reveals the prominent variables in user acceptance of social media technology including perceived usefulness, perceived ease of use, social influence, facilitating condition, and hedonic motivation. This study contributes to people who interests to the result of this study in revealing the prominent variables in research on user acceptance of social media. The recommendation of this study is the reducing or increasing user intention to use social media can be done by reducing or increasing the influence of factors or variables related to it. © 2022 IEEE.

关键词： Economic and social effects

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Examining User Acceptance of MOOCs: The Role of Openness, Task Technology Fit, and Self-Efficacy 7

Examining User Acceptance of MOOCs: The Role of Openness, Ta...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Harnadi, Bernardinus Prasetya, Fx Hendra Widiantoro, Albertus Dwiyoga Computer Science Faculty Soegijapranata Catholic University Information Systems Department Semarang Indonesia

ISBN: (纸本)9798350345711

This study has purpose to understand the main psychological processes in technology acceptance of massive open online courses (MOOCs). We proposed extending expectation confirmation model (ECM) model with openness, task technology fit, and self-efficacy. A sample of 210 high school and university students participated as respondents in this study. After the sample data passed the validity and reliability checking, the research hypotheses were examined with correlation analysis to prove the relationship among variables and causal effect analysis using structural equation analysis (SEM) to prove the causal effect among the variables. The results revealed that: openness and task technology fit have significantly direct effect on perceived usefulness and then on continue intention to use MOOCs;self-efficacy and task technology fit have significantly direct effect on satisfaction and then on continue intention;and the last, task technology fit has significantly direct effect on confirmation and then on perceived usefulness and satisfaction. This significantly factors of the study can contribute to people who interest on the development of MOOCs leaning model through keeping the factors to gain continue intention to use MOOCs. © 2022 IEEE.

关键词： Reliability analysis

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Personalized Shopping Experiences: Leveraging Big Data Analytics in E-commerce for Enhanced Customer Engagement

Personalized Shopping Experiences: Leveraging Big Data Analy...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Raja, Abdullah Siddique, Fatima Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Altaf, Junaid Kousar, Shagufta University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University Putra Malaysia Researcher Cyber Security Center Malaysia Bahria University Lahore Campus Department of Computer Science Lahore Pakistan Gulab Devi Educational Complex Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9798350369106

E-commerce systems have integrated big data analytics (BDA) as a core element, enabling personalized shopping experiences, crucial for customer loyalty and satisfaction. This research explores how intentional use of data analytics impacts customers and signals the combined effect of technology and market data in tailoring customer needs through innovation. We developed unique data mining processes from e-commerce archives to uncover patterns in customer behavior, aiming to simplify consumer analysis and identify factors influencing purchasing and loyalty barriers. Our research also tests personalization strategies on sample data to improve key performance indicators like conversion rates, average order value, and customer retention. Ethical data use and privacy are prioritized, ensuring trust and transparency between e-commerce platforms and consumers. This study is innovative, demonstrating the power of analytics to revolutionize e-commerce by prioritizing customer needs and sustainable growth. © 2024 IEEE.

关键词： Marketplaces

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Gamifying Python: Enhancing Learning Outcomes and Motivation through a Game-Based Approach

Gamifying Python: Enhancing Learning Outcomes and Motivation...

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International Conference on information Technology (INCIT)

作者： Nicha Sirikong Porawat Visutsak Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This work aimed to enhance Python programming education by developing a learning game that utilizes gamification techniques. The study focused on Mathayom Suksa 1 (7th grade in the U.S. education system) students who often face challenges in understanding complex programming concepts due to traditional teaching methods. The developed game incorporated gamification elements like points, rewards, leaderboards, and time constraints to create an engaging and interactive learning environment. The study compared students’ learning achievements before and after using the game and assessed their satisfaction levels. The findings indicated that the gamified learning game significantly improved students’ learning outcomes, with post-test scores (M = 12.90, SD = 4.27) being notably higher than pre-test scores (M = 7.28, SD = 1.60), t(39) = 11.82, p < .05. Furthermore, students expressed high levels of satisfaction with the gamified learning experience (M = 3.88, SD = 0.69) on a 5-point Likert scale, indicating its potential to foster positive attitudes towards learning Python. The study highlights the potential of gamification in addressing the challenges of teaching programming to novice learners and promoting effective and enjoyable learning experiences.

关键词： Education Collaboration Games Planning Time factors Problem-solving information technology Programming profession Faces Python

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Efficient Video Object Detection for EV Charging Plug Type Identification in Real-World Scenarios

Efficient Video Object Detection for EV Charging Plug Type I...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Phasuwut Chunnapiya Porawat Visutsak Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

The rapid growth of electric vehicles (EVs) necessitates a robust charging infrastructure, but the diversity of charging plug types poses a challenge for both EV users and automated charging systems. This paper addresses the accurate identification of EV charging plug types in real-world scenarios using video object detection. We evaluated four state-of-the-art YOLO models (V5s, V6s, V7, and V8s) on a custom dataset. YOLOv8s demonstrates superior overall accuracy (mAP@0.5:0.95 of 0.95, precision 0.998, F1-score 0.997), while YOLOv7 excels in specific metrics (mAP@0.5 of 0.997, recall 0.997). YOLOv6s boasts the fastest training time. YOLOv5s has the lowest Gigaflops and Parameter. We bridge research and application with a user-friendly website for real-time EV socket detection, empowering users and paving the way for automated charging. This research contributes to a more accessible and efficient EV charging ecosystem, fostering sustainable transportation.

关键词： YOLO Accuracy Biological system modeling Sockets Transportation Streaming media Electric vehicle charging Real-time systems Object recognition Plugs

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