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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fetoun AlZahrani Arwa Ghouti Tanzila Saba Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

With the increasing prevalence of computer usage, there has been a rise in cases of repetitive strain injuries (RSIs) like Carpal Tunnel Syndrome (CTS) among individuals who spend extended hours typing. Hand pose estimation plays a vital role in improving ergonomic and safe human-computer interaction (HCI). To address this challenge, we propose ErgoType, a real-time hand pose estimation application developed for Apple WatchOS. The application delivers posture classification results directly on the Apple Watch and integrates with a companion iPhone application for detailed feedback. The system employs a Support Vector Machine (SVM) model trained on smartwatch emulator data to analyze key hand-position indicators, such as wrist and elbow angles, achieving a classification accuracy of 98%. Immediate feedback is provided via on-screen notifications, alerting users to poor typing habits such as excessive wrist flexion or prolonged typing without rest. This feedback encourages users to adopt healthier typing practices. ErgoType's combination of wearable technology and real-time feedback offers a proactive solution for reducing typing-related injuries and promoting workplace wellness. It is designed to support both professionals and frequent computer users, enhancing productivity and well-being through improved ergonomics.

关键词： Support vector machines Wrist Hands Carpal tunnel syndrome Wearable Health Monitoring Systems Ergonomics Pose estimation Real-time systems Injuries Strain

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Suhail the Guiding Star: A Sustainable Chatbot for Saudi Arabia Culture

Suhail the Guiding Star: A Sustainable Chatbot for Saudi Ara...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Wahaj Saidam Lina Alnasi Sarah Ayman Rasha Alnaji Alqahtani Heyfa Ammar Anis Koubaa CCIS Prince Sultan University Riyadh Saudi Arabia College of Comp. and Info. Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics Lab. CCIS Prince Sultan University Riyadh Saudi Arabia University of Tunis ELManar Tunisia Alfaisal University Riyadh Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Protecting the cultural heritage is essential to ensure its survival for future generations while fostering cultural diversity and global understanding. It is then crucial to develop engaging, interactive and innovative approaches to inform and educate people about the significance of cultural heritage, making it accessible and appealing to diverse audiences. Particularly in the Kingdom of Saudi Arabia, various solutions have been proposed to assist visitors during their journey in the kingdom. However, the existing solutions lack localized content or fail to provide immersive dynamic user experiences, making them less effective in achieving cultural education goals. In this regard, the present work proposes Suhail: a novel AI-powered chatbot that allows for interactive communication, and improves understanding of Saudi cultural heritage through relevant responses. Suhail's reliability is underpinned by two novel datasets that we propose in this work, derived from trusted and verified sources. Our evaluation indicates an impressive accuracy score of 93.3%. Suhail's potential to advance Saudi cultural preservation and dissemination is underscored through expert interviews and survey responses in the direction of Saudi Vision 2030 and UN Sustainable Development Goals, specifically Quality Education (SDG 4) and Sustainable Cities (SDG 11).

关键词： Surveys Accuracy Education Urban areas Stars Chatbots Cultural differences Global communication Reliability Sustainable development

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Deep Learning and Shape-Driven Combined Approach for Breast Cancer Tumor Segmentation

Deep Learning and Shape-Driven Combined Approach for Breast ...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Mudassar Ali Haoji Hu Tufail Muhammad Muhammad Ahsan Qureshi Tariq Mahmood College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science Air University Islamabad Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia.

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Our aim is to enhance the performance of segmenting breast cancer from medical images by overcoming major challenges, including data insufficiency and complexity when training a model. Using the INbreast dataset, it proposes a hybrid method for improving the segmentation accuracy and computational efficiency by combining M3D-Neural Cellular Automata with Shape-Guided Segmentation. This work aims at optimal performance on complex tasks of tumor segmentation by leveraging shape priors and reducing the need for extensive training data. Results confirm that significant improvements in both accuracy and robustness are achieved, which reduces computation time and improves tumor boundary delineation. The hybrid approach provides a time- and cost-effective method for precise detection in breast cancer diagnosis and treatment.

关键词： Deep learning Training Image segmentation Accuracy Shape Training data Breast cancer data models Robustness Real-time systems

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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Leveraging Deep Learning for Cyberbullying Detection in Arabic Tweets

Leveraging Deep Learning for Cyberbullying Detection in Arab...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fatima AlShannaq Sawsan Alhalawani Heba Al-Jarrah Mohammad Shehab Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia College of Computer Science and Information Systems Prince Sultan University Riyadh Saudi Arabia Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan College of Computer Sciences and Informatics Amman Arab University Amman Jordan

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Cyberbullying is a growing concern in the digital age, particularly on social media platforms like X (formerly Twitter). This study presents a deep learning-based approach for detecting cyberbullying in Arabic tweets using the Arabic Cyberbullying Corpus (ArCybC). Leveraging the Arabic-BERT model, we conducted binary and multi-class classification tasks to identify cyberbullying content and its associated categories or domains. Our methodology incorporated advanced data-balancing techniques, including oversampling and undersam-pling, to address class imbalance challenges. The study aims to answer the research questions: (1) How effectively can deep learning models detect cyberbullying in Arabic tweets? (2) How do data balancing techniques impact classification performance? (3) Can Arabic-BERT outperform traditional classifiers in cy-berbullying detection? These questions are addressed through our methodology, where we employ Arabic-BERT, implement rebalancing techniques, and compare our model's performance with baseline classifiers. Experimental results demonstrate significant improvements in F1-scores, with 89.1% achieved in binary classification and 91.6% in category-level classification after oversampling. These findings highlight the effectiveness of fine-tuned deep learning models in tackling the linguistic complexity of Arabic and the nuanced nature of cyberbullying detection. This study provides a foundation for future research in addressing online harassment in Arabic-speaking digital communities.

关键词： Deep learning Blogs Cyberbullying Linguistics data science Information age data models Complexity theory

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Correction: MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

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International Journal of Computational intelligence Systems 2025年第1期18卷 1-2页

作者： Wang, Yu Xu, Juan Guan, Yucheng Ahmad, Faizan Mahmood, Tariq Rehman, Amjad Shandong Research Institute of Industrial Technology Shandong China Department of Endocrinology Taian City Central Hospital Taian China College of Engineering Northeastern University San Jose USA Qiqihar Medical University Heilongjiang Province China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Faculty of Information Sciences University of Education Vehari Campus Vehari Pakistan

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Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors

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Scientific Reports 2025年第1期15卷 1-23页

作者： Rehman, Amjad Mahmood, Tariq Alahmadi, Tahani Jaser Almasoud, Ahmed S. Saba, Tanzila Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Faculty of Information Sciences University of Education Vehari Campus Vehari Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

Wireless Sensor Networks (WSNs) are distributed sensor nodes that sense data from their surroundings and relay it to a central network for processing and analysis. Sensor localization is a crucial technique in WSNs, enabling precise positions of target nodes based on environmental signal perception. However, achieving high accuracy in node localization remains a challenge. This study introduces an improved DV-Hop positioning algorithm that integrates Long Short-Term Memory (OLSTM-DVHop) networks to enhance node position predictions. The algorithm processes original data through filtering, analysis, and feature extraction to improve predicted node positions. The study analyzed errors using a standard DV-Hop algorithm and designed a robust architecture for WSN positioning. Simulation experiments validated the proposed improvements, aligning with the algorithm’s accuracy requirements. The proposed error correction mechanism addresses uneven error distribution in the DV-Hop algorithm, adjusting the positions of nodes with significant deviations, reducing errors, and enhancing the positioning process’s reliability and accuracy. The effectiveness of the proposed algorithm is evaluated by comparing it with other localization algorithms across different terrain types. The improved DV-Hop algorithm significantly reduces localization errors and offers superior accuracy, outperforming other algorithms in various experimental scenarios. © The Author(s) 2025.

关键词： DV-Hop Error correction LSTM Positioning accuracy

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A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

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Computers, Materials & Continua 2022年第6期71卷 4785-4801页

作者： Tanzila Saba Mirza Naveed Shahzad Sonia Iqbal Amjad Rehman Ibrahim Abunadi Artificial Intelligence&Data Analytics Research Lab(AIDA) CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Statistics University of GujratGujrat50700Pakistan Department of Statistics University of SialkotSialkot51310Pakistan

Many countries developed and increased greenery in their country sights to attract international *** planning is now significantly contributing to their *** next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately *** accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal *** traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with *** contrast,the artificial neural network(ANN)model deals better with nonlinear time *** get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN ***,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and *** results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN ***,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

关键词： SARFIMA hybrid model tourists’arrival forecasting ANN

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Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

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Computers, Materials & Continua 2022年第1期70卷 505-521页

作者： Summra Saleem M.Usman Ghani Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Saeed Ali Bahaj Department of Computer Science UETLahorePakistan Al-Khwarizmi Institute of Computer Science UETLahorePakistan Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh11586Saudi Arabia MIS Department College of Business Administration Prince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal *** Edge GAN,the major contribution is attribute guided vector that enables high visual quality content *** research study proposes automatic face image realism from freehand sketches based on Edge *** propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute *** density level is projected into non-latent space,having a linear controlled function *** assists the user to appropriately devise the variant densities of facial sketches and image *** data set of Large Scale CelebFaces Attributes(ClebA),lab.lled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed *** solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.

关键词： Edge generator density variant sketch generator face translation recognition residual block

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Predicting mortality index for ICU inpatients based on clinical data extracted from electronic health record

Predicting mortality index for ICU inpatients based on clini...

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作者： Aisha Dawood Tanzila Saba Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

Predicting ICU inpatients mortality index needs to be improved to incorporate clinical *** is also helpful to reflect the patient's recovery and hospitals *** this research machine learning model LightGBM was trained and *** study used a dataset for ICU admissions for adult patients from six *** a total of 130,000 patient records were included in the *** final model achieved AUROC(95% CI) of 0.97,an accuracy of 0.95,and an F1 score of 0.81 on the *** on results,it is observed that machine learning models with the support of conventional mortality scoring indices can provide a successful and useful model for predicting the outcome of critical and severe cases in the ICU.

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