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Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference

Scandinavian Journal of Statistics 2025年第0期

作者： Beck, Jonas Langthaler, Patrick B. Bathke, Arne C. Intelligent Data Analytics (IDA) Lab Department of Artificial Intelligence and Human Interfaces (AIHI) Paris Lodron University of Salzburg Salzburg Austria Department of Neurology Paracelsus Medical University of Salzburg Salzburg Austria

A fundamental functional in nonparametric statistics is the Mann-Whitney functional (Formula presented.), which constitutes the basis for the most popular nonparametric procedures. The functional (Formula presented.) measures a location or stochastic tendency effect between two distributions. A limitation of (Formula presented.) is its inability to capture scale differences. If differences of this nature are to be detected, specific tests for scale or omnibus tests need to be employed. However, the latter often suffer from low power, and they do not yield interpretable effect measures. In this article, we extend (Formula presented.) by additionally incorporating the recently introduced distribution overlap index (nonparametric dispersion measure) (Formula presented.) that can be expressed in terms of the quantile process. We derive the joint asymptotic distribution of the respective estimators of (Formula presented.) and (Formula presented.) and construct confidence regions. Extending the Wilcoxon–Mann–Whitney test, we introduce a new test based on the joint use of these functionals. It results in much larger consistency regions while maintaining competitive power to the rank sum test for situations in which (Formula presented.) alone would suffice. Compared with classical omnibus tests, the simulated power is much improved. Additionally, the newly proposed inference method yields effect measures whose interpretation is surprisingly straightforward. © 2025 The Author(s). Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.

关键词： confidence region nonparametric test resampling simultaneous inference

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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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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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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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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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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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The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression: One-Armed Feasibility Study

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JMIR Formative Research 2025年 9卷 e65357页

作者： Keinert, Marie Schindler-Gmelch, Lena Rupp, Lydia Helene Sadeghi, Misha Richer, Robert Capito, Klara Eskofier, Bjoern M. Berking, Matthias Department of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum Muenchen Neuherberg Germany

Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility

关键词： cognitive reappraisal depression facial expression kinesthesia mobile phone smartphone-based intervention

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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as Intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine Learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing Machine learning Image segmentation

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