Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning by Najva Hassan, Abdul Saleem P.K

“…The SDAE is a neural network topology based on deep learning that can be used to determine the optimum covariance matrix. …”
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Supporting contraceptive self-care and reproductive empowerment with a digital health game in Barbados: Development and Pre-implementation study for What’s My Method? [version 2; p... by Clara Bertozzi-Villa, Nicole Alleyne, Erin Sabato, Anderson Langdon, Elena Bertozzi, Tiffany Jordan, Sonia Watson-Miller

“…Effective contraceptive education is essential to reducing unwanted pregnancy, increasing uptake of modern contraceptive methods, and thoughtfully planning desired births. …”
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Diagnostic Model for Transformer Core Loosening Faults Based on the Gram Angle Field and Multi-Head Attention Mechanism by Junyu Chen, Nana Duan, Xikun Zhou, Ziyu Wang

“…This method automatically learns effective fault features directly from GAF images without the need for manual feature extraction. …”
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Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation by SUN Xiujuan, SUN Fuzhen, LI Pengcheng, WANG Aofei, WANG Shaoqing

“…In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks, a model called GATSR, which is based on graph attention networks for sequential recommendation, is proposed. …”
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I-fp Convergence in Fuzzy Paranormed Spaces and Its Application to Robust Base-Stock Policies with Triangular Fuzzy Demand by Muhammed Recai Türkmen, Hasan Öğünmez

“…The policy is updated by a simple learning rule that can be implemented in any spreadsheet, requires no optimisation software, and remains insensitive to tuning choices. …”
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Cold Front Identification Using the DETR Model with Satellite Cloud Imagery by Yujing Qin, Qian Liu, Chuhan Lu

“…The cloud system characteristics within satellite cloud imagery play a crucial role in the meteorological operational analysis of cold fronts, and integrating satellite cloud imagery into automated frontal identification schemes can provide valuable insights for accurately determining the position and morphology of cold fronts. This study introduces Cloud-DETR, a deep learning identification method that uses the DETR model with satellite cloud imagery, to identify cold fronts from extensive datasets. …”
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A Small Object Detection Algorithm Integrated with ReFPN and Compound Attention Mechanism by ZHAO Yi-ming, WANG Jin-cong, REN Hong-e, ZHAO Long

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Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…The purpose of the present study was to develop an effective ML model for predicting ICU admission and ICU LOS for COVID-19 patients using a more comprehensive dataset including imaging findings. Methods: A COVID-19 hospital-based registry database that contained medical records of 6,854 patients was retrospectively reviewed. …”
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Educational Effectiveness of Using Big Data Based and Its Evaluation with Cluster Analysis and Qualification Framework in Financial Services and Management by Yujie Jiao, Ruiting Zhang, Ying Zhu

“…Using K-prototype clustering analysis and TF-IDF word frequency methods, the differences in different evaluations of job positions and vocational skill requirements of college graduates were analyzed. …”
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Exploring the use of retrieval-augmented generation models in higher education: A pilot study on artificial intelligence-based tutoring by Renáta Németh, Annamária Tátrai, Miklós Szabó, Péter Tibor Zaletnyik, Árpád Tamási

“…Four courses from three programs with different learning objectives and pedagogical methods were involved to provide as diverse a context as possible. …”
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A Two-Stage Strategy Integrating Gaussian Processes and TD3 for Leader–Follower Coordination in Multi-Agent Systems by Xicheng Zhang, Bingchun Jiang, Fuqin Deng, Min Zhao

“…Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. …”
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MixDiff-TTS: Mixture Alignment and Diffusion Model for Text-to-Speech by Yongqiu Long, Kai Yang, Yuan Ma, Ying Yang

“…In recent years, deep-learning-based speech synthesis has garnered substantial attention, achieving remarkable advancements in generating human-like speech. …”
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AI-Driven UAV Surveillance for Agricultural Fire Safety by Akmalbek Abdusalomov, Sabina Umirzakova, Komil Tashev, Nodir Egamberdiev, Guzalxon Belalova, Azizjon Meliboev, Ibragim Atadjanov, Zavqiddin Temirov, Young Im Cho

“…Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in mitigating fire-related damages. …”
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A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters by Murat Canayaz, Murat Demir, Zeynal Topalcengiz

“…The meta-heuristic optimization algorithms for the feature selection process followed by machine learning classification methods yielded a prediction accuracy between 93.57 and 95.55%. …”
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SIC based RL for massive MIMO NOMA signal detection for different modulation schemes under diverse channel conditions by Arun Kumar, Aziz Nanthaamornphong, Mohammed H. Alsharif, Mehedi Masud

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DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection by Yunlong Qin, Yanjun Li, Feifan Ji, Yan Liu, Yu Wang, Ji Xiang

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Simulation-Based Trauma-Informed Care Education Instills Empathy and Improves Clinician Practices Towards Refugee and Migrant Populations by Medha Palnati, Ashley E. Martinez, Aliyah Audil, Emily Tovar, Paul Macfarlane, Megan Gerber, Katherine Wagner

“…While measuring the impact on learners' empathy requires further assessment, this educational innovation's preliminary success provides a foundation for the role of simulation-based learning in medical education.…”
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Comparison and Evaluation of the Effectiveness of Traditional Neuroanatomy Teaching in Medical Education with Virtual-Reality Application Based On 3D Virtual by Ece Alim, Özlem Coşkun, Tuncay Veysel Peker

“…Conclusion: Our study contributes to the literature by demonstrating the positive long-term effects of VR-based neuroanatomy training on memory.…”
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A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images by Hassaan Haider Syed, Muhammad Attique Khan, Usman Tariq, Ammar Armghan, Fayadh Alenezi, Junaid Ali Khan, Seungmin Rho, Seifedine Kadry, Venkatesan Rajinikanth

“…In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. …”
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Can Peritumoral Radiomics Based on MRI Predict the Microvascular Invasion Status of Combined Hepatocellular Carcinoma and Cholangiocarcinoma Before Surgery? by Guo L, Huang C, Hao P, Jia N, Zhang L

“…Clinical models and radiomics models were constructed based on six classifiers. The model’s effectiveness was comprehensively evaluated using receiver operating characteristic (ROC), area under curve (AUC), and decision curve analysis (DCA), and the model results were output using Shapley Additive exPlans (SHAP).Results: The differences in HBeAg, capsule, target sign, and lymph node metastasis between MVI negative and positive groups were statistically significant (p < 0.05). …”
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