Enhancing Service Delivery through an Online Complaints System for Northern Ghana’s Tertiary Institutions by Mumuni Ali, Jacob Azaare, Wu Zhao, Gideon Mensah Engmann

“…The findings revealed that lack of education and training, and inadequate resources are the key factors hindering the implementation of online complaint system. …”
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The Opinions of Theology Faculty Undergraduates and Graduates on Interdisciplinary Learning by Saadet İder, Muhiddin Okumuşlar

“…The purpose of this study is to demonstrate the outcomes of interdisciplinary learning in higher religious education through a sample training. In the academic year 2023/2024, an interdisciplinary training was conducted on the common problems of theology and other disciplines, and the results were revealed through the views of 16 participants. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…The proposed PBNN is trained and evaluated on two geographical datasets and compared with state-of-the-art techniques. …”
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Resident assessment facilitation team: collaborative support for activated learning by Elissa Foster, Nicole Defenbaugh, Susan E. Hansen, Nyann Biery, Julie Dostal

“…Because healthcare knowledge, practices and systems change so rapidly, physicians-in-training need to develop skills related to lifelong learning. …”
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Dealing with foreign cultural paradigms: A systematic review on intercultural challenges of international medical graduates. by Kerstin Michalski, Nabeel Farhan, Edith Motschall, Werner Vach, Martin Boeker

“…The search results were synthesized and analyzed with the aid of coding systems. These coding systems were based on textual analysis and derived from the themes and topics of the results and discussion sections from the included studies. …”
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TS-Resformer: a model based on multimodal fusion for the classification of music signals by Yilin Zhang

“…Aiming at the problems of loss of time information, insufficient feature extraction, and low classification accuracy in music genre classification, firstly, we propose a Res-Transformer model that fuses the residual network with the Transformer coding layer. The model consists of two branches, the left branch is an improved residual network, which enhances the spectral feature extraction ability and network expression ability and realizes the dimensionality reduction; the right branch uses four Transformer coding layers to extract the time-series information of the Log Mel spectrum. …”
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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway

“…<h4>Methods</h4>We developed a novel machine learning algorithm, the 'Semi-supervised Set Covering Machine' (S3CM), and tested its ability to detect the presence of coronary angiogram results and ovarian cancer diagnoses in free text in the General Practice Research Database. For training the algorithm, we used texts classified as positive and negative according to their associated Read diagnostic codes, rather than by manual annotation. …”
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Improving the psychomotor performance of student nurses using remote clinical skills practice: a mixed-methods study by Ükke Karabacak, Hilal Yıldız Çelik

“…Finally, self-confidence, represented by skill practice in the laboratory, accounted for 9% of the codes. Conclusion This study demonstrates that educator-guided remote skill training can positively impact students’ psychomotor skill acquisition. …”
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A generative modeling approach to reconstructing 21 cm tomographic data by Nashwan Sabti, Ram Purandhar Reddy Sudha, Julian B Muñoz, Siddharth Mishra-Sharma, Taewook Youn

“…Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21 cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization. Code, pre-trained models, and scripts for making plots in this paper can be found https://github.com/NNSSA/Rec21 .…”
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A Comprehensive Polish Medical Speech Dataset for Enhancing Automatic Medical Dictation by Andrzej Czyżewski, Sebastian Cygert, Karolina Marciniuk, Maciej Szczodrak, Arkadiusz Harasimiuk, Piotr Odya, Marina Galanina, Piotr Szczuko, Bożena Kostek, Beata Graff, Dariusz Szplit, Mariusz Budzisz, Krzysztof Narkiewicz

“…Abstract Pre-trained models have become widely adopted for their strong zero-shot performance, often minimizing the need for task-specific data. …”
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Estimating Bolometric Luminosities of Type 1 Quasars with Self-organizing Maps by Jie Chen, Linhua Jiang, Shengxiu Sun, Zijian Zhang, Mouyuan Sun

“…We have also made our code publicly available.…”
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Enhanced mask R-CNN for luminaire detection through brightness balancing and distribution-guided optimization in tunnels by Xiaoqiong Qin, Bintao Xu, Chenchen Wang, Lirui Liu, Linfu Xie, Rong Liu, Mingwei Hu, Xiangsheng Chen

“…Leveraging tunnel gray-scale images and the Mask R-CNN object detection framework, a feature fusion network based on ResNet-FPN, trained via transfer learning, which enhances performance in detecting object luminaires. …”
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Deep learning-driven macroscopic AI segmentation model for brain tumor detection via digital pathology: Foundations for terahertz imaging-based AI diagnostics by Myeong Suk Yim, Yun Heung Kim, Hyeon Sang Bark, Seung Jae Oh, Inhee Maeng, Jin-Kyoung Shim, Jong Hee Chang, Seok-Gu Kang, Byeong Cheol Yoo, Jae Gwang Kwon, Jungsup Byun, Woon-Ha Yeo, Seung-Hwan Jung, Han-Cheol Ryu, Se Hoon Kim, Hyun Ju Choi, Young Bin Ji

“…A deep learning approach was executed through DEEP:PHI, which abstracts Python coding complexities, thereby simplifying the execution of AI training protocols for users. …”
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Polara-Keras2c: Supporting Vectorized AI Models on RISC-V Edge Devices by Nizar El Zarif, Mohammadhossein Askari Hemmat, Theo Dupuis, Jean-Pierre David, Yvon Savaria

“…Polara-Keras2c enhances compatibility with bare-metal systems, incorporates RISC-V vector extension optimization, and is customized for the Polara architecture. By converting pre-trained Keras models into optimized C code for bare-metal execution on edge devices, Polara-Keras2c enables advanced AI models to operate efficiently in resource-constrained environments. …”
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Defect Detection and Correction in OpenMP: A Static Analysis and Machine Learning-Based Solution by Norah A. Al-Johany, Fathy E. Eassa, Sanaa A. Sharaf, Eynas H. Balkhair, Sara M. Assiri

“…A rule-based correction engine is employed to repair detected defects through minimally invasive code transformations, such as inserting critical sections, correcting directive placement, and adjusting data-sharing clauses. …”
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Deep Learning-Based Metal Artifact Reduction With Masked Mean Squared Error Loss Function in Simulation CT for Radiation Therapy for Head and Neck Cancer by Juhyeong Ki, Wonjin Lee, Bitbyeol Kim, Dukju Kim, Seongmoon Jung, Jimin Lee

“…Applying the criterion function, a convolutional neural network-based metal artifact reduction model was trained on a synthetic dataset. We adopted a new data synthesis method to prevent tissue information loss by sinogram handling. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. …”
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Impediments to Physicians’ Workplace Contentment and Wellness: A Multifaceted Qualitative Inquiry by Saman Javed, Noshaba Batool, Nida Kamal, Bahaudin G. Mujtaba

“…The interview transcriptions were analyzed and categorized into codes and themes using qualitative data analysis (QDA miner) software. …”
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Information and cognitive technologies in the management of the quality of life through investments in the AIC: formal statement of the problem by T. P. Baranovskaya, E. V. Lutsenko

“…At the same time, the object and purpose of management and controlling factors will be determined, reference books of the future states of an object of modeling and factors affecting it will be developed, the source data will be coded and a training sample will be formed.…”
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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. …”
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