Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…Advances in machine learning (ML) and computing power, data analysis, and model generation using ML techniques, such as neural networks, enable predictive modeling for GET. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The subtle changes in hearing often go unnoticed, presenting a challenge for early hearing loss detection. …”
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Active Learning Versi Rasulullah dan Implementasinya dalam Pembelajaran PAI by Uyung Yuliza, Sunhaji Sunhaji, Wahab Wahab

“…Starting from this thought, this paper intends to provide input to the Islamic education environment by seeking to explore and present the learning methods used by the Prophet in his da'wah. …”
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Enhancing aspect-based financial sentiment analysis through contrastive learning by Viacheslav Ivanenko

“…Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3,  contrast learning, and LoRa fine-tuning. …”
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Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. …”
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Highly efficient stacking ensemble learning model for automated keratoconus screening by Zahra J. Muhsin, Rami Qahwaji, Ibrahim Ghafir, Mo’ath AlShawabkeh, Muawyah Al Bdour, Saif Aldeen AlRyalat, Majid Al-Taee

“…This paper presents a stacking ensemble learning method to enhance automated KC screening. …”
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Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations by Farida Siddiqi Prity, MD. Mehadi Hasan, Shakhawat Hossain Saif, Md. Maruf Hossain, Sazzad Hossain Bhuiyan, Md. Ariful Islam, Md Tousif Hasan Lavlu

“…Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. …”
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Detecting the left atrial appendage in CT localizers using deep learning by Aydin Demircioğlu, Denise Bos, Anton S. Quinsten, Lale Umutlu, Oliver Bruder, Michael Forsting, Kai Nassenstein

“…Abstract Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the LAA. To guide the imaging process, technologists first perform a localizer scan, which is a preliminary image used to identify the region of interest. …”
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Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications by Damjan Strnad, Stefan Kohek, Borut Zalik, Libor Vasa, Andrej Nerat

“…This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. …”
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INTERACTIVE WEB 2.0 TOOLS IN CONTENT AND LANGUAGE INTEGRATED LEARNING by Rinata R. Zaripova, Leila L. Salekhova, Andrew V. Danilov

“…The aim of present research is to reveal of the impact of using interactive Web 2.0 tools by Tatar-speaking students in the process of Content and Language Integrated Learning (CLIL) implementation while studying ICT. …”
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Paediatric oncology short learning programmes for nurses: a scoping review by Apatsa Selemani, Maureen Daisy Majamanda, Felix Chisoni, Irene Kearns, Johanna Maree

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Enhancing aspect-based financial sentiment analysis through contrastive learning by В’ячеслав Іваненко

“…Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3,  contrast learning, and LoRa fine-tuning. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The purpose of this paper is to present a machine-learning model for early detection of sexually transmitted infections that was developed. …”
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Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy by Xiuxiu Hu, Jinyue Yang, Yiping Li, Yuxiang Gong, Haifeng Ni, Qing Wei, Minyu Yang, Yu Zhang, Jing Huang, Cao Ma, Bizhen Wei, Kaijie Yu, Jiayun Xu, Siyu Xia, Taotao Tang, Pingsheng Chen

“…This study aims to develop a multimodal pathological diagnosis system to assist pathologists in diagnosing MN in morphology.Methods Using PASM-stained, immunofluorescence, and electron microscopy images from MN patients, we built three deep-learning models to detect lesions. The outputs of these models were combined to provide a comprehensive pathological diagnosis. …”
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Multi-Camera Machine Learning for Salt Marsh Species Classification and Mapping by Marco Moreno, Sagar Dalai, Grace Cott, Ben Bartlett, Matheus Santos, Tom Dorian, James Riordan, Chris McGonigle, Fabio Sacchetti, Gerard Dooly

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Technical Strategies and Learning Curve in Robotic-assisted Peripheral Nerve Surgery by Martin Aman, MD, PhD, Felix Struebing, MD, Jonathan Weigel, Amir K. Bigdeli, MD, Emre Gazyakan, MD, MSc, Ulrich Kneser, MD, Leila Harhaus, MD, Arne H. Boecker, MD

“…Conclusions:. The learning curve for RASPN was compared with early experiences with other surgical robots, emphasizing the importance of surgical proficiency and assistant training. …”
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Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

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SegRep: Mask-Supervised Learning for Segment Representation in Pathology Images by Chichun Yang, Daisuke Komura, Mieko Ochi, Miwako Kakiuchi, Hiroto Katoh, Tetsuo Ushiku, Shumpei Ishikawa

“…To address this limitation, we introduce a novel concept in pathology image analysis, namely segment representation learning, and present an algorithm, SegRep, for this purpose. …”
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Aprender Historia en ambientes virtuales / History learning in virtual environments by Manuel Moreno Castañeda

“…A propósito de lo histórico, habría que preguntarnos si antes de la aparición de todos estos aparatos no habría otros distractores para los estudiantes, que hicieran más atractiva la vida fuera de la escuela que dentro de ésta.Summary: This essay is based on the expansion and deepening of an earlier article entitled “Innovate to diversify and promote historical learning through virtual environments”, presented in academic events at the University of Guadalajara, the Autonomous University of Tamaulipas and the University of Extremadura in Spain. …”
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Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting by Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan

“…To address this issue, we design an Intensity Trend Module (ITM) to explicitly learn and model the intensity features of extreme precipitation events. …”
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