AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis by Bhekisipho Twala

“…Additionally, we developed and tested a hybrid machine learning model combining deep learning, computer vision, and natural language processing techniques for PD assessment using motor symptom analysis, voice pattern recognition, and gait analysis.ResultsThe systematic review identified 127 relevant studies demonstrating significant advances in AI-driven PD diagnosis, with accuracy rates ranging from 78 to 96%. …”
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Development of adaptive and emotionally intelligent educational assistants based on conversational AI by Rommel Gutierrez, William Villegas-Ch, Jaime Govea

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Advancing Monoclonal Antibody Manufacturing: Process Optimization, Cost Reduction Strategies, and Emerging Technologies by Ranbhor R

“…Although scaling issues remain, the combination of process innovation and computer technologies points to a revolutionary future for mAb production.Keywords: biosimilar, biomanufacturing, process optimization, continuous processing, cost reduction, machine learning, advanced analytics…”
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A Novel Spectral-Spatial Attention Network for Zero-Shot Pansharpening by Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Sergio Moreno-Alvarez, Guangsheng Chen, Weipeng Jing

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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects by Awal Ahmed Fime, Saifuddin Mahmud, Arpita Das, Md. Sunzidul Islam, Jong-Hoon Kim

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A new method for measuring the effectiveness of teacher evaluation instruments in improving pedagogical performance in higher education based on the neutrosophic 2-tuple linguistic... by Judith Ernestina Vera-León, Tatiana Daniela Ríos-López, Noemí Fernanda Caizaluisa-Barros, Andrea Fernanda Pazmiño-Arcos, Jessica Carolina Analuisa-Maiguashca

“…Assessment in a teaching-learning process allows teachers to determine to what degree students are assimilating the content and are meeting the objectives of the study program. …”
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Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence by Asma Aldrees, Stephen Ojo, James Wanliss, Muhammad Umer, Muhammad Attique Khan, Bayan Alabdullah, Shtwai Alsubai, Nisreen Innab

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Customers' sentiment on food delivery services: An Arabic text mining approach by Dheya Mustafa, Safaa M. Khabour, Ahmed S. Shatnawi

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A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support by Alexandre Hudon, Alexandre Hudon, Alexandre Hudon, Alexandre Hudon

“…Feature importance was also computed to assess the influence of each variable on the prediction outcome.ResultsThe hybrid model achieved an accuracy of 98.1%, precision of 93.3%, recall of 100%, and a F1 score of 96.6. …”
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Predicting Workplace Hazard, Stress and Burnout Among Public Health Inspectors: An AI-Driven Analysis in the Context of Climate Change by Ioannis Adamopoulos, Antonios Valamontes, Panagiotis Tsirkas, George Dounias

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Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions by Aasim Ayaz Wani

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Review of the 2021 ChinaCALL conference by Zhang Shuai, Wang Qi

“…The topics range from the traditional ones of teaching, learning and assessment to the emerging fields of intelligent computer-assisted language learning (iCALL), conversational agents and language development, the reconceptualisation of teacher digital literacy, reflections upon perfunctory gamification in language learning and particularly pedagogy in the post-pandemic era. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Funding This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/148/05. Plain language summary Heart disease, including heart attacks and strokes, is a major health problem worldwide. …”
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Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study by David RJ Snead, Ayesha S Azam, Jenny Thirlwall, Peter Kimani, Louise Hiller, Adam Bickers, Clinton Boyd, David Boyle, David Clark, Ian Ellis, Kishore Gopalakrishnan, Mohammad Ilyas, Paul Kelly, Maurice Loughrey, Desley Neil, Emad Rakha, Ian SD Roberts, Shatrughan Sah, Maria Soares, YeeWah Tsang, Manuel Salto-Tellez, Helen Higgins, Donna Howe, Abigail Takyi, Yan Chen, Agnieszka Ignatowicz, Jason Madan, Henry Nwankwo, George Partridge, Janet Dunn

“…Background Digital pathology refers to the conversion of histopathology slides to digital image files for examination on computer workstations as opposed to conventional microscopes. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/68/09) and is published in full in Health Technology Assessment; Vol. 29, No. 31. …”
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Exploring the potential of LLM to enhance teaching plans through teaching simulation by Bihao Hu, Jiayi Zhu, Yiying Pei, Xiaoqing Gu

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The OpenLAM Challenges: LAM Crystal Philately competition by Anyang Peng, Xinzijian Liu, Ming-Yu Guo, Linfeng Zhang, Han Wang

“…Inspired by the success of large language models, the development of large atomistic models (LAMs) has rapidly gained momentum in scientific computation. …”
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Benchmarking uncertainty quantification for protein engineering. by Kevin P Greenman, Ava P Amini, Kevin K Yang

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Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

“…By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. …”
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Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews by Heti Aprilianti, Khothibul Umam, Maya Rini Handayani

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