Classifying social and physical pain from multimodal physiological signals using machine learning by Eun-Hye Jang, Young-Ji Eum, Daesub Yoon, Sangwon Byun

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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. …”
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Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques by Muyiwa Lawrence Adedara, Ridwan Taiwo, Olusola Olaitan Ayeleru, Hans-Rudolf Bork

“…The findings reveal a low RDR of 0.37%, indicating that only approximately 2.47% (31,554.25 metric tonnes) of recyclables are recovered annually compared to a targeted 50% (638,750 metric tonnes). …”
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Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning by Li-Wei Zhang, Ji Yang, Hua-Wei Jiang, Hua-Wei Jiang, Xiu-Qiang Yang, Ya-Nan Chen, Wei-Dang Ying, Ying-Liang Deng, Min-hui Zhang, Hai Liu, Hong-Lei Zhang

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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

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An Efficient Analytical Method for Determining the Effects of Silver Nanoparticles on Vibrio parahaemolyticus in Estuarine Water by Xiangyi HOU, Xiaoyang WANG, Yuanyuan ZHANG, Ruohan LIANG, Feng LU, Qianqian YANG, Xiaodan PU, Yan ZHANG, Keming QU, Xuzhi ZHANG

“…Based on a 32-channel C4 detector and special algorithms, we developed a 32-channel electronic microbial growth analyzer (EMGA). …”
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A Shadow Detection Method Combining Topography and Spectra for Remote Sensing Images in Mountainous Environments by Huagui Xu, Jingxing Zhu, Feng Wang, Hongjian You, Wenzhi Wang

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Approximate Techniques in Solving Optimal Camera Placement Problems by Jian Zhao, Ruriko Yoshida, Sen-ching Samson Cheung, David Haws

“…Thus, we develop a comprehensive framework in comparing the merits of a wide variety of approximate algorithms in solving the optimal camera placement problems. …”
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Automatic identification of Dong Son antique glass artifacts using evolving learning by Anh-Khoi Ngo-Ho, Hoang-Bac Bui, Van-Trieu Pham

“…This approach, which has gained prominence in the field of archaeology worldwide over the past five years, aims to integrate advanced technology into Vietnamese archaeology. Our research is motivated by the unique conditions present in archaeology, where we seek to employ evolving learning algorithms to archaeological databases, comparing and selecting the most suitable model that aligns with the archeological dataset's performance. …”
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Assessment of structural changes in the left atrium myocardium according to cardiac magnetic resonance with contrast before and after balloon cryoablation in patients with persiste... by O. V. Stukalova, V. S. Kirilova, O. P. Aparina, E. B. Maykov

“…To study the structural changes of the left atrium myocardium according to magnetic resonance imaging with contrast in patients with persistent atrial fibrillation before and after balloon cryoablation and compare the results with the effectiveness of the intervention.Material and methods of research. …”
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Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data by Giambattista Albora, Andrea Zaccaria

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Machine learning in Alzheimer’s disease genetics by Matthew Bracher-Smith, Federico Melograna, Brittany Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Alejo J. Nevado, Peter Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Sven van der Lee, Atahualpa Castillo, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Nicolai Sandau, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John van Swieten, Inez Ramakers, Frans Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Gael Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Marco Spallazzi, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Carlo Masullo, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sánchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Agustín Ruiz, Alfredo Ramirez, EADB, Ruth Frikke-Schmidt, Najaf Amin, Gennady Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price

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Predictive value of subacromial motion metrics for the effectiveness of ultrasound-guided dual-target injection: a longitudinal follow-up cohort trial by Wei-Ting Wu, Che-Yu Lin, Yi-Chung Shu, Lan-Rong Chen, Levent Özçakar, Ke-Vin Chang

“…Future research should explore the predictive value of mVAHD with deep learning algorithms and evaluate the approach in adhesive capsulitis. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

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Artificial Intelligence‐Based Approaches for AAV Vector Engineering by Fangzhi Tan, Yue Dong, Jieyu Qi, Wenwu Yu, Renjie Chai

“…The advancement of artificial intelligence (AI), particularly machine learning, offers significant potential to accelerate capsid optimization while reducing development time and manufacturing costs. This review compares traditional and AI‐based methods of AAV vector engineering and highlights recent research in AAV engineering using AI algorithms.…”
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Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities by Alessandra Papetti, Marianna Ciccarelli, Andrea Manni, Andrea Caroppo, Gabriele Rescio

“…Although less extensively studied, EOG shows significant promise for comparable applications. Furthermore, the realm of human factors and ergonomics lacks sufficient research on the integration of wearable sensors, particularly in the evaluation of human work. …”
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A novel feature selection technique: Detection and classification of Android malware by Sandeep Sharma, Prachi, Rita Chhikara, Kavita Khanna

“…Experimental results using machine learning algorithms demonstrate that the technique proposed in this research effectively integrates the advantages of individual feature selection techniques and exhibits the potential to identify a brief set of pivotal features for detecting Android malware. …”
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Differing Effects of Alcohol Use on Epigenetic and Brain Age in Adult Children of Parents with Alcohol Use Disorder by Jamie L. Scholl, Kami Pearson, Kelene A. Fercho, Austin J. Van Asselt, Noah A. Kallsen, Erik. A. Ehli, Kari N. Potter, Kathleen A. Brown-Rice, Gina L. Forster, Lee A. Baugh

“…Conclusions: The results demonstrate a decreased brain age in the ACoAs compared to control, concurrent with increased epigenetic age specifically in the hazardous ACoA group, laying the foundation for future research to identify individuals with an increased susceptibility to developing hazardous alcohol use. …”
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Association of marital status with DNA methylation-based biological age among US older adults by Xian Cui, Jianghua Huo, Yaqian Xu, Hui Zhang, Chongyu Ding, Jun Du, Xiangwei Li

“…BackgroundDNA methylation (DNAm) algorithms, such as GrimAge and GrimAge2, have emerged as reliable measures of biological age acceleration and may offer additional insights into health outcomes. …”
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Psychometry of Latent Characteristics: Tools, Problems of Rank Information Processing, Solutions by V.G, Mitikhin

“…The algorithms of the AHP normative approach are presented, which allow obtaining numerical, personalized assessments of psychosocial characteristics. …”
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