Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…This paper addresses the problem of accurate energy forecasting by proposing an intelligent hybrid model that integrates advanced feature selection, signal decomposition, and deep learning techniques. Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
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Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model by Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Hai-Ha Pham, Thi-Trang Le, Tuan-Minh Nguyen, Davide Cassi, Francesco Scotognella, Roberto Alfieri, Michele Bellingeri

“…., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. …”
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2LE-BO-DeepTrade: an integrated deep learning framework for stock price prediction by Zinnet Duygu Akşehir, Erdal Kılıç

“…This study presents a novel, integrated deep-learning framework named 2LE-BO-DeepTrade for stock closing price prediction. …”
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning. by Yinghao Ren, Weiqiang Chen, Yuhao Lin, Zeyu Wang, Weiliang Wang

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Our Experiences and Learnings in Diagnosing MODY from Non-Institutional-Based Diabetes Care Clinics by Arunkumar R. Pande, Santosh Chaubey, Dinesh Kumar, Kumar P. Chandra, Thenral Geetha, Akshita Sharma

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Integrative bioinformatics and deep learning to identify common genetic pathways in Crohn’s disease and ischemic cardiomyopathy by Reza Maddah, Zahra Sadat Aghili, Fahimeh Ghanbari, Amirhossein Hajialiasgary Najafabadi, Sedigheh Asgary

“…Through differential expression analysis, we identified 60 common differentially expressed genes (CDEGs). Functional enrichment analysis revealed enrichment in inflammatory pathways, including NF-κB and TNF-α signaling, highlighting their role in disease pathogenesis. …”
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Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning by Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat

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A Multimodal Deep Learning Model Integrating CNN and Transformer for Predicting Chemotherapy-Induced Cardiotoxicity by Ahmed Bouatmane, Abdelaziz Daaif, Abdelmajid Bousselham, Bouchra Bouihi, Omar Bouattane

“…Our model combines clinical data (e.g., age and cardiovascular metrics) with Tissue Doppler Imaging (TDI), a functional imaging technique that captures myocardial velocity during the cardiac cycle. …”
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Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning by L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas

“…We estimate a total of six plant‐functional‐type‐specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. …”
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Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification by Lei Wang, Lei Wang, Xueying Jin, Yuchen Wu, Runing Qiu, Jianfang Wang

“…A clinically applicable nomogram integrating risk scores and clinical factors demonstrated robust predictive accuracy (AUC 0.829–0.845). Machine learning refined a 4-gene prognostic signature (CLN6, GMPR, AP1S2, ITGA6), with functional validation confirming the role of CLN6 in proliferation and migration.ConclusionThis study establishes a prognostic framework and therapeutic roadmap for precision immuno-oncology in melanoma, bridging multi-omics discovery with clinical translation.…”
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Task difficulty modulates motor learning benefits of balance exercises in community-dwelling older adults by Kazunori Akizuki, Kosuke Takeuchi, Kazuto Yamaguchi, Ryohei Yamamoto, Wataru Nakano, Jun Yabuki

“…Statistical analyses included analysis of variance and regression analyses to examine the impact of task difficulty on motor learning and the relationship between motor learning benefits and task difficulty. …”
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Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO by Yunxiang Chen, Jie Bao, Rongyao Chen, Bing Li, Yuan Yang, Lupita Renteria, Dillman Delgado, Brieanne Forbes, Amy E. Goldman, Manasi Simhan, Morgan E. Barnes, Maggi Laan, Sophia McKever, Z. Jason Hou, Xingyuan Chen, Timothy Scheibe, James Stegen

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Plant-based protein extrusion optimization: Comparison between machine learning and conventional experimental design by Yingfen Jiang, Noor Irsyad Bin Noor Azlee, Wing Shan Ko, Kaiqi Chen, Bee Gim Lim, Arif Z. Nelson

“…In contrast, Bayesian Optimization (BO), a machine learning technique, uses probabilistic surrogate models to efficiently explore parameter spaces and optimize black-box functions with fewer experiments. …”
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

“…The BN’s unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

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Problems of using phenol (hydroxybenzene) and parabens as pharmaceutical stabilizers: analysis using machine learning methods by I. Yu. Torshin, O. A. Gromova

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Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

“…Univariate COX analysis and 101 combinations of 10 machine learning algorithms were used to construct EIG prognostic signature (EIGPS), and survival analyses were performed. …”
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Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification by Eunji Lee, Eunji Lee, Ji-Hyun Kim, Jaeseok Park, Jaeseok Park, Sung-Phil Kim, Taehoon Shin, Taehoon Shin, Taehoon Shin

“…This study aimed to identify brain regions involved in the Aristotle illusion using functional magnetic resonance imaging (fMRI) and deep learning-based analysis of fMRI data.MethodsWhile three types of tactile stimuli (Aristotle, Reverse, Asynchronous) were applied to thirty participants’ fingers, we collected fMRI data, and recorded the number of stimuli each participant perceived. …”
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Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound by Tahani M. Ahmad, MD, ABR, Alessandro Guida, PhD, Sam Stewart, PhD, Noah Barrett, MSc, Michael J. Vincer, MD, Jehier K. Afifi, MD, MSc

“…Early and accurate identification of infants at risk for NDI enables referral to targeted interventions, which improves functional outcomes.…”
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Understanding continued use of smart learning platforms: psychological wellbeing in an extended TAM-ISCM model by Jinlei Li, Meilin Jin, Xiaowei Chen, Xiaowei Chen

“…Platforms that overlook psychological dimensions risk diminishing user satisfaction and long-term retention.ConclusionSustaining engagement with innovative learning platforms in higher vocational education requires a holistic approach that balances functional usability with mental wellbeing. …”
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