Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…To accomplish this goal, we have proposed an integrated system which encompasses the use of machine learning (ML) algorithms. This hybrid model is aimed at linking various activities to VAK model of learning and hence place students in their various class learning preferences derived from their activities and the patterns created during the learning processes. …”
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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry by Valeria Di Stefano, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, Luca Steardo

“…AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. …”
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Explainable AI for Spectral Analysis of Electromagnetic Fields by Dimitris Kalatzis, Agapi Ploussi, Ellas Spyratou, Theodor Panagiotakopoulos, Efstathios P. Efstathopoulos, Yiannis Kiouvrekis

“…The integration of SHAP enabled transparent feature attribution, revealing distinctive exposure patterns linked to urban topology, population density, and built environment metrics. …”
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Prediction of Rice Yields in a Changing Climate Using the Mobile Rice Yield Prediction Application by Mohamad Farhan Mohamad Mohsin, Mohamad Ghozali Hassan, Kamal Imran Mohd Sharif, Mohd Azril Ismail, Liew Jun Hong, Muhammad Khalifa Umana, Hairazi Rahim@Abdul Rahim

“… In Malaysia, rice production is crucial for national food security but faces significant challenges due to unpredictable climate patterns and climate change. Traditional prediction methods often struggle to capture the complex interaction between climatic and agricultural factors that cause a critical gap in accurately forecasting rice yields. …”
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Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma by Hugo Swenson, Ella Ittner, Lucas Werner, Elisabeth Werner Rönnerman, Claudia Mateoiu, Anikó Kovács, Pernilla Dahm-Kähler, Ghassan M. Saed, Szilárd Nemes, Per Karlsson, Toshima Z. Parris, Khalil Helou

“…Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers. …”
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Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication by Nicholas Brian Shannon, Narayanan Gopalakrishna lyer, Melvin Lee Kiang Chua

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EsoDetect: computational validation and algorithm development of a novel diagnostic and prognostic tool for dysplasia in Barrett’s esophagus by Migla Miskinyte, Benilde Pondeca, José B. Pereira-Leal, Joana Cardoso

“…Ultimately, a linear support vector machine algorithm incorporating ten genes was identified for diagnostic application, while a radial basis function support vector machine algorithm, also utilizing ten genes, was selected for prognostic prediction. …”
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Assessment of Large‐Scale Reservoirs' Impact on the Local Precipitation by Han Zhou, Jun Qiu, Mengjia Li, Houliang Lu, Fangfang Li

“…Further comparisons suggest that reservoir operation shifts the key influencing factors of local precipitation patterns from those characterized by high variability to those characterized by low variability. …”
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Optimizing Air Pollution Forecasting Across Temporal Scales: A Case Study in Salamanca, Mexico by Francisco-Javier Moreno-Vazquez, Felipe Trujillo-Romero, Amanda Enriqueta Violante Gavira

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Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC by Chao Zhang, Yisu Song, Xiaobo Cui, Yina Wang, Jiang Liu, Zhouji Shen

“…The distinct activated transcription factor patterns were uncovered as well as the essential ligand-receptor pairs in the interactions among different cell subtypes, such as CXCL12-CXCR4 and COL6A2-SDC4. …”
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The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature by Stefania Isola, Giuseppe Murdaca, Silvia Brunetto, Emanuela Zumbo, Alessandro Tonacci, Sebastiano Gangemi

“…Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. …”
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Obstacle Avoidance in Healthy Adults and People With Multiple Sclerosis: Preliminary fNIRS Study by Fares Al-Shargie, Michael Glassen, John DeLuca, Soha Saleh

“…The findings revealed distinct patterns of cortical activation and connectivity for each task condition in both groups. …”
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Long-Range Wide Area Network Intrusion Detection at the Edge by Gonçalo Esteves, Filipe Fidalgo, Nuno Cruz, José Simão

“…Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. …”
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Advances in gait research related to Alzheimer’s disease by Shuding Yan, Xiaoping Yun, Qiang Liu, Zhenmei Hong, Yufan Chen, Shuijing Zhang

“…It delineates the pertinent concepts of gait analysis and machine learning in AD and elucidates the intricate interplay between gait patterns and cognitive status.MethodsA comprehensive literature search was conducted within PubMed for all articles published until march 18, 2024, using a set of keywords, including “machine learning and gait “and “gait and Alzheimer.” original articles that met the selection criteria were included.Results and significanceA strong correlation exists between autonomous gait and cognitive attributes, necessitating further investigation into the selective interplay between gait and mental factors. …”
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Classification of Paddy Rice Planting Area Through Feature Selection Method Using Sentinel-1/2 Time Series Images by Shiyu Zhang, Pengao Li, Yong Xie, Wen Shao, Xueru Tian

“…The optimal feature subset was selected through the feature selection method, and machine learning algorithms were optimized for paddy rice planting area classification. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

“…The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. …”
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Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas by Wenshu Li, Zixiang Zhu, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang, Lingchuan Guo

“…For the TCGA samples, utilizing advanced machine learning algorithms, this study identified distinct patterns of bacterial infiltration within the LGG population and constructed a prognostically relevant intra-tumoral bacteria risk model (PRIBR Index). …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. …”
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Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits by F. Shokor, P. Croiseau, H. Gangloff, R. Saintilan, T. Tribout, T. Mary-Huard, B.C.D. Cuyabano

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Multilayer analysis of ethnically diverse blood and urine biomarkers for breast cancer risk and prognosis by Jia Feng, Xing Qi, Chen Chen, Baolin Li, Min Wang, Xuelong Xie, Kailan Yang, Xuan Liu, Rui min Chen, Tongtong Guo, Jinbo Liu

“…By enhancing the diagnostic power of a tailored biomarker panel through machine learning, this study contributes to the advancement of precision medicine in BC, offering strategies tailored to the unique genetic profiles and biomarker patterns across diverse populations.…”
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