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

“…Future work will refine the prediction accuracy and system utility by integrating different data sources and advanced machine-learning models. …”
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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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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwor... by Satrio Junaidi, Rani Valicia Anggela, Delsi Kariman

“…So, to deal with this problem, data mining classification is carried out to predict student graduation on time to find patterns for student on-time graduation predictions. …”
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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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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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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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Accurate Detection of scutellata‐Hybrids (Africanized Bees) Using a SNP‐Based Diagnostic Assay by Kathleen A. Dogantzis, Harshilkumar Patel, Stephen Rose, Ida M. Conflitti, Alivia Dey, Tanushree Tiwari, Nadine C. Chapman, Samir M. Kadri, Harland M. Patch, Elliud M. Muli, Abdulaziz S. Alqarni, Michael H. Allsopp, Amro Zayed

“…Here, we used an extensive population genomic dataset to assess the genomic composition of Apis mellifera native populations and patterns of genetic admixture in North and South American commercial honey bees. …”
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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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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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Intrinsic factors influence a physiological measure in a forest bird community: adults and females have higher H/L ratios than juveniles and males by Finja Strehmann, Markus Vogelbacher, Clara Guckenbiehl, Yvonne R. Schumm, Juan F. Masello, Petra Quillfeldt, Nikolaus Korfhage, Hicham Bellafkir, Markus Mühling, Bernd Freisleben, Nina Farwig, Dana G. Schabo, Sascha Rösner

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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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Personalized learning path planning for higher education based on deep generative models and quantum machine learning: a multimodal learning analysis method integrating transformer... by Changzhi Sun, Shijie Huang, Banghui Sun, Shiwei Chu

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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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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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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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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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