Brain functional connectivity analysis of fMRI-based Alzheimer's disease data by Maitha S. Alarjani, Badar A. Almarri

“…The core of this framework discovers and analyzes functional connectivity among regions of interest (ROIs) of a human brain. Multivariate Pattern Analysis (MVPA) is applied to extract features that reveal complex functional connectivity patterns in the brain. …”
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Web-Based Google Translate Inconsistencies in Bahasa-Arabic Translations from the ‎Arabic Thesis Writer's Perspective by Rahmat Satria Dinata, Ahmad Nur Mizan, Meliza Budiarti, Ilya Husna

“…This research aims to reveal the inconsistencies of machine translation from the perspective of students who use web-based Google Translate in their Arabic thesis. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…We evaluate SECONDGRAM on the UK Biobank dataset and show that it not only models MRI patterns better than existing baselines but also enhances training datasets to achieve better downstream results over naive approaches. …”
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Turning local anisotropy for macroscopic auxeticity: Design auxetic meta-laminae via systematic finite element simulations and machine learning approach by Shujing Dong, Ammar Batwa, Yaning Li

“…This investigation delves into patch pattern-property relationships, machine learning methods, and inverse design approaches tailored for specific properties. …”
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Evaluating climatic variability's impact on milk yield across climate zones: A machine learning-based comparative study of Switzerland and Thailand by Boonyarat Phadermrod, Varunya Attasena

“…Across all scenarios, previous milk yield is a stronger predictor than short-term meteorological variables, suggesting that recent production trends already reflect key weather effects. This pattern also holds within homogeneous sub-datasets. …”
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Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. …”
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Real‐time object detection for unmanned vehicles in Bangladesh: Dataset, implementation and evaluation by Muhammad Liakat Ali, Topu Biswas, Shahin Akter, Mohammed Farhan Jawad, Hadaate Ullah

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Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach by Daichi Sone, Noriko Sato, Yoko Shigemoto, Iman Beheshti, Yukio Kimura, Hiroshi Matsuda

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Geospatial analysis of short sleep duration and cognitive disability in US adults: a multi-state study using machine learning techniques by Tue T. Te, Alex A. T. Bui, Constance H. Fung, Mary Regina Boland

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Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach by P. Ravindra, M. Sankar, Suresh Rasappan, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, S. Sanal Kumar

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An assessment of the long-term change of the Mersin west coastline using digital shoreline analysis system and detection of pattern similarity using fuzzy C-means clustering by Ozcan Zorlu, Lutfiye Kusak

“…To identify spatial patterns in shoreline change, the Fuzzy C-Means (FCM) clustering algorithm was applied using the NSM, SCE, EPR, and LRR metrics. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

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ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target by Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

“…Methods We integrated large-scale datasets from TCGA and GEO databases to identify core modules by weighted gene co-expression network analysis (WGCNA), while mutation profiling and survival analysis verified clinical relevance. Multiple machine learning techniques, including GBM (gradient boosting machine), XGBoost (extreme gradient boosting machine), SVM (support vector machine), LASSO (least absolute shrinkage and selection operator) and random forest, as well as functional analysis, were used to systematically investigate the role of ATP6AP1 in HCC. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn’s disease and intestinal tuberculosis: insights from an integrated multi-coho... by Aditya Bajaj, Manasvini Markandey, Amit Samal, Sourav Goswami, Sudheer K. Vuyyuru, Srikant Mohta, Bhaskar Kante, Peeyush Kumar, Govind Makharia, Saurabh Kedia, Tarini Shankar Ghosh, Vineet Ahuja

“…Results Both diseases witness similar patterns of alterations in [alpha]-diversity, characterized by a significant reduction in gut bacterial (i.e., bacterial/archaeal) diversity and a concomitant increase in the fungal [alpha]-diversity. …”
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Tool Wear Monitoring Technology and Its Application in Heavy Cutting by CHENG Yao-nan, DING Ya, GAI Xiao-yu, GUAN Rui, MA Chun-jie

“…In the modern cutting system, the wear or damage of cutting tools will lead to the failure of the workpiece and even the damage of machine tools.Tool condition monitoring can effectively improve the adverse effects of wear and breakage on the cutting process, so it is of great significance to apply tool condition monitoring to the process of heavy cutting water chamber head to improve the cutting efficiency and machining quality of workpieces. …”
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PCA-GWO-KELM Optimization Gait Recognition Indoor Fusion Localization Method by Xiaoyu Ji, Xiaoyue Xu, Suqing Yan, Jianming Xiao, Qiang Fu, Kamarul Hawari Bin Ghazali

“…In this method, 30-dimensional motion features for different motion patterns are extracted from inertial measurement units. …”
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The dynamic linkage between covid-19 and nutrition: a review from a probiotics perspective using machine learning and bibliometric analysis by Christos Stefanis, Christina Tsigalou, Ioanna Bezirtzoglou, Gregoria Mitropoulou, Chrysoula Voidarou, Elisavet Stavropoulou

“…This study attempts to detect the relationship between dietary patterns and the disease of COVID-19 and emphasizes research on probiotics by mapping the knowledge produced during the pandemic until 2024.MethodsIn addition to bibliometrics, a machine-learning framework, ASReview, was used to structure the literature search. …”
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Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients by Bing Wang, Zhida Long, Xun Zou, Zhengang Sun, Yuanchu Xiao

“…High-risk patients exhibited worse prognosis and immunosuppressive infiltration patterns. Furthermore, consensus clustering identified two PAAD molecular subtypes with distinct PCDRGs expression patterns and survival outcomes. …”
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