A Multi-Modal Approach for the Molecular Subtype Classification of Breast Cancer by Using Vision Transformer and Novel SVM Polyvariant Kernel by Vinayak Shukla, Kaarthika, Archana Mathur, Piyush Narayan, Kunal Kishor

“…Despite advancements in machine learning, predicting these subtypes remains challenging due to tumor heterogeneity and complex data characteristics. …”
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Review on atomistic and quantum mechanical simulation approaches in chemical mechanical planarization by Seokgyu Ryu, Murali Ramu, Patrick Joohyun Kim, Junghyun Choi, Kangchun Lee, Jihoon Seo

“…Future research directions include development of machine learning-accelerated simulations, integration of multiphysics models connecting molecular-scale phenomena to wafer-scale uniformity, and predictive frameworks for novel slurry chemistries. …”
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POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN COMPUTER-AIDED DESIGN OF STAINED GLASS ART by Andrii Petrushevskyi, Yevhen Antonovych

“…The scientificance novelty The analysis of artificial intelligence capabilities in the form of automated algorithms for the development and optimization of stained glass design is a new research direction. …”
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Deep Reinforcement Learning-Based Dynamic Scheduling for Real-Time Applications in LTE and RAN Slicing for eMBB in 5G by Houssem Eddine Benmadani, Mohamed Azni, Turki Essa Alharbi, Mohammed S. Alzaidi, Mohamed Tounsi

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Beyond human-in-the-loop: Sensemaking between artificial intelligence and human intelligence collaboration by Xinyue Hao, Emrah Demir, Daniel Eyers

“…Moving beyond static models such as “human-in-the-loop” or “AI-assistance,” this study offers a novel framework that conceptualizes AI and HI collaboration as a sociotechnical system. …”
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Medium and short-term load forecasting based on NPMA-LSSVM algorithm in the case of unbalance and minority sample data by YANG Qiuyu, KUANG Shusen, ZHENG Xiaogang, YE Guoqi, ZHANG Zhongxin

“…Finally, the least square support vector machine (LSSVM) load forecasting model is established, and the improved mayfly algorithm with nonlinear inertia factor and polynomial variation is used to optimize the model parameters to achieve accurate load forecasts. …”
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AI-Assisted Detection and Localization of Spinal Metastatic Lesions by Edgars Edelmers, Artūrs Ņikuļins, Klinta Luīze Sprūdža, Patrīcija Stapulone, Niks Saimons Pūce, Elizabete Skrebele, Everita Elīna Siņicina, Viktorija Cīrule, Ance Kazuša, Katrina Boločko

“…Conclusions: The study concludes that AI-based models can augment radiologists’ capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. …”
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Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region by Mario Peña, Angel Vázquez-Patiño, Darío Zhiña, Martin Montenegro, Alex Avilés

“…To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. …”
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An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique by Narendra Singh Yadav, Vijay Prakash Sharma, D. Sikha Datta Reddy, Saswati Mishra

“…Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. …”
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Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. by Leland S Hu, Shuluo Ning, Jennifer M Eschbacher, Nathan Gaw, Amylou C Dueck, Kris A Smith, Peter Nakaji, Jonathan Plasencia, Sara Ranjbar, Stephen J Price, Nhan Tran, Joseph Loftus, Robert Jenkins, Brian P O'Neill, William Elmquist, Leslie C Baxter, Fei Gao, David Frakes, John P Karis, Christine Zwart, Kristin R Swanson, Jann Sarkaria, Teresa Wu, J Ross Mitchell, Jing Li

“…In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. …”
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CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement by Guohong Gao, Yuxin Ma, Jianping Wang, Zhiyu Li, Yan Wang, Haofan Bai

“…Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. …”
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Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. by Kenta Ninomiya, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng

“…The SVM model was built using the signature and optimized in a five-fold cross validation. …”
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Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer by Rui Zhang, Chunfan Jiang, Feng Li, Lin Li, Xiaomin Qin, Jiang Yang, Huabing Lv, Tao Ai, Lei Deng, Chencui Huang, Hui Xing, Feng Wu

“…Radiomic features were extracted from the whole-lesion tumor region of interest (ROItumor) and from peritumoral regions with 3 mm and 5 mm margins (ROI3mm and ROI5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. …”
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Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…All the 3 classification models were built using random forest (RF) machine learning. …”
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Intelligent Classifier for Identifying and Managing Sheep and Goat Faces Using Deep Learning by Chandra Shekhar Yadav, Antonio Augusto Teixeira Peixoto, Luis Alberto Linhares Rufino, Aedo Braga Silveira, Auzuir Ripardo de Alexandria

“…The proposed intelligent classifier was built on the Roboflow platform using the YOLOv8 model, trained with 35,204 images. Initially, a Convolutional Neural Network (CNN) model was developed, but its performance was not optimal. …”
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CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION by Steven Soewignjo, Ni Wayan Widya Septia Sari, Andini Putri Mediani, M. Aqil Zaidan Kamil, Dita Amelia, Nur Chamidah

“…The authors utilize the monthly selling exchange rate of CNY against IDR from January 2012 to October 2023 sourced from the “investing” platform. The optimal SVR model is obtained by splitting the data into 113 training samples and 28 testing samples and using the Radial Basis Function (RBF) kernel. …”
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Integrated CAD/CAM Approach for Parametric Design and High-Precision Microfabrication of Planar Functional Structures Comprising Radially Oriented V-Grooves by Jonas T. Churchill-Baird, O. Remus Tutunea-Fatan, Evgueni V. Bordatchev

“…MFB1 defines a single V-groove’s parametric model using specific input parameters. MFB2 transforms these parameters into equations to generate a CAD model of the surface. …”
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Design and Experiment of a Single-Disk Silage Corn Harvester by Wenxuan Wang, Wei Sun, Hui Li, Xiaokang Li, Yongwei Yuan

“…The ternary quadratic regression prediction model was constructed by using Isight 5.6 software, and the accuracy of the model was verified by variance analysis and field experiments. …”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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AI enabled, mobile soil pH classification with colorimetric paper sensors for sustainable agriculture. by Ademir Ferreira da Silva, Ricardo Luis Ohta, Jaione Tirapu Azpiroz, Matheus Esteves Ferreira, Daniel Vitor Marçal, André Botelho, Tulio Coppola, Allysson Flavio Melo de Oliveira, Murilo Bettarello, Lauren Schneider, Rodrigo Vilaça, Noorunisha Abdool, Vanderlei Junior, Wellington Furlaneti, Pedro Augusto Malanga, Mathias Steiner

“…The classification task is performed by a machine-learning model which was trained on the colorimetric pH indicators deployed on the paper sensor. …”
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