Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease by Xia Wang, Lin Zhang

“…Current therapies face challenges in targeting specific immune pathways and predicting treatment responses. Methods Multi-cohort transcriptomic data were integrated to identify inflammation-related genes (IRGs). …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to elucidate model predictions, improving transparency and trustworthiness. …”
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History of Location Objectives (Hilal Tracker): Development of Astronomical Observations from 2015-Present by Marataon Ritonga, Alya Fathi Muhammad Hasibuan, Rizqan Arya Perdana Nazara

“…This article explains the history of the development of hilal Tracker technology and methods, including integrating astronomical data, using satellite imagery, algorithms for predicting hilal visibility, and cooperation with various local and international institutions. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…A feedforward neural network is employed to predict the PCA-reduced S-parameters, serving as a surrogate model that enables faster decision-making within the Q-learning framework. …”
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Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT by Hafiyyan Putra Pratama, Dewi Indriati Hadi Putri, Hafiziani Eka Putri, Elysa Nensy Irawan, Makna A’raaf Kautsar

“…The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. …”
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Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. …”
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Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms’ accuracy and dependability. …”
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Knowledge graph convolutional networks with user preferences for course recommendation by Zhong Hua, Jianbai Yang, Weidong Ji

“…This paper introduces KGCN-UP (Knowledge Graph Convolutional Networks with User Preferences), a novel model for predicting the likelihood of a user interacting with a course based on user preferences and item relationships within a knowledge graph. …”
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Impact of Advanced Agriculture Technologies and Energy Consumption on Crop Yields in Modern Agriculture Using Deep Learning Techniques by Khan Baz, Zhu Zhen, Hashmat Ali

“…These machine learning techniques have successfully predicted the synergistic relationships, leading to significant improvements in cereal yield forecasting. …”
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Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes by Wava Lativa Azzahra, Jamaludin Indra, Rahmat Rahmat, Sutan Faisal

“…The SVM model was then applied to predict 971 unlabeled reviews, and the results showed a dominance of negative sentiment. …”
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H-DSAE: a hybrid technique to recognize heart disease by K. Uma Maheswari, A. Valarmathi

“…This method combines various classifiers to achieve highly accurate predictions. Due to the complexity of the task, the researchers decided to use deep learning methods to perform the heart disease classification task. …”
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Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories by Claire-Alice Hébert, Joshua E. Meyers, My H. Do, Patricia R. Burchat, LSST Dark Energy Science Collaboration

“…We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. …”
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A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. by Duhita Sengupta, Sk Nishan Ali, Aditya Bhattacharya, Joy Mustafi, Asima Mukhopadhyay, Kaushik Sengupta

“…It has already been elucidated that in ovarian cancer, the extent of alteration in nuclear shape and morphology can modulate genetic changes and thus can be utilized to predict the outcome of low to a high form of serous carcinoma. …”
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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. …”
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Optimizing energy cost in the residential sector through home energy management systems in a smart grid environment by Nabeeha Qayyum, Umar Jamil, Anzar Mahmood

“…The results show that GA achieved a 48% cost reduction compared to PSO, with significant peak load reduction and improved energy optimization when integrated with PV systems. …”
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Flexibility in load demand and PHEV parameters for clean and economic microgrid operation by Bishwajit Dey, Srikant Misra, Arnab Pal

“…Furthermore, a balanced economic emission dispatch is used to achieve the best trade-off between cost minimisation and pollution reduction, ensuring long-term MG functioning. Numerical results corroborate the improvement of load factor and reduction of peak demand using DSM methods. …”
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A robust multi-scale clustering framework for single-cell RNA-seq data analysis by Songrun Jiang, Chunyan Wang, Qiucheng Sun, Zhi Zhang

“…The source code for our algorithm is publicly available at https://github.com/DEREKJ24/scMSCF .…”
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Integrated energy microgrid bi-level game scheduling optimization taking into account electricity-heat-hydrogen enriched compressed natural gas coupling and shared energy storage by Changgang Wang, Xiaoning Sun

“…Finally, the proposed model is solved using the Black-winged Kite algorithm in conjunction with the CPLEX solver.Case studies demonstrate the effectiveness of the proposed method, showing revenue increases of 9.15% for integrated energy microgrid operators and 8.29% for load aggregators, alongside a reduction in carbon emissions by 8.76%.…”
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Integrated energy, environmental, and economic optimization for energy management systems in PHEVs considering traffic conditions by Morteza Montazeri-Gh, Ehsan Alimohammadi

“…In these vehicles, critical concerns include the reduction of fuel consumption, control of pollution, and the costs associated with battery degradation. …”
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Improving Scheduling Efficiency: A Mathematical Approach to Multi-Operation Optimization in MSMEs by Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Margarita De Miguel-Guzmán, Gelmar García-Vidal, Rodobaldo Martínez-Vivar

“…The application resulted in a 95.3% improvement in the level of utilization; a 79.3% reduction in the average completion time; a 90.5% reduction in machine waiting time; and a 90.9% decrease in product delivery delay. …”
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