Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach by Camilo Chacon Sartori, Christian Blum, Filippo Bistaffa, Guillem Rodriguez Corominas

“…The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. …”
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Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples by Qi Zhu, Ning Yuan, Donghai Guan

“…In recent years, self-paced learning (SPL) has attracted much attention due to its improvement to nonconvex optimization based machine learning algorithms. As a methodology introduced from human learning, SPL dynamically evaluates the learning difficulty of each sample and provides the weighted learning model against the negative effects from hard-learning samples. …”
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Trivariate Stochastic Weather Model for Predicting Maize Yield by Patrick Chidzalo, Phillip O. Ngare, Joseph K. Mung’atu

“…Its aggregated values predict maize yield with R2 up to 0.9765 and MAPE=1.94% under common machine learning algorithms. Comparatively, the R2 is 0.8829% and MAPE=4.18%, under the maize yield SDE. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. …”
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Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. by Malontema Katchali, Edward Richard, Henri E Z Tonnang, Chrysantus M Tanga, Dennis Beesigamukama, Kennedy Senagi

“…Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. …”
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Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images by Yixiao Lin, Quing Zhu

“…Additionally, statistics-based radiomic algorithms were applied to quantify differences in the lesion texture on US-PAT images. A machine learning model (US-PAT KNN model) was developed based on an optimized subset of eight US and PAT imaging features to classify a lesion as either cancer, one of four subtypes of benign lesions, or a normal ovary. …”
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Neural correlates of facial recognition deficits in autism spectrum disorder: a comprehensive review by Jianmei Liu, Jianmei Liu, Huihui Chen, Haijing Wang, Zhidan Wang

“…Future research should emphasize longitudinal, multimodal neuroimaging approaches to better understand developmental trajectories and design personalized interventions, leveraging AI and machine learning to optimize therapeutic outcomes for individuals with ASD.…”
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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. …”
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Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning by Yi Du, Jie Chen, Ting Zhang

“…Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. …”
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Reinforcement learning for deep portfolio optimization by Ruyu Yan, Jiafei Jin, Kun Han

“…DPO not only has the advantages of machine learning methods in investment decision-making, but also retains the essence of modern portfolio theory in portfolio optimization. …”
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Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods by Sunil Kumar Patel, Devina Panday

“…This study dives into the realm of machine learning, evaluating the performance of various algorithms - logistic regression (LR), decision tree (DT), and random forest (RF) - in detecting credit card fraud. …”
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Prediction of individual patient outcomes to psychotherapy vs medication for major depression by Devon LoParo, Boadie W. Dunlop, Charles B. Nemeroff, Helen S. Mayberg, W. Edward Craighead

“…Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. …”
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Stokes flows in a two-dimensional bifurcation by Yidan Xue, Stephen J. Payne, Sarah L. Waters

“…We parametrize the flow conductances of 2D bifurcation as functions of the dimensionless parameters of bifurcation geometry and a fixed object using a machine learning approach, which is simple to use and provides more accurate approximations than Poiseuille’s law. …”
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Comparison of the Performance of the C.45 Algorithm with Naive Bayes in Analyzing Book Borrowing at the Library Pringsewu Muhammadiyah University by Dani Wilian, Sriyanto Sriyanto

“…This research underscores the potential of data mining techniques to optimize library management and proposes avenues for future investigation, such as exploring advanced machine learning algorithms and expanding datasets for use in broader library contexts.…”
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Mining Stack Overflow for API class recommendation using DOC2VEC and LDA by Wai Keat Lee, Moon Ting Su

“…The benchmarking of the proposed approach against state‐of‐the‐art approaches, RACK and NLP2API, by using four performance metrics show that it is possible to produce comparable API recommendation results using a less complex approach that makes use of some basic machine learning models, in particular, Doc2Vec and LDA. …”
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Field Observations of Burial Mounds from the Soviet Military Topographic Maps for the Yambol Province, SE Bulgaria by Adela Sobotkova, Todor Valchev

“…The data have been used for studies of location preferences, cultural heritage decline, machine-learning training data and other methodological studies [7–10]. …”
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AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment. by Abozar Ghorbani, Mahsa Rostami, Elham Ashrafi-Dehkordi, Pietro Hiram Guzzi

“…We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. …”
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Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network by Mohamad Reda A. Refaai, Jyothilal Nayak Bharothu, T. V. V. Pavan Kumar, Chodagam Srinivas, M. Sudhakar, Anirudh Bhowmick

“…Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. …”
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Double layer federated security learning architecture for artificial intelligence of things by ZHENG Chengbo, YAN Haonan, FU Caili, ZHANG Dong, LI Hui, WANG Bin

“…Federated learning, as a distributed machine learning architecture, can complete model co-training while protecting data privacy, and is widely used in Artificial Intelligence of Things. …”
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Development of an Intelligent Communication Platform for Remote Practical Work in Education: Integrating AR, VR, and Cloud Technologies by El Gourari Abdelali, Ezzini Mustapha, Raoufi Mustapha, Skouri Mohammed, Achki Samira, Ait Lahcen Yassine, Mouachi Raja

“…By utilizing augmented reality (AR), virtual reality (VR), cloud computing, and machine learning, it bridges the gap between theoretical knowledge and hands-on application to enhance student engagement and learning outcomes. …”
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