Does managing a retirement portfolio via rate of return, Sharpe ratio and social interaction generate good returns? An analysis for the years 2017 to 2020 by Nicollas Mateus Gomes, Helberte João França Almeida, Rafael Jasper Feltrin, Guilherme de Oliveira

“…Innovatively, the machine learning algorithm optimizes the portfolio allocation using reinforcement learning, which rewards good decisions and punishes bad decisions based on individual and social criteria. …”
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SynergyAI: A Human–AI Pair Programming Tool Based on Dataflow by Le Jiang, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon

“…By visualizing decision tree models and integrating them with dataflow diagrams, SynergyAI effectively addresses the machine learning black-box problem. …”
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Inverse problem of life cycle assessment (LCA): its application in designing for environment (DfE) by Rybaczewska-Błażejowska Magdalena, Masternak-Janus Aneta, Gierulski Wacław

“…The notion of the inverse problem of life cycle assessment is explained here using a case study of a coffee machine (abstract model SimaPro, based on models Sima and Pro described in SimaPro 8.1 software). …”
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Modal Analysis of the Key Components of RV Reducer by Sun Yongsen, Zhou Yanfei, Yang Xiao, Zhang Xiang, Xia Tian

“…Taking the RV-40 E reducer as the research object,and the three-dimensional model of the cycloidal gear and the pinwheel is built. …”
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A Robust Ensemble Approach for Crop Recommendation Based on Environmental Parameters by Rinayat Bharti S., Dharaskar Bhagyashri

“…The proposed system is trained on a comprehensive dataset comprising environmental and agronomic parameters and aims to provide precise recommendations for optimal crop cultivation in a given region. The ensemble model, utilizing a voting mechanism, demonstrates superior performance in comparison to individual classifiers across multiple evaluation metrics, achieving a test accuracy of 97.40%. …”
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vFerryman: An Artificial Intelligence-Driven Personalized Companion Providing Calming Visuals and Social Interaction for Emotional Well-Being by Wei-Ji Wang

“…By utilizing large language model operations (LLMOps), vFerryman optimizes the performance of large language models to dynamically adapt to users’ emotional feedback. …”
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Credit approval classification through a WASD neuronet by Mourtas Spyridon D.

“…We improve the learning process of the WASD algorithm with a novel activation function for optimal adaptation to the credit approval model, motivated by the finding that WASD neuronets perform better than traditional back-propagation neuronets in terms of slow training speed and trapping in a local minima. …”
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Distrust Spillover in Sharing Accommodation: Evidence From Airbnb in Beijing by Xin Jin, Bo Wang, Ning Ma

“…Based on triadic reciprocal determinism and trust transfer theory, the study developed a model of distrust origins, transmission, and spillover. …”
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Dynamic Coordination Mechanism of DFIGs Based on Inertia Response by Zhaorui LIU, Qi JIA, Gangui YAN, Wenchao ZHAI, Yong SUN, Baoju LI

“…With a parallel system of two DFIGs as an example, the law of unbalanced power distribution between the DFIGs during inertia response is analyzed and extended to multi-machine parallel systems. A frequency response model of a system of integrated DFIGs and synchronous generators is built, and the frequency response characteristics in each stage of power disturbance are analyzed. …”
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Measurement of contact pattern displacement of hypoid gears with optical thermal camera by Stefan GERDHENRICHS, Manuel OEHLER, Daniel BUSCHMANN, Robert Heinrich SCHMITT, Oliver KOCH

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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

“…To address this challenge, this study presents a next-generation AI-based predictive framework utilizing three hybrid machine learning techniques: adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), convolutional neural networks with long short-term memory (CNN-LSTM), and multi-objective optimization (MOO). …”
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Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving by Hao Peng, Zehao Yu, Dandan Zhao, Zhiguo Ding, Jieshuai Yang, Bo Zhang, Jianming Han, Xuhong Zhang, Shouling Ji, Ming Zhong

“…Using reinforcement learning, MalAOI identifies optimal insertion points and benign opcode sequences to autonomously generate adversarial malware that evades GNN model detection. …”
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Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems by Kosmas Alexopoulos, Panagiotis Mavrothalassitis, Emmanouil Bakopoulos, Nikolaos Nikolakis, Dimitris Mourtzis

“…The DRL agent selects a dispatching rule to assign jobs to manufacturing resources. The model is trained, tested and evaluated using a discrete event simulation (DES) model that simulates a pilot case from the bicycle production industry. …”
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Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease by Qi Zhou, Mohammed Goryawala, Mercedes Cabrerizo, Warren Barker, Ranjan Duara, Malek Adjouadi

“…Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. …”
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Data-driven assessment of well stimulation in unconventional gas reservoirs by Jian Yang, Song Li, Ji Zeng, Zhaozhong Yang, Xiaogang Li, Tingting He, Liangping Yi, Bing Kong

“…This study introduces a new procedure for assessing well stimulation performance, which involves analyzing the EUR through Duong’s model, calculating the key performance indicator of the treatment, and establishing a data-driven model to predict the treatment KPI.…”
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Learning efficient haptic shape exploration with a rigid tactile sensor array. by Sascha Fleer, Alexandra Moringen, Roberta L Klatzky, Helge Ritter

“…In this field, the relative lack of good real-world interaction models-along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods-has so far rendered haptic exploration a largely underdeveloped skill. …”
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DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION by Mark Jayson Y. Sutayco, Eidref Simon Dela Cruz, Harold Aranza, Gian Fernan Collado, Kyle Benedict Lui

“…The pest classification model developed yielded notable recall rates, notably 76% for caterpillars, 93% for flea beetles, and 89% for other pests. …”
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Personalized Privacy-Preserving Data Utilization Approach Powered by Distributed-GAN by Shuo Wang, Chao Wang, Tianshuo Dong, Yunhua He, Ke Xiao

“…At last, we conduct sufficient privacy analysis and experimental validation, which demonstrate that our approach can optimize generative model with lower privacy cost and generate higher quality data for downstream tasks.…”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. …”
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The Evolution of the Interaction Between Urban Rail Transit and Land Use: A CiteSpace-Based Knowledge Mapping Approach by Haochen Yang, Nana Cui, Haishan Xia

“…Key findings include the following: (1) research on rail transit and land use has been steadily increasing, with a significant “US-China dual-core” distribution, where most studies are concentrated in the United States and China, with higher research density in Asia; (2) domestic and international research has primarily focused on themes such as the built environment, value capture, and public transportation, with a recent shift toward artificial intelligence and smart city technology applications; (3) research methods have evolved from foundational 3S technologies (GIS, GPS, RS) to spatial modeling tools (e.g., LUTI model, node-place model), and the current emergence of AI-driven analysis (e.g., machine learning, deep learning, digital twins). …”
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