TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency by Lesia Mochurad, Roman Levkovych

“…Meanwhile, QRNN reduces computational costs through parallelization of operations and an optimized architecture. …”
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A high-precision segmentation network for industrial surface defect detection by Hao Chen, Byung-Won Min

“…Accurate surface defect detection is essential for improving product quality and reducing manufacturing costs, particularly in high-precision industries. …”
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Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images by Hiroki Naito, Tomohiko Ota, Kota Shimomoto, Fumiki Hosoi, Tokihiro Fukatsu

“…The scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. …”
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A secured accreditation and equivalency certification using Merkle mountain range and transformer based deep learning model for the education ecosystem by Sumathy Krishnan, Surendran Rajendran, Mohammad Zakariah

“…However, most verification procedures are costly, hard, opaque, and time-consuming. This paper introduces a secured blockchain-based Accreditation and Equivalency certification prototype that effectively mitigates credential and equivalency frauds. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. …”
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Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks by Favour Ibude, Abayomi Otebolaku, Jude E. Ameh, Augustine Ikpehai

“…In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. …”
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The Use of Artificial Intelligence in Sturgeon Aquaculture by Dragoș Sebastian Cristea, Alexandru Adrian Gavrilă, Ștefan Mihai Petrea, Dan Munteanu, Sofia David, Cătălin Octavian Mănescu

“…It was found that the LAB colour space provided superior results in terms of precision and efficiency, but maximum accuracy was achieved using convolutional neural networks (YOLACT). The analysis of the project results confirms the significant advantages of using the AI system for biomass monitoring, advantages consisting of the reduction of unit costs with labour and feed, improvement of water quality, active optimisation of sturgeon growing conditions. …”
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A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis by Jie Xu, Erkang Jing, Yidong Chai

“…Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. …”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…Abstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. …”
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Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms by Xin Zeng, Guang-Peng Su, Wen-Feng Du, Bei Jiang, Yi Li, Zi-Zhong Yang

“…Abstract Background Accurately identifying targets not only guides treatments for diseases with unclear pathogenic mechanisms, but also reduces pharmaceutical costs and accelerates drug development timelines. …”
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Research on geomagnetic indoor high-precision positioning algorithm based on generative model by Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI

“…Aiming at the current bottleneck of constructing a fine geomagnetic fingerprint library that required a lot of labor costs, two generative models called the conditional variational autoencoder and the conditional confrontational generative network were proposed, which could collect a small number of data samples for a given location, and generate pseudo-label fingerprints.At the same time, in order to solve the problem of low positioning accuracy of single-point geomagnetic fingerprints, a geomagnetic sequence positioning algorithm based on attention mechanism of convolutional neural network-gated recurrent unit was designed, which could effectively use the spatial and temporal characteristics of fingerprints to achieve precise positioning.In addition, a real-time, portable mobile terminal data collection and positioning system was also designed and built.The actual test shows that the proposed model can effectively construct the available geomagnetic fingerprint database, and the average error of the proposed algorithm can reach 0.16 m.…”
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Divide-and-conquer routing for learning heterogeneous individualized capsules. by Hailei Yuan, Qiang Ren

“…Capsule Networks (CapsNets) have demonstrated an enhanced ability to capture spatial relationships and preserve hierarchical feature representations compared to Convolutional Neural Networks (CNNs). However, the dynamic routing mechanism in CapsNets introduces substantial computational costs and limits scalability. …”
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A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…Implementing a hybrid approach would prevent unexpected machine downtime, enhancing reliability and reducing maintenance times and costs.…”
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MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction by Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang

“…This model employs Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to extract features from the drug and protein sequences, respectively. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Traffic accidents remain major public safety concerns, often causing severe injuries, deaths, and economic costs, especially in rapidly urbanizing areas. Accurate traffic accident risk prediction is crucial for developing effective strategies to reduce accidents and enhance urban mobility. …”
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Innovative Approaches to Traffic Anomaly Detection and Classification Using AI by Borja Pérez, Mario Resino, Teresa Seco, Fernando García, Abdulla Al-Kaff

“…Key challenges identified include dependence on large labeled datasets, high computational costs, and limited model interpretability. The review outlines how recent research is addressing these issues through semi-supervised learning, model compression techniques, and explainable AI. …”
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GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation by A. F. M. Shahab Uddin, Maryam Qamar, Jueun Mun, Yuje Lee, Sung-Ho Bae

“…Studies suggest that a convolutional neural network’s receptive field follows a Gaussian distribution, with central pixels being more influential. …”
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Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms by Hong Zhang, Yuanshuai Dong, Yun Hou, Xiangjun Cheng, Peiwen Xie, Keming Di

“…To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. …”
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Predicting and Mitigating Delays in Cross-Dock Operations: A Data-Driven Approach by Amna Altaf, Adeel Mehmood, Adnen El Amraoui, François Delmotte, Christophe Lecoutre

“…Cross-docking operations are highly dependent on precise scheduling and timely truck arrivals to ensure streamlined logistics and minimal storage costs. Predicting potential delays in truck arrivals is essential to avoiding disruptions that can propagate throughout the cross-dock facility. …”
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Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning by Mehmet Erdogan, Mustafa Dogan

“…Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. …”
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