CNN Based Automatic Speech Recognition: A Comparative Study by Hilal Ilgaz, Beyza Akkoyun, Özlem Alpay, M. Ali Akcayol

“…The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. …”
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RiceNet: Efficient CNN for High-Throughput Image-Based Rice Panicle Detection and Counting by Kushwaha Ragini, Balkrishna Sutar Manisha

“…RiceNet achieves high accuracy and computational efficiency over traditional image processing and other CNN architectures on diverse rice field images of different rice varieties and stages of growth. Notably, the model can yield timely estimates of crop yield and manages the crop within 30 seconds, which is a significant reduction in panicle detection time. …”
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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. by Sheng Miao, Guoqing Ni, Guangze Kong, Xiuhe Yuan, Chao Liu, Xiang Shen, Weijun Gao

“…By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hydrocarbon content. …”
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Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning by Mingfei Li, Haibin Liu, Feng Xie

“…Simulation results show that the control strategy combining TSTMIPI and BSE not only eliminates the reliance on external markers but also significantly improves control precision under different noise levels and visual occlusion conditions, surpassing existing visual formation control methods in maintaining the desired distance and angular precision.…”
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Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…As the existing graph heterogeneous neural network can not learn different types of nodes and edge interaction, add multiple attention, design ablation experiments to verify its impact on the network. …”
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COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers by Syed Ali Jafar Zaidi, Indranath Chatterjee, Samir Brahim Belhaouari

“…As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. …”
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An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing by Chunping Dong, Jiaqiang Zhao

“…The decoder includes Multi-Head Attention (MHA) and Gated Recurrent Unit (GRU), which can adaptively enhance the relevant feature weights and extract long-term, deep different features. For the model training, a monotonicity loss function is defined. …”
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Semi-Supervised Atmospheric Turbulence Mitigation Based on Hybrid Models by Wenhao Chu, Zhi Cheng, Lixin He

“…We have conducted sufficient experiments on different types of turbulence data that the proposed framework can mitigate the motion blur and geometric distortion caused by atmospheric turbulence, thus resulting in a dramatic improvement in visual quality. …”
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Autonomous Maneuver Decision of UCAV Air Combat Based on Double Deep Q Network Algorithm and Stochastic Game Theory by Yuan Cao, Ying-Xin Kou, Zhan-Wu Li, An Xu

“…Air combat simulation results show that UCAV can choose maneuvers autonomously under different situations and occupy a dominant position quickly by this method, which greatly improves the combat effectiveness of UCAV.…”
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Wavelet attention-based implicit multi-granularity super-resolution network by Chen Boying, Shi Jie

“…Compared to existing self-attention modules, the wavelet attention module decomposes image features into different frequency components using wavelet transforms. …”
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Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation by Jiabao LI, Lichi AN, Yabing CHENG, Haoxiang WANG

“…To understand the impact of the MFCC order and the frame number on prediction accuracy, MFCC feature maps of different specifications are analyzed. The dataset is expanded threefold using fuzzy generation with an appropriate membership degree. …”
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Research on road surface damage detection based on SEA-YOLO v8. by Yuxi Zhao, Baoyong Shi, Xiaoguang Duan, Wenxing Zhu, Liying Ren, Chang Liao

“…Firstly, the SBS module is constructed to optimize the computational complexity, achieve real-time target detection under limited hardware resources, successfully reduce the model parameters, and make the model more lightweight; Secondly, we integrate the EMA attention mechanism module into the neck component, enabling the model to utilize feature information from different layers, enabling the model to selectively focus on key areas and improve feature representation; Then, an adaptive attention feature pyramid structure is proposed to enhance the feature fusion capability of the network; Finally, lightweight shared convolutional detection head (LSCD-Head) is introduced to improve feature representation and reduce the number of parameters. …”
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A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations by João Passagem dos Santos, Hugo Algarvio

“…Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. …”
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A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

“…To efficiently utilise different fine models on low-cost FPGAs with area minimisation, ZyCAP-based PR is adopted. …”
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Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning by Wenhao Zhou, Faqiang Liu, Hao Zheng, Rong Zhao

“…Here, we introduce shortcut hull learning, a diagnostic paradigm that unifies shortcut representations in probability space and utilizes diverse models with different inductive biases to efficiently learn and identify shortcuts. …”
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Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. …”
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Artificial Intelligence for Land Cover and Land Use Classification in Remote Sensing: Review Study by R. AlAli

“…This paper presents a comparative study of the different methods used in Land Cover Land Use Classification to find out the best available method based on their accuracy.…”
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Advancements in handwritten Devanagari character recognition: a study on transfer learning and VGG16 algorithm by Chetan Sharma, Shamneesh Sharma, Sakshi, Hsin-Yuan Chen

“…For future research, the authors intend to investigate deeper learning structures further and integrate a broader and more varied dataset to enhance the model’s accuracy and guarantee its suitability for different real-life situations.…”
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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. …”
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Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis. by Ramasamy Srinivasagan, Mohammed Shawky El Sayed, Mohammed Ibrahim Al-Rasheed, Ali Saeed Alzahrani

“…The study emphasizes accurately identifying and categorizing different chicken noises associated with emotional states such as discomfort, hunger, and satisfaction. …”
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