Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. …”
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Citrus Disease Classification Model Based on Improved ConvNeXt by Jichi Yan, Yongbin Mo, Yannan Yu, Shiqing Dou, Rongfeng Yang

“…Secondly, the multi-scale feature fusion module is incorporated to improve the model’s adaptability to disease features at different scales and improve the network classification performance. …”
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Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique by Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

“…This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. …”
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A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition by Qingjun Song, Shirong Sun, Qinghui Song, Bingrui Wang, Zihao Liu, Haiyan Jiang

“…Moreover, to strengthen the expression of key features, a feature weighting method based on the attention mechanism is combined to give adaptive weights to different features. Finally, the experimental platform of a tail beam of coal-gangue impact hydraulic support is built, and several comparative experiments are carried out. …”
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Automatic Morpheme Segmentation for Russian: Can an Algorithm Re-place Experts? by Дмитрий Алексеевич Морозов, Тимур Александрович Гарипов, Ольга Николаевна Ляшевская, Светлана Олеговна Савчук, Борис Леонидович Иомдин, Анна Валерьевна Глазкова

“… Introduction: Numerous algorithms have been proposed for the task of automatic morpheme segmentation of Russian words. Due to the differences in task formulation and datasets utilized, comparing the quality of these algorithms is challenging. …”
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Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Zhaokui Li, Qian Du, Shizhuo Deng, Tong Jia, Dongyue Chen

“…The SaG and SeG features of HSIs are extracted through a dual-branch design, preventing the feature coupling of different dimensions. In order to compress the model’s parameter search space, a local auxiliary global feature extraction strategy is devised. …”
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Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images by Peter Sykora, Patrik Kamencay, Roberta Hlavata, Robert Hudec

“…In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. …”
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Impact of Channel and System Parameters on Performance Evaluation of Frequency Extrapolation Using Machine Learning by Michael Neuman, Daoud Burghal, Andreas F. Molisch

“…We also find that, in particular, KNN results can be quantitatively and qualitatively different from CNN/MLP and AE. These investigations thus provide insights into meaningful parameter choices for the performance evaluation of new ML algorithms for frequency-domain channel extrapolation.…”
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Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis by Yisheng Chen, Yu Xiao, Hui Wu, Chongcheng Chen, Ding Lin

“…The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. …”
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Leveraging deep feature learning for handwriting biometric authentication by Parvaneh Afzali, Abdoreza Rezapour, Ahmad Rezaee Jordehi

“…Additionally, a combined loss function is employed to achieve more accurate discrimination between the handwriting styles of different writers. The designed model is trained with pairs of images, each comprising one authentic and one questioned handwritten word. …”
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Combined CNN-LSTM for Enhancing Clean and Noisy Speech Recognition by Noussaiba Djeffal, Djamel Addou, Hamza Kheddar, Sid Ahmed Selouani

“… This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) approach for Automatic Speech Recognition (ASR) using deep learning techniques on the Aurora-2 dataset. …”
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Bearing Fault Diagnosis in Induction Motors Using Low-Cost Triaxial ADXL355 Accelerometer and a Hybrid CWT-DCNN-LSTM Model by Muhammad Ahsan, Jose Rodriguez, Mohamed Abdelrahem

“…Three datasets, corresponding to different load conditions (100W, 200W, and 300W), were used to evaluate the model’s performance. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”
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MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion by Mustaqeem Khan, Phuong-Nam Tran, Nhat Truong Pham, Abdulmotaleb El Saddik, Alice Othmani

“…After this integration, different fusion techniques are applied before final emotion classification. …”
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Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry by ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai

“…Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. …”
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Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification by Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi

“…<b>Methods:</b> We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. …”
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RFSoC Modulation Classification With Streaming CNN: Data Set Generation &#x0026; Quantized-Aware Training by Andrew Maclellan, Louise H. Crockett, Robert W. Stewart

“…Furthermore, we explore quantised-aware training, producing three modulation classification models with different fixed-point weight precisions (16-bit, 8-bit, and 4-bit). …”
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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

“…To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. …”
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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms by Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang

“…Secondly, the receptive field is enlarged by cascading atrous convolutions with different dilation rates in the ASPP (atrous spatial pyramid pooling) module, and other feature maps are concatenated using the multi-scale attention module to enhance the extraction accuracy of the model. …”
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Offline reinforcement learning combining generalized advantage estimation and modality decomposition interaction by Kaixin Jin, Lifang Wang, Xiwen Wang, Wei Guo, Qiang Han, Xiaoqing Yu

“…In inter-modal interaction, the dual-Transformer architecture facilitates multimodal information exchange for states and actions separately, fully exploring potential correlations between different modal data to achieve deep cross-modal information interaction. …”
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