Artificial intelligence in acoustic ecology: Soundscape classification in the Cerrado by Bruno Daleffi da Silva, Linilson Rodrigues Padovese

“…The performance comparison of these models revealed the superiority of the Convolutional Neural Network (CNN), which, although requiring higher computational costs and training time, provided high accuracy in classifications and valuable insights through the application of the LIME explainability technique. …”
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CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv... by Xiaozong Qiu, Guohua Yan, Lihua Yin

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Automatic Number Plate Detection and Recognition System for Small-Sized Number Plates of Category L-Vehicles for Remote Emission Sensing Applications by Hafiz Hashim Imtiaz, Paul Schaffer, Paul Hesse, Martin Kupper, Alexander Bergmann

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An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model by Rajesh Kannan Megalingam, Kariparambil Sudheesh Sankardas, Sakthiprasad Kuttankulangara Manoharan

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Multi-View Stereo Using Perspective-Aware Features and Metadata to Improve Cost Volume by Zongcheng Zuo, Yuanxiang Li, Yu Zhou, Fan Mo

“…This paper proposes PAC-MVSNet, which integrates perspective-aware convolution (PAC) and metadata-enhanced cost volumes to address the challenges in reflective and texture-less regions. …”
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HCVNet: Binocular Stereo Matching via Hybrid Cost Volume Computation Module With Attention by Chenglin Dai, Qingling Chang, Tian Qiu, Xinglin Liu, Yan Cui

“…Finally, we adopt the Hybrid Cost Volume Computation Module (HCVCM) to construct and aggregate cost volume. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

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AI-driven demand forecasting for enhanced energy management in renewable microgrids: A hybrid LSTM-CNN approach by Bashiru olalekan ariyo, mutalub adesina lambe, olalekan ogunbiyi, musa abdulwaheed, bilkisu jimada ojuolape, monsurat omolara balogun

“…Methods: A hybrid forecasting model that combines long short-term memory (LSTM) networks and Convolutional Neural Networks (CNN) was proposed. The model leverages historical energy consumption and meteorological data for training, ensuring robust and accurate predictions. …”
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Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements by Shadfar Davoodi, Mohammed Ba Geri, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Alireza Soleimanian

“…Drilling operations depend on precisely controlling drilling fluid filtration volume (FV), which affects formation integrity, costs, and borehole stability. Maintaining optimal FV is essential to prevent well control issues, yet forecasting it is challenging due to process complexity and measurement limitations. …”
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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation by Thai la, Linh Tao, Dai Watanabe

“…Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Short-term wind power forecasting helps improve grid stability, optimize wind farm power generation plans, and reduce operating costs, enhancing the economic benefits of wind power and supporting the goals of low-carbon development. …”
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Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…Then, a dense multi-scale feature guided cost aggregation module was proposed, which adaptively fused the cost volume and dense multi-scale features while aggregating the cost volume, so that the subsequent decoding layers can decode more accurate and high-resolution geometry information with the guidance of multi-scale context information. …”
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Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system by Francisco A. Delgado-Rajó, Carlos M. Travieso-Gonzalez

“…By utilizing deep learning techniques, including convolutional neural networks (CNNs) trained on bird song datasets, the system performs real-time species detection at the edge, minimizing the need for high-bandwidth transmission. …”
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Online Calibration Method of LiDAR and Camera Based on Fusion of Multi-Scale Cost Volume by Xiaobo Han, Jie Luo, Xiaoxu Wei, Yongsheng Wang

“…To solve the above problems, we propose an online calibration algorithm based on multi-scale cost volume fusion. First, a multi-layer convolutional network is used to downsample and concatenate the camera RGB data and LiDAR point cloud data to obtain three-scale feature maps. …”
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Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters by Jiuxiao Zhao, Jianping Tao, Shirui Zhang, Jingjing Li, Teng Li, Feifei Shan, Wengang Zheng

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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM by ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

“…This method utilizes convolutional neural networks (CNNs) for feature extraction, and light gradient boosting machine (LightGBM) for carbon emission estimation based on extracted features. …”
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RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction by Jiangbo Zhang, Yunhui Peng, Feifei Cui, Zilong Zhang, Shankai Yan, Qingchen Zhang

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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…Such an integration acted as regular convolutional layers that subsequently substituted for the original layers to cut computational costs. …”
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A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju, Daniel Ford

“…In this work, a fog computing-based cost-effective smart health monitoring device is proposed for infectious disease applications. …”
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A rapid, low-cost deep learning system to classify strawberry disease based on cloud service by Guo-feng YANG, Yong YANG, Zi-kang HE, Xin-yu ZHANG, Yong HE

“…We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. …”
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