At present, most methods to improve the accuracy of emotion recognition based on electroencephalogram (EEG) are achieved by means of increasing the number of channels and feature types. This is to use the big data to train the classification model but it also increases the code complexity and consumes a large amount of computer time. We propose a method of Ant Colony Optimization with Convolutional Neural Networks and Long Short-Term Memory (ACO-CNN-LSTM) which can attain the dynamic optimal channels for lightweight data. First, transform the time-domain EEG signal to the frequency domain by Fast Fourier Transform (FFT), and the Differential Entropy (DE) of the three frequency bands (, and ) are extracted as the feature data; Then, based on the DE feature dataset, ACO is employed to plan the path where the electrodes are located in the brain map. The classification accuracy of CNN-LSTM is used as the objective function for path determination, and the electrodes on the optimal path are used as the optimal channels; Next, the initial learning rate and batchsize parameters are exactly matched the data characteristics, which can obtain the best initial learning rate and batchsize; Finally, the SJTU Emotion EEG Dataset (SEED) dataset is used for emotion recognition based on the ACO-CNN-LSTM. From the experimental results, it can be seen that: the average accuracy of three-classification (positive, neutral, negative) can achieve 96.59 %, which is based on the lightweight data by means of ACO-CNN-LSTM proposed in the paper. Meanwhile, the computer time consumed is reduced. The computational efficiency is increased by 15.85 % compared with the traditional CNN-LSTM method. The accuracy can achieve more than 90 % when the data volume is reduced to 50 %. In summary, the proposed method of ACO-CNN-LSTM in the paper can get higher efficiency and accuracy.
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http://dx.doi.org/10.1016/j.heliyon.2024.e30174 | DOI Listing |
Cogn Neurodyn
December 2025
School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing, 100081 China.
Enhancing the accuracy of emotion recognition models through multimodal learning is a common approach. However, challenges such as insufficient modal feature learning in multimodal inference and scarcity of sample data continue to pose obstacles that need to be overcome. Therefore, we propose a novel adaptive lightweight multimodal efficient feature inference network (ALME-FIN).
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, China.
In the cultivation of green chili peppers, the similarity between the fruit and background color, along with severe occlusion between fruits and leaves, significantly reduces the efficiency of harvesting robots. While increasing model depth can enhance detection accuracy, complex models are often difficult to deploy on low-cost agricultural devices. This paper presents an improved lightweight Pepper-YOLO model based on YOLOv8n-Pose, designed for simultaneous detection of green chili peppers and picking points.
View Article and Find Full Text PDFData Brief
February 2025
Department of Mechanical Engineering, Brawijaya University, Indonesia.
Crash box development is carried out continuously to obtain a lightweight design and high energy absorption. This dataset presents the results of a crash box experiments. Quasi-static testing of aluminum, honeycomb, and hybrid tube specimens was carried out using a universal testing machine.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
Neurocomputing (Amst)
January 2025
Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.
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