AIMC Topic: Neural Networks, Computer

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Advancing brain tumor classification: A robust framework using EfficientNetV2 transfer learning and statistical analysis.

Computers in biology and medicine
Brain tumors are a significant health risk threatening humanity, and they seem to be unique challenges due to their critical location and the complexity of accurate diagnosis and treatment planning. Accurate and timely diagnosis and appropriate treat...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...

Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.

Sensors (Basel, Switzerland)
Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and f...

3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.

Sensors (Basel, Switzerland)
Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing...

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

IEEE transactions on biomedical circuits and systems
This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neural network accelerator, achieved through algorithm-hardware co-design to optimize hardware resource...

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.

Journal of medical engineering & technology
The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the ...

Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

PloS one
Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and hig...

ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks.

Computers in biology and medicine
Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding si...

CE-Net: Cascade attention and context-aware cross-level fusion network via edge learning guidance for polyp segmentation.

Computers in biology and medicine
Colorectal polyps are one of the most direct causes of colorectal cancer. Polypectomy can effectively block the process of colorectal cancer, but accurate polyp segmentation methods are required as an auxiliary means. However, there are several chall...

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks.

Advanced materials (Deerfield Beach, Fla.)
The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial ...