Scientist turns people’s mental images into text using ‘mind-captioning’ technology

A scientist in Japan has developed a technique that uses brain scans and artificial intelligence to turn a person’s mental images into descriptive sentences.
Scientific Research Publishing

Next-Generation Cyber Defense: AI-Powered Predictive Analytics for National Security and Threat Resilience ()

Awurum, N.P. (2025) Next-Generation Cyber Defense: AI-Powered Predictive Analytics for National Security and Threat Resilience. Open Access Library Journal, 12, 1-17. doi: 10.4236/oalib.1114210 .

AI coding transforms data engineering: How dltHub's open-source Python library helps developers create data pipelines for AI in minutes

A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in ...
How-To Geek on MSN

How to run R-style linear regressions in Python the easy way

The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate correlation coefficient. If you look at the multiple regression we did, ...
IEEE

Adaptive Convolution for CNN-Based Speech Enhancement Models

Abstract: Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential ...
IEEE

Development of Scalable Deep Learning Frameworks Utilizing Advanced Hybrid Models like Transformer-CNN for Distributed Computing Environments

Abstract: Over the past few years especially in the context of communication and information processing the importance of Natural language processing which demands efficient deep learning models has ...
GitHub

tensorflow-keras-cnn-python-ai

Deep learning neural network for image classification using TensorFlow and Keras. Includes CNN architecture, data preprocessing, and model training scripts.
IEEE

LightAt-CNN: A Super Efficient CNN model based on Attention Mechanism

Abstract: This paper presents a highly efficient Convolutional Neural Network (CNN) architecture for traffic sign recognition that achieves state-of-the-art accuracy while dramatically reducing model ...
IEEE

Deep Learning-Based Classification of Coconut Leaf Diseases Using Optimized CNN Models

Abstract: Leaf diseases of coconut severely affect the yield and quality of the crops. Early detection is imperative for any control measures to be effective. Here, an approach on the deep ...
IEEE

Enhancing QoE Prediction for Video Streaming Using a Hybrid CNN-LSTM Model

Abstract: Quality of Experience (QoE) prediction in video streaming plays an important role in improving user satisfaction and optimizing adaptive strategies. The current nonhybrid QoE prediction ...
IEEE

PixTransNet: A Sensor-Aware CNN–Transformer Model for Magnetic Flux Leakage Defect Segmentation

Abstract: Magnetic flux leakage (MFL) is a widely used nondestructive evaluation technique for pipeline inspection. However, its signals are highly sensitive to noise and geometric distortions, ...
IEEE

Research on Polarity-Linear Attention-Based CNN Models for Emotion Recognition

Abstract: This study proposes an improved CNN model incorporating Polar Linear Attention Mechanism for facial expression recognition tasks. Addressing the limitations of traditional CNNs in capturing ...