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"A cost-sensitive deep learning based approach for network traffic clas" by Akbar Telikani, Amir H. Gandomi et al.

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ ....

Class Imbalance , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Ncrypted Traffic Classification , Generative Adversarial Networks , Intrusion Detection , Task Analysis ,

"Detection and Semantic Segmentation of Rib Fractures using a Convoluti" by Zineddine N. Haitaamar and Nidhal Abdulaziz

Rib Fractures are one of the most common bone injuries that can occur. Over 3 million cases are diagnosed in the United States alone yearly making it a very common fracture. Causes are usually from chest trauma such as falls and sports accidents. The detection of Rib Fractures is a common task in clinics and a labor-intensive such that a specialist radiologist is required to detect rib fractures. The proposed solution looks at processing CT Scans containing rib fractures with an Attention U-Net CNN Architecture. The system achieved a segmentation dice of 77.46% and an IoU of 63.21% with an improvement of 5.96% and 7.61% respectively from the currently published paper. ....

United States , Rib Fractures , Attentionu Net , Bone Fracture , Convolutional Neural Networks , Image Processing , Rib Fracture , You Net ,

"A Hybrid Machine Learning Approach for Sentiment Analysis of Partially" by May El Barachi, Manar Alkhatib et al.

With millions of images and videos uploaded on social media every day, facial sentiment analysis has gained significant attention as means of gaining large scale insights into people's emotions and sentiments. While several models have been proposed for sentiment and emotion analysis of complete, camera-facing pictures, the analysis of images appearing in natural settings and crowded scenes poses more challenges. In such settings, images typically contain a mix of complete and partially occluded faces (i.e. obstructed faces) presented with different angles, resolutions and distances from the camera. In this paper, we propose a hybrid machine learning model combining convolutional neural networks (CNNs) and support vector machines (SVMs) to achieve accurate facial sentiment and emotion analysis of incomplete and partially occluded facial images. The proposed model was successfully tested using 4, 690 images containing 25, 400 faces, collected from a large-scale public event. The mo ....

Convolutional Neural Networks , Facial Recognition , Sentiment Analysis , Support Vector Machines ,

Kneron releases its first automotive-grade chip for ADAS and AV systems – TechCrunch

Kneron has launched its first automotive-grade chip fueled by funds from Foxconn, Alibaba, Sequoia, Horizons Ventures and Qualcomm that it says could revolutionize the path to vehicle autonomy. The chip industry has been dominated by legacy and tech companies like Intel, AMD and Qualcomm. Kneron — which has raised over $100 million to […] ....

Albert Liu , Convolutional Neural Networks , Horizons Ventures , Vision Transformers ,