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"Context-Driven Satire Detection with Deep Learning" by Md Saifullah Razali, Alfian Abdul Halin et al.

This work discuss the task of automatically detecting satire instances in short articles. It is the study of extracting the most optimal features by using a deep learning architecture combined with carefully handcrafted contextual features. It is found that a few sets can perform well when they are used independently, but the others not so much. However, even the latter sets become very useful after the combination process with the former sets. This shows that each of the feature sets are significant. Finally, the combined feature sets undergoes the classification using well-known machine learning classification algorithms. The best algorithm for this task is found to be Logistic Regression. The outcome of all the experiments are good in all the metrics used. The result comparison to existing works in the same domain shows that the proposed method is slightly better with 0.94 in terms of F1-measure, while existing works managed to obtain 0.91 [1], 0.90 [2] and 0.88 [3]. The performance ....

Convolutional Neural Networks , Deep Learning , Feature Extraction , Machine Learning Algorithms , Natural Language Processing , Atire Detection , Support Vector Machines , Task Analysis ,

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al.

Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning on Big data hinders the scalability of IIoT intrusion detection systems. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-lev ....

Industrial Internet , Class Imbalance , Computational Modeling , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Edge Computing , Evolutionary Algorithms , Fog Computing , Industrial Internet Of Things , Industrial Internet Of Things Iiot , Intrusion Detection ,

Deep Learning for Image Classification in Python with CNN

[img]https://i120.fastpic.org/big/2022/0905/91/71854e8a8e29e285443285a47cabb091.jpg[/img] [b]Deep Learning for Image Classification in Python with CNN[/b] Published 09/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 37 lectures (1h 7m. ....

United States , Google Colab , Convolutional Neural Networks Cnns , Convolutional Neural Network , Image Classification , Neural Networks , Computer Vision With Keras , Convolutional Neural Networks , Image Processing Engineer ,

GlobalData ranked ZAC, the Cognitive Explainable-AI (Artificial Intelligence) Image Recognition startup, in top 5 companies worldwide, for a Web 3.0 fundamental category

/PRNewswire/ Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI) (based on Concept-Learning). ....

United States , Saied Tadayon , Lotfi Zadeh , David Lee Nobel , Robert Buhrman , Bijan Tadayon , Gholam Peyman , Mike Spencer , Mo Jamshidi , Z Advanced Computing Inc , Us Air , Sourcez Advanced Computing Inc , National Medal Of Technology , Us Army Science Board , Convolutional Neural Networks , Prnewswirez Advanced Computing Inc , Vice Provost Of Research , Advanced Computing , Artificial Intelligence , Next Revolution , Can Web , Key Players , Aerial Vision , Smart Appliance , Machine Learning , Deep Convolutional Neural Networks ,

The dark side of Graph Neural Networks

The current limitations of Graph Neural Networks. We continue our two part series on ML on Graphs, by asking: could graphs replace other domain specific formats and algorithms, such as Computer Vision (CV) or Natural Language Processing (NLP)? ....

Graph Neural Networks , Message Passing Neural Networks , Graph Neural Network , Convolutional Neural Networks Or Transformers , Graph Convolutional Network , Why Machine Learning , Computer Vision , Natural Language Processing , Convolutional Neural Networks , Hardware Lottery , Machine Learning , Graph Neural , Quantum Deep , Jure Leskovec ,