Outputs (57)

A comparative study of Persian sentiment analysis based on different feature combinations (2018)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., & Durrani, T. (2017, July). A comparative study of Persian sentiment analysis based on different feature combinations. Presented at International Conference in Communications, Signal Proces

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there... Read More about A comparative study of Persian sentiment analysis based on different feature combinations.

Toward's Arabic multi-modal sentiment analysis (2018)
Presentation / Conference Contribution
Alqarafi, A., Adeel, A., Gogate, M., Dashitpour, K., Hussain, A., & Durrani, T. (2019). Toward's Arabic multi-modal sentiment analysis. . https://doi.org/10.1007/978-981-10-6571-2_290

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information shari... Read More about Toward's Arabic multi-modal sentiment analysis.

Deep learning driven multimodal fusion for automated deception detection (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2018). Deep learning driven multimodal fusion for automated deception detection. . https://doi.org/10.1109/SSCI.2017.8285382

Humans ability to detect lies is no more accurate than chance according to the American Psychological Association. The state-of-the-art deception detection methods, such as deception detection stem from early theories and polygraph have proven to be... Read More about Deep learning driven multimodal fusion for automated deception detection.

A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition (2018)
Presentation / Conference Contribution
Gogate, M., Adeel, A., & Hussain, A. (2018). A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition. . https://doi.org/10.1109/SSCI.2017.8285377

The curse of dimensionality is a well-established phenomenon. However, the properties of high dimensional data are often poorly understood and overlooked during the process of data modelling and analysis. Similarly, how to optimally fuse different mo... Read More about A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017). Persian Named Entity Recognition. . https://doi.org/10.1109/ICCI-CC.2017.8109733

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification... Read More about Persian Named Entity Recognition.

Complex-valued computational model of hippocampal CA3 recurrent collaterals (2017)
Presentation / Conference Contribution
Shiva, A., Gogate, M., Howard, N., Graham, B., & Hussain, A. (2017). Complex-valued computational model of hippocampal CA3 recurrent collaterals. . https://doi.org/10.1109/ICCI-CC.2017.8109745

Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established... Read More about Complex-valued computational model of hippocampal CA3 recurrent collaterals.

A Generative Learning Approach to Sensor Fusion and Change Detection (2016)
Journal Article
Gepperth, A. R. T., Hecht, T., & Gogate, M. (2016). A Generative Learning Approach to Sensor Fusion and Change Detection. Cognitive Computation, 8(5), 806-817. https://doi.org/10.1007/s12559-016-9390-z

We present a system for performing multi-sensor fusion that learns from experience, i.e., from training data and propose that learning methods are the most appropriate approaches to real-world fusion problems, since they are largely model-free and th... Read More about A Generative Learning Approach to Sensor Fusion and Change Detection.