Deep Learning Based Hybrid Approach For Facial Emotion Detection

Nowadays, measuring customer satisfaction is an important strategic tool for companies; many manual methods exist to measure customer’s satisfaction. However, the results have not effective and efficient. In this paper, we propose a new method for facial emotion detection to recognize customer’s satisfaction using a deep learning model. We used a convolutional neural network to detect facial key points. These key points help us to extract geometric features from customer’s emotional faces. Indeed, we computed distances between neutral face and negative or positive feedback. After that, we classified these distances by using Support Vector Machine (SVM), KNN, Random Forest, and Decision Tree. To evaluate the performance of our approach, we tested our algorithm by using FACEDB and JAFFE datasets. We found that SVM is the most performant classifier. We obtained 96% as accuracy by using FACEDB dataset and 95% by using JAFFE dataset.

Download Full-text

Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era

Cardiology in the Young ◽

10.1017/s1047951120004503 ◽

2020 ◽

pp. 1-10

Author(s):

Colin J. McMahon ◽

Justin T. Tretter ◽

Theresa Faulkner ◽

R. Krishna Kumar ◽

Andrew N. Redington ◽

...

Keyword(s):

Deep Learning ◽

Carbon Footprint ◽

Quantitative Research ◽

Survey Design ◽

Hybrid Approach ◽

Disruptive Innovation ◽

Cross Sectional Survey ◽

Cross Sectional ◽

E Learning ◽

The Impact

Abstract Objective: This study investigated the impact of the Webinar on deep human learning of CHD. Materials and methods: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. Results: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they “agreed” or “strongly agreed” that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. Conclusion: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a “sustainable academia”.

Download Full-text

Age Bias in Emotion Detection: An Analysis of Facial Emotion Recognition Performance on Young, Middle-Aged, and Older Adults

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society ◽

10.1145/3461702.3462609 ◽

2021 ◽

Author(s):

Eugenia Kim ◽

De'Aira Bryant ◽

Deepak Srikanth ◽

Ayanna Howard

Keyword(s):

Older Adults ◽

Emotion Recognition ◽

Recognition Performance ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Emotion Detection ◽

Middle Aged ◽

Age Bias

Download Full-text

Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽

10.3390/en14082181 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2181

Author(s):

Rafik Nafkha ◽

Tomasz Ząbkowski ◽

Krzysztof Gajowniczek

Keyword(s):

Deep Learning ◽

Power Grid ◽

Hybrid Approach ◽

Electricity Consumption ◽

Maximum Power ◽

Learning Approaches ◽

Two Stage ◽

Load Optimization ◽

Optimal Capacity ◽

Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

Download Full-text