Other
seyed sadegh hosseini; Mohammadreza Yamaghani; Soodabeh Poorzaker Arabani
Abstract
Emotional computing synergizes the understanding and quantification of emotions, drawing on diverse data sources such as text, audio, and visual indicators. A challenge arises when attempting to discern authentic emotions from those concealed deliberately via facial cues, vocal nuances, and other communicative ...
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Emotional computing synergizes the understanding and quantification of emotions, drawing on diverse data sources such as text, audio, and visual indicators. A challenge arises when attempting to discern authentic emotions from those concealed deliberately via facial cues, vocal nuances, and other communicative behaviours. By integrating multiple physiological and behavioural signals, more profound insights into an individual's emotional state can be achieved. Historically, research has predominantly concentrated on a singular facet of emotional computing. In contrast, our study offers an in-depth exploration of its pivotal domains, encompassing emotional models, Databases (DBs), and contemporary developments. We commence by elucidating two prevalent emotional models, followed by an examination of a renowned sentiment analysis DB. Subsequently, we delve into cutting-edge methodologies for emotion detection and analysis across varied sensory channels, elaborating on their design and operational principles. In conclusion, the fundamental principles of emotional computing and its real-world implications are discussed. This review endeavours to provide researchers from academia and industry with a holistic understanding of the latest progress in this domain.
Computational modelling
Seyed Hamid Emadi; Abolfazl Sadeghian; Mozhde Rabbani; hassan dehghan dehnavi
Abstract
We consider a continuous model of the optimal control of the customer dynamics based on marketing policies as a non-autonomous system of ODEs. The model tracks the history of the simultaneous changes from the beginning to the current time for the evolution of the company’s regular, referral, and ...
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We consider a continuous model of the optimal control of the customer dynamics based on marketing policies as a non-autonomous system of ODEs. The model tracks the history of the simultaneous changes from the beginning to the current time for the evolution of the company’s regular, referral, and potential customers. We then present a new supervised machine learning algorithm for the numerical simulation of the problem. The proposed learning algorithm implements a polynomial kernel to simplify the formulation of the method. To avoid computational complexity, the Bernstein kernels are used to get a simple optimization marketing strategy by using the support vector regression in least-squares framework. Some numerical experiments are carried out to support the proposed model and the method. The method provides approximate numerical results with high accuracy by kernels of polynomials of low degree. The running time of the method is also illustrated versus the increasing number of training points to see the polynomial behavior of the running time.
Forecasting, production planning, and control
Akbar Abbaspour Ghadim Bonab
Abstract
Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze ...
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Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze the time series related to the demand for a type of women's luxury handbag based on a framework and using machine learning methods. For this purpose, five machine learning models including Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN), Discrete Wavelet Transform-Neural Networks (DWTNN), and Group Model of Data Handling (GMDH) were used. The comparison of the models was also based on the accuracy of the forecasting according to the values of forecasting errors. The RMSE, MAE error measures as well as the R, correlation coefficient were used to assess the forecasting accuracy of the models. The RBFNN model had the best performance among the studied models with the minimum error values and the highest correlation value between the observed values and the outputs of the model. But in general, by comparing the error values with the data range, it is concluded that the models performed reasonably well.
Data mining
Aboosaleh Mohammad Sharifi; Kaveh Khalili Damghani; Farshid Abdi; Soheila Sardar
Abstract
Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly ...
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Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.
