Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

Abstract-Estimationof human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.

The Heterogeneous Dial-a-Ride problem (HDARP) is an important problem in reduced mobility transportation. Recently, several extensions have been proposed towards more realistic applications of the problem. In this paper, a new variant called the Multi-Depot Multi-Trip Heterogeneous Dial-a-Ride Problem (MD-MT-HDARP) is considered. A mathematical programming formulation and three metaheuristics are proposed: an improved Adaptive Large Neighborhood Search (ALNS), Hybrid Bees Algorithm with Simulated Annealing (BA-SA), and Hybrid Bees Algorithm with Deterministic Annealing (BA-DA). Extensive experiments show the effectiveness of the proposed algorithms for solving the underlying problem. In addition, they are competitive to the current state-of-the-art algorithm on the MD-HDARP.

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The single vehicle pickup and delivery problem with time windows is an important practical problem, yet only a few researchers have tackled it. In this research, we compare three different approaches to the problem: a genetic algorithm, a simulated annealing approach, and a hill climbing algorithm. In all cases, we adopt a solution representation that depends on a duplicate code for both the pickup request and its delivery. We also present an intelligent neighborhood move, that is guided by the time window, aiming to overcome the difficult problem constraints efficiently. Results presented herein improve upon those that have been previously published.Keywords Pickup and delivery problem with time windows · Dial-a-ride · Vehicle routing · Genetic algorithms · Simulated annealing · Hill climbingThe pickup and delivery problem with time windows (PDPTW) is an important practical problem that is likely to assume even greater prominence in the future. Current concerns over global warming, resource depletion and the social impact of traffic congestion and pollution (and resulting legislation, increase in cost, and changes in public perceptions) are driving companies, government organizations and researchers to improve the efficiency of logistics and distribution operations. In addition, the rapid growth in parcel transportation as a result of e-commerce is likely to have an increasing impact. More cooperation between manufacturers, shippers and carriers in supply chains could greatly reduce the environmental impact of transport (the number of M.I. Hosny ( ) ·

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