Optimized PID Controller with Bacterial Foraging Algorithm

Fish robot precision depends on a variety of factors including the precision of motion sensors, mobility of links, elasticity of fish robot actuators system, and the precision of controllers. Among these factors, precision and efficiency of controllers play a key role in fish robot precision.  In the present paper, a robot fish has been designed with dynamics and swimming mechanism of a real fish. According to equations of motion, this fish robot is designed with 3 hinged links. Subsequently, its control system was defined based on the same equations. In this paper, an approach is suggested to control fish robot trajectory using optimized PID controller through Bacterial Foraging algorithm, so as to adjust the gains. Then, this controller is compared to the powerful Fuzzy controller and optimized PID controller through PSO algorithm when applying step and sine inputs. The research findings revealed that optimized PID controller through Bacterial Foraging Algorithm had better performance than other approaches in terms of decreasing of the settling time, reduction of the maximum overshoot and desired steady state error in response to step input. Efficiency of the suggested method has been analyzed by MATLAB software

Adaptive Energy-aware Cluster Based Routing Protocol for Mobile Ad Hoc Networks

Due to the downside characteristics of Mobile Ad hoc Networks (MANETs) such as dynamic topology and energy consumption and control overhead, network clustering is one of the promising solutions. Cluster Based Routing Protocol (CBRP) is a robust and scalable routing protocol for MANETs. Clustering formation algorithm used in CBRP is a variation of simple lowest-ID algorithm in which the node with a lowest ID among its neighbors is elected as the Cluster head. Neglecting mobility and energy for selecting cluster head is one of the weakness points of the algorithm. In order to increase stability of the network and to prevent re-clustering an adaptive energy-aware Cluster Based Routing Protocol (AECBRP) is proposed. Two algorithms have been introduced in AECBRP as enhancement to the CBRP: improving the cluster formation algorithm by considering relative mobility, residual energy and connectivity degree metrics, and add in an efficient cluster maintenance algorithm based on the aggregate energy metric of cluster head. Using NS-2 we evaluate the rate of cluster-head changes, the normalization routing overhead and the packet delivery ratio. Comparisons denote that the proposed AECBRP has better performances with respect to the original CBRP and Cross-CBRP

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Abstract Users of e‐commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website (www.Digikala.com), which is an Iranian e‐commerce company. In this research, a language‐independent framework is proposed that is adjustable to other languages. In this regard, after necessary text processing and preparation steps, the existence of an aspect in an opinion is determined using deep learning algorithms. The proposed model combines Convolutional Neural Network (CNN) and long‐short‐term memory (LSTM) deep learning approaches. CNN is one of the best algorithms for extracting latent features from data. On the other hand, LSTM can detect latent temporal relationships among different words in a text due to its memory ability and attention model. The approach is evaluated on six classes of opinion aspects. Based on the experiments, the proposed model's accuracy, precision, and recall are 70%, 60%, and 85%, respectively. The proposed model was compared in terms of the above criteria with CNN, Naive Bayes, and SVM algorithms and showed satisfying performance

A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems

Abstract Under-sampling is a technique to overcome imbalanced class problem, however, selecting the instances to be dropped and measuring their informativeness is an important concern. This paper tries to bring up a new point of view in this regard and exploit the structure of data to decide on the importance of the data points. For this purpose, a multi-manifold learning approach is proposed. Manifolds represent the underlying structures of data and can help extract the latent space for data distribution. However, there is no evidence that we can rely on a single manifold to extract the local neighborhood of the dataset. Therefore, this paper proposes an ensemble of manifold learning approaches and evaluates each manifold based on an information loss-based heuristic. Having computed the optimality score of each manifold, the centrality and marginality degrees of samples are computed on the manifolds and weighted by the corresponding score. A gradual elimination approach is proposed, which tries to balance the classes while avoiding a drop in the F measure on the validation dataset. The proposed method is evaluated on 22 imbalanced datasets from the KEEL and UCI repositories with different classification measures. The results of the experiments demonstrate that the proposed approach is more effective than other similar approaches and is far better than the previous approaches, especially when the imbalance ratio is very high