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The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.

The main aim of this study is to detect congestion and provide efficient route recovery mechanism using back pressure technique. In this study, we propose a fuzzy based congestion control and backpressure routing technique in wireless sensor networks. In the Fuzzy based congestion control technique, Fuzzy logic decision model is used to estimate the congestion status of each node based on the parameters number of contenders, buffer occupancy percentage of parent nodes and traffic load. In cluster based backpressure routing, clusters are formed and cluster heads are elected based on the congestion status of the nodes. By simulation results, we show that the proposed algorithm reduces the overhead and increases the routing efficiency.

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Today, a wealth of data is being produced over the internet from multiple sources, giving rise to the term big data. Much big data is contributed largely in the form of text. This work focuses on text classification of movie reviews dataset using Hybrid Word Embedding (HWE) models and deriving the optimal text classification model. However, in text processing, efficient handling and processing of the words and sentences in a document plays a vital role. In traditional methods like Bag of words (BoW) semantic correlation among the words does not exist. Further, the words in a document are not always processed in order, which results in certain words not being processed at all and creating problems with data sparsity. To overcome the data sparsity problem, the proposed work applied hybrid word embedding using WordNet repository. The hybrid model is built with three word embedding methods, namely, an embedding layer, Word2Vec and GloVe, in combination with the deep learning Convolutional Neural Network (CNN). The results obtained for the movie review dataset set was compared and the optimal classification model is identified. Various metrics considered for evaluation includes Log loss, Area under Curve (AUC), Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), Mean Absolute Error (MAE), Error Rate (ERR), Mathews Correlation Coefficient (MCC), Training Accuracy, Test Accuracy, Precision, Recall and F1 score. Finally, the experimental results proved that the word2vec is derived as the optimal hybrid word embedding model for classification of chosen movie review dataset. HIGHLIGHTS• Proposed Hybrid Word Embedding (HWE) models for Efficient Text classification.• Data Sparsity issue is reduced using WordNet repository along with proposed model.• Optimal model is derived based on the Performance evaluation on the model.