In the restructured power market, it is necessary to develop an appropriate pricing scheme that can provide the useful economic information to market participants, such as generation, transmission companies and customers. Proper pricing method is needed for transmission network to ensure reliability and secure operation of power system. Accurately estimating and allocating the transmission cost in the transmission pricing scheme still remains challenging task. This paper gives an overview of different costs incurred in transmission transaction, types of transmission transactions and the transmission pricing methodologies. Embedded as well as Incremental cost methods are explained. It mainly focussed on determining the embedded transmission cost by various methods and compared the results for 6bus, IEEE 14bus and RTS 24 bus systems.
Resource provisioning is the core function of cloud computing which is faced with serious challenges as demand grows. Several strategies of cloud computing resources optimization were considered by many researchers. Optimization algorithms used are still under reckoning and modification so as to enhance their potentials. As such, a dynamic scheme that can combine several algorithms' characteristics is required. Quite a number of optimization techniques have been reassessed based on metaheuristics and deterministic to map out with the challenges of resource provisioning in the Cloud. This research work proposes to involve the ant colony optimization (ACO) population-based mechanism by extending it to form a hybrid meta-heuristic through deterministic spanning tree (SPT) algorithm incorporation. Extensive experiment conducted in the cloudsim simulator provided an efficient result in terms of faster convergence, and makespan time minimization as compared to other population-based and deterministic algorithms as it significantly improves performance.
Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.
Polarity prediction is the field of study that discovers people’s opinions, feelings, assessments, perspectives and feelings about associations and their attributes as communicated in written text. It is one of the most active research areas in the field of text mining. Nowadays online reviews play an important role by giving a helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the organizations and government sectors to increase their quality of product and services. Pre-trained BERT (Bidirectional Encoder Representations from Transformers) is used for word embedding in this model. The fine-tuned BERT is used for better word representation which in turn improves the sentimental analysis classification accuracy. Bidirectional Long Short-Term Memory classifier is utilized for polarity prediction. To enhance the performance of Bidirectional Long Short-Term Memory, the weight parameters of Bi-directional LSTM are optimally selected by using APSO algorithm. Improved self-attention mechanism is added with BiLSTM for focusing on significant words in the context. For performance analysis, four bench mark datasets are used for experiments.
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