Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accur...
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Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed rider-based monarch butterfly optimization (rider-MBO) algorithm. The proposed rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.
Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. T...
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Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness. Design/methodology/approach This paper introduces a power quality enhancement model that is based on a new hybrid optimizationalgorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithmrider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections. Findings Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA);0.09 per cent better than GMDA and WOA;and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models. Originality/value This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors' knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.
Technology-enhanced learning provides various communication and information technologies for learning and teaching. The teachers also feel comfortable with the Open Educational Resources repositories that learn the ex...
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ISBN:
(纸本)9781728196008
Technology-enhanced learning provides various communication and information technologies for learning and teaching. The teachers also feel comfortable with the Open Educational Resources repositories that learn the existing learning materials when a new course is developed. Yet, this exists as a non-trivial problem because the learning environments should be flexible for the students to learn on the basis of their situations and characteristics. Hence, the main aim of this paper is to provide personalized dynamic and continuous recommendations for online learning systems. This paper plans to implement the novel recommendation system for online learning using intelligent techniques. The main steps of the proposed model are (a) Data collection, (b) Feature extraction, and (c) classification. Initially, the data are collected locally from the Ekhool learning application. Then the feature extraction techniques, such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Principle Component Analysis (PCA) are used for selecting the most relevant features. Further, the classifier termed as Fuzzy Logic Classifier is adopted as the recommendation system, where the improvement is made in the membership limits by optimizing it with the rider optimization algorithm (ROA). The superiority of the proposed method is proved by the performance analysis in terms of various performance measures.
Electricity plays an indispensable role in human lives. Due the increasing need for electricity in domestic, commercial and industrial applications and the deletion of conventional sources,the power generation system ...
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ISBN:
(数字)9781728146850
ISBN:
(纸本)9781728146850
Electricity plays an indispensable role in human lives. Due the increasing need for electricity in domestic, commercial and industrial applications and the deletion of conventional sources,the power generation system is switched on to systems with renewable energy sources. Therefore the power quality problems arises and research has been going on to improve the power quality. This paper is a study about the various power quality improving algorithms applied to the hybrid wind solar power generation with multilevel inverters. in comparison with various optimizationalgorithms, more control parameters with a new algorithm called rider optimization algorithm (ROA) is suggested.
Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massiv...
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Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional rider-Harmony search algorithm (Fractional rider-HSA) for resource allocation in the cloud. The Fractional rider-HSA combines fractional calculus, rider optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.
ABSTRACTCurrently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous se...
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ABSTRACTCurrently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the rider Horse Herd optimizationalgorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the rider optimization algorithm (ROA) and Horse herd optimizationalgorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.
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