Encryptions can play essential role in securely communication transmissions. It can be used to raise the reliability and security of any communication system such as satellite communication and wireless Local Area Network (LAN). Security should satisfy and reassure the data transmission arrival to a specific destination. In this paper, a well-defined and powerful encryption technique is suggested, it can be exploited for securely communication transmissions. This technique utilizes An artificial neural network (ANN) of Multi-Layer Perceptron (MLP). It is used to efficiently encrypt text information into useful codes. Significant accuracies of 100% are investigated for data encryption and decryption as well.

Sentiment Analysis/Opinion Mining integrates the discipline of Computer Science as well as Business Management. Sentiment Analysis on Fake and Genuine Reviews spotting is based on public emotional approaches. The main aim of this work is for helping customers to identify fake reviews on social media and websites for better decision on product purchases through online. Also it is helpful for sellers and manufacturers to notify fake and genuine reviews of their products. The most emerging techniques viz Fuzzy Logic, Neural Network, Hybrid of Fuzzy Logic, and Neural Network are used to splotch fake and genuine reviews. These techniques analyze social media comments which are then classified to fake and genuine reviews separately. These proposed technique’s performance are evaluated by evaluation metrics providing 96% of Accuracy Score and F1-Score. The higher percentage of evaluation metrics proves better classification of fake and genuine reviews.

In the proposed work is discussed the design and the development of a web app platform supporting the choice of financial announcements. The platform is based on digitized information extracted from a pdf file and on automatic text mining data processing. Models integrating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). are applied to implement a Decision Support System (DSS) based on Natural Language Processing (NLP), and to favor the choice of the announcement to follow, by guiding financial perspectives. The proposed DSS automatizes the choice process by enhancing announcement keywords, deadlines and requirements to participate to request for funds. The research is finalized to provide a dashboard suggesting automatically all the possibilities for a funding participation matching with company profiles. The paper introduces also an innovative approach based on a successive application of, CNN, LSTM-CNN, and of the combination of CNN + LSTM-CNN, thus furthermore improving text recognition performances. The combined CNN + LSTM-CNN approach provides the best Accuracy of about 95 %, and the best Loss of 0.006 for the specific case of study. The results are obtained within a framework of an industry project.

This paper proposes an optimal controller optimized by the meta-heuristic technique called coyote optimization algorithm (COA), for dealing with load frequency Control (LFC) issues. A filter with derivative term is integrated with PID controller to alleviate the impact of noise in the input signal. A time domain based-objective functions are investigated such as integral time-multiplied absolute value of the error (ITAE) and integral of absolute error (IAE). A two area non-reheat thermal systems are employed for the simulation procedures. It will be optimally tuning the PID parameters using the proposed COA. Also, the obtained results by the designed PID controller based on COA are compared with various reported techniques, such as particle swarm optimizer (PSO), differential evolution (DE), jaya optimizer and self-adaptive multi-population elitist (SAMPE) jaya, for three dissimilar test cases with various set of disturbances. These simulated comparisons declare the great efficiency with high superiority of the designed PID controller based on COA for handling the load frequency control in multi-area power systems. It gives robust results over various indices and cases

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Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DGs) or system reconfiguration (SRC). However, very few researches have been exerted for incorporating both where this merge represents further complexities in searching for maximizing their benefits and minimizing the investments. This paper sheds light on the simultaneous allocation of DGs with SRC in distribution systems. It suggests a multi-objective formulation considering multiple objectives: active power loss minimization, total cost reduction, and voltage profile improvement. For solving this problem, an improved spotted hyena optimizer (ISHO) is developed. The SHO demonstrates a good feature in finding the optimal or nearly optimal subset with simple structure to minimize the given fitness function. To support the exploration characteristics of the proposed ISHO, it augments a switch strategy in the updating mechanism of the spotted hyena’s positions and random positions are generated instead of the violated spotted hyenas. The simulations are investigated for the integration of a single and multiple DGs with SRC for two standard systems, which are IEEE 33 and 69-node test systems. The obtained results prove the efficiency of the suggested method for the network manager to find the optimal DGs allocation with SRC considering multiple criteria. These results are compared with previous literatures to demonstrate the effectiveness of the proposed algorithm

The return flow has been recognized as one of the factors to reduce irrigation efficiency in irrigation and drainage networks. hence a detailed estimation over various parameters and providing management strategies in quantitative reduction and/or its optimal use can have a considerable role in improving the performance and productivity of such systems. The present study aimed to develop a fuzzy tree model to predict the daily return flow of irrigation systems (Salimeh drainage basin; Dez irrigation system). Thus, fuzzy regression tree model were developed using five variabled of daily input flow, effective rainfall, water requirements, infiltration and evaporation from canals. The phases of training, verification and test of the models were conducted based on actual measured data during two years of 2014 and 2016. Model assessment illustrated that all fuzzy regression model had highly accuracy in identifying the return flow intensity in the studied area and hence application of of that is recommended is predicting the return flow network.

Our present-day Technology makes it possible to produce highly accurate gears with practically zero errors: even such accurate gears, often referred to as aircraft quality gears, are also subject to development of dynamic action especially when the running peripheral speeds are high (greater than 10m/s). The reason behind the enhancement of loading is to be sought in the dynamic phenomena that involve mechanical vibrations of the geared torsional. In most cases the vibrations are in the audio frequency range of 1 KHz to 5 KHz, emitting a note to which the human ears are most sensitive. Apart from noise pollution, such vibrations even in so called perfect gears result in surface deterioration of the mating pairs. In the present paper the authors consider the dynamics of a torsional system connecting two rotary inertias through a reduction gearing of high accuracy (zero pitch error). Equations of the geared torsional exhibit variable stiffness parameters (position dependent) and hence are analytically intractable. Recourse has, therefore, been made to digital simulation and the results of analysis bring forth many interesting points which could help the designer to decide upon the operating conditions of such installations