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Hiding the presence of data during communication has become a pressing concern in this overly digitalized world as a consequence of illegitimate access. These concerns have led to cryptography and steganography techniques as methods for securing data. This paper presents a modified information hiding technique based on an indirect least significant bit. Instead of saving each bit of the secret message in the least significant bit (LSB) of the cover media, each bit of the secret message is compared to a mask bit in the cover media. The result is saved in the cover media's LSB. In this paper, two steganography schemas are designed in which the cover media are image and audio, while the secret message is a text file. A simple encryption technique is used to transforn1 the secret message into an unreadable format before the hiding process begins. The experimental results indicate that the proposed algorithm achieves promising performance. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has reformed the meaning of traditional generation of power from large-scale to small-scale generation. The main issue of connecting the DG to the utility grid is the detection of unintended islanding. This paper shows the impact of the islanding phenomenon in the case of grid-connected photovoltaic arrays and how to develop a convenient technique to detect this phenomenon. A passive islanding detection algorithm is proposed for all types of DGs by varying and analyzing the DC-link voltage for voltage source converter in the photovoltaic inverter. The proposed algorithm is applied on the low and medium voltage scales. Furthermore, a comparison for applying the proposed technique with resistance load on the two scales is presented. In addition, the proposed technique for anti-islanding protection is performed and compared with a lot of techniques such as underfrequency, overfrequency, and rate of change of frequency according to the detection time of islanding. The simulation results using MATLAB/ SIMULINK platform illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Improving the accuracy of box office revenue forecasts is conducive to stimulating the creation, market investment, infrastructure construction, and rational allocation of public resources in the film market, as well as promoting social welfare and cultural prosperity. Since the existing box office revenue prediction algorithm does not consider film industry structure, the prediction accuracy is not satisfying. This paper firstly builds a two-stage human-machine collaborative feature processing framework. In the first stage, based on the box office data, the regression decision tree algorithm is used to process all the box office features preliminarily and delete the unimportant features automatically. In the second stage, feature processing is coupled with the built Artificial Neural Network (ANN). In this stage, the features processed by the machine are manually classified, and multiple, incompatible feature sets are divided. After designing the incompatible set network pruning algorithm, the neural network is pruned. We construct the data set with a total of 7098 movies crawled online on four platforms. Numerical experimental results show that the Mean Absolute Error (MAE) of the two-stage algorithm is significantly better than the baseline model, which can effectively reduce the noise caused by encoding between incompatible features directly, improve the prediction accuracy of ANN, accelerate the forward inference speed of ANN and reduce the consumption of computing resources. [ABSTRACT FROM AUTHOR]

The cycles in an interconnection network are one of the communication types that are considered as a factor to measure the efficiency and reliability of the networks’ topology. The network is said to be panconnected if there are cycles of length l between two nodes u and v, for all l = d(u, v), d(u, v) + 1, d(u, v) + 2, …, n - 1 where d(u, v) is the shortest distance between u and v in a given network, and n is the total number of nodes in the network. In this paper, we propose an algorithm that proves the existence of panconnectivity of Eisenstein-Jacobi networks by constructing all cycles between any two nodes in the network of length l such that 3 ≤ l < n. The correctness of the proposed algorithm is given with the time complexity O(n4 ). The proposed algorithm adopts and modifies the idea of Dynamic Source Routing (DSR) to find all possible shortest paths. The results of some test cases using the proposed algorithm are provided. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of sequential content load algorithm on regional transfer in OpenSimulator framework. It, then, presents an abstract parallel content load algorithm, which is implemented using the basic parallel for and chunk partitioning constructs to exploit the parallelism of multi-core architectures. This work used a set of five example virtual worlds content to investigate the effectiveness of the proposed work and its impact on the reallocation process of regions in OpenSimulator. Simulation results revealed that the implementation using parallel for reduced the timings by 47% to 57% while the implementation using chunk partitioning minimized it by 63% to 72%. Time impact on regional transfer was reduced by 39% to 51% using parallel for while 52% to 66% using chunk partitioning. [ABSTRACT FROM AUTHOR]

The arrangement which does not necessitate any infrastructure for doing discussion among nodes is called MANET. The direction-finding technique, a mixture of ACO (ant colony optimization) and particle swarm optimization (PSO) technique, is designed in the paper. The designed technique is judged against several alternatives of particle swarm optimization technique and ant colony optimization technique, i.e.,EDNR+PSO, AntChain Protocol, Improvised Ant Colony Routing (IACR), and ANTALG. Open-source simulator NS 2.3 is employed for carrying out the simulation. From the outcome, it has been verified that the designed technique is finest in contrast to other alternatives of PSO and ACO.Routing overheads of the designed algorithm are reduced in contrast to other methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several applications utilizing a set of red green blue (RGB) and near infrared (NIR) images have been emerging over recent years. The present work proposes a technique of enhancing an image by combining color (RGB) and near infrared information (NIR). In order to fuse the two types of images, the NIR-channel is considered as a luminance counterpart to the visible image. International standard database (RGB-NIR Scene Dataset) is used in this work for image fusion. The objective of the paper is to present a simple and hardware efficient fusion method, where the original RGB image is converted into two different color spaces, namely, HSV and YCbCr. Later, the luminance channel of the RGB image is replaced with the near infrared channel, thereby obtaining a fused enhanced image. The above procedure is effectively implemented on FPGA using the Xilinx HLS tool. RGB-NIR dataset is used in the present work for testing the proposed image fusion algorithm, and the quality of the fused image is measured through peak signal to noise ratio (PSNR). The experimental results indicate that HSV color space is more efficient in image fusion compared to YCbCr color space based on the average PSNR values of approximately 29db for HSV and 25db for YCbCr for various images, respectively. Finally, this complete fusion algorithm is implemented on Xilinx Nexys4 FPGA board to be able to obtain real-time outputs in the form of vivid, contrasted images that are pleasing to the observers. The experimental results illustrate that the Xilinx FPGA utilizes only 50% of the available hardware resources and consumes approximately 5.3 Watts to implement the fusion process. [ABSTRACT FROM AUTHOR]

The mobile device has become an essential utility tool for more effective computation, storage, and power, making it suitable for mobile cloud computing. The cloudlet is used as a connectivity establishment link between the mobile device and the cloud. The objective of this paper is to focus on mobile cloud computing facilitated with cloudlet-based computation. The latter possesses inter-cloudlet communication, which had been proposed within the mobile cloudletbased computing environment framework. The same had been further enhanced to scalable critical parameter yield of resources framework. Nevertheless, this was not taken to the criteria, which would impact the yield factor, in terms of availability. The present research endeavor aims to improve the algorithm by considering some more criterion and provides a new mobile cloud computing framework for data execution as a service using cloudlet. The outcome shows a positive result in the cloud- cloudlet based computation. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) system is always operated at the maximum power point (MPP) condition irrespective of the fluctuations in PV voltage. The maximum power point tracking (MPPT) employed in PV system is not effective during the presence of current ripple as normal tracking becomes increasingly complex during fluctuation in solar irradiation or due to change in MPP condition. This paper proposes a high-efficiency power point tracking algorithm to minimize the current ripple and power oscillation around the maximum power point. The developed algorithm is based on particle swarm optimization-support vector regression (PSO-SVR) technique. The proposed algorithm is implemented to select and tune the Support Vector Regression (SVR) parameters such as kernel parameters, variance, and the penalty factor for predicting the irradiation level as well as to determine the PV voltage corresponding of maximum power point. The PSO method is used to accelerate the process of optimizing the SVR parameters at different conditions and get knowledge about the corresponding global optimum. From the experimental results, the efficiency of maximum power point tracking is found to be 99.8%. The proposed algorithm PSO-SVR shows a better performance than using SVR alone. The stability and accuracy of MPPT have been validated during the rapid fluctuation of solar irradiation in the range of 25% to 100%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For online education platforms, a personalized recommendation system is crucial, and the collaborative filtering algorithm is the primary recommendation algorithm used. This study took the recommendation of crowdfunding platforms as a sample, and enhanced the collaborative filtering algorithm based on the user score and project attribute features of the crowdfunding platform, intending to resolve the cold start issue brought on by the platform's reliance on a single data source. The study concludes with experimental proof of the paper's suggested better method. This approach can alleviate the cold start issue to some degree. The prediction accuracy has been much enhanced in comparison with the conventionally advised method. The method can also adapt to user tastes over time, learning what they like and what they don't. It also has an excellent real-time suggestion impact. The performance verification of the algorithm in this research is also conducted using data from a live crowdfunding site, lending credence to the study's claim of greater practicality. [ABSTRACT FROM AUTHOR]

The trend of inter-disciplinary conceptual design synthesis requires designers to involve more and more distributed multi-disciplinary design resources. Therefore, this paper proposes a graph-based computerized optimal conceptual design synthesis to help designers explore novel design schemes within the distributed multi-disciplinary resource environment. The design resources tightly related to the design goal can be extracted from the huge resource environment by a proposed searching engine. The optimal design scheme can be generated from these related design resources by a proposed graph-based algorithm. A set of computer applications called Automatic Conceptual Design System (ACDS) is established to verify the feasibility of this proposed conceptual design synthesis, and a garbage power system’s conceptual design is completed by this software prototype. [ABSTRACT FROM AUTHOR]

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems. [ABSTRACT FROM AUTHOR]

The least trimmed squares (LTS) estimation has been successfully used in the robust linear regression models. This article extends the LTS estimation to the Jammalamadaka and Sarma (JS) circular regression model. The robustness of the proposed estimator is studied and the used algorithm for computation is discussed. Simulation studied, and real data show that the proposed robust circular estimator effectively fits JS circular models in the presence of vertical outliers and leverage points. [ABSTRACT FROM AUTHOR]

Oil and gas production represents an essential source of energy. Optimization of oil and gas production systems requires accurate calculation of pressure drop in tubing and flowlines. Many empirical correlations and mechanistic models exist to calculate pressure drop in tubing and flowlines. Previous work has shown that some correlations provide more accurate results under certain flow conditions, PVT data, and well configurations than others. However, the effects of errors in input data on the selection of which correlations to use have not been investigated. This paper studies different multiphase flow correlations to determine the effects of their input parameters on (1) the accuracy of calculated pressure drop and (2) the selection of best correlation. A database consisting of 33 oil wells and 32 gas wells was selected, and a commercial software was used to build different well models. A total of 715 well models were constructed and used to investigate the effects of errors in correlations inputs on both the calculated bottomhole pressure and the selection of best correlation(s). The methodology was based on perturbing the values of the selected input parameters and calculating the new predicted bottomhole flowing pressure. Then, the effects of error in input parameters on how the calculated bottomhole pressure was different from observed data were quantified. The effect of this error in input parameters was also checked against the algorithm that selects the best correlation(s). It was found that errors in input GOR have the greatest effects for oil wells, while gas specific gravity and the tubing roughness are the most effective parameters for gas wells. The results were integrated into a rule-based expert system. A new set of data, consisting of 220 cases from 10 new oil wells and 10 new gas wells, was used to validate the expert system. The expert system was found to predict the best correlation(s) with a success rate of 80%, and it also identifies the input parameters whose error would affect the value of calculated bottomhole pressure significantly. Finally, the rules of the expert system were programmed into a VBA-Code to ease its use. [ABSTRACT FROM AUTHOR]

Buffer capacity allocation plays a key role in the design of production lines. By means of polymorphism analysis on production capacity and capability, we investigate the buffer allocation problem in multistage production lines with unreliable machines. The allocated buffers simultaneously maximize the system theoretical production rate and minimize the system state entropy. A mathematical model for buffer capacity optimization was established and optimized using an extended vector universal generating function (UGF) and an improved adaptive nondominated sorting genetic algorithm II (NSGA-II). The feasibility and effectiveness of the proposed method were verified by applying the model to a production line of engine heads. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other e-commerce websites produce a large number of text reviews. These text reviews mostly describe the product features and their opinions, which are the most important to the product developers, launchers, or buyers for business development and decisionmaking processes. Therefore, we present an opinion-based co-occurrence network for product reviews. The main aim of this research is to identify the popularity of product features or popular terms, the number of connections of a term, the strong relationship between terms, grouping the product terms, and the sentiment polarity links between terms in both positive sentiment and negative sentiment. Also, we employed the Harel-Koren fast multiscale layout algorithm and CNM (Clauset-Newman-Moore) algorithm for visualizing and grouping the network. We then measured the overall graph metrics and vertex metrics to characterize the network. Additionally, the experimental result shows the ranked product features and their social strength between product features and sentiments. [ABSTRACT FROM AUTHOR]

The Lower Goru Formation (LGF) has produced from E-sand in Kadanwari gas field, composed of sand and shale. The estimation of reservoir properties, including porosity, mineralogy, and thickness, which could help to identify new possible drilling sites, is hindered by the heterogeneous nature of LGF, and intermix shale, and sand. Integration of elastic and petrophysical properties worked as an effective tool for evaluating reservoir properties, especially for handling the heterogeneities of sand reservoirs associated with pores and their fluids. The probabilistic neural networking (PNN) technique has been proven the most effective and is a widely applied algorithm for predicting petrophysical parameters away from control points. It can create nonlinear correlations between input and estimated properties by efficiently handling the intermix shale. For this study, PNN prediction demonstrated a reliable statistical approximation. The trends of the actual and predicted properties such as effective porosities (PHIE = ∅e) and water saturation (Sw) for E-sand level exhibited an excellent correlation of 0.98 and 0.99 with an error rate of 0.006 and 0.03 respectively, which is also evident through the regression lines. The promising zones of the reservoir are further assessed by the multivariate seismic attributes, i.e., amplitudes, sweetness, and RGBA blending of appropriate frequency bands, which illuminated the potential channelized sands. The methodology applied in this study eventually minimized the uncertainty associated with petrophysical properties such as ∅e and Sw of the producing sands in LGF. The findings suggest that when dealing with heterogeneous reservoirs, a similar approach could be employed to locate feasible prospects. [ABSTRACT FROM AUTHOR]

The utilization of back-propagation neural network in identification of internal dynamics of chaotic motion is found appropriate. However, during its training through Rumelhart algorithm, it is found that, a high learning rate (α) leads to rapid learning but the weights may oscillate, while a lower value of 'α' leads to slower learning process in weight updating formula Δvjk = αδjxi Momentum factor (μ) is to accelerate the convergence of error during the training in the equation wjk(t+1) = wjk(t) + αδkzj + μ{wjk(t) - wjk(t - 1)} vjk(t+1) = vjk(t) + αδkz + μ{vjk(t) - vjk(t-1) while transfer function sigmoid f(x) = 1/1+e-δx+n. It is the most complicated and experimental task to identify optimum value of 'α' and 'μ' during the training. To identiαy optimum value of 'α' and 'μ', firstly the network is trained with 10³ epochs under different values of 'α' in the close interval 0 < α <1 and μ = 1. At α = 0.3the convergence of initial weights and minimization of error (i.e., mean square error) process is found appropriate. Afterwards to find optimum value of μ, the network was trained again with α = 0.3 (fixed) and with different values of μ in the close interval 0 <μ < 1 for 10³ epochs. It was observed that the convergence of initial weights and minimization of error was appropriate with α = 0.3 and μ = 0.9. On this optimum value of α and μ the network was trained successfully from local minima of error = 1.67029292416874E-03 at 10³ epochs to global minima of error = 4.99180426869658E-04 at 15 105 epochs. At the global minima, the network has exhibited excellent performance in identification of internal dynamics of chaotic motion and in prediction of future values by past recorded data series. These essentials are presented through this research paper. [ABSTRACT FROM AUTHOR]

Automated recitation plays an important role in improving self-learning. It is based on Speech/Text recognition. The research in Arabic speech recognition is very limited. The few existing applications are only based on the Holy Qur’an. This article proposed a new system (Samee’a - سميع) to facilitate memorizing any kind of text such that poems, speeches and the Holy Qur’an. Samee’a system is based on Google Cloud Speech Recognition API to convert the Arabic speech to text and Jaro Winkler Distance algorithm to determine the similarity between the original and converted texts. The system has been tested using 70 collected files ranging between 12 to 400 words and some chapters from the Holy Qur’an. The average similarity achieved 83.33% for the 70 files and 69% for the selected chapters of the Holy Qur’an. These results were enhanced to 91.33 % and 95.66% after applying preprocessing operations on the text files and the Holly Qur’an respectively. To validate the obtained results, two comparison studies were performed. The Jaro Winker distance was successfully compared to the cosine and the Euclidean distance. In addition, the proposed system outperformed the related work with an improvement of the similarity reaching 5% when using section 30 of the Holy Qur’an. Finally, the user experience testing was carried out by 10 users of different ages (between 5 and 50-year-old) using small texts and some small chapters of the Holy Qur’an. The proposed system proved its efficiency. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) and differential evolution (DE) are metaheuristic algorithms that have shown a favorable performance in the optimization of complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multiobjective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for the optimal design of the RC frames. Using the weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables, and the design requirements of the ACI-318 are employed as constraints. Five numerical models are studied to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor and Actor Networks (WSANs) have been extensively employed in various domains ranging from elementary data collection to real-time control and monitoring for critical applications. Network connectivity is a vital robustness measure for overall network performance. Different network functions such as routing, scheduling, and QoS provisioning depends on network connectivity. The failure of articulation points in the network disassociates the network into disjoint segments. We proposed Distributed Partition Detection and Recovery using Unmanned Aerial Vehicle (UAV) (DPDRU) algorithm, as an optimal solution to recover the partitioned network. It consists of three steps: Initialization, Operational and Detection, and Recovery. In the Initialization phase sink node collects all the information about the network. In the Operational and Detection phase, network nodes communicate regularly by exchanging HEARTBEATS, detects failure if some nodes do not get a message from the neighbor node and send failure reports, and sink node identifies network partition. In the recovery phase, the sink node sends UAV at the positional coordinates of the failed node and examines network recovery after UAV reaches the desired location. Our approach primarily focuses on reducing message overhead by sending few update messages to sink node and energy consumption by engaging network nodes only for communication. The requirements of the recovery process (physical movement and communication) are fulfilled by UAV. The algorithm is tested according to the following parameters: Detection Time, Recovery Time, message overhead, and distance traveled by UAV. Simulation results validate the efficacy of the proposed algorithm based on these parameters to provide reliable results. The minimum and the maximum number of messages transmitted are 11 for 10 nodes and 24 for 100 nodes respectively. Hence these results demonstrate that the message overhead in our proposed solution is less as compared to other techniques when the number of nodes increases. [ABSTRACT FROM AUTHOR]

Tue manuscript proposes a time series prediction technique to enhance the response time of the coherency identification technique. Tue proposed methodology utilizes the nonlinear autoregressive exogenous neural network (NARX) algorithm to predict the generator speed deviations following a disturbance in the system. Consequently, the coherency identification technique based on independent component analysis (ICA) is utilized on the predicted system responses. Tue effectiveness of the proposed approach is demonstrated on of the IEEE 16-generator 68-bus test system model. The result shows that the proposed technique can predict 0.2s following a disturbance in the system accurately. Therefore, the NARX allows a 0.2s head start for the ICA to determine the real-time coherent generator group in the system. Furthermore, the result shows that the proposed approach can identify the coherent group of generators based on the predicted generator speed deviation in all cases considered in this study, accurately. Conclusively, the result implies that the proposed technique can speed up the overall coherency identification process in a power system operation. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure's property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule's consequence. [ABSTRACT FROM AUTHOR]

Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation. The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose. Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM). The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)