A novel hybrid requirement’s prioritization approach based on critical software project factors

Cognition, Technology & Work - Requirements prioritization is essential for development of quality software products. Requirements prioritization helps focus on the most important requirements...     

Comparison of the Microstructures and Mechanical Properties in the Overlapping Region of Low Carbon Steel Additive Bead Fabricated by WAAM and FSP

Metallurgical and Materials Transactions A - The periodic structural characteristics of the grains in the overlapping region of the WAAM deposited additive bead translate into spatially dependent...     

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.     

Towards efficient human–machine interaction for home energy management with seasonal scheduling using deep fuzzy neural optimizer

Cognition, Technology & Work - Maintaining the records of domestic consumers’ electricity consumption patterns is very complex task for the utilities, especially for extracting the...     

Impact of salicylic acid and calcium chloride on quality attributes of peach stored at refrigeration temperature

Food Science and Biotechnology - Salicylic acid (SA) in different concentration were used to assess its individual effect as well as combined impact with 3% calcium chloride (CaCl2) on Swat No. 8...     

Age and Gender Classification Using Backpropagation andBaggingAlgorithms

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.     

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms—Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.     

Conjugate local thermal nonequilibrium and non-Darcy flow inside porous enclosure: Analysis of localized heating and cooling arrangements

This study covers a simulation on conjugate free convective in a porous enclosure containing a side wall thickness and partially heated and cooled from sides under the considerations of local thermal nonequilibrium (LTNE) and non-Darcy flow. Interest has been focused on how the side wall thickness and the locations of cooled and heated parts affect the effectiveness of the Nusselt number (Nu). Three different cases of localized heating and cooling locations have been implemented for the following ranges: scaled heat transfer coefficient ($0.1\le H\le 100$), wall to fluid thermal conductivity ratio ($0.1{\le R}_k\le 100$), modified Rayleigh number ($200\le \mathit{Ra}*\le 1000$), wall width ($0.1\le \hat{Z}\le 0.5$), inertial parameter (${10}^{-4}{\le F}_s/P_r^{*}\le {10}^{-2}$), and thermal conductivity ratio ($0.1\le K_r\le 100$). Outcomes show that $\hat{Z}$ and the locations of cooled and heated parts have remarkable impacts on all the Nusselt numbers. The intensity of LTNE region considerably relies on $\mathit{Ra}*$, $K_r$ and $H$. The total average Nu_T is highly dependent on $R_k$, $\mathit{Ra}*$, $\hat{Z}$, $F_s/P_r^{*}$, and $K_r$ as compared to H. The increase in $\hat{Z}$ leads to change of the convective mechanism to conductive mode. The rise in $R_k$ guides to increase Nu, where $R_k$ can control the flow strength. The actions of $F_s/P_r^{*}$ on Nu_f is more evident than Nu_s. For low H and K_r, the size of LTNE zone is considerably affected by H as compared to K_r although K_r has a high influence on Nu. For high K_r and H, the LTNE zone has closely vanished. Findings display that the Case 2 provided the highest Nu for all tested parameters except the case of $K_r=0.1$. Finally, it is evident that for the problems that employed solid conduction wall with localized heating and cooling sections, Case 2 is recommended for future use in the applications that implement a porous medium and depend on free convection.