Service quality dealer identification: the optimization of K-Means clustering

Service quality and customer satisfaction directly influence company branding, reputation and customer loyalty. As a liaison between producers and consumers, dealers must preserve valuable consumer relationships to increase customer satisfaction and adherence. Lack of comprehensive measurement and standardization regarding service quality emerges as a consideration issue towards the company service excellence. Therefore, identifying the service quality performance and grouping develops into valuable contributions in decision-making to control and enhance the company's intention. This study applies the K-Means Algorithm by optimizing the number of clusters in identifying dealer service quality performance. Hence, the ultimate service quality formation will be performed. The analysis found three dealer identification categories, including Cluster One, with 125 dealers grouped as good performance; Cluster Two, with 30 dealers grouped as very good performance; and Cluster Three, with 38 dealers grouped as not good performance. In order to evaluate the efficacy of optimum k value, the lists of testing approaches are conducted and compared, whereby Calinski-Harabasz, Elbow, Silhouette Score, and Davies-Bouldin Index (DBI) contribute in k=3. As a result, the optimum clusters are determined through the highest performance of k values as three. These three clusters have successfully identified the service quality level of dealers effectively and administered the company guidelines for corrective actions and improvements in customer service quality instead of the standardized normal distribution grouping calculation.

Effect of corn supplementation on purine derivatives and rumen fermentation in sheep fed PKC and urea-treated rice straw

This study investigated the effect of different levels of corn supplementation as energy source into palm kernel cake–urea-treated rice straw basal diet on urinary excretion of purine derivatives, nitrogen utilization, rumen fermentation, and rumen microorganism populations. Twenty-seven Dorper lambs were randomly assigned to three treatment groups and kept in individual pens for a 120-day period. The animals were subjected to the dietary treatments as follows: T1: 75.3% PKC + 0% corn, T2: 70.3% PKC + 5% corn, and T3: 65.3% PKC + 10% corn. Hypoxanthine and uric acid excretion level were recorded similarly in lambs supplemented with corn. The microbial N yield and butyrate level was higher in corn-supplemented group, but fecal N excretion, T3 has the lowest level than other groups. Lambs fed T3 had a greater rumen protozoa population while the number of R. flavefaciens was recorded highest in T2. No significant differences were observed for total bacteria, F. succinogenes, R. albus, and methanogen population among all treatment. Based on these results, T3 could be fed to lambs without deleterious effect on the VFA and N balance

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature

The authentication of Halal dental materials using rapid Fourier Transform Infrared (FTIR) spectroscopy

The purpose of this study is to detect the presence of gelatin in orthodontic dental materials for halal authentication. The detection of gelatins was done using Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR). In this study, 21 samples were included and the spectrums were generated by OMNIC software of Nicolet iS50 FTIR. All data were also subjected to similarity match (SM) using TQ Analyst software. Four types of gelatin tested, namely porcine, bovine, fish and commercialized gelatin. From the studies, it was found that no samples exhibit similar spectra as the gelatins tested and SM found that no samples showed 100% similarities with porcine gelatin

Effects of corn supplementation on the antioxidant activity, selected minerals, and gene expression of selenoprotein and metallothionein in serum, liver, and kidney of sheep-fed palm kernel cake: urea-treated rice straw diets

This study aimed to determine influence of corn inclusion on glutathion peroxidase (GPx) activity, selected minerals concentration, and gene expression in sheep-fed palm kernel cake (PKC) and urea-treated rice straw. Twenty-seven of Dorper sheep were divided into three groups and fed a basal diet of (20% rice straw and 80% concentrate) with addition of ground corn at either 0% (T1), 5% (T2), or 10% (T3), respectively. After 120 days feeding trial, all animals were slaughtered and tissue samples of kidney, liver, and muscles were taken for enzyme and mineral analyses. The results showed that Cu concentration in the liver was lower treatment T3 compared to the control and T2. The serum activity of GPx was higher in T2 than in T3 at day 120 of experiment. Serum malondialdehyde (MDA) concentrations decreased at day 80 in sheep on T3, whereas MDA of liver increased linearly with increasing corn supplementation. The qRT-PCR analyses revealed significant up-regulation of ATP7A and MIa genes in T3, while hepatic Cu/Zn SOD, GPx1, and GPx4 mRNA showed a higher expression in lamb hepatocytes in T3 compared to those on T1. Present study results suggest that feeding PKC as basal diet can increase antioxidant activity, but cause liver dysfunction in sheep. Inclusion corn was found to regulate transcriptional levels of the GPx family and metallothionein genes. These genes may play a role in the antioxidant protection response and reduce incidence of toxicity associated with Cu

Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System

Skin cancer is considered as the most type of cancer that happens in humans. Three basic types of cancer occur which are basal cell carcinoma (BCC), Squamous cell carcinoma (SCC). Skin cancer leads to death if it is not diagnosed in an early stage. Fortunately, early diagnosis of skin cancer raises the survival rate of victims. Computer-aided has a great role to detect skin cancer which leads to saving human life. Based on that, this study proposes a computer-aided diagnosis (CAD) system that detects skin cancer using digital images, techniques of image processing, by using the case-based reasoning and expert system. The main goal for designing this system is to create a cheap, easy-to-use, and relatively accurate system for detecting skin cancer in an early stage to save human life, raises the survival rate, and decreases the cost of the dermoscopy test

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping

Breast density estimation on full-field-digital mammography (ffdm) by general radiologists: comparison of bi-rads and tabar classification sytems

Purpose of Study: The aim of the study is to compare inter-observer agreement between general radiologists in classification of mammographic breast density using TABAR’s pattern and BI-RADS classification systems on Full-Field Digital Mammography (FFDM). Materials and Method A 400 data set of mammograms in mediolateral and craniocaudal views was independently evaluated by three radiologists. Breast density was classified using BI-RADS and TABAR classification systems. The three radiologists interpreting the mammogram images were general radiologists with three, four and one years’ experience respectively. There was no special coaching conducted prior to data interpretation. Results: Inter-observer agreement for the BI-RADS are slight to fair (reviewer 1 vs reviewer 2: k=0.19, reviewer 1 vs reviewer 3, k=0.07 and reviewer 2 vs reviewer 3, k=0.49) and for TABAR is fair to moderate (reviewer 1 vs reviewer 2: k=0.23, reviewer1 vs reviewer 3, k=0.31 and reviewer 2 vs reviewer 3, k=0.50). Conclusion: Our study demonstrates that the assessment and classification of the breast density is difficult with slightly better performance using TABAR classification system compared to BI-RADS classification. If breast density is to be used as part of risk predictors of breast cancer, a consistent, quantitative and observer-independent method for characterizing mammographic breast density is needed in local clinical practice

An empirical comparison of commercial and open‐source web vulnerability scanners

Web vulnerability scanners (WVSs) are tools that can detect security vulnerabilities in web services. Although both commercial and open-source WVSs exist, their vulnerability detection capability and performance vary. In this article, we report on a comparative study to determine the vulnerability detection capabilities of eight WVSs (both open and commercial) using two vulnerable web applications: WebGoat and Damn vulnerable web application. The eight WVSs studied were: Acunetix; HP WebInspect; IBM AppScan; OWASP ZAP; Skipfish; Arachni; Vega; and Iron WASP. The performance was evaluated using multiple evaluation metrics: precision; recall; Youden index; OWASP web benchmark evaluation; and the web application security scanner evaluation criteria. The experimental results show that, while the commercial scanners are effective in detecting security vulnerabilities, some open-source scanners (such as ZAP and Skipfish) can also be effective. In summary, this study recommends improving the vulnerability detection capabilities of both the open-source and commercial scanners to enhance code coverage and the detection rate, and to reduce the number of false-positives

Effects of corn supplementation on the antioxidant activity, selected minerals and genes expression of selenoprotein and metallothionein in serum, liver and kidney of sheep fed Palm Kernel Cake - urea-treated rice straw diets

This study aimed to determine influence of corn inclusion on glutathion peroxidase (GPx) activity, selected minerals concentration, and gene expression in sheep-fed palm kernel cake (PKC) and urea-treated rice straw. Twenty-seven of Dorper sheep were divided into three groups and fed a basal diet of (20% rice straw and 80% concentrate) with addition of ground corn at either 0% (T1), 5% (T2), or 10% (T3), respectively. After 120 days feeding trial, all animals were slaughtered and tissue samples of kidney, liver, and muscles were taken for enzyme and mineral analyses. The results showed that Cu concentration in the liver was lower treatment T3 compared to the control and T2. The serum activity of GPx was higher in T2 than in T3 at day 120 of experiment. Serum malondialdehyde (MDA) concentrations decreased at day 80 in sheep on T3, whereas MDA of liver increased linearly with increasing corn supplementation. The qRT-PCR analyses revealed significant up-regulation of ATP7A and MIa genes in T3, while hepatic Cu/Zn SOD, GPx1, and GPx4 mRNA showed a higher expression in lamb hepatocytes in T3 compared to those on T1. Present study results suggest that feeding PKC as basal diet can increase antioxidant activity, but cause liver dysfunction in sheep. Inclusion corn was found to regulate transcriptional levels of the GPx family and metallothionein genes. These genes may play a role in the antioxidant protection response and reduce incidence of toxicity associated with Cu