Comparison between ANN and Multiple Linear Regression Models for Prediction of Warranty Cost

Nowadays, warranty has its own priority in business strategy for a manufacturer to protect their benefit as well as the intense competition between the manufacturers. In fact, warranty is a contract between manufacturer and buyer in which the manufacturer gives the buyer certain assurances as the condition of the property being sold. In industry, an accurate prediction of warranty costs is often counted by the manufacturer. Underestimation or overestimation of the warranty cost may have a high influence to the manufacturers. This paper presents a methodology to adapt historical maintenance warranty data with comparison between Artificial Neural Network (ANN) and multiple linear regression approach

Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students

Evaluation of boruta algorithm in DDoS detection

Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning's detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers

Engaging learning with game on food and nutrition for pre-school children / Cassandra Yen Pei Ying, Danakorn Nincarean Eh Phon and Mohd Faaizie Darmawan

With the growing rates of children obesity and concerns on the related health consequences, the awareness in transforming children’s eating habits to a healthier direction to reduce their threat of developing diet-related disease during adulthood has been growing in recent years. Recent evidence suggests that a game-based approaches have a potential in health and wellbeing sector. This research is conducted based on two main aspects: development of game application on food and nutrition for pre-school children and evaluation involving users on the usability of the developed game. This game application has been developed based on the ADDIE model which consist of five phases, Analysis, Design, Develop, Implementation and Evaluation. The usability evaluation on the game application involved children in early ages. The genre of the games is based on platform and it is a single-player game that contains three modules: food pyramid, healthy maze, and my plate. For the usability, the game application received positive feedbacks from the users where they gain some knowledge and enjoy playing the game. This finding provides a valuable knowledge to the design and development of the game application in food and nutritions sector

Comparison of two classification models for sex estimation based on bone length of hispanic population

One of the essential factors of conducting a forensic investigation is to determine sex. Although multiple studies have been conducted using hand bone, the studies using the Hispanic population are minimal. The purpose of this study is to develop the Discriminant Function Analysis (DFA) and Artificial Neural Network (ANN) model for sex estimation based on the Hispanic population using left-hand bone. The samples used are subjects ranged between age groups of infants and 18 years old which comprised of 91 females and 92 males. For the input, the length of nineteen bones from the subjects’ left hand is measured in centimeters and then normalized to become input for both models. The DFA model is chosen as a benchmark in this study to be compared with the ANN model based on accuracy percentage. The chosen DFA model is due to the widely used in estimating sex based on quantitative input. For the results, the DFA model produces a 72.7% accuracy percentage while the ANN produces 83.8%. Thus, the ANN model is selected to be the most ideal model in estimating sex compared to the DFA model

Malware detection using static analysis in android: A review of FeCO (features, classification, and obfuscation)

Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis

Age Estimation of Asian Using Soft Computing Model Based on Bone Length of Left Hand

Age estimation is applied in anthropology of forensic to facilitate the identification of a living person or the remains of individuals. Nevertheless, the uniqueness of the estimation models is only appropriate to a particular population. The common models are also inter and intra-observer variability where the dataset used based on qualitative which make the estimated age really depended on the expertise of the anthropologist. This paper propose age estimation focusing on Asian subjects from new-born to 18 years old using bone’ length in left hand. Two soft computing models are used to develop the estimation models which are Artificial Neural Network (ANN) and Support Vector Machine (SVM). The used of length of bone is to create a new age indicator based on quantitative data and the SVM and ANN is really suitable on quantitative data. Based on results produced by these models, the SVM is the best model which is produced the lowest mean square error (MSE) value of 1.917 and 3.775 for both male and female, respectively. To conclude, the SVM is the best model in estimating the age compared to the ANN, based on length of left hand. However, the used of this model is limited only for forensic practice or experimental purpose

Comparison between ANN and multiple linear regression models for prediction of warranty cost

Nowadays, warranty has its own priority in business strategy for a manufacturer to protect their benefit as well as the intense competition between the manufacturers. In fact, warranty is a contract between manufacturer and buyer in which the manufacturer gives the buyer certain assurances as the condition of the property being sold. In industry, an accurate prediction of warranty costs is often counted by the manufacturer. Underestimation or overestimation of the warranty cost may have a high influence to the manufacturers. This paper presents a methodology to adapt historical maintenance warranty data with comparison between Artificial Neural Network (ANN) and multiple linear regression approach