Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroup

Recent researches showed that DNA encoding and pattern matching can be used for the intrusion-detection system (IDS), with results of high rate of attack detection. The evaluation of these intrusion detection systems is based on datasets that are generated decades ago. However, numerous studies outlined that these datasets neither inclusively reflect the network traffic, nor the modern low footprint attacks, and do not cover the current network threat environment. In this paper, a new DNA encoding for misuse IDS based on UNSW-NB15 dataset is proposed. The proposed system is performed by building a DNA encoding for all values of 49 attributes. Then attack keys (based on attack signatures) are extracted and, finally, Raita algorithm is app

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automatically-supported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content.   When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniqu

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

In data transmission a change in single bit in the received data may lead to miss understanding or a disaster. Each bit in the sent information has high priority especially with information such as the address of the receiver. The importance of error detection with each single change is a key issue in data transmission field.
The ordinary single parity detection method can detect odd number of errors efficiently, but fails with even number of errors. Other detection methods such as two-dimensional and checksum showed better results and failed to cope with the increasing number of errors.
Two novel methods were suggested to detect the binary bit change errors when transmitting data in a noisy media.Those methods were: 2D-Checksum me

     The paired sample t-test is a type of classical test statistics that is used to test the difference between two means in paired data, but it is not robust against the violation of the normality assumption. In this paper, some alternative robust tests are suggested by combining the Jackknife resampling with each of the Wilcoxon signed-rank test for small sample size and Wilcoxon signed-rank test for large sample size, using normal approximation. The Monte Carlo simulation experiments were employed to study the performance of the test statistics of each of these tests depending on the type one error rates and the power rates of the test statistics. All these tests were applied on different sa

t-Self-Compacting Concrete (SCC) reduces environmental noise and has more workability. This research presents an investigation of the behavior of SCC under mechanical loading (impact loading). Two types of cement have been used to produce SCC mixtures, Ordinary Portland Cement (OPC) and Portland Limestone Cement (PLC), which reduces the emission of carbon dioxide during the manufacturing process. The mixes were reinforced with Carbon Fiber Reinforced Polymer (CFRP) which is usually used to improve the seismic performance of masonry walls, to replace lost steel reinforcements, or to increase column strength and ductility. Workability tests were carried out for fresh SCC. Prepared concrete slabs of 500×500×50mm were tested for lo