An improved artificial dendrite cell algorithm for abnormal signal detection

In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy

The effect of 21st century acculturation module on the students’ higher order thinking skills and academic achievement

This research was conducted to examine the effect of the 21st Century Acculturation Module on 21st Century Skills and academic achievement. The research sample comprised 120 form four students who took the Science subject from three secondary schools in Selangor and Perak. The experimental group consisted of students from Selangor, while the control group consisted of students from Perak. The quasi�experimental design was applied using quantitative data. Data collected were analysed using descriptive and inferential statistics, such as means, standard deviations, independent sample T-test and PLS-SEM. The result shows that for 21st Century Skills, namely Learning and Innovation (LI), the mean difference between control and experimental group was significant (t = -12.649, p = 0.000). As for the Life and Career (LC), the mean difference between control and experimental group was significant (t = -52.590, p = 0.000). In the case of Information, Media, and Technology (IMT) the mean difference between control and experimental group was significant (t = - 45.745, p = 0.000). Research finding also shows that there was a significant difference on students’ academic achievement (AA) (t = -12.700, p = 0.000) between the experimental and control groups. The result gathered from PLS-SEM indicated that the moderating role of the developed module on the relationship between LC and AA was significant. The proposed module derived from that the amalgamation of Dale’s Cone of Experiences and Revised Bloom’s Taxonomy frameworks along with the technology usage in a virtual learning environment is evident to enhance 21st century skills of the students and they also performed better in the academic achievement

A novel pooling method for CNN model based on discrete cosine transform

Deep learning can be used to learn huge volume of data, which will be processed through hidden layers and according to the number of hidden layers ,filter size and numbers and the required computation cost is increased because of the size of raw data, this problem can be avoided by using pooling techniques, different method s are proposed to extract the basic features of the signal instead of all signal, but unfortunately this operation may introduce some noise or omission because of elimination important data from the signal. In this paper, A novel pooling method are proposed based on discrete cosine transform , this method is utilized DCT technique to reduce spatial redundancy of image by transform the spatial domain into frequency domain , which can preserve the most significant image information from the other coefficients, which represents the other details information of the image, so discard these less important coefficients. Its effect will be slight and this can reduce the eliminated information as compared with other methods. After applying DCT, we crop the most significant coefficients to be used in the reconstructed data by applying inverse DCT . then the result is combined in different methods with Max pooling and average pooling methods, this new structure can reduce the effect of discarding most important information and reduce the drawbacks of average and Max. pooling method. لإhe results are proved that our proposed methods are outperformed some standard methods and can be used in more application

A comparative study between rough and decision tree classifiers

Rule-based classification system (RBC) has been widely used in many real world applications because of the easy interpretability of rules.RBC mines a collection of rule via knowledge which is hidden in dataset in order to accurately map new cases to the decision class.In the real world, the number of attribute of dataset could be very large due the capability of database technology to store much information.Following that, the large dataset may contain thousands of relationship and it will likely provide more knowledge since the interrelationship between data will give more description.Furthermore, it is also have the possibility to have most number of rules that contain unnecessary rule or redundancies in the model. Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice

Multi label ranking based on positive pairwise correlations among labels

Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. Two common approaches are being used to solve the problem of MLC: Problem Transformation Methods (PTMs) and Algorithm Adaptation Methods (AAMs). This Paper is more interested in the first approach; since it is more general and applicable to any domain. In specific, this paper aims to meet two objectives. The first objective is to propose a new multi-label ranking algorithm based on the positive pairwise correlations among labels, while the second objective aims to propose new simple PTMs that are based on labels correlations, and not based on labels frequency as in conventional PTMs. Experiments showed that the proposed algorithm overcomes the existing methods and algorithms on all evaluation metrics that have been used in the experiments. Also, the proposed PTMs show a superior performance when compared with the existing PTMs

Mysztech Solution (Mts) Point Of Sales (Pos) System

The MYSZTECH POS System with Table Layout is a comprehensive solution designed to enhance the customer experience and optimize operations for cafe and restaurant owners. Developed using the waterfall methodology and tailored to specific client requirements, this system offers a range of management features, including sales management, item management, thermal printer compatibility, discount management, tax management, and outlet management. The key highlight of this solution is its innovative table layout functionality, which enables efficient order tracking and streamlines the dining experience. With real-time insights into customer behavior and seamless table management, businesses can optimize their operations and make informed decisions to drive success in the competitive food service industry

Design and Development of Smart Home Security System for Disabled and Elderly People

This paper discusses an ongoing project that serves the needs of people with disabilities and the elderly at home. It uses the WiFi technology to establish communication between a user’s Smartphone and a controller board. The project uses a microcontroller to control the door lock and is equipped with a camera to identify the visitors. By connecting the servo motor and camera to the Raspberry Pi controller board, it can be controlled via WiFi to provide remote access from a smartphone

The design of F-CMS: A flexible conference management system

Conference management system (CMS) is designed to help the conference committee manages a conference well.The CMS which is available in market nowadays provides a well managed pre-conference function such as paper reviewing, paper submission, and participant registration system.However, payment module is not given priority by the existing CMS. This study argues that the payment management is importance ant to simplify the payment process, avoiding the unpaid paper being published in the proceeding. Also the conference committee can easily calculate the conference profit when the event ends. However, CMS is inflexible handling certain cases such as in case authors are unable to pay the fee before the conference day but need to submit the camera ready.Hence, this paper attempts to explain the design of a flexible conference management system (f-CMS).f-CMS is developed using RAD approach. It also includes the registration module during conference day.This paper presents the review of literatures and the early stages of the development of f-CMS