Identifying Purchasing Patterns of Arab and Malaysian Students Using Data Mining Technique

Currently, Universiti Utara Malaysia (UUM) has a significant number of international students. Since they are from various background and culture, their preferences towards purchasing products are different. This study intends to identify purchasing patterns of 2 groups of students: Arab and Malaysian. The 2 groups have been chosen because they represent the major groups of the postgraduate students. A questionnaire has been constructed and used to collect data. The sample of data consists of postgraduate students from Arab and Malaysia. The total number of postgraduate students is 2122 and the total number of the sample data is 547 (30% of the population). Apriori Algorithmn, which is a popular data mining technique has been used to identify the purchasing patterns. The study discovered that items such as fruits, vegetables, drinks and pickled food are frequently purchased by the Arabs. The Malaysians, however prefer items such as Pickled Foods, Snack Foods, and Other Stuff. A more comprehensive work in the future is suggested so that result obtained can be generalized. The study has been successful achieving all objectives. It is hope that the results could be use to strategic UUM as the patterns identified could be used to strategize UUM's retailing businesses and at the same time provide adequate facilities in terms of selling preferred products to its consumers

Using deep learning to detecting abnormal behavior in internet of things

The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy

Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%

Components and Analysis Method of Enterprise Resource Planning (ERP) Requirements in Small and Medium Enterprises (SMEs)

With the fast development of information technologies and enterprise software, Enterprise Resource Planning (ERP) systems are increasingly adopted by more small and medium enterprises (SMEs). Based on this trend, it is necessary to develop ERP systems in a manner that meets and fits the SMEs requirements and needs. This paper proposes conceptual components of ERP requirements that are required for generating ERP system functions. In addition, it proposes an ERP requirements analysis method for ERP system developments in order to produce the proper ERP system functions for SMEs. The advantage of this analysis method is that it is easy to analyze and integrate the special requirements of the ERP development for distinguishing a sub-sector of SMEs. In this paper, by analyzing the components of requirements and the relationship of the business process modelling, several basic concepts are given and the method of the process analysis and modelling is also expressed

A novel population-based local search for nurse rostering problem

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments