Malaysian Labour Market Information System
Nor Wahidah Nor Azelan and Maslina Mohd Basir
, Institute of Labour Market Information And Analysis (ILMIA), Cyberjaya, Selangor, Malaysia
A summary of the rationale for and the development of the centralised Labour Market Information System by ILMIA is provided. A data flow management system sets the basis for labour market data sharing among compilers of statistics by departments within MoHR and participating ministries or agencies. Users have access to crucial and useful labour market information that facilitates more comprehensive analysis for human capital planning and enhances the capability for big data analytics in the labour market. Future efforts by ILMIA will focus on expanding the participation of new data providers and increasing efforts on promotion and dissemination of the labour market information by producing labour market infographics posters and pamphlets. Strengthening the system with the latest security features to safeguard confidential data will receive appropriate attention.
Labour Market Information, Labour Market Information Data Warehouse, Data Warehouse, Centralised Database, Business Intelligence Tools, Dashboard, Mobile Application, Industrial 4.0, Big Data Analytics, Web Mining
USING MACHINE LEARNING FOR PREDICTION OF FACTORS AFFECTING CRIMES IN SAUDI ARABIA
Anadil Alsaqabi, Fatimah Aldhubayi and Saleh Albahli, Department of Information Technology, College of Computer, Qassim University, KSA
Crime rates are expected to increase in the whole world as the growth of many complex factors like:
unemployment, poverty, weather, violent ideologies and religion and etc. Obviously crimes have negatively influenced the development of society, economic progress and reputation of a nation. Hence, Analyzing large volume of data with machine learning algorithms can be used to predict the crime distribution over an area to provide indicators of specific areas which may become a criminal hotspot. The aim of this paper
is to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value. Our results show that Factor Analysis of Mixed Data (FAMD) as features selection methods showed more accurate on machine learning classifiers rather than Principal Component Analysis (PCA) method. Naïve Bayes classifier perform better than other classifiers on both features selections methods with accuracy 97.53% for FAMD and PCA equals to 97.10%..
Machine learning, Crime prediction, Crime Category, Predictive model
Video Classification Using Pre-Trained Models in the Convolutional Neural Networks
Isuru S. Jayasinghe1, Christin L. Senanayake1, Sarada I.M. Kahawandala1, Dumintha Wijesekara1, Nuwan Kodagoda2 and Kushnara Suriyawansa1, 1Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka and 2Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Automatically classifying images or video clips into a categorized gallery is a challenging and important
task in Convolution Neural Networks (CNNs). In this research paper, we find the most efficient solution to
classify videos using pre-trained models based on four different unique datasets. Our system will
automatically create a video or image gallery through the CNN models according to subcategories of main
datasets. We used an Image-Based classification method of the video styles where a single video is split
into multiple image frames, and then each frame is classified into one of the subcategoris. Various classifier
architectures were built on top of each state-of-the-art deep neural models, including Xception,
InceptionV3, MobileNet, VGG16, VGG19, DenseNet121, DenseNet201, NASNetMobile,
InceptionResNetV2, MobileNetV2, ResNet50 and NASNetLarge are evaluated and the comparison of the
results is shown.
Pre-trained models, Feature extraction, Convolution neural network (CNN), Video classification
Application Development using gamification techniques to simulate the admission exam to University
Michael Valencia Ramírez, Universidad Distrital Francisco José de Caldas Bogotá, Colombia
This article aims to show the advances in the development of a mobile application to simulate the admission exam to the National University of Colombia (UN), which makes use of gamification techniques. For young graduates of high school, one of the main concerns is to apply to the university and be admitted. The National University of Colombia, because it is public in nature and has a high academic level, it is in great demand in the country, each semester there are approximately 72 thousand applicants and only about 10% is accepted due to the low availability of university quotas. The selection process is carried out by means of an admission examination, which evaluates the skills in reading comprehension and logical-mathematical reasoning of the aspirant. Faced with this problem and with the unstoppable evolution of technology, it is vital to integrate the ICT tools (Information and Communication Technologies), as components in learning and preparing them, a mobile application is developed, with the aim of helping aspirants to have a better preparation for the exam. It is taken as population study of 20 students of the eleventh grade belonging to the Liceo Cultural Ernesto Guhl School in Bogota, Colombia.
Education, Gamification, University, ICT, Mobile application
Mobile application focused on knowledge training intended for “Saber 11 Test” using Augmented Reality Environments
Maily A. Quintero E
Universidad Distrital Francisco José de Caldas Bogotá, Colombia
In Colombia, the system of evaluation and promotion of education is carried out by the ICFES through state tests applied to grades 3rd, 5th, 7th, 9th and 11th. The Saber 11 Test is an assessment of the basic competences that is performed on grade 11th students. This test scores are used as an important requirement for admission to college.
In 2018, 37% of students scored low and failed to apply to the multiple benefits offered by government and universities. This is because the current training mechanisms do not generate impact and motivation in the student so that he can take advantage of the training material offered by various instances.
The development of a mobile application prototype for devices based on Android platform is presented, using Augmented Reality tools, which adds virtual elements to the real environment while providing information of interest to the user and takes advantage of the infrastructure of ICT (Information and Communication Technologies).
Being a motivational learning strategy helping the student to become familiar with the structure, areas and skills, and proposed questions on the exam know 11.
Education, Augmented Reality, Mobile Application, Technology, Saber 11
SURFACE CRACK DETECTION USING HIERARCHAL CONVOLUTIONAL NEURAL NETWORKS
Davis Bonsu Agyemang and Mohamed Bader-El-Den
The School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE.
Cracks on concrete walls can imply that a building possesses issues with its structural integrity. Surveyors who inspect these defects are expected to provide their customers with excellent evaluation regarding its severity. The process is currently conducted through visual inspection, resulting in occasions of subjective judgements being made on the classification and severity of the concrete crack which poses danger for customers and the environment as it not being analysed objectively. Many researchers have applied numerous classification techniques to tackle this issue but from the author's knowledge, their methods do not provide the severity levels of concrete cracks and have no feedback mechanism for adaptability of when their method classifies incorrectly. In this paper, the author proposes in building a mobile application with the 2 mentioned capabilities and using a trained Hierarchal-Convolutional Neural Network(H-CNN) to evaluate the concrete surface via images taken via the mobile device.
UVAS-BASED SMALL OBJECT DETECTION AND TRACKING IN VARIOUS COMPLEX SCENARIOS
Shicheng Zu, Yanwen Hu, Zhongzheng Yu, Wanqun Chen, and Kai Yang
, National Defense Science and Technology Coordination and Innovation Base, Nanjing, China
We have witnessed drastic progress in object detection in recent years due to the blooming development of neural networks. Most mainstream object detectors are sensitive to objects of regular scale because their detection largely depends on deep convolutional feature maps lacking low-level features, i.e. edges, and contours, which contribute to small object detection. Our study focuses on UVAs-based small object detection at a high altitude, i.e. 100 meters. We construct a pipeline by integrating the moving foreground segmentation algorithm, the conventional CNN model, the boosted cascaded classifier, and the tracker that can detect and track the small object progressively in a cascaded manner. We perform the qualitative and quantitative evaluation of the detection and tracking performance of our constructed pipeline in various complex conditions. The comparison study confirms its superiority in small object detection and strong robustness against various influential nuisances. Based on our constructed pipeline, we develop a real-time UVAs-based small object detection and tracking system. The systematic architecture and the general steps taken by the UVAs to realize small object detection are also presented. Finally, we qualitatively and quantitatively evaluate 8 popular trackers based on the attributes of the image sequences. The most
appropriate tracker can be determined in response to a specific condition. Our study testifies that by taking advantage of the merits of each algorithm germane to a given task, the performance can be further improved
UVAs, Small Object Detection, Foreground Segmentation Algorithm, CNN, Tracker
AUTOMATIC DESIGN SPACE EXPLORATION OF HETEROGENEOUS EMBEDDED SYSTEMS USING SPECIALIZED GENETIC ALGORITHM
Mouna HALIMA, Lobna Kriaa and Leila Azzouz Saidane, ENSI University, Manouba, Tunisia
Design space exploration (DSE) techniques have become essential in order to find the best compromise between different design goals and their trade-off. Designers need effective optimization techniques to support their design decisions. However, the partitioning problem is one of the most important issues.Many heuristic methods have been proposed to solve this problem. This paper introduces an automatic design space exploration approach which is based on a customized Genetic Algorithm, named Binary Genetic Algorithm (BGA). This new approach helps to find rapidly the best solution satisfying a set of systems requirements and/or constraints. Different steps, from the system specification to the target architecture including mapping and scheduling objectives, compose our flow.
Codesign embedded system, DSE, architecture exploration, partitioning, optimization algorithms,Genetic Algorithm
THE IMPACT OF QUANTUM GENETIC ALGORITHMS IN MINIMIZING TASK MIGRATION’ OVERHEADS
Department of Computer Engineering, Kasdi Merbah Ouargla University, Ouargla, Algeria
Genetic algorithms are widely used to solve problems in complex system for its easiest implementation and achievability of global optimum with a proper approximate solution, but quantum-inspired evolutionary algorithms have been surpassing the classical algorithms for their abilities to solve problems of polynomial-time that is considered it as impossible to be solved but with million years while their advantage is to balance between exploration and exploitation of the solution space and also obtain better solutions, even with a small population. In this paper, we propose a novel task migration algorithm that minimize the overall overheads caused during the migration process in network on chip (NoC) while searching the appropriate checkpoints during run-time using quantum genetic algorithm.
Genetic Algorithms, Quantum Computing, Mapping, Scheduling, Network on Chip, Task Migration