bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Combined Weighting for the Feature-Based Method on Topological Parameters in Semantic Taxonomy Using Social Media
Authors: Muttaleb Hasan Ali , Mohd Noor Noorhuzaimi , Hussein Rassem Taha , Muttaleb Hasan Ahmed , Hammood Waleed A. 
Published: June 2020

AbstractThe textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as1WordNet 3.1 in these tasks are how to integrate the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.
Preserving the Topology of Self-Organizing Maps for Data Analysis: A Review
Authors: Yusob B , Mustaffa Z , Shamsuddin S M 
Published: June 2020

Abstract
In Kohonen’s Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM’s architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm.
A Model Of Factors Influencing Users’ Adoption Of Internet Of Things Services: A Case Study Of Iraqi Educational Institutions
Authors: Salah Hashim Hayder , Amin Al-Sulami Zainab 
Published: June 2020

Abstract
In the revolution of information technology, the appearance of Internet of things (IoT) is considered a significant evolution that can enhance the efficiency and prosperity of daily activities. In this context, a few people are aware of the important benefits that can be gained from using IoT services. Generally, previous researchers have studied the technical aspects of IoT, such as architectural dimensions, models, wireless sensor networks and attribute-based signature. The aim of this study is to identify the factors that influence users’ adoption of IoT services at an educational institution in Iraq. The stratified sampling techniques were used to collect data from 221 users (students and lecturers) at university of Basra. Meanwhile, results corroborate that social influence is the most important factor in relation to the behavioral intention (BI) to use IoT services follow by effort expectancy, security and, performance expectancy. Facilitating conditions and BI have an effective influence on the use behavior of IoT services. Ultimately, contributions and recommendations were demonstrated.
IoT-Based Smart Environment Monitoring System for Air Pollutant Detection in Kuantan, Pahang, Malaysia
Authors: Saradatul Akmar Zulkifli Nor , Ridwan Satrial Mohammad , Zamri Osman Mohd , Syahidatul Nadiah Ismail Nor , Rusydi Muhammad Razif Muhammad 
Published: June 2020

Abstract
Air pollution is one of the pollutions that release harmful chemicals and if not controlled, it will affect the human respiratory system. Currently, there are two Air Pollution Monitoring system located in Kuantan district to record the pollution’s reading once it detects the air pollutant however, the origin of the pollution cannot be detected by the system. As a solution, a Smart Environment Monitoring System for Air Pollution Using Internet of Things (IoTs) is developed to overcome the weaknesses. This project involved both hardware and software developments, in which hardware development includes the Arduino Uno, Carbon Monoxide (MQ 7), smoke/gas (MQ 135) sensors, a buzzer and LCD display. This system is design so that the current reading can be viewed by admin in a real-time value. Besides, they can also view the data for every 8 hours. These data can be printed in PDF format. In addition, when the reading reached unhealthy value, it will warn the community via its alarm system embedded in this system. However, this system is subject to limitation especially on data delays from sensors to database. As only one existing monitoring system that located at Beserah, thus this new feature will add a sub-sensor that located at the designated area which potentially expose to the air pollution.
Unstructured Malay Text Analytics Model in Crime
Authors: Mohemad Rosmayati , Naziah Mohd Muhait Nazratul , Maizura Mohamad Noor Noor , Ali Othman Zulaiha 
Published: June 2020

AbstractThe concern in crime prevention to realize a safe environment for public’s welfare is one of the central agenda to government authorities. The increasing volume of crime reports with unstructured data that are heavily loaded with text presents the biggest challenge to human experts in analysing the data efficiently as it contains lots of hidden and valuable information such as modus operandi and common crime patterns. The capacity limitations in human information processing demand an effective data analytics model to uncover these hidden and potentially meaningful information from large-scale of data especially in Malay language for improving analysis and supporting decision-making in crime investigation. The objective of this paper is to propose Unstructured Malay Text Analytics Model to support serial crime investigation based on modus operandi patterns. The significance output of this paper is to assist experts especially Royal Malaysia Police in crime investigation based on modus operandi patterns analysis as one of the efforts in reducing rate of crime index.
A Model Of Biodiversity Taxonomy Database (iTaxo) For Managing And Analysing Kenyir Landscape Biodiversity Data
Authors: Mohamad Asmara Salwana , Man Mustafa , Tajuddin Abdullah Mohd 
Published: June 2020

Abstract
Biodiversity data management and analysis is a complex task that must be handled by biodiversity researchers. The large amount of data and complicated interaction of the data are the main problem in the biodiversity field. Data accessibility also becomes the main issue to create a data sharing environment among other biodiversity researchers. This is important element since the data is related to each other. A standardized form of data model for biodiversity data is not well implemented in real world. This study discussed about the database model for biodiversity taxonomy data (iTaxo) in order to manage the biodiversity data. We implement the Kenyir landscape which is located at the state of Terengganu in Malaysia as our case study data since the landscape is rich with biodiversity species and ecosystem. As a result, the model can be applied to manage, analyse and evaluate the biodiversity status at the landscape for conservation effort.
Predicting Graduate Employability based on Program Learning Outcomes
Authors: Nor Afiqah Wan Othman Wan , Abdullah Aziman , Romli Awanis 
Published: June 2020

AbstractMany studies based on the literature and adopted approach by Ministry of Higher Education regarding graduate employability are using survey. This approach is lack with on-demand analytical capability for impactful decision making. There is a lack of study that predicts the duration of graduate to get employed based on quantitative analysis. Since all institutions of higher education are compulsory to adopt and implement outcomes-based education (OBE), this study aims to develop a predictive model on GE based on program learning outcomes (PLO) data. There are two data sources used in this study, institutional academic database and online feedback from graduate. This study used simple linear regression to measure the degree of relationship between the category of PLO with the duration of graduate to get employed. This study received 47 responses from 216 with a response rate of 22%. PLO1 and PLO6 which are ‘knowledge’ and ‘problem solving and scientific skills’ respectively show high significance values on the duration of graduate to get employed. The linear models developed based on PLO1 and PLO6 were validated with error rate analysis and evaluated with error rate frequency analysis. The results show the model has potential value to be used to predict graduate employability performance within the time frame (6 months) as determined by Ministry of Higher Education. With prediction capacity from the developed model, more intervention program can be strategically planned to assure graduate can be employed in time and in-field.
A Conceptual IR Chatbot Framework with Automated Keywords-based Vector Representation Generation
Authors: Lokman A S , Ameedeen M A , Ghani N A 
Published: June 2020

Abstract
This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot’s machine learning training and knowledge base development. Within ten proposed framework’s components, we describe Ans2Q: a Neural Network model for question type approximation, and HR6: an IR score ranking calculation based on Ans2Q output. Fundamentally, these two components are the variance in which the proposed framework differs from others. Together with process flow explanation, we also provide several related formulas that hopefully can be used to implement this framework. Our general aim with this framework is to provide a tool that can be used to develop close domain chatbot with small knowledge and no readily available QA pair datasets.
Template Matching Analysis using Neural Network for Mobile Iris Recognition System
Authors: Farihan Mat Raffei Anis , Zulaikha Dzulkifli Siti , Shamsiah Abdul Rahman Nur 
Published: June 2020

Abstract
Today, the use of mobile phone among individuals with an advanced technology, acquisition and computation resources is crucial in securing personal data and services as well as in identifying a person. Furthermore, there has been growing interest of researchers using mobile as an acquisition device to capture an eye image in a non-cooperative environment (static motion and at different distances) for iris recognition. However, the use of mobile as an acquisition device still facing some noise factors because the quality of image captured in this environmental condition is low compared to a camera device. Besides, low awareness of user in handling the mobile devices and lack technical experiences in capturing iris images with this environmental condition is generally uncontrolled contribute to producing unpredictable low quality of eye images. Due to these issues, it led to incorrect segmentation of iris boundaries and subsequently lowered the ability to match the iris features. Hence, this condition contributes to performance degradation of iris recognition system. Therefore, to improve the ability to match the iris features in mobile iris recognition system, a combination with neural network is proposed. The iris database used to test against these methods is CASIA-Iris-M1-S2. The proposed method, a combination of Hamming distance and neural network has achieved a better result where it increases the existing method in term of accuracy for mobile iris recognition under non-cooperative environment.
RETRACTED: The prediction of undergraduate student performance in chemistry course using multilayer perceptron
Authors: Akmal Che Yahaya Che , Yahaya Yaakub Che , Firdaus Zainal Abidin Ahmad , Faizal Ab Razak Mohd , Fatin Hasbullah Nuresa , Fadli Zolkipli Mohamad 
Published: June 2020

Abstract
Chemical industry are key elements for changing crude materials to our ordinary objective merchandise. This has achieved an immense move in how things work. The disappointment pace of an understudy in a science course is additionally expanding with regards to requesting the compound specialists. Understudies who enlist the science course regularly bomb in the first or consequent semesters. Moreover, understudies are likewise unfit to comprehend in the event that they can adjust and graduate effectively in this program. The objective of this exploration is to foresee the future utilization of improved science for understudies to fizzled or graduate by upgraded Multilayer Perceptron (MLP) arrangement with Adaboost. The exactness of the outcomes is 92.23% percent.
Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network
Authors: Abu Hassan Hasliza , Yaakob Marzuqi , Ismail Sasni , Abd Rahman Juwairiyyah , Mat Rusni Izyani , Zabidi Azlee , Mohd Yassin Ihsan , Md. Tahir Nooritawati , Mohamad Shafie Suraiya 
Published: June 2020

Abstract
Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively.
Texture-based Feature using Multi-blocks Gray Level Co-occurrence Matrix for Ethnicity Identification
Authors: Osman M Z , Maarof M A , Rohani M F 
Published: June 2020

Abstract
Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, k-Nearest Neighbour (k-NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm.
GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network
Authors: Shahkhir Mozamir M. , Binti Abu Bakar Rohani , Isni Soffiah Wan Din Wan , Musa Zalili 
Published: June 2020

AbstractLocalization is one of the issue in Wireless Sensor Network (WSN). The problem is the estimation error in the localization of the node. In this paper, we propose an indoor localization in WSN based on Global best Local Neighbourhood Particle Swarm Optimization (GbLN-PSO) algorithm. Three anchors are used as the distance measurement for each unknown node to be localized. A comparison of the performances of PSO and GbLN-PSO in terms of localization error (accuracy) and computation time is presenting using simulation in Matlab. The result shows that GbLN-PSO improved the accuracy approximately by 0.01% and reduced the computational time by 86.02% compared to PSO technique.
Applying Graph-based SLAM Algorithm in a Simulated Environment
Authors: Azril Badioze Zaman Nazrul , Abdul-Rahman Shuzlina , Mutalib Sofianita , Razif Shamsuddin Mohd 
Published: June 2020

Abstract
This paper explores the capabilities of a graph optimization-based Simultaneous Localization and Mapping (SLAM) algorithm known as Cartographer in a simulated environment. SLAM refers to the problem in which an agent attempts to determine its location in the immediate environment as well as constructing the map(s) of its environment. SLAM is one of the most important aspects in the implementation of autonomous vehicle. In this paper, we explore the capabilites of the Cartographer algorithm which is based on the newer graph optimization approach in improving SLAM problems. A series of experiments were tested in order to discover its Cartographer capabilities in tackling SLAM problems. Then, we compare the results of Cartographer with Hector SLAM, another graph-based SLAM algorithm. We present the results from the experiments which show some promising findings based on the amount of computer resources used and the quality of the map(s) produced.
A survey on technique for solving web page classification problem
Authors: Hawa Apandi Siti , Sallim Jamaludin , Mohamed Rozlina 
Published: June 2020

Abstract
Nowadays, the number of web pages on the World Wide Web has been increasing due to the popularity of the Internet usage. The web page classification is needed in order to organize the increasing number of web pages. There are many web page classification techniques that have been proposed by the other researchers. However, there is no comprehensive survey on the performance of the techniques for the web page classification. In this paper, surveys of the different web page classification techniques with the result of the techniques achieved are presented. The existing works of web page classification are reviewed. Based on the survey, we found that the neural network technique namely Convolutional Neural Network (CNN) produce high F-measure value and meet the real-time requirement for classification compared to the other machine learning technique.
Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm
Authors: Solikhun  , Wahyudi Mochamad , Safii M. , Zarlis Muhammad 
Published: June 2020

Abstract
Education is one of the main indicators in national development efforts. The government in its efforts to realize national goals to educate the nation’s life has made a policy for compulsory 9-year schools, namely elementary and junior high schools. To find out how many residents use the education facilities provided by the government can be seen through school enrollment rates (SPR). A high School Participation Rate (SPR) means showing greater opportunities to access education in general. This study aims to optimize artificial neural networks with the One Step Secant (OSS) algorithm. Artificial Neural Network (ANN) is part of the artificial intelligence system (Artificial Intelligence, AI) which is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. The sample data used for optimization is SPR Indonesia data by province. Using 4 architectural models with 5 input variables, 1 shadow layer and 1 output. The best results obtained between architectures 5-4-1, 5-8-1, 5-16-1 and 5-32-1 are architectures 5-16-1. Obtained prediction accuracy comparisons using the One Step Secant (OSS) algorithm and standard algorithms namely 96.97% and 100%. The standard algorithm is superior in accuracy, the One Step Secant (OSS) algorithm is superior in terms of iterations.
Resilient Algorithm In Predicting Fertilizer Imports by Major Countries
Authors: Solikhun  , Wahyudi Mochamad , Safii M , Zarlis Muhammad 
Published: June 2020

Abstract
In the last five years (2013-2017) Indonesia’s fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.
Artificial Intelligence (AI): A Review of its Uses in Language Teaching and Learning
Authors: Ali Zuraina 
Published: June 2020

Abstract
The uses of Artificial Intelligence (AI) seems to be relevant in many fields nowadays due to its ability in providing a simulation of human intelligence processes that are handled by machines; in particular computer systems. This paper concerns with reviewing the uses of AI in language teaching and learning. In particular, it reviews the research on the uses of AI in its application in the learning and teaching of language. Qualitative research method; specifically content analysis, is employed as the technique to review the articles that are obtained from relevant databases. Findings from the study reveal that there are four (4) themes emerge in the uses of AI in relation to teaching and learning a language. The uses of AI for pedagogy, therefore, prove that its uses eases language teaching and learning.
A Hybrid Screen Size Independent Authentication Scheme for Smart Devices
Authors: Faizuwan Bin Welhendri Ahmad , Azad Saiful , Al Noman Ranak Md Sayfullah 
Published: June 2020

Abstract
Although, several authentication schemes have already been proposed for smart devices; however, most of these schemes does not consider the fact that smart devices come in different sizes. Hence, they are not screen size independent — which is the point of interest in this paper. Again, alongside screen size independence, a secure scheme also must defend the aforementioned attacks. Taking these concerns into account, in this paper, a hybrid screen size independent authentication scheme is proposed for smart devices that integrates Vibration Code or VC and Press Touch Code or PTC using a juggling-based approach. Here, VC ensures resilience against the shoulder surfing attack since it is a sense based technique and hybridization of both these schemes contribute to attaining resilience against the smudge and brute force attacks up to some extent. In addition, the proposed scheme does not require a space more than the placement of a finger on the screen; and thus, it is screen size independent. The proposed scheme is evaluated in terms of security and functionality; and compared with other similar schemes where it outperforms the others.