bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bibliometric Analysis of Data Mining on Medical Imaging
Authors: Karim Shahiratul A Abd , Nohuddin Puteri N E 
Published: August 2021

Abstract
Recently, data mining application on medical imaging have increased due to expansion usage of them for early disease detection. In this paper, a bibliometric analysis is used to analyze published documents research on medical image mining (MIM) based on Scopus database with search query “Data Mining” and “Medical Imaging”. This paper also interprets 696 publications based on the research productivity, documents and source types, language of publications and subject areas. The most influential journals, countries, institutions, keywords also analyzed. Some of the bibliometric indicators are visualized in network visualization map. As the results, it can be seen that the research growth of MIM has been gradually increased especially in the developed countries such as United States and China. This paper also allows related academicians to understand the status and trends in development in this field.
A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
Authors: Ali H , Ahmad Zaidi A F , Wan Ahmad W K , Zanar Azalan M S , Tengku Amran T S , Ahmad M R , Elshaikh M 
Published: August 2021

Abstract
Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator’s knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD & DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
A Hybrid Encryption and Decryption Algorithm using Caesar and Vigenere Cipher
Authors: Tan Camille Merlin S. , Arada Gerald P. , Abad Alexander C. , Magsino Elmer R. 
Published: August 2021

Abstract
This paper presents a hybrid encryption and decryption algorithm, which combines the concept of Caesar Cipher and Vigenere Cipher algorithms. This was done by comparing the proposed design with some of the well-known ciphers such as Hill Cipher, Caesar Cipher and Vigenere Cipher through MATLAB simulations. Parameters such as letter frequency and graph behavior were evaluated. Results show that the proposed algorithm performs excellently. It has the most evenly distributed graph among other ciphers being compared in terms of their frequency of the ciphertext. Also, the decryption algorithm was able to achieve 99.99% accuracy.
Fusion wind and solar generation forecasting via neural network
Authors: Al Asbahi M M Y M , Mansor M N , Manan M R , Aziz M A A , Kawi R Md , Mohd Faudzi F H 
Published: August 2021

Abstract
Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.
Framework Design of Patient Phlebotomy Identification System (PPIS) Mobile Applications for RFID Blood Test Tube
Authors: Lim C C , Markom M A , Teoh L M , Zaharin N F , Aziz I A A , Fadzli F S A , Rahman N H A 
Published: August 2021

Abstract
Phlebotomy procedures is the venepuncture, it's an important tool for diagnosing many medical conditions. However, there are a lot of human error and sample rejection during the phlebotomy procedures due to sample and patient identification. Currently, there are various advance production of medical computing system which able to provide significant benefits to medical staff and society. With this explosion of Smart mobile medical computing systems, a Patient Phlebotomy Identification System (PPIS) is designed to minimize the human error in phlebotomy practice that can caused blood specimen being rejected. The key of PISS is the continuous exchange and transfer of information between doctors and lab technologists. The conceptual framework of PPIS is designed in mobile application that can connect to RFID technology on the blood tube. The patient and blood sample identification data are stored in MySQL databases and real-time access by medical staff thru the PPIS mobile application. This mobile application able to perform data search and identification for work efficiency. This system is user-friendly and eco-friendly.
The Wavelet packet decomposition features applied in EEG based authentication system
Authors: Rosli Fatin Atiqah , Saidatul A. , Abdullah Azian Azamimi , Navea Roy Francis 
Published: August 2021

Abstract
Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have disadvantage in which it can be easily to be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for feature extraction method. The wavelet packet decomposition feature is represented in the average wavelet, root mean squared (RMS) wavelet and power wavelet were selected as features to extract a meaningful information from the original EEG signal based on the visual representation. These features were applied to classify between familiar and unfamiliar image responses (visual representation) and to recognize 13 subjects by using Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Random Forest (RF). The analysis of the classification between familiar and unfamiliar images responses obtained that gamma frequency (30 – 45 Hz) achieved the highest correct recognition rate (CRR) and KNN obtained the accuracy of 92.8% was obtained with KNN in the classification between familiar and unfamiliar image responses. Using the gamma frequency band, the classification between the EEG responses of the 13 subjects was evaluated using the percentage of false acceptance rate (FAR) and false rejection rate (FRR). From the overall view, the value of FAR is lower than FRR. These values were used in authentication system as threshold for security level. As the result of classification between the subjects, SVM performed better compared as KNN and RF in which the error rate for acceptance of unauthorized person and rejection of authorized person were the lowest.
Implementation of Artificial Neural Network in Electric Motor Control using Brain-Computer Interface
Authors: Navea RF , Alipaspas MA , Guillermo J , Mañosca AP , Awang SA 
Published: August 2021

Abstract
Brain-Computer Interface (BCI) is a technology that integrates analog brain signals to digital computer-based systems for the purpose of analysis, manipulation, and control. With modern machine learning algorithms, Artificial Neural Network (ANN) was used to develop the intelligence in processing brain signals for electric motor control. In this study, supervised and unsupervised learning methods were explored to train the ANN. Guided and unguided thought-task methods were used in manipulating a 5 and an 8 switching control variations of an electric motor. A brain wave filter was designed, and the different brain bands were explored. Significant signal features were obtained and were varied in terms of qualification. Simulation run in an offline and real-time mode. Results show high control accuracies in using the Gamma band with a supervised learning method. Guided thoughts with 5 switching controls, and 4 features gave better results. Control accuracies varies between off-line and real-time implementations.
Image Enhancement System for the Restoration of Old Jawi Malay Manuscripts using Binarization Method
Authors: Devadass Victoriah , Bakar Juhaida Abu , Harun Nor Hazlyna , Zamri Muhammad Faiz 
Published: August 2021

Abstract
Image enhancement is the process of refining a given image, so that the desired image features become easier to be perceived by the human visual system. Ancient manuscripts from hundreds to thousands of years often are often non-comprehendible condition. Several factors include the age of the manuscript, environmental influence, and ink quality. This includes Malay manuscripts, which are normally written in the Jawi scripts. These documents, which were written from the 16th to 19th century, are in possession to date, however, the quality has been degraded. The purpose of this study is to develop a system to protect historical manuscripts which are in worsening form and also to find solution to reduce image noise. Type of enhancement method employed is binarization or thresholding method. As a result of this study, Image Enhancement System (IES) was easy to use and has a great potential for improvement in future because IES provides high entropy value for enhanced image compared to original image.
A Web-Based Campus Navigation System with Mobile Augmented Reality Intervention
Authors: Nordin Noradila , Markom Marni Azira , Suhaimi Farhana Aqilah , Ishak Syaimaa’ 
Published: August 2021

Abstract
In this era of information technology, the introduction of new and greatly improved technologies is becoming vital especially with the progressive development of computing power to fulfill current technological needs. Augmented Reality (AR) is a part of the emerging technologies that are gaining great attention and interest with new and better innovations being developed. AR is an immersive technology where it complements real-world objects together with computer-generated objects to give more details and meaning to the objects in the real world. AR has been successfully growing nowadays in many industries, mainly in gaming and entertainment, education and navigation. However, AR has not been standardized and to be widely used in navigation systems even though there are many applications developed for this purpose. This paper proposed a web-based AR-UUM Campus Navigation System using ARToolKit, that is accessible via mobile devices that uses AR to overlay the information as images of the searched location on campus, especially for indoor navigations for lecture halls, tutorial rooms, laboratories, and offices which rarely are covered by the normal maps. Based on the respondents' evaluation, they managed to successfully interact with the AR-UUM Campus Navigation System, however, some improvements are required in terms of the functions expected from the system. Nevertheless, the use of AR in a navigation system shows promising results and has the potential to be widely used due to high interest in augmented reality.
A Deep Learning Based Multiclass Segregation of E-waste using Hardware Software Co-Simulation
Authors: Elangovan S. , Sasikala S. , Arun Kumar S. , Bharathi M. , Naveen Sangath’ E. , Subashini T. 
Published: August 2021

Abstract
Today, the advancement in technology has potentially changed the lifestyle of all the people. Though this innovation is beneficial, it has quite adverse effects on both human health and environmental health. One of the main causes is ‘E-Waste’ produced by electronic gadgets. Globally, the usage of electronic gadgets has increased the quantity of “e-waste” or electronic waste and it has now grown a major problem. An unproper disposal of e-waste is now becoming an environmental and public health issue, as this kind of waste has become the most rapidly increasing segment of the municipal waste stream in the world. But this ever-increasing waste is very complex in nature and is also a rich source of metals such as Neodymium, Indium, Palladium, Tantalum, Platinum, Gold, Silver, Aluminium and Copper which can be recovered from these wastes and brought back into the production cycle and day to day utilization. Hence, there is a need of proper e-waste segregation and management to recover the precious materials from these kinds of wastes. In this project, a Deep learning model implemented using NVIDIA’s Jetson Nano development kit has been proposed, to classify the waste components into two categories based Precious or Non – Precious metals present in the waste. The prototype model developed in turn segregates the waste with good accuracy and less time consumption.
Towards Building Intelligent Robotic Systems to Enhance the Safety of Firefighters
Authors: Kirubakaran M N , Arun Kumar S , Sasikala S , Gohithmugilan S , Muralidhar M 
Published: August 2021

Abstract
Firefighting robot is a human developed robot to safeguard human’s life. Nowadays, the accidents happening during the fire extinguishing is uncountable. The main role of the proposed robot is to detect the fire, to move towards the fire automatically, to move at any direction to extinguish the water and to send a notification to the nearest fire station. The automatic robot movement is fully controlled by a programmable ESP32. The automatic robot which is in a form of vehicle like will move right, left, front and back to detect and extinguish fire. The firefighting robot equipped with a camera is used to capture the live moment happening at a particular time. The live recording from the camera is sent to the nearest fire station which is already programmed. This recording helps the real fire fighters to know the intensity of the fire and they can plan accordingly to equip themselves. Cloud technology is used to send live recording to real fire fighters to identify the intense of the fire. The exact location of where the fire has happened is also sent to enable firefighters to arrive at the location very quickly.
A Conceptual model of E-Taxation satisfaction: How can taxpayers be tickled pink with the smart Web-Based taxation application?
Authors: Haruna Isyaku Uba , Nadzir Maslinda Mohd , Awang Hapini , Mohamed Latifah 
Published: August 2021

Abstract
Governments globally have adopted various types of E-Taxation systems as part of E-Government plans in providing taxpayers a media to lodge tax claims at their ease. As this article is concerned with user satisfaction, a critical analysis of satisfaction literature related to Information Systems (IS), E-Taxation, E-Government, and technology in general, has been thoroughly done. For instance, technology such as Artificial Intelligence is being introduced in practically all aspects of society, including E-Taxation. This led to the understanding of how different researchers have theorized the ‘Satisfaction’ concept in the related areas. We focused on identifying the factors that constitute user satisfaction with the E-Taxation system. As a result, it is discovered that to date, limited studies have been devoted to evaluating user satisfaction with E-Taxation systems. Also, we found the emergence of several dimensions that require further investigation. Therefore, in this article, a conceptual model to evaluate E-Taxation satisfaction among taxpayers is proposed based on DeLone and McLean IS Success Model (D&M). Finally, we urged that the model should be empirically validated so that it will be practical to be adopted to guide local government to improve the quality of services delivered through E-Taxation systems.
A review on image processing for fish disease detection
Authors: Pauzi S N , Hassan M G , Yusoff N , Harun N H , Abu Bakar A H , Kua B C 
Published: August 2021

Abstract
Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.
Graph-LSTM with Global Attribute for Scene Graph Generation
Authors: Shao Tong , Wu Dapeng Oliver 
Published: August 2021

Abstract
Lots of machine learning tasks require dealing with graph data, and among them, scene graph generation is a challenging one that calls for graph neural networks’ potential ability. In this paper, we present a definition of graph neural network (GNN) consists of node, edge and global attribute, as well as their corresponding update and aggregate functions. Based on this, we then propose a realization of GNN model called Graph-LSTM and use it in scene graph generation. The model first extracts the item features in the image as the initial states of the node-LSTM representing subject/object and edge-LSTM representing predicate. Two LSTMs update the states via LSTM’s timestep and aggregate information via message passing. Repeat the update-aggregate until convergence. Meanwhile, the tag feature, i.e., the generated probability distribution of image’s semantic concepts is sent to the LSTM through a semantic compositional network (SCN). The SCN-LSTM is trained in an ensemble style, and hence allows the tag feature to serve as the global attribute providing context information to all individuals. The LSTMs’ final states are input to inference modules and generate the triplet (subject, predicate, object) of the scene graph. Experimental results show that Graph-LSTM outperforms the Message Passing and the attention Graph Covolutional Network methods, proving the effectiveness of the proposed scheme.
The Research of BDPCA Identifying Emotion by EEG
Authors: Yan Chao , Nian Jintao , Lv Chao 
Published: August 2021

Abstract
Electroencephalogram (EEG) data contain wealthy information about the brain’s and body’s pathology and physiological state. It’s not easily to identify the truth that EEG contained. The unsupervised learning method don’t need to take label by human. Without subjective feeling, it greatly improve training accuracy. In this current paper, adopted improved Density Peak Clustering Algorithm (DPCA) to train EEG data. To solved the problem that difficult to determined cluster center number, Bayesian Information Criterion(BIC) was introduced. The algorithm was verified feasibility that in EEG processing by experiment which divided fatigue state level in lab. And used SJTU Emotion EEG Data set (SEED) identifying different emotions. Compared with other cluster algorithms, BDPCA accuracy totally raised about 5%. And BDPCA behavior was steadier in different emotion types.
Assistive control of brain-control multiple robots
Authors: Chi Weiming , Liu Ying , Yang Zhenge , Bi Luzheng 
Published: August 2021

Abstract
Brain-controlled robots have important application values in assisting persons with disabilities. However, few studies focus on brain-controlled multiple robots. This paper proposes an assistive controller based on model prediction with the leader-follower formation strategy for developing brain-controlled multiple robots. The controller is composed of a leader controller and a follower controller. The experimental results of brain-controlled multiple robots show that the proposed method can track the user’s lateral and longitudinal control intention and maintain the stability of the multi-robot formation while ensuring the safety of brain-controlled multiple robots. This work can advance the research and development of brain-controlled multiple robots.
Simulation of LPG tank truck leakage and explosion accident based on CFD
Authors: Kai Ni , Yuepeng Ma 
Published: August 2021

Abstract
Road transportation of hazardous chemicals has a high risk and is likely to cause serious road traffic accidents. In this paper, the most common LPG tanker in hazardous chemical transportation is taken as the research object. Based on theCFD method, we used FLACS software to simulate LPG tank car accident in Italy. The leakage process of LPG tank car after leakage was simulated, and the diffusion process of propane and butane mixture in one minute when the accident occurred. It was further analyzed that the leakage material exploded when it encountered the ignition source, and the shock wave overpressure was the highest at 2.5 seconds after the explosion, with the shock wave pressure exceeded 0.26MPa. Also, the attenuation of theblast wave is higher in the area far away from the explosion source. The research results can provide effective data support for on-site emergency disposal and personnel evacuation of similar accidents.
Machine vision problem for fast recognition of surface defects of thermoelectric cooler components based on deep learning method
Authors: Yu Z Q , Zhao M , Huang J L , Wen T X , Liao T D 
Published: August 2021

Abstract
During thermoelectric coolers (TEC) production, a complex industrial manufacturing process must be experienced, which may cause defects on the surface of the TEC component. To improve the efficiency of TEC component defect inspection, we propose a machine vision technology based on deep learning for surface defect detection. In order to make the deep learning method based on the you only look once (YOLO) model more efficient, first of all, we use a more lightweight network ResNet34 to improve the original network structure. Then, the loss function is improved to complete intersection over union (CIoU) loss. Experiments performed using the proposed model, show an obvious reduction in the number of parameters, the detection speed is as high as 6.5pcs/s, and the detection accuracy is 97.61%. This method lay a good foundation for the further application of deep learning methods in the field of industrial detection. The experimental results verify the feasibility and effectiveness of the model.