bluuebunny/crossref_metadata_2025 huggingface dataset:
A Bibliometric Analysis of Nonspecific Low Back Pain Research
Authors: Weng Lin-Man , Zheng Yi-Li , Peng Meng-Si , Chang Tian-Tian , Wu Bao , Wang Xue-Qiang
Published: March 2020

Background. Researchers are highly interested in the study of nonspecific low back pain (NSLBP). However, few have attempted to collect global data, analyze the emerging trends, and conduct reviews from the perspectives of visualization and bibliometrics. Purpose. We aimed to evaluate research situation and capture subsequent developmental dynamics regarding NSLBP via CiteSpace. Methods. Publications on NSLBP in recent 19 years were retrieved from the Web of Science Core Collection (WoSCC). We used CiteSpace to analyze publication outputs, document types, countries, institutions, journals, authors, references, and keywords. Knowledge foundation, hot topics, and future direction were then stated. Results. A total of 1099 papers were collected, and the trend of annual publications maintained growth with small fluctuations. Australia (188) and the University of Sydney (76) were the most prolific country and institution, respectively. The Netherlands (0.84) and the University of Sydney (0.47) had the maximum centrality, thus indicating that they have importance in this field. The journal Spine (publication: 87, cocitation counts: 942) ranked first in terms of the volume of publications and cocitation counts. Maher CG (52) who published the most papers and Waddell G (286) who was cited most frequently were the leading authors, thus making strong academic influences. “Motor control exercise” was the largest cluster, which contained most related research articles. 14 references with the strongest citation counts were cited until 2018, thus implying the future development trend. Current hotspots were treatment, meta-analysis, method, and risk factors. Spine, efficacy, adult, and meta-analysis can be regarded as research frontiers. Conclusion. This study offers insights into the trend of NSLBP to determine major research countries and institutions, core journals, pivotal authors, overall development tendency, hot topics, and research frontiers. Moreover, it will help researchers extract hidden valuable information for further study.
Household Food Insecurity Predicts Childhood Undernutrition: A Cross-Sectional Study in West Oromia (Ethiopia)
Authors: Berra Wondu Garoma
Published: March 2020

Background. Despite mixed reports, food insecurity emerges as a predictor of nutritional status, assumably limiting the quantity and quality of dietary intake. In Ethiopia, the prevalence of childhood undernutrition and food insecurity is highly pronounced. However, whether household food insecurity predicts undernutrition in children was not yet well established. Thus, the aim of the present study was to identify the link between household food access and undernutrition in children aged 6–23 months in West Oromia zones, Ethiopia. Methods. A cross-sectional study was conducted on a sample of 525 households during June–October 2016. Food access was measured as Household Food Insecurity Access Scale. Semistructured interviewer-administered questionnaires were employed to collect data on sociodemographics, child health, child dietary practices, household food security, and anthropometrics. The height and weight of children aged 6–23 months in each household were measured. Multivariate logistic regression models were constructed to assess the association between household food insecurity and child nutritional status measured from undernutrition indicators. Results. Overall, more than two-thirds (69%) of households were classified as food insecure (had insufficient access to adequate food), with a mean (SD) household food access score of 7.9 (7.7). The respective prevalence of mild and moderate food-insecure households was 56.6% and 12.4%. Higher proportions of children in food-insecure households were stunted (41.8% vs. 15.5%), underweight (22.0% vs. 6.1%), and wasted (14.9% vs. 6.1%). Overall, the prevalence of child undernutrition was 21.3% in the target population, with 16.2% stunted, 6.9% underweight, and 6.3% wasted. The present finding shows food-secure households were 54% protective (OR: 0.46, 95% CI: 0.25–0.84) for child undernutrition. Compared to children in food-secure households, children who were reportedly living in moderately food-insecure households were over twice more likely stunted (OR: 2.09, 95% CI: 1.02–4.28) and over 4 times more likely underweight (OR: 4.73, 95% CI: 1.81–12.35). However, household food insecurity was not a correlate for acute malnutrition (wasting) in children. Conclusions. The prevalence of household’s food insecurity situation is very common and more pronounced among households with undernourished children aged 6–23 months in Ethiopia. The analysis of this work shows that moderately food-insecure households are a salient predictor for composite undernutrition, stunting, and underweight, but not for wasting. Thus, this finding informs the need for multisectoral strategies and policies to combat household’s food insecurity and multiple forms of child undernutrition, beyond the socioeconomic wellbeing.
User Audit Model Based on Attribute Measurement and Similarity Measurement
Authors: Yang Xiaohui , Sun Ying
Published: March 2020

The Internet of Things (IoT) is an open network. And, there are a large number of malicious nodes in the network. These malicious nodes may tamper with the correct data and pass them to other nodes. The normal nodes will use the wrong data for information dissemination due to a lack of ability to verify the correctness of the messages received, resulting in the dissemination of false information on medical, social, and other networks. Auditing user attributes and behavior information to identify malicious user nodes is an important way to secure networks. In response to the user nodes audit problem, a user audit model based on attribute measurement and similarity measurement (AM-SM-UAM) is proposed. Firstly, the user attribute measurement algorithm is constructed, using a hierarchical decision model to construct a judgment matrix to analyze user attribute data. Secondly, the blog similarity measurement algorithm is constructed, evaluating the similarity of blog posts published by different users based on the improved Levenshtein distance. Finally, a user audit model based on a security degree is built, and malicious users are defined by security thresholds. Experimental results show that this model can comprehensively analyze the attribute and behavior data of users and have more accurate and stable performance in the practical application of the network platforms.
The Effectiveness of Neutrophil-Lymphocyte Ratio in Predicting in-Hospital Mortality in Non-ST-Elevation Myocardial Infarction
Authors: Avci Begüm Şeyda , Avci Akkan , Dönmez Yurdaer , Kaya Adem , Gülen Müge , Özer Ali İlker , Bulut Atilla , Koç Mevlüt , Nazik Hakan , Satar Salim
Published: March 2020

Background. Myocardial infarction is the most common cause of death all over the world. There are many studies in predicting mortality. The aim of this study was to determine the effectiveness of hematologic parameters measured at the moment of admission to the emergency room in predicting in-hospital mortality and to determine cutoff values of strongly predictive values. Methods. A total of 681 patients over 18 years of age, whose date could be obtained, were included in the study. From the hemogram parameters, white blood cells (WBC), red cell distribution width (RDW), mean platelet volume (MPV), and neutrophils-to-lymphocytes ratio (NLR) values were determined and recorded. CK-MB and high-sensitive troponin T values were recorded as cardiac markers. For statistical analysis, “SPSS for Windows version 21” package program was used. Results. 62.6% (n = 426) of the patients were male, and 37.4% (n = 255) of the patients were female. The NLR was found to be the strongest predictor (area under the curve (AUC), 0.783, SD = 0.052, 95% confidence interval (CI)). It was found that the WBC value came in the second place after NLR as a strong predictor of mortality (AUC, 0.702, SD = 0.075, 95% CI). Conclusion. According to the hemogram results which were acquired with a simple and cheap method, we found that WBC and especially NLR values obtained with a simple method can be used as powerful predictors.
Proportional PDC Design-Based Robust Stabilization and Tracking Control Strategies for Uncertain and Disturbed T-S Model
Authors: Ghorbel Chekib , Tiga Amira , Benhadj Braiek Naceur
Published: March 2020

This paper presents a proportional parallel distributed compensation (PPDC) design to the robust stabilization and tracking control of the nonlinear dynamic system, which is described by the uncertain and perturbed Takagi–Sugeno (T-S) fuzzy model. The proposed PPDC control design can greatly reduce the number of adjustable parameters involved in the original PDC and separate them from the feedback gain. Furthermore, the process of finding the common quadratic Lyapunov matrix is simplified. Then, the global asymptotic stability with decay rate and disturbance attenuation of the closed-loop T-S model affected by uncertainties and external disturbances are discussed using the H∞ synthesis and linear matrix inequality (LMI) tools. Finally, to illustrate the merit of our purpose, numerical simulation studies of stabilizing and tracking an inverted pendulum system are presented.
Relationship between Thyroid Hormone Levels and Crime Type: A Controlled Study in Prisoners
Authors: Acar Hasan , Ulgen Ayse
Published: March 2020

Various factors cause aggression, which can be related to imbalance of T3 and T4 hormones, which can act as neurotransmitters and are reported to be elevated during aggression. This indicates changes in the hypothalamic-pituitary-thyroid axis that cause long-term changes in aggressive behaviour, especially in criminals. Moreover, mental and behavioural disorders possibly occur in individuals with impairment in thyroid hormone balance. The main rationale for this study was to asses if high T3, high T4, and low TSH hormones may have an effect on aggression-related crime tendency. Furthermore, the study aimed to measure levels of thyroid hormones in prisoners and to examine relationships of the hormone levels with crime rates. Our study was conducted in Ankara Sincan Closed Prisons. The study group consisted of 208 male volunteers who were imprisoned and the control group included 82 male volunteers who were not imprisoned. Prisoners in the study group were divided into two groups: those who committed aggression-related crime (Group A, n = 96) and prisoners convicted of other crimes (Group B, n = 112). Pulse rates, T3, T4, and thyroid-stimulating hormone (TSH) levels, and theT3/T4 ratio were measured in these prisoners. Data were analysed using the Wilcoxon rank sum test and chi-square Fisher’s exact test to test for any statistically significant differences. Results showed that toxic goitre rates, T3 and T4 values, and pulse rates were significantly higher in Group A than in the control group. Significant increase in T3 and T4 levels and the presence of toxic goitre were associated with aggression-related crime. These examinations should be performed on prisoners in general, especially those convicted of violent crimes. Additional rehabilitation and research programs should also be developed for such patients.
A Theoretical Model of Listeriosis Driven by Cross Contamination of Ready-to-Eat Food Products
Authors: Chukwu C. W. , Nyabadza F.
Published: March 2020

Cross contamination that results in food-borne disease outbreaks remains a major problem in processed foods globally. In this paper, a mathematical model that takes into consideration cross contamination of Listeria monocytogenes from a food processing plant environment is formulated using a system of ordinary differential equations. The model has three equilibria: the disease-free equilibrium, Listeria-free equilibrium, and endemic equilibrium points. A contamination threshold ℛwf is determined. Analysis of the model shows that the disease-free equilibrium point is locally stable for ℛwf<1 while the Listeria-free and endemic equilibria are locally stable for ℛwf>1. The time-dependent sensitivity analysis is performed using Latin hypercube sampling to determine model input parameters that significantly affect the severity of the listeriosis. Numerical simulations are carried out, and the results are discussed. The results show that a reduction in the number of contaminated workers and removal of contaminated food products are essential in eliminating the disease in the human population and vice versa. The results have significant public health implications in the management and containment of any listeriosis disease outbreak.
Structural Analysis of Factual, Conceptual, Procedural, and Metacognitive Knowledge in a Multidimensional Knowledge Network
Authors: Vukić Đurđica , Martinčić-Ipšić Sanda , Meštrović Ana
Published: March 2020

Discovering the most suitable network structure of the learning domain represents one of the main challenges of knowledge delivery and acquisition. We propose a multidimensional knowledge network (MKN) consisting of three components: multilayer network and its two projections. Each network layer constitutes factual, conceptual, procedural, or metacognitive knowledge within the domain of databases as a standard course of computer science study. In the MKN layer, nodes are concepts or knowledge units and the edges are weighted with regard to Bloom's cognitive learning level. The projected network layers are contrasted with a monolayer network by comparing characterizations of the centrality measures: degree centrality, closeness centrality, betweenness centrality, and eccentricity. The study revealed indications of how concepts, supported with the higher number of previously introduced concepts, have a dominant role in knowledge acquisition, from a view of knowledge structure and content. The analysis of communities, assortativity coefficient, and overlap between MKN layers contributes to better structuring of knowledge. MKN enables systematic insights into the efficiency of knowledge integration across metacognitive layers, as well as the detection of crucial cognitive concepts that reduce/increase the cognitive load during learning.
3D modified wavelet block tree coding for hyperspectral images
Authors: Bajpai Shrish , Vikram Singh Harsh , Rahman Kidwai Naimur
Published: March 2020

A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors has been proposed. The proposed scheme uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients. By doing the reconstruction of the transform image cube, taking the difference between the frames, it increases the coding efficiency, reduces the memory requirement and complexity of the hyperspectral compression schemes in comparison with other state-of-the-art compression schemes.
Video spam comment features selection using machine learning techniques
Authors: Alias Nabilah , Mohd Foozy Cik Feresa , Najwa Ramli Sofia , Zainuddin Naqliyah
Published: March 2020

Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.
The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning
Authors: Ali M. , K. Sheng T. , M. Yusof K. , R. Suhaili M. , E. Ghazali N. , Ali S.
Published: March 2020

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.
Automatic smoke detection system with favoriot platform using internet of things (IoT)
Authors: Bin Suparman Mohd Alif , Ling Jong Siat
Published: March 2020

The available fire alarm system in the market unable to inform occupant that their house is on fire. The occupant remains unknown especially when they are away from their house. Most of the time, the house or building almost destroyed by fire when firefighter comes to the scene due to late inform to Fire and Rescue Department. To this aim, automatic smoke detection system using Internet of Things (IoT) is proposed. The proposed system not only able to monitor the smoke condition of a room but also able to alert user and Fire and Rescue Department when certain level of smoke is detected by a gas sensor via Favoriot platform. Arduino Uno is used in this work to control all the devices and WiFi shield acts as a medium to interconnect devices with the network so that the data from the smoke sensor can be read in the Favoriot platform. In this experiment, the condition of room is tested under several burning materials and the smoke levels are recorded. It is found that when the smoke level more than 100 ppm, it may cause to sore eyes, cough and hard breathing that can bring to death. Therefore, the best threshold level of the automatic smoke detection system is at 80 ppm. By using this system, the user able to take preliminary rescue action to save people and prevent fire breaks out.
Modeling of micro-grid with the consideration of total harmonic distortion analysis
Authors: Alhamrouni Ibrahim , Wahab Wira , Salem M. , Rahman Nadia H. A. , Awalin Lili
Published: March 2020

With the rapid expansion of electricity grid, there yet still places to be covered considering their remote location. Micro-grid (MG) is a solution in this scenario, in fact, there is actually many cases where MG is used in case of emergency and act as a backup to the main electrical grid. By disconnecting itself from the larger grid, a grid that can have many problems, the micro-grid becomes much more flexible in its operations and by continuing to power households and communities alike. Besides grid-connected mode, it is necessary for the MG to operate in autonomous mode. By operating in islanded mode, micro-grids must be able to supply critical load without interruption, run at specific values for voltage and power and extract the maximum power from the distributed generations (DG). Therefore, the modeling of microgrid network considering solar photovoltaic(PV) and wind turbine generation (WTG) system as the distributed generation have been modeled using Matlab/Simulink in this research. Apart from that, the observation of total harmonic distortion(THD) between two operation modes of grid-connected and the islanded-mode is presented in order to analyze the power quality stability towards two operations MG network with same loads size and network parameters.
Portable health monitoring kit using photolethysmogram signal
Authors: A. Jamal N. F. , A. Sidek K.
Published: March 2020

This study investigates the feasibility of photoplethysmogram (PPG) signals in monitoring health condition and designing a portable health monitoring kit which is suitable for home use. The aim of this study is to ease people in monitoring their health continuously without the need to go to the hospital which can save a lot of time. The focus of this study is to find heart rate and blood pressure recording. The type of PPG sensor used in this project is a non-invasive PPG which measures the blood volume changes in any part of the body. A total of 16 subjects consisting of male and female with age range of 20 to 60 years old were involved in this research. The heart rate and blood pressure for each subject were acquired and analyzed. Based on the result, it shows that higher heart rate reading is associated with female and younger age groups. Meanwhile, for blood pressure value, male subjects tend to have higher blood pressure as compared to female subjects. However, there is no specific pattern for blood pressure in terms of the age group. In the case of HRV analysis based on Kubios software, the SDNN value is higher for male and older age subjects. Meanwhile, the RMSSD value is lower for male and older age subjects. Therefore, PPG signal has the capability to monitor the health status of an individual, which acts as an alternative biological signal for the existing health monitoring systems.
Wireless water usage monitoring system for home / small premises
Authors: Chee Wei W.L. , Ab Ghafar A.S. , Hairul Rozi N.N. , A. Saparudin F.
Published: March 2020

The fourth Industrial Revolution has led to tremendous change in industrial automation. Measurement system can be seen as an important tool implemented in various fields because it enables us to access essential data from the environment or desired location. One of the essential measurement systems in industry, company or home is water usage monitoring. Water usage monitoring is the regular collection of information on the total amount of water drawn from sources during a given period. It enables a company or industry to understand water usage patterns and identify potential inefficiencies. For instance, a hotel premise who wants to monitor its water usage per room basis. Monitoring is also essential to set reduction targets of water used. The paper presents the development of wireless water usage monitoring system. This system consists of two nodes which are sensor node and sink node. The sensor node collects the water usage data and send them to the sink node. An ultrasonic sensor, Light-Emitting Diode (LED) and buzzer are attached to the sensor node as alert system for the user in case of water wastage occurrence. The sink node receives data from the sensor node wirelessly and mark this data time stamp by referring to a Real Time Clock (RTC) and store it in the database. The database is attached to sink node with Secure Digital (SD) card module. Furthermore, a Graphical User Interface (GUI) is used to display the water usage data in graphical form for easier user interpretation. The proposed wireless water usage monitoring system is suitable for home and small premises usage.
45 degree arrangement of compact array antenna for MIMO application
Authors: F. Ismail M. , A. Majid H. , Macwright C. , A. H. Shaabani M. N. , S. Mohd M. , M. Zahar M. , Zubir F. , A. Abdullah M.
Published: March 2020

A study on the compact array microstrip patch antenna for multiple-input multiple-output (MIMO) communication system based on the antenna arrangement is performed. The 2.45 GHz rectangular array are arranged in 45 degree slanted inward and outward for each other to reduce the mutual coupling effect between the patches. The antenna properties are analyzed and compact antenna design is determined based on the simulation results. The results show the antennas can very compact while maintaining low mutual coupling. The gain of the MIMO antenna is 11.3 dBi. The simulated and tested return losses, together with the radiation patterns, are presented and discussed.
Prospects of using the UFMC technology In 5g / Imt-2020 networks
Authors: Ghazi Behadili Ahmed Sabri , Abdualsada Alshebaney Emad Jadeen , Khudhair attaby Aqeel Lateef
Published: March 2020

The article considers the technology of frequency multiplexing with universal filtering UFMC, planned to be introduced in the fifth generation of mobile communication networks, which allows maximizing the rate of decay of the side lobes of the multifrequency signal spectrum that cause out-of-band emissions. As a method of investigation, a computational experiment was chosen. The parameters of the OFDM and UFMC signals were compared to determine the gain of the UFMC technology in the occupied bandwidth of the signal spectrum, as well as the number of arithmetic operations, required to generate a data symbol compared to the OFDM technology, on the basis of which, conclusions were made about the practical application of UFMC technology in networks mobile communication of the fifth generation. The conducted analysis can help to select the optimal number of sub-channels in groups in order to minimize the amount of computations during the UFMC symbol generation process.
Implementation of flow control over WirelessHART sensor network using WirelessHART adaptors
Authors: Miya Hassan Sabo , Bingi Kishore , Ibrahim Rosdiazli , Jin Chein Lim , Supramaniam ThasarathaRao
Published: March 2020

Despite the advantages of the industrial wireless standards such as WirelessHART, ISA100.11a and Wireless Networks for Industrial Automation-Process Automation (WIA-PA), their application still faces a lot of challenges especially when it comes to interfacing with the real plant. This is due to lack of adequate infrastructures such as interfacing circuitry to establish communication between the WirelessHART nodes and the actuators and sensors. Therefore, this paper presents the application of locally developed WirelessHART adaptors for flow process control. The adaptors serve as an interface between the WirelessHART network and the sensor and actuator of the plant. Experimental results of the controllers compared showed that wireless control is possible through the use of the adaptors.
Circularly polarized microstrip patch antenna array for GPS application
Authors: Khalifa Taher , M. Sahar N. , Ramli N. , T. Islam M.
Published: March 2020

The 12 elements antenna array for GPS system having high gain with circular polarization is presented in this paper. The circularly polarized antenna is very suitable for the use of various wireless systems such as Global Positioning System with operating frequency is 1.27 GHz with AR is less than 3 dB between 82˚ and 140˚.The antenna consists of twelve main radiation patches connected in parallel. The antennas array are designed on the Rogers RT5880 substrate with a dielectric constant (εr) of 2.2 and thickness is 0.787 mm. The antenna is excited with an inset feed line and it operates in L-band with a resonant frequency of 1.27 GHz.
Heterogeneous modelling framework for 5G urban macro ultra dense networks
Authors: Ghawbar Fayad , A. Saparudin Faiz , A. S. Jumadi , A. Ghafar Aimi S. , Katiran N.
Published: March 2020

The explosive growth of mobile devices is the main engine to continue evolution in the communications field. The amount of traffic generated by today’s users in applications such as high definition videos, cloud computing, and wearable devices, require a drastic change in mobile telecommunications. 5G Ultra Dense Network (UDN) is one of the key components leading in achieving the high capacity for all users. In UDN, the number of base stations or access nodes equals or exceeds the number of active users by unit area. In this paper, different modeling techniques of UDN are studied. Moreover, a heterogeneous framework modeling was proposed. This framework illustrated a system model for UDN based on Urban Macro (UMa) Scenario. The distance dependent path loss model for UMa was presented and analyzed. The Simulation results of path loss model indicated an increase in the path loss with increasing the distance range from 10m to 500m. The received power simulation results of User Terminal (UT) displayed the power is approaching zero when the distance between the BS and UT goes beyond 250m. Therefore, it is assumed that UTs located 250m away from the BS can reuse the subchannel of AN in another sector with negligible interference.
Design of metal plate temperature sensor based on fiber bragg grating (FBG)
Authors: Fadzlina Naim Nani , Anuar Nur Shahira , Sarnin Suzi Seroja , Yaa’cob Norsuzila
Published: March 2020

This paper presents the design and characterization for metal plate temperature sensor based on Fiber Bragg Grating (FBG). Five types of FBGs were used such as Acrylate FBG, Bare FBG, Bimetallic Acrylate FBG, Bimetallic Bare FBG and Ceramic FBG to determine the sensitivity on four different plates at 23℃ to 70℃, experimentally. The four different plates are Aluminium, Aluminium Coating, Zinc and Zinc Coating has been used because of their excellent thermal conductivity. Two metals which are Copper and Zinc have been chosen to design the Bimetallic Strips FBG due to the Coefficient Thermal Expansion (CTE) to improve their sensitivity. The results show that Ceramic FBG has been chosen as the best temperature sensor because it has the highest sensitivity compared to other FBGs with sensitivity value of 50.4 and 59.7 pm  16(â„ƒ)-1">  when in contact with Aluminium and Zinc plates, respectively. In addition, Aluminium and Zinc plates has been chosen as the most reliable metal plates where it can transfer heat efficiently due to high thermal conductive which is 237 W/mK and 116 W/mK thus it gives the highest sensitivity when measured using Bimetallic Acrylate, Bimetallic Bare and Ceramic FBG.
Automated brain tumor segmentation and classification for MRI analysis system
Authors: Mohd Saad Norhashimah , Faizal Yaakub Muhamad , Abdullah Abdul Rahim , Mohd Noor Nor Shahirah , Zainal Nur Azmina , Mohd Saad Wira Hidayat
Published: March 2020

This paper proposed a new analysis technique of brain tumor segmentation and classification for Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI). 25 FLAIR MRI images were collected from online database of Multimodal Brain Tumor Segmentation Challenge 2015 (BRaTS’15).  The analysis comprised four stages which are preprocessing, segmentation, feature extraction and classification. Fuzzy C-Means (FCM) was proposed for brain tumor segmentation. Mean, median, mode, standard deviation, area and perimeter were calculated and utilized as the features to be fed into a rule-based classifier. The segmentation performances were assessed based on Jaccard, Dice, False Positive and False Negative Rates (FPR and FNR). The results indicate that FCM offered high similarity indices which were 0.74 and 0.83 for Jaccard and Dice indices, respectively. The technique can possibly provide high accuracy and has the potential to detect and classify brain tumor from FLAIR MRI database.
Analysis of surface electromyography for hand gesure classification
Authors: A.M Najla Ilyana , Aini Z Nor , R. Sharvin , M.S Norlaili
Published: March 2020

Electromyography (EMG) is the measure of electrical activity produced by skeletal muscle. It is useful in prosthetic and rehabilitation technology as well as ability to handle electronic devices and robotics. If the EMG signal from the body especially hand movement can be apprehended, better value for people all around the world can be provided. Furthermore, it can be used to control smart-phone and be integrated with wearable technology. Another interesting application of this technology is in sign language recognition which is able to assist many disabled people in their daily lives. In this paper, hand gesture signals are acquired, extracted, analysed and classified. The EMG data from hand gesture which are rock, paper and scissors managed to be extracted. We use time domain feature to classified using Principal Component Analysis and regression tree. The result was highly accurate with 72.59% and 80.85% for PCA and regression tree respectively.
A review on data clustering using spiking neural network (SNN) models
Authors: Mohamed Siti Aisyah , Othman Muhaini , Hafizul Afifi Mohd
Published: March 2020

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.
Rain attenuation in broadband satellite service and worst month analysis
Authors: Abubakar Idrissa , Din Jafri Bin , Yin Lam Hong , Alhilali Manhal
Published: March 2020

Satellite link design, link margin and services at lower bands of satellite spectrum has been facing the challenges of meeting the demands for higher bandwidth requirements. Satellite operators and service providers are been compelled to migrate to the use of higher frequencies above 10 GHz. These higher frequencies were discovered to be vulnerable to atmospheric degradation creating the challenge of service availability especially for tropical rain zones with higher rainfall intensities and longer rain event durations. This study strive to evaluate the profile of rainfall and the monthly and annual variability to improve the design parameters of satellite propagation. Two yeas rainfall measurement campaign was conducted in Abuja at Nigcomsat-1R ground station with a view to understanding the characteristics of Abuja rain. The location of the site is on lat. 9.06o N and lon. 7.48o E. Tipping bucket rain gauge was used for point rain rate and 1.8 m VSAT antenna was installed to monitor the rain induced attenuation on satellite broadband signal. The results shows a huge variability between month to month as well as annual average between 2016 and 2017. The performance of broadband satellite service was found to largely to depend on the quality of the carrier power above the system noise rather than bandwidth capacity or the receive signal level while higher attenuations are associated with higher rain intensities and the slant path effects.
Development of a remote tending system for analog broadcast transmitters
Authors: Mappatao Gerino , Zebedia Bautista Isaiah Mari , Kevin Orsos Marc , Aldrine Ribo Mark , Castillo Joseph
Published: March 2020

Whenever a broadcast transmitter is on-air, it is required to be tended by licensed broadcast technicians. Technicians periodically monitor the transmitter performance, record the supply voltage and current at the last stage of the power amplifier and keeps a copy for at least two years. Also recorded are the times the transmitter is turned ON and OFF as well as the times the transmitter shuts down and returns back to air. This paper proposes a system to remotely tend analog transmitters in one central monitoring station. The remote tending of several transmitters in one location is made possible through internet connection. However, the challenge in the proposed system is in the gathering of transmitter data. A prototype of the system was constructed and tested in an FM broadcast transmitter. Test results on the prototype performance show that the proposed system can effectively be used to remotely monitor, record data and control the status of analog broadcast transmitters. The primary feature of the proposed system of monitoring several transmitters in one location will bring cost effective advantages to the broadcast operators. 
Modified brute force algorithm to solve the closest pair of points problem based on dynamic warping
Authors: Dellosa Rhowel M. , Fajardo Arnel C , Medina Ruji P.
Published: March 2020

This paper introduces an algorithm to solve the closest pair of points problem in a 2D plane based on dynamic warping. The algorithm computes all the distances between the set of points P(x, y) and a reference point R(i, j), records all the result in a grid and finally determines the minimum distance using schematic steps. Results show that the algorithm of finding the closest pair of points has achieved less number of comparisons in determining the closest pair of points compared with the brute force and divide-and-conquer methods of the closest pair of points. 
Radial basis function neural network for head roll prediction modelling in a motion sickness study
Authors: ‘Atifah Saruchi Sarah , Mohammed Ariff Mohd Hatta , Ibrahim Shapiai Mohd , Hassan Nurhaffizah , Wahid Nurbaiti , Jannah Zakaria Noor , Abdul Rahman Mohd Azizi , Zamzuri Hairi
Published: March 2020

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses.
Power system restoration in distribution network using minimum spanning tree - Kruskal’s algorithm
Authors: Mohamad Hasmaini , Faezy Wan Zalnidzham Wan Iqmal , Ashida Salim Nur , Shahbudin Shahrani , Mat Yasin Zuhaila
Published: March 2020

Events such as natural and manmade interference, line, transformer and feeder outages that occur in electric power distribution system negatively impact the continuity of power supply, thus affecting the power demand supply as well as customer’s satisfaction. In that cases, the restoration of power needs to be carried out immediately in order to guarantee the system’s reliability. The power flow path identification is considered as a difficult task especially in a huge system due to large number of switches. Kruskal’s algorithm is presented in this paper to find the minimum power flow path in a power distribution network. The comparison of performance between presented Kruskal’s algorithm and Binary Particle Swarm Optimization (BPSO) was made in solving a problem regarding network reconfiguration. The proposed load restoration approach is tested on IEEE 33-bus single feeder radial distribution system using MATLAB software. From the results, it is found that the presented Kruskal’s algorithm was able to search for the minimal power flow path that contribute to loss reduction for power restoration after the occurrence of fault.
Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment
Authors: Afifi Abdullah Mohd Hafizul , Othman Muhaini , Kasim Shahreen , Aisyah Mohamed Siti
Published: March 2020

Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.
Prioritization of network transformers in electrical distribution system by considering social welfare index
Authors: Krishna A. Prudhvi , Varma P. Srinivasa , Prakash R. B. R , Babu V. Kiran
Published: March 2020

To supply a meshed distribution system, network transformers are required. When few transformers are not in service, they must be repaired or replaced. A method is proposed for prioritizing the transformers considering the critical loads. Repair or replacement of transformers can be done by giving priority based on risk reduction. By addressing the possibility of network collapse due to failure of the feeder and impacted customers, risk can be predicted where the loads are extremely used at feeders section, network transformers and secondary mains. To select the transformer that needs to be replaced quickly and economically, an algorithm is proposed and it was tested on IEEE test system using GridLAB-D, MATLAB softwares. An index is proposed to give priority to emergency needs like hospitals and water pumping stations. Replacement or repair can be done by prioritizing network transformers incorporating social welfare index. 
Practical understanding of the operating principle of digital communication systems
Authors: Lema Gebrehiwet Gebrekrstos , Reda Teklehaymanot Baweke , Hailu Dawit , Sutikno Tole
Published: March 2020

There are many students and researcher who doesn’t really understand the practical operating principle of digital communication system. Hence in this paper, the digital communication system is studied and simulated. In the system kit development text and audio inputs are taken and encrypted with different encryption techniques including additive cipher, multiplicative cipher and affine ciphers. The encrypted data is converted in to an 8-bit binary, channel encoded with distinct channel coding styles like linear block encoder, cyclic encoder and convolutional encoder, line coded and band pass modulated by different digital modulation techniques. Finally, the developed software is tested with equivalent inputs of the current national TV broadcasting and the results found are correct according to the theoretical analysis of the discussion.
Performance analysis of emulated software defined wireless network
Authors: Assegie Tsehay Admassu
Published: March 2020

Software defined wireless network is a networking architecture and an emerging networking principle that is based on software defined network. A software defined network is a fundamental networking concept which separates the networking devices used in communication network form the program that runs on the top of these devices. In this paper we will explore the modeling tools used for software defined wireless network in our literature survey and based on the survey we have used mininet-Wi-Fi for modeling software defined wireless network, which is the best and most widely used emulation tool for software defined wireless network. Lastly, we have evaluated the performance of TCP traffic bandwidth for different number of emulated end stations and access points.
Cloud computing adoption reference model
Authors: Wan Mohd Isa Wan Abdul Rahim , Hakim Suhaimi Ahmad Iqbal , Noordin Nurulhuda , Fathiyah Harun Afdallyna , Ismail Juhaida , Awang Teh Rosshidayu
Published: March 2020

This paper presents a study to conceptualize a cloud computing adoption reference model. The case study on the cloud computing adoption was done in one local public university in Malaysia.  The case study was conducted to understand in-depth and real context phenomenon by investigating the influencing factors of cloud computing adoption. The main objective of this study is to design a cloud computing adoption reference model. This study applied Technology-Organizational-Environmental (TOE) Framework by Tornatzky & Fleischer and Diffusion of Innovation by Rogers as the theoretical background of the Cloud Computing Adoption Reference. Ten interviews were conducted with key informants. The theme pattern analysis of data were done by using qualitative computer programs, Atlas.ti. The findings are shown in summarize patterns that supports the conceptualization of cloud computing adoption reference model. Future work include the adaption of cloud computing adoption reference model specifically for the niche area of mobile computing.
Process Mining: Measuring Key Performance Indicator Container Dwell Time
Authors: Jokonowo Bambang , Sarno Riyanarto , Rochimah Siti , Priambodo Bagus
Published: March 2020

The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The L* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.
Comparison of malware detection techniques using machine learning algorithm
Authors: Syuhada Selamat Nur , Mohd Ali Fakariah Hani
Published: March 2020

Currently, the volume of malware grows faster each year and poses a thoughtful global security threat. The number of malware developed increases as computers became interconnected, at an alarming rate in the 1990s. This scenario resulted the increment of malware. It also caused many protections are built to fight the malware. Unfortunately, the current technology is no longer effective to handle more advanced malware. Malware authors have created them to become more difficult to be evaded from anti-virus detection. In the current research, Machine Learning (ML) algorithm techniques became more popular to the researchers to analyze malware detection. In this paper, researchers proposed a defense system which uses three ML algorithm techniques comparison and select them based on the high accuracy malware detection. The result indicates that Decision Tree algorithm is the best detection accuracy compares to others classifier with 99% and 0.021% False Positive Rate (FPR) on a relatively small dataset.