bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gender Prediction by Speech Analysis
Authors: Nazifa Nur Adlin , Fook Chong Yen , Chin Lim Chee , Vijean Vikneswaran , Kheng Eng Swee 
Published: November 2019

Abstract
Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensic analysis, the police need to detect criminal profile from any evidence such as voice from any calls and while in healthcare aspect, some vocal fold pathologies can be bias to a particular gender such as vocal fold cyst can be seen particularly in female patients and the patient will have problem with their voices. Three features are extract from the speech signal which are Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding (LPC) and Linear Prediction Coding Coefficient (LPCC). While for the classification, two classifier are used which are Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The recognition rate is higher for the combination of MFCC and LPCC compared to other features. SVM classifier had outperformed KNN classifier and obtained highest recognition rate of 97.45%. Lastly a graphical user interface system is develop that will record the voice of the speakers, pre-process the signal, extract MFCC and LPCC and classify it using SVM.
Study on Pulse Electric Field Exposure Effect on HeLa Cells For Wound Healing Application
Authors: Jamil Muhammad Mahadi Abdul , Zaltum Mohamed A. Milad , Youseffi Mansour , Javid Farideh 
Published: November 2019

Abstract
This study investigated the effect of pulse electric field (PEF) exposure on cervical cancer cells (HeLa cells) in an in-vitro wound repair model. The study mainly focused on the healing time of HeLa cell line wound model. During the experiments HeLa cells were maintained at 37°C in a modified Chamlide EC magnetic chamber where they were exposed to high electric fields. A Nikon inverted microscope (Ti-series) with Metamorph® time lapse software were used to monitor, image and capture photomicrographs and videos of the cells. The tests carried out during this study revealed that pulse electric field enhanced the migration of HeLa cells. Cells exposed to PEF (1kV/cm, 100μs, and single pulse) healed the wound in ∼2 hours (from initial wound gap of 54.53μm ± 0.55SD to 0.66μm ± 0.61SD). On the other hand, non-PEF (control) healed the wound in ~10 hours (from initial wound gap of 56.33μm ± 0.57SD to 0.46μm ± 0.45SD). It was therefore found that the healing rate with PEF is ∼five times faster than non-PEF group. It is believed that PEF usage on diseased biological cells would enable a novel method for assisting drug free wound repair systems and many other potential biomedical engineering applications such as treatment of neurological disorders including Alzheimers and Parkinsons.
Smart Medical Chatbot with Integrated Contactless Vital Sign Monitor
Authors: Zaki Wan Muhamad Asyraf Wan , Shakhih Muhammad Faiz Md , Ramlee Muhammad Hanif , Wahab Asnida Ab 
Published: November 2019

Abstract
Artificial intelligence is one of the important fields in modern technologies to help us strive for better life. Healthcare industries nowadays spend a lot of money researching on how artificial intelligence can help improve their services and give the highest satisfaction to their customers. Most healthcare organisations have a passive relationship to their patients when it comes to communication and this situation is often worsened because of a lack of inter-operability between client and provider. Mobile applications on the other hand have become one of the effective strategies in bridging the interaction between provider and end user. In this study, an automated self-learning system is designed to provide conversational healthcare for personalised proactive experience. This system is developed along with the in cooperation of contactless monitoring device using a vision-based real-time monitoring of vital signs which allow patients to monitor their oxygen level, heart rate and respiration rate. This system is also automatically calibrated across patients, allowing precise measurement using highest probability method and natural language processing. Results obtained from the comparative analysis show a promising result with an error of 1.16 for pulse sensor and 2.917 for ECG which are below the threshold error. This allows user to accurately measure vital signs in a non-obtrusive way, and to provide them with the data required to determine to the right timing for any intervention procedure needed. The developed system would also help to bridge the gap of interoperability between client and medical provider.
Segmentation of Irrelevant Regions Using Color Thresholding Method: Application in Breast Histopathology Images
Authors: Jian Tan Xiao , Nazahah Mustafa , Yusoff Mashor Mohd , Shakir Ab Rahman Khairul 
Published: November 2019

Abstract
Segmentation of irrelevant regions in the breast histopathology image is essentially performed in preliminary or post processing stages. This study presents a color thresholding method to segment the irrelevant regions, specifically blood cells and hemorrhage in the RGB breast histopathology images. The conventional gray level global thresholding method was extended to color images by modifying the thresholding algorithm. The color thresholding method was performed by decomposing the RGB input image into coherent regions based on similarity in color feature. Area Overlap Measure (AOM) and Combined Equal Importance (CEI) are two statistical metrics used to evaluate the performance of the color thresholding method. The obtained AOM and CEI for the overall dataset in blood cells are 0.92 and 0.89, respectively. The color thresholding method is found to be effective and is able to produce plausible thresholding results in segmentation of irrelevant regions in RGB breast histopathology images.
Prediction of Parkinson’s disease using Ensemble Machine Learning classification from acoustic analysis
Authors: Patra Amit Kumar , Ray Ratula , Abdullah Azian Azamimi , Dash Satya Ranjan 
Published: November 2019

Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder, which upon progression affects the movements. Tremors associated with Parkinson’s disease are the major symptoms to look out for in such cases. This generally results in breakdown of the neurons producing dopamine. Qualitative speech starts to decline as the disease progresses and the variability in the vocal cord vibration (also known as fundamental frequency) starts to occur. People with PD have shown to produce greater variability in frequency as compared to normal people. In our paper, we are focused on comparison of the voice measurement features of patient dataset to understand whether a patient is suffering from PD or not using Machine Learning classifiers. We have implemented Decision Trees, Logistic Regression and K-nearest neighbors as base classifiers and have compared their performance with Ensemble learning classifiers Bagging, Random Forest and Boosting. We have compared the accuracy (%) of the classifiers and discussed which one of them is more accurate at predicting the outcome of the disease. We also found out the most relevant features associated with the classification and ranked them based on feature importance. Our main aim here is the classification of healthy individuals from people suffering from PD by detection of dysphonia (difficulty in speaking due to declining health conditions).
Determination of Fracture Risk on Patient-specific Model of Femur with Osteogenesis Imperfecta
Authors: Ahmad Siti Fatima Zahra’ , Som Mohd Hanafi Mat , Basaruddin Khairul Salleh , Daud Ruslizam , Awang Mohd Shukrimi 
Published: November 2019

Abstract
Osteogenesis Imperfecta (OI) is a group of genetic disorder that affects the bones. It causes bone to break easily. For severe case, it can lead the patient to death. However, doctors still have no quantitative method to predict fractures. Therefore, this study aims to investigate the fracture risk for OI femoral bone under load of activities in daily living and various loading direction. A finite element model reconstructed from radiography CT images was developed. The variations of daily activities were subjected to the femoral model. In standing and walking configuration, the model of OI has the ability to perform without having any fracture. However, during jumping, both configurations cause the OI femoral fractured. We can conclude that when the OI patients more active in their daily life, the higher chances for fracture to occur. The risk of fracture increases with increasing the intensity of the activity. Thus, early prediction of fracture risk could help prevent or treating fracture for OI patients.
An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
Authors: Dzulkifli Fahmi Akmal , Mashor Mohd Yusoff , Jaafar Hasnan 
Published: November 2019

Abstract
Meningioma is a type of primary brain tumours. The meningiomas account for about one-third of all primary brain tumours. Image segmentation plays an important role in image analysis, especially detecting the tumours or cancerous areas in medical images. The output images from the segmentation prominently affect the system in detecting the tumour cells. Currently, the pathologists use the ‘eye-balling’ estimation technique to count the Ki67 cells. This technique was known as a time-saving measure. However, it has poor reliability and accuracy in counting the Ki67 cells. This paper proposed an automatic Ki67 cell counting in meningioma by using k-means clustering approach. The k-means clustering was used to segment the Ki67 cells and then the cells were classified into positive and negative Ki67 cells. The proposed system has been tested on 12 histopathological meningioma images. The proposed system is compared to the manually segmented images that have been validated in prior by the pathologists. The results show that the proposed system was able to segment the Ki67 cells with an average accuracy of 95.29%. The sensitivity and specificity of the proposed system were also high with an average of 93.56% and 97.39%, respectively.
Feature detection of mitral valve based on image processing technique: A review
Authors: Mahadi Lina Farhana , Ibrahim Nabilah , Zaluwi Mohd Thariq , Johan Muhammad Haniff S.M. 
Published: November 2019

Abstract
Mitral valve prolapse is a standout amongst the most common heart conditions, which may influence up to five to 10% of typical populace and a standout amongst the most questionable ones. The advent of computer aided technologies has contributed widely in medical application. This paper discussed on a methodological study on medical image processing to evaluate accurate diagnosis in the field of computer vision. The fundamental of image processing is to enhance the raw images obtain from machine so the useful information can be extracted from it. This paper deals with the application of the image processing on mitral valve images for further research purpose. Ultrasound data taken with subject diagnosed by mitral valve disease is analyse to determine the diameter of the valve. Based on the experiment, the average of mitral valve diameter is 21.82 mm which represent the abnormal length of mitral valve for adult.
Optimization of Dual Pathway AV Nodal Conduction Model
Authors: Ryzhii Maxim , Ryzhii Elena 
Published: November 2019

Abstract
In this work we tested our previously proposed heterogeneous oscillator model of atrioventricular node with respect to dual pathway physiology in order to optimize the model parameters. For this purpose we compared three model variants with different number of pathway node elements represented by nonlinear Aliev-Panfilov equations. The parameters controlling coupling between the pathway elements and the refractoriness of the fast pathway were selected for each model variant to obtain qualitative correspondence to the all range of functional atrioventricular node behavior. The results demonstrate that all considered model variants are able to reproduce normal cardiac signals conduction, AV node reentry, automaticity (pacemaking) and filtering function. However, the model variant with smallest number of the pathway elements better correspond to human electrophysiology, in particular to filtering high atrial rates by AV node. In addition, it is preferred for use in low-power systems with real-time simulations, such as ICD and pacemaker validation and testing hardware, as well as for educational and research purposes.
Effect of Water Content in Methanol on The Performance and Exhaust Emissions of Direct Injection Diesel Engines Fueled by Diesel Fuel and Jatropha Oil Blends with EGR System
Authors: Firmansyah Johan , Syaiful  , Yohana Eflita 
Published: November 2019

Abstract
Diesel engines are usually used because of their durability and high efficiency. Therefore the number of diesel engine uses is increasing. This results in an increase in the amount of diesel fuel consumption and air pollution from diesel engine exhaust gases, especially soot emissions. To reduce this soot emission, methanol which is rich in oxygen is added to diesel. The substitution of alcohol into diesel has an impact on the performance of the diesel engine. Therefore, this study is focused on investigating the effect of water content in methanol on diesel engine performance and soot emissions. The study was conducted using an Isuzu 4JB1 diesel engine equipped with a cold EGR system. The ratio of the diesel-jatropha-methanol mixture used is 55/30/15% on a volume basis. While the water content in methanol used is 0 to 25% at a 5% interval. The test is carried out at a stationary 2500 rpm and given a load of 25% to 100% at a 25% interval. A dynamite Land & Sea dynamometer and stages 898 smoke meter were used to measure performance and soot emissions. The experimental data shows that the higher water content in methanol in the mixture of diesel-jatropha-methanol fuel causes a reduction in performance and an increase in soot emissions of diesel engines.
Study of Desiccant Rice Husk Ash for Air Conditioning System
Authors: Syahbardia  , Fajar Berkah TK , Winoto SH. , Suryo MSK.Tony , Abdurrachiim L. 
Published: November 2019

Abstract
Indonesia is one of the producers and consumers of paddy or rice due to more than 95% of the Indonesian population consume rice as staple food. The by-product of paddy processing as to rice is also hush or rice hush. Up until now, in Indonesia, it is widely used as animal feed, fertilizer, mushroom planting media and fuel for making bricks, tiles and others. To increase the benefits of rice husk, the study of the usage of rice husk as an active ingredient in the SDC cooling system was conducted. The research method begins with the making of rice husk test samples and silica gel as a comparison. Rice husk was burned and heated using an oven until all carbons became ash. Then, the rice husk ash was blended and filtered, these applies to silica gel as well. Then these two samples are tested for characteristics with GSA and XRF instruments. From this test it can be concluded that the adsorption/desorption ability of ash is 1/6 time silica gel. If rice husk ash is used as an alternative for desiccant materials, it is necessary to modify the material.
Landslide Prone Maps Using Weighting Parameters in Pulung, Ponorogo East Java
Authors: Muriyatmoko D , Musthafa A , Effendi L , Harmini T , Rozaqi AJ 
Published: November 2019

Abstract
Pulung landslide occurred twice during 2017-2018. The landslide located in the village of Banaran and Wagirkidul, which caused losses. This study aimed to identify the susceptibility level of the landslide in areas of Pulung. This research using four parameters, namely land slope, land use, soil type, and rainfall. This study produces landslide prone maps divided into three levels. It is the level of “very vulnerable” covered area of 3.4% (433.67 ha), level of “vulnerable” covered area of 53.06% (6,767.803 ha), and level of “less vulnerable” covered area of 43.54% (5,553.527 ha). The results are useful for an early warning system in Pulung, especially rainy season. The map presented is also considered as the source for warning landslide system in surrounding Pulung.
Determination of Location and Numbers of Monorail Stops in Surabaya with Max Covering Problem Model
Authors: Ardiansyah Andika , Mardlijah  
Published: November 2019

Abstract
The Surabaya City Government plans to develop Mass Rapid Transit (MRT). The selected MRT is trams and monorails. The MRT procurement is expected to attract public attention to using public transportation so that it can reduce the use of private vehicles. With the reduction of private vehicles, it is expected to reduce congestion and traffic accidents. For the operation of the MRT, supporting facilities are needed, namely the stop for the monorail. So, the author intends to conduct research to determine the location and number of monorail stops. In this study, the determination of the location and number of monorail stops was determined by the Max Covering Problem model when there was a limit in the number of monorail stops established so as to maximize the number of passengers served, which was preceded by calculating the optimal number of monorail stops with the Set Covering Problem model. The results of calculations using the Set Covering Problem model indicate that there are 62 locations for monorail stops in order to serve the point of demand, totaling 151 locations along the route. If it turns out the government has budget constraints in the construction of monorail stops in 62 predetermined locations, then the calculation using the Max Covering Problem model is obtained by choosing 50 locations for monorail stops.
Indonesian Financial Crisis Prediction Using Combined Volatility and Markov Regime Switching Model Based on Indicators of Real Interest Rate on Deposits and Lending Interest Rate/Deposit Interest Rate
Authors: Sugiyanto  , Hidayah Aisyah Zahrotul 
Published: November 2019

Abstract
The financial crisis that occurred in mid-1997 made it aware of the importance of building an early detection system because the crisis had a severe impact on the Indonesian economy. The crisis occurred due to several macroeconomic indicators experiencing very high fluctuations and the existence of changes in the structure (regime). The combined volatility and Markov regime switching models are very suitable to explain the crisis. Indicators of real interest rates on deposits and lending interest rate/deposit interest rates from 1990 to 2018 were used to build the combined model. The result showed that the MRS-ARCH (2,1) model for real interest rate on deposits indicator and the MRS-GARCH (3,1,1) model for lending interest rate/deposit interest rate indicator could explain the crisis. Markov regime switching predictions indicate that in 2019 there are no signs of a financial crisis in Indonesia.
Exponential Distribution Parameter Estimation with Bayesian SELF Method in Survival Analysis
Authors: Triana Yuli , Purwadi Joko 
Published: November 2019

Abstract
This Paper discussed the Exponensial distribution parameter estimation using Bayesian SELF method in survival analysis with

θ
^

as SELF Bayesian estimator for the θparameter. Survival analysis corresponds to a method that relates to time starting from the start point to the occurrence of a particular event or an endpoint. The event referred in this research is the event of death. The distribution used in this research is an exponential distribution. Bayesian SELF estimation of

θ
^

on exponential distribution for the censored data was obtained by minimizing expectations of loss function. Application of Bayesian SELF method on acute coronary syndrome patient’s data, it was obtained

θ
^

= 0.18583 that indicates the patients’ probability to survive is high and the probability for the patients to fail is low.
The Monitoring of Corn Sprouts Growth Using The Region Growing Methods
Authors: Yudhana Anton , Umar Rusydi , Ayudewi Fadlillah Mukti 
Published: November 2019

Abstract
The aim of this research is to apply plant growth monitoring function using region growing method and Matlab application with an object of corn sprouts and parameters of length and time. The research process is done by taking pictures digitally change the color level (Red Green Blue) to grayscale, cropping the image and then calculate the growth of the plant using region growing. From the results of the study, region growing can be used to calculate the growth of plant length with parameters of length and time. The results of manual measurements show the growth of plant length by an average of 2.1 cm/day then the results of measurements with region growing show an average plant length of 2.3 cm/day, this result shows the comparison between manual and region growing measurements is 0.2 cm. The difference between manual calculation and region growing is caused by the accuracy of shooting and image cropping process.
Sustainability Strategy of Agarwood-Production Trees in Lombok Island using Harvesting Matrix
Authors: Romdhini M U , Fitriyani N , Kurnia A , Baskara I K A , Ito M , Yamada I , Hadi S , Mulyaningsih T 
Published: November 2019

Abstract
Eaglewood (Gyrinops verstiigee) is one example of a commercially valuable aromatic plant in the form of agarwood. The high selling value of agarwood encourages people to use it. The distribution area of Agarwood-producing trees is found in several regions in Indonesia. One of agarwood producers has been widely exploited in West Nusa Tenggara Province. The problem is the number of population and the quality of agarwood production which is declining due to exploitation carried out continuously and excessively without calculations and improper harvesting techniques. Therefore, a forest management model that cares for sustainability, called a sustainable harvesting model, could be used. This research used harvesting matrix to build this model. This model considers the initial configuration of the forest to be the same as the final configuration of the forest, so that the harvesting could be done continuously without damaging the forest configuration. The location of data collection was conducted in several areas, such as Orong Puncak, Lembah Sari, Orong Selatan, Kekait, Sepakek, East Lombok, and Kerujuk. The data obtained were divided into 5 intervals based on the diameter of the eaglewood tree. The research found that the number of trees that could be harvested on one harvesting period at each interval of trees diameter, i.e. 0 tree in 0-10 cm interval; 388 trees in 10-20 cm interval; 270 trees in 20-30 cm interval; 17 trees in 30-40 cm interval; and 3 trees in 40-50 cm interval. Harvesting matrix could be used to help the government in order to build a strategy for the sustainability of eaglewood trees in Lombok Island.