bluuebunny/crossref_metadata_2025_split huggingface dataset:
Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN
Authors: Singh Nimisha , Gill Rana 
Published: October 2019

<p class="Abstract">Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to detect the true retinal area. Artefacts like eyelids and eyelashes are come along with retinal part so removal of artefacts is the big task for better diagnosis of disease into the retinal part. In this paper, we have proposed the segmentation and use machine learning approaches to detect the true retinal part. Preprocessing is done on the original image using Gamma Normalization which helps to enhance the image that can gives detail information about the image. Then the segmentation is performed on the Gamma Normalized image by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Superpixel is used to reduce the complexity of image processing task and provide suitable primitive image pattern. Then feature generation must be done and machine learning approach helps to extract true retinal area. The experimental evaluation gives the better result with accuracy of 96%.</p>
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Induction Machine Control
Authors: Habbab Mohamed , Hazzab Abdeldjebar , Sicard Pierre 
Published: October 2019

<p>In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations. </p>
Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform Based Multifocus Image Fusion Using Fusion rules
Authors: N. Radha , Babu T.Ranga 
Published: October 2019

<p>In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures. </p>
Control of a Movable Robot Head Using Vision-Based Object Tracking
Authors: Khairudin Moh , Chen G.D. , Wu M.C. , Asnawi R. , Nurkhamid Nurkhamid 
Published: October 2019

<span lang="EN-US">This paper presents a visual tracking system to support the movement of the robot head for detecting the existence of objects. Object identification and object position estimation were conducted using image-based processing. The movement of the robot head was in four directions namely to the right, left, top, and bottom of the robot head. Based on the distance of the object, it shifted the object to many points to </span><span lang="IN">assess</span><span lang="EN-US"> the accuracy of the process of tracking the object. The targeted objects are detected through several processes, namely normalization of RGB images, thresholding, and object marking. The process of tracking the object conducted by the robot head varied in 40 various object points with high accuracy. The further the object’s distance to the robot, the smaller the corner of the movement of the robot produced compared to the movement of the robot head to track an object that was closer even though with the same distance stimulant shift object. However, for the distance and the shift of the same object, the level of accuracy showed almost the same results. The results showed the movement of the robot head to track the object under the head of the robot produced the movement with a larger angular error compared to the movement of the robot head in another direction even though with the stimulant distance of the same object position and the distance shift of the same object.</span>
Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects
Authors: Devnani Garima , Jaiswal Ayush , John Roshni , Chaurasia Rajat , Tirpude Neha 
Published: October 2019

<span lang="EN-US">Fine-tuning of a model is a method that is most often required to cater to the users’ explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations are not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset while allowing the users to use their externally available images to test the model’s accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</span>
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset
Authors: Rajkumar K. Varada , Yesubabu Adimulam , Subrahmanyam K. 
Published: October 2019

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
PSO-SVM hybrid system for melanoma detection from histo-pathological images
Authors: Takruri Maen , Abu Mahmoud Mohamed Khaled , Al-Jumaily Adel 
Published: October 2019

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.
Enhanced self-regulation nonlinear PID for industrial pneumatic actuator
Authors: S. Salim S. N , Rahmat M. F. , Abdullah L. , Shamsudin S. A. , Zainon M. , M. Amin A. F. 
Published: October 2019

<p class="3Abstract">The present article describes the improvement of Self-regulation Nonlinear PID (SN-PID) controller. A new function is introduced to improve the system performance in terms of transient without affecting the steady state performance. It is used to optimize the nonlinear function available on this controller. The signal error is reprocessed through this function, and the result is used to tune the nonlinear function of the controller. Furthermore, the presence of the dead zone on the proportional valve is solved using Dead Zone Compensator (DZC). Simulations and experiments were carried out on the pneumatic positioning system. Comparisons between the existing methods were examined and successfully demonstrated.</p>
Modified isotropic orthogonal transform algorithm-universal filtered multicarrier transceiver for 5G cognitive radio application
Authors: Dayana R. , Kumar R. 
Published: October 2019

Rapid developments in modern wireless communication permit the trade of spectrum scarcity. Higher data rate and wider bandwidth emerge the development in growing demand of wireless communication system. The innovative solution for the spectrum scarcity is cognitive radio (CR). Cognitive radio is the significant technology used to utilize the spectrum effectively. The important aspect of CR is sensing the spectrum band and detects the presence or absence of the primary user in the licensed band. Moreover, another serious issue in next generation (5G) wireless communication is to decide the less complex 5G waveform candidate for achieving higher data rate, low latency and better spectral efficiency. Universal filtered multi-carrier (UFMC) is one of the noticeable waveform candidates for 5G and its applications. In this article, we investigate the spectrum sensing methods in multi-carrier transmission for cognitive radio network applications. Especially, we integrate the sensing algorithm into UFMC transceiver to analyze the spectral efficiency, higher data rates and system complexity. Through the simulation results, we prove that the UFMC based cognitive radio applications outperform the existing Orthogonal Frequency Division Multiplexing (OFDM) based CR applications.
Comparing machine learning and ensemble learning in the field of football
Authors: Khan Shuaib , V. B Kirubanand 
Published: October 2019

Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features. There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions.
Detection of partial discharge acoustic emission in power transformer
Authors: Shaker Yomna Omran 
Published: October 2019

<span>Partial discharge is one the most important factor that leads to deteroration and failure of the power transformer transformer. Acoustic emission detection is effective method to evaluate the health index of the power transformer. Using acoustic emission (AE) sensors for partial discharge (PD) measurement is considered as one of the most promising techniques to detect and localize PD activities inside the transformer tank. On the other hand, AE waves suffer from high attenuation and reflections while traveling from the PD source to the AE sensor. The modeling of the AE wave can help to understand the behavior of the AE PD signal during its travel. In this paper, the AE PD signal is assumed to be composed of different frequencies. This work aims to investigate the influence of the frequency value on the attenuation and arrival time of the acoustic wave.</span>
Bivariate modified hotelling’s T2 charts using bootstrap data
Authors: Haddad Firas , Alsmadi Mutasem K. , Badawi Usama , Farag Tamer , Alkhasawneh Raed , Almarashdeh Ibrahim , Hassan Walaa 
Published: October 2019

The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.
Robust features extraction for general fish classification
Authors: Alsmadi Mutasem K. , Tayfour Mohammed , Alkhasawneh Raed A. , Badawi Usama , Almarashdeh Ibrahim , Haddad Firas 
Published: October 2019

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.
A new approach for enhancing LSB steganography using bidirectional coding scheme
Authors: Al-Momin Mohammed M. Saeed Abdullah , Abed Issa Ahmed , Leftah Hussein A. 
Published: October 2019

This paper proposes a new algorithm for embedding private information within a cover image. Unlike all other already existing algorithms, this one tends to employ the data of the carrier image more efficiently such that the image looks less distorted. As a consequence, the private data is maintained unperceived and the sent information stays unsuspicious. This task is achieved by dividing the least significant bit plane of the cover image into fixed size blocks, and then embedding the required top-secret message within each block using one of two opposite ways depending on the extent of similarity of each block with the private information needed to be hidden. This technique will contribute to lessen the number of bits needed to be changed in the cover image to accommodate the private data, and hence will substantially reduce the amount of distortion in the stego-image when compared to the classic LSB image steganography algorithms.
Knowledge and integrated data management model for personalized intercropping in rubber plantation
Authors: Phoksawat Kornkanok , Mahmuddin Massudi 
Published: October 2019

Selection and allocation of space for intercropping in rubber plantations to maximize yield and minimum costs for individual farmers involves Multi-Criteria Decision Making (MCDM) and several conditions. The problem is that the information is scattered in many related agencies, there are separate stores and some data is redundant. In addition, the format of the data varies depending on the purpose of the data. The knowledge of selecting plants to grow in the rubber plantation is the tacit knowledge acquired from the experience of successful farmers in rubber plantations and from agricultural experts. Therefore, this research involves an Integrated Ontology-based knowledge and Multi-Objective Optimization model for intercropping Decision Support Systems (DSS). This article presents the knowledge and integrated data management model for developing the Intercropping in Rubber Plantations Ontology by using the Triangulation in the method to verify the accuracy of the data and results. Moreover, propose ways to create recommendation rules that are easy to rule update and maintenance. Using an ontology for DSS helps to recommended plants according to the appropriate environment of the farmer area by rule-based inference to represent logical reasoning. It could also be applied to another domain that requires Intelligent DSS for MCDM.
Assessing completeness of a WEB site from quality perspective
Authors: Jammalamadaka Sasi Bhanu , DBK Kamesh , Sastry Jammalamadaka Kodanda Rama 
Published: October 2019

Assessing quality of a web site is most important especially because people world over are dependent on the content hosted on the WEB site for various purposes. May factors are to be considered for assessing the quality. Quality of every factor must be considered individual and also the total quality of the WEB site must be computed. This paper is primarily focused on the factor called “Completeness” which is one of the factors that can be considered for computing the quality of the WEB sites. There can be much of the disconnected in the content hosted on the web site such as missing href, columns in the tables and forms. The more of the disconnectedness in the content that is hosted on the WEB site, the less the quality, as the information hosted on the WEB site is incomplete and less readable
Design and implementation of smart farming system for fig using connected-argonomics
Authors: Zainal N. , Mohamood N. , Norman M. F. , Sanmutham D. 
Published: October 2019

<span lang="EN-US">This paper proposes a design and implementation approach of smart farming system using connected-agronomics technique for fig farm application. Nowadays, fig plants having a rapid growth in the current market demand due to its rich in natural health benefiting nutrients, antioxidants and vitamins where some farming systems have been used in maintaining fig plant’s environmental resources to grow without fail. Smart farming is a system applied to provide user with real time information and plan for desired plant such as time intervals for watering systems. There are two major problems on maintaining the fig fruit quality; watering system fail during emergency blackout and a contagious disease known as leaf rust due to external environments. The system implements two microcontrollers, the Arduino Uno &amp; Raspberry Pi along with smartphone Android application. The system performance is evaluated based on the requirement specification, irrigation soil, surrounding temperature and moisture. It is found that all data collected by the sensors are within the optimal range of values, which are 1500 µS/cm to 1599 µS/cm for the EC reading of the fertilizer while 6.0 to 6.5 for the pH value of the soil. This prototype of smart farming was well developed and can be applied to the fig plantation environment.</span>
Early Discontinuation of P2Y
12
Antagonists and Adverse Clinical Events Post–Percutaneous Coronary Intervention: A Hospital and Primary Care Linked Cohort
Authors: Harris Daniel E. , Lacey Arron , Akbari Ashley , Obaid Daniel R. , Smith Dave A. , Jenkins Geraint H. , Barry James P. , Gravenor Mike B. , Halcox Julian P. 
Published: October 2019

Background

Early discontinuation of P2Y
12
antagonists post–percutaneous coronary intervention may increase risk of stent thrombosis or nonstent recurrent myocardial infarction. Our aims were to (1) analyze the early discontinuation rate of P2Y
12
antagonists post–percutaneous coronary intervention, (2) explore factors associated with early discontinuation, and (3) analyze the risk of major adverse cardiovascular events (death, acute coronary syndrome, revascularization, or stroke) associated with discontinuation from a prespecified prescribing instruction of 1 year.

Method and Results
We studied 2090 patients (2011–2015) who were recommended for clopidogrel for 12 months (+aspirin) post–percutaneous coronary intervention within a retrospective observational population cohort. Relationships between clopidogrel discontinuation and major adverse cardiac events were evaluated over 18‐month follow‐up. Discontinuation of clopidogrel in the first 4 quarters was low at 1.1%, 2.6%, 3.7%, and 6.1%, respectively. Previous revascularization, previous ischemic stroke, and age >80 years were independent predictors of early discontinuation. In a time‐dependent multiple regression model, clopidogrel discontinuation and bleeding (hazard ratio=1.82 [1.01–3.30] and hazard ratio=5.30 [3.14–8.94], respectively) were independent predictors of major adverse cardiac events as were age <49 and ≥70 years (versus those aged 50–59 years), hypertension, chronic kidney disease stage 4+, previous revascularization, ischemic stroke, and thromboembolism. Furthermore, in those with both bleeding and clopidogrel discontinuation, hazard ratio for major adverse cardiac events was 9.34 (3.39–25.70).

Conclusions
Discontinuation of clopidogrel is low in the first year post–percutaneous coronary intervention, where a clear discharge instruction to treat for 1 year is provided. Whereas this is reassuring from the population level, at an individual level discontinuation earlier than the intended duration is associated with an increased rate of adverse events, most notably in those with both bleeding and discontinuation.