bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering
Authors: Cherrat El mehdi , Alaoui Rachid , Bouzahir Hassane
Published: October 2019

Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.
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

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 assess 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.
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

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.
Design and Optimization of Inductively Coupled Spiral Square Coils for Bio-Implantable Micro-System Device
Authors: Lakhdari Abdelghani , Mekkakia Maaza Nasr-Eddine , Dekmous Meriem
Published: October 2019

Due to the development of biomedical microsystems technologies, the use of wireless power transfer systems in biomedical application has become very largely used for powering the implanted devices. The wireless power transfer by inductive resonance coupling link, is a technic for powering implantable medical devices (IMDs) between the external and implanted circuits. In this paper we describe the design of an inductive resonance coupling link using for powering small bio-implanted devices such as implantable bio-microsystem, peacemaker and cochlear implants. We present the reduced design and an optimization of small size obtained spiral coils of a 9.5 mm2 implantable device with an operating frequency of 13.56 MHz according to the industrial scientific-medical (ISM). The model of the inductive coupling link based on spiral square coils design is developed using the theoretical analysis and optimization geometry of an inductive link. For a mutual distance between the two coils at 10mm, the power transfer efficiency is about 79% with , coupling coefficient of 0.075 and a mutual inductance value of 2µH. In comparison with previous works, the results obtained in this work showed better performance such as the weak inter coils distance, the hight efficiency power transfer and geometry.
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 < 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 m was evaluated to determine the distribution of membership values with variation in m 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.
Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES
Authors: Jena Tarakanta , Debnath Manoj Kumar , Sanyal Smaran Kumar
Published: October 2019

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system.
A noninvasive smart wearable for diaper moister quantification and notification
Authors: Khan Tareq
Published: October 2019

A baby feels uncomfortable in a wet diaper and it can cause health issues such as diaper rash. Diaper rash can be avoided by changing the diaper as soon as the baby urinates or passes stool. In this project, a smart wearable gadget is developed which sends an automatic notification to a caregiver’s smartphone whenever the baby urinates. The proposed wearable detects urination event noninvasively by sensing the temperature rise on the outer surface of the diaper and quantifies the event using a decision tree and a midpoint based k-nearest neighbor (KNN) hybrid classification algorithm. The gadget is a small size, low power, low cost and reusable electronic device that is attached externally to the outer surface of the diaper. The gadget can be used with any disposable diaper, thus no change in the diaper production process or price increase is required. The smartphone app shows the diaper change urgency score and logs all the urination events. This record can facilitate treating diseases where accurate records of urination are required. A prototype of the hardware gadget and a smartphone app is developed and tested.
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.
An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images
Authors: Del Carpio Christian , Dianderas Erwin , Zimic Mirko , Sheen Patricia , Coronel Jorge , Lavarello Roberto , Kemper Guillermo
Published: October 2019

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%.
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

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.
An IOT based smart metering development for energy management system
Authors: Priyadharshini S.G , Subramani C. , Roselyn J. Preetha
Published: October 2019

The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters.
An integration of uml use case diagram and activity diagram with Z language for formalization of library management system
Authors: Muhamad Zainab Hassan , Abdulmonim Dhafer AbdulAmeer , Alathari Bashar
Published: October 2019

Unified Modeling Language (UML) is the effective standard for modeling object-oriented software systems. However, the ambiguity of semantics and the absence of consistency among UML diagrams lead to lack of precisely defining the requirements of a system. On the other hand, formal methods are techniques and tools use the mathematical notations, and they involve the precise syntax and semantics of the unambiguous software requirements specification. It applied in early stages of Software Development Life Cycle (SDLC). Therefore, an integrated between UML specification and formal specification is required to reduce the requirements' ambiguity and error, and to improve the quality and security of software systems. This paper proposes an approach involves the combining UML use-case diagram and activity diagrams with Z language for formalization of Library Management System (LMS). The focus of this paper is on consistency between the UML diagrams to Z Schema, and then verified by using the Z / EVEs tool.
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.
Path based load balancing for data center networks using SDN
Authors: Chakravarthy V. Deeban , Amutha B.
Published: October 2019

Due to the increase in the number of users on the internet and the number of applications that is available in the cloud makes Data Center Networking (DCN) has the backbone for computing. These data centre requires high operational cost and also experience the link failures and congestions often. Hence the solution is to use Software Defined Networking (SDN) based load balancer which improves the efficiency of the network by distributing the traffic across multiple paths to optimize the efficiency of the network. Traditional load balancers are very expensive and inflexible. These SDN load balancers do not require costly hardware and can be programmed, which it makes it easier to implement user-defined algorithms and load balancing strategies. In this paper, we have proposed an efficient load balancing technique by considering different parameters to maintain the load efficiently using Open FlowSwitches connected to ONOS controller.
Resource allocation in cloud computing using advanced imperialist competitive algorithm
Authors: Javadi-Moghaddam Seyyed-Mohammad , Alipour Sara
Published: October 2019

Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms.
Offline signature verification using DAG-CNN
Authors: Pinzón-Arenas Javier O. , Jiménez-Moreno Robinson , Pachón-Suescún César G
Published: October 2019

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures.
Swarm algorithms in dynamic optimization problem of reactive power compensation units control
Authors: Manusov V.Z , Matrenin P.V. , Khasanzoda N.
Published: October 2019

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.
Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier
Authors: Jadhav Sachin B. , Udup Vishwanath R. , Patil Sanjay B.
Published: October 2019

Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by Blight, Frogeye leaf spot and Brown Spot were acquired by a digital camera. The acquired images are preprocessed using image enhancement techniques. The background of each image was removed by a thresholding method and the Region of Interest (ROI) is obtained. Color-based segmentation technique based on K-means clustering is applied to the region of interest for partitioning the diseased region. The severity of disease is estimated by quantifying a number of pixels in the diseased region and in total leaf region. Different color features of segmented diseased leaf region were extracted using RGB color space and texture features were extracted using Gray Level Co-occurrence Matrix (GLCM) to compose a feature database. Finally, the support vector machine (SVM) and K-Nearest Negbiour (KNN) classifiers are used for classifying the disease. This proposed classifers system is capable to classify the types of blight, brown spot, frogeye leaf spot diseases and Healthy samples with an accuracy of 87.3% and 83.6 % are achieved.
Random routing scheme with misleading dead ends
Authors: Rajarama Chitra , Sugatoor Jagadeesha Narasimhamurthy , T Yerri Swamy
Published: October 2019

A new method of sink location security in a Wireless Sensor Network is proposed. In the proposed scheme, all the node addresses are encrypted and an attacker cannot determine the real sink address by capturing the packets and analyzing its contents for the final destination. The main contribution of our proposed method is to use random routing scheme with misleading dead ends. This provides security against traffic analysis attack.
An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN
Authors: Mahboub Aziz , En-Naimi El Mokhtar , Arioua Mounir , Barkouk Hamid , El Assari Younes , El Oualkadi Ahmed
Published: October 2019

Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.
A cryptanalytic attack of simplified-AES using ant colony optimization
Authors: Grari Hicham , Azouaoui Ahmed , Zine-Dine Khalid
Published: October 2019

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.
The usage of gold and the investment analysis based on gold rate in India
Authors: S Vanitha , K Saravanakumar
Published: October 2019

Gold is one of the main commodities where the customers invest their money comparatively with bank for better interest. In the Indian context people purchase gold for their children’s marriages for later period. The investment in gold is better suits for easy conversion into money with quickest possible time from the bank and gold merchants. The appreciation or depreciation of gold based on other investment options like fixed deposit, provident fund, international crude oil price, stock market, mutual fund etc. The comparative analysis of gold with other investment options give an edge to the customer to clearly understand the investment pattern for their hard-earned money expected to give good returns in the future.
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.
Novel model for boosting security strength and energy efficiency in internet-of-things using multi-staged game
Authors: Ambore Bhagyashree , L Suresh
Published: October 2019

Security as well as energy efficiency is one of the most inevitable and challenging problems when it comes it large scale network deployment like INternet-of-Things (IoT). After reviewing existing research work on IoT, it was found that there are discrete set of solution for security as well as for energy. However, there is little research work that has jointly investigated both the problems with respect to IoT. Apart from this, there are also various form of attacks that cost energy of sensors that constitutes core physical devices in IoT. Therefore, these manuscripts present a novel idea for identifying and resisting the security breach within an IoT system ensuring energy efficiency too. Harnessing the modelling capability of game-theory, the proposed system offers a joint solution towards these problems. The simulated outcome of the study is found to offer balance performance for better energy efficiency and robust threat mitigation capability when compared with existing approaches.
Manifesting a mobile application on safety which ascertains women salus in Bangladesh
Authors: Hossain Md. Elias , Rahman Md. Wahidur , Islam Md. Tarequl , Hossain Md. Selim
Published: October 2019

This paper reflects on the indemnity of women in our society. The proposed model ensures the embodiment of a mobile application. The algorithm, we developed for this model focuses the safety issues which is applicable to both inside as well as outside of the house for the women in Bangladesh. The solution of this problems can be done through some interrelated features such as i) SOS button pressing which ensures automatic calling, instant location tracking system through GPS of the phone and sending tracked location to all trusted numbers, automatically secrete video recording system ii) voice command detection which assures exact same features as SOS button pressing iii) phone shaking features serve user instant immunity by calling a trusted number. This research also assures experimented data analysis at Dhaka city based on respond time, the time it takes to arrive the SMS and Phone call and current location of the victim. Also do a short comparison among the most popular safety related mobile applications.
Data visualization and toss related analysis of IPL teams and batsmen performances
Authors: Kanungo Vidit , B Tulasi
Published: October 2019

Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams.
Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments
Authors: V. D Chaithra
Published: October 2019

Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%.
Comparative Review of the Features of Automated Software Testing Tools
Authors: Gamido Heidilyn Veloso , Gamido Marlon Viray
Published: October 2019

Software testing is considered to be one of the most important processes in software development for it verifies if the system meets the user requirements and specification. Manual testing and automated testing are two ways of conducting software testing. Automated testing gives software testers the ease to automate the process of software testing thus considered more effective when time, cost and usability are concerned. There are a wide variety of automated testing tools available, either open source or commercial. This paper provides a comparative review of features of open source and commercial testing tools that may help users to select the appropriate software testing tool based on their requirements.
A new design algorithm for hybrid active power filter
Authors: Thuyen Chau Minh
Published: October 2019

The correct determination of the parameters of Hybrid Active Power Filter (HAPF) plays a decisive role in its performance. Therefore, this paper proposes a new design algorithm for HAPF based on the Social Spider Algorithm (SSA). This algorithm has the advantage that it is possible to determine the parameters of both the power circuit part and the control circuit part of HAPF. The achieved results are multi-purpose, such as: minimum total harmonic distortion of the supply current and source voltage, the maximum reactive power compensation into the system and satisfy many constraints such as: system stability, resonance conditions of the branches and the limits of the parameters. Compared to traditional design method using the Particle Swarm Optimization algorithm, the proposed algorithm shows the advantages of smaller total harmonic distortion of supply current and source voltage, and higher reactive power compensation into the grid while still meeting the constraints.
Detection of partial discharge acoustic emission in power transformer
Authors: Shaker Yomna Omran
Published: October 2019

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.
The exploitation of western and southern deserts in Iraq for the production of solar energy
Authors: Abass Ahmed Zkear , Pavlyuchenko D. A.
Published: October 2019

We have, an overview is presented of the potential future demands and possible supply of solar energy to Iraq. Solar energy, which is clean, unlimited, and environmentally friendly, is presented as a renewable energy resource. Many problems such as CO2 emissions, industry, human activities, and electricity distribution grids have attracted much attention because of the current state of crude oil production and its prices. Moreover, estimations of solar radiation levels and of the efficiencies of photovoltaics (PVs), concentrated solar power (CSP), and solar chimney towers, have all been investigated. Those systems that combine various sources of energy are called hybrids and they have received much attention in recent decades. The basic features of solar radiation in Iraq are outlined, and the selection of those sites with potential for development of solar plants is based on the local largest solar radiation. Moreover, longitudinal and latitudinal orientation, wind, solar intensity, dust, temperature, rain, humidity, and pollution factors are all considered in the calculation of PV/CSP efficiencies. We know there is an abundance of fossil fuels in Iraq, energy shortages began in 1991 because of the perturbation caused by the full ruin of the country. The obvious renewable energy resource available in Iraq is the solar energy, and its exploitation would provide a means to reduce CO2 emissions from the burning of fossil fuels and to achieve self-sufficiency of electric energy and export the rest to neighboring countries. Solar energy is becoming increasingly important because of the climatic change in the form of global warming.
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.
Modeling and simulation of graphene field effect transistor (GFET)
Authors: S. Nanda B. , S. Puttaswamy P.
Published: October 2019

Graphene based top-gated Field effect transistor (GFET) is designed and simulated using the device simulator packages. The paper describes fabrication process and the device simulation aspects of the GFET device. Two devices with different gate lengths of 200nm and 350nm are simulated. Device simulations are carried out in open source TCAD software package. The results indicate a depletion FET type operation in which ON/OFF current ratio of 2.25 is obtained.
Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node
Authors: El Ghmary Mohamed , Chanyour Tarik , Hmimz Youssef , Cherkaoui Malki Mohammed Ouçamah
Published: October 2019

With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.
Automatic summarization of Malayalam documents using clause identification method
Authors: C Sunitha , Jaya A , Ganesh Amal
Published: October 2019

Text summarization is an active research area in the field of natural language processing. Huge amount of information in the internet necessitates the development of automatic summarization systems. There are two types of summarization techniques: Extractive and Abstractive. Extractive summarization selects important sentences from the text and produces summary as it is present in the original document. Abstractive summarization systems will provide a summary of the input text as is generated by human beings. Abstractive summary requires semantic analysis of text. Limited works have been carried out in the area of abstractive summarization in Indian languages especially in Malayalam. Only extractive summarization methods are proposed in Malayalam. In this paper, an abstractive summarization system for Malayalam documents using clause identification method is proposed. As part of this research work, a POS tagger and a morphological analyzer for Malayalam words in cricket domain are also developed. The clauses from input sentences are identified using a modified clause identification algorithm. The clauses are then semantically analyzed using an algorithm to identify semantic triples - subject, object and predicate. The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score. Finally an algorithm is used to generate the sentences from the semantic triples of the selected clauses which is the abstractive summary of input documents.
A simplified hysteresis current control for cascaded converter fed switched reluctance motor
Authors: Tellapati Anuradha Devi , Kumar Malligunta Kiran
Published: October 2019

Simple constructional features with no windings on rotor circuit and robustness make switched reluctance motor (SRM) a most used motors in industrial applications. Peak motor voltage rating depends on the rated voltage of the power switches. In conventional asymmetrical converter driving SRM, voltage rating of the motor depends on rating of power electronic switches in converter. Demand to rise the motor rating insists to put pressure on converter switching components which results in increased switching losses. A cascaded converter topology for SRM reduces the rating of switching components as compared to conventional converters for SRM. This paper presents a cascaded converter fed SRM drive with reduced switching losses. The paper presents a simplified hysteresis current control (HCC) for cascaded converter fed SRM. Simplified HCC control method reduces switching losses as HCC is applied to only one bridge of cascaded converter. Though the performance of the SRM remains same with cascaded converter fed SRM with HCC applied to only one bridge or to two bridges and with conventional asymmetrical converter, the switching losses are reduced to a great extent when HCC applied to one bridge of cascaded converter fed SRM. Performance of SRM is illustrated with conventional asymmetrical converter fed SRM and is compared to cascaded converter while HCC applied to only one bridge and applied to two bridges of cascaded converter. Proposed work is simulated using MATLAB/SIMULINK and results are presented.
The direct power control of three-phase AC-DC converter under unbalance voltage condition
Authors: Yusoff Nor Azizah , Razali Azziddin M. , Karim Kasrul Abdul , Jidin Auzani
Published: October 2019

This paper has presents the integrated approach for three-phase PWM AC-DC converter for obtaining the symmetrical components under unbalanced supply condition. The input structures for conventional direct power control have been modified with three simpler sequence networks instead it coupled by a detailed three-phase system method. In the cases of an unbalanced three-phase system, it causes the presence of unbalanced current and voltages thus produce the negative components on the grid voltage. Otherwise, the unbalance voltage in a three-phase power system causes severe performance degradation of a grid-connected VSI. Therefore, the imbalance voltage can be resolved by separating from the individual elements of voltage and current into symmetrical components called as a sequencing network. Consequently, the input power is relatively improved during unbalanced condition. It proven through the measurement of Total Harmonic Distortion (THD) from the conventional direct power control in individual elements is much higher compared than it resolved in separate components. Therefore, three symmetrical components are necessary for imbalance supply condition to obtaining almost sinusoidal grid currents.
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.