bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparative study for bandwidth on demand using ONOS reactive and intent forwarding
Authors: Arif Kamarudin Fathul , Megat Mohamed Noor Megat Norulazmi , Ali Fuead
Published: February 2020

Telco operators need to tailor their networks to be agile, efficient and able manage the operational cost at moderate level. Furthermore, the norm of network volatility practiced by many enterprises open to a new challenge in managing an increasing traffic over the same WAN. For an enterprise, turning toward Bandwidth-on-Demand (BoD) approach promises to deliver extra network capacity when in demand without the complexity of running separate network. However, they need to manually transform these high-level policies into low-level configuration command and thus error-prone. This study was to address the effects of SDN (software-defined network) on the BoD performance towards the differences between the reactive and intent forwarding based on the assessment matrix. The testbed was performed under Mininet simulator which interacted with ONOS (Open Network Operating System). Mesh type topology with a predetermine parameter matrix was used using random topology generator. The expected outcome from this research was to show the effect on the BoD performance under two type of forwarding; reactive and intent based on the assessment matrix. This report contributed towards determining the effectiveness of SDN architecture on BoD performance and to improve future benchmarking on SDN-BoD testbed.
A rectangular CSRR based microstrip UHF reader patch antenna for RFID applications
Authors: Mohd Norzeli Syamimi , Ismail Ismarani , Md Din Norashidah , Tarmizi Ali Mohd , Abd Almisreb Ali , Ahmed Alkahtani Ammar
Published: February 2020

This paper presents a compact microstrip ultra-high frequency (UHF) reader patch antenna with complementary split ring resonator (CSRR) for radio frequency identification (RFID). The total size of the antenna is 208 × 208 × 1.6 mm3. The proposed antenna is designed, fabricated and measured in order to verify the proposed concept. The characterization for radiation parameters, like return loss, radiation pattern and antenna gain have been done experimentally. The proposed antenna is operated at 921 MHz for and achieved a gain of 8.285 dBi. All simulations in this work have been carried out by means of the commercial computer simulation technology (CST) software. In compare to the simulated results, the measured outcomes are promised.
Defining social content and social content management: Service science perspective
Authors: Zurita Wan Ahmad Wan Azlin , Mukhtar Muriati , Yahya Yazrina
Published: February 2020

Social media enables unlimited open interactions between organisations and customers. Social content resulting from social media interactions are of immense value and could be beneficial to both organisations and customers. Well managed social content could be used as a basis for producing service innovations offered by the organisations. However, the meaning of social content and its definition is not clearly articulated in the literature. This led to an equally limited meaning of social content management. In this regard, this article proposed that the definition of social content and social content management should be developed from the service science perspective. This is necessary to capture the essential facet of social interactions, that of value co-creation. Both definitions are developed through a triangulation process namely, literature review and a series of interviews with experts. With the proposed definition of social content and social content management, it is hoped that it would enhance the understanding of both concepts thus leading to a more effective way of managing social content.
Ozone prediction based on support vector machine
Authors: Tanaskuli M. , N. Ahmed Ali , Zaini Nuratiah , Abdullah Samsuri , A. Borhana Abdoulhdi , A. Mardhiah N. , Mathivanan Mathivanan
Published: February 2020

The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model.
Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach
Authors: Adnan Diwan Sinan
Published: February 2020

Real estate forecasting has become an integral part of the larger process of business planning and strategic management in real estate sector. This study covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature which need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. This aim of the paper is to propose and develop a computer assisted real estate property price forecasting model. This proposed framework will examine the current uses of artificial intelligence, particularly combining case base reasoning and artificial neural network, in the business-forecasting field and considers suitable applications in real estate. The methodology consists of five phases: 1) Data gathering b) Data cleaning c) ANN Training d) CBR (Case Base Reasoning) similarity retrieval e) Result retrieval. This research will investigate the influence of residential real estate property characteristics on property values (prices) in global context, it revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The results of the study will enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units.
An overview of wireless sensor network and its applications
Authors: Al-ani Khalid Waleed , Abdalkafor Ahmed Subhi , Nassar Aiman Majid
Published: February 2020

Small devices that can sense and collect data from the surrounding environment have been produced with the rapid development of wireless technology and digital electronics. As a result, a wireless sensor network (WSN) was constructed from these sensor devices to send collected data to the base station for monitoring and analysis. This paper provides an overview of WSN, as well as its architecture and applications.
Online geocode in postal address using GPS with synchronous database accessing
Authors: R. Sharba Mohammad Riyadh , Adil Kadhim Hakim , W. Al-Abassi Salam A. , Salih Ali Nabeel
Published: February 2020

Postal addressing information is a crucial issue in any organization for business targets, especially in developed countries. Thus, required to convert postal address data to an absolute value like latitude and longitude coordinates by a procedure called geocoding. In this paper, discuss how to make long coordinates into simple geocode via maps services. Besides, conduct a smartphone application to save the geocode with details information to be a GIS for the future used by end-users. The information includes address name, type, and phone number as well as a small note. The app can search for a particular location like hospitals, schools, restaurants, etc.
An improved ACS algorithm for data clustering
Authors: Mohammed Jabbar Ayad , Ku-Mahamud Ku Ruhana , Sagban Rafid
Published: February 2020

Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. 
Medical documents classification using topic modeling
Authors: Nuser Maryam , Al-Horani Enas
Published: February 2020

The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can use these documents easily and the effort and time in reading and searching for a specific topic will be reduced. Therefore, an automatic way to extract latent topics from these text documents is needed. Topic modeling is one of the techniques used to deal with this problem. In this paper, a medical collection of documents is used; this collection contains documents from three types of widespread diseases (Heart Diseases, Blood Pressure and Cholesterol). LDA topic modeling technique is applied to classify these documents into the previous mentioned topics. An evaluation of the algorithm’s results is done and the LDA shows a good level of classification accuracy.
IR4.0: unmanned store apps
Authors: Rahah Hamidi Saidatul , Muhamad Yusof Muhammad Afiq , Mohamed Shuhidan Shuhaida , Abd Kadir Shamsiah
Published: February 2020

The Internet of Things (IoT) is predicted to be one of the major megatrends in technology that applies worldwide including in Malaysia. Current advantages and expectations of using a connected device has become a reason to use applications in many areas, including the field of retail industry. The evolvement of technology has changed the retail landscape that replaces the manpower in their operations to improve the efficiency, quality and work ethics. Hence, this paper discussed comparative study of technology innovation applied and process flow in unmanned store from different country. Next, prototype of unmanned store apps was designed according to the most preferred technology based on the comparative study. IoT retailer model can be used to improve the retail experience for the customers. Thus, it may perform strategic marketing and gain retail intelligence. Besides, the revolution of using technology in this retail industry is contributing towards Industry 4.0 through digital transformation.
Using wireless and mobile technologies to enhance teaching and learning strategies
Authors: Tabassum Kahkashan
Published: February 2020

The tremendous development and sensational popularity  of smart mobile devices that offer remarkable availability of seamless  and uninterrupted  communication services at anytime, anywhere,  to any content as well as any prospects represent a new cohort of technological tools that could be utilized for inculcating creative skills in young children. Now a days many companies are  manufacturing lot of apps that target children education and entertainment. Both the Parents and educators find a huge responsibility in evaluating and  selecting such devices and decide which of these offer potential educational benefits for their children. This decision is critical to them since they  restricted tools to evaluate the applications available in the market. Hence the development of an  interactive and multisensory component based mobile application environment with integrated physical and virtual reality scenario could solve the issue related to the children education and entertainment. This is not just  limited to education and the entertainment domain but will explore essential features of virtual reality environment and interactivity. Children with special education needs find it difficult to understand  new knowledge and concept but by the use of virtual reality worlds produced for children could enhance their level of understanding. Therefore this paper dscusses the development of  adaptable, customizable and virtual reality based compliant applications for children. These applications would be useful to enhance learning essentials and improve  diverse sensory and cognitive impairments in even children with special needs by resolving  mobility issues. This application could be made available on i-pads, tablets, etc  and will be intelligent to perform switch between the different intellectual ranges of children accessing it. In other words it will present self-adaptability feature. This research study also focuses on the activities that are suitable for learning purposes with students with impairments.
A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images
Authors: Cherrat El mehdi , Alaoui Rachid , Bouzahir Hassane
Published: February 2020

In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.
Partial discharge measurement in solid dielectric of H.V Cross-linked polyethylene (XLPE) submarine cable
Authors: Mohammed Rasha Abdul-nafaa , Nathem Hamoodi Ali , M. Salih Bashar
Published: February 2020

A partial discharge described as anon-linear electrical break-down even that happened in a section of insulating area between two conduct which are at different potentials the damage of the insulating material, these conductors have a different insulating material potential of damage, under AC voltage discharge interval process. In this paper, we propose a Matlab /Simulation software. A detailed analysis of the partial discharge (PD) signal in the underground electric power conductor performed for monitoring, and investigation of numerous effects associated with the partial discharge event, such as heat ,phonic and electrical. Thus, to gain the important data of the insulating material status.
Ensuring sustainable development through groundwater management, area one, south western desert, Egypt
Authors: H. El Saeed Gamal , B. Abdelmageed Neveen , Riad Peter , Komy M
Published: February 2020

Darb El-Arbeain area lies between long. 29o 00/ and 31o 00/ E and lat. 22o 00/ and 24o 30/ N. It is divided into three separate areas; The northern part extends 90 km to the south from Paris town and has an area of 90 km2. In this study four suggested scenarios of pumping rates have been explored to fit with the Egyptian ministry of irrigation using the three dimensional finite difference flow model (MODFLOW) to simulate the flow system. These scenarios include: first, model will run with abstraction from the aquifers equal 110 %, 180%, 280%, and 370% of calculated initial recharge. Results indicate that the second scenario has the most economic scenario on the area. The fourth scenario caused the highest increase of drawdown values which should be avoided.
Energy saving control system development for external lighting
Authors: Ashryatov A.A. , Kulikov V.G. , Panteleyev A.V.
Published: February 2020

Currently, energy saving requires the development of simple and efficient street lighting control systems. In order to create such a control system, it is necessary to develop an original principle of its operation. They considered the advantages of electronic starting devices in street lighting control systems. They performed the analysis of the existing state of street lighting means, their shortcomings and solutions have been determined, and they developed the method of lighting device automatic control. They performed the assessment of the economic effect from loss reduction associated with reactive power and due to power reduction during deep night. They presented the example of economic effect achievement from the use of an electronic starting device with automatic power reduction.
Third harmonic injection pulse-width modulation technique for a five-phase voltage source inverter
Authors: Ameen Yasir M.Y. , Fadheel Bashar A. , Mahdi Ali J
Published: February 2020

Nowadays application of advanced power electronic technology enables producing an AC supply with a phase number higher than three-phase. Five-phase voltage source inverters (5ph-VSI) have been used in advanced power electronic drives to improve the reliability of the drive system and to boost the power capability of the converter as well as to their other inherent merits. This paper shows the mathematical analysis and simulation of the third harmonic injection pulse width modulation (THI-PWM) 5ph-VSI connected with inductive load in three scenarios i.e. star, Pentagon and pentacle. The presented THI-PWM technique for 5ph-VSI aims to increase the inverter fundamental voltage and hence to maximize the utilization of the DC bus without causing over-modulation. The simulation results are compared with typical sinusoidal pulse width modulation (SPWM) and the 10-step mode operation. The proposed scheme may be considered as a compromise case between the two reference cases;  as low harmonic components compared with 10-step mode operation and a high utilization factor compared with SPWM.
Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm
Authors: Salkuti Surender Reddy
Published: February 2020

This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.
Parallelizable cipher of color image based on two-dimensional chaotic system
Authors: Mahmood Sawsen Abdulhadi , Hussein Khalid Ali , Jurn Yaseen Naser , Albahrani Ekhlas Abbas
Published: February 2020

This paper aims to present a parallel implementation based color image encryption using non -linear chaotic system. The adopted chaotic system was suggested and approved in our previous work [1] which generates key streams with chaotic behavior. In this paper, pixel level permutation algorithm based on chaotic map generation is investigated and analyzed. The encryption–decryption schemes are achieved in parallel and composed of three main phases: chaotic keys generation, pixel-level permutation and bit-level diffusion phase. Both permutation and diffusion processes are achieved according to the chaotic keys. The parallel implementation of the proposed image encryption system is realized and inspired with parallel computing library offered by Matlab 2018, which equips highly performance than the pipeline ones and would be helpful to utilize in image encryption/decryption for real time application. Security and statistical analysis in addition to the main differential attacks analysis are specified to evaluate the performance of the proposed image encryption algorithm with parallel implementation. From the experimental results, the output image of the encryption task shows a higher randomness of the encrypted image which can be effectively resistant to attacker. Furthermore, the run time of encryption process is faster than other research works.
Denoising of MRI images using fast NLM
Authors: Hanchate Vandana , Joshi Kalyani
Published: February 2020

Denoising of image is a very crucial step which should retain fine details but should remove noise. Making the difference between noise and actual edge related data is very difficult. NLM filter helps to make a differentiation between image data and noise data. Its weight function decides the weightage of the neighboring pixel depending upon the similarity with the pixel to process. It helps to retain the edges and avoid it from smoothening. This paper discusses the implementation of NLM filter using hardware platform Spartan 6. After implementaion of this on FPGA, not only denoise the image but preseve edges and there is a tremendous saving in time compared to its matlab implementation. Denoised image performance is calculated using various objective metrics such as MSE, PSNR, SSIM, PFOM etc. FPGA implementation shows clearly the advntages over its  matlab implementation.
Outage probability and ergodic capacity analysis in power splitting based full-duplex relaying networks of double rayleigh fading channel for vehicular communications
Authors: Tin Phu Tran , Ha Duy-Hung , Vu Q. S. , Thanh Trang Tran
Published: February 2020

In this research, we present and investigate the system performance analysis in terms of the outage probability (OP) and ergodic capacity (EC) of the Full-duplex (FD) relaying networks via double Rayleigh fading channel. In this system model, we propose the power splitting (PS) protocol for the vehicular communications network. Firstly, we analyze and derive the integral form of the system OP and EC. Then, the correctness of the OP and EC expressions are verified by the Monte Carlo simulation. Finally, we can see that the analytical and the simulation results are matched with each other in the connection with the main system parameter.
Short-term wind speed prediction based on MLP and NARX network models
Authors: Amellas Yousra , Bakkali Outman El , Djebli Abdelouahed , Echchelh Adil
Published: February 2020

The article aims to predict the wind speed by two artificial neural network’s models. The first model is a multilayer perceptron (MLP) treated by back-propagation algorithm and the second one is a recurrent neuron network type, processed by the NARX model. The two models having the same Network’s structure, which they are composed by 4 Inputs layers (Wind Speed, Pressure Temperature and Humidity), an intermediate layer defined by 20 neurons and an activation function, as well as a single output layer characterized by wind speed and a linear function. NARX shows the best results with a regression coefficient R = 0.984 et RMSE = 0.314.
Smart monitor of pacemaker patient by using IoT cloud in real time
Authors: Abed Jameel Kadhim , Abd-aljabar Hassnaa Mustafa
Published: February 2020

People severing from heart diseases are increasing day after day, now it's more important to focus on this destructive problem and provide a good health care and remote patient monitoring .An effective monitoring system has been discovered to monitor patients who are implanting a pacemaker and need long-term follow-up. So extensive research has been done to monitor ECG and analyze it by using the free open source website and linking it with portable ECG system that are responsible for the presentation and storage of data to the doctor and patient. In this research we will develop a new method of ECG monitoring for patients who are implanting pacemakers and help them to transfer their ECG signal for long distances depending on the Internet things (IOT). Once the device is worn, it will start collecting ECG information and sent it directly via Wi-Fi to the IOT cloud. Reviewing ECG data through the web has led to the alleviation of the problem of the inability of the doctor to monitor the patient from the home as well as in the event that the doctor is being in another country ,the device also helps to discover the magnetic field that confuses the pacemaker by giving the patient a warning. The results of the test are able to detect that the system would be reliable for displaying real-time ECG data, which could help facilitate patient follow-up after implantation of the pacemaker at home with ease of use and cheaper price.
Optimal tuning of PI controller using genetic algorithm for wind turbine application
Authors: Belgaid Yamina , Helaimi M’hamed , Taleb Rachid , Benali Youcef Mohammed
Published: February 2020

Nowadays, wind turbine energy has an increased importance in electrical power applications since when it is considered as an essential inexhaustible and broadly available energy resource. An aerogenerator is a device that transforms a part of the kinetic energy of the wind into available mechanical energy on a transmission shaft, and then into electrical energy through a generator, which is in our case a dual power asynchronous machine. An important characteristic of a wind turbine is that the avail, able maximum power is provided only in a single given operating point, called Maximum Power Point. Many classical methods and controllers have been widely developed and implemented to track the maximum power point. Among drawbacks of a classical PI controller is that its parameters are not constant, these conventional control laws may be are insufficient because they are not robust, especially when the accuracy requirements and other dynamic characteristics of the system are strict. The new idea in this paper is to introduce the Genetic Algorithms theory into the controlstrategy that used inthe conversion chain of the wind turbine, in order to improve stability. Simulation results approve that the application of Genetic Algorithms to the PI regulator, minimize or eliminate the drawbacks of the classical PI regulator, and greatly promote the efficiency and stability of the wind turbine systems.
A review on vehicle to vehicle communication system applications
Authors: Ameen Hussein Ali , Mahamad Abd Kadir , Saon Sharifah , Nor Danial Md. , Ghazi Kareem
Published: February 2020

The field of automated vehicle technology is developing rapidly developing. While it is likely to be many years before self-driving cars are commercially viable and used in a wide range of conditions by the general public, technological advances are speeding along the automated technology continuum towards this destination. Automated vehicle technologies troth with significant social benefits such as reduced injuries and deaths, increased road efficiency, mobility. Automated vehicles can improve traffic safety, balance traffic flows, maximize road usage by offering driver warnings and/or assuming vehicle control in dangerous situations, as well as provide motorists with the best end-to-end transportation experience and reduce emissions, which are the most important goals of modern smart traffic control infrastructures. Exchanging data and integration of such systems with Vehicle-to-Vehicle (V2V) may be a keystone to successful readying of vehicular ad-hoc networks (VANETs) and will simply be the following step of this evolution, with dynamic period of time data exchange between all the players of the traffic dominant system and fostering cooperative urban quality. One of the applications of this concept is to provide vehicles and roads with the ability to make road time more enjoyable and also to make roads safer. These applications are typical examples of what an Intelligent Transportation System (ITS) is called, whose objective is to improve security by using new information and communication technologies (NTIC). In this paper, we will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.
Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance
Authors: Sabry Sana Sabah , Kaittan Nada Mahdi
Published: February 2020

Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the   the network’s state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length.
Developed third iterative dichotomizer based on feature decisive values for educational data mining
Authors: Ahmed Saja Taha , Al-Hamdani Rafah , Croock Muayad Sadik
Published: February 2020

Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement.
 The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values featuresselection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. 
Detection of partially overlapped masses in mammograms
Authors: Alhabib Mustafa Haitham , Alhabib Omar Haitham
Published: February 2020

Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis.
Forecasting rupiah exchange rate with learning vector quantization neural network
Authors: Linawati Linawati , Sudarma Made , Oka Wisnawa I Putu
Published: February 2020

The classification technique and data forecasting will probably be one of the techniques that will often be needed in handling or managing big data. So, from that the author analyzes the possible development of the existing algorithms. The purpose is to find possibilities in the use of reliable algorithms in a particular field, then can be adopted and implemented to develop forecasting techniques in the future. Based on these considerations, the authors conducted experiments by applying LVQNN to conduct shortterm forecasting on daily period of the Rupiah exchange rate. The literature that is used as a reference is the discovery of architectural data classification processes that resemble forecasting techniques. So, when there is a combination of Rupiah exchange histories, it is possible to find these combinations into certain classes based on predetermined parameters and historical data combination data and forecast values in the past. In this research the factors chosen as indicators that affect the Rupiah exchange rate are the amount of exports, the amount of imports, the inflation rate and also the world oil price. In this research the highest accuracy value in the testing process for the population reached 99.0991%. The increase in the percentage value of forecasting accuracy is influenced by the composition of the data. In this study the formation of data composition is influenced by distinct data. The selection of parameters which become distinct claused determines how the composition of the data will be formed. If the composition of the data is not correct, the test results will not be good. If the number of weights vector is smaller than the input data, the forecasting accuracy will decrease. Because the weight vector cannot represent data combinations that used during training or testing.
Segmentation of the human body based on frequency of human electromagnetic radiation
Authors: A. Jalil Siti Zura , Mohd Aris Siti Armiza , Aini Bani Nurul , Nabil Muhtazaruddin Mohd , Usman Sahnius
Published: February 2020

This paper discusses the body segment recognition based on human electromagnetic radiation frequency. Twenty-three points of human electromagnetic radiation are studied experimentally from thirty-three healthy human subjects. Three human body segments are considered, namely Left, Right and Chakra. For the purpose of recognition, k-Nearest Neighbor (KNN) algorithm is used to classify the segments of the human body. Then, the performances of classification are determined based on the accuracy and Receiving Operating Characteristic (ROC) analysis. It is found that the proposed technique accurately classifies the body segments with 100% accuracy, thus suggest that the proposed technique is significant to classify human body segments.
CMOS based thermal detector for processor
Authors: Che Yang Lee , Mat Jubadi Warsuzarina
Published: February 2020

This project proposed a design of low power CMOS-based thermal detector which can detect the temperature of processor such as in Central Processing Unit. By re-designing temperature detector circuit using CMOS technology, the reduction in power consumption and area size of the thermal detector can be obtained.  In this paper, the design of thermal detector consists of temperature sensing core, amplifier, and Analog to Digital Converter (ADC), respectively. The sensor was designed using 0.13 µm CMOS technology and operates by sensing the temperature of processor and produced a digital output value. The temperature detection range was setup between 0 °C to 80 °C with 10 °C resolution. The temperature detector was capable to show temperature readings in binary value. It consumed an average power of 558.2 µW and a space occupancy of 0.0118 mm².
Influence of annealing temperature on the sensitivity of nickel oxide nanosheet films in humidity sensing applications
Authors: Parimon N. , Mamat M. H. , Ismail A. S. , Banu I. B. Shameem , Ahmad M. K. , Suriani A. B. , Rusop M.
Published: February 2020

Nickel oxide (NiO) nanosheet films were successfully grown onto NiO seed-coated glass substrates at different annealing temperatures for humidity sensing applications. NiO seed layers and NiO nanosheet films were prepared using a sol-gel spin coating and sonicated sol-gel immersion techniques, respectively. The properties of NiO nanosheet films at as-deposited, 300 ℃, and 500 ℃-annealed were examined by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), ultraviolet-visible (UV-vis) spectroscopy, and humidity sensor measurement system. The XRD patterns demonstrate that the grown NiO films have crystalline cubic structures at temperature of 300 ℃ and 500 ℃. The FESEM images show that the large porous nanosheet network spread over the layers as the annealing temperature increased. The UV-vis spectra revealed that all the nanosheet films have the average transmittance below than 50% in the visible region, with absorption edges ~ 350 nm. The optical band gap energy was evaluated in ranges of 3.39 to 3.61 eV. From the obtained humidity sensing results, it shows that 500 ℃-annealed film exhibited the best sensitivity of 257, as well as the slowest response time, and the fastest recovery time compared with others.
Training on professional foreign language for students of engineering specialties: implementation of electronic technology
Authors: V.Smirnov Sergei , Z. Ibatova Aygul
Published: February 2020

The purpose of our work is to check the effectiveness of the principle of systematic in the process of teaching professional foreign language to the engineering specialties. The laboratory work contains information lexical programs for the introduction of active lexical lessons, vocabulary and grammar exercises, and questions to the texts of lessons, designated for self-control and mutual control. The authors presented the results of the implementation of the system principle in the pedagogical process, namely the possibility of providing systematic construction of methodical literature and construction of intra subject links. The laboratory work, thanks to which there is an actualization and improvement of basic knowledge and skills, as well as the motivation of educational activity - is the form of realization of this principle. The students have an opportunity to demonstrate the independence in performing communicative actions in the professional sphere, provided by the program, to automate their skills, to conduct the self-study and self-monitoring of knowledge and skills, as well as to make their correction.
Investigation of monthly variations in the efficiencies of photovoltaics due to sunrise and sunset times
Authors: Njok Armstrong O. , Ogbulezie Julie C. , Panjwani Manoj Kumar , Larik Raja Masood
Published: February 2020

The effect of time of day and month on the efficient conversion of solar energy to electrical energy using a polycrystalline (PV) module in calabar was studied. A KT-908 precision digital hygrometer and thermometer, and a M890C+ digital multimeter were used in the process. Results obtained shows that photovoltaic produce different levels of peak efficiencies at different times of the day for different months due to the difference in sunrise and sunset times for the months. The results also indicated that photovoltaics will be more efficient in months with low average relative humidity couple with low panel temperature. A peak efficiency of 77% at 12:30 in the month of April was observed before dropping to 73% at 12:00 in the month of May, indicating that there might be further drop in efficiency as we proceed further into the year. Results also show that photovoltaics are more efficient before noon in the month of May than in April while the reverse will be observed in the afternoon. 
A review paper on artificial intelligence at the service of human resources management
Authors: Berhil Siham , Benlahmar Habib , Labani Nasser
Published: February 2020

In the last few years, all companies have been interested in the analysis of data related to Human Resources and have focused on human capital, which is considered as the major factor influencing the company’s development and all its activities at all levels of human resource policies. Data analysis (HR analytics) will significantly improve business profitability over the next years.We started with an extensive survey of different human resources problems and risks reported by HR specialists, then a comprehensive review of recent research efforts on computer science techniques proposed to solve these problems and finally focusing on suggested artificial intelligence methods. This review article will be an archive and a reference for computer scientists working on HR by summarizing the IT solutions already made in human resources for the period between 2008 and 2018. It aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions. It summarizes at the same time the recent and different methods, IT approaches and tools already used by highlighting those using artificial intelligence.
Functional analysis of cancer gene subtype from co-clustering and classification
Authors: Machap Logenthiran , Abdullah Afnizanfaizal , Abdullah Afnizanfaizal
Published: February 2020

Cancer is a heterogeneity genetic disease with huge phenotypic alterations among dissimilar cancers types or even between same cancer types. Recent expansions of genome-wide profiling technologies offer a chance to explore molecular changes variations throughout advancement of cancer. Therefore, various statistical and machine learning algorithms have been designed and developed for the handling and interpretation of high-throughput microarray molecular data. Discovery of molecular subtypes studies have permitted the cancer to be allocated into similar groups that are deliberated to port similar molecular and clinical characteristics. Thus, the main objective of this research is to discover cancer gene subtypes and classify genes to obtain higher accuracy. In particular improved co-clustering algorithm used to discover cancer subtypes. And then supervised infinite feature selection gene selection method was combined with multi class SVM for classification of selected genes and further biological analysis. The analysis on breast cancer and glioblastoma multiforme evidences that top genes involved in cancer and the pathways present in both cancer top genes. The functional analysis is useful in medical and pharmaceutical field for cancer diagnosis and prognosis.
Higher performance and lower complexity turbo decoding scheme for 4G-LTE using unpunctured turbo trellis-coded modulation
Authors: Abderraouf Elarbi , Bassou Abdesselam , Rida Lahcene Mohamed Rida Lahcene
Published: February 2020

Thanks to the success of smart phones and mobile-ready laptops, data traffic has recently grown exponentially, and the demand for mobile data has risen very dramatically. These requests in large capacity can only be satisfied by a high efficiency and a very good optimization of the infrastructures of the mobile networks, while taking into account the constraints which are the power, bandwidth and a limited complexity. The task of developing mobile technologies has also evolved from a national or regional focus to a complex and growing mission, supported by global standards development organizations such as 3GPP (3rd Group Partnership Project). Through this research, we present everything related to the simulation of the 4G mobile network system (LTE), which can provide high data flow with good quality, through three model channels known as (EPA, EVA, ETU). In this work we focus on the block ‘iterative decoding channel encoder’ in the LTE system, where the iterative channel coding called ‘Turbo-code’ (TC) is substituted by the iterative coding channel called ‘Unpunctured Turbo Trellis-coded Modulation’ (UTTCM). The simulation results showed that with less decoding complexities, UTTCM's LTE system gives good performance (in terms of BER).
Multiple watermarking technique using optimal threshold
Authors: Alias Noruhida , Ernawan Ferda
Published: February 2020

The multiple watermarking technique has drawn more attention due to high demand for embedding more than one copyright. This paper proposes a multiple watermarking scheme using DWT-SVD by utilizing HVS characteristics. Our scheme embeds multiple watermarks in red and blue colors. The proposed scheme examines the coefficients of U orthogonal matrix for embedding and extracting watermarks. Watermarks are scrambled by Arnold transform before embedded in the host image in order provide additional security. The optimal thresholds for red and blue colors are revealed by finding a trade-off between normalized-cross correlation and imperceptibility from quantization steps. The experimental results demonstrate that our scheme achieves high resistant under JPEG and JPEG2000 compression for both inserted watermarks. 