bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical homogenization of thermal conductivity of particle-filled thermal interface material by fast Fourier transform method
Authors: Lu Xiaoxin , Fu Xueqiong , Lu Jibao , Sun Rong , Xu Jianbin , Yan Changzeng , Wong Ching-Ping
Published: March 2021

Thermal interface material (TIM) is pivotal for the heat dissipation between layers of high-density electronic packaging. The most widely used TIMs are particle-filled composite materials, in which highly conductive particulate fillers are added into the polymer matrix to promote heat conduction. The numerical simulation of heat transfer in the composites is essential for the design of TIMs; however, the widely used finite element method (FEM) requires large memory and presents limited computational time for the composites with dense particles. In this work, a numerical homogenization algorithm based on fast Fourier transform was adopted to estimate the thermal conductivity of composites with randomly dispersed particles in 3D space. The unit cell problem is solved by means of a polarization-based iterative scheme, which can accelerate the convergence procedure regardless of the contrast between various components. The algorithm shows good precision and requires dramatically reduced computation time and cost compared with FEM. Moreover, the effect of the particle volume fraction, interface thermal resistance between particles (R-PP), interface thermal resistance between particle and matrix (R-PM), and particle size have been estimated. It turns out that the effective conductivity of the particulate composites increases sharply at a critical filler volume fraction, after which it is sensitive to the variation of filler loading. We can observe that the effective thermal conductivity of the composites with low filler volume fraction is sensitive to R-PM, whereas the it is governed by R-PP for the composites with high filler content. The algorithm presents excellent efficiency and accuracy, showing potential for the future design of highly thermally conductive TIMs.
Motility and energetics of randomly flashing ratchets
Authors: Xu Xining , Zhang Yunxin
Published: March 2021

We consider randomly flashing ratchets, where the potential acting can be switched to another at random time instants with Poisson statistics. Using coupled Fokker–Planck equations, we formulate explicit expressions of mean velocity, dispersion and quantities measuring thermodynamics. How potential landscapes and transitions affect the motility and energetics is exemplified by numerical calculations on random on-off ratchets. Randomly flashing ratchets with shifted sawtooth potentials are further discussed. We find that the dynamics and output power of such system present symmetry w.r.t. the shift between the two potentials Δmax + Δmin, which is the sum of the shift between the two peaks (Δmax) and the shift between the two bottoms (Δmin). The mean velocity and output power both reach the optimal performance at Δmax + Δmin = 1, provided that the asymmetry α

i
of potential U

i
implies a positive flux respectively, i.e.,
α

i
> 0.5 for i = 1, 2.
An Efficient Prediction Model based on Machine Learning Techniques for Prediction of the Stock Market
Authors: Madeeh Omar D. , Abdullah Hasanen S.
Published: March 2021

Nowadays, the stock market’s prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction. The work involved three stages, the first stage involved preprocessing for the stock market data set, then the second stage which involved employing two from supervised machine learning techniques namely K-Nearest Neighbor (K-NN) and Random Forest (RF), and finally, the evaluation stage of accuracy and efficiency of the prediction for the two proposed models. The results experiments showed that the two proposed models achieved a high accuracy ratio and the RF model was the best of prediction accuracy, where it reached 93.23%, 93.12% and 93.17% respectively according to evaluation measures precision, recall, and F-measure.
The Convergence of Iterative Methods for Quasi δ-Contraction Mappings
Authors: Thajil A Q , Maibed Z H
Published: March 2021

In this article, we will present a quasi-contraction mapping approach for D iteration, and we will prove that this iteration with modified SP iteration has the same convergence rate. At the other hand, we prove that the D iteration approach for quasi-contraction maps is faster than certain current leading iteration methods such as, Mann and Ishikawa. We are giving a numerical example, too.
Mixed Penalty with Gradient, Gradient Projection and Frank Wolfe Methods for Solving Nonlinear Hyperbolic Optimal Control Sate Constraints
Authors: Al-Hawasy J A , Al-Rawdanee E H
Published: March 2021

This paper is focused on studying the numerical solution (NUSO) for the discrete classical optimal control problem (DISCOPCP) ruled by a nonlinear hyperbolic boundary value problem (NHYBVP) with state constraints (SCONs). When the discrete classical control (DISCC) is given, the existence and uniqueness theorem for the discrete classical solution of the discrete weak form (DISWF) is proved. The proof for the existence theorem of the discrete classical optimal control (DISCOPC) and the necessary and sufficient conditions (NECOs and SUCOs) of the problem are given. Moreover. The DISCOPCP is found numerically from the Galerkin finite element method (GFE) for variable space and implicit finite difference scheme (IFD) for time variable (GFEIFDM) to find the NUSO of the DISWF and then the DISCOPC is found from solving the optimization problem (OPTP) (the minimum of discrete cost functional (DISCF)) by using the mixed Penalty method with the Gradient method (PGMTH), the Gradient projection method (PGPMTH) and the Frank Wolfe method (PFWMTH). Inside these three methods, the Armijo step option (ASO) is used to get a better direction of the optimal search. Finally, illustrative example for the problem is given to exam the accuracy and efficiency of these methods.
E-Satisfaction based on E-service Quality among university students
Authors: Jameel Alaa S , Hamdi Sameer S. , Karem Mohammed A. , Raewf Manaf B. , Ahmad Abd Rahman
Published: March 2021

The importance of e-service and e-satisfaction in the education sector has increased nowadays due to the COVID 19 pandemic and most of the universities have shifted to electronic platforms to communicate with their students. Some studies had been conducted in Iraq and other developing countries to measure e-service and e-satisfaction in the education sector. the purpose of this study was to investigate the impact of e-service on e-satisfaction among undergraduate students. The survey was conducted at three private universities located in Erbil, Iraq. The study employed a convenience sample and the data was collected using questionnaires. Later, the questionnaire responses were analysed by AMOS after ensuring the validity and reliability of the model. The results showed that the responsiveness and privacy and security had a positive and significant effect on students’ e-satisfaction. On the other hand, efficiency, reliability and ease of use had an insignificant impact on e-satisfaction among students. The results indicated that responsiveness was the most important factor and led to an increase in the e-satisfaction among students. As such, the decision-makers at universities should be concerned and work towards enhancing the responsiveness as well the privacy and security for students.
Speech Scrambler Based on Discrete Cosine Transform and Novel Seven-Dimension Hyper Chaotic System
Authors: Zghair Hayder Kadhim , Mehdi Sadiq A. , Sadkhan Sattar B.
Published: March 2021

Because of their inherent features related to cryptography, the hyperchaotic system has strong potential in security systems. In this paper, a new cryptography scheme for speech scrambling has been proposed which gives it more security level. Also, it introduced a novel seven dimensions (7D) nonlinear hyperchaotic system in third-order which has twelve positive parameters. A chaotic behavior for the proposed novel system is analyzed. The hyperchaotic system is demonstrated that beside it appropriates for speech scrambler. PRNG (Pseudo Random Number Generated) is created based on the proposed 7D hyperchaotic result. The proposed scheme depends on this novel hyperchaotic system as PRNG to generate a random keystream bit. The input speech signal in this work is first squeezed by discrete cosine transformation (DCT) to minimize the residual intelligibility (R. I.). Increasing the number of system parameters accomplished a high key space, therefore, improves the security to resist several cryptographic attacks. The scheme is measured using various tests like. Peak Signal-to-Noise Ratio (PSNR), Signal to noise ratio (SNR), percentage of Difference (P. Diff.), Segmental Signal to Noise Ratio Measure (SSNR), Crest factor measure (CF), Correlation coefficient Measure (R_C), root mean square measure (RMS), histogram, key sensitivity, spectrogram, and waveform The results of simulation and comparison prove that the algorithm can achieve better performance in encryption that means the scheme is stronger and highly secure against various types of attacks than many recent similar audio encryption schemes. Simulated and implemented the result using Mathematica and MATLAB programs to simulation results, provided qualitatively, and figures.
Predication of Quality of Service (QoS) in Cloud Services: A Survey
Authors: Abdullah Muayad N. , Bhaya Wesam S.
Published: March 2021

Quality of Service (QoS) in cloud computing and web service has been commonly used as an essential measure for evaluating services of nonfunctional attributes. However, QoS prediction has become one of the key factors in services recommendation and selection to build high-quality software systems, so it becomes an urgent and crucial research area in both academia and industry. Therefore, several methods and techniques of service recommendation based on collaborative filtering (CF) have been exist for optimizing service selection. We investigate the different exist methods to solve challenging problems. Thence, we discuss and compare these approaches to provide an overview of the latest researches in the field.
Analysis the Results of Laboratory Tests of Gypsum Soil Samples at Salah-Aldeen City by Using the GIS Program
Authors: Al-Hassany Gheidaa Sabeeh Hadi , Abdulwadood Huda Wogod , Mustafa Reem Ibrahim
Published: March 2021

This paper has dealing with experimentally works which includes properties of materials and testing program. The testing program includes rotine characterization tests, chemical, and physical tests for samples of gypseous soil. Samples of disturbed and undisturbed soil was obtained of seven different locations of Salah-Aldeen province. The unified classification system was adopted of study region. Except sample 7, soil categorization (as poorly graded sand) was a good graded sand soil. Samples had non plasticity rate (NP). The results of laboratory tests (by using Arc-Map GIS program) were enhanced by spatial interpolation mapping utilizing Inverse Distance Weighted Scheme.
Integrated Spectrum Sensing and Frequency Reconfigurable Antennas for Inter-Weave Cognitive-Radio Applications
Authors: Mohammed Ali A. , Abdullah Abdulkareem S.
Published: March 2021

A dual-port antenna configuration composed of an ultra-wideband (UWB) sensing antenna and a dual-band reconfigurable communicating antenna is proposed and studied in this paper. The sensing antenna is an elliptical disk radiator covers a frequency range of (1.8-11) GHz. The communicating antenna is a modified dual-band rectangular radiator. By controlling the length of a stub attached to the communicating antenna with the aid of two PIN-diodes, the dual-band antenna can reconfigure its resonant frequencies to (2, 2.2) GHz, (3.25, 3.8) GHz, and (4.3, 5.6) GHz. Both simulated and experimental results for the return loss, mutual coupling, radiation patterns and gain are presented, and they reveal the antenna compatibility with the inter-weave cognitive radio applications.
Hyperparameter Determines the Best Learning Curve on Single, Multi-Layer and Deep Neural Network of Student Grade Prediction of Pokhara University Nepal
Authors: Rimal Yagyanath , Pandit Palvi , Gocchait Saikat , Butt Shariq Aziz , Obaid Ahmed J.
Published: March 2021

This primary study tries to explore the best machine learning model of hyperparameter tuning using single, multiple, and deep neural networks for predicting engineering student grade of Pokhara University Nepal in the year 2019. Generally, hyperparameter algorithms are adjusted from the data sets automatically when the model was designed. This is not a good idea for all sort of algorithm, with less flexibility of user choices. Therefore this article ties to meet the research gap between automatic calculation vs external model hyperparameter calculation on the same data sets applying comparison. The single neuron predicts 92 percent accuracy of the student’s A, B, C, D grade of university examination with correlation with their internal marks respectively. The five-neuron multilayer neural network produces 72 percent accuracy and deep neural network with drop-out layer (32, 64) examines, the most suitable hyperparameter determines to produce an accurate result of 50 percent accurate when 32 hidden levels were used over 64 hyperparameters produce quite less accuracy (44 percent) of their final score in constituent campus Pokhara. The main objective of this study is to examine the comparison between various neural networks for educational settings for optimization of model deployment accurately when categorical large data prediction of student grade. Therefore, this study compares three comparisons of the neural network model with the same data sets for best hypermeter prediction.
Design and Implementation of Electronic Enterprise University Human Resource Management System
Authors: Shukur Hanan M. , Zeebaree Subhi R. M. , Zebari Rizgar R. , Hussan Bzar Kh. , Jader Omid H. , Haji Lialan M.
Published: March 2021

Electronic Human Resource Management System (EHRMS) is a paperless-based system, which plays a vital role in facilitating organizational processes, overcoming all obstacles of a paper-based system, reducing cost, time, and efforts, enhancing the quality of services (QoS), and providing more accurate data. In addition, it is beneficial for competitive advantages and it eases the tasks of the HR managers to make decisions. In this paper, an efficient EHRMS is proposed, designed, and implemented. The system is called Enterprise Electronic University Human Resource Management System (EEUHRMS). The proposed system consists of fourteen modules that provide four groups of services. The first group is related to applicant services: Online Recruitment. The second group is related to staff services: Registration, Acknowledgements/Punishments, Annual Premium, Leaves, Leave Deduction, Archive, Dispatch, Extra Fees, Salary, and Service Summary. The third group is related to institutions and presidency services: Post and Statistics. While the fourth group is related to university services: Authentication and Statistics. The proposed system is evaluated by using the System Usability Scale (SUS) to get results via specific questionnaires that are checked by the university staff. The evaluation score of the questionnaire was about (85) which is considered a good result. The proposed system is developed using the Laravel framework.
Development of Model Scenarios for Forecasting Food Production and Consumption in Asia in Context of Solving Global Food Problem
Authors: Stezhko Nadiia
Published: March 2021

The article investigates the prospects of solving the global food problem by using trend, adaptive and integrated autoregressive models (Box-Jenkins and GLM models). The used models meet the criteria of statistical reliability and accuracy, which confirms the objectivity of the obtained estimates. Calculations have been performed world-wide, for Asia, West Asia and Iraq. The obtained results of the calculations show that in Asia, due to domestic resources, the population is provided with opportunities to meet the demand (according to the WHO standards) for cereals, fish and fish products, eggs and sugar plants. The volume of vegetable oils has almost reached parity; there is a slight excess of production, from 2.0% to 7.6%. At the same time, there is a significant shortage of potatoes, meat, milk, and vegetables, provided that the production maintains the trends which have developed in the last ten years. A rather difficult situation with the possibility of providing basic food products by the domestic market at the expense of own resources has developed in the countries of Western Asia. As for the most of basic products according to estimated assessment there has been a significant food deficit of food products. A particularly unfavorable and alarming situation might develop in Iraq. The results of calculations for all food products have shown, that there is no possibility to meet the needs of the population at the expense of own production. It requires the development of modern strategies for solving the global food problem and providing more favorable conditions for access to the world food market by improving the mechanism of regulating international food trade.
Influence of Heat and Mass Transfer on Peristaltic Transport of Viscoplastic Fluid in Presence of Magnetic Field through Symmetric Channel with Porous Medium
Authors: Murad Mohammed Ali , Abdulhadi Ahmed M
Published: March 2021

In the present paper, we discussed the influence of heat and mass transfer on peristaltic transport of viscoplastic fluid in presence of magnetic field through symmetric channel with porous medium. The constitutive equation of Bingham plastic model is chosen to describe viscoplastic material. The nonlinear partial differential equations that described the motion of flow are simplified under assumptions of low Reynolds number and long wavelength. These equations are solved by mean of the regular perturbation method which is restricted to the smaller values of Bingham and Grashof numbers. Series solution for the axial velocity, temperature and concentration distribution have been computed. The flow quantities have been illustrated graphically for different interesting parameters. The pressure rise and trapping phenomena are also examined graphically. MATHEMATICA software is used to plot all figures.
The use of the spatial trend surface model to predict the distribution of health cadres from pharmacists in Wasit Governorate
Authors: Al-Anbagy Ass. Professor D. Auday Taha Raheem , manhal Sjad hade
Published: March 2021

Wasit governorate suffers from a poor distribution of health personnel from pharmacists, their concentration in some aspects, and their lack of others. In order to determine the pattern and distribution of the cadres of pharmacists, some of the spatial inference models were used in the prediction process to reach a fair distribution for pharmacists in the governorate, which is the trend surface model of the first, second and third degrees, reviewing the results using the Geo Data program. We estimated the trend surface model after assuming a certain spatial covariance function. In terms of parameters called covariance components, the components were estimated using the MLE method that adopted the Newton-Raphson iterative algorithm Then we compared these models to determine the best model that represents these data, using the criteria (AIC, BIC R2) and we concluded that the best model is the first-order trend surface model. To find out whether there is fairness in the distribution of pharmacists in the governorate the two statistical indicators were calculated the Lorenz curve and the Gini coefficient for the original and predicted data using the i-xplore program. The Lorenz curve showed the lack of equality in the distribution of pharmacists in the governorate and this was confirmed by the Gini coefficient, either for The data predicted, the Lorenz curve showed that there is fairness in the distribution of pharmacists within the governorate, and that the Gini coefficient proved that there is no discrepancy in the distribution.
Bayesian Computational Methods of the Logistic Regression Model
Authors: Al-Khairullah Najla A. , Al-Baldawi Tasnim H. K.
Published: March 2021

In this paper, we will discuss the performance of Bayesian computational approaches for estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo (MCMC) algorithms was the base estimation procedure. We present two algorithms: Random Walk Metropolis (RWM) and Hamiltonian Monte Carlo (HMC). We also applied these approaches to a real data set.
Essential T-small quasi-Dedekind modules
Authors: Fandi Firas sh. , Yaseen Sahira M.
Published: March 2021

Let M be an R-module, where R be a commutative; ring with identity. In this paper, we defined a new kind of submodules, namely T-small quasi-Dedekind module(T-small Q-D-M) and essential T-small quasi-Dedekind module(ET-small Q-D-M). Let T be a proper submodule of an R-module M, M is called an (T-small Q-D-M) if, for all f ∊ End(M), f ≠ 0, implies Kerf is an T-small submodule of M (Kerf«T
M), if T≠ 0 then T ⊈ Kerf. In case Kerf is an essential T-small submodule of M (Kerf <<
ETM), Then M is called (ET-small Q-D-M). We illustrate them by some examples and discuss some of the basic properties of this types of modules and we present some relationships between this types of modules and other modules such as, T-hollow modules and T-generalized hopfian (T-gH ) modules.
Moduli of Smoothness for Weighted Lp
,0 < p < 1 Approximation
Authors: Sharba Zainab Abdulmunim , Bhaya Eman Samir
Published: March 2021

Moduli of smoothness are intended for mathematicians working in approximation theory, numerical analysis and real analysis. Measuring the smoothness of a function by differentiability is too crude for many purposes in approximation theory. For many purposes many authors defined many types of moduli of smoothness. For easier work and for weighted approximation, we introduce some types of moduli of smoothness. In this paper we introduce a new moduli of smoothness for functions ƒ Lp
[-1,1] n Cr_1 (-1,1), 0 < p < 1, r ≥ 1, that have an (r — 1)st locally bsolutely cotinous derivative in (-1,1), and such that φrfris in Lp
[-1,1], where φ(x)= (1 - x
2)1/2. These moduli of smoothness are used to describe the smoothness of the derivative of functions in Lp
spaces for 0 < p < 1, also it is equivalent to specific “weighted Ditzian-Totik (DT) moduli” of smoothness, but our definition is more straightforward and easier. we also introduce some relate properties with some relations and we prove the equivalence to “K- functional”
Accuracy Comparison of 30m Resolution SRTM and ASTER Derived Digital Elevation Models over AL-khamisah Region Using RTK-DGPS Data
Authors: Hamid Huda M.
Published: March 2021

Digital elevation models are topographic models of the Earth’s terrain that have had the heights of vegetation, and other cultural landscapes digitally removed. The accuracy and morphologic details of the height models represent the quality. DEMS provide one of the must usefule digital data sets for a wide range of users. This study is aimed at comparing and validating (in terms of elevation accuracy) SRTM and ASTER Global Digital Elevation Model (GDEM) with DGPS Points for AL-Khamisah region. Root-Mean-Square Error (RMSE) is used as the standard measure of accuracy for feature positioning. The results, of this study showed that vertical (RMSE) for ASTER DEM 4.92 m and for SRTM 2.10 m. The objectives of this study are to effectively evaluate the accuracy of ASTER and SRTM DEM datasets and determine which DEM most accurately represents low relief terrain. Findings of this analysis may be used as resources to determine the relevance of a particular DEM dataset in low relief terrain.
Growth and experimental evidences for doped nanofilms by X-ray photoemission spectroscopy data analyses
Authors: Hamodi A. , Hökelek Tuncer , Mahmood Natheer B. , Hamodi Y. I. , Bdair T. , Salih A. T. , Naji K. K.
Published: March 2021

By using the molecular beam epitaxy growth technique, the impacts of growth temperatures for Bi2Te3 thin films were investigated, besides of the substrate temperatures on growth. Moreover, the full width half maximums, based on the X-ray photoemission spectroscopy measurements, have been shown clear results for Cr-Te bonds in chromium doped Bi2Te3 thin films. The current aim is a spotlight on enlightening how chromium is joined within Bi2Te3 epitaxial thin films in the process of doping concentration by providing detailed experimental evidences through the systematic structure and electronic investigations of the compound.
Commutativity of Prime Γ-Semirings with Derivations and Generalized Derivations
Authors: Hamil Shahed A.
Published: March 2021

The concept of Γ-semiring is a generalization of semiring. Two important mappings of Γ-semirings are derivations and generalized derivations. The commutativity of prime Γ-semiring with derivation and generalized derivation f associated with the d derivation is discussed in this article.
Generalized Reverse Derivations and Commutativity of Prime Γ-Semirings
Authors: Hamil Shahed A.
Published: March 2021

We present the notion of generalized reverse derivation on Γ-semiring in this article and we prove that a prime Γ-semiring with center CΓ stratifying assumption (*) aαbβc = aβbαc, for all a, b, c S and α, Γ and I be a non-zero right ideal of S such that S admits a generalized reverse derivation f associated with a reverse derivation d satisfying d(CΓ) ≠ 0, if S satisfies any one of the properties:

(i) f(aαb) + aαb Γ

(ii) f([a, b]α) + [f(a), b]α Γ

(iii) f([a, b]α) + [f(a), f(b)]α Γ

(iv) [f(a), b]α + [a, f(b)]α Γ

For of the all a, b I and α Γ, then S is commutative.
Comparative Study of QPSO and other methods in Blind Source Separation
Authors: Salman Hussein M. , Abbas Nidaa A.
Published: March 2021

Many techniques are introduced as solutions of the Blind Source Separation mechanisms, as an Independent Component Analysis (ICA), which became most commonly used in this field. ICA methods exploit one of two properties: sample independency and/or non-Gaussianity. In our study, cocktail-party problem processed using ICA method. In this paper, we studied the performance of three technics with independent component analysis are standard FastICA, PSO, and QPSO; and compare the results of each algorithm with others according to the number of metrics (objective as SNR and SDR and subjective as signals plotting and playing). The implement of these algorithms were be made with two source signals and three source signals. As in evaluation process, the QPSO gives more accuracy results than other technics in the signals separation process. Many input speech signals of sampling frequency 8KHz, that achieve IID. also well condition, were tested for different speeches for men and/or women, also music.
A New Modified TR Algorithm with Adaptive Radius to Solve a Nonlinear Systems of Equations
Authors: Dwail Hasan H. , Mahdi Mohammed M. , Wasi H. A. , Hashim Karrar H. , Dreeb Nabiha k. , Hussein Hussein A. , Shiker Mushtak A. K.
Published: March 2021

The trust region method (TRM) is a very important technique to solve both of linear and nonlinear systems of equations. In this work, a new modified algorithm of a TRM with adaptive radius is introduced in purpose of solving systems of nonlinear equations. At each iteration, the new algorithm changes the trust region radius (TRR) automatically to reduce the subproblems resolving number when the current radius is rejected. The global convergence results of the new procedure under some appropriate conditions is established. The numerical effects indicate that the suggested algorithm is interesting and robustness.
A Survey Paper on Image Mining Techniques and Classification Brain Tumor
Authors: Jasm Dhamea A. , Hamad Murtadha M , Alrawi Azmi Tawfek Hussein
Published: March 2021

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).
A Combination Techniques of Intrusion Prevention and Detection for Cloud Computing
Authors: Alturfi Sabah M. , Muhsen Dena Kadhim , Mohammed Mohammed A. , Aziz Israa T. , Aljshamee Mustafa
Published: March 2021

Cloud computing provides scalable, on-demand, and highly available computing services over the Internet to both the public and organizations on a pay-per-use basis. It provides a variety of services such as networking, storage space, and applications. The key issue for cloud computing is ensuring the confidentiality and privacy of cloud resources and data. Enticing the user to purchase cloud services requires their trust which cannot be guaranteed unless the infrastructure is effectively protected because attacks at this level will threaten the whole system. To this end, we propose the Integrated Intrusion Prevention and Detection System (IIPDS) to prevent and detect different types of attacks to the infrastructure level of the cloud system. The proposed design uses Trusted Third Party (TTP) services and SSL/TLS protocols as intrusion prevention methods to secure the communication between the cloud provider and the user. It also uses multiple Intrusion Detection Systems (IDS) distributed over multiple cloud regions. The IDS system is capable of detecting known and unknown attacks using anomaly and rule-based (signature) intrusion detection techniques. The simulation results proved the efficiency of the system in detecting a wide range of attacks with low false positive alerts and low computational overhead.
A Dual-Polarized MIMO Array Antenna System for Future Smartphone
Authors: Khalil Al-Ani Nada M. , Shareef Al-Ani Oras A. , Mosleh Mahmood F. , Abd-Alhameed Read A.
Published: March 2021

In this paper, a dual-polarization four antennas Multiple Input Multiple Output (MIMO) scheme is proposed. The four elements are arranged over a surface area of 67×139mm2 printed circuit board (PCB) using dielectric substrate FR-4 with epsilon 4.4 and thermal conductivity 0.025. A circular slot radiator is etched in ground plane in order to improve the radiation characteristics. To mitigate the mutual coupling in the proposed MIMO scheme, two rectangular parasitics with one open rib are embedded across square slot radiator. The proposed single antenna model covers a frequency range of 5.81-6.66 GHz at -10dB impedance bandwidth. However, the bandwidth is raised up to 1.47 MHz (5.48-6.95) GHz at -6dB. The results show that -20 dB return losses can be obtained, while -45 dB isolation was achieved for a dual microstrip line. The proposed antenna can be integrated with new smart-mobile devices for future 5G wireless communications.
Network Performance Evaluation of Different MANET Routing Protocols Configured on Heterogeneous Nodes
Authors: Alturfi Sabah M. , Muhsen Dena Kadhim , Mohammed Mohammed A.
Published: March 2021

The recent communication technology tends to be wirelessly for the mobility reason. Mobile Ad-hoc Network (MANET) can be established without any infrastructure or centralized controller. It can be constructed quickly in rural areas, military zones, and in emergency cases like earthquake. Three different types of routing protocols were categorized according to their work’s nature, which are Proactive, Reactive and Hybrid routing protocols. Due to the mobility characteristic of MANET nodes, routing protocols play important role in determining the network efficiency where the network topology is changed frequently. Many studies are conducted to analyze network performance and specify the best routing protocol using different simulation tools. In this paper, the performance of network is analyzed and evaluated when heterogeneous nodes are configured with these three types of protocols under heavy traffic load (VOIP). The result of our simulation shows that OLSR protocol has the highest throughput among others while DSR protocol shows the best performance in terms of Network load. Generally, the performance of routing protocol differs depending on the network type and use.
Fixed Point Theorems of A Set-Valued Integral Type Mapping in Complete Partial IMetric Spaces
Authors: khaleel Liqaa J. , Ahmed Buthainah A. A.
Published: March 2021

In our paper we prove that the set valued mappings of integral type with lower semi continuous non-negative lebesgue integrable mapping have a fixed point in complete partial metric spaces. The same result was proved without needed to the lower semi continuous condition. We give two examples to illustrating the necessity for non-negative and lower semi continuous conditions.
An Interactive Network security for Evaluating Linear Regression Models in Cancer Mortality Analysis and Self-Correlation of Errors by Using Durbin-Watson Tests in Babylon/Iraq
Authors: Ashoor Asmaa Shaker , Kazem Ali Abdul Karim , Gore Sharad
Published: March 2021

The present study addresses Interactive Network security of the model of simple and frequent linear regression constructing method in describing the phenomenon of deaths of patients with cancer for the time variable in two aspects, one of which provides a theoretical background of the construction of the model of simple linear regression and the adoption of the principle of errors in the description of the standard deviation between the estimated value of the model and its true value, the other one is practical which shows the mechanism of building the model of simple linear regression and multiplexing and analysis of the results using two programs ( SPSS and Minitab ). And the study of the behaviour of errors for each model and drawn in the form of normal probability plot was also used the coefficient of selection R2 and Coefficient Selection Factor(adj) R2 in comparing the models and determining the best model in explaining the growth phenomenon of cancer deaths.
The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques
Authors: Hammoodi Mahmood Shakir , Essa Hasanain Ali Al , Hanon Wial Abbas
Published: March 2021

WEKA and MOA are a free open-source software project specific for data mining and data stream mining, respectively. They are written in Java and developed at the University of Waikato, New Zealand. This research paper presents a comprehensive study of both consists of algorithms, evaluation, visualization, correlation between WEKA and MOA, workflow of implementation, and the classification accuracy.
Distributed Denial of Service Attacks Detection System by Machine Learning Based on Dimensionality Reduction
Authors: Abbas Sara Abdalelah , Almhanna Mahdi S.
Published: March 2021

Data mining algorithms have essential methods and rules that can contribute in detecting and preventing various types of network attacks. These methods are utilized with the intrusion detection systems that can be designed and developed preserve the information in organizations from damage. Specifically, the data mining technique allows users to effectively distinguish between normal and malicious traffic with good accuracy. In this paper, a methodology for revealing and detecting (DDOS) network attack was suggested using DM algorithms. The utilized methodology is divided especially into four parts, each part has its own rules, as the following: First one is the pre-processing which consists of three sub-steps: (i) encoding, (ii) log2, and (iii) PCA. Encoding is used by converting the original nominal packets into numeric features. Standardization of data was performed using logarithmic algorithm. Finally the PCA technique is applied eight times for several different features to reduce the dimensions of the dataset. The second stage is an anomaly detection model, (RF) algorithm is implemented for the extraction of data patterns while classification the types of the given features in training step, (NB) algorithm was also used in classifying the data to compare the results of its classification with the results of using the classifier (RF). In the third stage, the outcomes were tested by implementing the already trained datasets. In the fourth stage, the proposed system performance evaluation metrics were collected such as the rates of accuracy, false alarm, detection, precision, and F.measure.

MIX dataset were utilized to train and test the proposed model which resulted from merging two datasets (PORTMAP+LDAP), which are used from the CICDDOS2019 datasets, each consisting of several types of attack packets, and benign packets.

Several metrics were utilized in the evaluation of the proposed system. The best outcomes were obtained for detection by using the log2 algorithm and PCA technique in the preprocessing step and using (RF)classifier to classify the dataset. the accuracy when using MIX dataset was 99.9764%, the detection rate was 100%, false alarm rate ≍ 0, and the F.measure was 99.9% when PCA = 25.
Arabic voice system to help illiterate or blind for using computer
Authors: Ali Shaker K. , Mahdi Zahraa M.
Published: March 2021

Speech recognition is one type of technology, which make a computer to recognize the voice of words that an individual speaks through a microphone and convert it into the written text. In this paper, the proposed system for helping illiterate and blind peoples to open applications with them voices.

The proposed system includes two parts; the first part is the training part while the second part is used for the testing. The system contains seven-steps; the first step is the recording of voices and the second step is voice pre-processing. The third step is the feature extraction using MFCC (Mel Frequency Cepstrum Coefficient) method that involves seven steps. The fourth step is for classification voices, there are 1400 voices samples used in training by using a Naïve Bayesian method as a classifier. The fifth step is the matching step using the Correlation Coefficient, there are 200 voices samples in testing. The sixth step was to convert voice into text and the seventh step for execution one of 20 commands.

The accuracy of results from using the Naïve Bayesian algorithm in the training phase gives (100 %) while the accuracy of results in the testing phase using Correlation Coefficient gives (98%).
Simulation of Hall Effect in Semiconductor for Current Sensors Applications
Authors: Mahmood Natheer B , Hamodi Ali , Jaffar Zahraa M , Saeed Farqad R
Published: March 2021

Hall effect simulation program was designed using a numerical direct and iterative method. The validity of this program was checked by comparing it with practical results. This program can be used as the basis for designing electrical current sensors, magnetic sensors, and integrated circuits depending on Hall effect, Moreover the program support researchers for their practical tests. The designed program was used to study the effect of different parameters on the designing of sensors built on the Hall effect, these parameters are magnetic field, electrical current, type of semiconductor, and the dimension of material. The results showed that the Hall voltage is sensitive to the magnetic field and electric current changes, while the hall voltage is constant with a variation of the sample dimensions. The results showed that the Hall experiment is sensitive to these parameters, especially to the thickness. The simulation software results will enhance the practical applications of the sensors.
Detecting fraudulent of fuel level on LED digitized display using IoT sensors
Authors: Varsha G. , Allam Sharath Kumar , Ramireddy Vasundhara
Published: March 2021

Fuel prices are soaring day by day. Because of this, there is a great deal of increment in fuel fraud cases. In order to aid people in their vehicle’s fuel theft detection, this paper examines the ability of the system to perform a task which detects the fraud at the filling station. The real-time problem which is now happening in the filling station is the Fuel Level in the fuel tank. The appliance has a display screen which is used to show the amount of fuel already existing in the tank of the vehicle before filling and after filling the tank it shows the total level of fuel in the tank that is already existing plus the amount after filling accurately thereby knowing the exact amount of fuel which was filled in the vehicle. The difference in total fuel and amount already existing in the tank gives the amount of fuel filled. This helps to reduce the fraud at the filling station. The Arduino stage has gotten very famous with individuals simply beginning with gadgets, and all things considered. We used the Ultrasonic sensor which is placed above the tank temporarily as it is a cost-effective solution.
Backpropagation Algorithm and its Hardware Implementations: A Review
Authors: Kuninti Shivani , Rooban S
Published: March 2021

Nowadays, artificial intelligence is gearing up at faster pace. Hardware integration with Artificial Neural Networks (ANN) have paved the way to different applications in areas like control engineering, robotics and navigation. There has been extensive research in the field of machine learning to use the system in advanced applications. The combination of hardware and software modules help to build systems in a robust manner. This paper focuses mainly on back propagation algorithm and its different hardware implementation using FPGA, ASIC, Memristor and Microcontroller. Implementation of back propagation algorithm on Field programmable gate arrays is analysed in detail. FPGAs parallel processing feature makes it unique for neural networks. The parallel processing models implement back propagation algorithms for calculating errors in the hidden layers of neural networks. This review helps the researchers to understand the implementation of various hardware with back propagation algorithm also the comparative analysis on the parameters helps to identify the suitable hardware based on their requirements.
Augmentation and Detection of Individual Pose using CUDA
Authors: Muzammil Parvez M , Inthiyaz Syed , Praghash K , Suresh Kumar K , Sreevardhan cheerla
Published: March 2021

The collective real-time pose prediction is a leading element in allowing algorithms to understand Individuals in videos and pictures. In this study, we are presenting a strategy to determining the pose of various individuals in a picture in real time. Non-parametric procedure is used as representation that we refer this to Part-Affinity-Fields (PAFs) to understand how to connect parts of the body with individuals. This base up framework accomplishes high exactness and real time execution, paying little mind to the quantity of individuals in the picture. In past work of computer vision researchers, PAFs and body part area estimation were refined at the same time across preparing different steps. We show that PAF just clarifying as opposed to both the PAF and content part area refinement brings about a significant increment in each of runtime execution and precision. We likewise propose the main joined body and foot key point identifier, considering an inner commented on foot dataset that we have freely discharged. The joined finder not just diminishes the derivation time contrasted with running them successively, yet in addition keeps up the exactness of every part separately. The work was completed in appearance to Open Pose, chief opensource continuous structure to multiindividual 2D present disclosure, including foot, body, hand, & facial central issues.
Design and Analysis of Dynamic Comparator Implemented Using Stacking Technique
Authors: Suman Shruti , Hruthik Sai Upendra P. , Sai Sree Vishnu Y.
Published: March 2021

Comparatorsareone of the basic devices that is mostly used in analog-to-digital converters (ADC). Designing low-power circuits with CMOS technology has been a serious research problem for several years. Nowadays the need of low power electronics became vital in various fields. In this paper, stacking technique is used to reduce the power consumed by the comparator.65nm CMOS process is used to design and simulate the comparator. The power consumption ofproposed comparator is compared with thepower consumption of double tail dynamic latch comparator. The proposed approach obtained less power consumption results.
Implementation of High Performance 6T-SRAM Cell
Authors: Venkata Lakshmi T. , Kamaraju M.
Published: March 2021

Now a days each and every device equipped with large capacity memories in order to full fill all the needs of customers. And there are some other parameters which plays an important role in the determination of performance of the device they are power consumption and delay. In many widgets, memory is an integral part and its size also scales down as the device size is reduced. For this reason, every computerized device has low power and high speed is a prime concern. Current day scenario suggests that 6T SRAM is commonly used for the SRAM based memorydesignsas they are advantageouscompared to other cells. Low power is the major concern of today’s electronics industries where, Static as well as Dynamic power dissipation are the two key parameters that should be taken into consideration. To meet consumer needs high bandwidth and low power and high speed consuming storages are also required. This paper mainly focuses on reducing powerdissipation of Static Random Access Memory (SRAM). Power reduction and Delay reductions are the major challenge of digital Industry. A simple and advantageous configuration of a SRAM cell is by connecting two CMOS inverters back to back. This configuration has good noise immunity.
Design and analysis of Jeans based Wearable monopole antenna with enhanced gain using AMC backing
Authors: Srilatha K , Madhav B T P , Anil Babu B , Rishikesh Raj M , Somala Tulasi , Nimmaraju Vyshnavi , Rao M C
Published: March 2021

A monopole antenna with enhanced gain, low Specific Absorption Rate (SAR) and low profile backed with an artificial magnetic conductor (AMC) for wireless communications is designed and presented in this article. This antenna presents AMC design, which isapplicableto reduce the SAR for the portion of the EM waves where electromagnetic pollution is commonly observed. To reduce the radiation of the back lobe and to improve the antenna gain, AMC is designed and backed under the monopole antenna. The antenna resonates in a single band which is from 2.38GHz to 2.44GHz with the reflection coefficient of 2.41Ghz. With the obtained band, the proposed model can be utilisedfor wearable applications.