bluuebunny/crossref_metadata_2025 huggingface dataset:
A new technique for data compression
Authors: Latef Thajel DhyaaAlrahman , Mahdi Hashim Kadhim
Published: November 2020

Data compression has become more important than ever, due to the increasing demand for internet use and the exchange of a huge amount of images, videos, audio and documents as well as the growing demand for electronic archiving by government departments that produce thousands of documents per day. In this paper, the proposed technique for document compression will be presented.The proposed technique is a lossless and completed technique it is consists of two parts the compression part and decompression part. The compression part contains of some basic stages such as: pre-processing, blocks processing, run length encoding(RLE), replace maximum values by unused values, minimize levels, delta encoding, compression of ones values, encryption. After the encryption process is complete, the outputs are stored in two separate binary files with the extension of bmp;one of them is the header file and is considered a key for the second file which contains the compressed data. This technique applied on twenty documents and compared with other methods compression such as RLE, jpeg, tiff and png. The experimental results showed that the proposed technique gives a higher compression ratio than the rest of the methods.
Numerical simulation of surface curvature effect on aerodynamic performance of different types of airfoils
Authors: Muhsin Elewe Adel
Published: November 2020

The development of car modification using rear wings or spoilers, underlies the research on the aerodynamic performance of airfoil. The influence of aerodynamic forces will produce a down force to the bottom of the vehicle. The present paper investigate airfoil NACA 0012, NACA 4415, GOE 528 and GOE 652 to estimate the effectiveness of airfoil shape based on different angles of attack. The numerical simulation has been done using ANSYS Fluent to obtain drag coefficient, lift coefficient and lift-to-drag ratio of all cases. In addition, the drag and down forces have been calculated with respect to airfoil geometry features. The main objective in this paper is finding the effect of airfoil surface specification as a part of airfoil geometry features. The results observe that the highest lift coefficient value was achieved by GOE 652 which was equal to CL=1.9310 at 7 degree angle while the highest lift-to-drag ratio achieved by NACA 4415 which was equal to 100.8359. The highest down force was 1906.847 N at 15 degree by GOE 652 airfoil. The results show that the airfoil GOE 652 has the most effective surface area among the four airfoils.
Dirichlet Tessellation’s technique to compress a true color image using a Lossy compression
Authors: Noori Amani Y. , Majeed Suhad F.
Published: November 2020

In this paper, we show how to use the concept of Dirichlet Tessellations to compress, store and reconstruct an image without affecting on its dimensions and represent it with an acceptable quality, where a true color image has compressed by 60.05% with mean square error (MSE) = 9.6081 which represents the error between the restored image and the original image, and peak signal-to-noise ratio (PSNR) =38.3044 dB which represents the similarity between the restored image and the original image, using MATLAB R2017a. Dirichlet Tessellation has simply defined as dividing the space into geometric shapes by generating finite set of distinct points, each shape contains one of the distinct points and comprising that part of the space nearer to that distinct point than to any of the other points. We have used two algorithms for image compression, First algorithm selects set of distinct points distributed uniformly in an image and store their locations along with pixel values. In the second algorithm random selection of distinct points, which distributed in regions containing more details, using the edges detector algorithm to detect these details. In order to reconstruct the image, Saved distinct points placed at their corresponding locations in a new image that is formed, where two algorithms used, the first algorithm based on the concept of a growing region. It’s Region -Based image segmentation method, by checking the pixels adjacent to the saved distinct points and delimiting whether the pixels should add to the regions of saved distinct points depending on the region’s membership criteria such as pixel intensity. The second algorithm uses one of the Dirichlet Tessellations characteristics, Which divides an image into polygonal regions based on the distinct points that saved, Each pixel in the confined plane of saved distinct points will have the same characteristics of this point, This is done by taking each pixel in an image and calculating the minimum distance between pixel location and saved sites using the distance equation, This process repeated until each pixel assigned its value and specifying all color regions in the image.
A Survey of Scaling Distributed System Via Machine Learning and An Insight on Hadoop and Spark
Authors: Sabih Shaker Atheel
Published: November 2020

This survey present and discuss distributed computing framework and distributed machine learning with evaluating the results of 10 papers on machine learning and big data. The first half discussed distributed computing framework, Hadoop and Spark. We briefly explained each structure and compared the key features between them. The second half consists of the survey in distributed machine learning. We briefly described the representative techniques as well as popular frameworks and discussed the major problem and challenges behind them used in different papers for scaling of distributed system via machine learning and big data techniques.
Energy improvement using Massive MIMO for soft cell in cellular communication
Authors: Ahmed AHMED Saadaldeen Rashid , Ahmed Najm Ihab , Talib Abdulqader Abeer , Basem Fadhil Khansaa
Published: November 2020

The process of energy efficiency improvement in any cellular network will require that the network design is densified to enhance higher spatial reuse while preserving the quality-of-service at the user. This study will analyze the combination of two densification techniques namely the small cell access point and the massive multiple-input-multiple-output (MIMO) base. The former is operator-deployed in the spatial soft-cell approach which is implemented after multiple transmitters which are in a system where noncoherent multi-flow beam forming is serving the users. In the study, the total power consumptions will be minimized to satisfy the QoS constraints. The technique has hidden convexity that will increase efficiency in the solution algorithms. This solution will promote exclusive assignments of users to the respective transmitters. The simulations in this research have promising results illustrating that the cumulative power usage can be significantly enhanced via the combination of the massive MIMO and small cell approaches. The results are viable for the optimal and low-complexity beam forming
Implementing an Electronic Management System for Managing Graduate Students’ Information in Iraqi Universities
Authors: Hadi Saleh Hassan , Adnan Mishkhal Israa , ali hussein Abid , Salam Mohammed Riyad
Published: November 2020

E-management means an electronic system implemented to transform the administrative work from manual management to upgraded electronic-management used computer application within an institute. The electronic administration and management are concentrated on the branches of the E-Systems that reduce administrative working costs and upgrade performance achievement and overcome the problem of geographical and temporal dimensions. In another meaning, it tries to develop the administrative structure of society and the development of a working mechanism. Moreover, it overcomes the daily business problems with informatics infrastructure safe and robust and compatible with each other, through resource management and operations using communication networks. This paper focuses on providing an approach to e-governance that is currently the environment of the University of Diyala using. The proposed model is for controlling off the flow of data and protecting it. The verification list questionnaire and the data collection were then implemented from several levels of students and staff at the Faculty of Physical Education and Sport Science from the University of Diyala. The obtained results from the questionnaire showed that (54.8%) of the tested sample accepted the new e management model, the other (45.2%) does not accept this model for various reasons such as lack of computer use and lack of familiarity with electronic applications, or limited community culture.
An Enhanced Performance of K-Nearest Neighbor (K-NN) Classifier to Meet New Big Data Necessities
Authors: L.Hussein Alsammak Ihab , M. Abdul Sahib Humam , H.Itwee Wasan
Published: November 2020

The rapid increase in the growth of text information over the past two decades has led to the need for the use of text classification techniques, particularly in the area of information retrieval, data mining and data management. The precise results and simplicity of the K-Nearest Neighbor Classification Algorithm (K-NN) in knowledge mining is the reason that made it one of the most important classification algorithms used in many tasks such as pattern recognition, regression, and text classification. Through experiments and analysis of the results of the use of the traditional algorithm of the (K-NN), there are some deficiencies in their performance, especially when the data are large such as the algorithm was unable to process big data by rapid extraction with minimal storage space and generate useless samples computation and probability problems.

In this paper, we have developed an enhanced algorithm and get the best results and perform better than that in the traditional algorithm. The significant improvement in our model performance is due to the improvement by removing unnecessary computational samples in the traditional algorithm. The performance is further improved by using the lost value computational method to define results as a prelude to avoid wasting time by correcting and filtering noise, examining the database, and eliminating unwanted records. Additionally, the inverse logarithmic function was used to solve the probability problems the algorithm encounters. The experimental results showed the efficiency of the modified algorithm in reducing the sample size and speeding up the search for the required data.
Utilizing Structured Query Language Database And Application XDK In Advanced Mobile Phones: Iraqi Police Model
Authors: Hashim Hassan B.
Published: November 2020

This Query obtains a fast response from the system which was designed to know the criminal history of people. The current problem is how to obtain the data stored in the database about criminals during the same time crime in Iraq capital Baghdad. which increases the speed of response to avoid possible risks and arrest criminals. The system is designed to inquire about the data stored in the database Structured Query Language My SQL to make the appropriate decision. This application contains the improved parts of the outline of ‘smartphone’ for the Federal Iraqi Police FIP. The principle movement engaged with configuration is how to spare the application capacity of the gadget to help capture offenders, or on the other hand, aroma who has a criminal record in the database by cops and their records in data frameworks FIP. The research adopted a Methodology database My SQL contains records of lawbreakers and Criminals wanted to eliminate. A foot watch officer can send information through this application and find the solution rapidly. That is added to the advanced messaging methods below the error rate. The MySQL database tables are planned with the Application Development Kit XD to coordinate this proposed and appropriate model through the three Stations test tables in Baghdad city that chose by Population density and danger levels. The search method was used in designing a database My SQL and application software XDK For smart devices. The results show that 75.07 % of the system’s response to the data available in the database that designed and will reduce the effort and time by searching for wanting criminals, and a criminal history in the virtual stations, that used in the city Related a large population density. New data access, the management system has been developed from the master database designed for this purpose and we can retain and retrieve data as needed.
Path-Planning Dynamic 3D Space Using Modified A* Algorithm
Authors: Hussein Qahtan Al Gburi , Ali Emad AL-Baiati
Published: November 2020

Path planning is critical to the development of any robotic system or any autonomous moving vehicle in the modern world. The traditional planning algorithms such as the A*, D*, APF algorithms and more have scored great deal of efficiency along the way of path planning and navigating though some like the A* and D* lacks the ability to adopt the three-dimensional environments, since they have been intended to be light and fast in execution. This paper shows a modified version of the A* algorithm that works on 3D sphere space in both simple and cluttered environments. Giving ability to the algorithm to operate with float values and navigate around sphere obstacles safely, which is a closer scenario to the real world environments. The modified algorithm has the ability to avoid dynamic as well as static obstacles, generating semi-smooth and completely safe path from start point to the goal.
Skin Dermatitis Detection using Image Segmentation Techniques
Authors: Haider Metib Maryam , Faris Abdulhssien Marwa , Anwer Abdulmunem Ashwan
Published: November 2020

This work, the image segmentation methods are proposed to provide a solution to a vital problem in health sector, which is detection of skin disease areas. Detection skin diseases has an important role to diagnose the affected area and treat them. In Health community considers this problem is vital because the accuracy of the disease recognition based on this step. We use three of image segmentation techniques namely, Morphology, K-mean and texture segmentation algorithms. These three methods have been applied on collected dataset of different skin dermis diseases. The results show these methods give reliable results to detect successfully the affected area on human skin.
Student Performance Prediction Model based on Supervised Machine Learning Algorithms
Authors: Salah Hashim Ali , Akeel Awadh Wid , Khalaf Hamoud Alaa
Published: November 2020

Higher education institutions aim to forecast student success which is an important research subject. Forecasting student success can enable teachers to prevent students from dropping out before final examinations, identify those who need additional help and boost institution ranking and prestige. Machine learning techniques in educational data mining aim to develop a model for discovering meaningful hidden patterns and exploring useful information from educational settings. The key traditional characteristics of students (demographic, academic background and behavioural features) are the main essential factors that can represent the training dataset for supervised machine learning algorithms. In this study, we compared the performances of several supervised machine learning algorithms, such as Decision Tree, Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Sequential Minimal Optimisation and Neural Network. We trained a model by using datasets provided by courses in the bachelor study programmes of the College of Computer Science and Information Technology, University of Basra, for academic years 2017–2018 and 2018–2019 to predict student performance on final examinations. Results indicated that logistic regression classifier is the most accurate in predicting the exact final grades of students (68.7% for passed and 88.8% for failed).
RETRACTED: Electroencephalogram Signals Classification Based on Feature Normalization
Authors: S. Yousif Enas , Shawkat Abdulbaqi Azmi , Zaily Hameed Abduladheem , Al-din M. N Saif
Published: November 2020

Data standardization is a fundamental process in which binary or multi- classification systems incorporate it as a sub-system in the classification- based question. Standardization can be called a mapping function moving from one space to another. Depending on the quality of the data, various types of normalization methods have been suggested. Research is underway recently on whether this approach is actually necessary. In this article, the various standardization methods efficiency is measured for the purpose of categorizing signal-based emotion with EEG. Binary classifier based on Naïve Bayes Classifier to classify the emotions. Only various kernel functions are considered for Naïve Bayes Classifier. While the experimental results may not show a substantial difference in performance between varrious types of normalization, the process of normalization generally improves emotion recognition classification efficiency.
Security in social media policies: guidelines for strategies
Authors: Ameer Jabir Hiba
Published: November 2020

This research investigates the security in social media and provided the guidelines and strategies toward minimizing the security risks in social media to collect parts of information and increase better practices to assist nations with tending to social media security changes adequately. Besides, this work further features more in the writing dependent on social media security dangers and rules to lessen it and furthermore outlined the key bits of knowledge to push our nations to effectively address these issues of social middle security chance. Great associations don’t contain a coercive social media security way set up and are dubious of how to make sturdy social media security systems to forestall social media security risks. This work can be filled in as a trend to associations to moderate social media security hazards that might compromise the associations. The flow investigates further merges the divided conversation in writing and gives a profundity investigation of social media security dangers, rules, and counteraction systems. Reasonable bits of knowledge are distinct and outlined from a wide investigation. Sharing this interesting information could possibly energize more conversation on bestead pursuits for contraction the risks of social media to those associations.
Age and Gender Classification using Multiple Convolutional Neural Network
Authors: Rahman Hassan Khaled , Hadi Ali Israa
Published: November 2020

Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.
Lifetime Enhancement to Improve Data Transmitted for Clustering Protocol in Heterogeneous Wireless Sensor Networks
Authors: Hussain Alaa A. , Abood Basim , Mahdi Hashim Kadhim
Published: November 2020

The Routing Protocols in Sensor Networks come from many different ways from mandatory registration in fixed networks: there is no architecture in WSN, wireless connections are inaccurate, sensor networks may fail and routing protocols have to meet stringent energy saving criteria. There have been several routing algorithms commonly set up WSN. In the sensor network, a large number of inexpensive sensors with small batteries are deployed in the area to be monitored, which has to survive for a long time, where it is difficult or even impossible to implement the sensor battery replacement. Consequently, power consumption is an important factor to consider when designing WSN. We proposed a new scheme to investigate the cluster in heterogeneous WSN, the clustering by using Fuzzy-Logic Stable Election Protocol (SEP_ FUZZY ), since the Fuzzy Logic Inference System (FIS)is used in the clustering process. FIS work with two input (nodes’ remaining-energy(R-energy), and its distance from the base station(D2BS)). Results from the simulation indicate that the lifespan of the network reached by the suggested approach will be improved by almost (47 %, 35.7 %, and 23.8 %) more than that gained by the protocols “(LEACH, PEGASIS, and SEP)”.
Effects of Volatility and Trend Indicator for Improving Price Prediction of Cryptocurrency
Authors: Dahham Dahham Ahmed Zahim , Abdu Ibrahim Abdullahi
Published: November 2020

The purpose of this research is to identify how effective the determinants of the improved price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then we treated them as time series data to run dynamic trend indicator and first-differencing volatility regression model. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are predictable. It is easy to say that major stakeholders (Apple, Amazon, Facebook, Google, Tesla) affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is strong. We have evaluated all the major cryptocurrency prices with exact accuracy of 95.38% using the volatility regression model effectively. All the cryptocurrencies are evaluated against US dollars in regard of different cryptocurrency like Bitcoin, Ethereum, Litecoin and Ripple digital currency. Cryptocurrencies shouldn’t be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
Near Real Time Twitter Sentiment Analysis and Visualization
Authors: Albaldawi Wafaa S. , Almuttairi Rafah M.
Published: November 2020

Twitter can be considered as a large scale network. People’s opinions matter a lot to analyze how knowledge spreads impact lives. In this project, we took advantage of the Apache Spark Streaming fast and memory computing platform to retrieve live tweets and perform sentiment analysis. The primary purpose is to provide a tool to evaluate the score of sentiments in streams. This paper reports on the nature of an analysis of emotions, collecting vast numbers of tweets. Results identify the view of users through tweets into positive, neutral and negative about coronavirus.

This project on Spark Streaming to analyze tweets, hashtags or specific keyword/keywords such as (corona) from live twitter data streams. Data is collected from input sources like Twitter and processed downstream using Spark Streaming. Then, how sentiment scores can be generated for tweets and build visualization dashboards on the data using Elasticsearch and Kibana.
PROPOSED METHOD FOR IMAGE SEGMENTATION USING GRAPH THEORY
Authors: Hashim Kalleel Balsam , M.Hashim Kadhim
Published: November 2020

This paper presents an image segmentation technique using graph tools for object detection. Graph theoretical systems have many good features among different segmentation schemes. It organizes the image elements into mathematically and structural form, and makes the problem formulation more flexible, and the computation more efficient. In this paper, the work, consists of two stages, in the first stage we apply the pixel-based labeling algorithm to the binary image, the algorithm works similarly to the eight connectivity labeling, but it is broader than it in terms of the search area, where two (horizontal and vertical) thresholds are first defined so that the search is in a block whose height is the vertical threshold and its width (2 * horizontal threshold), in the second stage the output of the first stage is mapping into an undirected weighted graph, in which each vertex represent region rather than pixel. We evaluate the results by comparing it with other method using (RI) parameter. We use 50 image from online source and image taken from Berkeley database.
Similarity metrics for classification: A Review
Authors: Ali Shaker K. , Aydam Zahoor M. , Rashed Biadaa M.
Published: November 2020

In this paper, fourteen similarity metrics are reviews, which will be the most important part in Diagnosis, Classification, Clustering and Recognition. Most researchers may not sure to choose which metric will be powerful and give high accuracy in them researches. Therefore, this paper will be as a guide for them to select which metric useful for them research by try one of fourteen metrics that listed in this paper and can compare one of these metrics by advantage and disadvantage of each one. In addition, there are new metrics modified to give more accuracy by testing them in some clustering application.
An Overview of Periodic Wireless Sensor Networks to The Internet of Things
Authors: M. Al-Qurabat Ali Kadhum , Abdulhussein Abdulzahra Suha
Published: November 2020

Through developments in digital electronics and wireless technology, a variety of tiny devices have begun to be used in many aspects of everyday life. These devices can sense, compute and communicate. These typically consist of low-power radios, many smart sensors, and integrated CPUs. Such devices are utilized to establish a wireless sensor network (WSN) essential for the delivery of sensing services and monitoring of weather conditions. The concept of the Internet of things (IoT) is formed in conjunction with WSNs, where IoT can be described as an interconnection between recognizable devices in sensing and monitoring processes inside the internet networks. This paper offers a description of Periodic WSNs in general. It also offers an overview of the PWSN applications and challenges.
Face Recognition Using Various Feature Extraction Approaches
Authors: Haitham Najim Ahmed , Majeed Ali Alaa
Published: November 2020

This paper introduces an experimental study on the recognition of the person’s face by utilizing three Techniques of extraction: Principle Components Analysis (PCA), Linear Discriminant Analysis (LDA) and Contourlet- Curvelet Transform (CCT). The results of these approaches were observed and compared to discover the perfect scheme for identification of human faces. The tests have been carried out on the faces databases of (ORL),(UMIST), and (JAFFE). The results acquired by the methods were quantified by altering the ratio of train to test photos in three categories: 75/25, 55/45 and 35/65. The evaluation results showed that the CCT extraction method provides better results than the others. The highest recognition rate was recorded for the CCT approach (recognition rate=98.980%) when the (train/test) photos ratio is (75/25). Furthermore, the best recognition rates for the LDA and PCA were 96.391% and 95.127% respectively. The Matlab R2019b program was used for implementing and testing the algorithms.
A Technique for Big Data Deduplication based on Content Attributes and Dictionary Indexing
Authors: S. Naji Duaa , E. George Loay
Published: November 2020

In recent years, the quick expansion of the data such as text, image, audio, video, data centers, and backup data has caused to a lot of problems in both storage and recovery processes. The companies spend plenty of money to store the data. Hence, a need for an efficient technique becomes necessary for handling enormous data. In this paper, we propose to set up for new de-duplication for the contents of big data set. The divisors are selected in an automated way using the fields separator, different dictionary indexing methods will be used to de-duplicate the fields contents those have bounded variability. Also a set of computationally low-cost hash functions will be used for speeding up the deduplication for fields consist of long strings. The number, nature, and length of fields will be tested. Besides that, certain kinds of indexing and clustering methodology will be applied to define the optimal way to decrease the data size before making de-duplication.
Design and implementation smart multi language sound guide using raspberry pi and RFID system
Authors: Al-Lami Ahmed Qasim , Alobaedy Firas , Alzubaydi Dhia
Published: November 2020

A conventional phonetic expression into the museums, exhibits, and spectacular places, is hard to ensure the quality demands of the visitors, on account of the languages difference and the large numbers of visitors. Hence, it is essential to develop a Multilanguage sound system, which can answer the different demands of various language users for the same exhibition. The system included RFID technology and a raspberry pi device. The system can define the language of the expression according to the demands of visitors, and then it could provide visitors extensive information about the exhibition in a clear, noise-free sound, as Clear and understandable. This will reduce the workforce and increase performance, but it also can completely ensure the quality of the expression.
Algorithm for CPU resource allocator case study and compering between ordinary and ML Algorithms
Authors: Ammar Emad Azeez , Yulia Valentinovna Bondarenko
Published: November 2020

The problem of resource allocation of scarce is a very important task especially in dynamic and non-stable environments when the importance and priority of recourse are changing while the system is already working in our work firstly we simulate the problem by mathematical model than the essential part is to use intelligent algorithms like comparing random scheduling, hill climbing, simulating annealing and genetic algorithms, by using these tactics of such kind of algorithms we designed an algorithm that can handle with every type of multi-agent learning-based resource allocation for distributed systems that act place of most promising approaches to these kinds of allocating systems problems. The approach will be authorized by achieving on different types of large scale systems than correlated with the regular algorithms to compare the result and get the efficacy of new algorithm.
Augmented Reality Technology in Education
Authors: Abd Al-hassan Hind Adai , H.Suad Jamila , H. Ali Hatem
Published: November 2020

Augmented reality technology has entered the field of education, and augmented reality is one of the innovations of technology. The educational institutions that allow uniqueness and enrichment of educational jobs through feeding them with many different sources and alternatives, which constitute an integrated combined methodological unit aimed at achieving ideal learning that is characterized by a large degree of effectiveness, efficiency and mastery and supports the learning process through its new and different by adding audio, 3D, and video. This technology transmits information by adding a new virtual image (audio visual information) to the still image (the real world) and this allows the learner to interact with this technology by dealing with additional information or objects that are displayed in the real world. In this paper, the proposed method for promoting and supporting the traditional method of teaching and converting difficult and complex materials into easy materials is presented easy to understand and memorize, which is called Technology Enhanced Learning (TEL). In this technique is produced virtual elements such as explanatory video, audio and three-dimensional shapes, these elements are stored in the database and linked to the note in the study material created when the learner focus the phone camera towards the mark, then camera picks up the mark, and opens the default elements on the phone screen.
Analysis of Criminal Spatial Events in GIS for predicting hotspots
Authors: F. Mohammed Abbas , R. Baiee Wadhah
Published: November 2020

The crime rate increasing in developing countries cause of the unequal distribution of psychological, economic situation. This research aims to identify the crime mapping and investigate the hotspots and analyzing the spatial crime dataset and the predict of Spatio-temporal hotspot in Baltimore city for a period from 2012 to 2018. Analyzing crime data using data mining algorithms and The Geographic Information System (GIS) of Geographic dataset visualize and it possible for law enforcement to detect spatial crime patterns map easy and flexible and different analysis to identify the crime hotspot region efficiently. analysis crime hotspot using GIS is a useful way to the recognition for crime pattern and predicting hotspot over spatial correlation, analysis spatial data and revile crime pattern future detection. using spatial correlation, the G* statistic has been done with hotspot analysis the Getis-Ord Gi* to find the result of the spatial statistics pattern. analysis the crime to predict hotspot uses spatial variation and density crimes for clarifying the positions of statistically significant crime predict hotspots and cold spots and GIS interpolation method is used for more efficient visualization. This research using Grid network hotspots are applied to the crime data of Baltimore, Maryland state to recognize the hotspots for crime data like Shooting, Homicide and Assault by threat.
Evaluation of Transfer Learning with CNN to classify the Jaw Tumors
Authors: Ismael Ahmed Khalid , Khidhir AbdulSattar Mohammed
Published: November 2020

Artificial Intelligence” (AI) This term refers to the idea that the machines can perform human tasks. Recently, researchers, professionals and companies around the world introduce deep learning and image processing systems that can analyze hundreds of X-Ray and Computer Tomography (CT) images rapidly to speed up the diagnosis of medical image and help to contain them. Dental diseases analysis is among the most innovative research fields, offering diagnostic and decision-making facilities for a variety of diseases, such as oral and maxillofacial diseases. Inside this paper, we present a comparison of recent architectures of the Deep Convolutional Neural Network (DCNN) for the automatic classification of two diseases depending on transfer learning with fined tuned using a pre-trained network (VGG16, VGG19). The proposed work was tested using a small scale X-Ray panoramic dataset containing 116 images (58 ameloblastoma and 58 Complex Odontoma). As a result, we can assume that the pre-trained network (VGG19) demonstrates highly satisfactory results with a rate of increase in the accuracy of training and validation. Unlike CNN, pre-trained network (VGG16) demonstrates less performance when a small image dataset is available.
Selecting Best CPU frequency for energy saving in cluster using genetic algorithm
Authors: A. Abdulazeez Zainab , B. M. Fanfakh Ahmed , H. Alwan Esraa
Published: November 2020

Dynamic voltage and frequency scaling (DVFS) is a technique mainly used for reducing the consumed energy of computer’s processor. Its only drawback detracting the performance of parallel application when executing them over parallel platform. However, genetic algorithm is introduced and applied in a heterogeneous cluster architecture for modeling the best trade-off between the energy saving and performance degradation of the parallel application in the same time. The suggested algorithm selects the best frequencies vector to achieve that targets by offering equal trade-off between them. The genetic algorithm simultaneously gives minimum energy consumption and minimal performance degradation via its objective function. All experiment will apply using SimGrid simulator. The experiments show that the algorithm reduces the energy consumption of the message passing application by (24% with 8000 problem size and 22% for 4000 problem size) and in almost cases improve the application performance up to 3%.
A Comparison Study for Intelligent Wheelchair Based on MEMS Sensors
Authors: Satar Al_Dosary Duaa Abdul , M Ali Alheeti Khattab , Sleibi Al-Rawi Salah
Published: November 2020

Today, the security of the system must be improved. A new approach is proposed that achievements the Integrated Circuit Metrics (ICMetric) technology to support identification of devices. ICMetric is a novel technology that generates the identification of devices based on the internal behavior of device features. In this paper, a comparative study is presented between two of our systems. These protection systems are designed to provide protection systems for intelligent wheelchairs. However, we proved that node identification can be provided by utilizing the three types of MEMS sensors. Extracted readings are acquired from the sensors and analyzed mathematically and statistically to provide an ICMetric basis that used to introduce identification of the device. Simulation results show that the proposed system can increase authentication and security.
Fuzzy Association Rule Classification System For Human DNA profile Identification
Authors: H. Faleh Najah , H. Karidi Karim
Published: November 2020

Recently, numerous identification methods are adopted in human identification systems particularly for forensic affairs. These methods are used to reduce the human efforts, related errors, cost, and consumed time in addition to increase the accuracy of human identification systems. The definition of STR DNA profile of a person’s, is associated to the reduction or detection of criminal investigation associated to the identification of the suspect, or to an investigation in counter terrorism as well as in the National Security Service. DNA is used for disaster victims or suspected individuals as a main means of identification. It possesses a high degree of verification in the courts when there is biological evidence, and is also used to identify persons accused and innocent people in criminal investigation with incredible accuracy. This thesis proposes a forensic human identification system based on Deoxyribonucleic Acid (DNA) classification. This system is depend on the concept of fuzzy logic and data mining in identification, that play role important in established a plenty of effective applications in DNA profile analysis and human identification, and reduce error or prevent and so they are chosen for their high accuracy. The main contribution of the presented system is, used association rules for classification the DNA motifs in addition to the fuzzy inference to correct errors, that can offer a high range of useful information. This information is permanently used to solve the crimes and identify damaged bodies. Additionally, this overcomes the lack problem in database information. It is important to note that the proposed system includes DNA based human identifications to cover all investigation cases. The aim of this thesis is to build a forensic human identification system that uses data mining techniques to access dataset by enforce particular algorithms for discovering advisable information or good patterns from present datasets for certain objective. To improve the data mining work, fuzzy logic is used to increase the flexibility for supporting DNA profile forensic human identification in making decisions. It utilizes sixteen Short Tandem Repeats (STRs) DNA loci used in identification methods for Population. It is important to note that the proposed system has been verified In terms of matching of provided samples with the samples stored in the database through conducting several experiments. The results were satisfactory and characterized by large percentage and high accuracy. Finally, performance of this system is evaluated and in turns the proposed system proves its capability in forensic human identification and scalability to handle huge amounts of data.
PROPOSED KNOWLEDGE MANAGEMENT CONCEPTUAL MODEL TO ENHANCE TEACHING ACTIVITIES THROUGHOUT WEB 2.0 TECHNOLOGY FOR ACADEMICIANS
Authors: Al-Sulami Zainab Amin , Hashim Hayder Salah
Published: November 2020

In the era of information technology, knowledge management became urgent need for raising the competitive advantage level. Universities considered as knowledge intensive base organizations. The knowledge are embedded in the academicians minds are sharing within students throughout various teaching activities. A valuable amount of knowledge that accumulated from academicians need to collected for sharing among the academicians community. The successful knowledge management processes of academician experiences lead to enhance the teaching activities. The learning process enhance by assisting academician to learn from each other and product high quality teaching activities. Most of academician’s knowledge is faded due to the absence of standard system that could collect and share the teaching experiences. This research proposed knowledge management conceptual model that can enhance the knowledge processes of acquiring, storing, sharing, and reusing teaching experiences. Data collected by interviewing academicians from Iraq universities and obtaining the required data. The findings deployed to develop knowledge management model that could work efficiently and affectivity to enhance the teaching activities in Iraq University.
Prediction of Link Weight of bitcoin Network by Leveraging the Community Structure
Authors: Rashid Atiya Hiba , Naji Nawaf Dr.Huda
Published: November 2020

In this work, the issue of predicting the edge weight in Bitcoin network has been addressed by leveraging community structure that involves members who trust with each other in their transactions. The proposed model consists of two main stages; the first one is the detection of trusted Bitcoin communities by implementing Newman- Girvan algorithm. In the context, the attributes of node have been modeling in different ways to get different structure of communities each time. Secondly, prediction the missing edge weight based on the neighbors of edge-source in community. In other words, the trust values that pointed to edge-target by neighbors are averaged to represent the prediction of missing edge weight. Practically, the model has been evaluated using two real-world datasets; Bitcoin-OTC and Bitcoin-Alpha datasets. The experimental results explicate the effectiveness of the proposed model comparable with other methods, where the minimization percentage for Bitcoin-OTC dataset is 4% and 18% for all and partial edges respectively. As for Bitcoin-Alpha dataset are 0% and 30% for all and partial edges respectively.
3D scenes semantic segmentation using deep learning based Survey
Authors: Y.Noori Amani , H. Shaker Shaimaa , abdulaali azeez Raghad
Published: November 2020

Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the power and popular tool for data and image processing in computer vision, used for many applications like “image recognition”, “object detection”, “semantic segmentation”, In this research paper, provide survey a background for many techniques designed to 3 Dimensions point cloud semantic segmentation in different domains on many several available free datasets and also making a comparison between these methods.
Using Digital Image as a Cover of Information Hiding: Review Paper
Authors: saadi Abdullah Ahmed , Mohammed Hussein Qasim
Published: November 2020

One of the most important topics that concern many researchers is how to make information that has a high degree of confidentiality hidden and not accessible except by the people who have the authority to view it, images are one of the most important multimedia that can be used as a cover to include the data, because the image contains how much A huge amount of data that can include confidential information in it, as well as the image is one of the most multimedia that is sent and received thus that the process of messaging with pictures does not arouse suspicion, this research provided a detailed study of a group of articles that used the image as a cover in the inclusion of information Confidentiality, and focus was on the most important domains of strength in each article. Articles were studied from 2013 to 2020 that were published at the containers of Scopes, and Google Scholar.
Computing the Filled Julia Set
Authors: Hammood Altameemi Ali yasir
Published: November 2020

The Julia set is one of the most remarkable and exciting fields of dynamic mathematics Because it contains very beautiful graphics.

Through this study we will highlight how the filled Julia set is computed. It is worth noting that there is an integrative work between computers and mathematical calculations to get great results to complete Julia Set filling process
Hausdorff Topological of Path in Graph
Authors: Jabor Ali A. , Omran Ahmed A.
Published: November 2020

Previous research has dealt with the topic of topological graph in several aspects and has studied the set of vertices or set of edges as a sub basis for topological space. The topic of domination has received the attention of many researchers in the recent period, because has many applications in the field of computers and networks. The main purpose of this paper is join between topological space and minimal dominating sets of a graph G, By using all minimal dominating sets (MDS) as open set of topology on V (G) . this paper discussed T
2 – MDS (hausdorff property) of path Pn
with n ≥ 4.
Some Approximation Properties of New Family of Baskakov-Type Operators
Authors: Sadiq Haneen J.
Published: November 2020

The aim of this paper is to establish a new family of Baskakov – type operators represented by summation type generalized Baskakov operators. Primarily, we discuss the properties of this sequences of L. P. O.. Further we view some approximation properties which lead us to establish a Voronovskaja theorem on this operators. Also, we study the rate of convergence when we show this new family preserve properties of modulus of continuity on a continuous function.
A Look at Sobolev Spaces on Graphs
Authors: Flaih Zainab , Samir Bhaya Eman
Published: November 2020

We study the Sobolev inqualities of real functions defined on graphs, when 0 < p < 1. These spaces are important than that the space of continuous functions.

In this paper we define the canonical embedding and study the existence of Sp
(G) which far from

l

p
m

when 0 < p < 1. Also we estimate the subspace of S

p
(G) isomorphic to

l

p
d

by Banach-Mazur distance of the corresponding dimension for 0 < p < 1.
Local Search Methods to Find Approximate Solution for The Sum of Two Criteria with Unequal Ready Times in Machine Scheduling Problem
Authors: Jabbar Khraibet Tahani , Khalid Jaber Waleed , Abed Dawood Lafta
Published: November 2020

This research concentrates on the study of SA / TS which is regarded as two of the approaches of modern artificial intelligence, to solve the problems of scheduling by single machine to minimize bi _ criteria

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