bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

AbstractThis 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractThis 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.