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Concepts of statistical learning and classification in machine learning: An overview
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Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in categorical outcomes, with the overarching goal of supervised learning being to enhance models capable of predicting class labels based on input features. This review endeavors to furnish a concise, yet insightful reference manual on machine learning, intertwined with the tapestry of statistical learning theory (SLT), elucidating their symbiotic relationship. It demystifies the foundational concepts of classification, shedding light on the overarching principles that govern it. This panoramic view aims to offer a holistic perspective on classification, serving as a valuable resource for researchers, practitioners, and enthusiasts entering the domains of machine learning, artificial intelligence and statistics, by introducing concepts, methods and differences that lead to enhancing their understanding of classification methods.

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Publication Date
Sun Dec 31 2023
Journal Name
Iraqi Journal Of Information And Communication Technology
EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier
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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

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Publication Date
Tue Oct 25 2022
Journal Name
Minar Congress 6
HANDWRITTEN DIGITS CLASSIFICATION BASED ON DISCRETE WAVELET TRANSFORM AND SPIKE NEURAL NETWORK
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In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

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Publication Date
Tue Dec 03 2013
Journal Name
Baghdad Science Journal
Satellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
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Publication Date
Tue Aug 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
Use of Remote Sensing in the assessment and classification of land degradation in the district of Mahmudiya for the period 1990-2007
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The study consisted in the development and use of a practical method to detect and
monitor, analyze and produce maps of changes in land use and land cover in the district of
Mahmudiya in Baghdad during the period 1990-2007 using the applications of remote sensing
techniques and with the assisstant of geographic information systems (GIS),as a valuable
contribution to land degradation studies.
This study is based maiuly on the processing on two subsets of landsat5 TM images picked up
in August 1990 and 2007 respectively in order to facilitate comparision and were thengeometrically and radiometrcally calibrated ,to used for digital classification purposes using
maximum liklihoods classification or six spectral bands of

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Publication Date
Sun Jul 03 2016
Journal Name
Journal Of Educational And Psychological Researches
Effect of Exemplar of Wettli On Acquiring Grammatical Concepts Among Basic 7th Class Female Students in Kurdish Language Grammar
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The current paper aims at knowing the effect of  Exemplar of Wettli On Acquiring Grammatical Concepts Among Basic 7th Class Female Students in Kurdish Language Grammar.

The current paper is confined to a sample of  Basic 7th Class Female Students-  Khaniqin –belonged Morning schools for the academic year 2014-2015 . The sample amounted 50 female students  distributed on two sections ( A,B) . Section A represented  experimental group ( 24) female  students  studied according to the exemplar of ( Wettli) and (B) represented control group (26) female students who have studied with traditional method. The researcher has qualified between these two groups  b

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Publication Date
Mon Feb 25 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Using Classification of Brown risks in Evaluation of the internal control system: Application Research in Karbala University
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Internal control system is a safety valve that preserves economic units assets and ensure the accuracy of financial data, as well as to obligation in the laws, regulations, administrative policies ,and improve the efficiency, effectiveness and economic of operation, so it has become imperative for these units attention to internal and developed control system The research problem in exposure the economic units when the exercise of their business to many of the risks to growth or hinder the achievement of its objectives and the risks (financial, operational, strategy, risk) and not it rely on risk Assessment according to modern scientific methods, as in Brown's risk Classification, Which led to the weakness of the internal control identif

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data
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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Assessment of surface water quality using statistical analysis methods: Orontes River (Case study)
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The study investigates the water quality of the Orontes River, which is considered one of the important water recourses in Syria, as it is used for drinking, irrigation, swimming and industrial needs. A database of 660 measurements for 13 parameters concentrations used, were taken from 11 monitoring points distributed along the Orontes River for a period of five years from 2015-2019, and to study the correlation between parameters and their impact on water quality, statistical analysis was applied using (SPSS) program. Cluster analysis was applied in order to classify the pollution areas along the river, and two groups were given: (low pollution - high pollution), where the areas were classified according to the sources of pollution to w

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Publication Date
Fri Jun 01 2012
Journal Name
Journal Of Engineering
Statistical Quality Control of Industrial Products at the General Company for Vegetable Oils
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This research includes the using of statistical to improve the quality of can plastics which is produced at the state company for Vegetable oils (Almaamon factory ) by using the percentage defective control chart ( p-chart ) of a fixed sample. A sample of size (450) cans daily for (30) days was selected to determine the rejected product . Operations research with a (win QSB ) package for ( p-chart ) was used to determine test quality level required for product specification to justify that the process that is statistically controlled.
The results show high degree of accuracy by using the program and the mathematical operations (primary and secondary ) which used to draw the control limits charts and to reject the statistically uncontr

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