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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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ARO. The Scientific Journal of Koya University
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2410-9355 - ISSN (Online) 2307-549X
Published by Koya University Homepage  [1 journal]
  • A Computational Model for Temperature Monitoring During Human Liver
           Treatment by Nd:YaG Laser Interstitial Thermal Therapy (LITT)

    • Authors: Bazhdar N. Mohammed; Dilshad S. Ismael
      Abstract: Describing heat transfer in biological organs is absolutely challenging because it is involved with many complex phenomena. Therefore, understanding the optical and thermal properties of living system during external irradiation sources such as laser interstitial thermal therapy (LITT) are too important for therapeutic purposes, especially for hyperthermia treatments. The purpose of this study was to determine a proper laser power and irradiation time for LITT applicator to irradiate liver tissue during hyperthermia treatment. For this aim, bioheat equation in one-dimensional spherical coordinate is solved by Green function method to simulate temperature distribution and rate of damage around irradiated target and how thermal and optical properties such as laser power, laser exposure time, and blood perfusion rate affect the rate of temperature distribution. Guiding equations according to the suggested boundary conditions are written and solved by MATLAB software. The outcomes show that increasing laser exposure time and power increase the temperature, especially at the nearest distance from the center of diffusion. Accordingly, a decrease in blood perfusion rate leads to decrease temperature distribution. The findings show that the model is useful to help the physicians to monitor the amount of heat diffusion by laser power during the treatment to protect healthy cells.
      PubDate: Mon, 26 Sep 2022 00:00:00 +000
       
  • An Investigation on Disparity Responds of Machine Learning Algorithms to
           Data Normalization Method

    • Authors: Haval A. Ahmed; Peshawa J. Muhammad Ali, Abdulbasit K. Faeq, Saman M. Abdullah
      Abstract: Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset.
      PubDate: Mon, 19 Sep 2022 11:57:40 +000
       
  • Train Support Vector Machine Using Fuzzy C-means Without a Prior Knowledge
           for Hyperspectral Image Content Classification

    • Authors: Akar H. Taher
      Abstract: In this paper, a new cooperative classification method called auto-train support vector machine (SVM) is proposed. This new method converts indirectly SVM to an unsupervised classification method. The main disadvantage of conventional SVM is that it needs a priori knowledge about the data to train it. To avoid using this knowledge that is strictly required to train SVM, in this cooperative method, the data, that is, hyperspectral images (HSIs), are first clustered using Fuzzy C-means (FCM); then, the created labels are used to train SVM. At this stage, the image content is classified using the auto-trained SVM. Using FCM, clustering reveals how strongly a pixel is assigned to a class thanks to the fuzzification process. This information leads to gaining two advantages, the first one is that no prior knowledge about the data (known labels) is needed and the second one is that the training data selection is not done randomly (the training data are selected according to their degree of membership to a class). The proposed method gives very promising results. The method is tested on two HSIs, which are Indian Pines and Pavia University. The results obtained have a very high accuracy of the classification and exceed the existing manually trained methods in the literature.
      PubDate: Sat, 10 Sep 2022 07:56:23 +000
       
  • Machine Learning Algorithms for Detecting and Analyzing Social Bots Using
           a Novel Dataset

    • Authors: Niyaz Jalal; Kayhan Z. Ghafoor
      Abstract: Social media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling.
      PubDate: Sat, 10 Sep 2022 07:55:28 +000
       
  • A New Design Approach for a Compact Microstrip Diplexer with Good Passband
           Characteristics

    • Authors: Abbas Rezaei; Salah I. Yahya
      Abstract: This paper presents an efficient theoretical design approach of a very compact microstrip diplexer for modern wireless communication system applications. The proposed basic resonator is made of coupled lines, simple transmission line and a shunt stub. The coupled lines and transmission line make a U-shape resonator while the shunt stub is loaded inside the U-shape cell to save the size significantly, where the overall size of the presented diplexer is only 0.008 λg2 . The configuration of this resonator is analyzed to increase intuitive understanding of the structure and easier optimization. The first and second resonance frequencies are f o1 = 895 MHz and f o2 = 2.2 GHz. Both channels have good properties so that the best simulated insertion loss at the first channel (0.075 dB) and the best simulated common port return losses at both channels (40.3 dB and 31.77 dB) are achieved. The presented diplexer can suppress the harmonics acceptably up to 3 GHz (3.3 fo1 ). Another feature is having 31% fractional bandwidth at the first channel.
      PubDate: Thu, 25 Aug 2022 00:00:00 +000
       
  • In Silico Domain Structural Model Analysis of Coronavirus ORF1ab
           Polyprotein

    • Authors: Mohammed I. Jameel; Rabar J. Noori, Soma F. Rasul
      Abstract: The world today is battling with a coronavirus infection that is considered a global pandemic. Coronavirus infection is mainly attribute to the varying technique of the replication and release of different genomic components of the virus. The present study aims to establish the physical and chemical features, as well as the basic structural and functional properties of Coronavirus ORF1ab domain. A molecular approach was adopt in this study using the Swiss Model and Phyre2 server whereas the prediction of the active ligand binding sites was done using Phyre2. The analysis of the structure of the protein showed that it has good structural and heat stability, as well as better hydrophilic features and acidic in nature. Based on the Homology modeling, only two binding active sites were noted with catalytic function being mediated by Zn2+ as the metallic heterogeneous ligand for binding sites prediction. The proteins mostly exhibited helical secondary configurations. This study can help in predicting and understanding the role of this domain protein in active coronavirus infection.
      PubDate: Thu, 25 Aug 2022 00:00:00 +000
       
 
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