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Articles 151 - 180 of 235
Full-Text Articles in Computer Engineering
Retracted: Data Envelopment Analysis Using Stochastic Frontier Analysis And Bootstrap Confidence Intervals, Fahad F. Alqahtani
Retracted: Data Envelopment Analysis Using Stochastic Frontier Analysis And Bootstrap Confidence Intervals, Fahad F. Alqahtani
Iraqi Journal for Computer Science and Mathematics
Evaluating company growth potential has moved away from traditional financial focused ratios and ratios analysis that has origins in the early twentieth-century economics. However, these conventional methods might not be accurate in measuring such efficient factors as this combined proposed framework of Data Envelopment Analysis (DEA) and improved mathematical models do. The present research focuses on the prospect of growth of companies through evaluating the performance of 40 DMUs in terms of efficiency DEA and MMTs. DEA is used to determine the efficient DMUs while SFA underline the factors such as investment on research and development, effective marketing strategies and …
A Hybrid Technique For Approximating The Solution Of Fractional Order Partial Integro-Differential Equations, Ahmed K. Mohsin, Fajir A. Abdulkhaleq, Osama H. Mohammed
A Hybrid Technique For Approximating The Solution Of Fractional Order Partial Integro-Differential Equations, Ahmed K. Mohsin, Fajir A. Abdulkhaleq, Osama H. Mohammed
Iraqi Journal for Computer Science and Mathematics
In this paper, we discuss the numerical solution of fractional order partial integro-differential equations. The type of fractional derivative used is a Caputo derivative. The method proposed in this paper known as transform optimal perturbation iteration method. This method combines the optimal perturbation iteration method and the Laplace transform in order to converge to the exact solution. The proposed method is highly efficient and provides the means of controlling the approximate solutions convergence. Illustrative examples prove that the suggested approach is very accurate when compared with the exact solution and the results of the existing methods.
Quantum Machine And Deep Learning For Medical Image Classification: A Systematic Review Of Trends, Methodologies, And Future Directions, Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed
Quantum Machine And Deep Learning For Medical Image Classification: A Systematic Review Of Trends, Methodologies, And Future Directions, Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed
Iraqi Journal for Computer Science and Mathematics
Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This systematic review provides a comprehensive critical assessment of the current status of QL techniques developed for medical image classification, with a specific focus on trends, methodologies, and future directions in this rapidly evolving field. A thorough literature search was conducted across five major databases, resulting in a total of 28 relevant studies published between 2018 and 2024. The studies were analyzed and classified based on the type of quantum algorithm, …
Different Methods To Estimate Stress-Strength Reliability Function For Modified Exponentiated Lomax Distribution, Raya Salim Al-Rassam, Khalida Ahmed Mohammed, Safwan Nathem Rashed
Different Methods To Estimate Stress-Strength Reliability Function For Modified Exponentiated Lomax Distribution, Raya Salim Al-Rassam, Khalida Ahmed Mohammed, Safwan Nathem Rashed
Iraqi Journal for Computer Science and Mathematics
When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than …
A New Ridge-Type Estimator In The Zero-Inflated Bell Regression Model, Nawal Mahmood Hammood, Nadwa Khazaal Rashad, Zakariya Yahya Algamal
A New Ridge-Type Estimator In The Zero-Inflated Bell Regression Model, Nawal Mahmood Hammood, Nadwa Khazaal Rashad, Zakariya Yahya Algamal
Iraqi Journal for Computer Science and Mathematics
Count data modeling requires usage of the Poisson regression model as a primary analytic method. Excess dispersion in variables makes the model unfit to use when the Poisson distribution mean value differs from its variance value. Data fits well with the results obtained by using the Bell regression model. Excess zeros occur frequently in the observed count data records. The Zero-Inflated Bell regression model is a substitute for the Bell regression model in this situation. The approach of maximum likelihood is mostly used to estimate the Zero-Inflated Bell regression model's parameters. When modeling the link between the response variable and …
An Improvement Finger Vein Authentication System Based On Pcanet Deep Learning And Hyper Parameter Machine Learning, Raniah Ali Mustafa, Tarek Abbes
An Improvement Finger Vein Authentication System Based On Pcanet Deep Learning And Hyper Parameter Machine Learning, Raniah Ali Mustafa, Tarek Abbes
Iraqi Journal for Computer Science and Mathematics
Finger vein authentication is a biometric technique that uses unique vein patterns within the finger to verify identity, ensuring security by scanning veins with near-infrared light to prevent counterfeiting. The reliability of this technology and its resistance to skin disorders make it highly appreciated. The motivation for using PCANet deep learning in finger vein authentication arises from the need for a more precise, secure, and effective biometric system. Conventional methods have trouble with image quality, noise, and illumination, whereas PCANet improves classification accuracy by employing principal component analysis (PCA) to extract deep features. Its lightweight structure guarantees computational efficiency while …
Liberated Arabic Handwritten Text Recognition Using Convolutional Recurrent Neural Networks, Ahmad Abdulqadir Alrababah, Mohammed Khalid Aljahdali, Abdulrahim Abdulhamid Al Jahdali, Mohammed Saleh Alghanmi, Israa Ibraheem Al_Barazanchi
Liberated Arabic Handwritten Text Recognition Using Convolutional Recurrent Neural Networks, Ahmad Abdulqadir Alrababah, Mohammed Khalid Aljahdali, Abdulrahim Abdulhamid Al Jahdali, Mohammed Saleh Alghanmi, Israa Ibraheem Al_Barazanchi
Iraqi Journal for Computer Science and Mathematics
Arabic script is exhibited in a cursive style, which is a departure from the norm in many common languages, and the shapes of letters are contingent on their positions within words. The form of the first letter is influenced by the subsequent letter, middle letters are shaped by both preceding and succeeding letters, and the shape of the final letter is determined by the preceding letter. Additionally, certain letters are found to have strikingly similar shapes, making Arabic text recognition a formidable challenge in computer vision. The challenge of detecting and recognizing Arabic handwritten text is addressed in this paper …
Robust Inner Knuckle Print Recognition System Using Densenet201 And Inceptionv3 Models, Haitham Salman Chyad, Tarek Abbes
Robust Inner Knuckle Print Recognition System Using Densenet201 And Inceptionv3 Models, Haitham Salman Chyad, Tarek Abbes
Iraqi Journal for Computer Science and Mathematics
Texture features and stability have generated significant interest in biometric recognition. The inner knuckle print is distinctive and difficult to fake, making it extensively used in individual identification, criminal investigation, and various other domains. In recent years, the rapid progress of deep learning technology has created new prospects for internal knuckle recognition. This paper proposes a robust inner-knuckle-print recognition system (RIKP-RS) depending on two deep learning (DL) models. This paper focuses on the key components of the inner surface of the hand namely the little finger, ring finger, middle finger, index finger, and thumb finger that are used for human …
Improving Heart Attack Prediction Accuracy Performance Using Machine Learning And Deep Learning Algorithms, Mosleh Hmoud Al-Adhaileh, Mohammed Ibrahim Ahmed Al-Mashhadani, Eidah M Alzahrani, Theyazn H.H. Aldhyani
Improving Heart Attack Prediction Accuracy Performance Using Machine Learning And Deep Learning Algorithms, Mosleh Hmoud Al-Adhaileh, Mohammed Ibrahim Ahmed Al-Mashhadani, Eidah M Alzahrani, Theyazn H.H. Aldhyani
Iraqi Journal for Computer Science and Mathematics
Accurate classification of cardiovascular diseases (CVDs) is of utmost importance for cardiologists to provide appropriate treatments. Diagnosing and predicting cardiovascular conditions are crucial medical responsibilities in this context. The healthcare sector is increasingly utilizing deep learning (DL) and machine learning (ML) algorithms due to their ability to identify patterns in data. Diagnosticians may reduce the number of misdiagnoses by using DL and ML techniques for the categorization of cardiovascular disease incidence. To reduce the mortality linked to CVDs, this research offers a unique model that properly predicts and classifies these problems. This research presents approaches such as deep learning, random …
Solving Multidimensional Fractional Telegraph Equation By Using Yang Hussein Jassim Method, Naser Rhaif Swain, Hassan Kamil Jassim
Solving Multidimensional Fractional Telegraph Equation By Using Yang Hussein Jassim Method, Naser Rhaif Swain, Hassan Kamil Jassim
Iraqi Journal for Computer Science and Mathematics
This study employs the Young Hussein Jassim (YHJ)technique to examine the exact solutions of the space-time telegraphequation analytically (ST-TE). The YHJ approach is an innovative andappealing hybrid transformation integration method, effectivelycombining the HJ and Young methods. Through a simplified iterativeprocess with minimal computational requirements, this approach quicklyprovides convergent, sequential solutions. The reliability of the methodis demonstrated by applying it to two case studies of the ST-TE withinthe framework of the Tania derivative, which includes the definition ofnon-singular kernel functions. The study also includes extensivecomparisons between approximate, exact, and relevant literature-basedsolutions to assess the technique's accuracy andeffectiveness. Graphical representations illustrate the …
A Lightweight U-Net Model For Accurate Skin Lesion Segmentation, Fallah H. Najjar, Karrar A. Kadhim, Farhan Mohamed, Mohd Shafry Mohd Rahim, Asniyani Nur Haidar Abdullah
A Lightweight U-Net Model For Accurate Skin Lesion Segmentation, Fallah H. Najjar, Karrar A. Kadhim, Farhan Mohamed, Mohd Shafry Mohd Rahim, Asniyani Nur Haidar Abdullah
Iraqi Journal for Computer Science and Mathematics
In this paper, a new lightweight U-Net deep learning-based neural network designed for the segmentation of skin lesions is proposed. Segmentation of skin lesions is the most critical step in computer-aided dermatology diagnosis for the early detection of melanoma and other diseases. However, we address the difficulty related to the precise definition of the lesion margins with an eye on the computation cost. We have demonstrated the state-of-the-art performance of DeepSkinSeg in most metrics on dermoscopic images using the PH2 and Human Against Machine (HAM10000) datasets. The metrics of the DeepSkinSeg model were robustness measured as the Intersection over Union …
Retracted: Image Denoising: Smooth Total Variation Minimization For 5g Enhanced Mobile Broadband Transmission System, Shehab Ahmed Ibrahem, Walled Khalid Khalid Abdulwahab, Moceheb Lazam Lazam Shuwandy
Retracted: Image Denoising: Smooth Total Variation Minimization For 5g Enhanced Mobile Broadband Transmission System, Shehab Ahmed Ibrahem, Walled Khalid Khalid Abdulwahab, Moceheb Lazam Lazam Shuwandy
Iraqi Journal for Computer Science and Mathematics
Image denoising is an important area of computer vision. Rudin-Osher-Fatemi model based on a gradient is one of the simplest models used in image denoising to solve the problem of restoring the clear image. The challenge in solving this model is the non-differentiability of Total Variation function (TV-function) minimization. Image transmission is widespread over wireless systems, including the fifth generation (5G) cellular network. Transmission impairment can affect transmitted images, including noise, attenuation, and distortion. This study proposed a new smoothing technique to make the TV-function differentiable and smooth. The new smoothed function was used for de-noising images with the help …
War Strategy Algorithm- Based Hybrid Optimization For Accurate And Rapid Speech Recognition, Shahad Thamear Abd Al-Latief, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, Ahmed Alkhayyat
War Strategy Algorithm- Based Hybrid Optimization For Accurate And Rapid Speech Recognition, Shahad Thamear Abd Al-Latief, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, Ahmed Alkhayyat
Iraqi Journal for Computer Science and Mathematics
Speech recognition-based applications increased and developed as a result of artificial intelligence's rapid growth, particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related to human-computer interaction, and natural language processing. The complexity and diversity of speech signals provides challenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuning is a crucial step in machine learning that has a significant role in optimizing the performance and generalization by determining the optimal values for the model's hyperparameters. This paper employed the recently developed WAR Strategy optimization algorithm for …
Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi
Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi
Iraqi Journal for Computer Science and Mathematics
The face morphing process blends two or more facial images to produce a singular morphed facial image that shows the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a significant risk to national security. This research presents a hybrid approach for feature extraction from facial images. The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, …
Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Mariem Hassan Lafta, Zahir Abdul Haddi Hassan
Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Mariem Hassan Lafta, Zahir Abdul Haddi Hassan
Iraqi Journal for Computer Science and Mathematics
Most real-world technological systems are highly complex, making it challenging to examine their reliability. Many systems can be represented as Complex Parallel-Series Networks (CPSN). The large number of components and subnetworks, along with their intricate connection, complicates the identification, evaluation, and potential failure of the CPSN. A minimal path set is a minimal set of components whose proper functioning (success) guarantees the success (operability) of the system. The set is minimal in the sense that removing any component from it means it no longer guarantees system success. The primary research problem is to identify these minimal path sets, both for …
Double Dual Convolutional Neural Network (D2cnn): A Deep Learning Model Based On Feature Extraction For Skin Cancer Classification, Raya Sattar Shahadh, Belal Al-Khateeb
Double Dual Convolutional Neural Network (D2cnn): A Deep Learning Model Based On Feature Extraction For Skin Cancer Classification, Raya Sattar Shahadh, Belal Al-Khateeb
Iraqi Journal for Computer Science and Mathematics
Artificial intelligence, especially in the field of ``deep learning'', is still promising when it comes to skin cancer detection and diagnosis. Among deep learning algorithms, convolutional neural networks (CNNs) give a high level of accuracy in identifying and classifying different types of skin cancer. CNNs have a strong coordination due to understanding the important features from medical images that are extracted from convolutional layers. However, there is still a problem which is the high imbalance in the dataset with high noise in the images. This paper presents a new solution that combines different architectural structures of convolutional neural networks (CNNs) …
Federated Learning-Driven Iot And Edge Cloud Networks For Smart Wheelchair Systems In Assistive Robotics, Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Abdullah Lakhan, Bourair Al-Attar, Waleed Khaled
Federated Learning-Driven Iot And Edge Cloud Networks For Smart Wheelchair Systems In Assistive Robotics, Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Abdullah Lakhan, Bourair Al-Attar, Waleed Khaled
Iraqi Journal for Computer Science and Mathematics
These days, assistive robotics and their applications for people with disabilities have become a revolutionary field in medical care. It combines edge-cutting technologies such as the Internet of Things (IoT), edge computing networks, and federated learning, offering the best services to disabled people for mobility and navigation in the environment. However, in the state of the art, many conceptual models are presented, and less effort is put into the practical implementation of assistive robots for disabled people in the environment. With this motivation, we propose an intelligent assistive robotics wheelchair system that enhances disabled care in federated learning-enabled IoT and …
Facial Swap Detection Based On Deep Learning: Comprehensive Analysis And Evaluation, Israa Mishkhal, Nibras Abdullah, Hassan H. Saleh, Nur Intan Raihana Ruhaiyem, Fadratul Hafinaz Hassan
Facial Swap Detection Based On Deep Learning: Comprehensive Analysis And Evaluation, Israa Mishkhal, Nibras Abdullah, Hassan H. Saleh, Nur Intan Raihana Ruhaiyem, Fadratul Hafinaz Hassan
Iraqi Journal for Computer Science and Mathematics
In recent years, Advancements in Artificial Intelligence (AI), particularly deep learning (DL), have made great strides in the creation of highly realistic deepfakes, which manipulate facial forensics to generate convincing fake faces or expressions. These manipulations pose significant threats to individual privacy and the integrity of legal, political, and social institutions. In fact, several existing studies have recently pursued the development of machine learning techniques for detecting deepfake content, with the overarching aim of protecting the victim's privacy or curbing the rise of picture fabrication. Despite extensive research on DL-based deepfake detection systems, challenges such as detecting facial swaps under …
Retracted: Deep Learning-Based Beamforming Optimization For Reconfigurable Intelligent Surface-Assisted Wireless Communication Systems, Mohammed Firas Jassim, Alhamzah Taher Mohammed, Osamah Abdullah
Retracted: Deep Learning-Based Beamforming Optimization For Reconfigurable Intelligent Surface-Assisted Wireless Communication Systems, Mohammed Firas Jassim, Alhamzah Taher Mohammed, Osamah Abdullah
Iraqi Journal for Computer Science and Mathematics
This research investigates how deep learning might be used to optimize beamforming in wireless communication systems that are helped by Reconfigurable Intelligent Surfaces (RIS). Our goal is to increase the possible data rates by dynamically forecasting the best phase shifts for RIS elements by utilizing Convolutional Neural Networks (CNN) and hybrid CNN-Long Short-Term Memory (CNN-LSTM) models. We assess the performance of these deep learning models against conventional genie-aided techniques by simulating real-world wireless settings using the DeepMIMO dataset. The findings demonstrate that beamforming based on deep learning can reach near-optimal performance, greatly lowering the overhead associated with channel estimation while …
The Permutation Annihilator Ideals In Commutative Permutation Bck–Algebras With Their Applications, Shuker Khalil, Ali Abbas Asmae
The Permutation Annihilator Ideals In Commutative Permutation Bck–Algebras With Their Applications, Shuker Khalil, Ali Abbas Asmae
Iraqi Journal for Computer Science and Mathematics
This paper introduces new concepts such as permutation BCK--algebra, permutation involutory ideal, commutative permutation BCK--algebra, and prime permutation ideal. Additionally, their attributes are examined. This paper elucidates a method for determining a relationship between the chemical structure of atoms for the chemical element Cadmium, and some of our suggestions are given here. In this work, the structure of the sets 𝒜 and λnβ∗𝒜 are defined. Next, we show that if 𝒜 is a permutation ideal, then λnβ∗𝒜 is a permutation ideal that contains 𝒜. Also, in any commutative permutation BCK--algebra the …
Integrating Fuzzy Set Theory With Association Rule Mining For Advanced E-Commerce Recommendations, Hind Raad Ibraheem, Murtadha Mohammed Hamad
Integrating Fuzzy Set Theory With Association Rule Mining For Advanced E-Commerce Recommendations, Hind Raad Ibraheem, Murtadha Mohammed Hamad
Iraqi Journal for Computer Science and Mathematics
The dynamic nature of e-commerce necessitates the adoption of cutting-edge technologies to improve the online shopping experience. Our research introduces a groundbreaking methodology called Fuzzy Association Rule Mining (FARM), combining fuzzy set theory with traditional Association Rule Mining (ARM). Unlike conventional ARM, which focuses solely on the frequency of jointly purchased items, FARM also considers the sold quantities, leveraging the Apriori algorithm to discern customer preferences from historical sales data across the UCI Online Retail II, Market Basket, and Movielens datasets. This hybrid of fuzzy set theory with ARM enables a better understanding of complicated consumer behaviors and associations between …
Liu-Type Estimator In Inverse Gaussian Regression Model Based On (R-(K-D)) Class Estimator, Zeina Ameer Hadied, Oday Esam Al-Saqal, Zakariya Yahya Algamal
Liu-Type Estimator In Inverse Gaussian Regression Model Based On (R-(K-D)) Class Estimator, Zeina Ameer Hadied, Oday Esam Al-Saqal, Zakariya Yahya Algamal
Iraqi Journal for Computer Science and Mathematics
When multicollinearity arises in the inverse Gaussian regression (IGR), there is a substantially unstable variance in the maximum likelihood estimator. Based on the (r-(k-d)) class estimation method, we present a novel Liu-type estimator in the IGR model in this study. The study examines the e ectiveness of the suggested estimator and draws comparisons with alternative estimators. Based on simulation and real data results, the suggested estimate performs better than the other estimators in terms of mean squared error.
The Efficacy Of Utilizing Artificial Intelligence Techniques In Developing Critical Thinking In Mathematics Among Secondary School Students And Their Attitudes Toward It, Mohammad A. Tashtoush, Aida B. Qasimi, Nawal H. Shirawia, Lubna A. Hussein
The Efficacy Of Utilizing Artificial Intelligence Techniques In Developing Critical Thinking In Mathematics Among Secondary School Students And Their Attitudes Toward It, Mohammad A. Tashtoush, Aida B. Qasimi, Nawal H. Shirawia, Lubna A. Hussein
Iraqi Journal for Computer Science and Mathematics
The aim of this study is to investigate the efficacy of Artificial Intelligence (AI) techniques and programs in developing Critical Thinking Skills (CTSs) in mathematics among secondary school students, as well as their attitudes towards it. This study employed an experimental methodology, which was applied to a sample of 91 students. A critical thinking test and a scale to measure students' Attitudes Towards Mathematics (ATM) were also utilized. This study revealed significant improvements in the mean scores of critical thinking skills among secondary students who were exposed to Artificial Intelligence Techniques (AITs), particularly in deduction, interpretation, inference, and evaluation. Additionally, …
Email Spam Classification Based On Deep Learning Methods: A Review, Ekramul Haque Tusher, Mohd Arfian Ismail, Anis Farihan Mat Raffei
Email Spam Classification Based On Deep Learning Methods: A Review, Ekramul Haque Tusher, Mohd Arfian Ismail, Anis Farihan Mat Raffei
Iraqi Journal for Computer Science and Mathematics
Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes …
Retracted: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Israa Faisal Jassam, Abdulrahman Abbas Mukhlif, Ahmed Adil Nafea, Mustafa Adnan Tharthar, Ahmed Isam Khudhair
Retracted: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Israa Faisal Jassam, Abdulrahman Abbas Mukhlif, Ahmed Adil Nafea, Mustafa Adnan Tharthar, Ahmed Isam Khudhair
Iraqi Journal for Computer Science and Mathematics
This paper comprehensively reviews the classification of breast cancer histological images. The paper discusses the research objectives, methodologies used, and conclusions drawn, as well as suggestions for the future. The study is based on the ICIAR 2018 database, which is considered one of the largest databases available to support this research. The paper also addresses major challenges such as lack of data, variation in tissue preparation, class imbalance, and computational requirements. Advanced techniques such as deep learning (DL), transfer learning and data augmentation are explored, along with innovative models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). …
Exploration Of Energy Efficient Location Based Routing Protocols For Wireless Sensor Networks, Huthiafa Q. Qadori, Idris Abubakar Umar, Mohammed Khalaf
Exploration Of Energy Efficient Location Based Routing Protocols For Wireless Sensor Networks, Huthiafa Q. Qadori, Idris Abubakar Umar, Mohammed Khalaf
Iraqi Journal for Computer Science and Mathematics
Few routing protocols designed for wireless sensor networks (WSN) have been adopted for commercial use in today's technology. This is because when designing the protocols, there is a need to trade-off some features to improve others, but for some designs, these compromises are deemed adamant especially when resources are constrained. An Ideal sensor node is expected to have a small code size capable of coordinating communication activities with the least energy possible. This survey studies some energy-efficient location-based routing protocols that were proposed over the years, with a key interest in factors influencing energy utilization, as it is the most …
Enhancing Smartphone Authentication By Integrating Decision-Making Model With Touch Pressure, Finger Location Data, And Advanced Cybersecurity Techniques, Maytham M. Hamood, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida
Enhancing Smartphone Authentication By Integrating Decision-Making Model With Touch Pressure, Finger Location Data, And Advanced Cybersecurity Techniques, Maytham M. Hamood, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida
Iraqi Journal for Computer Science and Mathematics
Smartphone authentication methods face significant challenges in achieving high accuracy, robustness, and usability within cybersecurity applications. Traditional methods, such as passwords and biometric recognition, often lack adaptability and are prone to high false-positive rates, impacting security and user acceptance. This study presents a novel hybrid approach incorporating machine learning (ML) and the Analytic Hierarchy Process (AHP) in a framework to facilitate decision-making abilities and improve smartphone authentication. A novel dataset was constructed based on 3D touch sensor data (pressure levels and spatial dynamics) collected from 20 participants performing tasks per task over sessions, where AHP was used to rank/choose relevant …
Enhancing Accuracy In Predicting Continuous Values Through Regression, Ahmed Aljuboori, M. M. A. Abdulrazzq
Enhancing Accuracy In Predicting Continuous Values Through Regression, Ahmed Aljuboori, M. M. A. Abdulrazzq
Iraqi Journal for Computer Science and Mathematics
Enhancing the accuracy in predicting continuous values remains a significant challenge, especially when dealing with imbalanced data and choosing appropriate models. Regression techniques are widely used in data mining, and machine learning fields for this purpose. However, the traditional algorithms struggle to achieve high accuracy because of the limitations in dealing with complex data and imbalanced distribution. This study addresses these gaps by proposing a new framework that evaluates multiple regression models using the Boston House Pricing Dataset (BHD). The examined models involve simple linear, multiple linear, Polynomial, Lasso, Ridge, Random Forest, Keras and Gradient Boosting regression. The models are …
Enhancing Multi-Robot Slam: Centralized Lidar-Based Loop Closure Detection Approach, Basma Ahmed Jalil, Ibraheem Kasim Ibraheem
Enhancing Multi-Robot Slam: Centralized Lidar-Based Loop Closure Detection Approach, Basma Ahmed Jalil, Ibraheem Kasim Ibraheem
Iraqi Journal for Computer Science and Mathematics
The loop closure detection is crucial for global mapping and route correction in multi-robot simultaneous localization and mapping (SLAM). However, including loop closure detection algorithms in MR-SLAM increases the computational complexity and the required resources on the robot board and at the base station. In this paper, An Enhanced Multi-Robot Fast Localization Odometry and Mapping (EMR-FLOAM) to deal with computation complexity issue. The EMR-FLOAM algorithm addresses computational complexity and resource requirements by utilizing a two-stage non-iterative distortion compensation technique, resulting in optimized code and accelerated localization and map construction processes. The simulation of the proposed work has been tested on …
Evaluating The Effect Of Students' Behavioural Intention To Use Social Media For Collaborative Learning, Nur Shamsiah Abdul Rahman, Noor Azida Sahabudin
Evaluating The Effect Of Students' Behavioural Intention To Use Social Media For Collaborative Learning, Nur Shamsiah Abdul Rahman, Noor Azida Sahabudin
Iraqi Journal for Computer Science and Mathematics
With the rise of social media technologies, investigating the use of social media for learning has become all the more important. However, far too little research has been conducted to investigate factors that contribute towards students’ attitude and behavioural intention to use social media for collaborative learning in Malaysian higher education. This study aims to examine the determinants that influence students’ attitude and behaviour intention to use social media for collaborative learning by applying the Theory Acceptance Model (TAM) and Unified Theory of Acceptance and Usage of Technology (UTAUT). 243 respondents participated in this study. The Structural Equation modelling (SEM) …