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Papers by Ahmed Adil Nafea

Research paper thumbnail of Deep Learning Algorithm for Face Recognition Using a Hybrid Model

This study presents a robust hybrid model for face recognition, which synergistically integrates ... more This study presents a robust hybrid model for face recognition, which synergistically integrates the VGG16 convolutional neural network (CNN) for feature extraction with an autoencoder for dimensionality reduction and representation learning. This paper proposed VGG16 and Autoencoder architecture efficiently extracts high-level features from images, much reducing computational complexity while the classify of the extracted features used Support Vector Machine (SVM). The proposed hybrid model has achieved a high accuracy of 98% in face recognition tasks on benchmark datasets. This high accuracy highlights efficiency of combination VGG16-based feature extraction with autoencoder of a dimensionality reduction technique and SVM classification in advancing the state-of-the-art in face recognition approaches .

Research paper thumbnail of An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network

Research paper thumbnail of Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review

Research paper thumbnail of Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques

Research paper thumbnail of A Birds Species Detection Utilizing an Effective Hybrid Model

Research paper thumbnail of Intelligent System for Student Performance Prediction Using Machine Learning

Mağallaẗ baġdād li-l-ʿulūm, May 19, 2024

Research paper thumbnail of A Brief Review of Big Data in Healthcare: Challenges and Issues, Recent Developments, and Future Directions

Babylonian journal of Internet of things, Feb 26, 2024

Research paper thumbnail of An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

Deleted Journal, Jul 23, 2023

Research paper thumbnail of A Review of Using Chatgpt for Scientific Manuscript Writing

Deleted Journal, Jan 10, 2024

Research paper thumbnail of A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision

Deleted Journal, Feb 11, 2024

Research paper thumbnail of An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning

Research paper thumbnail of Ensemble Model for Prostate Cancer Detection Using MRI Images

Research paper thumbnail of A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection

Mağallaẗ baġdād li-l-ʿulūm, Mar 19, 2024

Research paper thumbnail of Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants

Research paper thumbnail of A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3

Research paper thumbnail of An Ensemble Model for Detection of Adverse Drug Reactions

ARO, Feb 20, 2024

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety... more The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant underreporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, twoterm representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.

Research paper thumbnail of Enhancing Student's Performance Classification Using Ensemble Modeling

Iraqi Journal For Computer Science and Mathematics

A precise prediction of student performance is an important aspect within educational institutio... more A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of students using a group modeling. This approach combines the strengths of multiplealgorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robustand reliable predictive model because it considers The experiments were evaluated using the Open UniversityLearning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive datasetcontaining various characteristics related to the student’s activities thereby five cases based on the utilized datasetwere investigated...

Research paper thumbnail of Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Baghdad Science Journal

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech,... more Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Chi...

Research paper thumbnail of Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Baghdad Science Journal, 2023

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech,... more Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder.

Research paper thumbnail of Secure Smart Contract Based on Blockchain to Prevent the Non-Repudiation Phenomenon

Baghdad Science Journal, 2023

Blockchain is an innovative technology that has gained interest in all sectors in the era of digi... more Blockchain is an innovative technology that has gained interest in all sectors in the era of digital transformation where it manages transactions and saves them in a database. With the increasing financial transactions and the rapidly developed society with growing businesses many people looking for the dream of a better financially independent life, stray from large corporations and organizations to form startups and small businesses. Recently, the increasing demand for employees or institutes to prepare and manage contracts, papers, and the verifications process, in addition to human mistakes led to the emergence of a smart contract. The smart contract has been developed to save time and provide more confidence while dealing, as well as to cover the security aspects of digital management and to solve negotiation concerns. The smart contract was employed in creating a distributed ledger to eliminate the need for centralization. In this paper, a simple prototype has been implemented for the smart contract integrated with blockchain which is simulated in a local server with a set of nodes. Several security objectives, such as confidentiality, authorization, integrity, and non-repudiation, have been achieved in the proposed system. Besides, the paper discussed the importance of using the Blockchain technique, and how it contributed to the management of transactions in addition to how it was implemented in highly transparent real-estate scenarios. The smart contract was employed in creating a distributed ledger to eliminate the need for centralization. The ellipticcurve public key has been adopted as an alternative for the RSA in a signature generation/verification process and encryption protocol. For secure transactions, The Secure Socket Layer (SSL) also has been adopted as a secure layer in the web browser. The results have been investigated and evaluated from different aspects and the implementation was in a restricted environment. Experiments showed us the complexity of time and cost when using the (ECC) algorithm and using (RSA) algorithm depending on the size and length of the key. So if the size of the key in (ECC) equals (160) bits, and it corresponds to (1024) bits in (RSA), which is equivalent to 40% for (ECC) and 30% for (RSA). As a result, the (ECC) algorithm is complex, its key is smaller and the process of generating the key is faster, so it has achieved a high level of security.

Research paper thumbnail of Deep Learning Algorithm for Face Recognition Using a Hybrid Model

This study presents a robust hybrid model for face recognition, which synergistically integrates ... more This study presents a robust hybrid model for face recognition, which synergistically integrates the VGG16 convolutional neural network (CNN) for feature extraction with an autoencoder for dimensionality reduction and representation learning. This paper proposed VGG16 and Autoencoder architecture efficiently extracts high-level features from images, much reducing computational complexity while the classify of the extracted features used Support Vector Machine (SVM). The proposed hybrid model has achieved a high accuracy of 98% in face recognition tasks on benchmark datasets. This high accuracy highlights efficiency of combination VGG16-based feature extraction with autoencoder of a dimensionality reduction technique and SVM classification in advancing the state-of-the-art in face recognition approaches .

Research paper thumbnail of An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network

Research paper thumbnail of Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review

Research paper thumbnail of Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques

Research paper thumbnail of A Birds Species Detection Utilizing an Effective Hybrid Model

Research paper thumbnail of Intelligent System for Student Performance Prediction Using Machine Learning

Mağallaẗ baġdād li-l-ʿulūm, May 19, 2024

Research paper thumbnail of A Brief Review of Big Data in Healthcare: Challenges and Issues, Recent Developments, and Future Directions

Babylonian journal of Internet of things, Feb 26, 2024

Research paper thumbnail of An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

Deleted Journal, Jul 23, 2023

Research paper thumbnail of A Review of Using Chatgpt for Scientific Manuscript Writing

Deleted Journal, Jan 10, 2024

Research paper thumbnail of A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision

Deleted Journal, Feb 11, 2024

Research paper thumbnail of An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning

Research paper thumbnail of Ensemble Model for Prostate Cancer Detection Using MRI Images

Research paper thumbnail of A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection

Mağallaẗ baġdād li-l-ʿulūm, Mar 19, 2024

Research paper thumbnail of Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants

Research paper thumbnail of A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3

Research paper thumbnail of An Ensemble Model for Detection of Adverse Drug Reactions

ARO, Feb 20, 2024

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety... more The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant underreporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, twoterm representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.

Research paper thumbnail of Enhancing Student's Performance Classification Using Ensemble Modeling

Iraqi Journal For Computer Science and Mathematics

A precise prediction of student performance is an important aspect within educational institutio... more A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of students using a group modeling. This approach combines the strengths of multiplealgorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robustand reliable predictive model because it considers The experiments were evaluated using the Open UniversityLearning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive datasetcontaining various characteristics related to the student’s activities thereby five cases based on the utilized datasetwere investigated...

Research paper thumbnail of Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Baghdad Science Journal

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech,... more Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Chi...

Research paper thumbnail of Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Baghdad Science Journal, 2023

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech,... more Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder.

Research paper thumbnail of Secure Smart Contract Based on Blockchain to Prevent the Non-Repudiation Phenomenon

Baghdad Science Journal, 2023

Blockchain is an innovative technology that has gained interest in all sectors in the era of digi... more Blockchain is an innovative technology that has gained interest in all sectors in the era of digital transformation where it manages transactions and saves them in a database. With the increasing financial transactions and the rapidly developed society with growing businesses many people looking for the dream of a better financially independent life, stray from large corporations and organizations to form startups and small businesses. Recently, the increasing demand for employees or institutes to prepare and manage contracts, papers, and the verifications process, in addition to human mistakes led to the emergence of a smart contract. The smart contract has been developed to save time and provide more confidence while dealing, as well as to cover the security aspects of digital management and to solve negotiation concerns. The smart contract was employed in creating a distributed ledger to eliminate the need for centralization. In this paper, a simple prototype has been implemented for the smart contract integrated with blockchain which is simulated in a local server with a set of nodes. Several security objectives, such as confidentiality, authorization, integrity, and non-repudiation, have been achieved in the proposed system. Besides, the paper discussed the importance of using the Blockchain technique, and how it contributed to the management of transactions in addition to how it was implemented in highly transparent real-estate scenarios. The smart contract was employed in creating a distributed ledger to eliminate the need for centralization. The ellipticcurve public key has been adopted as an alternative for the RSA in a signature generation/verification process and encryption protocol. For secure transactions, The Secure Socket Layer (SSL) also has been adopted as a secure layer in the web browser. The results have been investigated and evaluated from different aspects and the implementation was in a restricted environment. Experiments showed us the complexity of time and cost when using the (ECC) algorithm and using (RSA) algorithm depending on the size and length of the key. So if the size of the key in (ECC) equals (160) bits, and it corresponds to (1024) bits in (RSA), which is equivalent to 40% for (ECC) and 30% for (RSA). As a result, the (ECC) algorithm is complex, its key is smaller and the process of generating the key is faster, so it has achieved a high level of security.

Research paper thumbnail of Deep Learning Algorithm for Face Recognition Using a Hybrid Model

5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24), 2024

This study presents a robust hybrid model for face recognition, which synergistically integrates ... more This study presents a robust hybrid model for face recognition, which synergistically integrates the VGG16 convolutional neural network (CNN) for feature extraction with an autoencoder for dimensionality reduction and representation learning. This paper proposed VGG16 and Autoencoder architecture efficiently extracts high-level features from images, much reducing computational complexity while the classify of the extracted features used Support Vector Machine (SVM). The proposed hybrid model has achieved a high accuracy of 98% in face recognition tasks on benchmark datasets. This high accuracy highlights efficiency of combination VGG16-based feature extraction with autoencoder of a dimensionality reduction technique and SVM classification in advancing the state-of-the-art in face recognition approaches .