Ahmed K A R E E M Oleiwi | Zhengzhou University (original) (raw)

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Papers by Ahmed K A R E E M Oleiwi

Research paper thumbnail of Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

BioMed Research International, 2022

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely ... more Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.

Research paper thumbnail of Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

BioMed Research International, 2022

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more preva... more Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

Research paper thumbnail of Metaheuristics Based Modeling and Simulation Analysis of New Integrated Mechanized Operation Solution and Position Servo System

Mathematical Problems in Engineering , 2022

Inadequate environmental protection is a problem, and to address it, the e ciency and quality gra... more Inadequate environmental protection is a problem, and to address it, the e ciency and quality grade of mechanical operations should be improved. A new comprehensive mechanized operation solution is proposed. e position control of a servo motor utilizing a PID (proportional, integral, derivative) controller and soft computing optimization approaches is explored in this paper. e essential technological realization techniques are reviewed and assessed, which serves as a reference for comprehensive mechanization solutions. e simulation model may be used to examine system features and explore control strategy by re ecting the characteristics of the actual system. A PID controller is designed by establishing a mathematical model based on the position servo system. It is veri ed that the PID control link is added to the pneumatic position servo control system. rough the comparison of the PID control experiment and PID control simulation data, the system can achieve high-precision position control, and the error is less than ±20 mm, meeting the requirements of rust removal and spraying design, and showing that the control system works stably and reliably to achieve high-precision simulation of the pneumatic position servo system.

Research paper thumbnail of A Comparative Analysis and Risk Prediction of Diabetes at Early Stage using Machine Learning Approach

International Journal of Future Generation Communication and Networking, 2020

Nowadays, diabetes is one of the fastest growing chronic life threatening diseases has become a c... more Nowadays, diabetes is one of the fastest growing chronic life threatening diseases has become a common disease to the mankind from young to the old persons.The growth of the diabetic patients that has already affected 422 million people worldwide according to the report of World Health Organization (WHO), in 2018, now also it is increasing day by-day due to various causes such as bacterial or viral infection, toxic or chemical contents mix with the food, auto immune reaction, obesity, bad diet, change in lifestyles, eating habit, environment pollution, etc. Hence, diagnosing the diabetes is very essential to save the human life from diabetes. Around 50% of all people suffering from diabetes are undiagnosed because of its long-term asymptomatic phase is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body's cells do not respond properly to insulin. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. Moreover, this paper presents a diabetes prediction system to diagnosis diabetes and to improve the accuracy in diabetes prediction using medical data with various machine learning algorithms.Finally, the result shows the Multilayer Perceptron (MLP) algorithm and the Radial Basis Function Network (RBF/RBFN) has the highest specificity of 95% and 98.72%, respectively holds best for the analysis of diabetic data. Using tenfold Cross-Validation evaluation techniquesRadial Basis Function Network outcome states the best accuracy of 98.80%.

Research paper thumbnail of Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

BioMed Research International, 2022

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely ... more Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.

Research paper thumbnail of Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

BioMed Research International, 2022

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more preva... more Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

Research paper thumbnail of Metaheuristics Based Modeling and Simulation Analysis of New Integrated Mechanized Operation Solution and Position Servo System

Mathematical Problems in Engineering , 2022

Inadequate environmental protection is a problem, and to address it, the e ciency and quality gra... more Inadequate environmental protection is a problem, and to address it, the e ciency and quality grade of mechanical operations should be improved. A new comprehensive mechanized operation solution is proposed. e position control of a servo motor utilizing a PID (proportional, integral, derivative) controller and soft computing optimization approaches is explored in this paper. e essential technological realization techniques are reviewed and assessed, which serves as a reference for comprehensive mechanization solutions. e simulation model may be used to examine system features and explore control strategy by re ecting the characteristics of the actual system. A PID controller is designed by establishing a mathematical model based on the position servo system. It is veri ed that the PID control link is added to the pneumatic position servo control system. rough the comparison of the PID control experiment and PID control simulation data, the system can achieve high-precision position control, and the error is less than ±20 mm, meeting the requirements of rust removal and spraying design, and showing that the control system works stably and reliably to achieve high-precision simulation of the pneumatic position servo system.

Research paper thumbnail of A Comparative Analysis and Risk Prediction of Diabetes at Early Stage using Machine Learning Approach

International Journal of Future Generation Communication and Networking, 2020

Nowadays, diabetes is one of the fastest growing chronic life threatening diseases has become a c... more Nowadays, diabetes is one of the fastest growing chronic life threatening diseases has become a common disease to the mankind from young to the old persons.The growth of the diabetic patients that has already affected 422 million people worldwide according to the report of World Health Organization (WHO), in 2018, now also it is increasing day by-day due to various causes such as bacterial or viral infection, toxic or chemical contents mix with the food, auto immune reaction, obesity, bad diet, change in lifestyles, eating habit, environment pollution, etc. Hence, diagnosing the diabetes is very essential to save the human life from diabetes. Around 50% of all people suffering from diabetes are undiagnosed because of its long-term asymptomatic phase is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body's cells do not respond properly to insulin. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. Moreover, this paper presents a diabetes prediction system to diagnosis diabetes and to improve the accuracy in diabetes prediction using medical data with various machine learning algorithms.Finally, the result shows the Multilayer Perceptron (MLP) algorithm and the Radial Basis Function Network (RBF/RBFN) has the highest specificity of 95% and 98.72%, respectively holds best for the analysis of diabetic data. Using tenfold Cross-Validation evaluation techniquesRadial Basis Function Network outcome states the best accuracy of 98.80%.