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Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

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FUNCTIONAL & INTEGRATIVE GENOMICS 2024年第1期24卷 23-23页

作者： Rehman, Amjad Mujahid, Muhammad Saba, Tanzila Jeon, Gwanggil Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Incheon Natl Univ Dept Embedded Syst Engn Incheon 610101 South Korea

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%;for multifactorial diseases, it is 97.5%;and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomed

关键词： Genomics Machine learning Detection Healthcare Medical history

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第5期85卷 1926-1936页

作者： Amin, Javaria Sharif, Muhammad Fernandes, Steven Lawrence Wang, Shui-Hua Saba, Tanzila Khan, Amjad Rehman Univ Wah Dept Comp Sci Quaid Ave Wah Cantt 4740 Pakistan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt Pakistan Creighton Univ Dept Comp Sci Design & Journalism Omaha NE 68178 USA Univ Leicester Sch Math & Actuarial Sci Leicester Leics England Prince Sultan Univ Artificial Intelligence & Data Lab AIDA CCIS Riyadh 11586 Saudi Arabia

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. Highlights This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.

关键词： 4-qubit-quantum circuit breast cancer deeplabv3 health care public health ReLU xception

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A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features

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IEEE ACCESS 2023年 11卷 133717-133729页

作者： Arif, Rahat Maqsood Aslam, Muhammad Al-Otaibi, Shaha Martinez-Enriquez, Ana Maria Saba, Tanzila Bahaj, Saeed Ali Rehman, Amjad Univ Engn & Technol Dept Comp Sci Lahore 39161 Pakistan Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia CINVESTAV IPN Dept CS Gustavo A Madero 07360 Mexico Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

In the internet of things (IoT) networks, machine learning (ML) is significantly used for malware and adversary detection. Recently, research has shown that adversarial attacks have put ML-based models at risk. This problem is exacerbated in an IoT environment because of the absence of adequate security measures. Consequently, it is crucial to evaluate the strength of such malware detectors using powerful adversarial samples. The existing adversarial sample generation strategies either rely on high-level image features or an unfiltered feature set, making it challenging to determine which feature modifications are crucial in evading malware detection systems, without compromising the malware functionality. This encourages us to propose an evasion framework named IF-MalEvade, based on Generative Adversarial Network (GAN) and Deep Reinforcement Learning (DRL) that effectively generates fully-working, malware samples with several effective perturbations such as header Section manipulation and benign bytes insertion. The DRL framework selects a few suitable action sequences to change malicious samples, thus allowing our malware samples to bypass various black-box ML based malware detectors and the detection search engines of VirusTotal, while maintaining the executability and malicious behavior of the original malware samples. The neural networks of GAN take in the unfiltered feature set of malware dataset and using minimax objective function yields a set of useful features that are subsequently used by the DRL agent to make effective changes. Experimental results illustrated that by utilizing the influential features in sequence of transformations, the adversarial samples generated by our model outperformed the state-of-the-art evasion models with an impressive evasion rate. Additionally, the detection rate of well-known machine learning models was also brought down to up to 97%. Furthermore, when the machine learning models were retrained using adversarial samples, a

关键词： Malware Generative adversarial networks Detectors Feature extraction Closed box Internet of Things Reinforcement learning Generative adversarial network portable executable PE malware adversarial attack malware evasion deep reinforcement learning technological development

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Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique

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MICROSCOPY RESEARCH AND TECHNIQUE 2024年第8期87卷 1862-1888页

作者： Rehman, Amjad Mahmood, Tariq Alamri, Faten S. Saba, Tanzila Naseem, Shahid CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari Pakistan Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh Saudi Arabia Univ Educ Fac Informat Sci Div Sci & Technol Lahore Pakistan Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci POB 84428 Riyadh 11671 Saudi Arabia

Breast cancer is a major health threat, with early detection crucial for improving cure and survival rates. Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, and improve medical care for breast cancer patients. The study explores the challenges in identifying benign and malignant breast cancer lesions using microscopic image datasets. It introduces a low-dimensional multiple-channel feature-based method for breast cancer microscopic image recognition, overcoming limitations in feature utilization and computational complexity. The method uses RGB channels for image processing and extracts features using level co-occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. This approach aims to improve diagnostic efficiency and accuracy in breast cancer treatment. The core of our method is the SqE-DDConvNet algorithm, which utilizes a 3 x 1 convolution kernel, SqE-DenseNet module, bilinear interpolation, and global average pooling to enhance recognition accuracy and training efficiency. Additionally, we incorporate transfer learning with pre-trained models, including mVVGNet16, EfficientNetV2B3, ResNet101V2, and CN2XNet, preserving spatial information and achieving higher accuracy under varying magnification conditions. The method achieves higher accuracy compared to baseline models, including texture and deep semantic features. This deep learning-based methodology contributes to more accurate image classification and unique image recognition in breast cancer microscopic *** Highlights Introduces a low-dimensional multiple-channel feature-based method for breast cancer microscopic image recognition. Uses RGB channels for image processing and extracts features using level co-occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. Employs the SqE-DDConvNet algorithm for enhanced recognition accuracy and training efficiency. Transfer learning wit

关键词： cancer deep semantic feature feature classification microscopic breast images transfer learning

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An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

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IEEE ACCESS 2023年 11卷 138813-138826页

作者： Butt, Ateeq Ur Rehman Mahmood, Tariq Saba, Tanzila Bahaj, Saeed Ali Omer Alamri, Faten S. Iqbal, Muhammad Waseem Khan, Amjad R. Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Univ Educ Vehari Campus Fac Informat Sci Vehari 61100 Pakistan Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 84428 Saudi Arabia Super Univ Dept Software Engn Lahore 54000 Pakistan

In today's world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment's multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System's (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user's behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user's trust value, ***-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

关键词： E-health role-based access control trust cloud environment data management IEEE health issue

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Detection of Chronic Kidney Disease Using Machine Learning

Detection of Chronic Kidney Disease Using Machine Learning

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2024 Multimedia University Engineering Conference, MECON 2024

作者： Khan Ghauri, Abdur Rehman Danish, Muhammad Nadeem, Lubna Amin, Yasar Mahmood, Tariq Sheraz, Muhammad Chuah, Teong Chee Bin Roslee, Mardeni University of Engg & Tech Dept of Telecom Engineering Taxila Pakistan University of Engg & Tech Dept of Telecom Engg Taxila Pakistan CCIS Prince Sultan University Artificial Intelligence and Data Analytics Saudi Arabia Centre for Wireless Technology Multimedia University Cyberjaya63100 Malaysia Centre for Wireless Technology Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia

ISBN: (纸本)9798331530747

One of the world's worst diseases are chronic kidney disease (CKD), Diabetes and Hypertension which affects millions of people. The development of CKD towards end-stage renal disease can be halted by early identification and prompt treatment. The purpose of this research is to create and assess a machine-learning model for detecting CKD among Diabetes and Hypertension patients. Accuracy of 6 models is improved to their highest, using a dataset of medical record of people already having diabetes and hypertension. Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Native Buyer (GNB) and XG Boost models are employed in this work. The k-neighbor, random forest, logistic regression, Support vector machine, GNB models performed at 96.30%, 98.46%, 98.21%, 98.13, 92.78 and XG Boost with highest accuracy of 98.88%. It is evident that the machine learning models which can be beneficial in the clinical diagnosis of CKD are the random forest and logistic regression models. The model's high reliability will aid in the early detection of CKD, allowing for more effective treatment. However, larger and more varied datasets are needed to further validate the model and investigate its potential therapeutic applicability. © 2024 IEEE.

关键词： Adversarial machine learning

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DeepFert: An Intelligent Fertility Rate Prediction Approach for Men Based on Deep Learning Neural Networks

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IEEE ACCESS 2023年 11卷 75006-75022页

作者： Naseem, Shahid Mahmood, Tariq Saba, Tanzila Alamri, Faten S. S. Bahaj, Saeed Ali Omer Ateeq, Hammad Farooq, Umer Univ Educ Dept Informat Sci Div Sci & Technol Lahore 54770 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Princess Nourah bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia

Men's fertility depends on their bodies making normal sperm and delivering them. Semen analysis has been the test of choice for assessing the male partner in an infertile couple using a single threshold value to distinguish 'abnormal' and 'normal' parameters. In the semen analysis process, rA gime issues might affect the semen morphology, quality, and spermatozoa, and also reduce the risks of fertility due to food regimen including glycemic content and limited intake of nutrients. Determination of the connotation between adjustable rA gime and semen morphology is a complex task to determine fertility. The goal is this study is the prediction of men's fertility rate to analyze the connection between spermatozoa and the level of lifeblood. Impaired semen parameters alone cannot be used to predict fertility more accurately. Some factors might affect the spermatozoa, like impairing sperm function, morphology, and sustainability, and can reduce the men's fertility rate. In this article, deep learning on convolutional neural network (DLNN) is used to predict the men's fertility rate more quickly, accurately, and consistently from different age spam of men between 18-50 years old. The convolutional neural network performs the segmentation of sperm heads, while the deep learning algorithm allows us to calculate the movement speed of sperm heads. After the application of DLNN, we have achieved semen prediction 80.952% and sperm concentration 85.714% accuracy of sperm head detection on human spermatozoa sperm samples. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

关键词： Fertility deep learning neural networks dwindled spermatozoa

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Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model

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IEEE ACCESS 2022年 10卷 73079-73086页

作者： Abbood, Saif Hameed Hamed, Haza Nuzly Abdull Rahim, Mohd Shafry Mohd Rehman, Amjad Saba, Tanzila Bahaj, Saeed Ali Univ Technol Malaysia UTM Fac Engn Sch Comp Skudai 81310 Johor Malaysia Iraqi Minist Hlth Wasit Hlth Directorate Al Kut 52007 Wasit Iraq Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 11586 Saudi Arabia Prince Sattam bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Al Mukalla 50512 Yemen

Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It's clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.

关键词： Retina Image enhancement Feature extraction Image color analysis Histograms Diabetes Image segmentation Image enhancement deep learning diabetic retinopathy retina fundus image healthcare health risks

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Enhancing Prognosis Accuracy for Ischemic Cardiovascular Disease Using K Nearest Neighbor Algorithm: A Robust Approach

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IEEE ACCESS 2023年 11卷 97879-97895页

作者： Muhammad, Ghulam Naveed, Saad Nadeem, Lubna Mahmood, Tariq Khan, Amjad R. Amin, Yasar Bahaj, Saeed Ali Omer Univ Engn & Technol Dept Telecommun Engn Taxila 47050 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Mukalla Hadhramout Yemen

Ischemic Cardiovascular diseases are one of the deadliest diseases in the world. However, the mortality rate can be significantly reduced if we can detect the disease precisely and effectively. Machine Learning (ML) models offer substantial assistance to individuals requiring early treatment and disease detection in the realm of cardiovascular health. In response to this critical need, this study developed a robust system to predict ischemic disease accurately using ML-based algorithms. The dataset obtained from Kaggle encompasses a comprehensive collection of over 918 observations, encompassing 12 essential features crucial for predicting ischemic disease. In contrast, much-existing research relies primarily on datasets comprising only 303 instances from the UCI repository. Six ML-based algorithms, including K Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Trees (DT), are trained on the ischemic heart data. The effectiveness of the proposed methodologies is meticulously evaluated and benchmarked against cutting-edge techniques, employing a range of performance criteria. The empirical findings manifest that the KNN classifier produced optimized results with 91.8% accuracy, 91.4% recall, 91.9% F1 score, 92.5% precision, and AUC of 90.27%.

关键词： Diseases Classification tree analysis Predictive models Support vector machines Prediction algorithms Heart Radio frequency Cardiovascular diseases Risk management Ischemic cardiovascular diseases healthcare and health risks SVM RF GNB KNN

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