Browsing by Author "De Boer, Friso"

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A Comprehensive Unsupervised Framework for Chronic Kidney Disease Prediction
(Scopus, 2021) Antony, Linta; Azam, Sami; Ignatious, Eva; Quadir, Ryana; Beeravolu, Abhijith Reddy; Jonkman, Mirjam; De Boer, Friso
The incidence, prevalence, and progression of chronic kidney disease (CKD) conditions have evolved over time, especially in countries that have varied social determinants of health. In most countries, diabetics and hypertension are the main causes of CKDs. The global guidelines classify CKD as a condition that results in decreased kidney function over time, as indicated by glomerular filtration rate (GFR) and markers of kidney damage. People with CKDs are likely to die at an early age. It is crucial for doctors to diagnose various conditions associated with CKD in an early stage because early detection may prevent or even reverse kidney damage. Early detection can provide better treatment and proper care to the patients. In many regional hospital/clinics, there is a shortage of nephrologists or general medical persons who diagnose the symptoms. This has resulted in patients waiting longer to get a diagnosis. Therefore, this research believes developing an intelligent system to classify a patient into classes of `CKD' or `Non-CKD' can help the doctors to deal with multiple patients and provide diagnosis faster. In time, organizations can implement the proposed machine learning framework in regional clinics that have lower medical expert retention, this can provide early diagnosis to patients in regional areas. Although, several researchers have tried to address the situation by developing intelligent systems using supervised machine learning methods, till date limited studies have used unsupervised machine learning algorithms. The primary aim of this research is to implement and compare the performance of various unsupervised algorithms and identify best possible combinations that can provide better accuracy and detection rate. This research has implemented five unsupervised algorithms, K-Means Clustering, DB-Scan, I-Forest, and Autoencoder. And integrating them with various feature selection methods. Integrating feature reduction methods with K-Means Clustering algo...
A Framework to Address Security Concerns in Three Layers of IoT
(IEEE, 2020-11) Jose, Alwyn; Azam, Sami; Karim, Asif; Shanmugam, Bharanidharan; Faisal, Fahad; Islam, Ashraful; De Boer, Friso; Jonkma, Mirjam
The Internet of Things (IoT) is becoming part of many aspects of our life, including healthcare, home utilities, retail, energy, logistics, etc. This prolific and ubiquitous nature of IoT based systems brings with it the threats of cyber-attacks in a variety of forms. An IoT framework is a set of controlling rules, standards and protocols which makes implementation of IoT applications somewhat streamlined. However, due to the existence of a plethora of IoT devices, applications and technologies, standardization of IoT protocols is a complex undertaking. Several well-known IT organizations have their own customized standards for the IoT platform. However, the lack of stable standardization has been a prime concern for quite some time. This research outlines the overall technologies used in IoT security implementation and an overview of different threats faced by IoT devices. The work also recommends a security framework that can effectively be implemented with various IoT based systems.
A Framework to Address Security Concerns in Three Layers of IoT
(IEEE, 2020-11-20) Jose, Alwyn; Azam, Sami; Karim, Asif; Shanmugam, Bharanidharan; Faisal, Fahad; Islam, Ashraful; De Boer, Friso; Jonkman, Mirjam
The Internet of Things (IoT) is becoming part of many aspects of our life, including healthcare, home utilities, retail, energy, logistics, etc. This prolific and ubiquitous nature of IoT based systems brings with it the threats of cyber-attacks in a variety of forms. An IoT framework is a set of controlling rules, standards and protocols which makes implementation of IoT applications somewhat streamlined. However, due to the existence of a plethora of IoT devices, applications and technologies, standardization of IoT protocols is a complex undertaking. Several well-known IT organizations have their own customized standards for the IoT platform. However, the lack of stable standardization has been a prime concern for quite some time. This research outlines the overall technologies used in IoT security implementation and an overview of different threats faced by IoT devices. The work also recommends a security framework that can effectively be implemented with various IoT based systems.
Breastnet18
(Biology, 2021-11-13) Montaha, Sidratul; Azam, Sami; Muhammad Rakibul Haque Rafid, Abul Kalam; Ghosh, Pronab; Hasan, Md. Zahid; Jonkman, Mirjam; De Boer, Friso
Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible over fitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.