Data Classification Framework for Medical Data through Machine Learning Techniques in Cloud Computing

2020 ◽  
pp. 391-416
Author(s):  
Saurabh Sharma ◽  
Harish K. Shakya ◽  
Ashish Mishra
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Classification ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Classification Framework ◽  
Learning Techniques

scholarly journals Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Choudhary Sobhan Shakeel ◽  
Saad Jawaid Khan ◽  
Beenish Chaudhry ◽  
Syeda Fatima Aijaz ◽  
Umer Hassan
Keyword(s):  
Machine Learning ◽  
Alopecia Areata ◽  
Nearest Neighbor ◽  
Autoimmune Disorder ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Framework ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Image Set

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T -test showed significant differences between the two accuracies with a p < 0.001 . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Omar Farooq ◽  
Parminder Singh
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Classification ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Right ◽  
Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

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