Kushal Dalal

The current study analyzes the impact of IoT sensors in health care units in association with cloud computing. The advantages, disadvantages and risks associated with the pragmatic implementation is discussed elaborately along with future benefits. The study aims to establish a standardized viewpoint with respect to the adoption of IoT sensors in the daily applications.

Point-of-Care Test (POCT) is considered as the diagnostic test which is administrated in the outside of the central laboratory ator near the patient location. Therefore, it can be stated that the use of POCT devices in the department of emergency represents a significant benefit to the management of the patient. It can be able to give proper governance to credible policy, safe practice, and effective process performance by the healthcare clinics. The major contribution of the POCT is it can… Show more

Point-of-Care Test (POCT) is considered as the diagnostic test which is administrated in the outside of the central laboratory ator near the patient location. Therefore, it can be stated that the use of POCT devices in the department of emergency represents a significant benefit to the management of the patient. It can be able to give proper governance to credible policy, safe practice, and effective process performance by the healthcare clinics. The major contribution of the POCT is it can give a huge improvement over the test result in the context of turnaround time. Here this research paper considers the process of development framework of POCT in the emergency department (ED). Moreover, the challenges faced by the clinical staff during the usage of previous POCT and the implementation process are also discussed to give a clear clarification about the essentiality of the developed framework of POCT. Show less

The implementation of machine learning (ML) in clinical settings reflects the promising opportunities for improving the delivery of healthcare. Private healthcare businesses are considering and utilizing the development of ML in the process of decision making, adopting and supporting medical fields. This strong subcategory of AI appears familiar in several cases like the use of speech recognition by voice assistants, and the creation of personalized medical experiences. This report aims to… Show more

The implementation of machine learning (ML) in clinical settings reflects the promising opportunities for improving the delivery of healthcare. Private healthcare businesses are considering and utilizing the development of ML in the process of decision making, adopting and supporting medical fields. This strong subcategory of AI appears familiar in several cases like the use of speech recognition by voice assistants, and the creation of personalized medical experiences. This report aims to critically analyse the implementation of ML in the healthcare industry by understanding how ML is revolutionizing the industry and what its full potential is for the industry. Show less

To harness the value of data generated from IoT, there is a crucial requirement of new mechanisms. Machine learning (ML) is among the most suitable paradigms of computation which embeds strong intelligence within IoT devices. Various ML techniques are being widely utilised for improving network security in IoT. These techniques include reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. This report aims to critically analyse the role played by… Show more

To harness the value of data generated from IoT, there is a crucial requirement of new mechanisms. Machine learning (ML) is among the most suitable paradigms of computation which embeds strong intelligence within IoT devices. Various ML techniques are being widely utilised for improving network security in IoT. These techniques include reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. This report aims to critically analyse the role played by supervised and unsupervised ML for the enhancement of IoT security. Show less

Data security is a critical aspect in this digital world. Data security algorithms are designed to suit specific applications. Electronic Health Records (EHR) are the digital documents that contain the digital data of the patients. The data includes personal information of the patient along with the medical and treatment histories. The radiology images and medical information is also contained in the digital form in the EHRs. This data is usually stored in the cloud servers because of the data… Show more

Data security is a critical aspect in this digital world. Data security algorithms are designed to suit specific applications. Electronic Health Records (EHR) are the digital documents that contain the digital data of the patients. The data includes personal information of the patient along with the medical and treatment histories. The radiology images and medical information is also contained in the digital form in the EHRs. This data is usually stored in the cloud servers because of the data storage issues. The common practice is that the data is divided into multiple segments and stored in different cloud servers. The issue of reconstruction using searchable metrics is the problem that is addressed in this paper using a novel hybrid algorithm. The personal data of the patient is preserved using the k-Nearest Neighbor classification algorithm to secure the data. The proposed method is novel and works in different stages. Show less

A threat can be anything that causes potential
damage to the network system. These threats can turn out to be a
attack to the system. Threat may occur in any forms like viruses,
outright attack, and phishing attack from hackers to gain
information. Such attacks put a user’s system and also business
system at risks. Cyber security aims at the protection of system
from attacks like unauthorized network access, intrusions attacks
etc. This paper presents a novel architecture… Show more

A threat can be anything that causes potential
damage to the network system. These threats can turn out to be a
attack to the system. Threat may occur in any forms like viruses,
outright attack, and phishing attack from hackers to gain
information. Such attacks put a user’s system and also business
system at risks. Cyber security aims at the protection of system
from attacks like unauthorized network access, intrusions attacks
etc. This paper presents a novel architecture model based on
machine learning for the prediction of Cyber security malware
that requires execution in a sandbox environment. In order to
prevent the attackers from infiltrating the system Machine
Learning approach is adapted. Show less

Machine learning is one among the widely used techniques in the field of Computer Science, and it finds wide applications in cybersecurity, natural language processing, image processing, pattern recognition and other fields. Despite successful applications of machine learning techniques in various cases like intrusion detection, data security, network security and cyber security, the performance of such techniques significantly decreases because these machine learning algorithms and equivalent… Show more

Machine learning is one among the widely used techniques in the field of Computer Science, and it finds wide applications in cybersecurity, natural language processing, image processing, pattern recognition and other fields. Despite successful applications of machine learning techniques in various cases like intrusion detection, data security, network security and cyber security, the performance of such techniques significantly decreases because these machine learning algorithms and equivalent training data are susceptible to various security attacks. This calls for in depth analysis of security threats and related defensive techniques of machine learning intensively which leads to the motivation of this review paper. An exploratory research on the existing methodology of machine learning techniques are studied and the research gap in the field of adaption of machine learning defensive techniques to cybersecurity is analysed. Show less