Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

Machine Learning has gained attention since the introduction of high computing machines and the availability of huge amount of data also known as big data. Today, machine learning is used in many types of industries from medical image processing to autonomous car. Detecting objects in images has also become one of the important research areas and now computers are able to not only detect objects but also able to draw bounding boxes on it. This is also known as computer vision. In this paper, we proposed the implementation of computer vision machine learning algorithms to detect object and use it to aid visually impaired and blind persons. This paper explain how convolution neural network are trained on ImageNet dataset that can detect objects and narrate detected objects information to the visually impairs person. This implementation can be used with any device using a camera that includes computers, tablets and mobile phones.

The smart grid is the digitized, modernized, updated version of archaic traditional electric grid. Advanced Metering Infrastructure (AMI) is an imperative part of the smart grid. It has replaced legacy metering, as it reports the energy consumption to the utility automatically through communication channels. AMI is also featuring to communicate control functions from utility to the smart meters. So, both the consumption data and control data needs to be securely transmitted to their ends. Any leak and tempering may produce the dire effects to both utility and consumers. Hence security of AMI is a very important concern to take real advantage of this technology. In this paper, first we analyze the security threats faced by AMI components and then propose the countermeasures to tackle them. The aim is to visualize the possible threats in the context of confidentiality, integrity and availability (CIA Triad) context and propose countermeasure.

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