Recent advances in machine-to-machine (M2M) communications, mini-hardware manufacturing, and micro computing have led to the development of the Internet of Things (IoT). The IoT is integrated with medical devices in order to enable better treatment, cost-effective medical solutions, improved patient monitoring, and enhanced personalized healthcare. This has led to the development of more complex and heterogeneous Internet of Medical Things (IoMT) systems that have their own operating systems and protocols. Even though such pervasive and low-cost sensing devices can bring about enormous changes in the healthcare sector, these are prone to numerous security and privacy issues. Security is thus a major challenge in these critical systems, one that inhibits their widespread adoption. However, significant inroads have been made by the on-going research, which powers the IoMT applications by incorporating prevalent security measures. In this regard, this paper highlights the significance of implementing key security measures, and essential aspects of the IoMT that make it useful for interconnecting various internal and external working domains of healthcare. This paper presents state-of-the-art techniques for securing IoMT systems, in terms of data transmission, collection, and storage. Furthermore, the paper also explores various security requirements, inherent design challenges, and various security techniques that could make the IoMT more secure and sustainable. Finally, the paper gives a panoramic view of the current status of research in the field and outlines some future research directions in this area.

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model’s training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U‐Net‐based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content‐based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U‐Net‐based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low‐level and high‐level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U‐Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U‐Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.

Twitter Data is one of the large amounts of sized data, because it is having a millions of tweets every day. It is one of the largest social media site. We are using this twitter data for the business purpose and industrial or social purpose according to our data requirement and processing the data. It is very large amount of sized data increasing every second that is known as big data. Because of large amount of data increasing every day we cannot easily analysis this data. We are using here new technology is HADOOP; with the help of HADOOP we can easily analysis the large amount of sized data. In this paper, we are using HADOOP for the analyzing the twitter data which is also known as a big data.

A Object Radar is a device (Tool) that is used to measure the distance from the object to the device Which is used for the purposes of surveying, determining focus in photography, or getting the information about the location and angle of the object. In this technical project, we had made simple object radar using the ULTRASONIC SENSOR (HC-SR04). This Radar Device works by calculating a distance range from 4 cm to 40 cm with angle range between 15° to 165°. In this device Servo Motor is used for the rotation of the ultrasonic sensor between the angles. "Processing Development Environment" software is used to demonstrate the result on a PC Screen after processing the information received by the Ultrasonic Sensor.