A hybrid segmentation algorithm is proposed is this paper to extract the blood vessels from the fundus image of retina. Fundus camera captures the posterior surface of the eye and the captured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinal haemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysis of vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the field of ophthalmology. It is derived from the literature review that no unique segmentation algorithm is suitable for images of different eye-related diseases and the degradation of the vessels differ from patient to patient. If the blood vessels are extracted from the fundus images, it will make the diagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithm exclusively for the extraction of blood vessels from the fundus image. The proposed algorithm is hybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement (MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, the area-based morphological operator is applied to highlight the blood vessels. To validate the proposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus (HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments the blood vessels with more accuracy than the existing algorithms.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

This paper proposes a prediction system to identify the type of eye diseases like glaucoma and diabetic retinopathy. The proposed system processes the images captured using the fundus camera that is connected to the computer. The acquired fundus images are fed into the proposed prediction system which can be deployed in the cloud, and it identifies the type of disease. This forms a cyber-physical system. Underdeveloped countries which do not have the necessary infrastructure can utilize this service when this system is deployed in the cloud. For identifying these diseases, ophthalmologists extract parameters manually from the fundus image, which is a difficult task. Hence, this research work attempts to develop a system to automate the feature extraction from fundus images and with the extracted features, eye diseases are predicted. From the literature, it is found that many research works were focused on the binary classification of any one disease. In this paper, a novel classification methodology is proposed that helps the experts and clinicians to classify Diabetic Retinopathy, Glaucoma and healthy eye images with more accuracy. The proposed system with high accuracy is designed with the following phases: i) image acquisition, ii) image enhancement, iii) local features extraction using Speeded Up Robust Feature (SURF), iv) Bag of Features/Visual Words (BoF/BoVW) obtained through k-means clustering of local features, and v) classification using Error-Correcting Output Code (ECOC) linear SVM. It is inferred from the results that proposed method of classification using BoVW provided a maximum accuracy of 92% when compared to other state-of-the-art recent literature.

Data replication can be defined as one of the most vital approaches in the case of distributed storage networks and is used for achieving reliability, availability and performance. Nevertheless, future issues are still real, including security threats, expansion limitations, and optimization inconveniences. This paper researches secure and scalable data replication, focusing on a comparative analysis of the techniques and their performance index based on analytical and scientific data. Encryption, access control, and blockchain-based ways of ascertaining the integrity of a message are included in this study to address the security issue while minimizing resource consumption and redundancy. The findings in the simulation show that by applying the proposed method, high availability can indeed be achieved while at the same time having a very low latency as compared to the existing ones with improved security

This paper discusses the setup of the research platform to engage middle and high school students, undergraduate and graduate engineering students in STEM project experiences which evolve from the basic to the advanced according to the STEM preparation of the student in each category. The research platform comprises wireless sensor nodes which are configured to record radio frequency signals within a grid or network of nodes. Radio frequency signals are used to perform non-invasive and device-free target localization of objects or entities in space. Radio tomographic imaging uses wireless sensor networks to form images from the attenuation of the radio frequency signals. The system is comprised of three subsystems the wireless sensor network, the command and data collection platform, and the user interface. STEM project activities are identified on the proposed research platform at different levels of complexity, specific to the category of students assigned to work on the platform.