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In today's society, voting is crucial to choosing the representatives of the people. The current voting process is filled with a vast array of disputes and manipulations. The leader must be selected in a precise manner without any malpractices. In addition, the people and authorities are not happy with the election results and label them unpredictable. We offer a better solution to the current problems, such as tampering, non-residents voting outside of the polling place, quick results analysis, quick counting, and reduced use of staff and funds during the electoral franchise process. In this offer, blockchain technology is used to create the distributed application (dApp) framework that will be used for the proposed e-voting system. Additionally, it offers unique characteristics such as immutability, transparency, privacy, and reception freedom that reduce crimes involving the processing of sensitive data in the electoral process. Ganache, MetaMask, and specified dagger hashing algorithm are used to develop the dApp. A key strength of this paper is the statistical analysis of transactions on the blockchain. Moreover, it also provides security to voters' identity and leads to immediate acceptable counting results with more accuracy. [ABSTRACT FROM AUTHOR]

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work's main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN. [ABSTRACT FROM AUTHOR]

The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust. [ABSTRACT FROM AUTHOR]

Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof. [ABSTRACT FROM AUTHOR]

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims' devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy. [ABSTRACT FROM AUTHOR]