Search

The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others in a smart grid. However, psychiatrists and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results, confirming the transformation. The present research was based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically, the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on the prediction capability of the proposed module and their overall satisfaction. The participants were strongly satisfied with the prediction capability (~81%) of the proposed module and future recommendations (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In the future, experimentation using different therapies and integrating more advanced techniques are anticipated to open new gateways in healthcare.

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.

The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.