Detection of plant crop diseases has become an active field of research day by day due to increasing the demand for such systems and techniques as crop diseases are now become a common part of agriculture. Focusing on this demand and need, we have developed a Convolutional neural network (CNN)-based Deep learning (DL) multi-classification model which classifies the total of 900 real-time collected images of potato crop plants having healthy and potato blight (PB) disease images based on their PB disease severity level, along with this binary classification has also been done to simply classify the healthy and disease crop leaf. A total of four disease severity levels have been taken into account which resulted in a binary classification accuracy of 90.77% and 94.77% of best multi-classification accuracy. This work will be a great contribution in the field of potato disease recognition and detection using DL approaches.

The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

The most significant disturbance now affecting all economies and financial institutions is the digital transformation of economies. The world’s economy and financial institutions are digitizing at an unprecedented rate. Bitcoin is a devolved crypto-currency, or digital asset, that uses blockchain technology to expedite peer-to-peer financial transactions. Price volatility is one of the primary issues with decentralized cryptocurrencies, highlighting the need of examining the underlying price mechanism. Additionally, Bitcoin prices display non-stationary behaviour, meaning that their statistical distribution fluctuates over time. Bitcoin prices are stochastic, and no one set of characteristics can be used to forecast them completely. Nonetheless, academics have demonstrated varying degrees of effectiveness in estimating Bitcoin values using various feature sets. This article explains how to forecast Bitcoin price movements and prices using machine learning approaches. We intend to apply ARIMA, Facebook Prophet and XGBoost techniques for price prediction.

In modern society the role of Internet and computer system is well recognized. People are greatly benefited with the development of networking and cyber space but some people are using this development in unethical way to have some illegal benefits. Recently different types of Social-networking attacks are witnessed by social networking sites user. Internal Revenue Service (IRS) impersonation scams, along with technical support scams are the most common type of tricks used by the attackers on unsuspecting victims in order to achieve financial benefits. The ratio of cyber-crime in India is constantly rising due to various reasons. Cyber-crlminals are very difficult to trace and this advantage is fully utilize by scammers. In this paper an intensive review has been done on cyber-crime in India. The studies shows that fraud cases are increasing and the victims are mostly in the age group of 20 – 29 years. Mostly children and women are affected. Thus, awareness programs are required for preventing or avoiding cyber-crime in India.

A very few innovations have been noticed in the area of wheelchair production. This area is of utmost importance but yet unexplored. The innovations in this area can directly benefit disabled and old-age people and the medical staff would be the indirect beneficiaries. This research work is proposing a smart IoT enabled wheelchair cum bed keeping stability, safety, durability and affordability in mind. In addition, this research work also emphasized to make it foldable and versatile. The transformation of a wheelchair to bed and vice-versa was achieved by making use of hydraulic and linear actuators which in turn improved the patients comfort and functioning of a device. The entire system was tested in both the lab environment as well as in a real environment. A 3G Internet connectivity was used to transmit the patient’s vital parameters and to establish a face-to-face communication with a doctor. The system performed well even in a 3G environment. Thus, it can be predicted that it will perform even better in 4G environments.

advent of the internet and its impact on healthcare domain made it possible to store, access and update medical records anywhere and anytime. The term “Electronic Health Record(EHR)” refers to a digital format in which patient information is kept. Because the records are patient-centered, any authorised user can access them from any location, at any time, and from any device. Blockchain is a new technology, booming in most of the industries. Blockchain contains a number of built-in characteristics, including distributed ledger, decentralized storage, authenticity, confidentiality, and immutability and it has progressed actual applications in industries like healthcare. Blockchain achieves fortifying and averting the modification of electronic health records. Blockchain supports the quality of healthcare services to the benefits of both patients and hospitals. This paper investigates the existing techniques used in healthcare blockchain system in the domain of authentication, data integrity, and confidentiality.