The Indian market has become one of the largest cyprtocurrency markets in the world. The demand for the trade of cryptocurrencies is evident from the number of Indian exchanges that have taken root and grown in the past few years. The drivers instigating investor movement towards crypto-assets from the traditional option available among Indians is being evaluated in this study. The study aims to analyse and identify the key factors driving the investor sentiments towards trading in the highly volatile cryptocurrencies. It attempts to gain insight into the reasoning behind the investor decision to trade in them. Extensive review of literature sheds light on the factors driving investment in cryptocurrencies. It is evident that the awareness levels for crypto, the future of crypto investments and the potential returns that they are offering have created a huge interest among the general public. The reasoning behind trading and investing in cryptocurrencies are also unique in their nature. The adoption of taxation policies by the government although may seem like a deterrent however, the 'Fear-of-Missing-Out' or 'FOMO' seems to have caught hold of the investors in pushing such investment decisions. The sample consists of the retail investor and the investment advisors in India using the structured questionnaire. Stratified Sampling Technique was utilized for the purposes of this study. The Independent Samples t-test was used to analyse the data and final results were interpreted from them. The results showcase certain parameters such as awareness, promotion, global acceptance, future of investment, 'Fear-of-Missing-Out' or 'FOMO' etc. as some of the factors driving trade and investment in cryptocurrencies. There are significant differences in sentiments of Retail Investors and Investment Advisors. Gender also plays a role in sentiments driving these investment decisions and finally education levels of individuals are also critical as well.

Multi-criteria decision making (MCDM) have been utilized by a variety of researchers beginning in the early 1970 s with the goal of improving decision-making in the field manufacturing industry. MCDM is an operational research method that also incorporates software. This method assists in decision making by assessing numerous contradicting criteria or objectives all at once. The MCDM approaches have evolved throughout the course of the years, and as a result, several new ways have been developed. This review article performs a literature survey of various optimization techniques that are utilized to select the best possible option among various options in various criteria such as selection of supplier, selection of best raw material and optimization of machining parameters in manufacturing processes. The analysis of a number of approaches that was carried out in the study enables a specific approach to be chosen that is appropriate for the circumstance.

The fast progress of technology has led to a rise in recent decades in privacy and cyberattack issues. This effort focuses on job in keeping and anonymity-enhancing safe cloud computing services using a blockchain named. It is developed with two features—anonymous files and searches for illegally submitted content. On, cloud users may identify all users inside the application layer and access data using payment systems. Analysis is done on how well three different implementations pure ledger, composite block chain with a cache and a convention database—perform when it comes to obtaining data. The results show that the work with the caches beats the pure network and the conventional by 50% and 53.19 percent, respectfully.

The growth of digital technology presents organisations with both huge potential and significant obstacles. Given the growing awareness of “loT and Big Data”, the goal of this study was to lay out the present status of business digitizing and to add to existing theory. The “Internet of Things” (IoT), Big Data, and data analytics along with cloud platforms, all provide opportunities for industrial enterprises to use technology to transform their approaches, particularly in the implementation of “new service-oriented marketing strategies”. In other words, marketing managers can, in general, incorporate new gadgets into old processes or enhance the technical content of products or services. Businesses may gather and evaluate review information and data, allowing them to develop relevant marketing strategies. A collection of innovations constitutes the foundations of today's technology landscape, as well as the catalysts of the resulting business change. Among these technologies, the “Internet of Things” (IoT), particularly the “Industrial Internet of Things” (1IoT), is crucial in assisting businesses in increasing the utilization of their machines and in the creation of service-based products in manufacturing organisations. This research paper has considered survey or primary quantitative method for data collection based on impact of loT towards creating digital transformation. In this context, 55 random people are selected and collect their responses.

An automatic vending machine is designed to supply people with a variety of items, such as snacks, beverages, newspapers, and tickets without any human intervention. According to the money that is deposited into a vending machine as well as the product that has been selected by the user, the machine will determine the item and will distribute it to the user. In the proposed work, the vending machine has been designed to distribute fruits to the user as per their requirement. Classification algorithms have been used to predict the type of fruits required by the user with the help of the input provided by camera. The load cell is used to measure the kilogram or the quantity of the fruits as per the requirement by using some input peripherals like keyboard. The proposed system is also a user interactive based once. Here, there is a display device that has interfaced with the system and the display device will provide information such as the fruit which has been chosen and the quantity of the fruit that the user has entered and also shares the information on the status of the requirements. So, it will be useful for the user to know the process going in the vending machine. The raspberry pi microprocessor has employed here as a processor along the required input and output peripherals like LCD, Keypad, Load cell, camera, and motors. The machine learning algorithm like a support vector machine has been employed to predict the type of fruit as per the requirements of the user. The insertion of intelligence like machine learning algorithms in the vending machine is comparatively providing better performance. The long-term objective is to equip a vending machine solution that is both affordable and efficient, therefore boosting the shopping experience of customers and increasing the need for widespread deployment of intelligence in smart vending machines.

A composite material is a mixture of two materials which have different chemical and physical properties. When both these different materials are combined, a new specialized material is formed to perform a particular job, for example, an insulated material for electric components. There are different methods of making composite materials such as open molding manufacturing, closed molding manufacturing, etc. Out of various manufacturing methods the research study has considered open molding and closed molding manufacturing. The main purpose of the research study was to know the impact of various open molding and closed molding processes on the productivity of the composite materials. For open molding manufacturing, Filament Winding, Hand Lay-up, and Spray-up processes were considered. For closed molding manufacturing, Continuous Lamination, Pultrusion Molding, Compress Molding, Reinforced Reaction Injection Molding, Resin Transfer Molding and Vacuum Bag processes were considered. A comparison was done between open molding manufacturing processes and closed molding manufacturing processes by using multiple regression analysis tool in Mumbai city. Total 140 manufacturing units have been selected. The result of the study has shown the effectiveness of the processes in the productivity of the composite materials and found out a clear distinction between open molding and closed molding processes.

Customers spend for agro - based items made by farmers in agro - based supply chain operations (ASCs). People stress the significance of agri-food quality throughout this operation, while producers anticipate higher revenues. Effective tracing and governance for agri-food commodities encounter enormous hurdles as a result of the size and dynamism of ASCs. Nevertheless, the majority of the currently available solutions are unable to adequately address the accountability and administration needs of ASCs. First, in order to enable product tracing and provide organizational unit for the agri-food tracking data in ASCs, we develop a blockchain-based ASC architecture. The manufacturing and preservation of agri-food goods are then effectively decided in order to maximize profit using a Learning Based training-based Supply Chain Administration technology. To show the efficiency of the suggested cryptocurrency system and the DR-SCM approach in various ASC contexts, detailed simulation tests are conducted. The findings indicate that the proposed ledger ASC architecture provides a strong assurance of trustworthy product traceability. Moreover, compared to intuitive and Q-learning approaches, the DR-SCM may provide larger product profitability.

The information is accessible to everyone using a block chain-capable app. Each single set of information that comes has its own block. When a blocks is loaded with information and linked to the block before it, a historic path for data are generated. The openness and virtual moral purity of IoT technologies shield smart devices from cyber-attacks. The brick chain's ability to store data in events and validate these activities with node may be utilized to provide secure connection amongst Connected systems. Automation has advanced due to the Internet of Things (IOT). By using block chain, we can increase the safety and confidentiality. This paper's focus is on the architecture and functionality of blockchain-based IoT technologies for industrial automation. Along with the Blockchain's numerous characteristics, its benefits are being investigated. The use cases and blockchain appropriateness studies for secure industrial automation have also been performed. In the last section, We examine the security components of the blockchain for a comparative analysis in both Man-in-the-middle and denial-of-service attack scenarios.

With greater frequency and granularity than ever before, artificial intelligence (AI) and blockchain technology can help businesses achieve next-level performance in the supply chain. Blockchain can reliably construct the framework for recording data across endpoints, whereas AI can assist in data matching. This study investigates how to apply blockchain technology and artificial intelligence together in supply chains to push the boundaries of operational effectiveness, promote sustainable growth, and monetize data. This study has examined how blockchain can be used to track financial operations in the supply chain and also aims to determine how artificial intelligence contributes to supply chain management. The study’s anticipated results will increase knowledge of blockchain technology and artificial intelligence in supply chain risk management and encourage practitioners and researchers to think about using these technologies in future context-aware research.

The universal blockchain consensus mechanism was then examined, consists of a review of its features, highlighting benefits and drawbacks. Following that, we offered a taxonomic taxonomy as well as a detailed evaluation of previous approaches and approaches that employ machine learning (ML) approaches to address common security issues and abnormal behaviours in Bitcoin systems and cryptocurrencies. Furthermore, researchers discussed several unanswered research questions and potential research areas, as well as some closing thoughts. This paper evaluates the present degree of awareness on the many types of cryptocurrency crime that persist today or may occur in the future, as well as full explanations of the scams found. It is unknown how useful cryptocurrencies will be in the long term as terrorist techniques and cryptocurrencies evolve. Unfortunately, some recent advancements in cryptocurrency will make it easier for the most skilled terrorist groups to utilise them to attack Developed democracies, and cryptocurrencies will be incredibly beneficial for people who currently participate in overseas fundraising and illegal operations. Machine learning mechanism is developed for detecting raising threats in cryptocurrency. The suggested approach comprises of a detection technique that is integrated into many Bitcoin entities. Prior to creating the initial analysers, the suggested method employs a capture mechanism to extract an antigen symbol from the activity. To collect data, survey or primary quantitative data is conducted.

Stream processing is used to collect information from large-scale wireless networks, which is the subject of the current article. We describe the basic techniques for sample selection, information gathering, and network surveillance in wireless networks, as well as how understanding extracting may be thought of as a ML challenge on large data stream processing systems. We demonstrate the most important developments in big data stream processing technologies in this paper. Furthermore, we investigate the information preparation, extraction of features, and machine learning methods that may be implemented to the case of wireless network analytics in more detail. We talk about difficulties and academic research in wireless network surveillance & stream analysis, as well as their outcomes. Furthermore, different innovations in stream processing, including such deep learning or reinforcement learning, are expected to be explored further.

Electricity plays a major role in our daily life in the home, offices, industries, public places, schools, colleges, hospitals, and everywhere in the world. The energy meter in our homes will display the electricity consumed by the customer and pay the Electric Bill (EB) to the government according to the power consumed in the customer’s home. The energy is transferred from the transformer to the customer’s home. In most of those cases, when transferring the power from the transformer to the homes, there will be a power theft for many reasons.It is essential to analyze if there is power theft to reduce it. The main aim of this paper is to detect whether the power is theft or not by using the Internet of Things (IoT), NodeMCU microcontroller, Arduino IoT Cloud, voltage sensor, and,current sensor. The voltage and current are collected by using the sensors in the transformer and customer’s home. The power is calculated by using the voltage and current through NodeMCU, then it sends the data to the Arduino IoT cloud. When there is a power difference between the transformer and the home, then the power is theft. When there is no power difference between the transformer and the home, then the power is not theft. Customers can view the data through the mobile application of the Arduino IoT cloud.

In the early days, greenhouse energy did not pay much attention to coating inspections and new applications, spending more attention on repair solar energy projects instead. However, these attitudes have recently changed. Energy producers realize that preventing corrosion and deterioration is less expensive than solving the greenhouse problems when they occur. The proposed model also provides coating, paint control, and error analysis services within the scope of solar machinery and equipment-related services while the greenhouse equipment reached a low energy level. The greenhouse monitoring services ensure that a solar plant is economical, reliable, and of high quality, meets legal requirements, conforms to standards published by domestic and foreign organizations, and determines conditions that cause short circuits or power outages. In this context, with the help of cloud computing-based Internet of things (IOT), the industrial power stations, high-voltage substations, low-voltage networks, power stations that comply with legal regulations on safety from electricity, electrical installations for machinery, alarm systems, fire alarm systems, cathodic corrosion protection mechanisms in oil tanks and pipelines, emergency power supply installations, electrical installations in buildings, and gas alarm systems are inspected and documented.

Combining cloud computing and Internet of Things concept is the emerging trend for online management efficiently and the sensor data processing. Pervasive healthcare sector or using the Internet of Things paradigm and the study offers the platform the disc based on cloud computing for the administration of information healthcare department. The development of clear and organized healthcare monitoring systems is made possible by the Cloud IoT convergence of the Cloud and IoT. Different types of healthcare applications use various IoT based and cloud-based application for transmission of data and to connect with each other that enables the provision of clinical healthcare solutions effectively. The research study proposes a novel hybrid system that uses IoT smart appliance using metaheuristic swarm intelligence technique for storing data into the cloud then to balance the load with real-time providers at low-cost that receives data from the sensor devices of hospital and stored in cloud. The computer resources are revolutionizing algorithms for individualized inference and actions in health management and the data is protected using blockchain technology.

The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

Now a day’s health care cost has been increasing very rapidly. The healthcare services, hospitals and clinics cost for providing services to their patients have been increased very sharply. Therefore, these organizations are always struggling to cut down the cost so that high quality services can be provided to every segment of the people in less price. By doing this, every patient can get good services from the hospitals or clinics which will be monetarily under their reach. 5S methodology helps to achieve the goal of reduction in time, wastages and non-operative time. Which results in cost savings and good services to their patients. This enhances the productivity of the nursing staff and makes organization effective and efficient. A sample of total 40 nursing staff were selected among the existing hospitals in Nagpur city. The nursing staff comprising males and females were considered for the study. To find out the impact of 5S before and after implementation on the productivity of the nursing staff in hospitals, paired sample t-test was used.The research study has shown the influence of the 5S in enhancing the productivity of the nursing staff in terms of reduction in staff non-operative time, staff overtime, patients waiting time, theatre preparation time and cost involved in the processes.

Entrepreneurial Intention inspects a desire of and individual to start a new business. If the desire is high then the individual is motivated to go for a new business. The individual’s motivation is affected more or less by different demographic factors.This study focuses on the impact of demographic factors such as family business experience, region, category, specializationand gender on entrepreneurial intention of management students of Nagpur University in India. The survey sample includes 200 students from different reputed management colleges of Nagpur University. Factors analysis and multiple regression analysis were conducted to the data. It was found that demographic factors had a positive effect on the entrepreneurial intention. It was also found out that the region and specialization of students are the most influencing factors among the given demographic factors that affect entrepreneurial intention.

Conflict management is the ability to be able to identify and handle conflicts sensibly, fairly, and efficiently. This paper highlights the impact of demographics like age, gender, years of experience on the conflict resolution style of employees. For the purpose of measuring the impact of demographics on the conflict management style, a sample of 30 respondents is selected. The sample is a fair representation of both the genders.The various factors of conflict resolution are put in a 5 point rating scale and then questionnaire is totaled and the most preferred style of conflict resolution is identified. The five conflict resolution styles are Accommodating, Avoiding, Compromising, Collaborating and Dominating. For the purpose of analysis, Two way Anova is used to reflect the impact of age on the conflict resolution style.

Conflict management is the ability to be able to identify and handle conflicts sensibly, fairly, and efficiently. This paper highlights the various consequences of conflicts in a workplace and how it impacts the performance of an employee in the organisation.For the purpose of measuring the impact of conflict on the performance, a sample of 40 respondents is selected. Conflict Management factors like proper management of conflicts helps to strengthen employees’ relationships, resolve problems quickly and effectively, decreases overall tension which is good for a better working environment, leads to greater levels of productivity and creativity,quick decision-making, improves commitment and communication, reduces absenteeism and turnover,reduces stress, leads to collaboration, innovations and new ideas and leads to effective interaction with customers and other stakeholders are selected and put in a well designed questionnaire consisting of 5 point rating scale.For the purpose of analysis, Factor Analysis is used to reflect crucial factors that are responsible for the effective management of conflicts.