Jaypee Institute of Information Technology

Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work, Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price.

Nanostructured lipid carrier (NLC) is second generation smarter drug carrier system having solid matrix at room temperature. This carrier system is made up of physiological, biodegradable and biocompatible lipid materials and surfactants and is accepted by regulatory authorities for application in different drug delivery systems. The availability of many products in the market in short span of time reveals the success story of this delivery system. Since the introduction of the first product, around 30 NLC preparations are commercially available. NLC exhibit superior advantages over other colloidal carriers viz., nanoemulsions, polymeric nanoparticles, liposomes, SLN etc. and thus, have been explored to more extent in pharmaceutical technology. The whole set of unique advantages such as enhanced drug loading capacity, prevention of drug expulsion, leads to more flexibility for modulation of drug release and makes NLC versatile delivery system for various routes of administration. The present review gives insights on the definitions and characterization of NLC as colloidal carriers including the production techniques and suitable formulations. This review paper also highlights the importance of NLC in pharmaceutical applications for the various routes of drug delivery viz., topical, oral, pulmonary, ocular and parenteral administration and its future perspective as a pharmaceutical carrier.

Escherichia coli biofilm consists of a bacterial colony embedded in a matrix of extracellular polymeric substances (EPS) which protects the microbes from adverse environmental conditions and results in infection. Besides being the major causative agent for recurrent urinary tract infections, E. coli biofilm is also responsible for indwelling medical device-related infectivity. The cell-to-cell communication within the biofilm occurs due to quorum sensors that can modulate the key biochemical players enabling the bacteria to proliferate and intensify the resultant infections. The diversity in structural components of biofilm gets compounded due to the development of antibiotic resistance, hampering its eradication. Conventionally used antimicrobial agents have a restricted range of cellular targets and limited efficacy on biofilms. This emphasizes the need to explore the alternate therapeuticals like anti-adhesion compounds, phytochemicals, nanomaterials for effective drug delivery to restrict the growth of biofilm. The current review focuses on various aspects of E. coli biofilm development and the possible therapeutic approaches for prevention and treatment of biofilm-related infections.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

The wide spread of World Wide Web has brought a new way of expressing the sentiments of individuals. It is also a medium with a huge amount of information where users can view the opinion of other users that are classified into different sentiment classes and are increasingly growing as a key factor in decision making. This paper contributes to the sentiment analysis for customers' review classification which is helpful to analyze the information in the form of the number of tweets where opinions are highly unstructured and are either positive or negative, or somewhere in between of these two. For this we first pre-processed the dataset, after that extracted the adjective from the dataset that have some meaning which is called feature vector, then selected the feature vector list and thereafter applied machine learning based classification algorithms namely: Naive Bayes, Maximum entropy and SVM along with the Semantic Orientation based WordNet which extracts synonyms and similarity for the content feature. Finally we measured the performance of classifier in terms of recall, precision and accuracy.

Nanotechnology has seen exponential growth in last decade due to its unique physicochemical properties; however, the risk associated with this emerging technology has withdrawn ample attention in the past decade. Nanotoxicity is majorly contributed to the small size and large surface area of nanomaterials, which allow easy dispersion and invasion of anatomical barriers in human body. Unique physio-chemical properties of nanoparticles make the investigation of their toxic consequences intricate and challenging. This makes it important to have an in-depth knowledge of different mechanisms involved in nanomaterials's action and toxicity. Nano-toxicity has various effects on human health and diseases as they can easily enter into the humans via different routes, mainly respiratory, dermal, and gastrointestinal routes. This also limits the use of nanomaterials as therapeutic and diagnostic tools. This review focuses on the nanomaterial-cell interactions leading to toxicological responses. Different mechanisms involved in nanoparticle-mediated toxicity with the main focus on oxidative stress, genotoxic, and carcinogenic potential has also been discussed. Different methods and techniques used for the characterization of nanomaterials in food and other biological matrices have also been discussed in detail. Nano-toxicity on different organs-with the major focus on the cardiac and respiratory system-have been discussed. Conclusively, the risk management of nanotoxicity is also summarized. This review provides a better understanding of the current scenario of the nanotoxicology, disease progression due to nanomaterials, and their use in the food industry and medical therapeutics. Briefly, the required rules, regulations, and the need of policy makers has been discussed critically.

Apoptosis, a genetically programmed cellular event leads to biochemical and morphological changes in cells. Alterations in DNA caused by several factors affect nucleus and ultimately the entire cell leading to compromised function of the organ and organism. DNA, a master regulator of the cellular events, is an important biomolecule with regards to cell growth, cell death, cell migration and cell differentiation. It is therefore imperative to develop the staining techniques that may lead to visualize the changes in nucleus where DNA is housed, to comprehend the cellular pathophysiology. Over the years a number of nuclear staining techniques such as propidium iodide, Hoechst-33342, 4', 6-diamidino-2-phenylindole (DAPI), Acridine orange-Ethidium bromide staining, among others have been developed to assess the changes in DNA. Some nonnuclear staining techniques such as Annexin-V staining, which although does not stain DNA, but helps to identify the events that result from DNA alteration and leads to initiation of apoptotic cell death. In this review, we have briefly discussed some of the most commonly used fluorescent and nonfluorescent staining techniques that identify apoptotic changes in cell, DNA and the nucleus. These techniques help in differentiating several cellular and nuclear phenotypes that result from DNA damage and have been identified as specific to necrosis or early and late apoptosis as well as scores of other nuclear deformities occurring inside the cells.

In today’s world, everyone is expressive in one way or other. Many social websites and android applications whether being Facebook, WhatsApp or Twitter, in this highly advance and the modernized world is flooded with views and data. One of the most global and popular platforms is Twitter. This is seen as the main source of sentiments where almost every enthusiastic or social person tends to express his or her views in form of comments. These comments not only express the people but also give the understanding of their mood. Text present on these medias are unstructured in nature, so to process them firstly we need to pre-process, six pre-processing techniques are used and then features are extracted from the pre-processed data. There are so many feature extraction techniques such as Bag of Words, TF-IDF, word embedding, NLP(Natural Language Processing) based features like word count, noun count etc. In this paper we analysed the impact of two features TF-IDF word level and, N-Gram on SS-Tweet dataset of sentiment analysis. We found that by using TF-IDF word level (Term Frequency-Inverse Document Frequency) performance of sentiment analysis is 3-4% higher than using N-gram features, analysis is done using six classification algorithms(Decision Tree, Support vector Machine, K-Nearest Neighbour, Random Forest, Logistic Regression, Naive Bayes) and considering F-Score, Accuracy, Precision, and Recall performance parameters.

Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network. In present times, a smart healthcare system with a blockchain data network and healthcare 4.0 processes provides transparency, easy and faster accessibility, security, efficiency, etc. Healthcare 4.0 trends include industry 4.0 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc. The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 3.0 and Healthcare 4.0 in consideration with healthcare ground-realities. Here, healthcare 4.0 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms. The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3.js, Athena, etc. Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems. The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc. A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes. This work has explored optimization algorithms applicable to Healthcare 4.0 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system. Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems. This work has considered simulation and implementation to validate the proposed approach. Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits. The proposed system is active because block utilization lies above 80%. Automated smart contract execution is below 20 seconds. A good number (average 3 per simulation time) is generated in the network that indicates a health competition. Although there is error observed in simulation and implementation that lies between 0.55% and 4.24%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible.

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.

The advancement of Artificial Intelligence (AI) technology has accelerated the development of several systems that are elicited from it. This boom has made the systems vulnerable to security attacks and allows considerable bias in order to handle errors in the system. This puts humans at risk and leaves machines, robots, and data defenseless. Trustworthy AI (TAI) guarantees human value and the environment. In this paper, we present a comprehensive review of the state-of-the-art on how to build a Trustworthy and eXplainable AI, taking into account that AI is a black box with little insight into its underlying structure. The paper also discusses various TAI components, their corresponding bias, and inclinations that make the system unreliable. The study also discusses the necessity for TAI in many verticals, including banking, healthcare, autonomous system, and IoT. We unite the ways of building trust in all fragmented areas of data protection, pricing, expense, reliability, assurance, and decision-making processes utilizing TAI in several diverse industries and to differing degrees. It also emphasizes the importance of transparent and post hoc explanation models in the construction of an eXplainable AI and lists the potential drawbacks and pitfalls of building eXplainable AI. Finally, the policies for developing TAI in the autonomous vehicle construction sectors are thoroughly examined and eclectic ways of building a reliable, interpretable, eXplainable, and Trustworthy AI systems are explained to guarantee safe autonomous vehicle systems.

Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

Blended learning incorporates online learning experiences and helps students for meaningful learning through flexible online information and communication technologies, reduced overcrowded classroom presence, and planned teaching and learning experience. This study has conducted surveys of various tools, techniques, frameworks, and models useful for blended learning. This article has prepared a comprehensive survey of student, teacher, and management experiences in blended learning courses during COVID-19 and pre-COVID-19 times. The survey will be useful to faculty members, students, and management to adopt new tools and mindsets for positive outcomes. This work reports on implementing and assessing blended learning at two different universities (University of Petroleum and Energy Studies, India, and Jaypee Institute of Information Technology, Noida, India). The assessments prepare the benefits and challenges of learning (by students) and teaching (by faculty) blended learning courses with different online learning tools. Additionally, student performance in the traditional and blended learning courses was compared to list the concerns about effectively shifting the face-to-face courses to a blended learning model in emergencies like COVID-19. As a result, it has been observed that blended learning is helpful for school, university, and professional training. A large set of online and e-learning platforms are developed in recent times that can be used in blended learning to improve the learner's abilities. The use of similar tools (Blackboard, CodeTantra, and g suite) has fulfilled the requirements of the two universities, and timely conducted and completed all academic activities during pandemic times.

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

Mental stress is a major issue nowadays, especially among youngsters. The age that was considered once most carefree is now under a large amount of stress. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. In this paper, we are calculating the mental stress of students one week before the exam and during the usage of the internet. Our objective is to analyze stress in the college students at different points in his life. The effect that exam pressure or recruitments stress has on the student which often goes unnoticed. We will perform an analysis on how these factors affect the mind of a student and will also correlate this stress with the time spent on the internet. The dataset was taken from Jaypee Institute of Information Technology and it consisted of 206 student’s data. Four classification algorithms Linear Regression, Naïve Bayes, Random Forest, and SVM is applied and sensitivity, specificity, and accuracy are used as a performance parameter. The accuracy and performance of data are further enhanced by applying 10-Fold Cross-Validation. The highest accuracy recorded was by Support Vector Machine (85.71%).

The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for micro-transactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

INTRODUCTION: Green tea contains polyphenolic flavanoids such as epigallocatechin-3- gallate (EGCG), epicatechin-3-gallate (ECG), epigallocatechin (EGC) and epicatechin (EC). EGCG is the most abundant and active compound in green tea. Extensive research has shown that it has significant antioxidant, anti-carcinogenic, anti-microbial, and neuroprotective properties and has therapeutic potential against various human diseases. AREAS COVERED: This review focuses on the applications of EGCG alone, and in combination with other compounds, for the treatment of various types of cancers, metabolic, neurodegenerative, and microbial diseases, and discusses its mechanism of action in cell line and animal modesl. Recent advances, which include the use of nanoencapsulated EGCG to enhance the drug delivery and reduce cell toxicity, have also been discussed along with the comprehensive analysis of the specific granted patents associated with EGCG. EXPERT OPINION: Under the current scenario, the role of EGCG as a therapeutic agent is being utilised and new approaches are being formulated to overcome the problem of stability and bioavailability of EGCG. EGCG and its derivatives could be used for the development of drugs for the treatment of cancer, as well as various microbial, metabolic, and neurodegenerative diseases.