ICT Analysis and Applications

Anthrax is an antiquated and most serious disease that influences a wide scope of creature species and is brought about by bacterium Bacillus anthracis, which is a spore-framing bacterium. Harmful types of Bacillus anthracis have two enormous pathogenicity associated plasmids pXO1 and pXO2. pXO1 has the distinctive Bacillus anthracis poison qualities pagA, lef, and cya, whereas pXO2 has the qualities liable for capsule synthesis and degradation. B. anthracis express its pathogenic movement generally over the case and the creation of a poisonous compound including three proteins identified as defensive antigen (namely PA), deadly factor (namely LF), and edema factor (namely EF). These two enormous plasmids of Bacillus anthracis are fundamental for full pathogenicity, rejection of both of the plasmids very debilitates the harmfulness of B. anthracis. In the current assessment, we coordinated the investigation of the codon usage bias of destructive qualities stay alive in pXO1 plasmid of Bacillus anthracis. Codon usage bias assumes a significant part at the degree of quality articulation. Codon usage pattern of Bacillus anthracis is significant for understanding the transformative qualities in the species. To research codon usage pattern of the Bacillus anthracis genome, nucleotide arrangements of the virulent genes viz cya, lef, and pag were gathered from National Center for Biotechnology Information (NCBI). The phylogenetic investigation is done using MEGA X and Figtree. The RSCU of the selected genes were determined and these outcomes designated that codon usage bias exists in the virulent genes remains alive in pXO1 plasmid of Bacillus anthracis genome. These outcomes could be used for additional investigation on the transformative examination of the genome.

Xuan, Van Luu Van, Hoa Doan Hong, Dung LuuE-government is the latest trend in many countries in which the government is using the online system for delivering government services to citizens, and the digital signature is used to validate contents and authorize users who are involving in the E-governance system. Although there are many digital signature schemes currently known as RSA, Elgamal, the approach of improving the safety level of digital signature schemes based on the hardness of solving simultaneous difficult assumptions has still remained underdeveloped and attracted rising attentions from researchers. In this research, with the target to improve safety level of digital signature schemes, we present a new approach for constructing digital signature scheme. Algorithms that used in this digital signature scheme are built based on a new key form. This new key format is constructed using a new difficulty problem without high-efficiency solution, and it can be applied to construct digital signature algorithms. According to this research, a high-security digital signature class can be constructed by the proposed method in some actual situation.

Internet of Things (IoT) a tired framework necessitates the use of diverse technologies, for capturing, processing, analyzing, and storing large unprocessed data in cloud-based data centers for the IoTs to function as desired. The security and privacy are some of the critical concerns with increased communication among billons of IoT-connected devices. In this article, an overview of three-layered IoT architecture is provided, and the vulnerabilities and threats in all the three layers have been discussed. It also discusses the security practices that can be enforced in each layer due to limiting functionalities of current hardware technology. The article also explores some of the countermeasures and protection methods such as authentication, authorization, lightweight symmetric, and asymmetric algorithms that can be implemented against various attacks on all the three layers.

Vasagam, Swamiraj Nithiyanantha Sornam, MadasamyDuring the leather processing, identification of defect is highly critical because each and every square foot are significant while calculating the value in terms of currency. This process is unaviodable to make decision in the quality of intermittent leather which is predominately carried out by manually. However, it requires to be handled with the involvement of a human expertise, but there could be always risk for human error. To overcome this, a novel method is proposed which has been derived from Black Hat and Hough Transformations. The feature selection is done with the ensemble algorithms. The proposed method exhibits classification accuracy of 94.25% on validation dataset. Further, performance measures such as precision, ROC curve, sensitivity, error rate, specificity, and false positive rate have been reported to verify the effectiveness of the proposed approach.

The on-road traffic increases due to the exponential growth of the number of vehicles on the road. That leads to undesirable road conditions and makes urban population life uncomfortable. It includes traffic congestion, traffic jams, and long queues at traffic signals and toll booths. Many people violate road traffic rules and regulations rules every day. Manual human effort and supervision are not enough to resolve these issues. Still, many road traffic managements follow the traditional approach, which results increase in road accidents that makes humans seriously injured and even loss of life. To address these issues, we have proposed four strategies as (1) congestion status on square, (2) efficient toll tax collection, (3) fine collection on road for the people who violate road traffic rules and (4) shortest pathfinder for drivers. This research work mainly focuses on the various strategies and their implementation with the help of the MapReduce framework. We have combined video surveillance, big data analytics algorithm and implemented efficient road traffic management system.

Gomes, Elroy Gour, Tryambak Sinha, Arushi Dakshayani, R.In a world, where travelling is now not just limited to leisure family trips, an up-to-date optimal solution for business tours, event travelling is yet awaited. The aim is to accomplish this by creating a web application, called TravelBel-Business, Events and Leisure Travels (will be called TravelBel hereafter), using simple route based optimization algorithms, which processes real time data and gives the user(s), i.e. business travellers or people travelling for various events like competitions, weddings, etc., a complete travel package from the start to the end; which includes stay and travel with the best possible plans at the current time for the associated party, all in one place. The web application will take certain inputs from the users regarding the trip, which will be used to select and create the most relevant and optimal tour packages for the user(s). The web application gives the users various travel packages for the enterprise/planners who can chose one among the given options and edit a few particulars in it, for, e.g. the required level of luxury. In turn, the user, instead of doing different bookings through different websites, will get to complete all travel related bookings in one place, with tailor-made packages. Thus, making the booking and travelling experience simplified and efficient for all stakeholders.

The Internet of Things (IoT) is used in a variety of fields, covering almost all the aspects of the individuals’ day-today-life, organisations and society. With the Internet revolution, the IoT framework provides a platform for connecting many smart and self-configuring objects, such as watches, cars, tablets, wearable devices and home appliances. Despite the technological and societal advantages and the enormous economic potential of IoT, the issue of security and privacy in IoT is a major concern. IoT systems have problems with scalability, centralization, data security and privacy. This article provides an overview of IoT systems and reviews the IoT's typical security and privacy issues taking into consideration the biomedical domain. In addition, this article explores the convergence of blockchain and computing technologies for IoT applications and blockchain based mitigation of DDoS attacks.

Prupose—The term 'Industry 4.0' commonly used for fourth industrial revolution. It is mainly grounded on Internet of Things (IoT) where cloud based data exchange is done between machines or machine and human beings. In Industry 4.0, Internet of Things is widely used in manufacturing industry. Industry 4.0's design emphasizes worldwide machine networks in an intelligent plant environment that can exchange data and control each other autonomously. Manufacturing industries are adopting digitalization and automation to cope up with competition. Productivity improvement is possible by the fast development in manufacturing techniques and Digitalization. There are many issues and challenges while implementing Industry 4.0. The aim of this study is to offer an outline of research conducted on Industry 4.0, to identify difficulties and problems that arise with execution of Industry 4.0 and to identify gaps between academic research and industry requirement. Methodology—A Systematic Literature Review method is used in this study. Findings—Authors have outlined prominence of research in Industry 4.0 and at end given further research directions. Originality/Value—The findings in this paper is very crucial for industry leaders, policy makers, governments, researchers, decision makers and professionals.

Fog computing—an add-on of cloud computing—is an emerging computing paradigm having the capabilities to address the requirements of Internet of Things (IoT). Fog is well suited to find the key for latency, and bandwidth issue exists in cloud computing to fulfill the demand of IoT. Due to decentralized distributed nature of fog, several security and privacy challenges exist when fog nodes work together and exchange data to execute certain piece of work. One of the issues is how to establish reliable and trusted communication between fog nodes in fog layer. Trust is the level of assurance that is calculated on the basis of communication behavior used to detect malicious fog nodes in network (Patwary, Abdullah Al-Noman, et al. "FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain." Computer Communications 162 (2020): 212–224.). The conventional authentication methods (password based, certificated based and biometric based) will not work in fog because of its unique architecture. Additionally, it also consumes significantly more computation power and provokes latency issue. This survey paper discusses the fog computing three-layer architecture, state-of-the-art models and identifies various trust and security open challenges in fog computing.

When we exchange the data via network, we need more security. Security is main part in data communication. In this paper, we have to discuss how fuzzy logic is used in RSA algorithm for securing communication. Multimodal images use the fuzzy logic FIS rules to design 64 bits blowfish algorithm which increase security and improve the performance. This algorithm helps to protect the data from unauthorized access and runs faster. The proposed algorithm is designed using MATLAB R2017a. We also discussed advantages and disadvantages of fuzzy and RSA algorithm.

This paper studies the concepts that come into play when Administrative Reform initiatives attempt to leverage ICT to meet their reform objectives. ‘Grounded theory for literature review’ is used as a methodology to extract the concept and their dimensions that influence the use of ICT in an Administrative reform environment. The literature review revealed that the factors affecting the ICT implementation in government have been well documented. But when ICT is used as part of an Administrative reform, consideration of factors at a project level alone was found to be insufficient. The initiatives should also consider the effect of the big picture on the projects. This consists of two important contextual concepts; ICT context and Reform context which are often ignored. In other words, why and how ICT is being used and why and how the administration is being reformed are two important questions that need to be answered by the initiative. Based on the outcomes of the review a descriptive framework consisting of four concepts and twenty-one dimensions has been theorized. This framework can be used both by the practitioners and the political/social leadership as a risk evaluation framework. This study makes an original contribution by incorporating the two macro-level concepts that affect ICT implementation in a reform environment. Thus, the model developed helps to mitigate the risks of neglecting the two critical elements of ICT-context and Reform-context.

Cloud computing provides various range of services over the internet such as storage services virtual servers applications infrastructure, etc. Continuously growing cloud data centers are majorly dependent on physical machines and virtualization. The reliability of cloud is calculated based on service availability of its physical and virtual servers. In this paper, a brief analysis study is done on virtualization. Firstly, the concept of failure and its types are presented. Secondly, the existing failure rectification models and strategies are studied and analyzed. Finally, a summary is presented to conclude common failure causes in virtual environment. Thereafter, it is proposed that failure detection can be improved by two-level detection strategy by introducing confidence factor, α, as a counter in heartbeat strategy for fault detection.

Our world is making way towards complete digitalization, with more and more of data going to Internet. Thus, security of this data has become one of our utmost responsibilities. In 2018, 30 million cyber-attacks occurred all over the world costing $3.86 million on an average. With the statistics getting worse every year, it is very important to invest more in securing the data and more importantly design modern cryptographic techniques that are capable of mitigating modern adversaries. In this paper, we study different cryptographic algorithms that were designed after 1970 and are being used till date, and which were adopted and trusted to secure the data across the globe. Comparisons between different algorithms are being done on basis of flexibility, resistance against common attacks, execution speed, memory utilization, applications and the conclusion on best-suited encryption/cryptographic algorithms for securing our data is drawn. The study and comparisons of various algorithms have shown that asymmetric key encryption is far more secure than symmetric key encryption because of the application of two keys in place of one key and the algebraic complexity of their algorithms. Comparative analysis between symmetric key algorithms concluded that AES is the most versatile, secure and efficient algorithm. Comparative analysis between asymmetric key algorithms reveals that elliptical curve cryptography (ECC) is very secure due to its algebraic structure dependency on finite fields and elliptic curves.

The wireless data transmission has emerged as a residential data distribution process which is nowadays used for dissemination of public users in order to cater a large integer of mobile users. In this research paper, we have proposed a hybrid indexing schemes for data transmission which is based on the dispersion of indexing using hash table with Huffman-tree index coding. In the proposed work, we have theoretically explained the performance of existing indexing scheme using a detailed study and afterward have compared their performances. In our proposed approach we have used an indexing technology for retrieving geographic information in Wireless Environment. The proposed technology has been evaluated in terms of efficiency and time difficulty and proved to be very efficient during our experimental analysis.

The recognition and detection of activities of human have gathered a significant research importance in the field of computer vision. In the current paper, a performance evaluation on various techniques of Human Action Recognition (HAR) is carried out in detail. The applications of HAR are widespread and they range from Security Surveillance to Patient Health Monitoring Systems, Human-to-Computer interaction to Robotics, etc. In the recent past, many research articles have been published on HAR systems and HAR system is broadly divided into Pre-processing stage, Segmentation stage, Feature extraction stage, Action classifier stage and Action identification stage. The goal of the paper is to consider different stages of HAR system and to compare various algorithms used in these stages for input video datasets.

Finding high utility itemset (HUI) from transactional databases like customer transaction data is not an easy task. The generated HUI should represent the real-world condition which contains set of items that likely to be purchased together and also has high profit. Many algorithms have been introduced to overcome that issue. Parallelism is one of the good architectures that can be adapted for mining algorithms, and it is rarely used for HUI Mining. The basic idea that can be adapted is by dividing the mining process based on subsearch spaces in concurrent time to boost the performance. This paper proposed two new frameworks that adapted parallel mining namely CLB and PLB which are extension of ULB-Miner algorithm. CLB is hybrid algorithm from CTU-PRO and ULB-Miner, and PLB is proposed because of inefficient process in CLB. From the experimental evaluation, PLB outperforms ULB-Miner in all cases regarding execution time.

Electrical insulation is the most important component in generation, transmission and utilization of electrical energy. Progress in this domain is interrelated with the advancement in material sciences. This paper has incorporated detailed view of the research carried out over the last half century that has contributed to the progress of high-voltage transmission in a broad sense. The paper further explores thoroughly the attention needed to improve the characteristics of dielectric materials so that their range of applications can be expanded. The article also highlights some gaps and suggests some future areas of research.

Recorded healthcare data by using electronic health record (EHR) can be shared with numerous medical management units, hospitals, health centers, laboratory for identification of disease. Regardless of sharing data, it is vulnerable because it could be tempered or harmed as security and privacy have not been promised. In current time, it is shown that medical data is using cloud for storage, but there is also security, privacy and trust which has not been guaranteed. Therefore, researchers have come up with the solution known as blockchain technology to preserve data. By integrating cloud with this technology, data becomes invulnerable. As matter of fact, blockchain-based cloud technology can resolve lot of issues concerning healthcare industry. In this paper, systematic review of previous literature has been discussed. Also, state of the art on blockchain-based medical healthcare system has been presented by analyzing a few works. Also, the blockchain-based healthcare sector is gripped with some challenges. Significant among them being adoption of blockchain-based technique as it is quite different from the traditional approach, its adaptability and need for more research in the applications of blockchain for ensuring security and privacy of data. These issues have been mentioned in this paper. Some future directions can be of significant value for researchers and healthcare practitioners.

The exponential growth in technological development boosted the IC industry in production of IC chips in a span of 12–18 months. It has paved way for increased outsourcing which leads to malicious modification by inserting hardware trojans. It becomes necessary to detect these defects before IC fabrication in pre-silicon production stage to save the production cost. In this paper, we propose a method for HT detection using gate level netlist. Extracted features from the netlist of circuits are used for dataset creation. From the extracted features, designed deep learning neural network model figures out salient trojan features required for HT classification of nets into trojan-free and trojan infected nets. In addition to this, data imbalance problem occurs in dataset is taken care by preprocessing data using generative adversarial network. Deep learning-based hardware trojan classifier is evaluated with performance metrics like confusion matrix, F-measure, accuracy for ISCAS’85,’89 benchmark circuits and Trust-Hub circuits.

As dependency on credit cards and online transactions is increasing, the scope for unauthorized payments or fraudulent transactions is on the rise. Huge financial losses are borne by banks every year due to fraudulent transactions. The process of identifying such transactions among millions of transaction records can be perfected using machine learning models, even more so by ensembling them. This study takes a real-time financial dataset and analyzes the efficiency of various machine learning models in predicting fraudulent transactions. Individual ML models are also analysed, ensembled and tested on the dataset. Upon extensive analysis and research, it is concluded that an ensembled model of LightGBM, decision tree classifier and XGBoost gave the highest AuC score of 87%. Further, a user-interactive tool is also created, which predicts fraudulent transactions from real-time transactions. The tool makes use of the model with highest prediction efficiency and presents it in an interactive manner. This study and practical demonstration show advancement in prediction of fraudulent transactions when compared to existing ML models and its user-friendly software incorporation.

Analysts and professionals keep on drawing fluctuated interests in blockchain (BC) technology. The idea has- some key highlights, for example, decentralization, dependability, security, and information veracity. In any case, even with the developing interest, much more has to be done concerning the current state of knowledge and practice in relation to the use of BC technology in education. Accordingly, this study presents a review of the current investigations on BC-based learning applications in the education sector. This paper has focused on three major themes: learning applications that have been made using BC technology, the contribution and merits of blockchain technology to the education sector, and the problems experienced while integrating BC technology in the education sector. A concentrated conversation is introduced as well as a complete outcome examination of each topic. This paper also highlights the key areas of the education sector which tend to benefit from the application of Blockchain technology.

Image features play pivotal role in digital image processing and computer vision problems. To retrieve image features accurately and effectively, various texture descriptors are needed. This paper presents a comprehensive evaluation of various descriptors such as local binary pattern (LBP), significant local binary pattern (SLBP), gradient binary pattern (GBP) and its effectiveness in plethora of applications such as medical image analysis, object detection and classification. Moreover, the descriptors are validated using brain tumorous magnetic resonance image (MRI).

Women’s safety is one of the major concerns that have been the most important topic till date. As per National Crime Record Bureau (NCRB) reports, many women of various ages have been subjected to violence, domestic abuse and rape. When ladies normally need to drive late at night, it’s necessary to be alert and safe. While the government takes the measures appropriate for their protection, there are also free apps for women’s safety that can help them remain safe. These days, many women carry their cell phone with them, so it is critical to have at least one activated personal security app. In one way or the opposite, such a women security app will definitely make it easier. The current applications either web applications or the android apps had given a focus on the alert generation and informing the close ones or the police people for the emergency help when the victim is in need of help when a person is in danger. Main goal of this research review is to propose a women safety solution to generate a swift alert to change the route when the user reaches an area which is found to be risky for travel because of the identified reasons. We propose a system to develop the android application which maintains the currently available feature in the app by adding the new feature which helps by guiding them on the basis of “Prevention is better than cure”.

Education is a vital part of everyone’s life. With the advancement of the internet, online learning has gained a wide scope. With the availability of a wide variety of courses and course content available online, learners usually struggle while choosing the course that will be most beneficial to them. It’s mainly because of the learners having different learning styles, different level of understanding, and different knowledge domains. Along with this, the learners also suffer from improper monitoring and evaluation. The adaptive learning system adapts itself as per the learning style of the learner and an intelligent tutoring system helps in the monitoring and evaluation of the learner’s performance. An intelligent tutoring system also provides an immediate and customized response to the learner. With the combination of Adaptive learning system and intelligent learning system, the online learning system can be developed with advanced capabilities and can be more beneficial to the learners than traditional online learning system. There are various learning styles in which learners can be categorized. Various learning style models are Felder Silverman model, VAK model, David Kolb learning style model etc. The paper presents a discussion on the research work done in the area, showing the work done on adaptive learning systems and intelligent tutoring systems.

A Background gives an extra advantage to one’s presentation. In today’s world, we are digitalizing every section of the professional lives of humans, and often we need to be present digitally in an event. In those times, we look for a place which is has a decent background where we can present ourselves while maintaining our privacy. Privacy is important to everyone and we try to ensure our private details are not made public in any way. However, it is not possible for everyone to ensure a common background. If we can select our background digitally then it would solve this issue. Computer vision can help us in manipulation of background during video calls and ensure safety of our personal information. For this purpose, we are going to use Open CV to change the background to a video of our choice. We can select a video of our choice and upload it to the module and get the desired background by replacing it. The proposed technique is more efficient and execution time is lesser compared to existing techniques.

While giving the reviews the customers intentionally/unintentionally use the swear/bad words to express their disappointment to the service provider. Customer care representatives spend each day of their working lives dealing with a lot of angry customers and the casual use of curse words. Even when it is not any fault of their own, they have to be patient and help resolve issues with composure. This kind of interaction affects the mental health of the employees who have to work despite these unkind words, and it does not solve the problem rather it makes the world of the internet an unkind place. So, we want to make the internet savvy millennial aware that with a little effort, we could speak kindly to strangers, even in stressful scenarios. This paper proposes an application which helps users respect the person on the other side of a chat by offering an autocorrect option that replaces the swear word with the name of the related item for which user is complaining about.

It is a common practice to integrate data from more than one resource in this age of cloud and big data. It has never been this easy in the past to get such huge chunks of memory in one place and allow processing at the present high speeds. In the process of integrating data from various sources, it is seen that there could be a number of repeated tuples in the big data. This hampers all analysis and also could lead to serious problems of false implications in analysis leading to absolute failure of purpose of data research. The paper presents a review of existing novel method commonly used in detecting duplicate tuples which are different but provide same meaning to the real world. The paper discusses the need and uses of fuzzy logic to detect such records with or without the intervention of the user for confirmation for deletion or removal of such records. The degree of similarity is the key review in this paper.

In the age of digital change, IoT is a crucial technological trend nowadays. It is expected that the number of IoT devices will rise by 25.1 billion by 2025. In the IoT family, there is a subset that is SIoT (Social Internet of Things), which is a relatively recent concept. And a method of integrating IoT with social networking. SIoT is a simulation of human-to-human and object-to-object social networks where Humans are called intellectual and relational objects. They build their social network to accomplish common objectives such as enhancing accessibility, success, and productivity, as well as providing the services they need. The main contribution of this review article is to understand the concepts of SIoT. SIoT can be best described by its architecture which includes its components, the relationship.

In machine learning, text annotation is used to train models. Annotation is a precursor to named entity recognition (NER) in natural language processing problems. It is the labeling or highlighting of certain keywords, phrases or sentences through means of tags to help learning models better understand the complexities of human languages. During the annotation process, the features of the dataset are marked up using metadata tags. Annotation tools can make this process easier for users, reducing manual labor involved. A simple web tool has been developed which allows only authenticated users to manually upload and annotate text files and download the output in JSON format for internal use by the employees of optimum data analytics to prepare training data for spaCy models. Its UI is simple and intuitive, making it easy to use by the average user for NLP tasks. This paper discusses in detail about the need for STAT and its implementation aspects.

Ali, Hala S. Sridevi, R.While the number of devices is potentially increased in IoT system, cloud computing will face several challenges such as mobility support, distribution and location awareness, transmission delay, as well as the security threats. Substantially, fog computing is recently introduced as an extension of the cloud to address these tremendous challenges. Fog nodes are located geographically closer to IoT devices in order to shift the cloud services partially at the edge of network. However, IoT devices are resource-constrained and unable to secure themselves, and hence, they can be effortlessly hacked. Therefore, this paper proposes a credential-based authentication mechanism at the fog layer to provide the security requirements of IoT applications (e.g., confidentiality, privacy preserving, integrity and access control). Furthermore, simulations are conducted to evaluate the performance of the proposed approach using iFogSim simulator and Java Cryptography Architecture (JCA) library. The results show that the proposed mechanism ensures confidentiality of the secret key, privacy-preserving of the device attributes, in addition to detect the compromised IoT device.

The project/proposed product hinges on three domains of computational technology, i.e., machine learning, convolutional neural networks, and Internet of Things. The aim of the project is to invent a product that is helpful to the disabled section of society as ideally as possible try to as well as to acquaint ourselves with the much talked about and ever-growing domains of computer technology. The main functions that our proposed product will offer are detection of the obstructing object and alerting via a speaker (along with classification and distance of the object from the user) and a navigation system (which obtains live data of the current location of the user with the help of the UBLOX GPS module). The proposed product is designed in such a way so as to provide an all in one multitasking and hassle-free solution to our user and to ease the burden that comes along with the disability of blindness. The proposed product is touted as a boon to our users since it not only will help them in identifying the obstructions ahead them but will also help them to navigate from their current location to their destination with freedom and no fear.

Coronavirus (COVID-19), one of the deadliest pandemic diseases of the century, escalated at such a fast rate that around 25 million people around the world got infected. The impact of the virus made it a compulsion for the people to wear masks and apply sanitizer at regular intervals. Thus, for the safe and hygiene buying process of masks, sanitizer and other pharmaceutical products, an idea of a cashless and contactless dispenser was brought up named Hygieia. Hygieia is the name of the Greek goddess of health and sanitization. The purpose of this project is to make the buying process fast as well as ensure the safety of the customers by using digital payment methods. The customer can place the order with the help of speech instead of touching the keypads which is observed in most of the vending machines. Cashless payment option is also provided which is making this product a smart and viable automated dispenser.

In recent years, India has seen markets develop for a wide range of new crops, many of which have not been traditionally cultivated in the region. Various factors affect the productivity and profitability of crops, and farmers often have limited know-how regarding the suitability of new crops and varieties to soil and weather conditions, the requirement of fertilizers and other inputs, and cultivation practices. Their knowledge about market conditions is also lacking in such cases. In addition, even among conventional crops, hundreds of varieties with different characteristics are now available to farmers. In order to make informed decisions regarding crop and variety selection, a large amount of information must be processed. This information is often highly technical and is unavailable in regional languages. With an ever-expanding number of crops, varieties and processes, as well as rapidly changing climatic and market conditions, knowledge transfer through established agricultural extension services has become inadequate. The growth in mobile phone usage over recent years can promote the use of mobile applications for this purpose. Such an application can process agricultural information to help farmers make informed decisions regarding crop selection and help them achieve greater productivity and profitability. This paper discusses the frameworks used to develop Krishi Mitr (Farmer Friend), a mobile application that can enable farmers to select crops that are best suited to their soil and weather while factoring in the costs of inputs and labor, sustainability, pests and diseases, as well as market conditions. The variables that are included in arriving at the selection, the crop-selection algorithm, and the deployment of the Krishi Mitr mobile application has been detailed in this paper.

Since tiny pocket-sized individual gadgets (e.g., smartphones) together with big computing appliances or solutions (e.g., cloud computing or net banking) are pervasive in today's world, innumerable data bytes are created, stored, interchanged, yielded, and used to produce results in particular applications every minute. As a result, protecting data, computers (gadgets), as well as consumer anonymity in cyber sphere comes out to be a top priority for oneself, businesses, and governing bodies around the world. Machine learning (ML) is becoming popular in cybersecurity in recent years, with applications such as intrusion disclosure and biometric-based customer validation. However, both the training and testing phases of machine learning algorithms are prone to attacks, resulting in significant performance drops and security breaches. This paper intends to provide an overview of challenges faced by machine learning solutions for ICS and IIoT devices. It also presents thorough discussion of literature regarding security concerns of IIoT.

The Coronavirus outbreak in 2020 brought the world to a standstill. In the aftermath of COVID-19, communication technologies were greeted as exquisite mode of virtual interaction and collaboration. However, COVID-19 soon started stripping several sectors off their digital pride by exposing under-digitalisation, rate of technology adoption and/or infrastructural gaps particularly in low-income countries. This paper looks at Pakistan’s approach towards ICTs and digitalisation particularly during the pandemic. The paper highlights the alternative channels of communication that has led to an increasing number of electronic services despite the reduced teledensity and broadband penetration. The paper explores the case of branchless banking as well as the role of digital identity that has been crucial for health, education and social sector in Pakistan.

Very Large-Scale Integration (VLSI) is the process of creating integrated circuit (IC) by combining millions of transistors into a single chip. This technology is used in most applications in all domains. In this paper, we describe the patent pending work of development of intelligent machine to do VLSI design modeling from the requirements. The Vinyas Design Bot is an intelligent bot which automates the process of capturing design requirements in the form of chats, discussion notes and conversation and generates Verilog design database from it. The work involves extracting specification from the requirements by Natural Language Processing (NLP), deriving logic from specifications by convolutional neural networks (CNN) and performing optimization by artificial intelligence (AI) to generate the project database with Verilog HDL models and electronic design automation (EDA) run scripts. The functionality of Vinyas Design Bot is demonstrated by two case studies – ALU design and I2C design.

The brain tumor is a devastating disease, which shortens the life expectancy quite a bit in their highest grade. We aim to present a multiclass brain tumor classification algorithm using deep networks convolution neural networks with loss function as the binary cross-entropy and optimizer as Adam and ResNet50 on multiple datasets of Magnetic Resonance Images. Deep learning has earned a lot of development in the medical field in the last few years. MRI has been a constructive tool for medical research in current years and has many applications like brain tumor detection. In the detection of tumors, MRI images of the brain were classified as tumorous or non-tumorous using deep network CNN. In the classification, contrast-enhanced MR images are used to classify the images into three types of brain tumor: pituitary, glioma, and meningioma. By making use of this, we can identify and give the required medication to the patients.

Tasnim, Jarin Alam, Tanvir Saha, Samapty Rabbi, Md ForhadWhile ICT is burgeoning in southeast Asia, online food delivery (OFD) picking upstream due to its concrete influence on the mob’s experience. The COVID-19 pandemic caused an unprecedented impact in most commerce including OFD due to the escalation of safety aspects. On account of the explosion of the pandemic and to prevent the spread of COVID-19, socio- and economic factors arise that likely turn OFD potential user’s attitudes and behavior. This report highlights a six-month-long online survey (n = 158) in Bangladesh that analyzes the fluctuation in OFD consumer tendency, identifying the polarization of potential purchasers during the COVID-19 and considering the safety, e-satisfaction, and e-trust. Besides, we also discussed the associations between these determinants that are responsible for polarizing the consumers into marginal groups during the pandemic in developing countries like Bangladesh and proposed some suggestions for OFD service providers based on our findings.

“Road Accident Alert System” is being created keeping in mind the large number of road accidents and related casualties that occur every day in Bengaluru, India. Knowing the conditions under which these accidents happen and where they happen is a very powerful information that can be used to take action to avoid them. Our main objective is to identify the accident hotspots in the city with the help of thorough and discrete data given about the various road accidents taking place in the city. Our app will alert the user regarding the accident hotspots while using the Web app for navigating from source to destination. The proposed idea precisely focuses on the accident-prone zones in Bengaluru. Using the dataset available, the blackspots are identified and clustered to form accident zones within the city. This paper will focus on the implementation of clustering algorithm to generate the list of blackspots, the technical requirements and voice alerts given to the user.

Oftentimes, we as humans want an instant answer and reply to the many questions in our heads, and it is not always possible to get answers for our queries at one go. However, with the advancement in the field of artificial intelligence, we can overcome these challenges with the help of a chatbot. This chatbot is a software influenced by an AI that gives an automatic response to the user. In this paper, the authors have utilized a tool called Dialogflow for implementation of a chatbot that can simulate a language translator for Paite. Dialogflow has a pre-trained machine learning-based model that allows us to feed in our data for automatic response. The proposed application is to give translation in Paite for the input words and sentences. The Chatbot serves as a Paite language teaching bot in this application. This application is beneficial for both the end user and the developer as the user gets his/her desired response, and the developer saves his/her time giving response to the many queries received daily. Furthermore, we will discuss in detail about the workflow and the methodology behind implementation of this work.

Diabetes is a metabolic disease that severely affects the human body causing high sugar levels in the blood. Machine learning has been used for predicting various diseases. Researchers across the globe are motivated to experiment with different methods to predict diabetes at an early stage. Different classifiers are used for predicting diabetes, yet some classifiers are barely analyzed. Predicting diabetes at an early stage with easy to measure factors will help patients and healthcare workers to tackle the consequences of diabetes at starting stage and save lives. Also, feature selection plays an important role in building any machine learning model, and it becomes even more major aspect when dealing with medical data. In this paper, various techniques for feature selection and prediction of diabetes are reviewed.

The pandemic period is an opportunity and threat to be able to meet national food needs. Restrictions on outdoor activities change consumer behavior in meeting their daily food needs, like food purchases that were done at brick and mortar saw a shift to online. Technological developments support the fulfillment of food needs with the availability of an e-marketplace offering a variety of agricultural products. This study discusses consumer purchase decisions on e-marketplaces for agricultural food products. Population in this study is consumers who have used e-marketplace to purchase agricultural products. The results showed that the elements of marketing mix and e-marketplace performance simultaneously effect on consumer purchasing decisions. Collectively, this impact is 43.6% meaning that marketing mix and e-marketplace Web site hold high impact on purchase decisions of agricultural products. Additionally, by the use of e-marketplaces by paying attention to the element of marketing mix, farmers can cut the length of distribution chain, thereby increasing their income.

Internet of things (IoT) is helping us to develop smart cities. Smart cities need security. Human authentication through surveillance is one of the important issues. Several machine learning algorithms can be used to identify a person on basis of their movements and gestures. In this paper, we devised a supervised machine learning technique for the classification of images. We tested our method on the CASIA-B data set which is available in the public domain. It consists of 124 subjects. We found our method outperforming the existing schemes, viz support vector machine (SVM), k-nearest neighbor (kNN), neural network (NN), convolutional neural network (CNN), fuzzy set theory and discrete Fourier transform.

Blockchain-enabled e-voting system (BEV) is the need of the hour for many organizations and for democracies. With allegations being raised on validity of the voting system and mass protests, e.g., in the USA as well as the allegations of some hired tech companies interfering in elections of different countries, it is indispensable to have a reliable voting system to know the opinion of the voters. As paper ballot has its own issues of processing, safety and ease of e-voting is the way forward. Blockchain-based e-voting system can fix the loopholes that lead to voting frauds by first providing a unique ID to the voter and the eligible voters can cast a ballot anonymously at his comfort using a PC or smartphone. Second, the BEV’s encrypted key and tamper-proof personal IDs can help to ensure the validity of the submitted vote, if any discrepancy happens it can be easily identified by the modified blockchain. The blockchain-enabled e-voting system relies on the encrypted data transfer and storage on distributed ledger that can be confirmed from any node of the blockchain. The voter ID cards are issued to the eligible voters, this ID card contains encrypted files that identify the owner and allows him/her to carry out a number of online transactions; say, for example, for a single time voting a single “coin” can be uploaded to the wallet of the user account that can be used to cast their vote only once. The voters are allowed to change their vote as many times they wish for a particular duration, say three days till the voting closes. Once the user confirms the vote by using the verification PIN or credentials, it is verified for authenticity. In the next stage, the identified authentic votes are transferred to the counting server and accounted for as advised by the voter. A non-blockchain e-voting system may leave many points where the frauds can happen for example under reporting of votes received, selecting a few responses that favor some candidate, corrupting the data, or erasing the data rendering the voting exercise invalid, etc. Blockchain proposes the possible solutions to each of these problems of the existing e-voting systems in practice. The BEV can provide the solution to the challenges any voting system in any level of organization may face, e.g., no coercion of the voters, anonymity of the voter, proof of a valid vote, correct counting and accounting of votes, avoiding single entity control on vote tallying and election result announcement, weeding out ineligible voters. All these issues will be discussed in detail in the chapter. This chapter often uses blockchain currency analogy to keep the reader interested and makes it easier for the reader who is not familiar with the blockchain technology or is familiar only with the bitcoins and focuses on major issues like voter access and voter fraud. Some used cases are also provided to illustrate the benefits and challenges in implementing the blockchain-based e-voting system industry that has remained at the forefront when it comes to technology adaptation and it seems the case for adaptation of blockchain technology. Blockchain technology has appeared as a landmark revolution in data encryption and storage. The distributed ledger system allows the hashes to be available with multiple nodes any time and tampering and modification create a new hash hence makes it easy to confirm the authenticity of the data files and track the node where the changes happened. Blockchain technology promises a great future for digital data security for digital transactions be it informational or monetary. Industries that deal with information exchange between multiple stakeholders like aviation industry have found multiple applications of blockchain technology, and the adaptations in the industry are advancing but still there is a long way to go. In this chapter, we discuss various transaction situations in the aviation industry where the blockchain technology can be of use or have already been put to practice.

This paper presents a novel approach on development of autonomous-based navigation system with the visual from camera interface and without depending on global positioning system. This system consists of several parameters which lead to development of complex operation to be performed. Angle prediction is used as to steer the UAV to its original path. By remote sensor and camera, the data get feed on the system path by few iteration of overall dataset. This system realizes on deep convolutional neural network (CNN) combined with the steering path with inclusion the degree of freedom like yaw, roll, and pitch. The system is then able to get calculated by the generated dataset helped form Udacity simulator. By this simulator, we get the prediction of the direction of the way points. Then, the model gets trained on the behalf of this content, and finally, run on the simulation model on gazebo to check the error loss in the performance of them. The model is suitable for development in autonomous-drone navigation system that exhibits similar location such as environmental and desertification areas.

The aviation industry has remained at the forefront when it comes to technology adaptation and it seems the case for adaptation of blockchain technology. Blockchain technology has appeared as a landmark revolution in data encryption and storage. The distributed ledger system allows the hashes to be available with multiple nodes any time and tampering or modification creates a new hash hence makes it easy to confirm the authenticity of the data files and track the node where the changes happened. Blockchain technology promises a great future for digital data security for digital transactions be it informational or monetary. Industries that deal with information exchange between multiple stakeholders like aviation industry have found multiple applications of blockchain technology, the adaptations in the industry are advancing but still, there is a long way to go. In this chapter, we discuss various transaction situations in the aviation industry where the blockchain technology can be of use or have already been put to practice.

In today’s world, the food you take in your body is not as healthy as it should be. Our body takes energy from the food, and there are some use cases where our body prevents this system by not properly consuming energy from the food we eat. The main disease that arises from this condition is diabetes mellitus. This disease is a silent killer that makes our body weaker by producing less insulin which is important. As diabetes is a chronic disease, it would be more dangerous in extreme cases. It has attracted the research area a lot. Machine learning schemes help in predicting this disease. Therefore, we devised a machine learning-based scheme for diabetes prediction. Missing features need to be found, then only the correlation will be better to other segments of the body. We must compensate for missing values in the dataset by allowing some algorithm to predict them. So, we used an algorithm, viz., naive Bayes to balance the dataset class and reduce the class imbalance factor. Then, we performed classification using an RF classifier. Our proposed work speeds up the execution time by introducing the algorithm of ensemble learning techniques like hyperparameter optimization. Here, we used RandomsearchCV. We used the best estimator like the XGBoost algorithm which increases the speed of execution and, thus, the performance. We used the PIMA dataset (PIMA Indian diabetes dataset) from California University and UCI (Irvine) repository for testing our scheme.

Artificial intelligence (AI) is playing very significant role in every domain of life and spreading widely all across the globe. It covers all major fields like automation, hospitality industries, medical sectors, technology industry, and defense services. AI assists the painstaking development of data compilation which is a fundamental element of AI expansion. Since, all are using AI without knowing much about the ethics and fairness of AI and using it only as per their requirements. AI ethics is a set of values, principles, and techniques that utilize generally established values of accurate and inaccurate to direct ethical behavior in the expansion and use of AI technologies. The Organization for Economic Co-operation and Development (OECD) principles on AI introduced to encourage artificial intelligence that is pioneering and reliable and respects human rights and ethical values. Here, in this paper, it is tried to demonstrate FAST track principles, i.e., fairness, accountability, sustainability, and transparency along with OECD. These principles will also can be utilized when we are using this technology in hospitality industry. During the international pandemic situation (COVID-19) when everyone used to avoid stay and visits to hotels and restaurants, AI and some other technologies make it possible to provide safe and hygienic stay to the customers. Here, in this paper, we also cover the benefits and advantages of AI in this domain.

Human bodies constantly generate the signal or information which has valuable information about the health. These signals should be extracted from the body, and it has to be processed to diagnose the disease. Using these informations, the blood pressure, haemoglobin levels in blood, brain activities, heart functions, and etc. can be measured invasive and non-invasively. The signal obtained from the human body will contain noises, and it has low-amplitude raw data which cannot be used directly for the diagnose purpose. In order to make it to be useful, the signal should be filtered to remove the noise and amplified to extract the exact information. Signal processing contributes a major role in the medical technologies to diagnose the disease and provide solution to cure. In this review, applications of signal processing particularly in ECG medical technologies are discussed. The methods and algorithms to overcome the ECG noises are discussed briefly. Also, the improvement of those technologies for the betterment of human health care is also discussed.

The motivation behind this investigation is to figure out which pointers are more equipped for demonstrating more precise trade and purchase indicators on the NIFTYFIFTY record by utilizing the oscillator marker MACD, BB and RSI. An outcomes in current examination demonstrate that the trade sign be able to caught well by the BB and MACDmarkers, yet it cannot be caught appropriately through RSI, and capacity can be little or caption and inside manners, despite the fact the MACD assumes a too moderate function in catching the sign purchase contrasted with Bollinger groups and RSI. The utilization of a solitary pointer will not ever appearance a purchase and trade signal which is truly exact, and this depends on the aftereffects of exploration that spectacles the distinction in idealness in BB, RSI and furthermore MACD which is the fusion of a few sorts of markers will be improved contrasted with utilizing single markers. Despite the fact that in measurements there are no critical contrasts, there are just contrasts in augmentation and improvement in the situation of existing qualities; however, in this situation, the request for this worth is pivotal for dealers since it requires high precision to decide the correct choice in day by day exchanges. Further, the investigation has indicated that, however, both RSI and MACD generate signals earlier, yet they are unsafe as the quantity of bogus signs produced by them is additionally discovered to be very high. The examination is significant as the discoveries can be utilized by financial specialists, choice brokers and portfolio administrators to get create beneficial exchanging signals and acquire great danger to compensate proportions.

Ribu Hassini, S. Gireesh Kumar, T. Kowshik Hurshan, S.The two major components in monitoring and controlling industrial processes are SCADA (Supervisory Control and Data Acquisition) and ICS (Industrial Control System). Since their demand has been increased all over the World, they have gained more attention and because of their efficiency and high performance, these systems became mandatory in all countries. In recent times it has become an interesting target for adversaries because most of the infrastructure is automated but security is not strong enough to protect the entire system. Many loopholes make the attacker a way to exploit the vulnerability, hence protecting these systems is becoming more critical. A Hardware-in-the-loop testbed is used in this paper and its main purpose is to simulate power generation units and various attacks are performed on this testbed as well as the attack dataset is also exploited. In this research paper different machine learning algorithms are applied to the dataset and it is found that AdaBoost has better accuracy and performance compared to other algorithms and when it comes to deep learning CNN has the best accuracy compared to other ones.

The statistics on the vehicle such as locations visited, driver status logs and usage of the vehicle is needed by the authority for validating the vehicular information. We need a black box solution to capture the live details of the vehicle. GNSS can be used as a tool to view the digital map with the help of digital tachograph. Using the coordinates of different countries in the form of latitude and longitude, we are proposing a solution to view the digital map of different digital tachograph-supported countries from European Union. The proposed solution is implemented by keeping the road safety rules suggested by European Union. The solution framework is deployed as part of vehicle unit and hence can fetch and store the coordinates of locations traveled by the vehicle. The unit of time is implemented as per recommended minimum specification sentences. The sentences are used to validate the coordinates. If the position is valid, the tachograph can record the location details, otherwise location is ignored. With the valid result, the tachograph in the vehicle extracts the value from HDOP from GSA. The security policy is applied and the value available on the settler system is considered to process the validated values. The DTCO with version R4.1 has a highly sensitive integrated GNSS module with access to satellite system such as Galileo, GPS and GLONASS. GNSS uses NMEA0183 conventions and generates time conflict and GNSS time difference more than one minute. GNSS allow users with capable device to determine their position, velocity and time from the satellite and we propose a solution as visual verification tool to map the DTCO R4.1 GNSS. The tool uses the data and protocol deployed in the vehicle unit and accurate positioning information data is transferred.

The COVID-19 has bought drastic changes in the life styles of the people. It mainly changed the educational system from offline class room teaching to online virtual teaching from schools to higher post-graduation studies. This paper discusses various comparisons and challenges of online teaching tools available in India during pandemic. Different pros and cons of the mostly used virtual platforms are mentioned here so that educators can choose the better tool to make their e-learning easy. Different challenges and constrains of the virtual or online teaching are showcased and their possible solutions are also enlightened. Constrains and solutions include the involvement of students, teachers and also the parents to make their e-learning most efficient.

With the advent of cloud computing, data can be distributed in remote servers. Data is distributed in remote servers instead of keeping all the data in the local server, and the principles of cloud computing are applied to all the servers that hold data. This paper uses a cloud simulator that works in a distributed environment using two server models that holds data from the Computer Science and Engineering department of our college. Then, we apply the dogma of cloud computing to the data present in the servers. Distributed cloud environment reduces overheads of communication, latencies and costs by offering efficient storage resources and orderly computation. This paper tries a build a model of networking as service that gives a virtual and flexible environment to the users for doing computation work. It also helps the users to dispatch their requirements using this virtual domain.

Insider threats are security threats by actors having access to the organization’s IT infrastructure like employees, contractors, etc. Conventional network security systems can fail in detecting insider threats as the malicious activity never crosses the endpoint. Most researchers focus on monitoring a user’s activity on a host machine to detect threats. However, in the absence of a monitoring mechanism when responding to an insider threat attack, it is impossible to detect the threats. In our paper, we apply forensic analysis of a Windows machine to detect insider attacks.

Optical character recognition (OCR) is the process of extracting the characters from a digital image. The concept behind OCR is to acquire a text in a video or image formats and extract the characters from that image and present it to the user in an editable format. In this study, a convolutional neural network (CNN) is applied, which is a mathematical representation of the functionality of the human brain, using back-propagation algorithm with test case files of English alphabets and numbers. The purpose of this study is to test systems capable of recognizing vehicle plate number English alphabets and numbers with different fonts, and to be familiar with CNN and digital image processing applied for character recognition. Scientific journals and reports were used to research the relevant information required for the thesis project. The chosen software was then trained and tested with both computer and video output files. The tests revealed that the OCR software can recognize both vehicular plate and computer alphabets and learns to do it better with each iteration. The study shows that although the system needs more training for vehicular plate characters than computerized fonts, and the use of CNN in OCR is of great benefit and allows for quicker and better character recognition.

Teaching in the hotel management courses involves both practical and theory classes, as this is a skill based professional and practical oriented course were teaching has to encompass 50% of practical lab classes to develop the skills and talents of the students and the remaining 50% for teaching theory, which imparts knowledge to understand the concepts, and ideas accordingly to operate in the departments of hotel industry. Since learning provides an opportunity to student on acquiring new knowledge and skill with a positive attitude that motivates them to work in hotel industry, due to the outbreak corona virus which has disrupted the classroom teaching and thrown into disarray of practical and theory classes along with exam schedules, in addition to force the students and teachers to stay in indoors. The purpose of this study is to recognize the best tools in handling online classes. Furthermore, to know their participation in online classes with comparison of offline classes. However, teaching and learning process have not been stopped with the latest technology to meet the drift by Covid-19 pandemic situation, social media application (SMA) acts as a right tool for teaching and learning process.

Traveling salesman problem (TSP) is one of the most fundamental constraint satisfaction and optimization problem in computer science and engineering. In this paper, we have adapted the plate tectonics based neighborhood search optimization technique on this problem. This theory explains and describes the large scale motions of Earth’s lithosphere. This actually motivates the application of plate tectonics and create a new paradigm, i.e., plate tectonics based neighborhood search optimization. PBO search technique find solutions for the traveling salesman problem (TSP) which depend very much on the way the initialization part is handled. We have used biogeography based optimization BBO for it and also tried many crossover operators in it. As the movements along the paths in solving TSP problem are continual and happen to be very stable, it naturally induces optimized solution of a function by choosing the most stable position. The proposed algorithm can exploit the local information available from the gradients of the function, while also allowing for exploration to scout for better solutions throughout the search space. This allows the algorithm to improve the known solution in each iteration and locate the global minimum efficient. Our results are comparable to ACO and PSO. For the dataset Att48, the number of cities are 48, and the optimal cost is 33522. The cost using ACO and PSO are 33,514.1 and 33,534.2, respectively, whereas the cost using PBO is 33, 537.1. For this case, the cost by PBO lies between ACO and PSO. The cost is better than ACO. Comparable results of many datasets are discussed in the paper.

The access point (AP) assignment of IEEE 802.11 standards is a universal network challenge. It can be smartphones, Wi-Fi, etc., in terms of network equipment. Two methods may be carried out depending on the flow-type or the frequency of the RSSI signal for the globally assigned Software defined network (SDN) AP. The flow style AP assignment is performed in this article. As a researcher studying various algorithms that are helpful to SDN AP assignments and also various feature types that are useful to make current work more accurate. This paper discusses the mobile connectivity AP assignment algorithm using SDN architecture.

Nowadays, the world suffers from a terrible problem with COVID-19. Some researchers worked for a facemask detection biometric system by using Residual Networks such as Resnet50 or Resnet100. This research focuses on depreciating the transmission of Corona Virus Disease (COVID) from person to person. To wear a facemask plays an excellent role in not spreading COVID to the community. Moreover, to enhance social life, automatic facemask detection and facemask recognition systems are needed. Additionally, this system is helpful for security enhancement through face recognition. Here, the research for developing and implementing a biometric system for banks, offices, universities, and other public areas. This system helps in detecting the facemask for wearing by the human With Artificial Intelligence (AI), Deep Learning (DL), and Digital Image Processing technologies, this system allows the person to enter inside or stay outside because of not wearing a facemask. Besides designing and implementing a new model, the authors will discuss pre-defined the VGG16 deep learning model of CNN (Convolution Neural Network). Further, conclude the performance of both CNN models. This paper proposes a Facemask Detection model for solving the problem of manual care. This proposed model is a combination of most trending technologies such as AI, DL, and Image processing. In the future, describe this newly implemented model by appending Gaussian noise with images and make the system more reliable for actual use.

The growth of multinational enterprises has created a platform for Cloud cafeteria by digitizing their workplaces through mobile application assisted by ICT (Information and Communication Technology) and IoT (Internet of Things), for making their food preferences before their meal by placing their order through mobile app from workstation and visiting the counter once their food is ready to serve. This lessens the turnaround time for the decided food choice and furthermore decreases the heap on the cafeteria particularly during top long periods especially for dining, some of the multinational enterprises provides access to WIFI and furthermore as another option to utilize this facility on contract. This innovation stage of interface facilitates the Corporate Employees to communicate with food vendors and the Company Administration continuously, through a personalized application for employees of corporate companies. The aim of this research is to identify the part of mobile app on providing the convenience for corporate employees during their lunch and dinner sessions and secondly focused on the association of ICT with this mobile app.

Deception, an act of misleading into false belief, has been proven to be an effective method to counter cyber-attacks. Although prior research in deception in cybersecurity has focused on the network size and the proportion of honeypots in games, there has been little research on the influence of probing action costs on adversarial decisions. In this research, using a deception game (DG), we investigate the impact of different cost functions in the probe stage on adversarial decisions. The DG involved a game DG (n, k, γ), where n was the number of webservers, k was the number of honeypot webservers in the network, and γ was the number of probes an adversary could make before choosing to attack the network. In an experiment, three between-subject conditions that differed in the cost of probing actions included: increasing-cost (40 participants), constant-cost (40 participants), and no-cost (40 participants). In increasing-cost, the cost for probing honeypots increased linearly over trials. However, in constant-cost, the cost for probing a honeypot webserver remained constant. In no-cost, probing a webserver did not cost the adversary. Results revealed that the probing cost did not influence the probe and attack actions and that there was a significant interaction between different cost conditions and the regular webserver probe actions over blocks. The main implication of the research is that the probing action costs may not influence adversarial decisions.

Digital wellbeing is frequently defined in terms of the skills that people require to prosperously make use of digital technologies. Social, personal, and professional life is affected by the way people handle digital life which affects the overall development of an individual. The paper discusses and analyzes how digital wellbeing apps are useful for people in various scenarios and performed a survey on youths to identify what effect does it have on youngsters. The paper explains the role of digital wellbeing in different fields such as education system, health care, and agriculture which are the pillars of society.

The purpose of this paper is to present an optimized approach for clustering the social media community. The current system works on traditional algorithms and uses rule-based classification and clustering algorithms. This paper presents an approach wherein current pre-processing techniques are enhanced and an optimized approach for an augmented target user base leading to an effective advertisement is presented. The categories for classification are based on professions, stages of life, activities, etc.

The optimal allocation of distributed generation at optimal bus so that the performances of power system networks have been improved which is presented using Particle Swarm Optimisation Technique. The voltage stability and power losses are main concern for safe operations of power system networks. Recently due to their stressed operations for increasing electrical power loads, voltage instability and voltage collapse are evitable, which are main problems in the power system networks. By using different type of DG of optimal size at optimal location, the overall power losses are reduced and voltage profile of power system network is improved. The particle swarm optimization (PSO) technique has been used for optimal location of distributed generation (DG type-1) of the Optimal value to reduce overall power losses and maintained voltage profile. The results show that the overall power losses of the power system networks are reduced and at the same time the voltage profile is maintained using optimally locating DG in the power system networks. The proposed methodologies are tested on IEEE-9 bus, IEEE-24 bus and IEEE-30 bus distribution systems using MATLAB tool-box and MATPOWER 6.0 software package.

E-Aid is a study that aims to develop an application based on the convolutional neural network algorithm. The central idea for the creation of E-Aid is to provide a mobile application which offers more advanced capabilities and leads to a strong emergence for the medical health applications in the market. The reliability for the usage of CNN as an algorithm produces positive results which is essential for this study. The researchers trained CNN model that will be used later on during the execution of the CCN algorithm, and this CNN model must be able to identify 4 types of open wounds (laceration, puncture, abrasion and avulsion) and 4 types of skin burns (1st-, 2nd-, 3rd- and 4th-degree burn) and also must be able to classify it whether the wound is infected or not infected. The researchers tested the accuracy of the CNN model before sending to our respondents. The researchers tested the accuracy by getting a random image of open wounds and skin burns in the Internet and run it on the E-Aid app. After the researchers finish testing the accuracy of the app, they distributed the app to their respondents to test furthermore the accuracy and reliability of the app. The researchers’ respondents are composed of 6 medical professionals (doctors/nurses), 5 IT/CS professionals and 14 students (in the field of medicine and computer studies).

In rural area, the photovoltaic system is augmented by additional resources like wind, diesel generator. Such systems are called hybrid energy systems and mainly used for supplying electrical energy to independent loads in small towns where the access of electricity is lacking. In this research paper, an analysis is conducted to examine the control strategy of a PV generator connected with the grid and facilitated with maximum power point tracking (MPPT) control unit. The DC-to-DC boost amplifier is used to increase the voltage at the output of photovoltaic (PV) generator to the required voltage at the converter input side. In addition, the boost amplifier enables the tracing of the efficient operating point of the PV panels which ensures the peak output power production. An Elman neural network (ENN) online trained controller is designed to track the effective and excellent/maximum operating point of the PV generator. Employing the developed ENN, a better steady state and stable response is achieved with reduced fluctuations near the maximum power point (MPP) in comparison with the perturb and observe (P&O) technique findings. The simulation results prevailed using MATLAB 2019a/Simulink exemplify the dynamic performance of the online trained intelligent ENN controller.

Outline of a website starts with creating models for each web page either by hand or using graphic design as well as expert model creation tools. Then the model is converted into systematic HTML or equivalent code by software engineers. This process is usually done frequently until the desired template is created. Our system will detect the text, input box, check box, buttons, etc. from the input image and convert it into Html code. To implement the proposed system, some deep learning methods are used. Any non-developer can create web pages using our system in a smaller amount of time with efficient results. 96% method accuracy and 73% validation accuracy will be achieved by the proposed system.

There exist businesses and applications that involve huge amount of data generated be it in any form to be processed & stored on daily basis. It is an implicit requirement to be able to carry out quick search through this enormous data in order to deal with the high amount of document and data generated. Documents are being digitized in all possible fields as collecting the required data from these documents manually is very time consuming as well as a tedious task. We have been able to save a huge amount of efforts in creating, processing, and saving scanned documents using OCR. It proves to be very efficient due to its use in variety of applications in Healthcare, Education, Banking, Insurance industries, etc. There exists sufficient researches and papers that describe the methods for converting the data residing in the documents into machine readable form. This paper describes a detailed overview of general extraction methods from different types of documents with different forms of data and in addition to this, we have also illustrated on various OCR platforms. The current study is expected to advance OCR research, providing better understanding and assist researchers to determine which method is ideal for OCR.

During the time of this pandemic, the dependency on Internet has increased manifolds. Employees are doing Work from Home, Students are Studying from Home, Teachers are teaching from Home, Everyone is shopping from Home and the list continues. All this has given rise to Cyber Security Vulnerability attacks like phishing attacks, malware attacks, ransomware attacks, social engineering attacks, identity theft, and a denial-of-service attack. This paper discusses the basic concept of cybersecurity and with a detailed review of recent cybersecurity attacks from January 2020 to March 2021 with a detailed analysis of motivation behind such attacks, attack techniques used, and targets of the attacks. This paper will also discuss cloud breaches, data breaches, and leaky buckets which have increased from 2021. The paper will discuss the types of cyber attacks, attack types, and targets of those attacks.

Sutar, Swapnil Malladi, Kishore Mekala, Priyanka Goel, SupriyaBluetooth technology operates on industrial, scientific and medical (ISM) unlicensed band which is an open wireless medium. Such radio waves are susceptible to various wireless attacks. Major smart devices manufacturers have integrated the Bluetooth with their latest smart devices to form a wireless personal area network (WPAN) with enhanced data rate (EDR) support. This paper demonstrates powerful open-source tools with its experimental set-up to exploit the vulnerabilities in Bluetooth technology. The collected results from each tools are analysed to identify and map the illegitimate device in the vicinity of legitimate areas.

Many AI/ML and data analysis-based recommendation systems are being used in various fields like education, e-business, and course selection to invoke the computerized intelligence solutions based on some basic knowledge of domain. In that role recommendations, course recommendations, job recommendations are also considered as one of the challenging tasks to do. Having this role recommendation system for the College Voting system or at university level voting system can be helpful. However, traditional voting needs to break the barrier and use new approaches to motivate students to vote in an effective way. The online voting system is one of the most important and convenient ways nowadays. So, choosing the correct responsibility and role for a candidate is also crucial. For this, the best solution is a recommendation system based on skills. This skill-based recommendation system which focuses on the abilities and interests of candidates and recommends the most suitable role to each candidate. Therefore, the system will illuminate the candidate’s vision for selecting the role unambiguously. Finally, this paper presents an application scenario where the system analyzes and processes the data based on some inbuilt filters and presents the most appropriate solution.

Write-up is about the basics of finite element method (FEM) starts with the introduction of finite element analysis followed by the implementation of artificial neural networks (ANN) in brief, and finally, article explains the combination of FEM with the emerging ANN to provide an accurate mesh generation technique. It tells us the importance of FEM in the real-world engineering applications and moving on to the discretization of the domain for the generation of FEM mesh and the implementing steps of it. It gives us a glance of how a mesh refinement can be made from the coarse mesh using few refinement techniques and also the standard guidelines to be followed before computing for an adaptive mesh. Later on moving on to the working of an ANN in the feed forward process and the activation function which is used widely. It explains the cost/error function and, finally, computing for the desired output using back propagation algorithm. The final section explains the importance of implementing the FEM mesh using ANN. The elements that are generated in FEM domain are extracted using ANN algorithm by considering the neurons with the FEM nodes and the physical space of the domain with the weight space of the neural network

With the advancement in communication and digitization, the need of having a reliable hardware is extremely necessary. Radars and radar systems play a vital role in various fields like geographical studies, aviation, etc. One important part of these radar systems is an antenna. Antennas have been in use in several fields, right from aircrafts, ships, military applications and space shuttles to comparatively smaller systems like cars, a radio, mobile phones and many more. This paper introduces a particular type of antenna: tapered slot Vivaldi antenna (TSA) with ellipse slot. The presented unique Vivaldi antenna is specially designed for 5G applications. The design has FR4 as the substrate with a dielectric constant of 4.4 and a thickness of 0.8 mm. The dimensions of this design are 11 mm × 8 mm. From the simulated results, it can be seen that this antenna has a good return loss at two frequencies: 24.77 GHz and 44.88 GHz. The impedance bandwidth for these two frequencies is approximately 4 GHz and 14.5 GHz, respectively. The S11 value at 24.27 GHz is -31.75 dB and that at 45 GHz is -26.27 dB. The overall gain of this antenna is 4 dB. VSWR values at 24.77 GHz and 44.88 GHz are 1.0531 and 1.1021, respectively. The main advantage of this size is that it can easily fit in smaller appliances and hence can be used extensively for mobile phones.

Liver diseases are common nowadays, and it is often described as silent disease since detection of such diseases is difficult since the affected person will not show any of the symptoms until the progressive stage. Researches show that over fifty million people globally are suffering from any type of hepatic diseases. Data mining has vital score in the diagnosis and prognosis of liver diseases using big data, machine learning (ML) and deep learning since detection procedures that are noninvasive have high-accurate results with ML support than clinical invasive procedures. Regarding this, study was done for the review of the benefits of applying these mining algorithms and their high contribution to the healthcare sector by its potential prediction capabilities and thus providing clinical decisions and a quality life style for the liver patients.

Verification is a major bottleneck in the design of complex system designs, accounting for nearly 70% of the project development period. Directed test verification is currently a tedious, time-consuming, and repetitive task for verifying complex systems, and many uncovered scenarios will be left out. So, it is necessary to construct robust, scalable, and reusable SoC verification environment. Universal Verification Methodology (UVM) along with SystemVerilog work together to build a coverage-driven constrained random environment for verification. In this paper, UVM test bench is implemented for verifying the frequency generated from PLL, which in turn is used as the clock source for QDSP core, hence enhancing the reusability of verification components. PLLs (Phase Lock Loop) are an integral part in almost all SoCs, used as a frequency synthesizer. And the output of PLL is fed to the Clock Generator along with 2 different clock sources. Clock Generator is a mini clock macro used to generate the clock root which takes in 2 other raw clock sources. Clock Generator contains source muxing and clock divide (integer, half integer, fractional divide) ability.

Due to tremendous rise in the use of microservices and smart devices, there has been an increase in distributed denial of service (DDoS) attacks. Distributed denial of service is the attack where the perpetrator aims to make a network resource or machine unavailable to its users for its services. The detection of these attacks is not very effective due to manual configuration errors and predefined rule sets. This paper proposes a hybrid web intrusion detection system (HWIDS) which detects DDoS attacks. The system focuses on detection of five different types of hypertext transfer protocol (HTTP) based application layer DDoS attacks and unknown variants of these known attacks. An ensemble learning technique is used to combine the advantages of multiple algorithms. The results show that the ensemble learning model has an accuracy of 95.96% and false negative rate of 4.10% for detecting known attacks. The system has an accuracy of 93.48% and false negative rate of 6.52% in detecting unknown attacks. As given by the result statistics, the system detects known attacks and unknown variants of known attacks with high accuracy and no human intervention, providing an advantage in curbing the adverse effects of the security breach.

Manasa, Koduganti Sai Sudha Geetha, P.Groundwater is one of the principal sources that contribute to the total annual supply. Understanding the groundwater potential zones is significant for water management and planning for any city. Situated in Andhra Pradesh, the Vijayawada–Mangalagiri region is majorly known for its industrial and agricultural activities. However, the rapid urbanization, inappropriate land use methods, increased anthropogenic activities, and poor irrigation practices led to the depletion of groundwater during the last few years. This research aspires to discriminate the groundwater potential zones using the combination of geospatial technology and analytical hierarchy process (AHP) which augment the source of groundwater. Different thematic layers such as digital elevation model (DEM), drainage density, slope, soil type, land use/land cover (LULC), and rainfall pattern were prepared and analyzed by employing primary and secondary resources. The weighted overlay analysis has been performed on various thematic layers based on their importance using AHP. A groundwater potential map with five zones has been classified as poor, fair, good, very good, and excellent.

In Rwanda installing, fire extinguishers are mandatory in all buildings. The number of staff trained in its efficient usage is still insufficient. Many are not aware of the fire extinguisher handling. Although alerting a fire outbreak detection on a real-time basis is still a big challenge. Furthermore, as people delay informing fire brigades, the administrative procedures also delay resulting in several people having fire injuries with valuable asset damage. Developing a system that detects fire smartly, records the building’s occupants sending alert information to the fire brigade with other stakeholders on a real-time basis is the motivation of this paper. This paper presents a system based on the Internet of Things (IoT) that detects spruce fire in a building. The IoT system consists of various sensors that measure important factors like recording the number of occupants in the buildings through an Infrared proximity sensor. The smoke sensor sends the notification updates to the fire brigade’s dashboard providing an alert in case of fire. The actuators are initialized for a quick exit from the spruce fire. The developed system helps to evacuate buildings' occupants against fire outbreaks on real-time information. This reduces the number of casualties, decreasing the extent of property damage to save resources during the fire outbreaks.

According to Census 2011, India has majority of the populations residing in six lakh and above villages. With over seventy-five years of development, the government still strives to help the masses cross the threshold of poverty through its various interventions. However, the rural people are still to get access to basic amenities including drinking water, a proper house, a secure livelihood, education and social security. In this study, the authors study Burgula Panchayat that also has tribal thandas (hamlets) and is near the capital city of Hyderabad. The authors use survey method and Open Data Kit to collect data and GIS application to map the assets and create thematic maps for visualisation of data. An interactive Performance Management Dashboard is created to monitor and analyse the pace of development with enhanced citizen engagement. An attempt has been made by the authors to map already existing development entities and to identify the gap areas and suggest for future village development plans.

Lohani, Rupal Suresh, Vidya Varghese, Eric George Thara, S.The rise of coronavirus has exposed a trivial response to this major community disaster. The National Disaster Management Authority (NDMA), headed by the Prime Minister of India, is the main body for COVID-19 in India. It has been widespread in India despite the efforts of suppressing it. Due to its high transmission rate, India has taken several countermeasures and clinical trials to study the mutations and other antibodies. This study aims to visualize the main cause of the difference in the severity of cases among the states of India, taking into consideration the factors that mobilized the spread, along with the actions undertaken by the Indian government to control the spread of the virus. This paper introduces the data set and provides a comprehensive overview of related data visualization tools, existing techniques, and systems for analyzing the results. Finally, we have enabled the presentation of data with graphics, making comparisons more understandable, to help allocate resources (worldwide) to the states in India that are in need.

Introduction of data protection regulation and acts by different countries, especially guidelines like Fair Information Privacy Principle (FIPP), has made it almost mandatory for enterprises to implement data privacy protection throughout their information infrastructure. Data privacy is mostly managed either as confidentiality issue, risk management issue, or as policy/control implementation issue. If privacy is considered a control implementation issue, there must be a correspondence between privacy requirements/clauses with the control components. A scrutiny of privacy controls and privacy regulations reveals that privacy of personal data can be protected if specific privacy properties of data items are protected throughout the journey of the data in enterprise infrastructure. In this paper, an analysis of correspondence among FIPP clauses, data lifecycle phases, and privacy properties are presented. This analysis would help data controllers to identify and implement privacy controls more accurately and enable them to streamline data compliance activities.

This paper will examine the feasibility of installing a cost-effective Radio Frequency Man-Made Noise (RF MMN) monitoring station onto public transport. In utilizing public transport, the RF MMN monitoring system can opportunistically collect measurements while moving through a specific area. These measurements will provide valuable data about RF spectrum use over a larger area compared to a single location-based station approach. A model of the RF MMN monitoring station is simulated to optimize the sample rate of measurements, maximize the accuracy and sensitivity of this approach. To optimize the sensitivity, an investigation is conducted on the noise floor generated by the inherent (internal) electronic noise of the system and the vehicle electronics that the system is installed in. A hardware prototype was designed to demonstrate the feasibility of the system and a pilot test run was conducted, and the measurement is plotted on a scatter plot map. The recorded measurements serve as a tool for analyzing RF MMN over the geographic area in which public transport frequently travels. The RF MMN measurement system will contribute to enhancing traditional Man-Made noise databases with the sensors moving around through public vehicles. This will assist in identifying the primary, secondary, and illegal users, confirm spectrum database accuracy, and will assist in refining current propagation models within the RF band in which the samples were measured. The RF MMN monitoring system is possibly the new “eyes and ears” of the regulatory body to actively monitor spectrum use.

Blockchain is an emerging technology, nowadays looked at it as a solution for most of the security issues of any system. It is a breakthrough to the traditional way of developing the applications which provides robust features in security, sharing the information, and immutability. Blockchain is digital ledger, and every transaction is considered as one record. This data is stored in encrypted format, and the series of records are linked in such a way that any tampering of the data is close to impossible. This literature study is focusing on the usage of blockchain in agriculture domain in supply chain. Researchers have provided various innovative solutions to improve on traceability in supply chain of food, fruit, vegetables, and seeds. The study of literatures helps to understand the new technical concepts that make blockchain as an effective solution, for e.g., BigchainDB to avoid decentralized database issues, use of activation key for authorization, and combination of blockchain and SQL database to improve the performance and efficiency. It is also observed that the combination of IoT with blockchain will bring error-free data entry and easy to use it improving traceability in the supply chain. This paper aims to understand all these concepts from various studies.

The paper presents a new design of creating a household powerline model with parameters provided in random. The actual parameters on the powerline communication are approximately calculated with required accuracy. The previous and present studies in modelling of powerline communication are explained briefly in this paper. The transmission line variables are measured and verified with powerline modelling. Powerlines are basically varied from telephone lines in terms of topology and impedance of the loading. In this, ABCD parameters of the separate modules are used to study the powerline channel which consists of 10 bridge taps with overall distance of 600 m length. Among the noise profiles, periodic impulse noise is included as it is the primary among the noises present in powerline communication aside of AWGN. The impulsive noise is described as a high PSD that reaches more than 50 dB than the background noise. Discrete multi-tone transmission (DMT) is applied in VDSL2 with bandwidth of over 30 MHz in order to get the profiles of tone loading. This leads to the fact in order to achieve rates of conventional VDSL2, lower transmit PSD is sufficient with open bridge taps. But, due to the inductive loads in the residential areas, we need to (1) use the existing VDSL2 setup with adjustable impedances in the front-end so that the load impedance can be matched conjugate and (2) ability to increase transmit PSD and to further increase the sub-bands in order to achieve the required rates smoothly like VDSL2.

Over the last decade, there has been a quantum leap in terms of the evolution of new methodologies to better our quest to understand artificial intelligence and machine learning. One such field, where there has been an unparalleled advancement, is computer vision. The paper aims to design and structure an automated monitoring system that automates the monitoring of the number of people in this COVID-19 scenario in a designated enclosure. We have deployed the system on Raspberry Pi module and integrated a HOG detector which transcends ordinary Haar cascades in terms of performance. This model can then subsequently be connected and integrated with other modules to further enhance its applicability and spectrum of usage.

Sign language is learned by deaf and dumb, and usually, it is not known to normal people, so it becomes a challenge for communication between a normal and hearing-impaired person. Thus, we decided to bridge the gap between hearing impaired and normal people and make the conversation easier. Sign language is one of the oldest and most natural forms of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real-time method using neural networks for finger-spelling-based American sign language. In this research, a hand image is first passed through a filter and after the filter has applied the image is passed through a classifier that predicts the class of the hand gesture. Our method provides above 98% accuracy for the 26 letters of the alphabet. The paper implements that a functional real-time vision-based American sign language recognition for D&M people has been developed for ASL alphabets.

Dey, Mitra Tithi Chatterjee, PunyashaThe influence of Covid-19 is reshaping our daily life. Along with protecting lives, management of contaminated covid wastes is very essential to reduce environmental and human health risks. Inappropriate management of covid wastes can cause serious possibilities of disease transmission to health workers, waste pickers, patients, and the community in general through spreading of Corona virus. Poor management of these infected wastes has created huge problems in healthcare waste handling as the amount of waste generated due to pandemic is excessively large. In this paper, we have presented an Internet of Things(IoT)-based smart framework for covid waste management. We have proposed two types of smart covid waste bins here, one for collecting covid wastes for home isolated patients and other for covid medical wastes, generated from hospitals/nursing homes, etc. These intelligent bins can capture waste information automatically by attached sensors, and the collected data is sent wirelessly to remote municipality server for further analysis. Municipality officials can take decisions such as number of bins to be deployed, at which time intervals wastes are to be cleared, etc., based on those information. The proposed framework minimizes the human interaction with the covid waste and restricts the spreading of virus.

Sudhakar, Vivek John Mahalingam, Srimathi Venkatesh, Vaman Vetriselvi, V.The number of cyber attacks which are carried out are increasing day by day and pose a serious threat worldwide. The most common methods of launching these attacks include delivering phishing URLs and exploiting built in vulnerabilities present in web applications due to faulty and error prone code. Most of these vulnerabilities occur due to the absence of input user data validation before processing. This paper presents solutions to identify phishing URLs and input validation and sanitization based vulnerabilities, i.e., SQL injection, Cross Site Scripting, and File Inclusion in PHP code using machine learning models, after the extraction of certain IVS attributes.

Increasing efficiency in the power sector does not occur only by improving the technical losses. Although many thoughtful improvisations like power line communication, advanced metering infrastructure have been done to decrease non-technical losses, but not at large scale. The idea of this work is to attain accurate detection of power theft without installing expensive meters. Proposed system combines and improvises conventional consumer meter and smart meter features to design and construct a small-scaled advanced metering infrastructure and enhancing it to detect and mitigate different methods of power theft with reliable communication using a cloud platform rather than GSM network. Proposed system is capable of locating the theft area and controls the illegal load usage, by placing a separate energy meter. The observer meter energy reading is compared with the value at the main energy meter situated at the individual subscriber’s premises. If any difference occurs between both meter readings, an error signal is sent to concerned authority stating power theft at particular location. ThingSpeak is handed down as a platform for cloud services and data transfer and IFTTT (if this then that), and an interface for sending an alert message without a GSM module is used. The proposed system is simulated on a Proteus platform for apartment models and household models and tested successfully.

In the original version of the book, the affiliations of authors “Ashish Sharma, Ara Miran, Zanyar Rzgar Ahmed” have been updated in the Chapter “The 3D Facemask Recognition: Minimization for SpreadingCOVID-19 and Enhance Security”. The chapter and book have been updated with the changes.