Emerging Technology for Sustainable Development

Road accidents are the most unfortunate things to happen for people. This road accident takes place when a vehicle gets collided with another vehicle, pedestrian, animal, or other stationary obstruction, such as a tree or building. While road safety is improving day by day, the procedure debris moderate and misaligned with enacted targets, this moderate development is partly due to the active and complicated nature of road traffic and safety performance. This article aims at design and implementation of automatic road reflectors which reduces eye strain to the road users and the road accidents. This suggests automating the traditional road reflectors, so that it senses the intensity of ambient light and work accordingly which is alike the auto-brightness feature in mobiles. So, by the implementation of automatic road safety reflectors, we can reduce the accidents and provide road safety for the road users same as the use of automatic street lighting nowadays.

Remotely piloted aircraft (RPAs) or drones are commonly referred to as unmanned/autonomous aerial vehicles. Over the past 5–6 years, there has been a boom in the use of these vehicles in civil and defense applications worldwide. Despite very protective use guidelines, RPAs are used for surveillance and remote-sensing purposes worldwide, and few delivery services are active. On the Indian Subcontinent, the use of drones/RPAs is limited to government organizations and a few private operators that work with educational institutions. Using a case study from India, the paper explores the various requirements, current objectives, technology, and future challenges for RPA/drone operations that can be conducted in all-weather conditions. India’s technology and future benefits are also examined in the paper. Additionally, this paper examines the options for monitoring civil aviation applications related to remote sensing, cargo transport, search and rescue services, and civil surveillance. The utilization of RPAs for the novel COVID-19 pandemic in India has also been reported.

Biomedical and regenerative medicine has significantly contributed to developing new, specific techniques and technologies to improve patient care. A significant leap has been made with innovative materials, cell development, scaffold design, and fabrication in tissue engineering. The tissue engineering process focuses on regenerating tissues that have been lost accidentally or encounter defects such as osteosarcoma, osteoporosis, and osteoarthritis. India has a high number of people who suffer from bone diseases. An estimated 15–20% of the population suffers from osteoporosis. Bone scaffolds are proving to be an excellent treatment for osseous anomalies and defects. Scaffolds are porous, three-dimensional structures that enhance the growth of new tissues. Bone scaffolds are designed to facilitate osteoinductive cells’ growth, expansion, and migration on their surface. The purpose of this paper is to review possible polymeric materials for bone scaffolds and provide a suitable combination in terms of cost of material and cost of technology for tissue-engineered bone scaffolds.

In the engineering industry, mechanical damage done by abrasive wear is probably the most dominant. In the present study, the two body abrasive wear characteristics of reformer steel (HP40Nb) are investigated. The design of experiments was done by using Taguchi technique. For performing the experiments, three parameters each of three levels were chosen. The L27 orthogonal array was selected. The combined effect of parameters on specific wear is studied, and interactions of parameters on specific wear are also studied.

The most exhibited consequence of the population explosion is seen within the style of solid waste disposal issue. This is because of incorrect analysis of waste disposal sites, setting up and transportation. This project deals with the designing of a vehicle that is powered by human energy to drive it. The vehicle has been designed to gather and manage dispose of the solid waste of approximately 500 L with proper, healthy way. The aim of this project is to overcome the effort and to provide safe and easy disposal of domestic solid waste. The existing design for the three-wheeler vehicles for collection of municipal waste is in a poor condition. The safety issues, improper and unhealthy transportation of the waste, etc., are several key issues in the current waste assortment tricycles in the country. It is of the utmost requirement to design and develop a vehicle which is safe, comfort in riding, cost-effective, etc. There are many designs of human-powered vehicles, but they have some issues related to human comfort, proper and safe and hygienic way of transporting wastes.

Unmanned aerial vehicles (UAV) have occupied a pertinent space in the field of remote sensing in recent years. UAVs are powered aircraft with pre-programmed flight planning. They are operated remotely. UAVs also known as drones can propose a practical and sustainable alternative to traditional platforms when it comes to procuring cost-effective high-resolution remote sensing data, greater versatility and operational flexibility. The practical use of UAV remote sensing in various fields like environmental studies, disaster management, flood monitoring system, archaeology, land-use dynamic monitoring, meteorological disaster monitoring, fighting COVID-19 pandemic, etc., is considered to have enhanced the application domains. Moreover, the effective use of UAVs for acquiring digital elevation model (DEM) data renders analytical advantages in a wide range of remote sensing applications. The need of the hour is to examine and religiously work on the diverse research aspects and trends of UAV remote sensing to augment its regular use. This paper reviews and examines the innumerable benefits and significance of the use of UAV platform for remote sensing applications in various fields.

Sediment dynamics plays a very important part in erosion, floods and morphological changes in the river. The sediment dynamics can be analysed by studying the basin and river parameters. There are different techniques to investigate the sediment dynamics. Numerical modelling of rivers for studying sediment dynamics is rapidly being utilized to investigate river pattern changes, and the information acquired from these studies is used by environmental managers and decision makers. There are a lot of numerical models present for these studies. But all of these models vary in some way or the other. They may vary in their intricacy, inputs and outputs, advantages and disadvantages and so on. In light of these issues, this research examines various numerical models and their approaches with regard to sediment dynamics. Upgradation of these numerical models from the time it was first presented has been discussed in the paper. A summary of some specific numerical models is given. The purpose of this paper is to understand different numerical models and the usage of these models in a given research, so that the sediment dynamics of a river can be studied.

Due to climate change, the overheating risk of the buildings becomes alarming worldwide, thus increasing energy demand for thermal comfort of the inhabitant. This study was conducted to provide overheating risk reduction measures using passive cooling design for both existing buildings and those yet to be constructed. Simulations were done in Revit software and Autodesk Insight, and the results were compared with the first simulation to see its effect. The building study typology is an office building of Mizoram University. The simulations were done using thermal wall absorptance, thermal transmittance through glazing, wall and roof insulation using expanded polystyrene and polyurethane, natural shading by trees and changing the window-to-wall ratios (WWR) by measuring the heating and cooling loads from Revit and also through optimizations done in Insight. This study concluded that the best thermal comfort measure can range from building to building. Since the building used in this study is an office building, having high WWR, the windows played the most important role in the overheating of the building. This study highlights the significance of thermal comfort and overheating risk measures.

As water demand is rising due to the increase in the population and urbanization, the use of spring water as supplementary water sources becomes imminent. Mizoram state, which is located in the North Eastern part of India, located at the top of a ridge where the main sources of water are generally streams, spring and rainfall. As many of the springs are reportedly drying up lately, this may be due to the anthropogenic and the local effect of climate change as the area received intense rain during monsoon and longer dry period of little or no rain during winter, which may result in the very low discharge or drying up during this time of year. The general lithology consists of alternations of siltstone, shale, and sandstone, wherein the porosity of the rocks is very limited. Identifying the recharge of spring water is of prime importance for sustainable management strategies. The hydrogeological strata and the geochemical composition in mountain scale study were conducted, and it was observed that water is young and immature type, and the lower residence time of the water relates with MFR mountain front recharge mechanism being the major portion of groundwater recharge. Artificial recharge is a necessity for the growing populations which demands more water and as more storage of water is needed to save water in times of water surplus for use in times of water shortage.

Despite the fact that cage culture has been in practice for centuries, commercialization has only begun in the last few decades. Farmers are keen to relocate to offshore locations since most nearshore sites are entirely utilized, with limited potential for additional fish production and environmental groups are raising concerns about pollution, conservation, and recreation. The cage culture system classifies into nearshore and offshore farming. In this review, the nearshore cage system investigates on cage materials, classifications, and net shapes. It provides a brief overview of cage deformation, velocity reduction, cage hydrodynamic features, motion responses, mooring line tension of single cage and grid system. The definitions of offshore and the design challenges that must overcome to relocate offshore are discussed. The offshore system analyses distinct designs in open cages depending on submergence depth and closed contaminant tanks under harsh environmental conditions. An assessment is carried out on experimental studies, in-situ tests, and numerical and analytical methods of the cage culture system. The evolution, development, and present scenario of sea cage culture in India are also reviewed.

Water is a very important element for living organisms, and it is helpful in the circulation and transmission of nutrients in the biosphere. Due to industrialization, urbanization, and rapid increase in human population, the demand for water has increased sharply, and the quality has declined drastically. Although water has the ability to purify itself, when the concentration of pollutants generated from man-made sources becomes so high that it exceeds the self-purifying ability of water, then the water becomes polluted. Degradation of the physical, chemical, and biological characteristics of water by natural and man-made processes in such a way that it is unsuitable for humans and other biological communities. This is called water pollution.

Since the large demand has been placed on building material industry especially in the last decade owing to the increasing population which causes a chronic shortage of building materials, the civil engineers have been challenged to convert waste to useful building and construction material. Recycling of such waste as raw material alternatives may contribute in the exhaustion of the natural resources; the conservation of not renewable resources; improvement of the population health and security preoccupation with environmental matters and reduction in waste disposal costs. Different low-cost/waste materials like plastic bottles, polythene, saw dust, styrofoam, fine aggregates and coarse aggregates from different places are taken for regenerating building blocks. Automatic compaction machine has been used to find the compressive strength (fck) of prepared blocks. Based on the experimental investigation on plastic–aggregate blocks, it was found that the blocks with the proportion of 26% plastic, 74% fine aggregate have the highest strength when comparing with all other proportions of the plastic–aggregate blocks. Hence, it was concluded that polyethylene terephthalate (PET) bottle can be used as main constitution for the preparation of paver blocks with the increased strength. Though the compressive strength is low when compared to the concrete paver block, but it can be used in gardens, pedestrian path and cycle way, non-traffic and light traffic road, etc.

Steel members have become one of the most popular building materials in the construction industry owing to its high tensile and compressive strength, high ductility, durability, longer span, aesthetic looks, etc. Steel members can be classified into various types based on the manufacturing process, chemical composition, types of section, etc. Steel members used in construction are subjected to various loading such as tension, compression, bending, and torsion. Only few studies (Ridley Ellis in Rectangular hollow sections with circular web openings fundamental behaviour in torsion, bending and shear. University of Nottingham, 2000; Devi et al. in J Constr Steel Res 162, 2019) have been conducted on steel members subjected to torsion. Based on the literatures, it has been concluded that perforated members have lower torsional load resisting capacity than unperforated members and square steel members are better in resisting torsional stiffness than rectangular steel members.

Nowadays there is concern about the environment, and ecology has ended up a worldwide issue and is putting nearly each industry accentuation on the use of eco-friendly materials, technology, etc. The road construction field is one of them. There has been a colossal increment in the cost of bituminous asphaltic material. As we all know, “day-by-day the mines are vanishing with broad mining work and as recycling”. Recycling materials can be utilized in a few development areas as well as in road development. In expansion, much consideration is centered on the utilization of recovered black-top asphalt material in modern asphalt pavement design. So, reusing is a choice by which there are more concerns. As coordinated to moderate and decrease characteristic assets, natural effect can be of utilizing new asphalt binders. One more major issue on earth is plastic waste. Plastic waste has many bad impacts on soil, surface water, groundwater, and air (due to burning). Scientists and researchers are doing a major concentration on this area to use/reduce plastic waste. Government had banned single-use plastic. Now being fashion on packed food, online food ordering, and packed drinking water bottles. Some packages of food as necessary to protect from moisture and time duration cannot be stopped instantly. Daily plastic waste collection is in millions of tones which is a major issue to handle and dispose of municipals. Plastic waste is now used in road construction at various methods and stages. Utilizing reclaimed asphalt pavement and the use of waste plastic in road construction can solve many environmental problems to a great extent.

Inefficient solid waste management of plastics and polymeric materials is one among the biggest challenges worldwide, resulting in environmental deterioration. Observations in the domain of plastic waste management have indicated that the process of incineration has become the most widely accepted disposal strategy worldwide. However, due to poor maintenance of incinerators, it releases several harmful gases including dioxins and furans in case of chlorinated and brominated plastic waste thus raising several environmental issues. This challenge has brought alarming concerns regarding minimizing the volume of such wastes released into the environment. This review paper presents an extensive study and proposes a solution to this problem to some extent by reuse, recycling, and efficient conversion of waste materials into alternative application such as utilization of plastic waste in road construction, co-processing of plastic waste in cement kilns etc. Some of such newly employed recycling and conversion techniques of plastic wastes, and possible future alternatives with recommendations are reviewed in this paper, with emphasis given to the recycling potential, specifically in the construction industry.

Earthquakes are the sign of change in our earth's inner crust. The past earthquake encounters have exhibited gigantic death threats and infrastructure, influencing the social and monetary states of a. However, it is unimaginable to expect to prevent an earthquake, and all today's technology is capable of making infrastructure earthquake safe. With the advancement in our comprehension of the earthquakes, most of the nations have commanded the cooperation of seismic provisions in building plan and engineering. In case of an earthquake, the seismic waves starting from the centre are sent in all the potential bearings. These shock waves propagate as body waves and surface waves through the interior of earth and are profoundly arbitrary in nature. These ground movements cause buildings to vibrate and instigate dormancy powers in the structures. Without a seismic plan, the structure may fall leading to catastrophic events. The seismic plan theory intends to principally ensure life safety and gets the usefulness of the structure. The paper means to make a statement about the earthquake-safe structures in different seismic zones. The impacts of plan and form configuration on irregularly shaped structures are discussed in this study. Seismic activity affects buildings with uneven geometry in diverse ways. The plan geometry is the parameter that determines how well it performs under various loading situations. Using the structural analysis programme STAAD Pro. V8i, the influence of irregularity (plan and form) on structure was investigated. There are numerous elements that influence how a building behaves, and storey drift and lateral displacement are two of the most significant in understanding how a structure behaves. Graphs and bar charts are used to display the results. According to the research, a basic layout and configuration must be selected at the planning stage to reduce earthquake effects.

This study aims at replacing sand and cement with ROBO sand and ground granulated blast furnace slag (GGBS) in the concrete mix. Here, GGBS and ROBO sand are chemically and physically characterized and used to replace cement and sand in different proportions. Tests on workability were conducted for fresh concrete, various strength tests were conducted for hardened M30 grade of concrete, and a comparative study was done with normal concrete and composite concrete with GGBS and ROBO sand in different proportions. Increase in compressive strength after 28 days was observed with 15% GGBS along with 20% ROBO sand. Similar trend was observed in the case of split tensile strength and flexural strength test.

This paper presents the water quality status of Deepor Beel which is a perennial freshwater lake and the Ramsar site in Assam. Now, its ecological health is being affected by developmental works; this study is aimed to find out the status of water quality so that remedial measures can be initiated to protect it. Various analyses including physical and chemical quantification of important parameters of the water samples were collected from different locations of Deepor Beel. Samples were found to have higher concentration of alkalinity, BOD, iron, TS, TDS, and TSS, in some locations, and DO was found less than the minimum requirement in few locations. Based on the correlation coefficient, alkalinity, acidity, total solids, total suspended solids, DO, and chloride were identified as sensitive parameters which were influencing the BOD level. Iron is the parameter which is least correlated with any of the parameters. An attempt was made to model the ecosystem study by using artificial neural network using the identified sensitive parameters as inputs and BOD as output. The number of hidden neurons was 10, and the transfer functions were tangent and logarithmic sigmoidal functions in the input layer and in the output layer, respectively, for the best configured neural network model.

Detecting diseases from the leaf images of a plant is an important and challenging task. Various image processing techniques like pre-processing, segmentation, classification, etc., are performed to detect plant diseases from its leaf images. Image segmentation is one of the important steps in the process of disease detection in leaf images of plants. A well segmented image increases the accuracy of prediction. In this paper, we have implemented the K-means algorithm to segment leaf images of tea infected with red rust disease caused by algae. The value of K in K-means needs to be set manually. Determining the optimum value of K is crucial to obtain a well segmented image. So, the elbow method and silhouette coefficient determination are employed for this purpose.

Deep learning (DL) has become a critical component of medical image processing. Over time, DL methods have changed. Advances in the field of DL have resulted in a computer-aided diagnosis system (CADs) that is more sophisticated and self-reliant. In medical image analysis, convolutional neural networks (CNN) are becoming increasingly extensively employed as a DL approach. This study aims to survey state-of-the-art approaches of breast mass classification using CNNs. The breast cancer mammography repositories have also been examined. Various limitations that demand further examination are also discussed. We looked at articles published on well-known publishing platforms like Google Scholar, PubMed, Science Direct, and IEEE Xplore to conduct the literature study. These papers are all SCOPUS/SCI/SCIE indexed and focus on using CNN algorithms in mammogram images. We also present the advancements and challenges of CNNs for breast cancer diagnosis. When it comes to medical image processing, using CNNs has proved to be more beneficial to researchers than using a traditional approach. However, better architectures, larger datasets that address class imbalance issues and improved optimization methods are still required.

Tumour can be detected early and prevented, although this is not always practicable. Image augmentation and segmentation is an important method used to enhance the properties and abilities of deep learning architectures and can be generalised with the regularisation of the image data. This method plays an important role where the number of original training image data is limited and deriving new attributes from image data becomes expensive and time-consuming. This is a general and common issue in medical image analytics, especially when it is about brain tumour classification and prediction. In this paper, we reviewed the recent enhancement in the field of cancer image generation algorithms used over a number of magnetic resonance brain tumour images. For more understanding of the practical and real aspects of most of the algorithms, our work investigates the articles written and submitted on challenges faced for multimodal brain tumour segmentation. This review also verifies which image augmentation techniques were exploited and what were the research impacts on the capabilities with supervised learning scenarios. In the end, we highlighted the use of pre-trained CNN-based architectural methods such as H2NF, GoogleNet, UNet, etc., in order to serialise and synthesise high-quality automated brain tumour examples which can give a boost to the capabilities of deep learning models.

One of the most perplexing issues confronting higher education institutions today is how to increase student placement performance. Placement estimation becomes more complex as the number of educational institutions increases. Educational organizations explore more efficient technologies to assist them in improving their decision-making practices, as well as in developing creative methods. Providing new insight into instructional processes is a critical component in resolving quality issues. Machine learning methods are used to extract information from historical data contained in the libraries of educational organizations. Our model will generate a recommendation system that forecasts the placement level of a student. This model assists an organization’s selection cell in identifying prospective students, assessing their technical and interpersonal abilities, and assisting them in developing them. Students in their pre-final and final years of B. Tech programs may also use this work to determine their individual placement status and probability of achieving it. This enables them to exert additional effort in order to get placements in organizations with higher hierarchies.

Load forecasting is a technique used by power companies to predict the energy needed to balance the supply and load demand at all times. It is also a mandatory requirement for proper functioning of the electrical power supply industry. Load forecasting is critical to provide decision-making support for power generation. An accurate load forecasting can ensure sufficient power being generated to fulfill actual need of the community and reduce waste of generation. In this paper, a regression-based method is presented for load forecasting in Meghalaya. The efficiency of the methodology is evaluated on the dataset and the predicted values are compared with the actual maximum load demands. The method is found to be effective in accurately forecasting of maximum load demand during the winter season in Meghalaya. This method can be applied to any place in the world having colder climate, with load dependency on heating elements, especially during the winter season.

Internet of Things (IoT) is an evolutionary as well as emerging concept which is in one of the pioneering positions in the transformation of the real-time objects (things) into smart components. It finds its place in a wide range of application domains such as smart grid, healthcare, defence, agriculture, etc. This technology has been successful in creating revolutionary solutions in agriculture in the form of creating artificial greenhouses, precision farming as well as monitoring of livestock. In this article, a novel long range wide area network (LoRaWAN) cattle monitoring and tracking system (CMTS) is proposed which assures a framework that is operated wirelessly over radio frequency having long ranges and lower power consumption. This device not only enables to locate the animal in an unknown area but also proved support in terms of monitoring the vital conditions of the same. The information accumulated by the device is transmitted to the cloud-based server which could be accessed through mobile application for identifying health abnormalities of the animal or location coordinates of the same.

Monitoring the water quality of a wetland is very essential as it forms a major ecosystem in the environment. Although in situ wa-ter quality measurements are precise, high costs limit their applications. Remote sensing has demonstrated high potential as a cost-effective alter-native to traditional water quality monitoring techniques. In this study, the remotely sensed Lansat 8 dataset was used for estimating the water quality parameters of Deepor Beel, Assam. Using in situ data, regression models were created to establish the relation of water quality parameters—pH, total dissolved solids (TDS), and turbidity; with the bands of the Landsat data. For linear regression, R2 values for all the three parameters were more than 0.70. With R2 values over 0.90 for all parameters, the decision tree regression model produced more promising results. The analysis also showed that the pH value had a substantial correlation with the NIR band of the Landsat dataset. TDS, on the other hand, had a significant impact on the reflectance of the SWIR 1 and the green bands. For estimation of turbidity, the modified normalized difference water index (MNDWI) and red bands were found to be vital.

Multi-scale detection in pedestrian detection plays a vital step due to its use in detecting pedestrians in different scales. However, multi-scale detection is a challenging task due to various reasons like selection of appropriate scale factor, low resolution and loss of sharp edges at deeper pyramidal levels, etc. In this paper, new pedestrian detectors are presented which uses non-linear scale space for multi-scale detection. The proposed detector uses histogram of oriented gradient features in combination with dense local difference binary features. Different classifiers like linear SVM and cascade of boosted classifiers are used to train the detector. INRIA pedestrian dataset is used to train and test the proposed detectors. The proposed system is evaluated in terms of precision versus recall and miss-rate versus FPPW/ FPPI as well as computational speed. The performance of the proposed detectors is also compared with some similar existing pedestrian detectors.

Millions of users have been a victim of cyberattacks, and thousands of companies are affected as well. This paper proposes Machine Learning to be used as a method to improve the detection rates of cyberthreats in a network which is better than the traditional signature or anomaly-based methods. Machine Learning can be used to detect threats and protect systems in real time thereby reducing the damage caused by attacks to a very high extent. In this paper, five Supervised Machine Learning algorithms, Random Forest, Logistic Regression, SVM, Decision Tree and Naive Bayes, have been used with optimized parameters and tuning and lastly, a deep learning algorithm; Convolutional Neural Network (CNN) has been used, and the performances have been compared among them. The algorithms performed well with Random Forest model being the highest. The results achieved prove that Machine Learning can be implemented to develop a threat detection system for a network which would be much more secure compared to the existing methods of detection and prevention.

The earth is filled with a different kinds of medicinal plants. These medicinal plants are used in some useful ways such as formulation of drugs, herbal products made from it, and common ailments and diseases cured by making medicines out of the medicinal plants. There are many medicinal plants in the wilderness. Recognition of those medicinal plants by human sight are going to take a long time, slow, tiresome, and not accurate. As many of them are under extinction as per the IUCN records, image processing comes into play by identifying the endangered plants and helping in preserving it. The Mendeley dataset has a collection of different species of healthy medicinal herbs such as Alpinia Galanga (Rasna), Citrus Limon (Lemon), and Moringa Oleifera (Drumstick), and 30 different medicinal plants with 1500–2000 images are available in Mendeley’s dataset. In each respective medicinal plant folder, 50–100 high-quality images are present. The species botanical/scientific name are named as the folder name which will be used to train the model. In this paper, it proposed a system that adopts the deep learning method to obtain high accuracy in the classification and recognition of medicinal plants. Convolutional Neural Network (CNN) is used as the system for classifying of medicinal plant images based on deep learning.

Aggressive drivers are often considered to violate traffic rules and adopt dangerous driving behavior. This requires the development of effective and robust classifiers for unsafe drivers. Driving behavior analysis is the classification of driving behavior based on the driver’s GPS trajectory. With ever-increasing GPS trajectory data, dangerous driving behavior can be thoroughly analyzed and better classified using a deep learning model. Behavioral analytics can help us analyze and identify dangerous drivers that contribute to traffic safety and promote safe driving behavior. In this paper, we propose a novel feature extraction model using a statistical approach to extract the important features from the GPS trajectory data and label the trajectory. To overcome the dataset dependency, we propose to use a deep learning model on our labeled data and finally classify the safe and unsafe drivers. The proposed method demonstrates high accuracy with reduced computational overhead.

Agricultural sector requires a supply chain for the availability of different farm products throughout the usable premises to gain more profit. Many existing researches address the issue, but no one is comfortable with payment, authentication, and integrity. Centralized models are not flexible. Blockchain is used along with the supply chain by the current research works to decentralize the system where the scalability issue is not addressed. Our scheme is the first blockchain-based supply chain model, which can resolve the scalability issue to make the payment faster. We have used Perun virtual channel to resolve the issue. In faster payment in blockchain, Perun payment system is comparable with visa and master card also. Our scheme is user-friendly, as we use data integrity with the current date and time at sending and receiving. The proposed scheme is fully decentralized with scalable blockchain. Availability of the whole system publicly and to all the participant entities makes a public verifiable system. We have used smart contracts and analyzed the gas consumption in the result section. Performance of the resultant work shows that our scheme is cost-effective and feels like easy payment in the blockchain. The payment time in the proposed scheme is almost 99% less than the existing works. The application of the resultant work may consider for other current sectors.

Analysis of tweets accompanying a catastrophic situation is a crucial chore. Sentiment analysis is the field of study to analyze the varied opinions shared by diverse users on social networking platforms on various social phenomena. In this paper, an analysis of the sentiments on thousands of tweets collected from Kaggle on the ongoing pandemic of COVID-19 is carried out. Data preprocessing technique followed by TF-IDF approach for uni-gram and bi-gram features is extracted. Three different supervised machine learning classifiers such as Bernoulli’s Naïve Bayes (BNB), Gaussian Naïve Bayes (GNB), and Random Forest (RF) models are applied. Experimental results suggest that on both the feature extraction models, i.e., uni-gram and bi-gram feature extraction techniques, RF classifier has performed better than the other two models. With 70%-30% train–test set, RF has achieved an accuracy of 90.06% to classify the tweets into negative, neutral, and positive classes.

Reversible logic is a prominent area of research due to its interesting characteristics. Reversible logic is also useful in low power computing. The quantum computing technique is also reversible, and so another attraction is attached with this logic. In computing, adders are very important circuit. Adders play a major role in the design of various computing devices. And so, adders are extensively studied. There are adders proposed in many literatures, even though there is always an ample scope to improve the designs. In this manuscript, two reversible gates also called microblocks are presented. Then using these gates, two new adders are designed. One of the designs is a ripple carry adder, and the other design is an improved design in terms of quantum cost and delay. These proposed circuits are constructed using reversible logic. The presented adders are compared with some available literatures on full adders, and it is found that the performance of the proposed adders is of comparable standard. Two designs of 2-bit adders are also presented, and using these deigns, n-bit realization of adders can be achieved.

The concept of face anti-spoofing is an important part in the face recognition system. It has great importance for fiscal payment and different networking systems in today’s modern world. A new system has been introduced using a three-layer convolutional neural network. Accordingly, in this paper, we present a deep neural network strategy for face anti-spoofing. This paper proposes a system of detecting spoofing using convolutional neural network (CNN) classifier. The convolutional neural network system is constructed to arrest the spoofed faces from piercing in the name of genuine person. We have considered 3 layers of CNN in order to make the detection of the images in a more clear format. Self-datasets of real and fake images are created to train the neural network. The two datasets are trained singly to resolve the absolute outgrowth. The accuracy achieved by our model is quite satisfactory. The experimental results over the validation dataset and training dataset show that this system shows better performance and has demonstrated a satisfactory accuracy over other models.

Greenhouse is a structure that consists of walls and gates, and a transparent roof which maintains a climatic condition that is favourable for the growth of plants inside the greenhouse. Greenhouse system is maintained by human beings which reduces the labour work inside the greenhouse. The automatic greenhouse formed by the utilization of Internet of Things helps in overcoming the problem faced by the farmers and provides with an automatic monitoring of the greenhouse system. This paper proposes an automation system that uses Arduino NANO and various sensors for detecting the moisture, temperature, light and humidity to get a rise in the production.

Effective or efficient management of e-waste is considered as preeminent vital challenges of the modern days. The massive scale of e-waste generated and dumped in open landfills or oceans without proper treatment poses severe threat to the environment around the globe. E-waste can be considered as one of the prime root causes of different types of pollution like air, soil, and water. Furthermore, the absence of stringent rules and regulations for proper e-waste treatment, duping, and management also adds to the problem. With these issues as a motivation factor, this paper proposes an efficient e-waste management framework for effectively managing the e-waste. The paper discusses the various issues and challenges of e-waste management. Furthermore, the role of various enabling technologies like IoT, WSN, blockchain, and artificial intelligence in effective e-waste management is also discussed. Finally, some of the best practices and future research directions are proposed.

When a nerve signal propagates through a nerve fiber, it is subject to a number of processes, including those of the extracellular space (ECS) attenuation due to the longitudinal or axial resistance, etc. The information content of the signal is of much importance as it has to reach the desired location with maximum amount to signal having being retained throughout its journey along the fiber. Studies have shown that the nerve anatomy along with the surrounding extracellular medium plays a very important role in facilitating the signal transmission from the site of generation to distant places along the fiber. In this work, effort has been made to understand the similarity of the neuronal signal between two distinct locations of an axon. Initially, an action potential or nerve impulse is considered to be generated at a node of Ranvier, and the similarity of the signal at the subsequent node of Ranvier after propagating via a myelinated segment is computed. The results obtained show that the length of the nerve fiber has a key role to play in retaining the overall information content of the nerve signal, and also it is observed that there must be some critical length of the nerve fiber so that the information is not lost as it propagates from one region of the fiber to the other.

With the advent of current electronics technology and access to the Internet, it is now possible to obtain distant medical diagnostics and counseling at a distance. However, because of the size and cost of such medical equipment, third-world and developing countries continue to face barriers to primary medical consultation and testing. One such medical situation is the early assessment of patients’ cardiovascular well-being. An attempt has been made in this suggested framework to build and implement a low-cost ECG collection combined with a local storage server system capable of capturing and storing patient data locally. When the system is connected to the Internet, the server can send patients’ ECG data to a group of registered cardiologists for immediate assessment of a patient’s criticality and generate complete assessment reports of individuals by collecting tagged information from the cardiologists’ responses in addition to local storage. These comprehensive assessment reports can then be printed for the patient or electronically mailed to others who need to know. This system is portable, low-cost, and seamlessly implementable into the Android UI for better usability over uninterrupted Internet service.

For 5G applications, a novel two-port linearly polarized multiple-inputs multiple-outputs (MIMO) proximity coupled antenna is presented and discussed. The orthogonal arrangement of the antenna components enables us to achieve more than 30 dB isolation. FEM-based ANSYS high-frequency structure simulator (HFSS) electromagnetic solver is used for the modeling and simulation of this module. The suggested antenna works at a single frequency band, 5.35–5.60 GHz, thereby addressing the critical sub-6 GHz regions for 5G outdoor applications. The significant difference between co-polarization and cross-polarization levels justifies the radiation attributes of the antenna. The envelope correlation coefficient (ECC) and diversity gain (dB) are calculated to justify its MIMO antenna performance. At resonating frequency, positive gain value and radiation efficiency of more than 80% are recorded.

Global healthcare systems are effectively dealing with the pandemic arising out of COVID-19 virus yet there are requirements to formulate appropriate risk minimization methods. In the absence of effective medical resources, certain alternatives are recommended to stem the infection. Mask wearing is regarded to be a non-pharmaceutical intervention measure to prevent rapid spread of the virus from an infected individual. As part of measures to ensure wearing of marks in public places, certain methods of monitoring are being developed. Majority of these are based on learning-aided pattern recognition methods. This paper discusses the design of a few learning-based methods like Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Support Vector Machine (SVM) and also includes discussion on the performance of these methods which are configured and trained to find the best suitable approach for checking mask wearing in a real-time situation. It is observed that the proposed technique based on VGG-16 CNN achieves high accuracy (99.0%) during the testing phase but when implemented with SVM during training, similar results are obtained.

In this paper, we propose a method for a robotic arm based on human–machine interface, which can assist physically disabled people to perform activities of their day-to-day life. The interface can be implemented in several ways, viz. by using voice command or hand gesture or through electrooculography. In the propose system, human voice command is used to control the prototype devices. An HM2007 IC is used for the voice command processing and recognition. The analog input voice command given by the user is converted to the digital output signal by the speech recognition IC. The microcontroller further processes these digital outputs for motion and directional control of the robotic arm. The paper includes the results obtained by testing the system for both speaker dependent and speaker independent cases, respectively, under noise-free and noisy environment.

The analysis of radar signals is a critical task in Electronic Warfare (EW) environments and decides the nature of counter employments. For an Electronic Support (ES) system, the challenge is to efficiently recognize the source of the threat radiation. In dense EW situations, detection of Pulse Repetition Interval (PRI) modulation modes of radar signal significantly aids the manifestation of emitter in the process of radar emitter recognition. Developments in Artificial Intelligence (AI) methods suggest that this emerging technology can be very effective for such purposes. In this direction, an automatic approach for recognizing eight kinds of complex PRI modulation types based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN) is proposed. The CWT is used to decompose the PRI modulation sequence to obtain different time–frequency components, and the CNN is used to extract features from the 2D scalogram composed of temporal and spectral elements for deriving appropriate class decisions. The simulation result shows that the proposed method not only enhances the performance but is also robust in an environment with noisy content. The recognition accuracy is 98.2% with 30% spurious pulses in the simulation environment.

Joint source and channel coding (JSCC) has been found to be effective in ensuring better link reliability and quality of service (QoS) while using the already scarce spectrum efficiently. For better spectrum management, universal filter multi-carrier (UFMC) has been receiving greater attention for achieving high throughput. Multi-user (MU) multi-input multi-output (MIMO) antenna diversity has already been accepted to be a key element and also for upscaling data rates, while millimeter wave (MMW) has been regarded to be a critical technology for 5G wireless communication. In this paper, JSCC and UFMC have been deployed over a MUMIMO antenna diversity aided by hybrid beamforming. The composite setup is experimented with a range of scenarios including the adoption of the framework in a MMW arrangement. Experimental results involving peak-to-average power ratio (PAPR), power spectral density (PSD) and bit error rate (BER) have shown improvement in performance. Further, there is an increase in reliability in data recovery and channel capacity.

A simple structured dual-band omnidirectional as well as directional parasitic coupled, quadrilateral shaped dielectric resonator antenna (DRA) feeding by a coaxial connection is presented. It consists of a FR4 substrate having a plus shaped patch, excited by a coaxial connector at the center of the structure. Four rhombus shaped DRA is diagonally placed above the microstrip line. Variation of height of the parasitic DRA affecting the antenna radiation pattern is investigated in this paper. The antenna has an omnidirectional radiation property at the first resonance frequency 3 GHz and directional radiation pattern at the second resonance frequency 4.5 GHz is also observed. The DRA is used to enhance the radiation field at resonance frequency. The proposed antenna demonstrates an omnidirectional radiation pattern at lower resonance frequency and four directional radiation patterns at second resonance frequency.

This paper presents a technique for forecasting solar power generation using weather forecast data. Solar power generation mainly depends on the relative position of the sun and some extrinsic as well as intrinsic factors. Extrinsic factors, such as cloud cover, temperature, rainfall, humidity, and wind speed, are used for the prediction of solar generation. Apart from these, the intrinsic factors are also taken as inputs for the proposed prediction technique. The artificial intelligence–based techniques of linear regression, polynomial regression, and artificial neural networks are used for prediction purposes, with input data of all the months of the year 2021. After developing different AI models, their accuracies are compared before selecting the best technique for solar generation prediction. The AI model found to be accurate during the present work is applicable to all solar generation systems, for generation level prediction.

Microwave scatterometers have been used in both ground-based and platform-mounted remote sensing applications. The primary use of microwave scatterometers in ground-based applications is measuring and monitoring soil moisture. However, the determination of suitable frequency of the scatterometer for detecting soil saturation for different soil types is a topic still under study. The present study is an extensive field-based experimental work done in the state of Assam in India, considering alluvium-rich soil of the Brahmaputra valley. The L-band of microwave range is considered for the field testing and determining suitable configuration of the scatterometer, for monitoring soil wetness, as presented in detail in the paper. The configuration is found to be suitable in detecting the changes in soil moisture and identifying pre-saturation conditions of the soil also.

Blockchain is a composite technology that combines cryptography and consensus algorithms to solve traditional distributed database synchronization problem. Due to the features like immutability and traceability, blockchain is considered to be a reliable platform to store shared information. It is an integral part of various modern multi-field infrastructures, including cryptocurrency. The popularity of blockchain gained spectacularly after the introduction of Bitcoin in the financial world. The consensus mechanism used in Bitcoin, called Proof-of-Work (PoW), also received a similar acceptance and even used in the present blockchain technologies. However, PoW has faced criticism for being less responsive to transaction throughput and for demanding huge computational power. In this paper, we make an attempt to improve the PoW mechanism by incorporating it with another popular consensus mechanism called Proof-of-Stake (PoS). From our experiments, it is observed that such a collaboration can greatly improve the block creation time in the blockchain network and also increase fairness as both higher computational power owners and higher stakeholders get a fair chance to contribute to the chain.

In the background of a rapidly aging population, the widespread deployment of smart wearable devices might help ease the societal burden created by the growing demand for healthcare and support among the elderly. Despite the fact that technology plays an important part in attaining these goals, any solution needs to be planned, implemented, and verified via domain expertise. This chapter examines the technologies that are employed in healthcare. Machine Learning (ML), Cloud Computing, Big Data, the Internet of Things (IoT), Artificial Intelligence (AI) and remote and wearable sensor network devices are among the technologies studied. Similarly, the data for a research article are gathered from a previously published study 92 papers from reputable publications. The essay also includes a systematic review technique. In addition, the study was prompted by Cloud Computing and IoT technology because of its high accuracy rate, manageable interface, and effective and efficient outcomes. They will need both financial and technical aid to collaborate with healthcare specialists and provide both the technology, knowledge needed for treatments to be beneficial.

Nowadays, most of the major industries, such as healthcare, are losing millions of valuable data and information, and therefore, many of these major industries have implemented blockchain technology in order to save and secure their valuable data since blockchain's major feature is to store information in an immutable and permanent manner. Blockchain provides greater transparency, enhanced security, instant traceability, increased efficiency, and speed. Though when we talk about blockchain, it is mainly the mining of transactions that draws our attention and of course, mining thus consumes huge computational power. In this chapter, we have implemented a lightweight blockchain in smartphones. We have built a simple blockchain that can store the contents of any smartphone user and this lightweight blockchain can perform all the operations that a normal blockchain does, like the mining of transactions, updating the chain, and checking if there is any pending transaction. From our experimental study, we have observed that implementing blockchain in smartphones does consume a huge computational power, that is, the smartphone starts to heat up and the battery power decreases rapidly.

This chapter proposes a framework which is based on Internet of Things (IoT) and Long Range (LoRa) communication framework which helps to collect relevant data from inundated areas and make the data available for use by the disaster management authority and for the general population. This chapter also presents a detailed review of IoT- and LoRa-based contributions in the domain of detection and analysis of flash flood. The proposed framework has the potential of reducing the dependency over internet to transmit data over long ranges during disaster by using LoRa and its associated protocols. The entire framework is integrated with a number of sensors in three distinct layers which would be used to collect data which are relevant for the occurrence of flashflood. Overall, this system would be highly advantageous for the protection, prevention and rescue of lives and property during flash floods.

Linear increase in population which results in overcrowding has become an unavoidable element in any public gathering. Public safety under such condition has become a very vital problem in areas like streets, malls and railway stations during weekends, festive seasons, holidays, concerts, etc., normally or in any pandemic situation. The massive disasters that can occur includes numerous instances of fatality where people gather in form of throng. In present time, surveillance cameras are deployed to maintain peace, security and manage crowd, as surveillance videos for proper analysis of crowd activities is an important issue for communal harmony and security; however, some major limitations in video surveillance system are that includes picture getting blurred, peculiarities among person cannot be identified automatically with respect to surroundings during live video streaming, along with that to save the information a lot of storage spaces is also required and hence it becomes costly to run and maintain. The present study proposes a method that is based on principle of Histogram of Oriented Gradients (HOG) and OpenCV that efficiently keeps in track count of the people in the scene which helps in efficient crowd management. OpenCV-based method used for crowd estimation written in Python used in this study in order to count the number of heads in live streaming and helps in crowd management according to requirement in an economical way.

Indian economy is largely agriculture based. With the increasing population, there is need for technological advancements in the field of agriculture to increase the yield and to satisfy the entire population. Determination of soil pH is important in finding the crop suitability. Soil pH measures the acidity or alkalinity of the soil. Soil has various elements like nitrogen, phosphorus, potassium, calcium, magnesium etc. Their varying amount determines the soil pH and the color of the soil. Crop suitability and the amount of nutrients available is based on the range of soil pH. In our work, RGB-based soil pH detection and crop suggestion system is developed which makes use of image processing techniques and ANN, in finding out the pH value and composite nutrients of the soil. The system provides 95% accuracy compared to laboratory results. This system is fast, simple and cost-effective means to provide soil pH values for the captured soil images and can be used for various applications. Our application suggests the suitable crops based on the pH value.