bluuebunny/crossref_metadata_2025 huggingface dataset:
Faster as Well as Early Measurements from Big Data Predictive Analytics Model
Authors: Gupta Shashi Kant , Tiwari Sharda , Jamil Azlin Abd , Singh Prabhdeep
Published: April 2022

Data processing in large scale system and analyses is the key problems of today's distributed systems in real-time and close to real time. These systems should be able, while meeting these constraints, to process large-scale data inputs, to expand to terabytes or larger. Volume of processing the data over the systems of large scale becomes too large. These systems use parallelization of the data and similar techniques to increase performance. The standards are increasing, though: data analyses are expected almost in real time. Among all the data treatment models Map Reduce is mostly adopted. Due to the processing of data design in this method, once work is finished, the results are returned. The growth of data is not always a preferred batch operating environment: large numbers of applications can benefit from early results. The online aggregation method used in connection databases was first discussed. In this work analysis we discuss techniques that can allow early results to be estimated. We are proposing some changes to the online system for Map Reduce. We show that the properties required for accurate evaluation of the effects of our proposed device design changes. The reduction of data bias algorithm and sampling at the block levels are provided. Consequently, with a series of particular applications and sets of data, the implementation of the proposed design of the system is defined and evaluated.
Development of Decision Tree-Based Framework for Efficient Classification and Prediction of Heart Disease Data
Authors: Carpenter Sudhir , Anand Vijay
Published: April 2022

Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. A massive amount of information must be analyzed in order to come up with a sound strategy for dealing with disease. There are a number of common scenarios, including as early disease detection, severity assessment, and prediction. This will decrease the progression of the disease, improve the well-being of the patients, and reduce the associated costs of medical care. In this regard, machine learning techniques have been employed. This article presents a machine learning based framework for heart disease data classification and prediction.
Consumer Purchase Behavior Influenced by Green Marketing: A Study of Gujarat State
Authors: Panchasara Dignesh Shaileshbhai , Dangarwala Umesh Rajnikant
Published: April 2022

Today consumers have strong concern about nature and environment safety due to high level of pollution and global warming. In this situation, organizations need to develop the products in a way that the products can be reuse and recycle. Green marketing is a new trend that fulfills the objective of reducing the pollution and helpful to reduce global warming. To understand the awareness and consumption behavior motivation towards green marketing, researches have collected 168 responses across the Gujarat state and analyzed data with SPSS 20.0 version. Mean, standard deviation, correlation, and chi-square test have used to analyze the collected data. The finding of the study shows that occupation and consumption behavior motivation towards green marketing is negatively correlated, while other demographic variables are positively correlated. Chi-square test found that there is a significant relationship between selected demographic variables and consumption behavior motivation towards green marketing.
Product Design of Hydrogen Sensing System
Authors: Chakravarthi Sharanya , Meda Ujwal Shreenag , Harti Akshit
Published: April 2022

Hydrogen gas is an essential commodity in various sectors starting from manufacturing chemicals to transportation. In the era where fuel-celled vehicles are gaining importance, there is a growing need for hydrogen sensing systems that give quick and accurate information about hydrogen gas that is both qualitative and quantitative. Effective product design of hydrogen sensing systems is essential to bring them to the market and target specific end users. In this paper, an attempt is made to develop a hydrogen sensing system highlighting various aspects of product design. Three design variants are developed for different applications using Solidworks and Tinkercad. As circuit design is essential to determine the exact dimensions of the product, a general Wi-Fi-enabled circuit for a generic hydrogen sensing system was utilized. A simulation of a gas sensing system was done on Tinkercad using Arduino. The paper concludes with discussions about the suggested models and future scope of gas sensors.
Variation in NO2 Concentrations for Kota City (India) Associated with COVID-19
Authors: Kuldeep Kuldeep , Sisodiya Sohil , Mathur Anil K.
Published: April 2022

This study focuses on how the covid-19 pandemic impacts NO2 concentration in Kota City, Rajasthan (India), under different metrological conditions (Temperature, Precipitation, Relative Humidity, and Wind speed and direction). A reduction in NO2 concentration (-55%) was observed due to the Lockdown in Kota city. A sharp decrease of -37% in NO2 levels was observed in lockdown compared to the same period of 2019. However, a massive increase of 57% in NO2 levels was also seen in 2021 compared to the 2020 lockdown period. This study shows the lockdown effect on atmospheric composition, mainly through massive traffic and anthropogenic emissions reduction, significantly reducing NO2 concentrations. The findings can give confidence to stakeholders involved in air quality policy that if better pollution control plans are put in place and strictly implemented, pollution prevention can be done well. The natural environment and air quality have greatly improved due to lockdown. Keywords: Covid-19, Lockdown, Air Pollutants, NO2, Air Quality
Phyllanthus Emblica: A Herbal Remedy for Healthy Life
Authors: Mal Anuradha , Meena Devender Singh
Published: April 2022

Phyllanthus emblica is an important medicinal plant widely used in Ayurveda and Unani medicine systems. It is distributed in tropical and subtropical regions of the world. Different parts of this plant are used in various forms, edible and therapeutic. P. emblica is rich in Vitamin C, amino acids, and minerals. This plant contains phytochemicals like tannins, vitamins, minerals, anthocyanin, flavonoids, phenolics, etc. Apart from these it also contains a potent antioxidant, ellagic acid, which helps prevent gene mutations and repair chromosomal defects. It can scavenge a wide range of ROS and reactive nitrogen species because of its four hydroxyl and two lactone functional groups.
Design of Machine Learning Based Model to Predict Students Academic Performance
Authors: Arcinas Myla M.
Published: April 2022

Artificial intelligence relies heavily on machine learning. Classifying and forecasting outcomes may be done using machine learning. Student performance assessment demonstrates how much effort educational institutions should put in to help the underachieving or average student. Using EDM models is important because they use previous student data to predict future student performance. To help students and teachers improve their performance, educational institutions use a range of ways to gather information on the characteristics of students who are actively engaged in the learning process. Students' performance may be predicted using this machine learning-based system. Support vector machine, ID3, and Regression Analysis are the three machine learning methods used in the model. The results of the experiments have shown that SVM's performance is concerned with the performance of the students.
Different Power Quality Issues of the Energy Power System with Their Analysis and Mitigation Techniques
Authors: Singh Rajendra
Published: April 2022

The power systems have the utilization of the power electronics devices for electric power supply becomes a very common trend. These power electronics devices work like as a nonlinear loads and produce the distortion in the system which introduces unwanted harmonics for reduction of the PQ supply system. The power quality of the system becomes very important issue due to the utilization of the power electronics devices and their application of day to day life. Power suppliers faced a number of problems for transferring the power supply from generating station to the end users and load demand centers with minimum investment or high reliability of the system. Different power quality monitoring techniques describe with their applicable solution of the issues. Custom power devices are utilized for achieving the better power quality through the mitigation techniques like UPS, TVSS for the system. Due to the different advantages of the power quality S-TATCOM can be utilize for renewable energy system. Spinning reserve is the traditional method for improving the power quality of the system. Flow chart of the power quality of the system provide the information of the about the problem identification and evaluation of the problem of power quality in first stage. It can be divided into five parts of the problem evaluation like voltage sag, transients, flicker, voltage regulation and harmonics distortion. Standards of the power quality with their different issues in the energy system for monitoring techniques have been discussed with use of particular custom power devices.
Various Soft Computing Based Techniques for Developing Intrusion Detection Management System
Authors: Sajjaa Guna Sekhar , Pallathadka Harikumar , Naved Mohd , Phasinam Khongdet
Published: April 2022

Protecting networks and data requires an effective Intrusion Detection System (IDS). Contextual knowledge processing may be used to identify attacks that are specific to certain applications and networks, which is becoming more common due to the fast advancement of network technology. A hybrid intrusion detection system may help overcome this problem (IDS). Packet flooding is a common tactic employed in DoS attacks, which aims to overload the victim's infrastructure. It is now possible to interrupt networks of virtually any size with these kinds of assaults. Testing a high-performance hybrid IDS is hampered by having to deal with massive amounts of data with many characteristics. In order to identify dangerous patterns, a high number of features might make it harder, leading to a protracted training and testing procedure, an increased resource demand, and a lower detection rate. As a result, minimizing and deleting irrelevant features from the benchmark dataset is needed. Preprocessing removes out-of-range values, unlikely data combinations, and missing values from the dataset. Feature Selection (FS) methods are used to reduce the dimensionality of a dataset by deleting irrelevant and redundant attributes, thus improving classifier accuracy. To solve this problem, the AODV protocol, in conjunction with soft computing techniques, is very useful.
Consumer Inclination to Purchase Personal Care Products in Salem City
Authors: V P Rameshkumaaar , B Balaji
Published: April 2022

The study's goal is to assess consumers' proclivity to buy personal care items in Salem. The data for the study was collected by using direct interview with the consumers who were purchasing personal care products via an interview schedule. The study collects data from 794 consumers in different parts of Salem City. The study was commenced with the motive to test demographic profile of consumers, factors influencing consumer inclination and satisfaction of consumers while purchasing personal care products. The data was appropriately collated and analyzed using basic frequencies and percentages, exploratory factors, weighted ranking, as well as multiple regression. The study confirmed that consumer inclination mainly relies on store ambience, product quality, product promotion, product price, interest to purchase, and external influences. Consumer satisfaction was mainly influenced by the package quality, variety of product, and quality offered by the personal care products. It is concluded that several factors determine consumer inclination to procure personal care products.
Modeling Work Trip Mode Choice in Post-Metro Bengaluru City
Authors: Kumar Dheeraj N , Kumar S Sanjay , Kunhikrishnan Parthan , Jagadeesh H. S.
Published: April 2022

Traffic congestion and emissions are the acute problems faced by most cities in the country. The cause for these problems could be increased usage of personal vehicles and lesser popularity of public transport. The aggregate effect of individual mode choice decisions reflects in traffic congestion and emissions in the city. To study these problems, it is essential to know the mode choice and the factors affecting it. Commute trips like work and school trips contribute heavily to these typical urban transportation problems. In the present study, attempts have been made to study the mode choice of working commuters in Bengaluru City. To achieve this, a household survey is conducted. Multinomial logit models are used to analyze the mode choice behavior of these individuals. Findings from the model revealed that individual characteristics like age, vehicle ownership, level of service characteristics such as travel time and travel cost, and subjective factors like comfort, security, and station environment significantly affect mode choice. Using the model developed, a policy has been proposed to predict the effect of restricting ownership of the second car on mode choice, resulting in about 2% decrease in car shares, which could reduce traffic congestion and pollution in the city.
Bypassing Confines of Feature Extraction in Brain Tumor Retrieval via MR Images by CBIR
Authors: Saraswat Amar , Sharma Neeta
Published: April 2022

To check out the health of the patient, digital images are generated every single day and are used by the radiologist for extracting out the details and anomalies. The complicated part is to figure out the disease in those images. By the manual diagnosis of the images through the radiologists, the doctors can get to know exact scenario of the abnormalities in images, but is considerably more difficult with Content Based Image Retrieval (CBIR) to get those finer details from MR images. These CBIR approaches are now frequently employed in the automatic diagnosis of disease from MR images, mammograms, and other sources. Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique we propose, and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.
Web Enhanced Instruction in Learning Mathematics: Adaption Analysis during COVID-19 Pandemic
Authors: Rastogi Ashish , Sikhwal Omprakash
Published: April 2022

Due to the current pandemic situation most of the Higher Educational Institutes (HEI’s) adapted the online learning and evaluation in all the courses. In this study the objective is to is to identify and investigate the characteristics that may influence foundation students' adoption of Web enhanced Instruction in Learning Mathematics (WEILM). The model is based on the Technology Acceptance Model (TAM) [1][2] as shown in fig.1 and social cognitive theory (SCT) [3] see fig.2, which was developed using the structural equation modelling (SEM) technique and analyses the components and their impact on the successful adaptation of Web Enhanced Instruction in Learning Mathematics (WEILM). The factors that were investigated were perceived usefulness (PU), computer self-efficacy (CSE), social influence (SI), availability of IT services (AIT), enjoyment (ENJ), received feedback (RF), comparative feedback (CF), attitude (AT), and behavioural intention (BI). To test the theoretical concept, primary data is obtained from foundation level students via a survey questionnaire. The total number of responses were 387, with 348 of them providing legitimate responses. Our research findings differ by about 80% from what we expected. The summary of the finding is shown in table 1 reveals that all the 12 hypotheses are supported and is consistent with the previous results [4][5][6], and all the factors have a substantial beneficial correlation. The study's findings may give instructors and designers with a better grasp of how to successfully adapt the WEILM.
STAY HOME and Teach”: Educational Reforms and Mental Health Due to COVID 19
Authors: Pillai Rajani H. , G Sitara , H R Kruthika
Published: April 2022

COVID 19 created a large disturbance in the education system. The locking down of education institutions has majorly affected large number of students and teachers. The managements of educational institutions, with a lot of thought process have worked on the implementation of innovative teaching and learning. This paper majorly focuses on the intellectual innovation and education reforms initiated by the teaching fraternity to reach the students through the virtual mode. The need of the hour is to innovate the teaching methods with a systematic plan. The teaching fraternity who mostly used chalk and talk were forced to adapt to online mode for teaching which was considered as an opportunity for them to relearn and rethink on implementing the virtual teaching
A Preliminary Study on Detection of Serogenic E.coli in Fishes to Conduct a Microbial Assessment of Coastal Water
Authors: Bose Sanchali , Ponnusamy Senthil Kumar , G Pooja
Published: April 2022

Escherichia coli is a bacterial pathogen mostly responsible for gut infections and related serious illnesses. There have been some studies in the past which detected the presence of E.coli in edible fishes. The diarrheagenic E.coli strains are mainly responsible for such health issues. E.coli is an indicator of the microbiological quality of water as it indicates faecal contamination. In this paper, it is intended to understand the microbiological quality of coastal water inhabited by fish. The edible fishes consumed along the Vishakhapatnam coast were subjected to various tests like Indole production test, Citrate utilization test, Urease test to confirm the presence of E.coli. It was thereby confirmed through Polymerase Chain reaction that Indian Mackerel and Indian Salmon were infected with Sta (Heat stable toxin) serotype and SLT2 (Shiga toxin) respectively. This indicates the poor microbiological quality of the coastal water along with poor handling practices.
Puri-Konark Tourist Circuit of Odisha: Coastal Tourism Hotspots, Calamities, and Resurgence
Authors: Seal Manisha , Samanta Sahoo Shwetasaibal , Mohanta Giridhari , B Balaji
Published: April 2022

Natural hazards take place periodically every year in the sub-continent India, devastating the life of several residents placed around eastern and western coastal areas. The tourism phenomenon is not immune to natural disasters. Due to the intrinsic scenery many tourism developments are often located in regions that are exposed to natural disasters, in particular beach and coastal areas promoting coastal tourism. Tourists travel to a destination only for a short time period and may not be aware of such local hazards and finally they may become the victims of such natural disasters which results in creating a negative impact on the image of the destination that can be both serious and long-lasting. The study focuses on Puri and Konark, two most preferred tourist destinations in Odisha, prone to natural calamity called cyclonic storms. The research paper may contribute a valuable insight and guidelines for policy makers and other stakeholders concerned to restore and reduce the hazards of natural disaster in the Puri-Konark tourist.
Electrocatalytic Activity of PMMA–Anatase TiO2 Nanocomposites
Authors: Pon Maha Lakshmi P. , Vedhi C. , Rajakani P.
Published: April 2022

We characterized optically and electrochemically synthesised pure PMMA and PMMA–TiO2 nanocomposites developed through various proportions of anatase TiO2 (TiO2 (A)) added by chemical polymerization at room temperature. The characterization of the nanocomposite was carried out using FT-IR, UV-Vis, XRD, and FESEM. Cyclic voltammetric studies exhibit good adherent behaviour on the electrode surface at pH 7.0 for PMMA and pH 4.0 for PMMA–TiO2 nanocomposite. It has been observed that PMMA–TiO2 nanocomposite modified GCE has good oxygen reduction ability, and also enhances resistance behaviour.
Blockchain and Corporate Banking
Authors: Pathrose Eugin Prakash
Published: April 2022

Blockchain technology has grown in prominence after the introduction of cryptocurrencies. The appeal of cryptocurrencies is undeniably based on the legitimacy of the same, which is ensured by blockchain technology. Blockchain technology is extremely common due to three main characteristics. The first is decentralization, which means that there is no central authority or single point of failure that can be used to bring the system down so each node is self-contained. The second feature is immutability, which prevents false entries. Third, since any entry must be checked by all nodes in the scheme, it allows for openness. If no consensus is reached, the submission is denied. The use of blockchain technologies has expanded across a wide range of sectors. Banking is one sector that is going to be greatly affected by blockchain technologies. It facilitates the transfer of large sums of money while still posing significant security risks. As a result, blockchain technology makes it simple to fix these two issues. Blockchain technologies can be used to automate several operations in the banking industry. This paper aims to describe such processes and demonstrate how blockchain technology can be used in various areas of the banking industry.
A Comparative Study and Impact Analysis of Different Oversampling Techniques for CIP
Authors: Bora Dibya Jyoti , Navlani Avinash , Mohari Ankit
Published: April 2022

In the area of data science, machine learning based classification techniques are the first choice for an accurate analysis of a huge amount of data. The first requirement while developing such classification techniques is the robustness that it will imply for an accurate and efficient classification. However, it is seen that in many times these algorithms suffer from “class-imbalance problem”, shortly CIP. Due to CIP, many difficulties arise during the learning process, which as a whole results a poor classification process. Resampling the data set is one common technique for dealing with CIP where in general oversampling the size of the rare class is made. We have different oversampling techniques available in the literature like SMOTE, ADYSN, and Random Oversample are the noted ones. In this paper, an effort is made to compare these different techniques as well as their impact on classification performance.
Exploitation of Motherhood in Stanadayani and Maratthottil
Authors: Sree Hari Sree Harsha , Saraswat Surbhi
Published: April 2022

The Bengali short story Stanadayini or ‘Breast Giver’ by Maha Swetadevi and the Malayalam short story Maratthottil or ‘The Wooden Cradle’ by Lalithambika Antherjanam outline and explore women’s identity as a mother and how the romanticism of motherhood for some woman is nothing more than a successful ploy to misuse and exploit her. Both Jashoda and Nangelipennu spend their lives rearing the children of their masters only to die after being rejected by the families. The stories acts as a window to the caste, class, gender, sex, culture, identity, body, and power that plays its role in the society. The status of both the women as care giver or mother collapses as soon as her material worth to the family fades. They swiftly revert to a normal woman who, as a result of her illness, becomes a liability. This research paper attempts to explore the vulnerability of a lower class/caste women by the hands of the upper class/caste custodians, thus making them vulnerable to exploitation and thereby negating her identity.
Evaluation of Impact of Concomitant Drugs in Human Plasma on LC-MS/MS Based Quantification of Olmesartan and Hydrochlorothiazide
Authors: Kumar Arvind , Prakash Surya , Prasad Tulika
Published: April 2022

Liquid chromatography tandem mass spectrometer (LC-MS/MS) is an advanced analytical technique, which requires a validated method for a particular drug, prior to analysis of study samples. The analytical method must be selective with ability to distinguish between analyte of interest and endogenous or exogenous interferents (salts, proteins, lipids, metabolites, and concomitant medications). Concomitant medications are general treatment taken by subject population. Regulatory guidelines advocate evaluation of effects of co-medication on analytical methods, but detailed description of experiment designs is not yet reported. In the present study, a simple procedure to evaluate the co-medication effects on LC-MS/MS based simultaneous quantitation of Olmesartan and hydrochlorothiazide is explained. This is the first report that provides details on experimental design for assessing effects of co-medications on LC-MS/MS based quantitation of drugs in biological samples. This experimental design is likely to facilitate assessment of effect of co-medications on analytical methods applied in pharmacokinetics, drug development, and therapeutic monitoring.
Automatic Text Summarization of Hindi Text Using Extractive Approach
Authors: Jain Rekha
Published: April 2022

Text summarization is one of the applications of NLP (Natural Language Processing) that is having a large impact on users’ lives. The resultant text must convey the important information to intended users. A lot of users face the problem of content repetition while reading from the internet. Most of the time same information is repeated in majority of documents. It wastes valuable time of users. This problem raises the need of automatic text summarization. Automatic text summarization belongs to the NLP area of Machine Learning. This technique generates the shorter version of text with the help of some automated tool. The shorter version of text conveys relevant and important information to users. In the proposed work, the author is introducing an extractive approach for summarization of text. This approach is based on cosine similarity technique, which automatically generates the summarized text while keeping the relevant and meaningful information intact.
AMBA AHB Bus with Multiple Masters Using VHDL
Authors: Saluja Hitanshu
Published: April 2022

This article depicts execution of DDR3 controller of AMBA Bus with four experts is portrayed in this paper. DDR3 controller is associated with the AMBA AHB Bus. It empowers development of squares of information from fringe to memory, memory to fringe, memory to memory, and fringe to fringe. This development of information lessens the heap on the processor. A DDR3 controller saves power in a framework by placing the CPU in a low force state. An AXI 4.0 execution in SOC configuration lessens the limits of correspondence by decreasing information rates. Proposed configuration utilizes AXI 4.0 conventions with memory regulator interface. AXI 4.0 convention upholds various IDs and channel handshaking rather than judge. In AXI 4.0, five individual exchange channels is proposed to convey in different expert and various slave frameworks so numerous bosses can discuss all the while with numerous slaves and wonderful parallelism is accomplished. All the control circuitry is synthesized using Xilinx 13.1 and simulation waveforms are achieved by using Modelsim 6.5e.
Market Study of Hydrogen Sensors and Sensing Systems
Authors: U Sripriya , Meda Ujwal Shreenag
Published: April 2022

The future of hydrogen is abundant with numerous applications in refineries, fertilizer industries, nuclear plants, and other upcoming technologies like environment friendly fuels. With the market for hydrogen being so dynamic, a sensing system in place becomes a prerequisite. Hydrogen sensors with various working principles, price ranges, and with different applications are commercially available. This paper aims to give a broad comparison between different types of hydrogen sensors as well as metal oxide hydrogen sensors and evaluate the existing market, the future scope, economy, and challenges. A comprehensive study has been put together which focuses on different types of hydrogen sensors, ways of sensing of hydrogen and its quantification, prospective applications of hydrogen, challenges in the field of hydrogen sensing as well as its use as fuel, market study global share of hydrogen production and consumption through the years, hydrogen sensing systems, and the major players in this field.
Optimal Control Measures of COVID-19 in Infected Prey with Harvesting Effort
Authors: S Saranya , S Vijaya
Published: April 2022

This paper presents a mathematical model for a prey-predator system in which there are two prey species and one predator species where prey population is taken as competitive of the infected-susceptible species. We analyze and propose the positive, boundedness, and positive equilibrium point of trivial, disease-free, and interior equilibrium point with stability analysis criteria for local stability and applied harvesting effort to control the spread of disease. We analyze numerical simulations showing different scenarios from the maple 15 and 20 programming.
Rice Plant Diseases Identification Using Similar Image Analysis in Deep Unsupervised Learning
Authors: Sharma Pooja
Published: April 2022

The prerequisite for a (extremely) huge dataset is for the most part the fundamental part that is formed against deep learning algorithms. The testing accuracy of deep learning models is not sufficient, as they require huge datasets for preparing. The testing accuracy of deep learning model is not good for unseen images or those not used at the time of training. Earlier research on deep unsupervised learning is also limited. There is an application of image search, which is based on content based searching. This content-based image searching is the solution for the problem of large dataset where the model is trained with fewer images. Similar images are retrieved by calculating distance to find nearest neighbors. In this paper pre train VGG16, deep convolution neural network is used to train the model using a rice image dataset. This image-based dataset includes four diseases of rice plant, the models learn the image features, and similar images are retrieved by calculating nearest neighbors. The input feature length also reduced with the help of PCA. At last comparison of nearest neighbor algorithm is also performed.
Automatic Classification and Diseases Identification of Indian Medicinal Plants Using Deep Learning Models
Authors: Sharma Pooja
Published: April 2022

In India, there are various plants which are used in making Ayurveda medicines. To identify and classify the kind of plants is a tedious task which needs testing from an expert. So there should be a system for early identification of medicinal plants. As there is good availability of smartphone with farmers now, various deep learning models based applications can automatic classify the plant diseases. In the current study, VGG16 and VGG19 pretrained CNN models performance are analyzed to classify diseases of various plants like basil, bael, Arjun, etc. There are 21 classes in the dataset. The validation accuracy of VGG16 produced best results as 98.52, where VGG19 shows 98.08 accuracy. Various other factors related with CNN models like performance, testing on real time data, and future scope also elucidated.
Finite Element Analysis for Design Optimization of MEMS Double Ended Tuning Fork Resonator
Authors: Sharma Anshu , Kumar Vijay , Kaur Harsimran Jit
Published: April 2022

A resonator design is very crucial for a resonant sensor since it affects the sensitivity, linearity, operational range, and resolution of the sensor. In this study, a DETF resonator is the object of analysis, to obtain a proper selection of boundary conditions and suitable structure for vibrational model. In order to achieve high Q values, proper vibrational mode selection is an important criterion for the operation of DETF resonator. This mode selection depends on the dimensional parameters, such as length, width, thickness, and gap of the tines of the DETF resonator. This paper presents a numerical approach-based analysis for obtaining optimum geometric configuration of a DETF resonator. The impact of DETF geometry on modal resonant frequency and mode separation is elaborated with the help of simulations using COMSOL Multiphysics tool.
Exploring the Relationship Between Teachers Well Being and Professional Commitment
Authors: Abrol Monika , Bansal Kanchan , Kishore Nand
Published: April 2022

This article focuses on exploring the relationship between teachers well being and professional commitment. The investigator employed quantitative descriptive study and data was taken from 200 (50 male and 50 female) secondary school teachers from government schools and (50 male and 50 female) from private schools through scales of well being (1) and professional commitment scale (2) from Punjab state. The results found a positive and significant correlation of well being with the professional commitment of secondary school teachers. The study also indicates that well being and professional commitment of male and female teachers of both government and private secondary schools exhibit positive and significant correlation. The study further suggests that government should frame such policies and programs that foster the professional growth as well as commitment among teachers.
Advances in Incorporation of Nanomaterials Onto Fabrics
Authors: Kiran Samhita , Meda Ujwal Shreenag , Ranga Shravan , M a L Antony Raj , R J Basavaraja
Published: April 2022

Textile is a necessary commodity and is of paramount importance in a variety of areas. The properties of textiles can be further enhanced and the horizon of their applications can be broadened by the incorporation of nanoparticles using nanotechnology. Nanoproducts ranging from nanofibers, nanocomposite fibers, and intelligent polymeric nanocoatings are frequently incorporated in conventional textiles to provide improved performance and new functionality. Nanocoatings offer numerous specific properties like antimicrobial, wrinkle resistance, and protection against UV radiations. These properties lead to a variety of textile applications, including medicinal textiles and sportswear. In the COVID-19 pandemic situation, nanotechnology has paved the way for need-based customization, such as antiviral masks, PPE kits, etc. This paper provides an overview of the nanomaterials suitable for textile industry, enhancement in the properties of fabrics due to incorporation of nanomaterials, advantages, disadvantages, and a few applications.
Online Consumer Shopping Behavior with Special References to COVID-19 Pandemic in Pune City
Authors: Bhalerao Harshali Rahul , Phule Prashant Namdev , Saxena Vimal Deep
Published: April 2022

The COVID-19 pandemic and lockdown has brought about various changes in the shopping behavior of the consumers. The risk, perceptions, and precautions related to the COVID-19 pandemic influenced the online consumer shopping behavior. A sudden growth and numerous opportunities are being created in the field of online marketing. In the current paper, the researchers tried to find out how customers responded to online shopping during the pandemic and lockdown times. The study involved 140 respondents from the different areas of Pune City. The data was collected using an online survey method. The data collected was analyzed using tests like T-Test and ANOVA. The current research will give a broad guideline to the online sellers to devise strategies for selling of their products and services online. The after effects of the pandemic may be carried ahead leading to a new normal, where sellers could make the best of this opportunity of changed consumer behavior towards online shopping.
Detection of Rice Leaf Diseases Using Convolutional Neural Network
Authors: R Poorni , Kalaiselvan Preethi , Thomas Nikhil , T Srinivasan
Published: April 2022

Rice is one of the most important crops grown in India, and it is afflicted by a number of diseases which pose a significant menace to global food security. Furthermore, farmers find it challenging to manually recognize these infections with their naked eyes. The proposed work uses rice leaf images for disease classification since early diagnosis of rice leaf disease is critical. Therefore, a collection of various rice leaf disease images is acquired and trained on the best model Convolutional Neural Network (CNN), and then new rice leaf images are tested using the weights obtained from the CNN. Transfer learning is performed using the Inception v3 model to obtain the ideal possible weights for testing and validation in the proposed approach. The model's accuracy is 94.48 percent, and the results indicate that our developed model outperforms existing models in terms of detection performance.
Numerical Solution to Sixth Order Ordinary Differential Equation Using Three Stage Eighth Order Runge-Kutta Type Method
Authors: Kaur Manpreet , Kumar Sangeet , Bhatti Jasdev
Published: April 2022

The present paper provides an innovative approach in developing numerical structure with minimal processing and maximum precision for sixth order ordinary differential equation satisfying underlying associated initial conditions. The algebraic order conditions for the three stage eighth order Runge-Kutta methods (RKSD8) been well designed for evaluating local and global truncated errors towards sixth order ordinary differential equation having form vvi = f (u,v,v',v''). The convergence of a numerical method is well explained and proved beneficial in evaluating norms of error and zero stability of RKSD8 with effective values of weights and nodes in the form of Butcher tableau.
A Review on Emotion Recognition with Machine Learning Using EEG Signals
Authors: Islam Mir Salim Ul , Kumar Ashok
Published: April 2022

Emotions are critical in people's daily lives since their decision making, interaction, intelligence, and perception are all influenced by the emotions they display. Emotion recognition with machine learning based on EEG signals has been an exciting topic and employed in several areas, such as health care, social security, and safe driving. In this paper, a review on emotion recognition using EEG signals employing machine learning is carried out based on various factors, such as the stimulus used, equipment, modalities, filters, features, classifiers, and detected emotions, along with the limitations. This paper identifies the basic methodology used in the emotion recognition process with various tools and technologies utilized in it. Finally, it gives the issues and challenges for future research directions.
Energy Improvement in Distribution Network Using Sliding Mode Controller Based SVC System
Authors: G Ramya , M Vinil , J Ajay Daniel , M Moovendan
Published: April 2022

Static Var Compensator is a shunt type reactive power compensation device used to enhance the energy in the distribution system. This paper presents the power quality improvement using SVC in 14 bus system using Proposed Resonance and Sliding Mode (SM) Controller to improve the power quality. Minimized power losses and improved power transfer capacity to stabilize the weak system are the main functions of the SVC system. The instantaneous response of SVC is the main important advantage in comparison with mechanically switched compensation devices. SVC is installed at the point of common coupling to improve the reactive power. The performance of the multibus system is analyzed in terms of real power, reactive power, and RMS voltage of the system, and is simulated using MATLAB Simulink.
Analysis of Algorithms to Control the Congestion by Improve Energy Efficiency in WSN
Authors: G Vanitha , P Amudha , S Sivakumari
Published: April 2022

Congestion is a major significant challenge in WSNs because it directly impacts energy efficiency and the network lifetime of sensor nodes in the network. This paper aims to analyze the different congestion control or avoidance routing protocols performances in WSNs with their drawbacks for identifying the upcoming scope of congestion-aware routing protocols in WSNs. This article proposed an adaptive queuing system with the WPDDRC called a proficient control (PRC) algorithm to tackle this issue. In this algorithm, two independent virtual queues are considered single physical length (lines), which accumulate the input packets from every child's node depending on the source's traffic significance and priority. A Proficient Rate Control (PRC) technique develops using traffic type priority and virtual queue conditions. Here, a PRC with fair bandwidth allocation (PRC-FBA) technique is compared and analyzed. It must handle the congestion due to the mix of RT and Non-RT (NRT) packets effectively in network.
Watermarked Hashing As a Video Content Authentication Technique
Authors: Saini Preeti , Ahuja Rakesh
Published: April 2022

The Authentication is the primary problem of cyber security as a dimension. It is just a method of approving a digital proof of identification. Different levels of access or completion of tasks are granted to different individuals, depending on who has signed up for the task at hand. There are many different types of authentication, ranging from passwords to fingerprints to bio metrics. These technologies add a layer of protection and prevent security lapses like data infringements. Digital video authentication is a method of evaluating whether the recorded video is genuine and has not been tampered with. Video hashing is commonly deployed in closer detection, video authentication and video tamper detection and a look for anti-piracy. Perceptual hashing has emerged as a powerful tool for verifying the validity of multimedia content and it continues to pique the interest of experts working in analyzing multimedia domains and spotting illegal or counterfeit videos on the internet.
An Application Module of IOT for Environment Protection
Authors: Arora Yojna , Chugh Aarti , Jain Charu
Published: April 2022

Internet of Things is the connection of physical objects which are embedded with sensors and software for exchanging data with other devices over the internet. It is a collaboration of multiple technologies, such as machine learning, real time analytics, and embedded system. IOT has helped in dealing with various real world problems. One such application area is in the field of environment protection. Due to the enhanced levels of particles and toxic materials in the environment it has become essential to monitor the levels on continuous basis so that required notifications can be generated. The purpose of this paper is to develop an IOT module which can sense the amount of toxic particles in air using the MQ135 sensor which can be later displayed on Thingspeak server. However, when the toxicity in the air crosses the threshold value it will give the notification on twitter. This module can be installed anywhere and when pollution crosses the safe point some external devices can be triggered and send a notification using a WiFi module.
Novel Framework for Rumor Detection Using Emotionally Infused LSTM
Authors: Sharma Osheen , Ahuja Sachin
Published: April 2022

In modern life, Microblog has become a popular and convenient platform for sharing, posting, and seeking information. The use of social media is increasing due to its connectivity and availability. However, it has also increased the rate of generation and propagation of rumors which can become a cause of social detriment. Rumors are the unverified information spread via social media without checking the facts with some hidden agenda or motive. Detecting rumors on social media is important for the betterment of the society. A lot of research has already been done for rumor detection on social media. The paper discusses the proposed emotionally infused LSTM and data mining-based framework. Along with this the prominent research done in rumor detection has also been discussed.
An Investigation on Drug Binding Ability of Cationic Surfactant CTAB
Authors: Kaur Jasmeet , Kumar Harsh , Awasthi Pamita
Published: April 2022

Taking in consideration the application of surfactants in drug delivery, the interactions of cationic surfactant cetyltrimethylammonium bromide (CTAB) with drug Betaine Hydrochloride have been scrutinized. The micellization behaviour of surfactant is investigated at three different temperatures (298.15, 308.15, and 318.15)K in presence of various concentrations (0.10, 0.25, and 0.50) mmolkg-1 of drug Betaine hydrochloride via conductivity measurements The critical micelle concentration (CMC) attained though conductometric measurements has been utilized to obtain different thermodynamic parameters of micellization i.e. standard free energy of micellization (ΔG0
m), standard enthalpy of micellization (ΔH0
m), and standard entropy of micellization (ΔS0
m).
Slavery and Racism: Portrayal of Huck as a White Slave in Mark Twain’s Novel T
he Adventures of Huckleberry Finn
Authors: Khalil Zeenat , Jahan Mursalin
Published: April 2022

Slavery and racism are complicated, contentious issues that have been intimately interwoven elements of American society. Slaves have suffered a wide range of wounds and afflictions at the hands of others, including members of their own community and family. Slavery and racism in Mark Twain’s The Adventures of Huckleberry Finn is a fundamental concern for Huck, a white boy of thirteen who observes the pinnacle of injustice and cruelty, which is ethically repugnant and blatantly anti-human. In the novel, Huck Finn’s struggle with his inner conscience due to the heinous practice of slavery, the harsh realities of white culture, and their cold-hearted attitude have horrible implications on his heart and mind. This research emphasizes the necessity for deliberate moral reflection on how a white person’s honesty and kindness became a justification for his enslavement among the white community and also how he fights against the atrocities of the whites.
Optimization Techniques Analysis in Underwater Wireless Sensor Network
Authors: Mamta Mamta , Goyal Nitin , Nain Mamta
Published: April 2022

By the existence of Underwater Wireless Sensor Network (UWSN) there exists now ample opportunities for researchers, academicians, and industry experts to study and explore marine resources. For exploring and monitoring these marine resources, ocean surveillance, disaster prediction, etc. UWSN connects the rest of the world with the ocean environment. This UWSN network contains enormous number of sensor nodes deployed in the water at different profundity and are interwoven for real-time data analysis, optimized and with a secure data collection. So, to gratify the necessities for rapid development of UWSN, optimization plays an important role. In this paper we first present the introduction about the UWSN and its architecture. Furthermore, explanation about node deployment, node localization, and data collection are presented and also discussed about the optimization strategies for each one. Using simulation results for localization, performance of different optimization techniques is compared. Finally, the paper provides an outlook on challenges that exists in UWSN and act as torchbearer for researchers.