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Machine Learning Technique to Detect Radiations in the Brain
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Pt Nanospheres Decorated Graphene-?-CD Modified Pencil Graphite Electrode for the Electrochemical Determination of Vitamin B6
An electrochemical sensor for Vitamin B6 determination has been prepared by the electrochemical deposition of Pt nanospheres on graphene-?-CD coated Pencil Graphite Electrode (PGE). Cyclic voltammetric (CV) and electrochemical impedance spectroscopic (EIS) studies were employed to explore the electrochemical properties of the modified electrode. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and optical profilometric studies. The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Vitamin B6 was found to be 5nM to 205nM. The low level of detection limit (1.2nM) implies the high sensitivity of the process. The suggested method was effectively employed for the electrocatalytic evaluation of Vitamin B6 in different juice samples. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications
This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%. 2022 SulaimaLebbe Abdul Haleem et al. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
Photocatalytic degradation of methylene blue and metanil yellow dyes using green synthesized zinc oxide (Zno) nanocrystals
In this work, ZnO nanocrystals (NCs) have been effectively synthesized by a simple, efficient and cost-effective method using coconut husk extract as a novel fuel. The synthesized NCs are characterized by UV-Vis, XRD, FT-IR, SEM, EDX, Raman and PL studies. The obtained ZnO were found to be UV-active with a bandgap of 2.93 eV. The X-ray diffraction pattern confirms the crystallinity of the ZnO with hexagonally structured ZnO with a crystallite size of 48 nm, while the SEM analysis reveals the hexagonal bipyramid morphology. Photocatalytic activities of the synthesized ZnO NCs are used to degrade methylene blue and metanil yellow dyes. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
The Impact of Website Design on Online Customer Buying Satisfaction and Loyalty to E-Tailers: An Exploratory Study of E-Tailers in India
The popularity of e-tailers has distorted the retail industry in India. Websites are becoming an important means through which customers get product information and purchase items for their needs. This research paper focuses on four dimensions (i.e., user interface, convenience, personalized recommendations, and perceived security of the website) to assess their impact on online customer satisfaction with and loyalty towards e-tailers. The study questionnaire used established measures. The data was collected from four large cities in India, namely Chennai, Mumbai, Kolkata, and Delhi. Analysis of the survey results suggests that perceived website security is the most important dimension for customer loyalty. E-tailers have to ensure adequate security provisions in their websites to build up consumer perceptions of trust and so repeat business loyalty. Copyright 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. -
(Mes-Acr-Me)+ClO4 Catalyzed Visible Light-Supported, One-Pot Green Synthesis of 1,8-Naphthyridine-3-Carbonitriles
Abstract: A novel, four-component one-pot green synthesis of biologically active 1,8-naphthyridines by a reaction of diverse aromatic aldehyde, malononitrile, 4-hydroxy substituted 1,6-dimethylpyridin-2(1H)-one, corresponding aniline in EtOH catalyzed by 9-Mesityl-10-methylacridinium perchlorate [(Mes-Acr-Me)+ClO4] under visible light generated from a 24W Blue LED wavelength 450460nm at 26C is reported. In contrast with the reported procedure, our methodology is diverse, versatile and has several favourable factors such as metal-free, excellent yields, shorter reaction durations, chromatography free and straightforward extraction process. Graphical Abstract: [Figure not available: see fulltext.]. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Financial well-being A Generation Z perspective using a Structural Equation Modeling approach
The current pandemic situation in the global economy has urged the need to revolutionize the financial services industry with a keen eye on consumers financial needs for sound financial decisions, which is necessary for financial well-being. The purpose of the study is to assess the financial well-being of Indian Gen Z students in relation to financial literacy, financial fragility, financial behavior, and financial technology. In addition, the study also tries to determine how Gen Z students financial well-being is influenced by other factors such as gender, age, parental education, employment status, and monthly income in India. The study uses the scientific data analysis approach, Partial Least Squares-SEM model to estimate, predict, and assess the hypotheses. A sample of 271 University students from India was surveyed using a self-administered structured questionnaire. Questions were incorporated to understand the effect of financial literacy, technology, fragility, behavior, demographic and parental characteristics on financial well-being. The results indicate that financial behavior is positively related to financial well-being, while financial fragility is negatively associated. However, financial literacy and financial technology do not significantly affect financial well-being. The results also show that financial well-being is significantly influenced by gender, parental education, employment status, and monthly income change. Understanding Indian Gen Z student financial well-being will expand the students understanding of the importance of financial literacy for well-planned financial behavior and informed decisions, hence high levels of financial well-being. Government and financial institutions can more effectively identify gaps and deficiencies in student financial well-being. 2022 LLC CPC Business Perspectives. All rights reserved. -
Derris Indica Leaves Extract as a Green Inhibitor for the Corrosion of Aluminium in Alkaline Medium
The corrosion inhibitive effect of Derris indica leaves extract (DILE) on aluminium in 1 M NaOH is investigated at different temperatures. For this purpose, weight loss studies and electrochemical methods including potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) technique are employed. Surface analysis of the treated and untreated aluminium coupons are done by using metallurgical microscopy. About 60.2% of maximum corrosion inhibition efficiency is attained with an optimum inhibitor concentration of 1.2 g/L. Both weight loss and electrochemical studies confirmed that DILE plays a crucial role in the formation of a protective layer over metal surfaces. Also, electrochemical measurements revealed that DILE behaves as a mixed type of corrosion inhibitor. The kinetic parameters and thermodynamic parameters are calculated using Arrhenius theory and transition state theory. Langmuir adsorption isotherm was found to be the best fit and physical adsorption mechanism was proposed. En ineered Science Publisher LLC 2022 -
Consumer perception and factors influencing consumption of millets
Consumers purchase intention and preferences are influenced by price, quality, health-related benefits, and awareness about the product. This paper aims to know and understand the consumer perception of millets and to recognize the factors that influence their purchase. The primary data was collected through an online questionnaire covering fourteen districts of Kerala, India. Factor Analysis, Friedman test, T-test, and One-way ANOVA were used for testing the objectives and hypothesis. Factors identified were grouped as perceived value, essential nutrients, and a healthy lifestyle. Friedman test revealed that there wasa significant difference among the mean values of most nutritious cereals, and maize was the most preferred cereal over others in Kerala. Based on the findings, the study recommends certain strategies like food manufacturing companies could introduce variety of millet-based snacks. In addition to this, the concerned food and health department could also devise certain policies that would be aimed at promoting millet-based food. 2022, Kerala Agricultural University. All rights reserved. -
A short review on environmental impacts and application of iron ore tailings in development of sustainable eco-friendly bricks
Increased mining activity of iron ore has led to the generation of voluminous wastes of various nature, especially during the different stages of its extraction and production. The improper disposal of such waste causes negative impact on the environment. One such waste which is generated during the beneficiation process of iron ore is waste iron ore tailings, which is also termed as IOT. Further, dumping of IOT on open ground creates huge dumping sites. This dumping sites have been a concern to the environment and human population in its close vicinity. Therefore, a need to effectively use IOT has become one of the subjects of interest for many researchers. This article provides a short review of environmental problems caused due to improper disposal of IOT, and also reviews on the reuse methods of IOT in the construction sector, which helps to alleviate the environmental pollution associated with improper disposal of IOT. Furthermore, reuse of IOT in construction sector reduces the exploitation of the virgin materials for production of construction material, and thus reducing depletion of natural resources. Based on the existing literatures and findings it was observed that the use of IOT to develop stable building blocks using unconventional methods showed great potential and improved performance, when compared with conventional materials such as clay fired bricks. 2021 -
Return and volatility spillover between India, UK, USA and European stock markets: The Brexit impact
The 2016 Brexit referendum created potential turmoil in financial markets. The purpose of this study is to examine the impact of the Brexit referendum on the return and volatility spillover between the EU, the UK, and the USA stock markets and the Indian stock market during the pre- and post-Brexit referendum period. The VAR and bivariate GARCH BEKK models were employed. The study results suggest that before the Brexit referendum, Indian stock market returns made no significant return spillover on the other markets. On the contrary, following the referendum, Indian stock returns significantly spilled over to France, Germany, the UK, and the USA stock market returns. The study results also identified a substantial increase in the bidirectional volatility spillover between India-France, India-UK, and India-USA during the post-Brexit referendum period. Therefore, the investors opportunity to invest simultaneously in India, UK, EU, and US stock markets for portfolio diversification is limited. India was affected mainly by its own past shocks before the Brexit referendum. However, after the Brexit referendum, Indian markets are getting more and more integrated with other markets. In order to reap the diversification benefits, a prudent investment strategy will need to be developed in the future, especially during times of economic and political uncertainty and market crisis. Sangeetha G Nagarakatte, Natchimuthu Natchimuthu, 2022 -
The mathematical and machine learning models to forecast the COVID-19 outbreaks in Bangladesh
The COVID-19 virus mutates in many different variants after its outbreak. Although several vaccines have been developed by many countries and implemented worldwide, it is difficult to prevent the outbreaks due to the pops out of different variants from its regular mutations. This study is an attempt to develop models which could precisely forecast the COVID-19 outbreaks in Bangladesh. In this study, we have developed a SEIRD based machine learning model to forecast the next possible one year outbreaks scenario in this country. We have tested the accuracy of this model by fitting the results with the considered historical data from March 08, 2020 to October 14, 2021. Also, we have validated this model by predicting the future inside the existing dataset, which is almost similar to the real dataset. It is observed that the final future forecasting results are very realistic compared to the current outbreak situation. Additionally, we have shown that the classical SEIRD model cannot predict the COVID-19 future outbreaks even it does not fit with the real datasets of outbreaks. Moreover, another machine learning time series forecasting model, FBProphet, has been implemented to forecast the future outbreaks of Bangladesh. Finally, we have analyzed and compared the forecasting results and hence identify the limitations of the proposed models which can improve future research in this field. 2022 Taru Publications. -
Mathematical foundations based statistical modeling of software source code for software system evolution
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling. 2022 the Author(s), licensee AIMS Press. -
Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, Dense-Net201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 2022 CRL Publishing. All rights reserved. -
Juice Jacking: Security Issues and Improvements in USB Technology
For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Weakly nonlinear stability analysis of salt-finger convection in a longitudinally infinite cavity
This paper is a two-dimensional linear and weakly nonlinear stability analyses of the three-dimensional problem of Chang et al. ["Three-dimensional stability analysis for a salt-finger convecting layer,"J. Fluid Mech. 841, 636-653 (2018)] concerning salt-finger convection, which is seen when there is sideways heating and salting along the vertical walls along with a linear variation of temperature and concentration on the horizontal walls. A two-dimensional linear stability analysis is first carried out in the problem with the knowledge that the result could be different from those of a three-dimensional study. A two-dimensional weakly nonlinear stability analysis, that is, then performed points to the possibility of the occurrence of sub-critical motions. Stability curves are drawn to depict various instability regions. With the help of a detailed stability analysis, the stationary mode is shown to be the preferred one compared to oscillatory. Local nonlinear stability analysis of the system is done in a neighborhood of the critical Rayleigh number to predict a sub-critical instability region. The existence of a stable solution at the onset of a weakly nonlinear convective regime is indicated, allowing one to perform a bifurcation study in the problem. Heat and mass transports are discussed by analyzing the Nusselt number, Nu, and Sherwood number, Sh, respectively. A simple relationship is obtained between the Nusselt number and the Sherwood number exclusively in terms of the Lewis number, Le. 2022 Author(s). -
Insider attack detection using deep belief neural network in cloud computing
Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine. 2022 CRL Publishing. All rights reserved. -
Augmented Reality-Enabled Instagram Game Filters: Key to Engaging Customers
The gamification concept is rapidly grabbing attention of different sectors in the current competitive business ecosystem. Companies are amalgamating game elements to enrich customer enhancement. However, empirical studies incorporating Augmented Reality (AR) elements in the same are lacking. Therefore, main objective of this research is to inspect elements of AR, impacting the customer brand engagement in game filters of Instagram. Drawing on S-D Logic the authors aim to explore the impact of gameful experience on creating customer engagement. The capability of Customer Brand Engagement (CBE) to influence Brand Satisfaction (BS) and Brand loyalty (BL) is also explored in the study. Convenient sampling method was adopted to gather 458 responses from Gen Z in India. Responses were gathered using self-administered questionnaire. Findings of the study expand CBE literature to a new technology and refines knowledge of relationship between AR and preexisting CBE dimensions (affective, cognitive and activation), leading to BS and BL. This study has some implications for managerial decision making in creating resilient and long-term relations with customers. 2021 Taylor & Francis Group, LLC. -
Deep learning based modeling of groundwater storage change
The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 20032025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 20032020 with a rate ranging from -5.88 1.2 mm/year to -14.12 1.2 mm/year and -3.5 1.5 to -10.7 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from -7.78 1.2 to -15.6 1.2 for TWSC and -4.97 1.5 to -12.21 1.5 for GWSC from 20202025. An interesting observation was a minor increase in rainfall during the study period for three basins. 2022 Tech Science Press. All rights reserved.