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Thermorheological and magnetorheological effects on Marangoni-Ferroconvection with internal heat generation
Marangoni convectiveinstability in a ferromagnetic fluid layer in the presence of a spatial heat sourceand viscosity variation is examined by means of the classical linear stability analysis. The higher order Rayleigh-Ritz technique is used to compute the critical Marangoni number. The effective viscosity of the ferromagnetic liquid is taken to be a quadratic function of both the temperature and magnetic field strength. It is shown that the ferromagnetic fluid is significantly influenced by the effect of viscosity variation and is more prone to instability in the presence of heat source compared to that when viscosity is constant. On comparing the corresponding results of heat source and heat sink it is found that heat sink works in tandem with the effect of viscosity variation if magnetic field dependence of viscosity dominates over temperature dependence. If the temperature dependence of viscosity dominates, the effects of viscosity variation and heat sink are mutually antagonistic. Published under licence by IOP Publishing Ltd. -
Bard-Taylor ferroconvection with time-dependent sinusoidal boundary temperatures
The combined effect of centrifugal acceleration and time-varying boundary temperatures on the onset of convective instability of a rotating magnetic fluid layer is investigated by means of the regular perturbation method. A perturbation expansion in terms of the amplitude of applied temperature field is implemented to effectively deal with the effects of temperature modulation. The criterion for the threshold is established based on the condition of stationary instability manifesting prior to oscillatory convection. The modulated critical Rayleigh number is computed in terms of Prandtl number, magnetic parameters, Taylor number and the frequency of thermal modulation. It is shown that subcritical motion exists only for symmetric excitation and the destabilizing effect of magnetic mechanism is perceived only for asymmetric and bottom wall excitations. It is also delineated that, for bottom wall modulation, rotation tends to stabilize the system at low frequencies and the opposite is true for moderate and large frequencies. Furthermore, it is established that, notwithstanding the type of thermal excitation, the modulation mechanism attenuates the influences of both magnetic stresses and rotation for moderate and large frequencies. Published under licence by IOP Publishing Ltd. -
An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Stability Analysis ofSalt Fingers forDifferent Non-uniform Temperature Profiles inaMicropolar Liquid
This paper describes the linear stability analysis of salt finger convection for different non-uniform temperature profiles by keeping the solutal concentration uniform throughout the system. The system consists of two parallel plates separated by a thin layer of micropolar liquid with infinite length, in which the system is heated and soluted from above the plate. Normal mode techniques are used to convert the system of partial differential equations into ordinary differential equations; further, Galerkian method is introduced to get the eigenvalue for isothermal, permeable with no-spin boundary conditions. The study also explains the effect of different micropolar parameters on the onset of convection. The phase of temperature flow for different boundary conditions explains the graphical solution of the energy equation and its gradients. It is shown that non-uniform temperature profiles, diffusivity ratio, coupling parameter, and solutal Rayleigh number influence the stability of the system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybrid Renewable Road Side Charging Station with I2V Communication Functionality
The faster adoption of Renewable-based Energy Sources for charging Electric Vehicles is highly required. The paper proposes a novel strategy of design and developing a hybrid Road Side Unit (RSU) that would be easy to install and provides easy access to Electric Vehicle charging. The system inculcates Infrastructure to Vehicle (I2V) communication framework enabling communication between the Infrastructure and the Vehicle to identify the nearest charging station based on the availability. The communication framework is based on Wi-Fi communication and enables bidirectional communication between the Vehicle and the Infrastructure as well. The modelling and development of the RSU, and the active power flow regulation from the RSU to the Charging Station is also developed, using a Fuzzy Controller. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Inphase and outphase concentration modulation on the onset of magneto-convection and mass transfer in weak electrically conducting micropolar fluids
The paper analyses the effect of concentration modulation at the onset of solute magneto-convection and heat transfer in a weak electrically conducting fluid by carrying out a linear and non-linear analysis. The Venezian approach is assented encompassing the correction Solute Rayleigh number and wave numbers for meagre amplitude concentration modulation. A multiscale method is applied to convert the analytically untraceable Lorenz model to an analytically traceable Ginzburg-Landau equation which is solved to quantify mass transfer through Sherwood number. It is observed that concentration modulation results in sub-critical motion however out-of-phase concentration modulation is more stable compare to others. 2019 Author(s). -
LULC Analysis of Green Cover Loss in Bangalore
Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. 2024 IEEE. -
Integrated Health Care Delivery and Telemedicine: Existing Legal Impediments in India
The technological innovation in the healthcare sector has contributed to the growth of telemedicine in India. Health services fall under State responsibility as per the Indian Constitution by virtue of Schedule 7although policy and planning framework are under the scope of Central government. Telemedicine cannot not work as an autonomous service, rather, ought to be subjected to different regulations having complex ethical, medico-legal manifestations. As far as India is concerned, Ministry of Health and Family Welfare of India (MoHFW) is the body responsible for initiating the policy of digitization of healthcare. However the point ishow far digital health services going appropriately in India. Based on NDHBs comprehensive architectural framework of Federated National Health Information System in January 2020 and as the pandemic strategy Medical Council of India and the NITI Aayog released new guidelines on telemedicine with respect to registered medical practitioners, this research needed to be checked. Thus, the examination was done in these aspects. Guidelines were revisited to see how the hospitals in Delhi and NOIDA function based on the records submitted in medical consultation given to patients using telemedicine. It is felt that telemedicine being a nebulous concept in India, it needs to be analyzed in the light of prospective opportunities it would offer. There is a need for collaborative approaches on digital health, revision in the prevailing legal and ethical frameworks, the clinical practices corresponding to standing medical guidelines. Also, it is found that there exist no uniform telemedicine practices balancing the privacy norms, medico-legal responsibility and regulatory standards. To arrive at conclusion, the best practices prevailed in other countries are examined and adopted. It is felt that the policies existing in telemedicine need to be bifurcated as digital consultation, digital photography, remote patient monitoring (RPM) separately. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentiment Analysis of Online Hotel Reviews Employing Bidirectional GRU with Attention Mechanism
Online hotel reviews are a more reliable resource for potential hotel guests. Sentiment analysis is a branch of text mining, Natural Processing Language that seeks to identify personality traits, emotions, and other factors. Deep Learning algorithms such as LSTM and GRU have successfully generated context information in sequence learning. However, deep learning cannot focus on the words that contribute the most and cannot capture important content information. This research aims to overcome the inability of LSTM and GRU to capture information. The results are satisfactory, with 93.12% accuracy, 95% ROCAUC, and 95.28% precision recall. This research paper helps managers identify areas to improve their products and services, target marketing campaigns, and identify customer churn. 2024 IEEE. -
A mathematical approach to the study on alkylating agents
There are several classes of anticancer drugs, among which our study focuses on alkylating agents. As a chemical graph invariant number, topological index, has crucial role in predicting the physical, chemical, biological and toxicity properties of a molecule. Different versions of Zagreb indices correlate well with various physio-chemical properties of a molecule. We are analysing physio-chemical properties of the class of alkylating agents using various Zagreb indices. In this paper we are able to predict the physico-chemical properties of a molecule which is not yet discovered using the Zagreb class. 2022 Author(s). -
Bounds for Zagreb class of indices on alkylating agents
The family of Zagreb indices have a pivotal role in predicting various physiochemical properties of molecules. Alkylating agents are some of the main classes of anticancer drugs. In this paper we find the bounds of some Zagreb indices. 2022 Author(s). -
Sentimental Analysis on Online Education Using Machine Learning Models
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students feelings. Students favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Some New Results on Non-zero Component Graphs of Vector Spaces Over Finite Fields
The non-zero component graph of a vector space with finite dimension over a finite field F is the graph G=(V,E), where vertices of G are the non-zero vectors in V, two of which are adjacent if they have at least one basis vector with non-zero coefficient common in their basic representation. In this paper, we discuss certain properties of the non-zero component graphs of vector spaces with finite dimension over finite fields and their graph invariants. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classifying AI-generated summaries And Human Summaries Based on Statistical Features
In an age where artificial intelligence knows no bounds, it's crucial to know if the textual content is reliable. But, the task of identifying AI-generated content within vast volumes of textual data is a big challenge. The existing studies in feature-based classification only explored prompt-based text responses. This paper explores methods to identify AI-generated summaries using feature-based machine-learning techniques. This study uses the BBC News Summary dataset. The summaries for the dataset are then generated using three of the top-performing summarisation models. Different statistical features like Zipf's Law Score, Flesch Reading Ease Score, and the Gunning Fog Index are used for extracting features for the classification model. The aim is to differentiate AI-generated summaries from human-written summaries. The main part of the study involves extracting the statistical features from the summarized texts, which are then classified using different classification models. Different models like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression models are used in the paper. Grid Search is also used to fine-tune SVM for the best results. The right model depends on what the need is. Whether it's accuracy, F1 score, or a mix of both, there are different options to lead you to the truth. The feature-based approach in this paper helps in more explainable classification and can compare how statistical text features are different for human-written summaries and generated summaries. 2024 IEEE. -
Fake News Detection: An Effective Content-Based Approach Using Machine Learning Techniques
Fake news is any information fabricated to mislead readers to spread an idea for certain gains (usually political or financial). In today's world, accessing and sharing information is very fast and almost free. Internet users are growing significantly than ever before. Therefore, online platforms are perfect grounds to spread information to a broader section of society. What could circulate between a relative few can now circulate globally overnight. This advantage also marked the increase in the number of fake news attacks by its users, which is unsuitable for a healthy society. Therefore, there is a need for good algorithms to identify and take down fake information as soon as they appear. This paper aims at solving the problem by automating the process of identifying fake news using its content. Evaluation metrics like the accuracy of correct classification, precision, recall and f1-score assess the performance of the approach. The machine learning approach achieved its best performance with 96.7 percentage accuracy, 96.2 percentage precision, 97.5 percentage recall and 96.9 percentage f1 score on the ISOT dataset. 2022 IEEE. -
A study of CNN models for re-identification of vehicles
Vehicle Re-identification has evolved in recent times. Initially, clicking a single picture of a vehicle or a car was done manually, inviting the workforce to complete a specified task. With the growth in technology, the method and techniques in Vehicle Re-Id also have advanced, transforming from manual to automation. Surveillance cameras were used to capture vehicle images and retrieve information about a specific vehicle. Re-trieving and identifying the images of the vehicle is done using computer vision, the most important branch of computer science and artificial intelligence. Earlier, Vehicle Re-Id implemented a single algorithm on a dataset, making the corresponding result insufficient to determine its effects. This paper proposes a brief survey of multi-modal techniques and methods for vehicle re-identification and fingerprinting. The different attributes of the vehicle are considered for ANPR (Automatic number plate recognition) for identifying the number plate, focusing on the vehicle's details or features as the initial phase of identification, and then the vehicle number plate. 2023 IEEE. -
Improved Computer Vision-based Framework for Electronic Toll Collection
The world is moving towards artificial intelligence and automation because time is the most crucial asset in today's scenario. This paper proposes an automatic vehicle fingerprinting system that avoids long waiting times in toll plazas with the help of computer vision. The number plate recognition and vehicle re-identification focus on this research. Day/night IR cameras are used to get the images of the vehicle and its number plate. The VeRi776 datum, which contains real-world vehicle images, is used to facilitate the research of vehicle re-identification. The proposed framework employs Siamese model architecture to identify the attributes such as color, model, and type of vehicle. The Car License Plate Detection datum is used to evaluate the efficiency of the proposed license plate recognition system. An ensemble of image localization techniques using CNNs and application of the OCR model on the localized snapshot is used to recognize the vehicle's license plate. A combination of license plate recognition and vehicle re-identification techniques is used in the proposed framework to improve the efficiency of identifying vehicles in toll plazas 2022 IEEE. -
IoT based continuous monitoring of cardiac patients using Raspberry Pi
In the recent development the Internet of Things (IoT) brings all electronics objects in to a single domain and it is easy to access everything through internet. The applications of IoT are Smart agriculture, Smart Home, Smart City, Smart health monitoring system etc. The automation of health care is one of the application which monitors the patient health status using IoT to make medical equipments more efficient by monitoring the patient's health, in which identifies the body conditions and reduces the human error. A health care monitoring system is used to monitor patient's body parameters for the particular disese and obtain the various values about it. The heart rate monitor is one of the in system using IoT to recognize the cardic patients condition and monitor the status in emergency situations. It monitors the heart rate of the patient with long term cardiovascular disease. Here the Arduino based microcontroller is used to communicate to the sensors such as pulse sensor and ECG Sensor. The system can analyze the signal, extract features from it, detect the normal or abnormal conditions with the help of Raspberry Pi and the results of the ECG signals is sent to the web server. It ensures the signal transmission of heart rate signal to the database through IoT. This also suggests doctors to care the patient follow-up their patient using the patient's data stored in the database. Thus IoT brings one of the solution for cardiac patient monitoring and also reduces the complexity between patient outcome and technology. 2018 Author(s). -
Experimental investigation on the effect of varying percentage of E-waste particulate filler in GFRP composite laminates
The advent of newer technology increases the electrical and electronic devices into the market in a rapid phase, thereby causing the previous generation gadgets to become obsolete, in spite of the gadgets being in good working condition. This is one of the main causes for the increase of E-waste. In the past two years itself the e-waste has gone up by 8% with respect to weight globally. An attempt is made to utilize the e-waste in a productive manner as a filler material and study its characteristics when subjected to different mechanical tests. This paper describes the fabrication and mechanical characteristics of new polymer composites consisting of E-glass fiber reinforcement along with filler material. Study of composites play a very important role in material science, metallurgy, chemistry, solid mechanics and engineering applications. The specimens were fabricated with the help of hand layup technique followed by vacuum bagging process. Mechanical tests viz., tensile test, Flexural test, and Shore D test has been performed. Samples were made of three different compositions of E-waste filler particulate, 5%, 10% and 15%. These tests have been conducted to find out the impact of varying percentage of filler material on the composite laminates. With the increase in the percentage of e-waste filler, there is a reduction in the tensile strength of the laminate, while the flexural strength of the laminates increased with increase in the filler material. The laminate with 5% filler material exhibited higher hardness than the other two samples. 2019 Elsevier Ltd. -
Factors Affecting the Growing Economic Inequality: An Empirical Study with Reference to BRICS Countries
Economic inequality refers to the uneven distribution ofearningsand opportunity between various groups in society and is a point of major concern in almost all the nations in the world. This study aims to analyse the effect of various factors over the increasing inequality in BRICS nations. The study takes into consideration factors like trade openness, credit, net foreign assets and health and tries to assess their impact as a driving force behind the increasing inequality in these countries. The augmented DickeyFuller test for stationarity has been applied followed by multiple regression. To explore causality, Granger causality test is applied. All the models are tested for autocorrelation using the BreuschGodfrey Lagrange Multiplier test. Wald test is applied to examine the significance of independent variables. The study provides statistical evidence about the positive and negative effects of trade openness, healthcare finance, net foreign assets and healthcare expenditure on income inequality in BRICS nations. Findings may help to work intensively on the relevant causes of inequality for these five countries. This paper will add to the already present literature on inequality which is one of the important problems of the countries across the world. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.