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Deterministic, Stochastic, and Deep Learning Approaches to Understand the Economic Fluctuations in India
In the present work, a new mathematical framework is proposed for studying the interrelation among population growth rate, GDP, inflation rate, and unemployment rate within deterministic and stochastic frameworks. The values of the parameters of the proposed model are estimated using real data from India. The local and global uniqueness of solutions is established for the stochastic model. The deterministic model is solved by using the Adams-Bashforth-Moulton predictor-corrector method, and Milstein's method is used for solving the stochastic model. Numerical simulations correlated quite strongly with observed data, while projections for the 20242030 period indicate that controlled population growth bodes well for the outlook of the economy for India, supporting economic prosperity alongside reduced inflation and better employment conditions. The findings presented in this work are correlational; therefore, to find the possible cause for this phenomenon, further research is required with detailed datasets. Comparing our model's GDP predictions with that obtained using a long short-term memory recurrent neural network model returned very high values of predictive accuracy, thus reinforcing the strength and reliability of our framework. 2025 John Wiley & Sons Ltd. -
Development and evaluation of the bootstrap resampling technique based statistical prediction model for Covid-19 real time data : A data driven approach
The objective of the article is to develop earlyR package based novel coronavirus disease (COVID-19) forecasting model. The reported COVID-19 serial interval data is applied for obtaining maximum likelihood value of the reproduction number (R0) using maximum likelihood approach and projections package is applied for getting trajectories of epidemic curve. The minimum, median, mean and maximum projected value of R0 with 95% confidence interval (CI) is obtained by using bootstrap resampling strategy and the predicted cumulative probable count of new cases is also presented with different quantile. To validate the results with real scenario, the past COVID-19 data is considered. The % error rate ranges from -7.91% to 21.27% for the developed model for the five Indian States. 2022 Taru Publications. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Mechanical and Wear Behavior of Aluminium Metal Matrix Composites Reinforced Ceramics Materials for Light Structures
Aluminium Alloy based Metal Matrix Composites (AAMMCs) has widely used in defense, aircraft and automobile applications because of their enhanced engineering properties with light weight metals. Nano sized silicon nitride (80 ?m) is used as a reinforcement in this study, whereas aluminium alloy 8011 is selected as the matrix material. Using the stir casting method, metal matrix composites made of aluminium alloy 8011 with varying weight percentages of Si3N4(0, 4, 8, 12, and 16) are created. The stir casted AL 8011/Si3N4composites further heated under T6 condition. The AL 8011/Si3N4 T6 composites are further subjected to Energy Dispersive X ray Analysis (EDAX) and Scanning Electron Microscope (SEM) to identify by the presence of elements and study the microstructure characterization, respectively. The density, microhardness and wear test are conducted by employing Archimedes principle, Vickers hardness tested and pin on disc equipment, respectively. The wear test is done at different sliding distances like (500, 1000, 1500 and 2000 m), applied load like (10, 20, 30 and 40 N) and kept sliding at a speed of 1 m/s. The increasing weight percentage of silicon nitride expands the increasing of density and Vickers hardness up to 12 wt % of silicon nitride and decreasing by 16 wt % addition. The wear resistances of AL 8011/12 wt % Si3N4T6 composite exhibits higher wear resistance than other Al8011 based composites. 2024, Informatics Publishing Limited. All rights reserved. -
Static analysis tool for identification of permission misuse by android applications
Android is one of the most important and widely used mobile operating systems in the world. The Android operating system utilizes the permission-based model, which permits Android applications to get user data, framework data, gadget data and other assets of Smartphone. These permissions are affirmations declared by the developer of an application. The permissions granted varies from one application to another, depending on its functionality. During installation, permissions to access the resources of the smartphone are requested by apps. Once the client grants the permission, the apps are allowed to access the granted resources as per its requirement. Android OS is susceptible to different security issues owing to the loopholes in security. This paper mainly focuses on identifying how the permissions granted to a specific application is misused by another application using SharedUserID. The paper also proposes a security tool that identifies a list of applications which are misusing the permissions in a user's Android smartphone. The viability of the tool is tested by using a Proof-of-Concept (PoC) implementation of the security tool. Research India Publications. -
Photocatalytic activity of bismuth silicate heterostructures synthesized via surfactant mediated sol-gel method
A surfactant mediated sol-gel method is employed to synthesize bismuth silicate heterostructures with tunable morphologies and properties. The synthesized nanoparticle samples were characterized by XRD, FTIR Spectroscopy, SEM-EDAX and UV-DRS. The synthesized bismuth silicates exhibit excellent photodegradation against malachite green and rhodamine B dyes in the aqueous medium. Bismuth silicates (10% SiO2-Bi2O3) show superior photocatalytic property and outstanding reusability compared to pure bismuth oxide. The kinetics of the photodegradation of the dyes shows that the reaction follows first-order kinetics with the regression coefficient of 0.99. Thus, enabling Bismuth silicates heterostructures practical application as a photocatalyst for clean water. 2019 Elsevier Ltd -
Synthesis of bismuth silicate nanostructures with tunable morphology and enhanced photocatalytic activity
Bismuth oxide due to its narrow bandgap has attracted significant attention as a photocatalyst. A facile and efficient method to synthesize bismuth silicate with tunable morphology and property is achieved in this study. Bismuth oxide and bismuth silicate have been synthesized by surfactant-assisted modified sol-gel method. The fabricated bismuth oxide nanoparticle samples are characterized by various analytical tools such as X-Ray diffractometer, Infra-Red spectroscopy, Scanning Electron microscopy and UV-Diffuse Reflectance spectroscopy. The synthesized nanoparticles exhibit excellent photocatalytic activity for the degradation of Rhodamine B dye in aqueous medium. Bismuth silicate exerts more satisfactory catalytic property and outstanding reusability compared to pure bismuth oxide. The superior stability and enhanced activity enables the application of bismuth silicate as a photocatalyst for environmental remediation. 2019, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved. -
Influence of Surfactant on the Phase Transformation of Bi 2 O 3 and its Photocatalytic Activity
Bismuth oxide with its unique narrow bandgap has gained significant attention in the field of photocatalysis. A new and efficient method to synthesise bismuth oxide with tuneable properties is proposed herein. A surfactant assisted modified sol-gel method is used to synthesise bismuth oxide with excellent photocatalytic activity for the degradation of Rhodamine B dye. Three different surfactants, namely polyethylene glycol-400, sodium lauryl sulfate, and cetyltrimethylammonium bromide (CTAB) have been used. The fabricated bismuth oxide nanoparticles were characterised by X-ray diffraction, IR, scanning electron microscopy, and UV-diffuse reflectance spectroscopy analysis. Evolution of both the ? and ? crystalline phases of bismuth oxide was observed. The bandgap of the synthesised bismuth oxides ranges from 2.03 to 2.37 eV. The CTAB assisted synthesised bismuth oxide with a bandgap of 2.19 eV showed the highest photocatalytic activity of 93.6 % under visible light for the degradation of Rhodamine B. This bismuth oxide based catalyst opens a new avenue for efficient photocatalysis for environmental remediation. 2019 CSIRO. -
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET
A kind of wireless network called a mobile ad hoc network (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFEs settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible. The Author(s), under exclusive licence to Shiraz University 2024. -
Identification of ambulance in traffic videos using image processing techniques
Traffic congestion is one of the commonly faced problems in the Urban areas. To eliminate these problems, there is a need for an Intelligent Transportation System (ITS) that proposes an efficient method to reduce the traffic problems and introduces the priority system for the Emergency vehicles. This paper proposes two frameworks that identify ambulance in traffic videos based on features such as color, siren and text. Frames are extracted from videos to employ methods like multilevel thresholding and region matching. Multilevel thresholding is used for segmenting the ambulance from the other occurring vehicles based on the white color. Region matching for text detection method is employed in the segmented vehicle. Color space thresholding is used for the detection of siren based on red or blue color feature. Optical character recognition (OCR) is employed to extract the text in the frame. Word comparison and Matching detects the ambulance text based on the outcome of OCR. The performance of Framework 1 and Framework 2 are evaluated based on Word accuracy and from the experimental results it is observed that Framework 2 is better from 75% word accuracy. 2018, Institute of Advanced Scientific Research, Inc. All Rights reserved. -
Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers
The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents. Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention. The current study highlights the influence of the perceived usefulness of using the Click & Collect online shopping mode on the purchase intention of customers. 2021 Transnational Press London -
Evaluate and design the mini-hexagon-shaped monopole antenna controller to minimize losses in the unit
Main Aim: Hexagon-shaped mono-pole transmitters are developed, computed, and evaluated in a range of applications. Their whole performance is being compared. Methods: Various hexagon-shaped mono-pole transmitters are built and modeled using the HFSS. These transmitters are built with Defective Ground Structure (DGS) but include openings in the patch antenna for High-Frequency Spread Spectrum (HFSS), also on the surface, but also. That influence including its position including its slot upon this radiation pattern is examined. Evaluate the modeling, the controller was designed for the broadcast subsystems and respective reflectivity and VSWR have been found. Findings: The specifications of the antenna is return losses, VSWR, amplification and switching frequency, among other things are assessed as are usually uncertain and VSWR for the manufactured device. The transmissions are continuously monitored. Another most unclear wavelength is around 10 dB among a large bandwidth and that they are less than 10 dB over a specific frequency range. The value of VSWR is less than 2. Applications: These transmitters may be utilized for wirelessly and interior activities via UWB technology. 2021, SciTechnol, All Rights Reserved. -
A qualitative causal analysis on incremental behavioural complexities due to fomo (Fear of missing out) in indian youth
FOMO (Fear of Missing Out), a new threatening dampener of youth is prevalent across the world, and is shaping up as a wicked problem to Indian youth, especially in the category of Teens, Adolescents, including educated youth. The vulnerability is getting deeper and severe in terms of behavioural problems that turns as outcome. This qualitative paper contemplates on the human behavior with invasive nature of a newer and stronger, psychological stimulus to youth via, the digital connectivity, social media and mobile phones, called, or abbreviated as FOMO. Why FOMO has become a huge discomfort to almost all the organizations even, at times, separate teams are set to put things at control. This article qualitatively with the secondary statistics carried out across the world, and contemporary research outcome on the FOMO, tries to correlate, how the other countries are impacted, and tries to find a feasible practical moderating factors, that can wane down the impact or pull down the severity, the FOMO is causing in the life of youth. What are the strategies that can be adopted to bring down the level of damages, with suggestions for handling and managing the situation, rather than controlling, as most of the worlds work life balance is happening because of the FOM O. Also this study tries to validate the utilities of JOMO, and will it be possible in the Indian environment, since the youth are not in a position to bring d own the situation. Is NOMO too is growing up vividly across silently, is what the study concludes. 2019 ETA-Florence Renewable Energies. -
IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques
In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indi-cator of diabetic retinopathy. With that in mind, the purpose of this work is to cre-ate an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS clas-sifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate. 2023, Tech Science Press. All rights reserved. -
Towards developing an automated technique for glaucomatous image classification and diagnosis (AT-GICD) using neural networks
Glaucoma is the eye defect that has become the second leading cause of blindness worldwide and also stated as incurable, may cause complete vision loss. The earlier diagnosis of glaucoma in Human Eye is a great confrontation and very important in present scenario, for providing efficient and appropriate treatments to the persons. Though there is much advancement in Ocular Imaging that affords methods for earlier detection, the appropriate results can be obtained by integrating the data from structural and functional evaluations. With that note, this paper involves in developing automated technique for glaucomatous image classification and diagnosis (AT-GICD). The model considers both the textural and energy features for effectively diagnosing the defect. Image Segmentation is processed for obtaining the exact area of optic nerve head; histogram gradient based conversion is employed for enhancing the fundus image features. Further, Wavelet Energy features are extracted and applied to the artificial neural networks (ANN) for classifying the NORMAL and GLAUCOMA images. The Accuracy rate based comparison with other existing models is carried out for evidencing the effectiveness of the proposed model in glaucomatous image classification. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
A novel security framework for healthcare data through IOT sensors
The Internet of Things (IoT) has played a crucial role in the distribution of health records and poses security issues to the patient-specific health information needed for remote hospital attention. The majority of publicly accessible security mechanisms for health information do not concentrate on the flow of information from IoT different sensors installed upon the person's blood through networking devices to primary health care centers. In this paper, we investigated the potential risks of unprotected transmission data, particularly among IoT sensor systems and network gateways. The study encourages the transmission of health insurance data to hospitals remotely. The proposed health care information model would encode immediately so that the sensing element before even being transferred to cryptographic techniques. To use a laboratory configuration with two-stage cryptography at the IoT sensor and two-stage decoding at the physician's surgery receptor, the prototype system was validated. The test results for a complete safety system for IoT - based on the transmission of healthcare data seem good. The study opens up new avenues for information security on IoT devices. 2022 The Authors -
Numerical modeling of novel cage-like cross-linked membranes for enhanced proton conductivity in a high temperature-polymer electrolyte membrane fuel cell
Phosphoric acid (PA)-doped polybenzimidazole (PBI) membranes have encountered several problems associated with high cost, chemical instability, poor solubility in organic solvents, and higher doping level which results in poor mechanical properties and faster degradation of the membrane. Alternative membranes with high proton conductivity and mechanical strength for high-temperature applications are of great interest, one such membrane being cPBI-IL X. The cage-like cross-linked structure of these membranes shows a dual proton transport path due to which proton conductivity is elevated. The ionic liquid content of these membranes improves the PA absorbing capability and shortens the proton transfer path. These membranes exhibit the highest proton conductivity of 13.3 S/m and better durability compared to existing PBI Membranes. A mathematical model is developed and validated versus published experimental results to account for the proton conductivity of these membranes. The developed model is further investigated for a detailed understanding of polarization phenomena and species distribution. 2023 Wiley Periodicals LLC. -
Data journalists perception and practice of transparency and interactivity in Indian newsrooms
Data journalism research recorded exponential growth during the last decade. However, the extant literature lacks comparative perspectives from the Asian region as it has been focused on select geographies (mainly Europe and the US). In this backdrop, the present study examined data journalism practices in the Indian media industry by conducting intensive interviews with 11 data journalists to investigate their perception of transparency and interactivity which are two of the core aspects of data journalism practice. Further, a content analysis of data stories published by two Indian news organizations for two years was conducted to assess the status of transparency and interactivity options in these stories. The findings showed that Indian data journalists acknowledge the importance of transparency and interactivity, but exhibit a cautious approach in using them. There is general apathy in practicing transparency among journalists in legacy organizations, drawing a stark contrast with their counterparts in digitally-native organizations. 2022 Asian Media Information and Communication Centre. -
Indexing of exoplanets in search for potential habitability: application to Mars-like worlds
Study of exoplanets is one of the main goals of present research in planetary sciences and astrobiology. Analysis of huge planetary data from space missions such as CoRoT and Kepler is directed ultimately at finding a planet similar to Earththe Earths twin, and answering the question of potential exo-habitability. The Earth Similarity Index (ESI) is a first step in this quest, ranging from1 (Earth) to0 (totally dissimilar to Earth). It was defined for the four physical parameters of a planet: radius, density, escape velocity and surface temperature. The ESI is further sub-divided into interior ESI (geometrical mean of radius and density) and surface ESI (geometrical mean of escape velocity and surface temperature). The challenge here is to determine which exoplanet parameter(s) is important in finding this similarity; how exactly the individual parameters entering the interior ESI and surface ESI are contributing to the global ESI. Since the surface temperature entering surface ESI is a non-observable quantity, it is difficult to determine its value. Using the known data for the Solar System objects, we established the calibration relation between surface and equilibrium temperatures to devise an effective way to estimate the value of the surface temperature of exoplanets. ESI is a first step in determining potential exo-habitability that may not be very similar to a terrestrial life. A new approach, called Mars Similarity Index (MSI), is introduced to identify planets that may be habitable to the extreme forms of life. MSI is defined in the range between 1 (present Mars) and 0 (dissimilar to present Mars) and uses the same physical parameters as ESI. We are interested in Mars-like planets to search for planets that may host the extreme life forms, such as the ones living in extreme environments on Earth; for example, methane on Mars may be a product of the methane-specific extremophile life form metabolism. 2017, Springer Science+Business Media B.V. -
Optical Spectroscopy of Classical Be Stars in Old Open Clusters
We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr. Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr. We found that the H? emission strength of most of the stars is less than 40 in agreement with previous studies. Our analysis further suggests that one of the stars, [KW97] 35-12, might be a weak H? emitter in nature, showing H? equivalent width of ?0.5 Interestingly, we also found that the newly detected classical Be star LS III +47 37b might be a component of the possible visual binary system LS III +47 37, where the other companion is also a classical Be star. Hence, the present study indicates the possible detection of a binary Be system. Moreover, it is observed that all 16 stars exhibit a lesser number of emission lines compared to classical Be stars younger than 100 Myr. Furthermore, the spectral type distribution analysis of B-type and classical Be stars for the selected clusters points out that the existence of CBe stars can depend on the spectral type distribution of B-type stars present in these clusters. 2023. National Astronomical Observatories, CAS and IOP Publishing Ltd.