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A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
A comprehensive study on the assessment of chemically modified Azolla pinnata as a potential cadmium sequestering agent
The major environmental issue raised throughout the world is the egression of toxic pollutants in water bodies. Hence, employment of novel technological interventions such as bioremediation and phytoremediation for mitigating the toxic effects caused by the pollutants has gained attention. The aquatic macrophyte, Azolla pinnata is utilized as a biofiltering agent in the present study for the chelation of metal toxicants from the artificial wastewater system. The nutritive value of A. pinnata was determined to be 268.99Kcal/100g energy and the mineral profiling showed the highest amount of calcium (54.7ppm), iron (14.04ppm) and manganese (7.96 ppm). The quantitative screening of total phenolic and total flavonoid contents showed a maximum of 402.334.29 mg/g GAE and 105.253.81 mg/g QE respectively and the sample exhibited strong antioxidant activity in quenching the DPPH radicals with an IC50 value of 88.27?g/ml. Similarly, the highest bioactivity was observed in methanolic and chloroform extract of A. pinnata biomass showing the zone of growth inhibition against E. coli (17mm) and S. aureus (18mm). The results recorded from the SEM-EDX, GCMS, FTIR and XRD confirmed the adsorptive properties of biomass. The chemically modified and unmodified Azolla exposed to cadmium metal solution showed the maximum adsorption of about 0.470.001 and 0.480.003 ppm in 60mins using the unmodified biomass with dosage of 0.75 and 1.0g respectively. Moreover, the results recorded from the instrumental characterization for the adsorptive properties of Azolla biomass proved that cadmium chelation is due to the modifications caused in porosity, surface structure and the addition of functional groups in the treated biomass surface. 2023 The Ceramic Society of Japan. -
A Comprehensive Study On The Consumer Preferences Towards Online Marketing In Consumer Goods
Online Marketing has become an integral part of peoples lives in recent years. Currently as per the developments that have taken place due to the improvements and importance that has been created by online marketing has covered each and every type of business sector. The purpose of the study is to examine the preferences of consumers towards online marketing and how it varies across different age group, income levels and across gender. This study is conducted with the help of 300 sample data collected from the working population of Bangalore city. To analyze the collected data, the statistical tools like Pearsons correlation, Posthoc ANOVA test and Scheffes and Tukey,s test has been used. This study found that male consumers are more influenced in purchasing products online then female consumers. -
A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification
Skin disorders occur more frequently than other kinds of diseases. Skin diseases can be attributed to a number of aspects, like fungi, bacteria, viruses, allergies, and so on. The rapid advancement of healthcare centered around lasers and photonics has rendered it feasible to diagnose skin disorders in a more accurate and timely manner. However, the cost of such a diagnostic remains extremely limited and prohibitively expensive. As a result, the use of image processing methods is beneficial in the initial phases of designing a computerized dermatology screening system. The retrieval of characteristics is an extremely important step in classifying skin disorders. The use of computer vision may play a crucial role in the diagnosis of a variety of skin conditions using a variety of approaches. The strategy we have proposed is straightforward and quick and requires no expensive technology besides a computer and a camera. When applied to the inputs of a colored picture, the method is successful. After that, resize a portion of the image to retrieve attributes with a pretrained convolutional neural network. The attribute was then classified using the multi-class XGBoost program. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Survey of Methods for Identifying Counterfeit Banknotes Using Image Processing and Machine Learning
The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on image processing techniques and machine learning algorithms are helpful in detecting counterfeit notes. This survey paper reviews the current strategies on fake banknote detection using image processing techniques and machine learning algorithms. We discuss various stages of the detection process, including image acquisition, preprocessing, feature extraction and classification. Furthermore, we analyze the limitations and comparative performance of different algorithms and approaches mentioned in the literature. The survey aims to provide insights into the various methodologies, challenges and future directions in the field of fake banknote detection, facilitating the development of more robust and effective counterfeit detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Survey on Decoder Design using Quantum-dot Cellular Automata
QCA offer a compelling alternative to CMOS technology, providing benefits such as low power consumption, high speed, high density, and the ability to surpass the nanoscale limitations of CMOS. QCA is increasingly being adopted in VLSI designs as a solution for reducing power consumption and thermal dissipation. This paper analyzes the area, cell count, and latency of different 2:4 decoders to determine the most efficient design based on these factors. Decoders play a critical role in Quantum-dot Cellular Automata(QCA) by enabling efficient data routing, memory addressing, and logic control while minimizing power consumption and reducing interconnect complexity. The study employs a specialized logic gate known as the Toffoli gate, which is renowned for its capability in reversible computing, allowing information processing without data loss. Future advancements in 2:4 decoders using QCA should prioritize optimizing clocking schemes, improving fault tolerance, and developing scalable architectures to address fabrication challenges and enhance reliability in practical applications. The circuits are simulated using QCA Designer software. 2025 IEEE. -
A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics
With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one's own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers. 2024 World Scientific Publishing Co. -
A comprehensive survey on features and methods for speech emotion detection
Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition. 2015 IEEE. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. -
A comprehensive view of artificial intelligence (ai)-based technologies for sustainable development goals (sdgs)
Agenda 2030, aimed at sustainable and inclusive development through seventeen SDGs formulated by the United Nations (UN), has become a massive challenge for most nations around the world. Many countries are setting a plan of action for achieving carbon neutrality by 2050. Due to this, industries are under immense pressure to mitigate harmful emissions and incorporate SD in their business activities. In the past decade, AI has grown as the dominating technology which influences nearly every aspect of human life, i.e., society, business, environment, etc. This chapter provides a comprehensive view of AI-driven technological applications in achieving SDGs. It provides a snapshot of the emerging relationship between AI applications and sustainable development and how AI could be used to create sustainable business models. Large-scale adoption of AI-driven technologies has enormous potential from the sustainable development perspective. The purpose of this chapter is to map the application of AI-based technological tools and solutions with the various SDGs. Further, this chapter also extends the discussion on AI-based technology as an enabler of or barrier to addressing sustainable development issues. It provides an important insight for policymakers, practitioners, investors, and other stakeholders about the conducive influence of AI on society, governance, and ecology in line with the priorities underlined in the UN SDGs. 2024 Walter de Gruyter GmbH, Berlin/Boston. -
A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
A compression system for unicode files using enhanced LZW method /
Patent Number: 202041003844, Applicant: Rincy T A.
Data compression becomes a vital and pivotal role in the process of computing as it helps in space reduction ocuupied by a file as well as to reduce the time taken to access the file. The present invention relates to a system for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Viz-welch (LZW) and method of operation thereof. -
A computational approach for shallow water forced KortewegDe Vries equation on critical flow over a hole with three fractional operators
The KortewegDe Vries (KdV) equation has always provided a venue to study and generalizes diverse physical phenomena. The pivotal aim of the study is to analyze the behaviors of forced KdV equation describing the free surface critical flow over a hole by finding the solution with the help of q-homotopy analysis transform technique (q-HATT). he projected method is elegant amalgamations of q-homotopy analysis scheme and Laplace transform. Three fractional operators are hired in the present study to show their essence in generalizing the models associated with power-law distribution, kernel singular, non-local and non-singular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and convergence for the solution is derived with Banach space. The projected scheme springs the series solution rapidly towards convergence and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional order the physical nature have been captured in plots. The achieved consequences illuminates, the hired solution procedure is reliable and highly methodical in investigating the behaviours of the nonlinear models of both integer and fractional order. 2021 Balikesir University. All rights reserved. -
A computational approach for the generalised GenesioTesi systems using a novel fractional operator
This article presents the novel fractional-order GenesioTesi system, along with discussions of its boundedness, stability of the equilibrium points, Lyapunov stability, uniqueness of the solution and bifurcation. The efficient predictorcorrector approach is employed to quantitatively analyse the GenesioTesi system in fractional order. The findings enable conceptualisation and visualisation of the presented novel fractional-order GenesioTesi systems. The modified systems are proposed for future study on chaos control and applying the same for secure communication. Bifurcation analysis is carried out to see the variation in the systems behaviour from stability to chaos. The results of the bifurcation analysis support the results obtained for the stability of the equilibrium points. The system behaves chaotically since all the equilibrium points are unstable. The findings demonstrate a torus attractor for some of the suggested systems and a chaotic attractor for some of the novel fractional-order GenesioTesi systems. The systems torus attractor changes into a steady state when the order is reduced from integer to fractional. Changing the parameter values for one of the modified systems also shifts the systems behaviour, with the point attractor replacing the torus attractor. The point attractor of one of the systems changes into a steady character when the systems order is reduced from integer to fractional. The behaviour for one modified system is the same for fractional and integer orders. This discovery paves the way for the future study of the modified GenesioTesi system. This article gives a new direction to utilise these proposed GenesioTesi systems and study them extensively. The chaotic behaviour of the modified system can be used for secure communication. The synchronisation and chaos control of the modified system is recommended. 2024, Indian Academy of Sciences. -
A Computational Data-Granular Model Highlighting the Evolving Fintech Landscape in India
The Fintech sector in India has undergone remarkable development, complementing the significant progress in financial technology designed to simplify financial services and provide innovative solutions. This study aims to discover and analyze two significant knowledge gaps in the Indian Fintech sector. It seeks to identify and examine the evolving patterns in web searches for potential career opportunities in the Fintech sector, providing perspectives into the trendline data from the country. Secondly, the study will assess employment in the Fintech Sector in India, emphasizing Position Titles, the geographical distribution of opportunities, and market trends from 2015 to 2023. Furthermore, it will examine the motivation and strategies essential for supporting and developing the Fintech sector in India. It performs a trend analysis on Fintech, Finance, and Accountancy searches and how they have changed over the years. By addressing these gaps, the research aims to provide valuable insights into the Fintech industry's dynamics and development in the Fintech job market over the years in the Indian context. To complement the trend analysis conducted in the paper, a computational modeling approach is used to predict future job trends in the Indian Fintech sector. The model relies on data from the years 2015 to 2023 on job openings, web searches, and geographical distribution. Therefore, the Autoregressive Integrated Moving Average (ARIMA) model has been used to understand the future patterns of job opportunities and skill requirements accordingly. This research will be helpful for companies and business owners to improve their financial operations in the long run. 2025, Bentham Books imprint. -
A COMPUTATIONAL MODEL FOR TEA LEAF PRICE PREDICTION BASED ON QUALITY FACTORS USING HYBRID MACHINE LEARNING TECHNIQUES
This document reflects the effort made to calculate and identify the grade of the tea leaves based on the assessment of the leaves' size and color. The leaves were classified based on their severity with the help of HSV. The leaves were further classified using the k prototypes clustering once their length and width were established. The leaves were then further categorized in line with that. Light, medium, and dark are the three-color categories into which it belongs. The leaves were further sorted according to their quality so that the farmer could sell the produce at a better price. With the machine learning method for the categorization part, we were able to show its values. All of the healthy leaves were considered in a different dataset, and the images were obtained using the feature selection method. The length and width of each individual leaf, along with its color and shape, were then measured using those leaves. We were able to differentiate between the various leaf grades based on the findings. The healthy leaves were separated from the diseased leaves using the textual features. Additionally, we were able to use the other criteria to obtain higher-grade leaves. Little Lion Scientific. -
A computer vision based system for stenosis detection and recognition in coronary angiogram image and a method thereof /
Patent Number: 202241013759, Applicant: Kavipriya K.
Coronary artery disease is becoming one of the most common heart diseases recently because of the unhealthy lifestyle from past few decades. The coronary artery supplies the oxygenated blood and nutrient to the heart muscle. If the artery is blocked or narrowed by the stenosis deposit on the wall of the artery it led to coronary artery disease. If the block is high it will lead to heart attack or stroke. Doctors do an Angiogram test to diagnosis the stenosis. -
A Computing Assisted Test Method in Healthcare Industry Using Artificial Intelligence
New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This research goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The creation of prediction models and tools to assess risk has become an important field of research as it can assist healthcare workers in identifying individuals who are more likely to acquire breast cancer and adapting screening and preventative programs accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more in-depth exploration of the subject and the various technologies, methods and factors involved in breast cancer prediction. 2025 IEEE.