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Nanobiosensors for COVID-19
Coronavirus Disease (COVID-19) is an internationally recognized public health emergency. The disease, which has an incredibly high propagation rate, was discovered at the end of December 2019 in Wuhan, Hubei Province, China. The virus that causes COVID-19 is referred to as severe acute respiratory illness. Real-time reverse transcriptase (RT)-PCR assay is the primary diagnostic practice as a reference method for accurate diagnosis of this disease. There is a need for strong technology to detect and monitor public health. Early notification on signs and symptoms of the disorder is important and may be managed up to a few extents. To analyze the early signs and side effects of COVID-19 explicit techniques were applied. Sensors have been used as one of the methods for detection. These sensors are cost effective. These sensors will combine with a systematic device. It is utilized to detect the chemical compound and combined with a biological component. It is detected through physiochemical detector. Nanomaterials represent a robust tool against COVID-19 since they will be designed to act directly toward the infection, increase the effectiveness of standard antiviral drugs, or maybe to trigger the response of the patient. In this paper, we investigate how nanotechnology has been used in the improvement of nanosensor and the latest things of these nanosensors for different infections. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
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. -
Hybrid short term load forecasting using ARIMA-SVM
In order to perform a stable and reliable operation of the power system network, short term load forecasting is vital. High forecasting accuracy and speed are the two most important requirements of short-term load forecasting. It is important to analyze the load characteristics and to identify the main factors affecting the load. ARIMA method is most commonly used, as it predict the load purely based on the historical loads and no other assumptions are considered. Therefore there is a need for Outlier detection and correction method as the prediction is based on historical data, the historical data may contain some abnormal or missing values called outliers. Also the load demand is influenced by several other external factors such as temperature, day of the week etc., the Artificial Intelligence techniques will incorporate these external factors which improves the accuracy further. In this paper a hybrid model ARIMA-SVM is used to predict the hourly demand. ARIMA is used to predict the demand after correcting the outliers using Percentage Error (PE) method and its deviation is corrected using SVM. Main objective of this method is to reduce the Mean Absolute percentage Error (MAPE) by introducing a hybrid method employing with outlier detection technique. The historical load data of 2014-2015 from a utility system of southern region is taken for the study. It is observed that the MAPE error got reduced and its convergence speed increased. 2017 IEEE. -
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. -
Impact of social media on consumer decisiveness in the food and grocery sector
Consumers are currently inclined to acknowledge online information but purchase food and grocery products offline. Also, the buyer's decision is coherent with the factors like income, age, social media influences, cost of products, etc. The chapter studies the Influence of Social Media on Consumer Decision-making in the food and Grocery Sector. As per the findings, the effectiveness of marketing tools and techniques has a homogeneous effect on all GenX, GenY, GenZ. Contrary to expectation, Gen X was most influenced by offers. Social media equally influenced all generations to make purchases, irrespective of their incomes. Post Covid there is a shift in consumption habits disregarding generations and income brackets of all the participants. 2023 by IGI Global. All rights reserved. -
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. -
Design and Development of Terahertz Medical Screening Devices
This paper highlights the prospect of design and development of a terahertz medical screening system, giving an overview of existing devices, systems, for THz spectroscopy and imaging of biological samples (e.g., cell, tissue imaging or screening). Considering the non-ionizing nature of THz waves along with its reasonable soft-tissue sensitivity, terahertz instrumentation has opened up possibilities for medical screening devices. Some THz imaging systems presently use raster scanning for calculation of image region of interest. Here, a particular system is proposed as a medical screening device and factors like signal-to-noise ratio, image resolution, image contrast, etc., have been described and correlated with relevant clinical results for exploring possible prospects in medical applications of terahertz waves. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Assessment of composite materials on encrypted secret message in image steganography using RSA algorithm
The use of the internet in this modern era is increased many fold. The communications between different peers take place in digital form. While sharing the messages between different recipients, the confidentiality of the messages is very important. For creating the high level of security while sharing the secret messages, the cryptographic algorithms are introduced along with steganography. Image Steganography is a methodology used to hide the messages inside of the cover image. Initially, the secret information is encrypted by using the RSA Algorithm. Then the encrypted secret information is hidden in the Least Significant Bit (LSB) of the different components of the color image in such a way that the original quality of the image to be minimized. The recipient of the message is able to retrieve the encrypted secret message from the LSB bit of stego_image and then the cipher text is converted into original plain text by using the RSA algorithm. The proposed algorithm verified and analysed its performance against the different combinations of key pairs. 2021 Elsevier Ltd. All rights reserved. -
Enhancing the confidentiality of text embedding using image steganography in spatial domain
Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components. 2021 American Institute of Physics Inc.. All rights reserved. -
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Role of Data Science in the Field of Genomics and Basic Analysis of Raw Genomic Data Using Python
The application of genomics in identifying the nature and cause of diseases has predominantly increased in this decade. This field of study in life sciences combined with new technologies, revealed an outbreak of certain large amounts of genomic sequences. Analysis of such huge data in an appropriate way will ensure accurate prediction of disease which helps to adopt preventive mechanisms which can ultimately improve the human quality of life. In order to achieve this, efficient comprehensive analysis tools and storage mechanisms for handling the enormous genomic data is essential. This research work gives an insight into the application of data science in genomics with a demonstration using Python. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Empowering Women Entrepreneurs in Trade: Challenges, Opportunities and the Role of Digital Marketplaces in Tamilnadu
In recent years, there is a revolution in the commercial sector due to digitalisation which provides an opportunity for the women entrepreneurs, especially in Tamil Nadu with unknown prospects. Nevertheless, all the women entrepreneurs do not get full advantage of these digitalised platforms like the women entrepreneurs who involved in MSME sectors with lack of access or restriction to capital, social constraints, legal constraints or lack of digital literacy. The current status of women entrepreneurship in Tamil Nadu is to identify their unique challenges they face in use of digital platforms and to overcome these challenges. The present study is to make an attempt to know how digital market places should improve women entrepreneurs in access to all the resources, opportunities and market places by evaluating the same. This current research adopts mixed-methods approach, which includes quantitative and qualitative methods. These methods are used to investigate how digital platforms helps the women entrepreneurs in removal of inequalities in reaching the market and also in elimination of financial access which in turn helps to improve the operational efficiency of women-led businesses. Based on the previous research, it is evident that digital markets help the entrepreneurs in getting connected with others and skill development along with access to a greater number of customers. This study focusses towards the interventions which has to be addressed which includes social constraints, lack of digital infrastructure, lack of training to access digital tools. The intent of this study is to focus in policy framing which promotes growth in trade that assists the women entrepreneurs. Thus, this research highlights the transformative potential of digital marketplaces in empowering women in trade, paving the way for an inclusive and dynamic entrepreneurial ecosystem in Tamil Nadu. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysing Customer Profile, Expectations and Satisfaction with Airport Retail in Coimbatore, Insights into the Airport Environment and Decision Making Dynamics
Airport retailing has become a crucial income source and a fundamental aspect of improving traveler experiences. Nevertheless, the intricate relationship between traveler characteristics, anticipations, shopping environments, promotional tactics, purchasing choices, and contentment remains insufficiently examined. This research fills this knowledge gap by exploring the structural associations among six key factors: Customer Profile (CP), Customer Expectation (CE), Airport Retailing Environment (ARE), Retail Marketing Strategy (RMS), Customer Preference Decision (CPD), and Customer Satisfaction (CS) in the context of airport retail environments A descriptive study framework was implemented, concentrating on travelers participating in retail purchases at Coimbatore airport. Firsthand information was obtained from 203 participants using a structured survey. The collected information was assessed using Structural Equation Modeling (SEM) to determine the interconnections among these elements and their overall effect on passenger contentment. The findings reveal that Customer Expectation is significantly influenced by Customer Profile, while Airport Retailing Environment is directly shaped by Customer Expectation but not by Customer Profile. Retail Marketing Strategy is strongly impacted by Airport Retailing Environment but shows no significant relationship with Customer Expectation. These findings emphasize the significance of analyzing traveler behavior and tailoring retail strategies to enhance contentment in airport shopping environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques
Computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. 2025 IEEE. -
Automated Detection Model (ADM) for Glaucoma, Exudate and Diabetic Retinopathy (DR) Diagnosis Using Fundus Images
A total of 15 million people in India suffer from blindness yet statistical analysis shows 75% of these cases can be treated. The research shows DR and Glaucoma lead to blindness in India. Long-term diabetes mainly causes diabetic retinopathy which stands as the primary cause of blindness. Glaucoma damages the optic nerve until blindness develops. The digitized format of fundus images provides useful diagnostic information about infected retinas for proper eye disease detection. Eye defect diagnosis at an early stage enables medical care that greatly decreases patient vision loss risk. An ophthalmologist conducted the disease screening process through examination of fundus image abnormalities. Higher rates of DR and glaucoma prevalence do not affect the number of available ophthalmologists for evaluating fundus images so the prevention of diseases has been delayed. An automated analytical system should be developed presently to help ophthalmologists enhance their diagnostic process efficiency. The paper introduces an artificial learning methodology that utilizes concatenate systems to detect input fundus images in three categories namely ND and GI and EI and DRI. No Diseases (ND), ii. Glaucoma (GI) iii. The classification groups include Exudate infected Images (EI) along with two other categories namely Glaucoma (GI) and DR Images (DRI). The proposed model Automated Detection Model (ADM) starts by analyzing input samples with histogram-based model and employs DenseNet121 and Inception-ResNetV2to facilitate further processing. The Convolution Neural Networks (CNN) function gathers and sorts the feature extraction data obtained from both models. The proposed approach demonstrates improved accuracy and recall plus average precision when used instead of a solitary model. The proposed machine-learning approach using fundus images proves successful for Glaucoma, Exudate and DR diagnosis according to this experiment. 2025 IEEE. -
Electrochemical behaviour of optically transparent, nanoporous LiFePO4cathodes grown via RF magnetron sputtering
The rapid growth of smart technology has accelerated the need for compact and durable microbatteries. Fabrication of thin-film microbatteries is effective to address the requirements of the evolving technology. In the present work, pristine, optically transparent, nanoporous LiFePO4 (LFP)is synthesized via RF magnetron sputtering. The effect of nanoporosity on the electrochemical properties and charge storage mechanisms of LFP is explored. The galvanostatic studies revealed an initial discharge capacity of 32 Ah cm2?m1 and stabilised to 17.5 Ah cm2?m1 after 100 cycles. The capacity fading can be attributed to the increased formation of SEI caused by the enhanced interaction between the cathode and electrolyte due to the nanoporosity. The films demonstrate good rate capability and reversibility. Optical studies reveal a bandgap of 3.74eV, highlighting the potential for usage in optically transparent microbatteries. This work provides key insights into the intrinsic electrochemical behaviour of pristine nanoporous LFP thin films, creating a pathway for its implementation in microbatteries. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Broad spectrum antibacterial activity of nanostructured Cu oxide thin films grown via glancing angle sputtering deposition
The demand for antibacterial surfaces has intensified since the recent pandemic, underscoring the need to prevent microbial adhesion on high-contact surfaces. Metallic and metal oxide nanostructures exhibit intrinsic antibacterial properties, motivating the development of scalable, cost-effective fabrication routes for functional coatings. In this study, copper oxide (CuO) thin films were deposited by magnetron sputtering and further nanostructured via glancing angle deposition (GLAD). The films exhibited pronounced antibacterial efficacy, inactivating Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) with efficiencies over 98% after 8h of exposure. Increasing the deposition angle enhanced surface roughness and hydrophobicity, which directly correlated with higher bacterial inactivation. Longer exposure further improved antibacterial performance, demonstrating time-dependent activity. These results establish GLAD-fabricated CuO thin films as a promising, industrially scalable strategy for next-generation antimicrobial surface coatings. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.