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Kernel granulometric texture analysis and light res-aspp-unet classification for covid-19 detection
This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. 2022 Tech Science Press. All rights reserved. -
Non-Invasive Early and Precise Detection of Breast Tumor with Novel UWB Radar Pulse
Impulse Radio Ultra-Wideband is emerging as a superior breast cancer detection technique compared to ultrasound, magnetic resonance newlineimaging and X-ray mammography due to its high resolution, nonionizing radiation, effectiveness in dense tissues and cost-effectiveness. Radar-based Ultra-Wideband technology is a viable, non-invasive newlinetechnique for detecting breast cancer. The Ultra-Wideband signal must be safe to penetrate deep into human breast with minimal attenuation and comply with Federal Communication Commission regulations to newlineensure early, precise detection of deep-rooted malignant tumor inside newlineheterogeneous breast. In this research work, a shaped Ultra-Wideband Gaussian pulse of newlineseventh order is employed in a radar-based breast cancer detection system. A sharp transition bandpass Finite Impulse Response filter is designed in this work for safe, deep penetration and optimal transmission through the heterogeneous breast. The pulse shaper filter design has a sharp transition with a low side lobe level and can be tuned newlineto any variable center frequency. This design is suitable for shaping very short-duration pulses, achieving higher data rate and less newlineinterference issues. Also, the pulse tightly fits the Federal Communication Commission spectral mask, thus achieving higher spectral utilization efficiency and meets the signal safety standards for transmission through the breast. The shaped pulse fed to the antenna of the radar system provides higher antenna radiation efficiency and radiating power due to the concentration of power in the main lobe. This research work employs bistatic and monostatic radar systems to detect the deep-rooted and smallest formation of the malignant tumor in the breast. Tumor detection is based on the time and frequency newlinedomain analysis of the backscattered signals from the malignant tumor. These signals have higher amplitude, higher electric field intensity variations and an increase in the scattering parameter values due to the newlinepresence of tumor. -
Performance Analysis of Pulse Shaper-Generated Gaussian Pulses for Effective V2V Communication Using Microstrip Patch Antenna
This research presents a comparative analysis of shaped fifth and seventh derivative Gaussian pulses as excitation sources for microstrip patch antennas in V2V communication at 3.69 GHz. The antenna was modeled and simulated using CST Studio Suite, and its performance was evaluated based on return loss, directivity, radiation efficiency, and total efficiency. An Ultra-Wideband pulse shaper was also designed to ensure FCC compliance, reduce sidelobe levels, and minimize interference. Results show that while the fifth derivative pulse yields slightly better return loss, the seventh derivative pulse offers improved frequency alignment, radiation efficiency, and gain, making it more suitable for dynamic vehicular environments. Overall, integrating pulse shaping with higher-order Gaussian pulses enhances the safety, reliability, and interference resilience of V2V communication systems. 2025 IEEE. -
Synthesis of UWB Pulse Shaper for Efficient Pulse Propagation in Human Tissue
In this paper, a filter based pulse shaper is proposed for efficient Ultra-wideband (UWB) pulse transmission through human tissues. A bandpass Finite Impulse Response (FIR) filter is synthesized and its closed form expression for the impulse response coefficients is obtained. The filter shapes the basic UWB pulse, to closely fit the desired Federal Communication Commission (FCC) mask specifications, to achieve high spectral utilization efficiency. In this approach, the effects due to Gibb's phenomenon are minimized thereby resulting in lower dominant sidelobe of the resultant UWB pulse. The interference between adjacent pulses of the UWB data stream is minimized thus it allows shorter duration UWB pulses to be synthesized leading to higher data rate transmission compared to some techniques in literature. 2020 IEEE. -
Pulse Shaper Design for UWB-Based Medical Imaging Applications
In this paper, a pulse shaping filter is designed to shape the higher-order derivatives of the basic UWB Gaussian pulse for efficient pulse transmission through human tissues for medical imaging applications. The shaped pulse for the desired center frequency fits the FCC mask and power spectral density (PSD) specifications with higher spectral efficiency being achieved. It is observed that the ringing effect of Gaussian pulse is reduced by using the proposed bandpass FIR shaping filter. The low ringing effect observed in the shaped pulse ensures better antenna power distribution and improved location accuracy which is critical factor for medical imaging applications. The pulses synthesized are highly orthogonal which aids in multi-access communication, improved bit error rate (BER) performance and short duration UWB pulses leading to higher data rate transmission. The drooping frequency response characteristics of the synthesized pulse have reduced clutter hence tightly focused image obtained for imaging applications. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Efficient Ultra Wideband Radar Based Non Invasive Early Breast Cancer Detection
Ultra Wideband radar systems have emerged as a good alternative for non-invasive and harmless breast cancer detection. In this paper, bistatic and monostatic radar systems are proposed, which detects the deep-rooted and smallest formation of the tumor in the breast. The source signal for transmission through the breast is a seventh derivative Gaussian Ultra Wideband pulse. This pulse is shaped using the proposed sharp transition bandpass Finite Impulse Response filter. The pulse shaper filter design has a sharp transition, hence efficient for shaping very short-duration pulses, achieving higher data rate and less interference issues. Also, the pulse tightly fits the Federal Communication Commission spectral mask, thus achieving higher spectral utilization efficiency and meeting the signal safety standards for transmission through the breast. The shaped pulse fed to the antenna of the radar system provides higher antenna radiation efficiency and radiating power due to the concentration of power in the main lobe, sidelobe suppression, and less channel loss. Tumor detection is based on the time and frequency domain analysis of the backscattered signals from the tumor. These signals have higher amplitude, higher electric field intensity variations, and an increase in the scattering parameter values due to the presence of tumor. Simulation results show significant changes in the electric field intensity for normal and malignant breast tissue for tumor sizes ranging from 4 mm to 0.5 mm. To accurately detect the location of tumor inside the breast, Specific Absorption Rate (SAR) analysis is carried out. It is observed that the energy absorption in the cancerous breast is higher than that of the normal breast, thereby aids to detect the location of the tumor accurately by identifying the coordinates of the maximum value of SAR. The results obtained with an experimental setup consisting of fabricated heterogeneous breast phantom with tumor and monostatic radar closely confirms with the simulation results. 2013 IEEE. -
Diabetic retinopathy detection via deep learning based dual features integrated classification model
Background: The primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images. Objective: The physical diagnostics for this condition was time-consuming and prone to fault. The development of computer-vision based intelligent systems has develop a main research area to effectually diagnosis the pathologies from an image. Methods: In this research, a novel Deep learning based Dual Features Integrated classification (DD-FIC) framework is designed to detect the DR from a color retinal image. Initially, the fundus images are denoised by Wavelet integrated Retinex (WIR) algorithm to remove the noise artifacts which provide high contrast image. This DD-FIC model contains two phases of feature extraction module to evaluation of several retinal areas. Initially, global features of the fundus image are retrieved by the assist of attention fused efficient model, whereas the attention module dynamically highlights the important features. Afterwards, the segmented retinal vessels data is converted into features for learning the local features. Results: Finally, the collective of features is processed into the Random Forest based feature selection model for the optimal prediction with five different classes using multi-class support vector machine (MCSVM). The efficacy of the proposed DD-FIC framework is estimated by Kaggle dataset with the detection accuracy of 98.6%. Conclusions: The proposed framework rises the accuracy of 1.54%, 3.65%, 13.79% and 6.28% for Multi-channel CNN, CNN, VGG NiN and Shallow CNN respectively. The Author(s) 2024. -
Credibility of advertisements on social media - people's perspective /
Social media has created a buzz ever since its inception. It has always created the curiosity factor amongst the people. There has been numerous literatures and information regarding social media and its influence on people, but the most important aspect is the bond that people seem to have developed with social media today. Considering the dependability of the audience on social media, the credibility factor has to be strictly monitored. -
A Study on the Influence of personality traits on entrepreneurial intention among working professionals in the Indian technical organizations
Pacific Business Review International, Vol. 9, Issue 5, pp. 12-19, ISSN 097X-438X -
A Study on the Influence of Personality Traits on Entrepreneurial intentio
Pacific Business Review International, Vol. 9, Issue 5. pp. 12-19, ISSN No. 0974-438X -
Impact of Digital Wallets Usage on Digital Banking Experience
The aim of this study is to explore the influence the digital wallets on digital banking experience of the bank customers. The study used e-wallet literacy scale constructs as independent variables and digital banking experience as dependent variable in order to identify the impact of digital wallets on digital baking experience of the bank customers. The primary data was collected from 300 bank customers who are using the digital wallet provided by the bank or any other third-party digital wallet. Structure equation modeling was executed to explore the influence of digital wallets on digital banking experience. The results indicate that Purchase Transactions (PT) and Investment Transactions (IT) are having high influence on digital banking experience followed by Fund Transfer Transactions (FTP), Method of Payment (MOP) having moderate impact, and finally, Credit Payment Transactions (CPT) and Bill Payment Transactions (BPT) are having low influence on digital banking experience. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC. -
Water Purification Using Subnanostructured Photocatalysts
Visible light is an abundant resource, and photocatalysts absorb this light and use it to energize chemical reactions. Of the many types of reactions that are catalyzed by photocatalysts, wastewater purification is an important area. Photocatalysis is an economical, eco-friendly, and sustainable method of purifying water, a precious resource for which need is increasing while availability is shrinking. Of the several types of photocatalytic materials available, atomically dispersed metals and metal oxides appear to be the most promising. In conventional materials, the efficiency of utilization of active photocatalytic material is rather poor because only a small fraction of those present on the surface can serve as active materials. As the particle size decreases, this efficiency increases. In this respect, subnanometric catalysts such as single-site heterogeneous catalysts, atomically dispersed catalysts, and single-atom catalysts have distinct advantages when compared with their bulk and nanometric counterparts. The challenges in preparing stable single-atom catalysts have largely been overcome, and several methods are now available for their preparation. Many atomically dispersed photocatalytic materials have been synthesized, and many new insights have been gained, unlocking the tremendous potential in purifying wastewater by utilizing solar radiation. The aspects of higher activity, improved selectivity, economical use of materials, and a better understanding of the structure-activity relationship offered by subnanometric photocatalysts have been explored in this chapter. 2020 American Chemical Society. -
Modelling and optimization of Rhodamine B degradation over Bi2WO6Bi2O3 heterojunction using response surface methodology
The Bi2O3/Bi2WO6 heterostructures of various compositions are prepared via the surfactant-assisted solgel method, which exhibits enhanced and synergistic photocatalytic activity towards the degradation of Rhodamine B (Rh B) using visible light irradiation. Characterization of these heterostructures has been done using X-ray diffraction, microscopic and spectroscopic methods. The 50% tungstate in bismuth oxide (BWO) nanocomposites having band gap of 2.85eV and an average size of 4080nm shows maximum dye removal up to 87% in 4h compared to pure Bi2O3 and other heterostructures of Bi2O3/Bi2WO6. The reusability studies demonstrate the excellent retention of photocatalytic activity without much loss in activity, implying the stability and efficiency of the prepared catalyst. The degradation of the Rh B dye is modeled mathematically to analyze the interactive effects of the key parameters like the time, amount of catalyst, and dye concentration, and to determine the optimal setting of these parameters to optimize the degradation process using the face-centered Central Composite Design (FC-CCD) of the Response Surface Methodology (RSM) analysis. An accurate full quadratic model has been developed with R2 = 99.41%. The sensitivity of the degradation was evaluated at all levels of the key parameters. At 0.1g of catalyst amount, it was found that the increment of the catalyst amount would be suitable for improved degradation as compared to allowing more time for the degradation. The maximum degradation was obtained for a dye concentration of 5ppm, and 0.1g catalyst for 4h. 2022, King Abdulaziz City for Science and Technology. -
Eco friendly nitration of toluene using modified zirconia
Nitration of toluene has been studied in the liquid phase over a series of modified zirconia catalysts. Zirconia, zirconia- ceria (Zr0.98Ce0.02)O2, sulfated zirconia and sulfated zirconia- ceria were synthesised by co precipitation method and were characterised by X-ray diffraction, BET surface area, Infra red spectroscopy analysis (FTIR), Thermogravimetric analysis (TGA), Scanning Electron Microscopy (SEM), and Energy Dispersive X ray analysis (EDAX). The acidity of the prepared catalysts was determined by FTIR pyridine adsorption study. X-ray diffraction studies reveal that the catalysts prepared mainly consist of tetragonal phase with the crystallite size in the nano range and the tetragonal phase of zirconia is stabilized by the addition of ceria. The modified zirconia samples have higher surface area and exhibits uniform pore size distribution aggregated by zirconia nanoparticles. The onset of sulfate decomposition was observed around 723 K for sulfated samples. The catalytic performance was determined for the liquid phase nitration of toluene to ortho-, meta- and para- nitro toluene. The effect of reaction temperature, concentration of nitric acid, catalyst reusability and reaction time was also investigated. 2013 BCREC UNDIP. -
Synthesis and characterization of CeO2/Bi2O3/gC3N4 ternary Z-scheme nanocomposite
An effective and facile phytogenic method was used to prepare CeO2/Bi2O3 and CeO2/Bi2O3/gC3N4 composites using Eichhornia crassipes phytoextract. The synthesized catalysts were characterized using techniques such as XRD, FTIR, UV-DRS, PL, SEM-EDAX, XPS, zeta potential, and TGA. These catalysts showed diverse photocatalytic and optical properties due to the alteration in the bandgap. The synthesized composites exhibited good photocatalytic activity by degrading Malachite green (MG) dye. The increase in the photocatalytic activity could be attributed to the p-n heterojunction of the catalysts with efficient charge separation and strong oxidative ability. The modified photocatalysts showed excellent catalytic activity and reusability under visible light. The superior efficiency and its applications in environmental remediation make these catalysts a potential candidate for photocatalysis. 2020 The American Ceramic Society -
Performance Analysis of Deterministic Finite Automata and Turing Machine Using JFLAP Tool
In real life, the increased data accessing speed and data storage ability is required by most of the machinery fields. However, the real-world problems can be studied effectively with the combination of scientific computational techniques with the mathematical models. Automata theory is known to be the popular mathematical model. Towards most of the software and hardware related applications, the computational methods are analyzed and designed using significant automata theory concepts (likely, pushdown automata (PDA), Turing machines (TMs) and finite automata (FA)). Hence, the conventional lecture-driven style has attracted the reflective preferences of learners using these abstract natured concepts. But the lecture-driven teaching style has less motivated the computer engineering learners. In order to learn automata theory and computational models, we introduce the PDA and TM in a virtual platform. However, this work has motivated the improvement of longitudinal experimental validation and learning using the modern technology. Java Formal Languages and Automata Package (JFLAP) tool is used to write our simulators in JAVA language and the results are obtained from each machine through simulating the input strings. 2021 World Scientific Publishing Company. -
Brand Loyalty Drivers among Generation Z Fashion Consumers: A Comparative Analysis
Brand loyalty is crucial in the competitive fashion market, particularly among Generation Z. Although previous studies have investigated what drives loyalty, there is still limited evidence from India, particularly about gender differences. This study adopts a context-specific and exploratory approach to examine brand loyalty and its drivers among Generation Z fashion consumers in Bangalore. The study adopts a quantitative research design with a structured questionnaire using a 5-point Likert scale. A sample of 100 Generation Z students in Bangalore was selected using convenience sampling to collect the data. Further descriptive and inferential statistical analyses were conducted using SPSS. The findings show positive associations among brand loyalty, brand awareness, perceived quality, emotional connection, and social influence. Independent-samples t-tests reveal no significant difference in overall brand loyalty between male and female respondents. However, regression analyses indicate that perceived quality and brand awareness are relatively stronger predictors of brand loyalty among male respondents. In contrast, emotional connection is a stronger predictor among female respondents. These findings suggest differences in motivational pathways rather than loyalty intensity. The study suggests that while overall brand loyalty levels are similar across genders, the motivational drivers underlying loyalty differ. These findings are context-specific and exploratory, and their generalizability is limited by convenience sampling and a restricted geographic scope. 2026 Journal of Computers, Mechanical and Management. -
Incorporating the metaverse into the green banking revolution: Spearheading the implementation of eco-friendly financial practices
This study aims to explore the awareness and perceptions of green banking among bankers and customers in rural and semi-urban areas of India. A structured questionnaire was employed to gather information from 807 customers and 200 officials of selected commercial banks, utilizing the snowball sampling method. The study utilized chi-square and factor analysis techniques. The chi-square test results revealed an association between educational status and the customer's opinion regarding green banks. Factor analysis derived three key factors influencing the adoption of green banking: convenience and environmental sustainability, financial and technological advantages, and customer retention and prestige. The findings indicate that green banking services provide more benefits to its customers than traditional banking. 2024, IGI Global.