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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. -
Effortless and beneficial processing of natural languages using transformers
Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components. 2022 Taru Publications. -
EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemics impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who arent wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This studys proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Eggshells biowaste for hydroxyapatite green synthesis using extract piper betel leaf - Evaluation of antibacterial and antibiofilm activity
The present research work reports the biosynthesis of hydroxyapatite (HAp) from eggshells and green synthesis of HAp from eggshells with incorporation of Piper betel leaf extract (PBL-HAp) using microwave conversion method. Although there are several works on synthesis of HAp from eggshells and other calcium and phosphorus rich substrates, the incorporation of herbal extract with HAp to promote antimicrobial and antibiofilm activity is less explored and reported. This research work highlights a simple and cost-effective method for development of antimicrobial biomaterials by combining the concepts of waste management, biomaterial science, and herbal medicine. In the present study, characterization of synthesized HAp was applied by X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy, and morphological analysis using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The characterization results indicated that the prepared HAp and PBL-HAp were pure b-type carbonated HAp. The PBL-HAp was checked for its antibacterial activity using the well diffusion method and biofilm inhibitory activity by crystal violet assay against some common pathogens. The antibacterial activities against Staphylococcus aureus and biofilm inhibitory activities against Escherichia coli, Vibrio harveyi, Pseudomonas aeruginosa, and Staphylococcus aureus of Piper betel leaf extract coated HAp (PBL-HAp) were showed to be significant and offered a promising role for the development of potent dental biomaterials. 2021 Elsevier Inc. -
EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society -
Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
ELCCFD: An Efficient and Enhanced Credit Card Fraud Detection using Enhanced Deep Learning Principle
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN's output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems. 2024 IEEE. -
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE. -
Electric Vehicle Control and Driving Safety Systems: A Review
The relevance of Electric Vehicles (EVs) and the overall market demands of the respective control units is in a never before leap all around the globe as seen from the news, business studies, research trends and technological innovations today. Compared to earlier years, the relevance of driving safety in EVs also gains special attention due to the unforeseen surge in promoting EVs by National, State and City administrations for better environment and societal changes in future. For EV, the scenario broadens to a wider landscape beyond the earlier passive safety design features, to a highly comfortable and safer possible road travel. Safety enhancements can be experimented and implemented on EVs in a reliable way with higher end control of the dynamics, stability and optimised utilisation of individual vehicle characteristics and driver behaviours. In this paper, an attempt is made to scrutinise different control design approaches and possible solution paths experimented upon in the past and currently for EV as seen in the published literature. The quest is also to explore optimisation strategies in an organised way to ensure best possible driving safety along with passenger safety in EVs. 2023 IETE. -
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE. -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
Electrical transport and magnetoresistance studies on the magnetic moment compensated Mn2V1-xCoxZ (Z=Ga, Al; x=0, 0.25, 0.5, 0.75, 1) Heusler alloys
We report the electrical resistivity and magnetoresistance properties of arc-melted Mn2V1-xCoxZ (Z=Ga, Al; x =0, 0.25, 0.5, 0.75, 1) alloys, which possess compensated ferrimagnetic behaviour with high TC when x=0.5. Apart from metallicity, the alloys in the Ga series with x= 0, 0.75, 1 composition showed a positive to negative crossover in the magnetoresistance versus temperature curves. This crossover was absent for Mn2V0.75Co0.25Ga and the fully compensated ferrimagnet Mn2V0.5Co0.5Ga. In contrast to this, Co-substituted Mn2VAl exhibits distinctly different resistive behaviour. While the alloys Mn2VAl and Mn2CoAl exhibit metallic and semiconducting behaviour respectively, the intermediate compositions show a gradual metallic to semiconducting transition as the Co concentration increases. The compensated ferrimagnet Mn2V0.5Co0.5Al showed a mixed transport behaviour of metallic and semiconducting nature with a resistivity minimum at 140 K. In contrast to this mixed response of the arc-melted bulk sample, the Mn2V0.5Co0.5Al melt-spun ribbon shows a clear semiconducting nature throughout the temperature range, indicating that the sample preparation methods could highly influence the electrical properties of the investigated compensated ferrimagnets. 2024 Elsevier B.V. -
Electrically small S-band antenna for cubesat applications
This research paper deals with the design and development of a circularly polarized S-band rectangular patch antenna providing performance suitable for application in CubeSat. A CubeSat is a type of miniaturized satellite used primarily by university research groups for demonstration of technology. They are low earth orbiting sun-synchronous (LEOSS) type of satellites. The design protocol specifies maximum outer dimensions equal to 100 mm00 mm00 mm and weighing a mass between 1.3-6 kg. However, being small in size, they pose some challenges such as low profile antenna, possibility for cross-link communication with other similar satellites and high reliability of communication in a swarm without the prior knowledge of their positions. Additionally CubeSats dictate the space limitation for placing the antenna within it. With all these, it also requires small antenna with high gain and wide directivity. The most suitable antennas that address most of the aforementioned challenges are planar antennas. The design and simulation of the proposed design of electrically small sband antenna for CubeSat achieves gain of 5.01 dBi with a narrow bandwidth of 100 MHz. The analysis is performed using MATLAB and HFSS (High Frequency Structural Simulator). 2017 IEEE. -
Electro fabrication of molecularly imprinted sensor based on Pd nanoparticles decorated poly-(3 thiophene acetic acid) for progesterone detection
In recent years, scientific community has witnessed substantial interest in the design and engineering of electrodes as sensing platforms towards sensitive and selective detection of hormones. An electrochemical strategy for the detection of progesterone was proposed by generating a composite film comprising of palladium nanoparticles with 3-thiophene acetic acid (3-TAA) coupled with molecular imprinting technology. Progesterone molecule was employed as the template while generating molecular imprints by electropolymerization on the surface of the Carbon Fibre Paper (CFP) electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry were used to analyse the various modified working electrodes (CV). Characterization methods included field emission scanning microscopy, energy dispersive X-ray spectrometry, optical profilometry, and X-ray photon electron spectroscopy. Pd nanoparticles resulted in enhanced sensitivity and molecular imprinting technology contributed to its specificity. Because of the molecular cavities created on the removal of the template molecule, Nyquist plots data showed that the MIP/Pd/CFP electrode had the lowest charge transfer resistance compared to other control electrodes. 2022 Elsevier Ltd -
Electro fabrication of molecularly imprinted sensor based on Pd nanoparticles decorated poly-(3 thiophene acetic acid) for progesterone detection /
Electrochimica Acta, Vol.408, ISSN No: 0013-4686.
In recent years, scientific community has witnessed substantial interest in the design and engineering of electrodes as sensing platforms towards sensitive and selective detection of hormones. An electrochemical strategy for the detection of progesterone was proposed by generating a composite film comprising of palladium nanoparticles with 3-thiophene acetic acid (3-TAA) coupled with molecular imprinting technology. Progesterone molecule was employed as the template while generating molecular imprints by electropolymerization on the surface of the Carbon Fibre Paper (CFP) electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry were used to analyse the various modified working electrodes (CV). -
Electro-osmotic effect on the three-layer flow of Binary nanoliquid between two concentric cylinders
The three-layer flow of an immiscible nanoliquid in composite annulus with an electro-kinetic effect is analyzed using Buongiornos model. This model helps in analyzing the impact of two major phenomena, namely thermophoresis and Brownian motion. In this model, an interfacial layer is formed between the liquids due to the immiscibility of the base liquids. The use of a multilayer model especially in cooling systems brings more applications in many industries such as nuclear, biomedical, and solar. Different from the earlier studies on multilayer channel flow, this paper explains the three-layer flow between two concentric cylinders in the presence of cross-diffusion which makes the work unique. Further, the middle region is assumed to be porous and heat source or sink is applied to the entire system. Also, the flux conservation condition for nanoparticle volume fraction is considered. The equations governing the problem are simplified and are solved using the differential transform method. The results indicate that the electroosmotic parameter enhances the velocity but reduces the electrostatic potential. Further, the diffusion ratio improves the temperature and decreases the solute concentration of the fluid. 2022, Akadiai Kiad Budapest, Hungary. -
Electro-sprayed Quaternary Composite of Poly(aniline-co-pyrrole), Graphene Oxide, and Iron Oxide as an Efficient Electrode for Hybrid Supercapacitor Application
Abstract: A novel quaternary nanocomposite has been developed using a cost-effective and user-friendly method called electro-spraying. This composite consists of poly(aniline-co-pyrrole), Graphene Oxide (GO), and Iron Oxide (Fe3O4), aimed at achieving improved electrochemical stability and performance. The composite electrodes displayed an impressive specific capacitance of 950 Fg1 at a current density of 0.5 Ag1 when tested in a 1 M H2SO4 solution. Furthermore, even after 2000 cycles at a current density of 1 Ag1, the electrode exhibited an outstanding capacitance retention rate of 91%, showcasing its remarkable stability and long-lasting performance. These exceptional properties can be attributed to the synergistic effects arising from the combination of the conducting polymer, metal oxide, and graphene oxide components within the electrode material. Additionally, significant advancements in other electrochemical properties make this nanocomposite a promising candidate for use as an electrode material in supercapacitors. Pleiades Publishing, Ltd. 2024. -
Electrocatalytic oxidation and determination of morin at a poly (2,5-dimercapto-1,3,4-thiadiazole) modified carbon fiber paper electrode /
Journal Of The Electrochemical Society, Vol.163, Issue 8, ISSN:0013-4651 (print) 1945-7111 (web).