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Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Ear Recognition Using Pretrained Convolutional Neural Networks
Ear biometrics, which involves the identification of a person from an ear image, is challenging under unconstrained image capturing scenarios. Studies in Ear biometrics reported that the Convolutional Neural Network is a better alternative to classical machine learning with handcrafted features. Two major concerns in CNN are the requirement of enormous computing resources and large datasets for training. The pretrained network concept helps to use CNN with smaller datasets and is less demanding on hardware. In this paper, three pre-trained CNN models, AlexNet, VGG16, and ResNet50 are used for ear recognition. The fully connected classification layers of the nets are trained with AWE, an unconstrained ear dataset. Alternatively, the CNN layers output (the CNN features) are extracted, and an SVM classification model is built. To improve the classification accuracy, the training dataset size is increased through data augmentation. Data augmentation improved the classification accuracy drastically. The results show that ResNet50, with the fully connected classification layer, results in higher accuracy. 2021, Springer Nature Switzerland AG. -
A Novel Approach to Automatic Ear Detection Using Banana Wavelets and Circular Hough Transform
Ear is an attractive biometric trait that maintain their structure with increasing age. Because of the complex geometry of ear, its detection is very difficult. This paper proposes a modified algorithm for automatic detection of 2D ear images using Banana wavelets and Hough transform. Banana wavelets derived from bank of stretched and curved Gabor wavelets are used to identify curvilinear ear structure. Addition of a preprocessing stage, prior to application of banana wavelets is found to improve the detection results further. The proposed algorithm is brought in to comparison with three existing algorithms and evaluated on standard databases. In addition to manual detection accuracy, this paper also calculates the efficiency of the proposed method using automatic classification techniques. The features like LBP and Gabor extracted from segmented ear image is used by different classifiers to determine whether the segmented portion of the image is class Ear or Non ear. 2019 IEEE. -
A modified invasive weed optimization for MPPT of PV based water pumping system driven by induction motor
A novel approach called Modified Invasive Weed Optimization (MIWO) technique has been developed and combined with the Perturb and Observes (P&O) algorithm to enhance the extraction of maximum power from photovoltaic (PV) panels in the presence of partial shading conditions. The conventional P&O algorithm falls short in extracting the maximum power from PV systems under partial shading conditions due to the existence of multiple maximum points. In such scenarios, optimization techniques can be employed to search for the global maximum point. The proposed MIWO-based P&O algorithm updates the reference voltage to ensure that the PV system operates at the Maximum Power Point (MPP) based on the prevailing weather conditions. This MIWO based PV system is further fed to water pumping system. A PV-based water pumping system is utilized for both irrigation and domestic purposes. Additionally, a sensorless vector control-based induction motor is employed in this study to drive the pump. The objective of this research is to demonstrate the achievement of an efficient PV-based water pumping system without the need for battery storage. Various results based on MIWO are compared with PSO and GWO. The results are presented based on various water pumping applications and the availability of solar irradiance during rapid climate changes. MATLAB/Simulink simulations, along with hardware-based experiments, are provided to validate the effectiveness of the proposed method under both transient and steady-state conditions. 2024 IOP Publishing Ltd. -
Role of mixed molecular weight PEO-PVDF polymers in improving the ionic conductivity of blended solid polymer electrolytes
Blended solid polymer electrolytes (BSPE) were prepared by mixing different molecular weight polymers PEO6 (Mw = 1 106 g/mol), PEO5 (Mw = 1 105 g/mol), and PVDF (Mw = 5.25 105 g/mol) complexed with lithium salt. Conductivity and dielectric studies at different temperatures were carried out on these BSPE systems by varying the wt% of PEO5 and PVDF with respect to PEO6, keeping the wt% of lithium salt constant. The electrical characterizations of BSPE systems have been investigated using impedance spectroscopy in the frequency range 0.1106 Hz. The conductivity data shows that inclusion of PEO5 and PVDF into the PEO6 matrix improved the overall lithium-ion dynamics in the polymer matrix. The composition, PEO6 (94 wt%)-PEO5 (3 wt%)/PVDF (3 wt%)-LiClO4, exhibited maximum conductivity of 6.44 10?4 Scm?1 at 303 K. TheDC conductivity variation with temperature of BSPE systems follows Arrhenius relation and variation of AC conductivities with frequency obeys Jonschers power law. The real and imaginary part of dielectric constant and the dielectric relaxation were also investigated. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Engineered biocorona on microplastics as a toxicity mitigation strategy in marine environment: Experiments with a marine crustacean Artemia salina
The marine environment has become a major sink for microplastics (MPs) wastes. When MPs interact with biological macromolecules, the biocorona forms on their surface, which can alter their biological reactivity and toxicity. In this study, we investigated the impact of biocorona formation on the toxicity of aminated (NH2) and carboxylated (COOH) polystyrene MPs towards the marine crustacean Artemia salina. Biocoronated MPs were prepared using cell-free extracts (CFEs) from microalgae Chlorella sp. (phytoplankton) and the brine shrimp Artemia salina (zooplankton). The results revealed that biocorona formation effectively reduced the toxicity of MPs. Pristine NH2-MPs exhibited higher reactive oxygen species production (ROS) (182%) compared to COOH-MPs (162%) in Artemia salina. Notably, NH2-MPs coronated with brine shrimp CFE exhibited a substantial reduction in ROS production (127%) than those coronated with algal CFE, with COOH-MPs showing a similar trend (120%). Biocorona formation also significantly decreased malondialdehyde (MDA) levels and antioxidant activity compared to pristine MPs. Molecular docking and dynamics simulations demonstrated a strong binding between polystyrene and acetylcholinesterase (AChE). In vitro studies indicated that pristine NH2-MPs exhibited more reduction in AChE activity (84%) compared to COOH-MPs (95%). However, no significant reduction in AChE activity was observed upon exposure to MPs coronated with either algal or brine shrimp cell-free extracts. Independent action modeling indicated an antagonistic interaction for MPs coronated with both the CFEs. Pearson correlation and cluster heatmap analysis suggested that the toxicity reduction in Artemia salina might be driven by decreased oxidative stress followed by the corona formation. Overall, this study provides valuable insights into the potential of biomolecules from phytoplankton and zooplankton to reduce MPs toxicity in Artemia salina, while highlighting their role in modulating the toxicity of other marine pollutants. 2024 The Author(s) -
An Analysis of Sentiment Using Aspect-Based Perspective
Opinions play a major role in almost every human practice. Finding product and service reviews is made easy online. Product reviews are readily available in huge quantities. Considering each review and making a concise decision about a product is not feasible or even possible. Aspect-based sentiment analysis (ABSA) is one of the best solutions to this problem. Summary and online reviews analysis is delivered in this paper. ABSA has made extensive use of machine learning techniques. Recent years have seen deep learning take off due to the growth of computer processing power and digitalization. When applied to various deep learning techniques, numerous NLP tasks produced futuristic results. An overview of various deep learning models used in the field of ABSA is presented in this chapter after an introduction to ABSA. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Breaking News Recognition Using OCR
Identifying and recognition of breaking news in most of the TV channels in different backgrounds with varying positions from a static image plays a significant role in journalism and multimedia image processing. Now a days its very challenging to isolate only breaking news from headlines due to overlapping of many categories of news, keeping all this in mind, a novel methodology is proposed in this paper for detecting specific text as a breaking news from a given multimedia image. Basic digital image processing techniques are used to detect text from the images. The methods like MSER (Maximally Stable Extremal Regions) and SWT (Stroke Width Transform) are used for text detection. The proposed work focuses on extraction of text in breaking news images also discusses the different methods to overcome existing challenges in text detection along with different types of breaking news datasets collected from various news channels are used to identify text from images and comparative study of different text detection methods. The comparative study proves that MSER and SWT is a better technique to detect text in images. Finally using OCR (Optical Character Recognition) technique to extract the breaking news text from the detected regions will help in easy indexing and analysis for journalism and common people. Extensive experiments are carried out to demonstrate the effectiveness of the proposed approach. 2019, Springer Nature Singapore Pte Ltd. -
Acetylcholine esterase inhibition activity of leaf extract of Saraca asoca using zebrafish as model organism
Alzheimers disease, also called as Senile Dementia, is a progressive neurogenerative disease that slowly destroys important mental functions like memory, reasoning and thinking. A plethora of factors including genetics, lifestyle, environment, age etc. play a part in determining its incidence. One of the commonly used techniques to slow down the progression of Alzheimers is to reduce the functioning of Acetylcholinesterase (AChE) enzyme which breaks down the neurotransmitter acetylcholine. Plants have been found to be natural sources of AChE inhibitors. Hence the present investigation was an attempt to screen Ashoka plant (Saraca asoca) for such inhibitors. Zebrafish (Danio rerio) was used as a model organism due to its genetic similarities with humans. Both in vivo and in vitro analyses using zebrafish indicated inhibitory action of the leaf extract on AChE. Gas Chromatography- Mass spectrometry (GCMS) analysis of the methanolic leaf extract and further docking studies of prominent phytochemicals revealed the AChE inhibitory potential of molecules like Stigmasterol, ?-sitosterol, Vitamin E etc. Hence these molecules can be thought of as targets in the therapy of Alzheimers disease. 2020 World Research Association. All rights reserved. -
Discovery of quasi-periodic oscillations in the persistent X-ray emission of accreting binary X-ray pulsar LMC X-4
We report the discovery of quasi-periodic oscillations (QPOs) in the high-mass X-ray binary (HMXB) pulsar LMC X-4 in its non-flaring (persistent) state using observations with XMM-Newton. In addition to the 74 mHz coherent pulsations, the persistent emission light curve shows a QPO feature in the frequency range of 20-30 mHz. Quasi-periodic flares have been previously observed from LMC X-4 in observations made with Rossi X-ray Timing Explorer (RXTE). However, this is the first time QPOs have been observed in the persistent emission observations of LMC X-4. QPOs in X-ray binaries are generally thought to be related to the rotation of the inhomogeneous matter distribution in the inner accretion disc. In HMXBs such as LMC X-4 where the compact object is a neutron star with a high magnetic field, the radius of the inner accretion disc is determined by the mass accretion rate and the magnetic moment of the neutron star. In such systems, the QPO feature, along with the pulse period and X-ray luminosity measurement, helps us to constrain the magnetic field strength of the neutron star. We use considerations of magnetospheric accretion to have an approximate value of the magnetic field strength of the neutron star in LMC X-4. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Flares during eclipses of high-mass X-ray binary systems Vela X-1, 4U 1700?37, and LMC X-4
In eclipsing X-ray binary systems, the direct X-ray emission is blocked by the companion star during the eclipse. We observe only reprocessed emission that contains clues about the environment of the compact object and its chemical composition, ionization levels, etc. We have found flares in some X-ray binaries during their eclipses. The study of eclipse flares provides additional clues regarding the size of the reprocessing region and helps distinguish between different components of the X-ray spectrum observed during the eclipse. In the archival data, we searched for flares during eclipses of high-mass X-ray binaries and found flares in three sources: Vela X-1, LMC X-4, and 4U 1700?37. Comparing spectral properties of the eclipse flare and non-flare data, we found changes in the power-law photon index in all three sources and multiple emission lines in Vela X-1 and 4U 1700-37. The fluxes of prominent emission lines showed a similar increase as the overall X-ray flux during the eclipse flare, suggesting the lines originate in the binary environment and not in the interstellar medium. We also observed a soft excess in 4U 1700-37 that remains unchanged during both eclipse flare and non-flare states. Our analysis suggests that this emission originates from the extremely thin shell of the stellar wind surrounding the photosphere of its companion star. The detection of short (100200 s) count-rate doubling time-scale in 4U 1700?37 and LMC X-4 indicates that the eclipse reprocessing occurs in a region larger than, but comparable to the size of the companion star. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Neural Network based Student Grade Prediction Model
Student final grade GPA is the collective efforts of their previous and ongoing efforts of each semester examination may predict accurately using the neural network which receives the input weight of each matrix element of variables to next neuron. The GPA prediction based on regular class performance and previous grades with background variables were found much significant. This research tries to explore the model comparison and evaluate student grade prediction using various neural network models. The single-layer half i.e., successful student model predicts 90 total accuracies than the single layer with five hidden layer neurons (88.5 percent). The multi-layer with two hidden layers (7,3) is 84 percent accuracy is less than one percent accuracy than multilayer with three hidden layers. Similarly, the multilayered with four hidden layered 25,12,7,3 model predicts the least accuracy (77 percent accuracy) for student grade. Similarly, the passed student prediction model has less accuracy than both students' 86 percent. 2022 IEEE. -
An Outlook on Sustainable Business Practices through Virtual Reality Marketing
Technologies and businesses blend progressively and work towards creating a sustainable future through the company's marketing strategies. The purpose of the study is to find out the various sustainable outcomes of Virtual Reality Marketing (VRM). The exploratory research identified immersive experience, experiential economy, positive image creation, positive travel decisions, and repeat purchase as the constructs of VRM, and a total of 418 people were surveyed to analyze those constructs. The data were analyzed through statistical tests such as t-test, One-way ANOVA, and Chi-square with the help of SPSS software. The study shows a positive relationship between customers and virtual reality marketing. The results predict that businesses that have incorporated VRM tend to likely have a high-profit margin and more sustainable returns compared to their peer competitors. 2024 IEEE. -
A Study on Factors Enhancing Immersive Virtual Reality Experiences
The objective of this study is to identify the various influential factors of immersive virtual reality (VR) experiences and examine the relationship between the immersion factors (technology, visuals, sound, interaction, and sound) and virtual reality experiential outcomes (satisfaction and loyalty). The survey comprises 412 participants who experienced VR games at the Orion Mall in Bangalore. The study has identified the prominent factors for enhancing the immersive experience. The factors are technology, visuals, sound, interaction, and sound. It also identified that there exists a positive association between VR experiential satisfaction and technology, visuals, sound, interaction, and sound. The results imply that service providers should focus on elevating immersive experience as it is closely associated with VR experiential satisfaction and VR experiential loyalty. This will increase the revisit intention and spread positive word of mouth about the virtual experiences. This paper provided valuable insights that pay way to analyze the association between immersion factors and VR experiential outcomes. 2024 IEEE. -
Evaluation of Virtual Reality Experiential Dimensions using Sentiment Analysis
Experiential technologies like Virtual Reality (VR) are revitalizing the gaming industries through higher immersive and interactive gaming experiences. The immersive technology has a considerable impact on the industry and will evolve simultaneously as the technology continues to update and improve further. Indian tech cities Bangalore, Delhi NCR, Mumbai, Kolkata, Chennai, Pune, and Hyderabad were chosen for the study and the user-generated content was scraped from the top gaming centers of each city. User Generated Content analysis is gaining immense interest among businesses for devising better decision-making and marketing strategies. The study devised an integrated framework comprised of web data scraping, data cleaning, data pre-processing, AI model designing, sentiment analysis, logistic regression model, and support vector machine model. Logistic Regression predicted the sentiment of the text and the Support Vector Machine classified the VR experiential dimensions and helped in understanding the most important dimension for customer satisfaction. The study has found that VR experiences are gaining positive responses among the customers and illusion emerges as the most significant dimension for their satisfaction. 2024 IEEE. -
Preprocessed text compression method for Malayalam text files
The increasing importance of Unicode for text files implies an increase in storage space required for data and the time for the transmission of data, with a corresponding need for compression of data. Conventional compressors fair purely on UTF-8 texts, where each character can span multiple bytes. Malayalam which is one among the four major languages of the Dravidian family, is represented by using Unicode characters. The contribution of this paper is a reversible transformation mapping of the input to reduce the actual size of the input file before a general purpose compression method. After the preprocessing, LZW compression achieves more compression to Malayalam text files containing any characters including ASCII characters. This method can be extended to any native language files containing mostly the characters of only one script. BEIESP. -
Implementation of AI in manufacturing industries a case study
Artificial intelligence (AI) is getting progressively integrated into nearly every facet of our existence. Its applications are ubiquitous and ever-evolving, spanning fields such as autonomous vehicles, geology, medicine, and art. AI has, however, posed as many questions as it has answered. These include the definition and application of the technology (viz., assisted, augmented, or independent intellect), the question of whether computers are thinking machines similarly to humans, the wider implications of the impact of automation on society, and the unexpected moral and principled quandaries. This chapter provides an overview of artificial intelligence in manufacturing intended for executives in manufacturing and industrial companies who want to integrate AI into their business. Its main objective is to apply AI to the engineering, testing, and production stages of the manufacturing value chain. The goal is to discuss business applications that technology, data, and automated processes can support, and how the appropriate personnel, organizational structure, and culture can support them. This article discusses current advancements, poses problems, asks questions, and attempts to bring cutting-edge concepts and research closer to business. 2025 Mohamed Arezki Mellal. All rights reserved. -
Data: A Key to HR Analytics for Talent Management
The past few years have witnessed a significant rise in job openings across various industries worldwide. This trend has highlighted the need for companies to plan and recruit better talent to keep up with the demand for skilled employees. As a result, Human Resource (HR) professionals are now using workforce planning and HR analytics to address the challenges of finding and retaining the right employees. With the help of technological advancements in HR systems, businesses are leveraging data and insights to understand workplace dynamics better. Workforce planning has thus become crucial for organizations of all sizes to ensure they have the necessary talent to achieve their goals in the present and future. This chapter delves deeper and examines the importance of workforce planning and how HR analytics can help companies achieve their strategic objectives. Talent Management is about analyzing the workforce skill requirements of the organization. It needs a strategic plan to ensure the appropriate people are in the right roles at the right times. Talent Management is a crucial element of every businesss performance. In this process, data play a pivotal role in evaluating the existing workforce and planning for future workforce needs. Based on this, a strategy is developed to fill gaps or prospective shortages. Organizations can achieve their goals by using talent planning and collecting data about upcoming projects and skill requirements based on market needs. For example, talent planning is essential in the healthcare sector to guarantee that hospitals and clinics have enough doctors, nurses, and other healthcare workers to fulfill the rising demand for healthcare services. HR analytics is the key to talent management, enabling organizations to stay competitive, enhance productivity, and achieve long-term strategic objectives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
MnO2 Nanoclusters Decorated on GrapheneModified Pencil Graphite Electrode for Non-Enzymatic Determination of Cholesterol
Electrochemically deposited MnO2 on graphene coated Pencil Graphite Electrode (PGE) has been used to develop a facile electrochemical sensor for the determination of Cholesterol. Cyclic voltammetric (CV) studies and electrochemical impedance spectroscopic (EIS) technique were used to investigate the electrochemical properties of the modified sensing platform. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Cholesterol was found to be 120?10 M2400?10 M under optimum conditions. The ultralow level of detection limit (0.42 nM) demonstrates the high sensitivity of the proposed method. The developed method was successfully applied for the non-enzymatic determination of Cholesterol in human blood samples at ultralow levels. 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
?-Cyclodextrin-PANI decorated pencil graphite electrode for the electrochemical sensing of morin in almonds and mulberry leaves
Morin (3,2,4,5,7-pentahydroxyflavone) is one of the natural flavonoids which is present in a variety of fruits and herbs. ?-cyclodextrin (?-CD) and polyaniline (PANI) decorated Pencil graphite electrode (PGE) has been successfully used as a sensitive and conducting electrode for the determination of morin. The hydroxyl groups of ?-CD attract the analyte towards the modified electrode through hydrogen bonding. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques were employed to study the electrochemical properties of the modified electrodes. The enhanced surface roughness of ?-CD-PANI/PGE has resulted in the increase of electrocatalytic activity of electrode towards the analyte. Opitical profilometric studies were performed to evaluate the surface roughness of electrodes and differential pulse votammetry (DPV) was used for the quantitative analysis of morin. Scanning electron microscopy (SEM), Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy were carried out to know the physicochemical characteristics of the modified electrodes. The experimental conditions such as scan rate, pH and concentration were optimized. The electrochemical process was found to be adsorption controlled and irreversible from scan rate studies. Under optimal conditions, the linear dynamic range for the quantification of morin was found to be 1.1732nM. The low detection limit (0.38nM) indicates ultrasensitivity of the proposed method. The suggested method has been effectively employed for the determination of morin in almonds and mulberry leaves. 2020, Springer Nature Switzerland AG.