Browse Items (16481 total)
Sort by:
-
Design and Analysis of Reliability Sampling Plans Based on the ToppLeone Generated Weibull Distribution
As part of this study, we design a reliability acceptance sampling plan under the assumption that the lifetime of a product follows the ToppLeone generated Weibull (TLGW) distribution, a model that exhibits structural symmetry in its hazard rate behavior and distributional form. The fundamental procedures for constructing such a plan are described. We compute and tabulate the minimum sample sizes required for given risk criteria using both binomial and Poisson models for the number of failures. We also provide the operating characteristic (OC) values for the proposed sampling plans, and determine the minimum ratios of true mean life to specified mean life needed to satisfy a given producers risk. The role of symmetry in the TLGW distribution is highlighted in its balanced tail properties and shape characteristics, which influence the performance of the acceptance sampling plan. Finally, we illustrate the applicability of the proposed plan with real-world data. 2025 by the authors. -
Design and Analysis of Conformal Antenna for Wearable Devices
This paper presents the design, modeling, and bending analysis of a conformal antenna operating at 2.4 GHz for wearable devices. The antenna's performance was evaluated using HFSS, focusing on return loss, geometry optimization, and bending effects. The optimal resonant frequency of 2.4 GHz was achieved for a patch length (Lp) of 41.78 mm and a width (Wp) of 50.20 mm, achieving a return loss of -34.94 dB and an impedance of 53.5 ohms. The antenna's width had minimal impact on the resonant frequency. When optimized on a Teflon substrate without bending, the antenna demonstrated excellent resonance and impedance matching, with a Voltage Standing Wave Ratio (VSWR) of 1.63 at 2.4 GHz, indicating minimal reflection. The E-plane and H-plane radiation patterns were analyzed at 2.4 GHz, showing a peak gain of 7.38 dB at a theta angle of 0 degrees. Bending analysis revealed that increased bending causes a negative shift in the resonance frequency and affects impedance matching. The proposed antenna is flexible, low-cost, and suitable for wearable medical devices and other applications in the 2.4 GHz frequency band, with return loss, VSWR, radiation pattern, and impedance results all within acceptable ranges. 2025 IEEE. -
Design an efficient protocol for secured energy efficient routing in large scale wireless sensor networks
Wireless Sensor Network has played a significant role in enabling communication and connectivity to human unreachable area since more than a last decade. Apart from its newlinebeneficial features, it also suffers from various problems that have yet not been solved till date in spite of massive research work in this area. The proposed work jointly addresses the problems of energy efficiency and secure routing in wireless sensor network. The existing literatures are found to provide a less scope of an effective solution to address such issues jointly. Hence, the prime goal of the proposed work is to introduce a mechanism that uses lightweight cryptosystem as a part of new hierarchical routing protocol. The mechanism of the proposed work is discussed in three different modules. Where each module is enhanced version of the previous module. The first module is named as Secured Tree Based Routing with Energy Efficiency (STREE), which newlineintroduces a new energy efficient selection of cluster head along with a very simple and newlinelightweight encryption mechanism for routing message using keccak, a newly launched newlinecryptographic hash function. The second module is named as Secured Authentication Based Routing (SABR) introduces node-to-node authentication along with identification newlineand compensation of network related delay owing to incorporated cryptography. The newlinethird module is named as Secured Anonymous Routing with Digital Signature (SARDS) newlinewhich introduces a distinct mechanism of using enhanced elliptical curve cryptography newlineand a new usage of digital signature. The modelling of proposed study is done using newlineanalytical research methodology and the outcome of the study has been compared with newlineexisting standard routing protocol SecLeach to find that proposed system presents a newlinesuperior mechanism of balancing security, energy efficiency, and communication newlineperformance in wireless sensor network. -
Design & Analysis of CPE Based Fractional Filters
In this paper, a design and analysis of a constant phase element (CPE) based fractional-order filter (FOF) is presented. This paper leverages a voltage differencing transconductance amplifier (VDTA) to design a current-mode fractional-order filter, capable of realizing four types: low-pass, high-pass, band-pass, and band-reject, all with just two VDTAs. The circuit utilizes both a standard integer-order capacitor and a novel fractional-order capacitor. The proposed filter is resistor-less and electronically tunable. Mathematical formulations are outlined for the transfer functions of FOF. All the filter responses are obtained at varying value of ?=0.5,0.6, 0.7, 0.8 and 0.9. All the simulations are carried out using Cadence Virtuoso at 45nm CMOS technology node. 2024 IEEE. -
DESI DR2 meets cosmography: a comparative study of Pad Chebyshev, and Taylor expansions
We perform a comprehensive cosmographic analysis of the late-time Universe using the latest Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) baryon acoustic oscillation (BAO) measurements, comparing Taylor, Pad and Chebyshev expansions as model-independent reconstructions of the background expansion. We consider Padapproximants of order (2,1) and (2,2), a Chebyshev expansion, and a third-order Taylor series. Due to its limited radius of convergence, the Taylor expansion is constrained using only the low-redshift DESI sub-set (z < 1), while the rational Padforms and the Chebyshev expansion are applied over the full DESI DR2 redshift range. Cosmographic parameters are inferred through a Bayesian Markov chain Monte Carlo (MCMC) analysis, and the resulting best-fitting reconstructions of H(z), dL(z), and BAO distance indicators are compared with the predictions of the Lambda cold dark matter (CDM) model. All methods are consistent with CDM at low redshift, but the Chebyshev expansion exhibits noticeable deviations at higher redshifts, while the Pad2,1) and Pad2,2) reconstructions remain closely aligned with CDM across the DESI DR2 range. A model-selection analysis based on Akaike Information Criterion and Bayesian Information Criterion shows a clear statistical preference for the Taylor expansion over low-zCDM, and a strong preference for Padcosmography over CDM when the full DESI DR2 data set is used. These results demonstrate the constraining power of DESI DR2 for cosmographic studies and highlight the utility of rational approximants, especially Padforms, in extending cosmography reliably to higher redshifts beyond the domain of traditional Taylor series. The Author(s) 2026. Published by Oxford University Press on behalf of Royal Astronomical Society. -
Descriptive Answer Evaluation using NLP Processes Integrated with Strategically Constructed Semantic Skill Ontologies
The world is moving towards an online methodology of education. One of the key challenges is the assessment of questions which do not have a definite answer and have several correct answers. To solve this problem, and for quality evaluation of descriptive answers online, an automatic evaluation methodology is proposed in this work. A language model is modelled from the expected answer key, and entity graphs are generated from the ontology modelled using the input answer to be evaluated. Natural Language Processing (NLP) techniques like Stemming, Summarization, and Polarity Analysis are integrated in this work with Ontologies for the efficient evaluation of descriptive answers. Several challenges which come across evaluating descriptive answers are discussed in this chapter, and they have been solved in order to obtain a dynamic and robust evaluating system. Finally, the system is evaluated using a user-feedback methodology comprising a panel of 100 students and 100 professors. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
DES J024008.08-551047.5: A new member of the polar ring galaxy family
Aims. This study presents the discovery of a new polar ring galaxy (PRG) candidate and highlights its unique features and characteristics. We provide evidence from photometric analysis that supports the inclusion of galaxy DES J024008.08-551047.5 (DJ0240) in the PRG catalogue. Methods. During the visual observations of optical imaging data obtained from the Dark Energy Camera Legacy Survey, a serendipitous discovery was made of a ringed galaxy, DJ0240. We conducted a one-dimensional isophotal analysis to determine the position angle of the ring component and its relative orientation to the host galaxy. A two-dimensional GALFIT analysis was performed to confirm the orthogonal nature of the ring galaxy and identify distinct components within the host galaxy. We compared the photometric properties of the host and ring components of DJ0240 with PRGs and other ring-type galaxies (RTGs), finding that DJ0240 shares similar properties with both of these galaxy types. Results. We have discovered the galaxy DJ0240, a PRG candidate with a ring component positioned almost perpendicular to the host galaxy. The position angles of the ring and host components are ?80 and ?10, respectively, indicating that they are nearly orthogonal to each other. The extension of the ring component is three times greater than that of the host galaxy and shows a distinct colour separation, being bluer than the host. The estimated g-r colour values of the host and ring components are 0.86 0.02 and 0.59 0.10 mag, respectively. The colour value of the ring component is similar to those of typical spiral galaxies. The host galaxy's colour and the presence of a bulge and disc components indicate that the host galaxy may be lenticular. Our findings reveal a subtle yet noticeable colour difference between the host and ring components of PRGs and RTGs. We observe that both the host and ring components of DJ0240 align more closely with PRGs than with RTGs. Furthermore, we compared the Sersic index values of the ring component (nring) of galaxy DJ0240 with a selected sample of PRGs and Hoag-type galaxies. The results show that DJ0240 has a remarkably low nring value of 0.13, supporting the galaxy's classification as a PRG. Hence, we suggest that the ring galaxy DJ0240 is a highly promising candidate for inclusion in the family of PRGs. 2024 The Authors. -
Derris Indica Leaves Extract as a Green Inhibitor for the Corrosion of Aluminium in Alkaline Medium
The corrosion inhibitive effect of Derris indica leaves extract (DILE) on aluminium in 1 M NaOH is investigated at different temperatures. For this purpose, weight loss studies and electrochemical methods including potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) technique are employed. Surface analysis of the treated and untreated aluminium coupons are done by using metallurgical microscopy. About 60.2% of maximum corrosion inhibition efficiency is attained with an optimum inhibitor concentration of 1.2 g/L. Both weight loss and electrochemical studies confirmed that DILE plays a crucial role in the formation of a protective layer over metal surfaces. Also, electrochemical measurements revealed that DILE behaves as a mixed type of corrosion inhibitor. The kinetic parameters and thermodynamic parameters are calculated using Arrhenius theory and transition state theory. Langmuir adsorption isotherm was found to be the best fit and physical adsorption mechanism was proposed. En ineered Science Publisher LLC 2022 -
DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection
The significance of early skin cancer detection for effective prevention and treatment is underscored by the limitations of traditional manual diagnostic methods used by dermatologists. Leveraging Convolutional Neural Networks (CNNs) and the HAM10000 dataset, this research aims to automate skin cancer classification through dermatoscopic image analysis. The primary objective of the research is an accurate classification system identifying seven specific skin cancer types. The novelty is the deployment of the classification system using a Mobile Application - DermAI. The trained CNN model, spanning 10 epochs, achieved remarkable precision, peaking at a 97.90 percentage test accuracy during the 7th epoch. Evaluation metrics like the confusion matrix confirm its reliability in categorizing lesions, minimizing misclassifications, and validating its efficiency as a diagnostic tool. Transforming the model into TensorFlow Lite format enables seamless integration into mobile platforms, optimizing computational resources. This allows users to access prompt skin cancer classification via an Android application, fostering accessibility to preliminary assessments. Early identification facilitates timely medical intervention, a crucial factor in enhancing prognosis. Through CNNs, TensorFlow Lite, and mobile deployment, this research strives to bridge technology and healthcare accessibility, empowering individuals to proactively manage their skin health based on classification results and initiate timely discussions with healthcare professionals. 2025 IEEE. -
Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE. -
Depth Comparison of Objects in 2D Images Using Mask RCNN
Getting distance of an object from a single 2D image has always been a task. Due to various reasons, it was difficult to compare from images whether an object is closer or farther from camera. In this paper, we propose an idea to compare multiple images taken from same focal length cameras and specifying the distance of an object in those images with respect to each other. Our dataset contains images of palm of hand with particular distance from camera, and the output difference can specify in which image the palm is closer to camera as compared to others and vice versa. For this model, we are using Mask RCNN to recognize the object; in our case, it has been trained to identify palm, and then giving the output of masked RCNN to a depth identifier model to specify the distance of the palm from the camera. Directly using depth identifier model cannot give correct output as distance of background from camera results in different value for distance of targeted object in different images. So, we will be using mask RCNN to specify which part of image depth model should find distance from the camera. In the final step, we take the output of the depth model and take the mean of the output generated by it and compare the means of various images to specify relative distance from each other. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deprotection induced modulation of excited state intramolecular proton transfer for selective detection of perborate and ammonia
Acetate protected Naphthalene Coupled Benzothiazole (NCB) has been designed and synthesized for selective detection of perborate (BO3) and ammonia (NH3) based on modulation of excited-state intramolecular proton transfer (ESIPT) process by chemodosimetric deacetylation pathway. In presence of nucleophilic species like BO3 and NH3, acetyl group deprotection of NCB resulted ESIPT within the molecule exhibiting a significant enhancement of absorption and emission signals at 425 nm and 472 nm respectively. The emission enhancement of NCB has been observed by 31-folds and 14-folds in presence of BO3 and NH3 respectively. The selectivity and fast sensitivity of NCB have been shown by the lower detection limit (1.32 M for BO3, 1.74 M for NH3 in UVvis study and 0.60 M for BO3 and 4.39 M for NH3 in fluorescence study) and fast response (rate constants: 12.36 s?1 and 5.54 s?1 for BO3 and NH3 respectively). Analytes induced deacetylation pathway of NCB followed by ESIPT has been clearly demonstrated by theoretical calculation. The test strips based on NCB with BO3 and NH3 are fabricated, which can act as a convenient and efficient test kits for both these analytes. In the practical applications, the sensor NCB can be utilized as low cost food spoilage indicator and soil analysis by fluorometric method. 2024 Elsevier B.V. -
Depression, anxiety, stress and marital adjustment among women
Marriage, especially for women in a patriarchal society involves a huge transition process. The struggle with new responsibilities and expectations is overwhelming in itself. But with the feelings of worthlessness and feeling trapped and bound in a loveless and thankless bond, come distress and adjustment issues. According to a recent Nielsen survey on 'Women of Tomorrow', out of 21 nations and 6500 women, India is a leading nation when it comes to stress in women. About 87% of women were stressed most of the time and 82% claimed that they did not find time to relax. Women in the age range from 22 years to 55 years are the most stressed and are struggling hard to strike a balance between their home lives, social activities and jobs. The present study aims to examine depression, stress, anxiety and adjustment issues among women. A total of 80 married women were selected for the study with 40 working and 40 non-working women. The Revised Dyadic Adjustment Scale and Depression Anxiety Stress Scales were administered to collect data. Negative relationship was obtained between stress, anxiety depression and marital adjustment among married women. Anxiety and Marital adjustment are moderately correlated (-.346) while Stress (-.454) and Depression (-0.487) are highly correlated with marital adjustment. 2020 Journal of International Women's Studies. -
Depression Severity Prediction Among Higher Education Students Using Neural Network Model
Depression significantly affects students' mental health and academic performance, highlighting the need for effective early detection methods. This study investigates machine learning approaches for automated classification of depression severity using responses from the Patient Health Questionnaire-9 (PHQ-9). Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and hybrid models combining structured PHQ-9 scores with descriptive text responses were evaluated. The experimental results show that the LSTM model achieved the highest classification precision (90%), demonstrating its ability to capture sequential relationships between items in PHQ-9. The findings indicate that sequence-based models are well suited to assess the severity of depression in student populations. Integrating such predictive models into digital mental health screening systems may support the early identification of at-risk students and enable timely, data-driven interventions in academic settings. 2026 IEEE. -
Deposition and characterization of ZnO/CdSe/SnSe ternary thin film based photocatalyst for an enhanced visible light-driven photodegradation of model pollutants
A heterogeneous photocatalytic pathway is a possible approach to global energy and environmental issues. Sol-gel spin coating and physical vapour deposition were used to create a new ternary ZnO/CdSe/SnSe nanocomposite thin film photocatalyst. X-ray diffractometry, energy-dispersive X-ray spectroscopy (EDS), field emission-scanning electron microscopy, UV-Vis, and photoluminescence (PL) spectrophotometers were used to characterize the deposited films. When exposed to solar light, the ternary photocatalyst exhibits high photocatalytic activity in photocatalytic dye degradation processes. it demonstrates excellent visible light absorption, enhanced charge carrier separation, and solar light simulation. It was proposed that the charge in the ternary ZnO/CdSe/SnSe photocatalyst moves in a double type-II and cascade manner between the various components. In this study, ternary thin film heterostructures are synthesized, exhibiting outstanding stability and solar light-induced photocatalytic activity.The thin film composed of ZnO/CdSe/SnSe exhibits a degradation efficiency of 96% when exposed to visible light, and a degradation efficiency of 90% for methylene blue under sunlight within a time period of 150 min. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Deposition and characterization of ZnO thin films on corning glass substrate using Magnetron sputtering
The Zinc Oxide (ZnO) thin films were deposited on corning glass substrates using RF Magnetron sputtering at a substrate temperature of 400 C and thicknesses of 1000 nm and 2000 nm. SEM, EDX, XRD, and UV-Vis spectrometers were used to analyse the thin films' morphological, structural, and optical characteristics. SEMwas used to analyse the surface morphology of the thin films. The composition of the created thin films was evaluated using EDX. XRD was used to examine the crystalline structure of the deposited ZnO films. Using the Debye-Scherrer equation, the average sample crystal size was determined. Uv-Vis was used to analyse the optical characteristics of the thin films. The findings showing how well-piezoelectric the produced thin films are may be useful in developing Surface Acoustic Wave Devices. 2024 Author(s). -
Deployment of Smart Surveillance System using Deep Learning to Recognize Cyber-criminals
This paper presents the development of the smart surveillance systems critical in identifying cybercriminals through the use of deep learning.The system utilizes deep learning algorithms for the identification of cybercriminals in physical and cyberspace.The systems apply neural networks to analyze images,video streams and cyber behavior for pattern recognition of suspicious activities and potential threats.Also the system analyze the online activities of users and flow of data within the network for signs of cybercriminals. Various technologies such as convolutional neural networks (CNN), and recurrent neural networks (RNN) are used to distinguish facial features, body language, and unusual online activities. This way, effective security measures are taken to prevent or reduce the impact of cybercriminals in various environments by combining intelligent monitoring systems with future threat prediction. The system is capable of evolving by identifying new criminal patterns to enhance its performance. This means the system is modified and updated as it receives more data, making it effective in multivariate settings such as any institution with financial activities, government networks, and high-security locations. 2025 IEEE. -
Deploying NLP techniques in Twitch application to comprehend online user behaviour
Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level. 2023 IEEE. -
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE. -
Deploying Fact-Checking Tools to Alleviate Misinformation Promulgation in Twitter Using Machine Learning Techniques
In the present era, the rising portion of our lives is spending interactions online with social media platforms. Thanks to the latest technology adoption as well as smartphones proliferation. Gaining news from the platforms of social media is quicker, easier as well as cheaper in comparison with other traditional media platforms such as T.V and newspapers. Hence, social media is being exploited in order to spread misinformation. The study tends to construct fake corpus that comprises tweets for a product advertisement. The FakeAds corpus objective is to explore the misinformation impact on the advertising and marketing materials for a particular product as well as what kinds of products are targeted mostly on Twitter to draw the consumers attention. Products include cosmetics, fashions, health, electronics, etc. The corpus is varied and novel to the topic (i.e., Twitter role in spreading misinformation in relation to production promotion and advertising) as well as in terms of fine-grained annotations. The guidelines of the annotations were framed through the guidance of domain experts as well as the annotation is done with two domain experts, which results in higher quality annotation, through the agreement rate F-scores as higher as 0.976 using text classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.