Browse Items (11807 total)
Sort by:
-
Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Facile synthesis of novel SrO 0.5:MnO 0.5 bimetallic oxide nanostructure as a high-performance electrode material for supercapacitors
Perovskite bimetallic oxides as electrode material blends can be an appropriate method to enhance the supercapacitor properties. In the present research, SrO 0.5:MnO 0.5 nanostructures (NS) were synthesized by a facile co-precipitation method and calcinated at 750800C. Crystal structure of SrO 0.5:MnO 0.5 NS were characterized by X-ray diffraction, surface chemical composition and chemical bond analysis, and dispersion of SrO into MnO was confirmed by X-ray photoelectron spectral studies. Structural morphology was analyzed from scanning electron microscopy. Optical properties of SrO 0.5:MnO 0.5 NS were studied using UV-Visible spectrophotometer and SrO 0.5 and MnO 0.5 NS showed ?75nm grain, ? 64nm grain boundary distance, with two maxima at 261nm and 345nm as intensity of absorption patterns, respectively. The synthesized SrO 0.5:MnO 0.5 NS exhibited high specific capacitance of 392.8F/g at a current density of 0.1A/g. Electrochemical impedance spectroscopy results indicated low resistance and very low time constant of 0.2s ?73% of the capacitance was retained after 1000 galvanostatic charge-discharge (GCD) cycles. These findings indicate that SrO 0.5:MnO 0.5 bimetallic oxide material could be a promising electrode material for electrochemical energy storage systems. The Author(s) 2022. -
Factors influencing purchase decision and brand switching in the passenger car segment in Bengaluru
This study identifies and analyses the Product Attributes of Passenger Cars and the demographic factors that influence consumer Purchase Decision and Brand Switching in the Indian context, specific to the city of Bengaluru. It discusses the existing knowledge pertaining to Passenger Cars and a conceptual framework is developed based on the review of literature. The research identifies what drives the Purchase Decision and Brand Switching for the Indian consumers and analyses how it differs based on demographic variables such as age, gender and income. Based on the model thus created, the research seeks to segment the Indian Passenger Car consumers according to the significant demographic variables thus identified. A questionnaire was administered to 200 respondents of different age, income and gender groups within the city of Bangalore. The data was then analyzed using Factor Analysis, One-way ANOVA and frequency analysis in SPSS.It was found that Quality, Aftersales Service, Safety and Price are the major value factors effecting purchase decision of Indian Passenger Car consumer. Age and income also has a significant influence of Purchase Decision and Brand Switching. It was also found that purchase intention varies between different age and income groups. The research was conducted within the city of Bangalore alone which may not be generalized to the entire country. 2020 SERSC. -
-
The Development of Structured Tele Based Medicine Concept Using Programmable System
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer. 2024 IEEE. -
Front-End Security Analysis forCloud-Based Data Backup Application Using Cybersecurity Tools
In this challenging, demanding, daunting, and competitive business world, the rise, and growth of cybercrimes are very high. With the proliferation of Cloud Computing techniques, usually in industrial arenas, business information and important clients data are stored and managed using cloud platforms. Application programs are developed to handle such valuable information assets of the organizations. Cloud backups are provided for these client data where security is the most concerning aspect. There are many vulnerabilities in the current scenario where intruders can cause havoc. Destruction of the product can happen by exploiting vulnerabilities that can put the company and the product in jeopardy. It may create a bad impression about the organization among the customers, competitors, and the public world. This paper shows the work done by a cyber security team whose main objective is to run vulnerability analysis and mitigate threats on an application that backs up the clients data to the cloud. Cyber Security is an important aspect in all types of businesses because it protects all categories of data such as fragile data, private information, intellectual property data, and other data including governmental and industrial information systems from theft and damage which concludes in huge financial loss and loss of client data. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Damaged Relay Station: EEG Neurofeedback Training in Isolated Bilateral Paramedian Thalamic Infarct
Stroke is a major public health concern and leads to significant disability. Bilateral thalamic infarcts are rare and can result in severe and chronic cognitive and behavioral disturbances - apathy, personality change, executive dysfunctions, and anterograde amnesia. There is a paucity of literature on neuropsychological rehabilitation in patients with bilateral thalamic infarcts. Mr. M., a 51 years old, married male, a mechanical engineer, working as a supervisor was referred for neuropsychological assessment and rehabilitation with the diagnosis of bilateral paramedian thalamic infarct after seven months of stroke. A pre-post comprehensive neuropsychological assessment of his cognition, mood, and behavior was carried out. The patient received 40 sessions of EEG-Neurofeedback Training. The results showed significant improvement in sleep, motivation, and executive functions, however, there was no significant improvement in memory. The case represents the challenges in the memory rehabilitation of patients with bilateral thalamic lesions. 2024 Neurology India, Neurological Society of India. -
Leveraging FinTech for the Advancement of Circular Economy
During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC. -
Understanding green economy
Resource efficiency, environmentally friendly consumption and production, and the green economy's contribution to sustainable development resource efficiency refer to the ways in which resources are used to deliver value to society and aims to reduce the amount of resources needed, as well as emissions and waste generated, per unit of product or service. Sustainable consumption and production aim to improve production processes and consumption practices to reduce resource consumption, waste generation, and emissions across the full life cycle of processes and products. A macroeconomic strategy for achieving sustainable economic growth is offered by the green economy, with a primary emphasis on investments, jobs, and skills. 2024, IGI Global. All rights reserved. -
Role of social media influencers in fashion and clothing
This study provides an overview of the influence of social media influencers in the fashion and clothing industry. With the increasing presence of social media in people 's lives, individuals are easily influenced by what they see online, leading to the adoption of trends promoted by influencers. Influencers, who have established their personal brand and gained a substantial following, play a key role in shaping consumer preferences and driving sales. Social media platforms allow fashion brands to connect with their audience, democratizing fashion shows and enabling direct interaction. The rise of fast fashion and the influence of fashion influencers have contributed to the growth of the clothing industry. Businesses are now utilizing AI-powered analytics and dedicated platforms to enhance their influencer marketing strategies. In summary, social media influencers have a significant impact on consumer behavior, driv- ing sales and shaping trends in the fashion and clothing industry. 2023, IGI Global. All rights reserved. -
Food wastage and consumerism in circular economy: a review and research directions
Purpose: Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerisms impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability. Design/methodology/approach: A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023. Findings: The findings reveal four prominent factors behavioural, environmental, socioeconomic and technological in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways. Research limitations/implications: Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour. Originality/value: Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends. 2024, Emerald Publishing Limited. -
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023. -
A Framework for Dress Code Monitoring System using Transfer Learning from Pre-Trained YOLOv4 Model
Maintaining a proper dress code in organizations or any environment is very important. It not only imbibes a sense of discipline but also reflects the personality and qualities of people as individuals. To follow this practice, some organizations like educational institutions and a few corporations have made it mandatory for the personnel to maintain proper attire as per their regulations. Manual checks are performed to adhere to the organizations' regulations which becomes tedious and erroneous most of the times. Having an automated system not only saves time but also there is very little scope of mistakes and errors. Taking this into context, the main aim and idea behind the project is to propose a model for detecting the dress code in such workplaces and educational institutions where the attire needs to be regularly monitored. The model detects Business Formals (Blazer, Shirt & Pants) worn by the personnel, for which CNN has been considered, along with YOLOv4, for performing the detection, due to its nature of giving the highest accuracy in comparison to the other object-detection models. Providing the Mean Average Precision of around 81%, it becomes evident that the model performs quite well in performing the detections. 2023 IEEE. -
The effect of airline service quality on customer satisfaction and loyalty in India
Indian Aviation Industry has been one of the world's fastest-growing aviation industries with private airlines representing more than 75 percent of the domestic aviation industry. With an 18 percent compound annual growth rate (CAGR) and 454 airports and airstrips in place in the country, 16 of which are designated as international airports, it has been stated that by 2011 the aviation sector will be witnessing a revival. In 2009, with traffic movement rising and revenues rising by nearly US$ 21.4 million, India's Airports Authority appears expected to earn better margins in 2009-10, as indicated by the Civil Aviation Ministry's latest estimates. The most crucial step in identifying and providing high-quality service is to understand exactly what customers expect. Quality of service is one of the best models for measuring customer expectations and perceptions. A company's performance results in customer satisfaction with a product or service. Passenger satisfaction is important to customer sovereignty. Customers can be loyal without being highly satisfied and being highly satisfied and yet not being loyal. Companies are required to gain a better understanding of the online environment relationship between satisfaction and behavioural intention, and to assign online marketing strategies between satisfaction initiatives and behavioural intention programme. In addition, the findings of this research will assist airline managers to better serve their customers, track and improve quality of service and achieve the highest level of satisfaction for their passengers. 2020 Elsevier Ltd. All rights reserved. -
Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection
Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications. 2023 IEEE. -
Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy
With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms. 2023 IEEE. -
Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Women empowerment in India: Are we on the right track?
Information and communication technologies (ICTs) have attracted the continued attention of national governments, international bodies, private organizations, civil societies, and NGOs. ICT has the potential to act as an influential method for promoting gender equality and social-economic and political empowerment of women. The chapter describes a number of ICT-backed initiatives in different countries targeted towards various concerns of women such as health, education, violence, governance, income, etc. It demonstrates the ability of ICT for empowering women especially those belonging to the marginalized group. This chapter examines the key challenges including technical, social, and economic to the usage of ICT for women's development as well as suggests initiatives for initiatives for national governments, policy makers, and organizations focusing on the issue of women empowerment. 2023, IGI Global. -
An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG.