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Theory and Applicability of the Weighted Modified Lindley Distribution
As a bridge between the exponential and Lindley distributions, the modified Lind-ley distribution was created. It has been used successfully in a variety of fields related to survival analysis. In this study, we present a novel distribution that extends the modified Lindley distribution using the traditional weighted (or length/size-biased) approach. It is named as weighted modified Lindley distribution. This idea is mainly used to flexibilize the former modified Lindley distribution through the use of a one-parameter polynomial weight. This weight is intended to modulate the functionalities of the new distribution, well beyond those of the former modified Lindley distribution. The related probability density function, cumulative density function, hazard rate function, moments, moment generating function and characteristic function are analysed from a theoretical and practical point of view. Estimation of the parameters is done by the classical method of maximum likelihood and a simulation study is carried out to check the consistency of the maximum likelihood estimates. A data set is used to illustrate the application of the proposed distribution. 2022, Society of Statistics, Computer and Applications. All rights reserved. -
Dynamic vibrational analysis on areca sheath fibre reinforced bio composites by fast fourier analysis
Natural fibre reinforced bio composites [6] are good alternative for conventional materials. Natural fibres are cheaper in cost, environmental friendly and biodegradable. In this project work the effect of varying fibre length is studied and Fast Fourier Technique is used for the analysis of dynamic frequency response. The naturally extracted areca sheath fibres are used as a reinforcement and epoxy L - 12 is used as polymer matrix. Fabrication is done by using hand lay-up method and compression molding technique at 100 - 110 bar pressure and 140 - 150C temperature. Each specimen is cured for 24 h and then test specimens were cut according to ASTM standards i.e., 150 X 150 mm in length and breadth. The dynamic frequency response of specimens with varying fibre length of 29, 27 and 25 mm and thickness 4, 3.5 and 2 mm is obtained by modal analysis. Finite Element Analysis for all specimens is carried out by ANSYS 14.5 and results are compared with the experimental values. These natural areca fibre reinforced polymer matrix composites are defined for particular applications based up on the mechanical and vibrational characteristics obtain from the experimental results. 2018 Elsevier Ltd. All rights reserved. -
Artificial Intelligence Technological Revolution in Education and Space for Next Generation
The goal of this research is to discover the various potential for the educational system using artificial intelligence (AI). The world today is dealing with AI in different sectors. This study specifically looked into the prospects for acquiring efficient and high-quality education for each student, automating administrative tasks, including regulating adaptive student support systems. AI has been leveraged and used in the education sector in various formations. AI initially took in the form of computers with the cognitive model, transformed to online learning, together with other technologies, the use of AI provides chatbots to perform instructors. Imagine you can access your classroom from anywhere at any time through an online learning system. These functionalities enable the education system to deal with the curriculum effectively. Using these facilities, teachers instruct the students to desire to achieve their goals efficiently. The primary aim of this article addresses the concepts in AI that serve to regulate and improve the overall quality of academic performance. The secondary aim of this article is to discuss AI involvement in the space domain. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The impact of excess CSR expenditure on firm value anddividend payout in India: ananalysis using firm age andsize dynamics
Purpose: The paper looks at the impact of excess amount of CSR expenditure (CSRE) in relation to mandatory CSRE in an Indian context on dividend payout (DP) and firm value (FV) where CSRE is mandatory, as well as how this relationship varies between firms based on their age and size. Design/methodology/approach: A sample of the 657 companies listed on the National Stock Exchange (NSE) from 201415 to 202021 is used in the study, for which spending on CSR was mandatory. A two-step generalised method of the moment is employed to examine the relationship between the variables of interest. Findings: The results show that excess CSREs neither increase the firms valuation nor benefit shareholders' economic benefits, i.e. dividend distribution. However, a deeper analysis reveals that excess CSRE is positively associated with FV in the case of smaller firms and also positively corresponds with DP in the case of younger firms. Originality/value: The present study explicitly considers the excess CSR spending beyond the mandated requirements. It investigates whether such spending contributes to firms improving their valuation and explores its connection to DPs. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0136. 2024, Emerald Publishing Limited. -
A Feminist Perspective on the Food and Gender based Marketing Narrative
Nutrition to the body is a basic element for sustenance and growth biologically, provided through food. This paper aims to understand why there is a difference between foods that are marketed gender-specifically to males and females separately. There have been a lot of participative changes in the household kitchen activities since the birth of the concept. However, certain things have continued to remain the same either as a result of tradition, preference, or systemic societal loop. This paper aims to categorically understand this patterned behaviour behind gender based food marketing and the consequent consumptions so as to find a more sustainable and inclusive approach for food marketing for the firms of this industry. The aim is also to shed light on the impact of such practices on the psychological level of the individual buyer that stems to form a pattern, creating a recurring practice out of habit, over internal choice. The Electrochemical Society -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
PE-v-SVR based Architecture to Predict and Prevent Low and Slow-Rate DDoS Attacks using Machine Learning
Distributed Denial of Service (DDoS) attacks continue to emerge; low and slow attacks pose a serious threat. These small-scale attacks often evade traditional security protections and increase the risk of long-term outages and loss of service. Our research aims to develop effective predictive models and strategic defences to detect and mitigate slow DDoS attacks. The proposed model combines Power Spectral entropy and V-Support Vector Regression. More importantly, the version achieves the first-class error price in the variety of zero to at least one, demonstrating its effectiveness in detecting and predicting DDoS attacks. Research results show the effectiveness of the proposed design using PSD (power spectral density) entropy and V-SVR. The best mean square error obtained further confirms the ability of the model in this context. V-SVR in low and sluggish DDoS assaults. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE. -
Beyond Automation: Understanding the Transformational Capabilities of AI in Management
The investigation explores at the various ways that artificial intelligence (AI) is affecting management techniques. The study highlights the dichotomy between automation and augmentation, highlighting how artificial intelligence (AI) can replace human work through automation, but its ultimate use in augmenting human capabilities (augmentation) leads to better organisational performance. This analysis reveals how AI-driven tactics enhance operational efficiency, decision-making, and productivity by synthesising research findings from a variety of domains, including manufacturing, banking, municipal sectors, and remote work environments. It also looks at how AI may change management through big data and data analytics, recommending a shift to an integrated strategy that combines automation and human understanding to promote creativity and long-term growth. 2024 IEEE. -
Optimal Feature Selection for the Classification of Hyperspectral Imagery Using Adaptive SpectralSpatial Clustering
Hyperspectral images captured through the hyperspectral sensors play an imperative part in remote sensing applications in the present context. Unlike traditional images sensed with few bands in the visible spectrum, the hyperspectral (HS) images are obtained with hundreds of spectral band ranges from infrared to ultraviolet regions. Because of its vast spatial and spectral data, it requires an extensive computational system for processing and its hidden features are needed to be unveiled in an effective manner specifically for the classification of HS imagery. This approach exploits the high spectral band correlation and rich spatial information of the HS images for the generation of feature vectors. To attain optimal feature space for the best probable classification, an adaptive approach is incorporated to adaptively choose spectralspatial features for feature selection to classify the pixels effectively. Furthermore, the HS image encompasses several bands including noisy bands. To categorize the images with great accuracy, it is suggested to eradicate the noisy bands whilst retaining the informative bands. In this research, an adaptive spectralspatial feature selection scheme is proposed for HS images where the extremely correlated representative bands are considered for analysis with uncorrelated and noisy spectral bands are judiciously discarded during its classification process. This hybrid approach not merely diminishes the computational time and also improves the general classification accuracy significantly. The empirical result displays that the proposed work surpasses the conventional approach of HS image classification systems. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
AI Applications Computer Vision and Natural Language Processing
Artificial intelligence (AI) applications in computer vision and natural language processing (NLP) have made major advances in recent years, challenging a number of sectors and areas. This multidisciplinary topic combines NLP, which examines the study of human language, and computer vision, which concentrates on the understanding of visual data. This study examines the wide range of applications that are included within this convergence, highlighting the revolutionary potential of AI technology. AI has made it possible to make significant advances in autonomous systems, object identification, and image recognition in the field of computer vision. These developments have stimulated innovation and increased efficiency, revolutionizing sectors including healthcare, autonomous vehicles, and security. Meanwhile, AI-driven advances in NLP have produced strong language models that can produce, comprehend, and translate text. These approaches have been utilized to improve accessibility and efficiency of communication in chatbots, sentiment analysis, and language translation services. This chapter explores the basic ideas and advancements in these two fields, emphasizing the opportunities and novel challenges that arise from integrating computer vision and NLP. Additionally covered are data privacy, ethical issues, and the possibility of prejudice in AI applications. The study also highlights the ongoing need for these fields' advancement and investigation in order to solve real-world problems and fully utilize AI's potential in the computer vision and NLP industries. 2025 The Institute of Electrical and Electronics Engineers, Inc. -
Deep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Disease
After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lattice thermal conduction in cadmium arsenide
Lattice thermal conductivity (LTC) of cadmium arsenide (Cd3As2) is studied over a wide temperature range (1-400 K) by employing the Callaway model. The acoustic phonons are considered to be the major carriers of heat and to be scattered by the sample boundaries, disorder, impurities, and other phonons via both Umklapp and normal phonon processes. Numerical calculations of LTC of Cd3As2 bring out the relative importance of the scattering mechanisms. Our systematic analysis of recent experimental data on thermal conductivity (TC) of Cd3As2 samples of different groups, presented in terms of LTC, ? L, using a nonlinear regression method, reveals good fits to the TC data of the samples considered for T < ? 50 K, and suggests a value of 0.2 for the Gruneisen parameter. It is, however, found that for T > 100 K the inclusion of the electronic component of TC, ? e, incorporating contributions from relevant electron scattering mechanisms, is needed to obtain good agreement with the TC data over the wide temperature range. More detailed investigations of TC of Cd3As2 are required to better understand its suitability in thermoelectric and thermal management devices. 2022 Chinese Physical Society and IOP Publishing Ltd. -
Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks
The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms. 2013 IEEE. -
Post Covid Scenario Effective E-Mentoring System in Higher Education
During Covid-19 pandemic many people and institutions preferred online coaching instead of in person education. The problem with online is that it will be difficult to carry on interconnections between students and professors in that environment. The main constraint for conducting online session is that the people in remote areas may find a difficulty to connect to online sessions having network issues. Electronic mentoring (e-mentoring) is implemented like a website in which the mentor and mentee can communicate with each other. With the help of this mentoring the project can provide a best solution for both the mentor and mentee. They can communicate with each other with the help of online platform and even with the help of emails.This proposed method will help them to keep the track of their academic progress and achievements of students. This article mainly focus on the mentoring through physical and virtual environment in which the mentee will be interacting with the mentor to know the progress of their academics. This article discusses about the website which is developed to fulfill the needs of the student and it discusses about the various stages of development that helped in building the website. Students can share their difficulties and their achievements with the mentor who are assigned for them particularly. In future planning to implement artificial intelligence technique to online mentoring process, this is for the betterment of student's growth. 2023 IEEE. -
Total Global Dominator Coloring of Trees and Unicyclic Graphs
A total global dominator coloring of a graph G is a proper vertex coloring of G with respect to which every vertex v in V dominates a color class, not containing v and does not dominate another color class. The minimum number of colors required in such a coloring of G is called the total global dominator chromatic number, denoted by Xtgd (G). In this paper, the total global dominator chromatic number of trees and unicyclic graphs are explored. 2023 University of Baghdad. All rights reserved. -
Total domination coloring of graphs
A total domination coloring of a graph G is a proper coloring of G in which open neighbourhood of each vertex contains at least one color class and each color class is dominated by at least one vertex. The minimum number of colors required for a total domination coloring of G is called the total domination chromatic number of G and is denoted by ctd(G). In this paper, we study the total domination chromatic number of some graph classes. The bounds of total domination chromatic number with respect to the graph parameters such as the domination number, chromatic number, total dominator chromatic number and total domination number are also studied. 2021 the author(s). -
IoT Enabled Energy Optimization Through an Intelligent Home Automation
The benefit of IoT devices is that they allow for automation; nevertheless, billions of connected devices connected with one another waste a substantial amount of energy. IoT systems will have difficulty in wide adoption if the energy requirements are not adequately managed. This study proposes a solution for IoT devices to regulate their energy consumption. Both hardware and software aspects are taken into consideration. Using a mobile computer or smartphone with Internet connectivity to interact with actual scenarios has grown more prevalent as technology has advanced over the years. An intelligent home automation system based on android applications has been developed to save electricity and human energy. This study aims to create comprehensive Energy optimization through intelligent home automation utilizing widely available mobile applications and Wi-Fi technologies. The devices are turned on and off using Wi-Fi. Intelligent home, in the area of electronics, automation is the most purposely misused term. Numerous technological revolutions have occurred as a result of this demand for automation. These were more essential than any other technologies due to their ease of use. These can be used in place of household current switches, resulting in sparks and, in rare instances, such as fires. A unique energy optimization system was developed to control household appliances while taking advantage of Wi-Fi benefits. 2023, Bentham Books imprint. -
HCI Authentication to Prevent Internal Threats in Cloud Computing
Cloud computing reduces physical resources and simplifies common management tasks. Over the past decade, cloud computing has become an important IT (information technology) industry, driving cost savings, flexibility, convenience, and scalability. Despite these advantages, many government organizations and companies are still cautious about using cloud computing. They continue to believe that the threats inherent in cloud computing technology are greater and deadly than traditional technologies. Cloud computing security threats typically include insider attacks, malware attacks, information leaks and losses, distributed denial of service, and application programming interface vulnerability attacks. Technical security improvements for virtual networks are actively researched, and many are working hard. But defending against internal attackers is more than just a technical solution but a complement to manuals and company policy. In reality, however, there are cases of damage by internal attackers, and the damage is getting bigger. Technically malicious internal attackers can relatively easily manipulate the control system and cause malfunctions. This paper provides comprehensive information about security threats in cloud computing, shows the severity of attacks by insiders, analyzes the latest authentication technologies for humancomputer interaction, and identifies the pros and cons. This shows how HCI (humancomputer interaction) technology can be applied to cloud computing management servers. The result is an innovative security certification model that can be applied. 2020, Springer Nature Switzerland AG.