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Verification and validation of MapReduce program model for parallel K-means algorithm on Hadoop cluster
With the development of information technology, a large volume of data is growing and getting stored electronically. Thus, the data volumes processing by many applications will routinely cross the petabyte threshold range, in that case it would increase the computational requirements. Efficient processing algorithms and implementation techniques are the key in meeting the scalability and performance requirements in such scientific data analyses. So for the same here, we have p analyzed the various MapReduce Programs and a parallel clustering algorithm (PKMeans) on Hadoop cluster, using the Concept of MapReduce. Here, in this experiment we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Means Clustering Algorithm. We have found that as the number of nodes increases the execution time decreases, but also some of the interesting cases has been found during the experiment and recorded the various performance change and drawn different performance graphs. This experiment is basically a research study of above MapReduce applications and also to verify and validate the MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster having four nodes. 2013 IEEE. -
A scientometric analysis of social entrepreneurship
Impactful studies in social entrepreneurship area has garnered attention of the researchers in recent times. The interest and importance is generated in this area because of its nature in addressing social problems and welfare of the communities and societies. The study aims at providing insight on scientometric analysis in the domain of social entrepreneurship. The study further identifies researchers exploring sub domai ns considering parameters like publication language, outlook of publication patterns that changed every year, contextual journals to perform a literature review, primary subject areas in which research is being conducted, most productive institutes/universities, most productive countries where research is being conducted in the domain of social entrepreneurship and the most prolific authors in the area of social entrepreneurship. This study is a pathfinder for researchers with plans to conduct studies in social entrepreneurship domain by leading them to relevant scholarly journals and authors for greater impact. IJSTR 2019. -
Improved Acceptance model: Unblocking Potential of Blockchain in Banking Space
Over the past ten years, blockchain has emerged as the new buzzword in the banking sector.The new technology is being adopted globally in many industries, including the business sector,because of its unique uses and features. However, no adoption model is available to help with this process.This research paper examines the new technology known as blockchain, which powers cryptocurrencies like Bitcoin and others. It looks at what blockchain technology is, how it works especially in the banking sector, and how it can change and upend the financial services sector. It outlines the features of the technology and discusses why these can have a significant effect on the financial industry as a whole in areas like identity services, payments, and settlements in addition to spawning new products based on things like 'smart contracts'. The adoption variables found in the literature study were used to gather, test, and evaluate the official papers that are currently available from regulatory organizations, practitioners, and research bodies. This study was able to classify adoption factors into three categories - supporting, impeding, and circumstantial - identify a new adoption factor, and determine the relative relevance of the factors. Consequently, an institutional adoption paradigm for blockchain technology in the banking sector is put out. In light of this, it is advised to conduct additional research on using the suggested model at banks using the new technology in order to assess its suitability. 2024 IEEE. -
Democratising Intelligent Farming Solutions to Develop Sustainable Agricultural Practices
In this chapter, the transformative potential of democratising intelligent farming solutions is discussed, primarily in the context of the sustainable farming. Technologies including the Internet of Things (IoT), global positioning systems (GPS), Unmanned Aerial Vehicles (UAVs), computer vision, and artificial intelligence (AI) have redefined farming activities. Such advances have allowed decision-making and optimised resource utilisation to be driven by real-time data. The democratisation of AI tools are meant to make AI-driven agriculture accessible to all. As such, this chapter discusses the interplay of bottom-up and top-down approaches, highlighting their roles in promoting the accessibility of AI tools and their benefits to farmers. The integration of such AI tools would transform contemporary agriculture into agriculture 4.0. This revolution would be characterised by real-time data, predictive analytics, and precision farming techniques. Further, the integration of technology such as wireless networks and the global navigation satellite system (GNSS) increases precision and the ability to monitor farming activities. The idea of democratising intelligent farming solutions is meant to herald agriculture 4.0, which would improve crop quality, climate resilience of crops, and the income of farmers. It would also improve broader macroeconomic aspects by promoting education and information and communication technology (ICT) skills and potentially reducing income inequality gap while promoting socio-economic well-being. 2025 selection and editorial matter, Sirisha Potluri, Suneeta Satpathy, Santi Swarup Basa, and Antonio Zuorro; individual chapters, the contributors. All rights reserved. -
Organizational contributions to emergency preparedness and response in Varanasi: A comprehensive analysis
India's unique geographical diversity and status as the world's most populous nation make it exceptionally susceptible to a wide array of hazards, both natural and human-induced. This vulnerability is further compounded by the intersection of diverse disasters and the dense population, leading to significant human and material losses. In response to these challenges, effective emergency preparedness plans are indispensable, requiring meticulous risk assessments, strategic resource allocation, capacity building initiatives, and active engagement of community-oriented organizations. Continuous monitoring and adaptation are essential for bolstering resilience and safeguarding socio-economic stability. Furthermore, the examination of developmental and disaster-specific organizations' roles in preparedness and response necessitates a systematic shift towards proactive paradigms, fostering an anticipatory culture rather than a reactive one. This study aims to dissect the intricate web of organizational efforts crucial for emergency preparedness in Varanasi, one of the world's oldest cities. By delving into these critical mechanisms, our goal is to enhance collective readiness against potential emergencies, safeguarding the city's rich heritage and its inhabitants. Through a mixed-method approach, this research illuminates the multifaceted involvement of organizations across various sectors, unraveling a complex tapestry of challenges that impede practical disaster preparedness. We scrutinize the coordination among governmental and non-governmental entities, funding dynamics, and grassroots alliances, revealing untapped resources for disaster resilience. Additionally, we analyze the strategies adopted by the national emergency preparedness and response force, highlighting both successes and shortcomings. Moreover, this study underscores the unique competencies of individuals involved in disaster preparedness, while identifying structural and functional gaps within organizational frameworks. Conversely, non-profit organizations face distinct challenges, including fundraising constraints and donor-imposed limitations, hindering their ability to develop comprehensive emergency preparedness and response capacities compared to public and private entities. In summary, this research serves as a comprehensive exploration of organizational dynamics in emergency preparedness within Varanasi, offering valuable insights into the complexities of disaster management efforts. By addressing these challenges, we aim to pave the way for more effective and inclusive disaster preparedness strategies, ultimately enhancing the resilience of Varanasi and similar communities globally. 2024 Elsevier Ltd -
Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT
The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication. 2022 Anand Kumar et al. -
Economic and sustainable management of wastes from rice industry: combating the potential threats
Rice is one of the imperative staple foods, particularly in the developing countries. The exponential boom in human population has resulted in the continuous expansion in the rice industry in order to meet the food demands. The various stages of paddy processing release huge quantity of solid wastes, mainly rice husk, rice husk ash and liquid wastes in the form of rice industry wastewater. The discharge of the rice industry wastewater imparts a substantial threat to the aquatic bodies and the nearby surrounding and, thus, consequently demands eco-benign treatment plan. As a result, different strategies are needed to enhance the effluent quality and minimize the operational cost of the treatment process. Therefore, efficient technological approach targeting the minimization of pollution as well as assuring the economic prosperity should be implemented. In this review article, several aspects related to the rice industry discussing the significant challenges involved in the generation of both solid and liquid wastes, mitigation experiments and future prospects have been meticulously elaborated. Furthermore, the article also focuses on the various processes utilized for reducing the pollution load and promoting the practice of reuse and recycle of waste rather than the discharge action for the sake of sustainability and the emergence of novel techniques for the generation of energy and value-added products. 2017, Springer-Verlag GmbH Germany. -
A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
In contemporary research on mild cognitive disorders (MCI) and Alzheimer's disease (AD), the predominant approach involves the utilization of double data modalities for making predictions related to AD stages. However, there is a growing recognition of the potential benefits that could be derived from the fusion of multiple data modalities to obtain a more comprehensive perspective in the analysis of AD staging. To address this, we have employed deep learning techniques to holistically assess data from various sources, including, genetic (single nucleotide polymorphisms (SNPs)), imaging (magnetic resonance imaging (MRI)), and clinical tests, with the objective of categorizing patients into distinct groups: AD, MCI, and controls (CN). For the analysis of imaging data, convolutional neural networks have been employed. Moreover, we have introduced a novel approach for data interpretation, enabling the identification of the most influential features learned by these deep models. This interpretation process incorporates clustering and perturbation analysis, shedding light on the crucial aspects of the data contributing to our classification results. Our experimentation, conducted on the dataset (i.e., ADNI), has yielded compelling results. Furthermore, our findings have underscored the significant advantage of integrating multi-modality data over solely relying on double modality models, as it has led to improvements in terms of accuracy, precision, recall, and mean F1 scores. 2024, Ismail Saritas. All rights reserved. -
Neuro-Leadership: A New Paradigm in Leadership Thought
Leadership is not a static occurrence. It is a dynamic one that constantly evolves. Leadership is seen as a means to enhance ones personal, professional, and social lives. Organizations believe that leaders bring in unique assets to the organization; which contribute to the bottom line of the company. The conclusions drawn from research findings on leadership portray an image of a process that is far more sophisticated and complex than the condensed view, popularly accepted. This chapter will provide a comprehensive evaluation of different approaches to leadership and highlight the importance of a new paradigm. Ground breaking insights have started to surface regarding neurosciences and brain functioning that has significantly influenced leadership thought. The traditional approaches of leadership could not adapt to the world of unlimited information which needed continuous evolution; however, our brain can adapt and change leading to the emergence of neuro-leadership. The chapter will trace the journey of neuroleadership and its increasing relevance in the current scenario, especially in terms of employee and organization performance. 2024 by Nova Science Publishers, Inc. -
How blockchain enables financial transactions in the banking sector
Blockchain technology is the most important technological revolution of the second decade of the 21st century. The banking sector is one of the major sectors where blockchain has played a significant role in recording and processing various financial transactions, inter-bank transfers, and digital format agreements through a distributed ledger system. It harms the transactional costs, which influence the financial markets. The global financial system being the most popular sector is prone to many errors and frauds. Blockchain technology can help prevent these problems by enabling a decentralised network that permits all parties to review. The present study attempted to analyse the problems in existing banking financial transactions, understand the importance of transparency and study the usage of blockchain in the banking sector. It suggests a research model for solving financial transaction problems by applying blockchain technology. The study uplifts the security and transparency of blockchain technology throughout the paper. Copyright 2022 Inderscience Enterprises Ltd. -
An empirical evaluation of stress and its impact on the engineering colleges faculty members in Tamil Nadu
The present study examined the specific causes, levels and effects of stress experienced by faculty members who worked in unaided private engineering and technology (UPET) colleges in the state of Tamil Nadu, India. Factor analysis and structural model were used to evaluate the relationship between the causes, subsequent effects at different level of stress and the appropriate personal and organisational stress coping strategies. Primary data were collected from 560 faculty members by employing convenience sampling method during the academic year 20172018. The study revealed that causes of stress influencing considerably the level of stress and the level of stress explained the high influence on the effect of stress variables. Both effects of stress and causes of stress were negatively influencing the stress coping strategies of personal as well as organisational level strategies. Copyright 2020 Inderscience Enterprises Ltd. -
Regression analysis and features of negative activation energy for MHD nanofluid flow model: A comparative study
This article elucidates the impact of activation energy on magnetohydrodynamic (MHD) stagnation point nanofluid flow over a slippery surface in a porous regime with thermophoretic and Brownian diffusions. Negative activation energy is scarce in practice, but the impact of negative activation energy could not be neglected as it is noticed in chemical processes. The rate of some Arrhenius-compliant reactions is retarded by increasing the temperature and is therefore associated with negative activation energies, such as exothermic binding of urea or water. In some processes, the temperature dependence of the pressure-induced unfolding and the urea-induced unfolding of proteins at ambient pressure give negative activation energies. The present mathematical model is solved with successive linearization method (a spectral technique). A comparison of results is made for negative and positive values of activation energy. Apart from it, the quadratic multiple regression model is discussed briefly and explained with bar diagrams. It is observed that with rise in unsteadiness parameter from 0 to 1 (taking positive activation energy), skin friction and Sherwood number are increased by 9.36% and 19% respectively, and Nusselt number is decreased by 26%. However, for negative activation energy, 9.36% and 112% enhancement is observed in skin friction and Sherwood number, respectively. 2023 The Authors -
Application of AI-Based Learning in Automated Applications and Soft Computing Mechanisms Applicable in Industries
The term artificial intelligence is used to describe a method through which computers may teach themselves new skills and develop themselves, without the help of humans or any predetermined instructions. Machines are fed data and trained to look for patterns; these patterns are then used as templates for further learning. They get the agency to choose their own actions and alter their habits accordingly. The term soft computing refers to a group of computational techniques that draw inspiration from both AI and natural selection. Solutions to difficult real-world situations that have no simple computer solution are provided, and they are both practical and cost-effective. Soft computing is an area of study in mathematics and computer science that has been around since the early 1990s. The idea for this project sprang from the fact that people can think of solutions that are close to the ones in the actual world. It is via the use of approximations that the science of soft computing is able to solve difficult computational challenges. Industrial automation is used by a diverse variety of industries and companies to improve the effectiveness of their processes by leveraging a number of technology developments. Many routine tasks are being changed by industrial applications. Industrial automation that reduces breakdowns and repairs quickly might help a business save money. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Implementation of Recent Advancements in Cyber Security Practices and Laws in India
In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. This essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. The community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
DISCOURSE OF DISSENT: LANGUAGING RESISTANCE AND CONSCIOUSNESS IN SUBALTERN LITERATURES DALIT AND BLACK
The paper highlights the pivotal role of language in Afro-American and Dalit movements, emphasizing identity affirmation and resistance to dominant aesthetic structures. It examines languages dynamic role in shaping subaltern experiences and fuelling revolutionary movements. While there is some analysis of the significance of literary trends and intellectual current in these parallel movements, a few scholarly inquiriesintegratethelinguisticandstylisticaspectscomprehensively. Thestudyaddresses this critical gap by comparing and contrasting the selected study of these two movements to see their convergences and divergences. We employ the theoretical framework of Subaltern Studies and Distributed Language (DL) to understand socio-political motifs of pre- and post-production of a particular kind of language. The selected poems are closely read and analysed through Critical Discourse Analysis, with close reading as a key technique. It allows for an exploration of the intricate relationship between the linguistic structure, use of lexical items, emotive use of language, connotational significations, and compositional semantics. While selected Black literature poems experimented with internal morpho-syntax and everyday language, Dalit literature bluntly presented harsh facts using multilingualism, a unique Indian linguistic trait, and everyday vocabulary. Copyright 2024 Chandan Kumar, Nivea Thomas K. -
Influence of Te doping on the dielectric and optical properties of InBi crystals grown by directional freezing
Stoichiometric pure and tellurium (Te) doped indium bismuthide (InBi) were grown using the directional freezing technique in a fabricated furnace. The X-ray diffraction profiles identified the crystallinity and phase composition. The surface topographical features were observed by scanning electron microscopy and atomic force microscopy. The energy dispersive analysis by X-rays was performed to identify the atomic proportion of elements. Studies on the temperature dependence of dielectric constant (?), loss tangent (tan?), and AC conductivity (?ac) reveal the existence of a ferroelectric phase transition in the doped material at 403 K. When InBi is doped with tellurium (4.04 at%), a band gap of 0.20 eV can be achieved, and this is confirmed using Fourier transform infrared studies. The results thus show the conversion of semimetallic InBi to a semiconductor with the optical properties suitable for use in infrared detectors. 2014 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
A homotopy-based computational scheme for two-dimensional fractional cable equation
In this paper, we examine the time-dependent two-dimensional cable equation of fractional order in terms of the Caputo fractional derivative. This cable equation plays a vital role in diverse areas of electrophysiology and modeling neuronal dynamics. This paper conveys a precise semi-analytical method called the q-homotopy analysis transform method to solve the fractional cable equation. The proposed method is based on the conjunction of the q-homotopy analysis method and Laplace transform. We explained the uniqueness of the solution produced by the suggested method with the help of Banach's fixed-point theory. The results obtained through the considered method are in the form of a series solution, and they converge rapidly. The obtained outcomes were in good agreement with the exact solution and are discussed through the 3D plots and graphs that express the physical representation of the considered equation. It shows that the proposed technique used here is reliable, well-organized and effective in analyzing the considered non-homogeneous fractional differential equations arising in various branches of science and engineering. 2024 World Scientific Publishing Company. -
Grey Wolf optimization-Elman neural network model for stock price prediction
Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman,CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1day ahead prediction and outperforms the benchmark models taken for comparison. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction
Stock market prediction is one of the most important financial subjects that have drawn researchers attention for many years. Several factors affecting the stock market make stock market forecasting highly complicated and a difficult task. The successful prediction of a stock market may promise attractive benefits. Various data mining methods such as artificial neural network (ANN), fuzzy system (FS), and adaptive neuro-fuzzy inference system (ANFIS) etc are being widely used for predicting stock prices. The goal of this paper is to find out an efficient soft computing technique for stock prediction. In this paper, time series prediction model of closing price via fusion of wavelet-adaptive network-based fuzzy inference system (WANFIS) is formulated, which is capable of predicting stock market. The data used in this study were collected from the internet sources. The fusion forecasting model uses the discrete wavelet transform (DWT) to decompose the financial time series data. The obtained approximation and detailed coefficients after decomposition of the original time series data are used as input variables of ANFIS to forecast the closing stock prices. The proposed model is applied on four different companies previous data such as opening price, lowest price, highest price and total volume share traded. The day end closing price of stock is the outcome of WANFIS model. Numerical illustration is provided to demonstrate the efficiency of the proposed model and is compared with the existing techniques namely ANN and hybrid of ANN and wavelet to prove its effectiveness. The experimental results reveal that the proposed fusion model achieves better forecasting accuracy than either of the models used separately. From the results, it is suggested that the fusion model WANFIS provides a promising alternative for stock market prediction and can be a useful tool for practitioners and economists dealing with the prediction of stock market. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Stock price prediction based on technical indicators with soft computing models
Stock market prediction is a very tough task in the finance world. Since stock prices are dynamic, noisy, non-scalable, non-linear, non-parametric and complicated. In recent years, soft computing techniques are used for developing stock prediction model. The main focus of this study is to develop and compare the efficiency of the three different soft computing techniques for predicting the intraday price of individual stocks. The proposed models are based on Time Delay Neural Network (TDNN), Radial Basis Function Neural Network (RBFNN) and Back Propagation Neural Network (BPNN). The predictive models are developed using technical indicators. Sixteen technical indicators were calculated from the historical price and used as inputs of the developed models. Historical prices from 01/01/2018 to 28/02/2018, where the time interval between samples is one minute, are utilized for developing models. The performance of the proposed models is evaluated by measuring some metrics. Also, this study compares the results with other existing models. The experimental result revealed that the BPNN outperforms TDNN, RBFNN as well as other existing models considered for comparison. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.