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An Assessment of Farmers' Perception and Adaptive Capacity for Climate Change
In the past decades, various regions in U.P. had experienced severe floods. The effects of climate change also affected agricultural production. This study investigated the farmers' perception of climate change and suggested strategies for mitigating its effects using a primary survey with the help of a pre-structured schedule. Change in rainfall pattern, problems in seed quality, the emergence of new pests and diseases, changes in the crop cycle were the few effects that farmers' perceived due to climate change. Even the most mitigation efforts by the farmers cannot prevent some of the impacts of climate change within the following decades. It makes adaptation a must-have for addressing these impacts. 2022, The Society of Economics and Development. All rights reserved. -
An asymmetric analysis of overall globalization on financial inclusion
Purpose: Financial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries. Design/methodology/approach: Applying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function. Findings: The estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested. Originality/value: To the best of the authors knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population. 2024, Emerald Publishing Limited. -
An augmented artificial bee colony algorithm for data aggregation in wireless sensor networks
As wireless sensor networks comprise of a vast number of resource constrained tiny sensor nodes which are designed to operate for a long period of time, it is inevitable to efficiently utilize the available resources. Even though energy harvesting approaches exist, energy efficiency in these networks remains the primary concern. Innovative data collection methods help in the optimal utilization of the confined resources like energy, memory and processing capabilities. Majority of the energy is consumed for data transmission in contrast to sensing and processing. Adopting self-organizing system intelligence of the nature for modern advancements is effective and efficient. This paper provides a gist of the existing bio-inspired routing algorithms and describes a new energy efficient data collection strategy with mobile sinks in wireless sensor networks. IAEME Publication. -
An Automated Deep Learning Model for Detecting Sarcastic Comments
The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models. 2021 IEEE. -
An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing
Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 4060% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. 2023 by the authors. -
An autonomic computing architecture for business applications
Though the vision of autonomic computing (AC) is highly ambitious, an objective analysis of autonomic computing and its growth in the last decade throw more incisive and decisive insights on its birth deformities and growth pains. Predominantly software-based solutions are being preferred to make IT infrastructures and platforms, adaptive and autonomic in their offerings, outputs, and outlooks. However the autonomic journey has not been as promising as originally envisaged by industry leaders and luminaries, and there are several reasons being quoted by professionals and pundits for that gap. Precisely speaking, there is a kind of slackness in articulating its unique characteristics, and the enormous potentials in business and IT acceleration. There are not many real-world applications to popularize the autonomic concept among the development community. Though, some inroads has been made into infrastructure areas like networking, load balancing etc., very few attempts has been exercised in application areas like ERP, SCM, or CRM. In this paper, we would like to dig and dive deeper to extract and explain where the pioneering and path-breaking autonomic computing stands today, and the varied opportunities and possibilities, which insists hot pursuit of the autonomic idea. A simplistic architecture for deployment of autonomic business applications is introduced and a sample implementation in an existing CRM system is described. This should form the basis of new start and ubiquitous application of AC concepts for business applications. 2012 IEEE. -
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE. -
An ecology intervention in an English studies programme: Contexts, Complexities and Choices
Over the past few decades, there has been a critical mass gained regarding the need to engage purposefully with Ecology. Unfortunately, this has not provoked any stimulating work within the Humanities and Social Sciences academia. In fact, alongside growing realisations about the necessity to address Ecology, there is a glaring absence of any significant engagement. In response to such a vexing reality, the Department of English at Christ University chose to initiate an Ecological venture within its Honours programme. This paper captures - the vigorous debates it lit up, the tough choices that had to be made, and the promise it offers - that complex journey. 2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore). -
An Econometric Approach Towards Exploring the Impact of Workers Remittances on Inflation: Empirical Evidence from India
This paper attempts to study short and long run impact of increased workers remittances on general price level. It uses the real GDP growth, real effective exchange rate (REER), M3 (broad money), fiscal deficit to gauge the impact of foreign remittances on inflation. The study makes use of VAR/VECM framework to gauge the impact of workers remittances on inflation. Inflation is measured in terms of CPI and WPI, real income or GDP at constant prices is taken as a measure of GDP growth, REER is used for exchange rates and M3 is taken as a proxy for money supply. Monthly data of all these variables has been taken from Bloomberg and World Bank data base. The findings provide important insights into the nature of association between remittances and inflation suggesting causality between inflation, remittances, real GDP, real effective exchange rates and money supply due to increased workers remittances. The findings have policy implications for decisions to channelize workers remittances in a way to increase real GDP growth and money supply while at the same time not causing the general price levels to soar. The present study focuses on how increased (decreased) workers remittances is leading to increase (decrease) in general price levels in India. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
AN ECONOMIC RELIABILITY TEST PLAN BASED ON TRUNCATED LIFE TESTS FOR MARSHALL-OLKIN POWER LOMAX DISTRIBUTION WITH APPLICATIONS
In every competitive enterprise, there has been a resurgence of interest in increasing the quality of products. In this paper, we create new acceptance sampling plans based on truncated life tests for the Marshall-Olkin power Lomax distribution. The minimum sample sizes needed to declare the specified mean life with respect to the newly developed sampling plans are obtained for different values of the model parameters. Besides, the operating characteristic function values, minimum ratios of the true value and the required value of the parameter with a given producer risk are discussed. Moreover, the results are illustrated using numerical examples, and a real data set is considered to illustrate the functioning of the recommended acceptance sampling plans. The result shows that the proposed plan is more adequate compared with other acceptance sampling plans available in the open literature. So, it can be used for industry applications. 2010 Mathematics Subject Classification. 60E05, 62E15, 62F10. 2022, Asia Pacific Academic. All rights reserved. -
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest. 2022 IEEE. -
An effective analytical method for fractional Brusselator reactiondiffusion system
In recent years, reactiondiffusion models have attracted researchers for their wide applications. In this article, we consider Brusselator reactiondiffusion system (BRDS), which is known for its cross diffusion and pattern formations in biology and chemistry. We derive an analytical solution of the fractional Brusselator reactiondiffusion system (FBRDS) with the help of the initial condition by a novel method, residual power series method (RPSM). The system solution has been analyzed by graph. 2023 John Wiley & Son Ltd. -
An effective Approach for Pneumonia Detection using Convolution Vision Transformer
Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. 2022 IEEE. -
An Effective BiLSTM-CRF Based Approach to Predict Student Achievement: An Experimental Evaluation
Currently, massive volumes of data are accumulated in databases when people configure new requirements and services. Data mining techniques and intelligent systems are emerging for managing large amounts of data and extracting actionable insights for policy development. As digital technology has grown, it has naturally become intertwined with e-learning practices. In order to facilitate communication between instructors and a diverse student body located all over the world, distance learning programs rely on Learning Management Systems (LMSs). Colleges can better accommodate their students' individual needs by using and analyzing interaction data that reveals variances in their learning progress. Predicting pupils' success or failure is a breeze with the help of learning analytics tools. Better learning outcomes might be achieved through early prediction leading to swift focused action. Preprocessing, feature selection, and model training are the three components of the proposed method. Data cleansing, data transformation, and data reduction are the preprocessing steps used here. It used a CFS to enable feature selection. This study has used a BiLSTM-CRF hybrid approach to train the model. When compared to tried-and-true techniques like CNN and CRF, the proposed method performs effectively. 2024 IEEE. -
An Effective Deep Learning Classification of Diabetes Based Eye Disease Grades: An Retinal Analysis Approach
Diabetic Retinopathy (DR) is a common misdiagnosis of diabetes mellitus, which damages the retina and impairs eyesight. It can lead to vision impairment if it is not caught early. Tragically, DR is an unbreakable cycle, and treatment only serves to reinforce the perception. Early detection of DR and effective treatment can significantly lower the risk of visual loss. In comparison to PC-aided conclusion frameworks, the manual analysis process used by ophthalmologists to diagnose DR retina fundus images takes a lot of time, effort, and money and is prone to error. As of late, profound learning has become quite possibly the most well-known procedure that has accomplished better execution in numerous areas, particularly in clinical picture examination and classification. Thereby, this paper brings an effective deep learning-based diabetes-based retinography in which the following are the stages: a) Data collection from MESSIDOR which contains 1200 images classified into 4 levels and graded from 03 followed by b) Preprocessing using grayscale normalized data. Then followed by c) feature extraction using Discrete Wavelet Transform (DWT), d) feature selection using Particle Swarm Optimization (PSO) and finally given for e) classification using Densenet 169. Experimental states that the proposed model outperforms and effectively classified grades compared to other state-of-art models (accuracy:0.95, sensitivity:0.96, specificity;0.97). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An effective face recognition system based on Cloud based IoT with a deep learning model
As of late, the Internet of Things (IoT) innovation has been utilized in applications, for example, transportation, medical care, video observation, and so on. The quick appropriation and development of IoT in these segments are producing an enormous measure of information. For instance, IoT gadgets, for example, cameras produce various pictures when utilized in medical clinic reconnaissance sees. Here, face acknowledgement is one of the most significant instruments that can be utilized for clinic affirmations, enthusiastic discovery, and identification of patients, location of fake gadgets. patient, and test clinic models. Programmed and shrewd face acknowledgement frameworks are profoundly precise in an overseen climate; notwithstanding, they are less exact in an unmanaged climate. Additionally, frameworks must keep on running on numerous occasions in different applications, for example, insightful wellbeing. This work presents a tree-based profound framework for programmed face acknowledgement in a cloud climate. The inside and out pattern have been proposed to cost less for the PC without focusing on unwavering quality. In the model, the additional size is isolated into a few sections, and a stick is made for each part. The tree is characterized by its branch area and stature. The branches are spoken to by a leftover capacity, which comprises of a twofold layer, a stack game plan, and a non-direct capacity. The proposed technique is assessed in an assortment of generally accessible information bases. An examination of the method is likewise finished with top to bottom craftsmanship models for the eye to eye connection. The aftereffects of the tests indicated that the example was considered to have accomplished a precision of 98.65%, 99.19%, and 95.84%. 2020 -
An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown
There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemics evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to Indias diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that Indias two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second waves severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
An Effective Time Series Analysis for Equity Market Prediction Using Deep Learning Model
A stock Exchange is a market where securities are traded. Every day, billions are traded at various stock exchanges across the world. In recent years prediction of movement of stock market is regarded as fascinating and has created a demand in financial market time series prediction. A precise forecasting of equity market is needed to provide higher returns for investors. Since there is high complexity in predicting stock market profits, developing models for it becomes difficult. The data mining and machine learning techniques has played an important role in Prediction of stock market movement. This study attempted to develop a deep learning model using Recurrent Neural Network for forecasting movement in the National Stock Exchange of India's benchmark broad based stock market index(NIFTY 50) for the Indian equity market. In this paper the NIFTY 50 index and INFYOSYS Ltd historical data from Yahoo finance companies has been selected for forecasting and analysis. 2019 IEEE. -
An efficient 2-Step DNA symmetric cryptography algorithm based on dynamic data structures
The security of text has become highly demanding in today's fast growing networking world. DNA computing is one of the emerging technologies in the arena of huge data storage and parallel computation. A single gram of DNA holds 5.5 petabytes of data. This leads to the increased risk in data communication. DNA in computers is mapped to human genome. Thus, the sequence of nucleotide base constructs the foundation of uniqueness. In this paper, a new scheme acronymed as -'Cryptography on DNA Storage'-CDS is provided. It performs the DNA data encryption in just two-step by using random private key for each letter in the plaintext and parallel swapping of the resultant text in small clusters. It is discussed keeping the time and space complexity of the algorithm in concern. 2018 Authors.