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Exploring the Influence of Service Learning on the Socio-Educational Commitment and Self- Efficacy of Graduate Educators in the Artificial Intelligence (AI) Domain.
This study, conducted by a distinguished university, aims to contribute significantly to the professional development of educators dedicated to creating a fair, sustainable, and socially conscious world. The research focuses on a pedagogical approach using Service Learning to foster civic and social skills in higher education students. The main goal is to examine how graduate students, actively participating in Service-Learning initiatives, develop socio-educational commitment and self-efficacy compared to traditional university volunteering. The study, involving 1562 aspiring educators, employs a quantitative correlational methodology. The hypothesis suggests that Service-Learning leads to more positive outcomes in socio-educational commitment, pedagogical self-efficacy, and crafting instructional materials. The findings, statistically significant (p < 0.01), highlight the increased development of these metrics among participants in Service-Learning programs. 2024 IEEE. -
Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
With the integration of artificial intelligence (AI), today's fast financial landscape increasingly promises the most efficient and accurate processes for decision-making in accounting practices. On the other hand, the opacity of models represents a truly difficult challenge, given that transparency and accountability are key for using AI in making financial decisions. This is a research paper that focuses on the explanation of an XAI model application as a way of improving transparency in financial decision-making within the accounting field. The paper begins by outlining how transparency is important and opens the room for trust and understanding in the process of financial decision-making. Traditional black-box AI models, although able to provide remarkable predictions, usually exhibit low interpretability; this entails that stakeholders may have a small degree of understanding regarding the rationale behind the decisions. This provides a cloudy appearance not to hamper trust and supports compliance with regulatory standards like GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The proposed work applies to the accounting domain and brings about some of the different XAI techniques that are designed under this domain. The following techniques aim at demystifying the AI algorithms for effective AI stakeholders' understanding of the model predictions and underlying decision-making processes. 2024 IEEE. -
Extraction of features from video files using different image algebraic point operations
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery. -
Extraction of Web News from Web Pages Using a Ternary Tree Approach
The spread of information available in the World Wide Web, it appears that the pursuit of quality data is effortless and simple but it has been a significant matter of concern. Various extractors, wrappers systems with advanced techniques have been studied that retrieves the desired data from a collection of web pages. In this paper we propose a method for extracting the news content from multiple news web sites considering the occurrence of similar pattern in their representation such as date, place and the content of the news that overcomes the cost and space constraint observed in previous studies which work on single web document at a time. The method is an unsupervised web extraction technique which builds a pattern representing the structure of the pages using the extraction rules learned from the web pages by creating a ternary tree which expands when a series of common tags are found in the web pages. The pattern can then be used to extract news from other new web pages. The analysis and the results on real time web sites validate the effectiveness of our approach. 2015 IEEE. -
Extractive Text Summarization Using Sentence Ranking
Automatic Text summarization is the technique to identify the most useful and necessary information in a text. It has two approaches 1)Abstractive text summarization and 2)Extractive text summarization. An extractive text summarization means an important information or sentence are extracted from the given text file or original document. In this paper, a novel statistical method to perform an extractive text summarization on single document is demonstrated. The method extraction of sentences, which gives the idea of the input text in a short form, is presented. Sentences are ranked by assigning weights and they are ranked based on their weights. Highly ranked sentences are extracted from the input document so it extracts important sentences which directs to a high-quality summary of the input document and store summary as audio. 2019 IEEE. -
Extremal Trees oftheReformulated andtheEntire Zagreb Indices
The first reformulated Zagreb index of trees can take any even positive integer greater than 8, whereas the second reformulated Zagreb index of trees can take all positive integers greater than 47 with a few exceptional values less than 8 and 47, respectively. The entire Zagreb index is defined in terms of edge degrees and incorporates the idea of intermolecular forces between atoms along with atoms and bonds. This intricate significance of studying the entire Zagreb index led to the generalization of the first entire Zagreb index of trees. The inverse problem on the first entire Zagreb of trees gives the existence of a tree for any even positive integer greater than 46. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Fabrication of Didactic Model to Demonstrate Bottle Filling System Using Programmable Logic Controller
Automation is the evolution of manufacturing process which will ideally lead to e-governance. It helps humans and machines to be connected at the hip, blending the cerebral aptitudes of human and specialized abilities of automated bots to immune the workspace. Automated system has enhanced the modern day market by increasing the quality of the product as well as making the fabricating process time efficient. Lights out technology in industry promotes the robots to do work even after working hours when the lights are shut down in industry. In this research paper, a unique approach is used to fabricate a didactic model which demonstrates the working of a bottling plant which may be preferred for medium and small scale industries. To implement the process, CODESYS is used to program CPX-CEC-C1 PLC, a digital computerized system which performs logical decisions and provides outputs based on sensor inputs. The main focus is towards interfacing pneumatics and hydraulic components with PLC. 2021, Springer Nature Singapore Pte Ltd. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Facial Expression Recognition with Transfer Learning: A Deep Dive
In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Facilitating the New Normal: Challenges and Opportunities of Facility Management Companies in India
Covid 19 has brought the world to a standstill. The hospitality, Travel and tourism being affected the most due to travel restrictions across the world and within India. One of the sectors that have been affected majorly are the industries providing Facility Management services to core businesses. FM is dependent on the type of client business, the client organisation's structure and the market sector. This paper aims to gauge the impact of the pandemic on the Facility management companies in India. This also captures the positive and negative aspect of pandemic on the FM. An empirical research design has adopted to address the study objectives. This research involves both primary and secondary data. Primary data for the study have been collected in the form of structured questionnaire distributed among 300 respondents who are senior executives heading selected Facility Management companies in India. The target respondents have been selected based on simple random sampling to ensure the normal distribution of data. The Electrochemical Society -
Factor Analysis for Portfolio Returns: Investigating How Macroeconomic Factors Impact the Performance of the equity Portfolio
This paper investigates the complex relationship between macroeconomic factors and equity portfolio performance using regression analysis. In today's volatile financial environment, it emphasizes the importance of understanding how variables such as interest rates, inflation, money supply and GDP influence investment outcomes. Exact statistical techniques and historical data from a specific time period are used to uncover hidden factors affecting portfolio returns, with a particular focus on interest rates, inflation, money supply, and GDP. The goal of the research is to provide a comprehensive understanding of how these macroeconomic factors influence the equity investments. 2024 IEEE. -
Factors Affecting Data-Privacy Protection and Promotion of Safe Digital Usage
India is facing the problem of the digital divide. Being developing countries and with low literacy rates, digital knowledge among the public is weak. Those who know a bit about digital operations on smartphones and computers are not having complete knowledge of data security and its peculiarities. Therefore, this study aimed to find determinants of data-privacy anxiety among Indians and to understand their stress and anxiety during the use of digital applications in their daily routines, especially amid the COVID-19 scenario. The current study adopted an inductive qualitative exploratory approach to delve into the above issues. This study employed a reflexive thematic analysis method to analyse interview data of 10 participants across young-adult to middle-adult age groups of male and female gender. Participants belonged to middle socio-economic status having urban background. The study found 6 themes and 26 subordinate themes as determinants of data-privacy anxiety. Emerging themes from the data indicated at the systemic determinants of data-security anxiety, the paradox of learned helplessness and convenience preference among participants. This paper employed the Foucauldian lens of bio-power to discuss the circumscribing function of ill-structured knowledge dissemination approaches. This paper argues in favor of a critical pedagogy approach in educating people about digital security, dealing with data-privacy anxiety, and promoting safe digital usage among all generations of Indians. It also suggests measures of modifications in policies and documentation processes of major online platforms and apps to curb uncertainty and sense of insecurity among users. 2022 Copyright for this paper by its authors. -
Factors Affecting the Growing Economic Inequality: An Empirical Study with Reference to BRICS Countries
Economic inequality refers to the uneven distribution ofearningsand opportunity between various groups in society and is a point of major concern in almost all the nations in the world. This study aims to analyse the effect of various factors over the increasing inequality in BRICS nations. The study takes into consideration factors like trade openness, credit, net foreign assets and health and tries to assess their impact as a driving force behind the increasing inequality in these countries. The augmented DickeyFuller test for stationarity has been applied followed by multiple regression. To explore causality, Granger causality test is applied. All the models are tested for autocorrelation using the BreuschGodfrey Lagrange Multiplier test. Wald test is applied to examine the significance of independent variables. The study provides statistical evidence about the positive and negative effects of trade openness, healthcare finance, net foreign assets and healthcare expenditure on income inequality in BRICS nations. Findings may help to work intensively on the relevant causes of inequality for these five countries. This paper will add to the already present literature on inequality which is one of the important problems of the countries across the world. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Factors Effecting on Work Values Towards Career Choices Among University Students
The pandemic effect of COVID-19 triggered a global recession in the year 2020. The unpredictability that surrounds the coronavirus is the most challenging problem that many people must confront, particularly in terms of making decisions regarding their careers, considering the significant shift in employment opportunities. The purpose of this research is to investigate the influence anxiety and the Covid-19 pandemic have on work values and the reality of career choices among university students. A quantitative research methodology was applied to 110 respondents from a nearby institution to achieve the study's objective. This was done through online surveys and the snowball sampling technique. In order to acquire the findings, a data analysis using SPSS and PLS-SEM was carried out. It is evident from the study's findings that students work values are impacted by anxiety and the COVID-19 pandemic. Moreover, the findings support the hypothesis that anxiety and the COVID-19 pandemic influence students employment decisions. The findings of the study provide insight into the body of knowledge. The influence of anxiety and the COVID-19 pandemic on current work values among university students about career choices are examined, and recommendations are made to various stakeholders, such as policymakers, university management, and career counselors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Factors Influencing Online Shopping Behaviour: An Empirical Study of Bangalore
Online shopping is growing rapidly in India, predominantly driven by tremendous and substantial divulgatory activities among millennial consumers. Online shopping is becoming more popular and attracts significant attention because it has excellent potential for both consumers and vendors. The convenience of online shopping makes it more successful and makes it an emerging trend among consumers. When all the companies are striving against one another, certain factors influence the behavior of customers. This paper analyses the relationship between the critical, independent variables, including consumer behavior, cultural, social, personal, psychological, and marketing mix factors. The results revealed that the influence of Brand as a factor had positively influenced the customers decisions in shopping online and evaluates the customers level of satisfaction with Online shopping. Results provided in this research could be employed as reference information for Ecommerce app builders and marketers regarding such issues in the city. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET
The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. 2023 IEEE. -
Fake News Detection and Classify the Category
A new type of disinformation has emerged: fake news, or untrue stories that have been presented as actual occurrences. We can no longer tell whether the information is true from fraudulent since so much information is published on social media these days. Artificial intelligence algorithms are helpful in resolving the fake news identification issue. In the field of natural language processing, fake news identification is a crucial yet difficult issue (NLP). In this article, we discuss similar duties as well as the difficulties associated with finding bogus news. Based on these findings, we suggest intriguing avenues for future study, such as developing more accurate, thorough, fair, and useful detection models. The average public's life is impacted by mass media since it happens regularly. Because of this, news stories are written that are somewhat true or even entirely untrue. Using online social networking sites, people deliberately promote these fake goods. It is crucial to decide whether the news is false owing to its potential to have detrimental social and national effects. The false news identification process made use of many criteria, including the headline and body content of the news piece. The suggested method works effectively in terms of producing results with excellent accuracy, precision, and memory. Comparing all the models employed in this study, it was discovered that Distillbert and multinomial nae bayes models perform better than Logistic and others ml models. The credibility of the story may be evaluated using a larger dataset for better results and additional variables like the author and publisher of the news. Grenze Scientific Society, 2023. -
Fake News Detection and Classify the Category
Everyone depends on numerous sources of E-news in today's world when the internet is ubiquitous. Online content abounds, especially social media feeds, many of which are unreliable and may not always be factual. For people to utilise social media platforms like Facebook, Twitter, and others, fake news is a topic that may be studied through Natural Language Processing techniques. Using ideas from natural language processing and machine learning applied to social media, our goal in this work is to conduct categorization of different news items that are available online. Our intention is to empower the user to utilise NLP (Natural Language Processing) methods to identify 'fake news,' which refers to misinformed material that may be categorised as genuine or false using software like Python. The model focuses on identifying false news sources based on several articles from a website, categorising the news as false or true, and determining its veracity using unreliable sources like scikit-learn and NLP for textual analysis of the website distributing the news. When a source is identified as a publisher of false news, which can be predicted with high vectorization and also suggested using the Python scikit-learn module to do tokenization and feature development, biased viewpoints may be identified and categorised in any subsequent articles from that source. 2022 IEEE. -
Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms
Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.