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A Comparative Analysis of Biodiesel Properties Derived from Meat Stall Wastes through Optimized Parameters
Biodiesel is considered as alternative green fuels that can be used in Internal Combustion engines as a replacement fuel for conventional diesel. Biodiesel is extracted from vegetable and animal sources which are rich in triglycerides. In this work, an attempt has been made to obtain and characterize the biodiesel from animal wastes such as chicken skin and pig tallow which are available in abundance and at an economical cost within the authors' geographical location. Initially, the feedstock is decontaminated and subjected to conventional heating to convert it into fatty oil. Heating is carried out at different temperatures and for varying time to find out the optimal combination of time and temperature, which would result in maximum fat yield. The fatty oil is then subjected to the trans-esterification process with methyl alcohol in the presence of a catalyst to extract crude biodiesel. A de-canter funnel is used to separate the glycerine and biodiesel from the crude extract. The extracted biodiesel is mixed in different volume percentages with conventional diesel, and various thermochemical properties were evaluated as per ASTM standards. The test result indicated that the properties of the biodiesel blends were well within the limits as prescribed by ASTM standards. Published under licence by IOP Publishing Ltd. -
Fortitude, and Sense of Coherence in achieving Financial Resilience and Financial Health of Micro and Small Entrepreneurs
The COVID 19 pandemic has brought economic shock s all over the world. India is not an exception to this. The pandemic has made the lives of poor, and downtrodden people, micro, and small entrepreneurs miserable. Micro and small enterprises struggle to bounce back financially and to achieve financial health. Micro and small entrepreneurs face many problems such as no adequate income and savings, debt repayment, rising costs, lack of funds to run the business, financial and mental stress, uncertain future, and so on. Despite these problems, the micro and small enterprises move on steadily to achieve the goal of financial health. What makes them move on steadily? How do they manage their resources to achieve financial resilience? To seek answers to these questions, this study would like to examine the role of fortitude and sense of coherence in achieving financial resilience and financial health of micro and small entrepreneurs. The Electrochemical Society -
Technologies Driving Digital Payments in India: Present and Future
The payments market in India has been witnessing a significant transformation in recent years. The Indian payments market has robustly and consistently been moving towards digitization due to enhanced digital infrastructure, favourable government policies, and initiatives, availability of new technologies, disruptive innovations, and changes in the mindset of the customers. India tops in the worlds real-time digital payments with 20.5billion transactions in the year 2020 despite the adverse effect of the COVID-19 pandemic. This article deals with the growth of the Indian digital payments market and the technologies that drive the digital payments space at present and in the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Differences in perceptions of employees towards knowledge management strategies in selected information technology companies in Bangalore
In older days, knowledge was passed verbally from one person to another. The industrial revolution changed the scenario and the emergence of factories and industries paved the need for systematic knowledge and it became more and more specialized as time passed. Since then, there has been an exponential growth in scientific and practical knowledge. In the twenty-first century, this process has taken rapid speed due to the improvements in information and communication technologies. Employees of the organizations have a varied opinion towards practicing knowledge management strategies in an organization. Some employees consider knowledge management as an opportunity and a few others like a burden. This article focuses on identifying and analyzing the differences in perceptions of employees towards knowledge management strategies in selected Information Technology companies in Bangalore. 2020 American Institute of Physics Inc.. All rights reserved. -
An Empirical Examination of the Factors of Big Data Analytics Implementation in Supply Chain Management and Logistics
Numerous companies have effectively exploited Big Data Analytics (BDA) potential to enhance their effectiveness in the Big Data period. Given that big data application in logistics and supply chain management (SCM) is nevertheless in its early stages, assessments of BDA could differ from various viewpoints, producing certain difficulties in comprehending the significance and potential of big data. Based on past research on BDA and SCM, this work examines the factors that influence organizations' willingness to implement BDA in their everyday activities. This research divides potential elements into 4 groups: technical, firm, ecological, and supply chain issues. A framework consisting of direct factors like technical, firm, and mediators was presented based on the technology diffusion hypothesis. The experimental findings demonstrated that anticipated advantages and high-level management assistance might have a considerable impact on intended adoption. Furthermore, ecological variables like competitive adoption, administration legislation, and supply chain connection can greatly alter the direct connections between influencing causes and intended adoption. 2023 IEEE. -
Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Effect of Heat Treatment on Fatigue Characteristics of En8 Steel
Fatigue failure is an important factor in most of the engineering applications, especially in steel materials, and among the steel materials, it is an important phenomena in medium carbon steels like EN8, which is very commonly used in components like shaft, gears etc., since it is prone to fatigue failure. Hence, without changing the composition, an attempt is made to enhance the fatigue strength by different heat treatment techniques. In this study, the investigation is carried out on heat treatment of EN8 steel material. Various kinds of heat treatment techniques like quench and temper, normalizing and annealing are performed on EN8 steel. After exposure to the heat treatment, the EN 8 steel material specimens are machined as per the ASTM standards and are subjected to RR MOORE test and SN-curves are plotted from the obtained results; the obtained results from the fatigue tests are further analyzed with the help of ANSYS software. Fatigue life and Factor of Safety (FOS) comparisons for EN 8 steel material is made with the structural steel material and it is found from the comparisons, that the heat treatment process enhances the fatigue strength and endurance limit. Published under licence by IOP Publishing Ltd. -
Impact of Childhood Trauma on Psychological Distress and Personality Pathology in Young Adults
Adulthood is a time of change, thus stressful. A predetermining factor to this is a provision for a safe environment during the crucial years of life (childhood). Children make meanings of everything and are more dynamic in the early developmental years. It is a basis for their overall development and defines their coping mechanisms during adulthood. Therefore, if they develop faulty meanings of themselves, others, and the world at large, it can alter their abilities to function during adulthood. It is fundamental to understand the psychological well-being and personality traits in adulthood by this very nature of traumatic experiences in childhood. This paper is a conceptual framework discussing a three-tier model to retrospectively understand the impact of childhood trauma on psychological distress and personality pathology in adulthood. This paper suggests future research to focus on developing intervention and prevention models for young adults (childhood trauma survivors) on positive parenting practices. The Electrochemical Society -
Perceived Reality of Self and Others with Two Childhood Trauma Survivors - An Idiographic Case Study
Impacts of childhood trauma can be crucial in understanding personality traits and psychological distress. However, it could be hard to predict if these individuals develop posttraumatic stress or growth. Several quantitative research studies have concluded the connections between childhood trauma and psychopathology or maladaptive personality traits. Various researchers have discovered the negative consequences of early childhood trauma and its long-term effects which may be rudimentary in understanding the causation of life-long psychological and medical deficiencies. This has been very elementary in understanding trait patterns and psychopathology for outcome generalizability and implementing prevention and intervention models. However, these studies still fail to spotlight the importance of the lived experiences of trauma survivors. Nevertheless, the present study is an idiographic single-case study research design used in the exploration of the lived experiences and perceived reality of self and others with two childhood trauma survivors. The Electrochemical Society -
Integral Transforms andGeneralized Quotient Space ontheTorus
In this chapter, we discuss one of the recent generalization of Schwartz distributions that has significantly influenced the expansion of various mathematical disciplines. Here, we study the space of generalized quotient on the torus. Different integral transforms are investigated on the space of generalized quotients on the torus BS?(Td). The space BS?(Td) is made of both distributions as well as space of hyperfunctions on the torus. Further, by introducing the relation between the Fourier and other integral transforms, the conditional theorems are proved for generalized quotients on tours. Moreover, we study the convergence structure of delta-convergence on the generalized quotient space, and an inversion theorem is proved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Quantum Computing: Navigating The Technological Landscape for Future Advancements
Quantum Computing represents a transformative paradigm in information processing, leveraging principles of quantum mechanics to enable computations that transcend the limitations of classical computing. This research paper explores the cutting-edge technologies employed in Quantum Computing, examining the key components that facilitate quantum information processing.The purpose of this study is to provide a comprehensive exploration of the state-of-the-art technologies in Quantum Computing, laying the groundwork for future advancements and applications in this rapidly evolving field.The methodology employed in this study integrates three analytical approaches: sentiment analysis, topic modeling, and thematic analysis. Sentiment analysis is utilized to discern and quantify emotional tones within the content. Topic modeling is applied to identify latent themes and patterns within the data, revealing underlying structures. Thematic analysis, on the other hand, involves a systematic identification and exploration of recurrent themes to provide a nuanced understanding of the subject matter. This tripartite methodology ensures a comprehensive examination of the data, facilitating a robust and multifaceted analysis of quantum computing technologies. 2024 IEEE. -
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess and measure which LLM is more vulnerable towards hallucination. We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations. 2023 Association for Computational Linguistics. -
Classification of Diseased Leaves in Plants Using Convolutional Neural Networks
The article focuses on the classification of diseased leaves using a machine learning algorithm. The main focus in agriculture is controlling pests and weeds, for which farmers spray chemical pesticides to get a good yield. The issue here is over-usage and under-usage of pesticides, which might harm the end consumer. To achieve the goal of reducing pesticide use and detecting pests in the crop early, the machine learning algorithm is deployed on the leaf image. The image data of the leaf of the cauliflower plant is collected for 40days. The data was collected from the day the plant was seeded in a pot until the day it was ready to be planted in the soil. From this data, the pest attack on the plants is tracked without the application of pesticides. To achieve this, the CNN algorithm is used on the collected image data. The outcome of the study would be to classify the diseased leaves based on the pest attack and know the right time to spray the pesticides to reduce the damage to the plant. This also reduces the use of pesticides and costs to the farmer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Machine Learning based Loan Eligibility Prediction using Random Forest Model
When one or more people, organizations, or other entities lend money to other people, organizations, or entities, it is known as a loan. The recipient (that is the borrower) takes on a debt for which he or she is normally accountable for interest payments until the loan is repaid. The major goal of this proposed model is to ensure that an individual, institution, or organization seeking for a loan is properly verified before granting them the loan they require. Before authorizing a loan for any individual or business several factors must be considered. That including gender, education, and the number of dependents. The goal of proposed model is to automate the method, which will save time and energy while improving the efficiency of the process. This particular process input is having two different kind of data set. First one is train data set and second set is test data set. The first date set that is train data set is generally used to train and assess the machine learning model accuracy. The loan eligibility predictions are generated using the test data set. To forecast loan eligibility and train this random forest, machine learning method called Random Forest. The proposed random forest model is providing higher accuracy level. This model is providing 28 % higher accuracy level compare to regular prediction. 2022 IEEE. -
Modernized energy management system: A review
The usage of renewable energy system (RES) and its management is vital for reliable electrical energy delivery without pollution. In the scenario of increase in distributed generations (DGs), to utilize the generated electricity from RES without any wastage, to avoid the consumption of electricity during peak hours, to store and retrieve energy in an efficient way from the battery, there is a need for overall energy management system (EMS). As the prices for electricity and pollution are reduced, the review of available methodologies is discussed in this paper. The EMS takes decision based on the predicted load demand. So, the different prediction methodologies and their benefits are also discussed here. Though the electric vehicles (EVs) are considered as load in power system, the storage facility of the EVs are also used as power backup facilities through vehicle to grid (V2G) technology. This paper provides a review on the complete management of RES, EVs, batteries and load. Published under licence by IOP Publishing Ltd. -
Deep Learning Based Age Estimation Model
To improve accuracy and resilience in demographic categorization, this research presents a novel use of Convolutional Neural Networks (CNNs) for age prediction. Deep learning is utilized to achieve this goal. Precise estimation of age has become essential in a variety of areas, including human-computer interaction, marketing, and healthcare. The ability of CNNs to handle the intricacies of facial features for accurate demographic forecasts is examined in this study. The research covers every step of the age prediction process, including dataset collection, prepossessing, model architecture, and assessment measures. The CNN is trained to automatically extract hierarchical characteristics from facial photos, which enables the model to recognize complex patterns related to age. The architecture's flexibility to different lighting conditions, facial expressions, and postures. In this research, we deal with deep learning-based perceived age estimation in still-face pictures. Our Convolution Neural Network models (CNNs) have been trained prior on Image Net for picture classification, as they use the VGG architecture. In addition, we analyze the effects of tailoring over Web photos having known age, considering a lack of apparent age-annotated annotated images. In addition, this work adds to the increasing library of studies on the use of deep learning methods for demographic data evaluation by showing the effectiveness of CNNs to predict age. The results show how, in practical situations, CNNs could significantly enhance the precision and dependability of age prediction systems. 2024 IEEE. -
Securing the Digital Realm: Unmasking Fraud in Online Transactions Using Supervised Machine Learning Techniques
A key component of contemporary banking systems and e-commerce platforms is identifying fraud in online transactions. Traditional rule-based techniques are insufficient for preventing sophisticated fraud schemes because of the increasing complexity and number of expanding online transactions. This research study examines the development of fraud detection methods, emphasizing data analytics and machine learning (ML) models. The study also focuses on the fact that developing efficient fraud detection systems requires continuous observation, data preprocessing, feature selection, and testing of models. Seven ML models, Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbors (kNN), Nae Bayes (NB), Support Vector Machine (SVM), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) are considered for classifying the dataset into fraudulent or not. During the experimentation study, it was observed that XGBoost yielded the highest accuracy of 99% when compared to other models. Users can determine which features significantly influence the model's predictions by using XGBoost's feature significance insights. Additionally, XGBoost provides integrated support for managing missing values in data, negating the requirement for imputation and other preprocessing procedures. Due to these, it performed better. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Contextual Recommendation System: A Revolutionary Approach Using Hadoop, Spark, NLP and LLMs
This study presents a novel framework for contextual recommendations on platforms like Wikipedia, integrating Hadoop, Spark, NLP, and LLMs. Leveraging these technologies, the framework aims to enhance user experiences by delivering personalized article suggestions aligned with their current interests. Through scalable data processing, advanced NLP techniques, and LLM-powered semantic understanding, the framework offers a transformative approach to recommendation systems, promising to revolutionize knowledge exploration on digital platforms. 2024 IEEE. -
Ethnic Food: The Food Way Forward
In the context of food security, two things are significant. To ensure availability, affordability and accessibility of adequate food to people throughout the country. Also, to promote entrepreneurship for sustainable food production and supply. This paper highlights differences between food security and food insecurity. The global population in 2050 is predicted at 9 billion in which case the output must double considering the dwindling and degrading resources. This may be a challenge for agronomists and policy-makers. Considering that food security must be achieved at individual, household, district, national and global levels, India may need an Integrated Farming System (IFS) to take agriculture further. There are numerous challenges besides the environment that must be considered for this. It is important to ensure that the dignity of the farmer is not compromised while strategizing food security. Currently, private-public partnerships are being introduced in some places as a potential model. However, all stakeholders in food security have their task cut out (1). This paper is a review of existing literature to understand the level of information we have documented. It tries to highlight ways in which consumption of ethnic food could be a way forward in terms of food security and sustainability. The Electrochemical Society -
Preventing Data Leakage and Traffic Optimization in Software-Defined Programmable Networks
The first widely used communication infrastructure was the telephone network, often known as a connection-oriented or circuit-switched network. While making a phone call, these networks will first set up a connection, and then tear it down after the call has ended. The connection made during the call would not be used again. Thus, connectionless or packet-switched networks have been introduced, with an aim to send voice signals as data packets. When compared to conventional network architecture, SDN's separation of the data plane and control plane of networking devices makes the management of these devices directly programmable via a centralised controller. It uses a MAS-based distributed architecture to categorise network flows, and it's called the Traffic Classification Module. Each host or server's high-priority application traffic is isolated via Deep Packet Inspection (DPI). The time consumed for a packet to travel from one endpoint to another is referred to as the average packet delay, whereas the controller's reaction time is twice the average packet delay. Few works existed that utilised routing strategies to decrease the typical packet delay in SDN. To reduce the controller's response time, Software-Defined Networks (SDNs) need a routing algorithm that reduces the average packet delay. Each of the proposed modules and the whole combined SDN-MASTE framework were put through their paces in a series of experiments and emulation-based tests to see how well they performed. 2023 IEEE.