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Analyzing Job Satisfaction, Job Performance, and Attrition in International Business Machines Corporation through Python
Since workers significantly impact the firm's operation, businesses invest heavily in them. They must deliver better and more excellent performance to compete with the increasing competition. Employee performance is becoming more and more important for business success and staying ahead of the competition, so companies are putting more money into things like training, growth centers, and careers. The target audience was the employees working in International Business Machines Corporation. The data was analyzed through the process of Exploratory Data Analysis using Python. There is a 0.002297 link between Job Satisfaction and Performance Rating, and a 0.002572 correlation between Work Life Balance and Performance Rating. The relationship between work-life balance and job involvement is -0.01462, indicating a negative impact on work-life balance for people who are heavily interested in their occupations. The study would help Human Resources Managers formulate their policies and understand the employees better in the current environment. Here, Job Satisfaction and Performance Rating served as mediators, and the findings show that their influence on Attrition is minimal at this firm. 2024 IEEE. -
HydroIoT: An IoT and Edge Computing based Multi-Level Hydroponics System
The depleting area of cultivable lands is increasing demands for implementing improved techniques that could use less space and produce more than traditional farming. This situation is common in all the developing and under developed countries. With a motivation to contribute towards providing solution to this growing problem of food scarcity, a Multi-Level Hydroponics System is proposed. The proposed system combines best of all trending technologies like IoT, Edge Computing and Computer Vision and applies it to Hydroponics. A cultivation estimation system based on image processing is implemented and accuracy of the same is tested with actual produce. The crop used for the proposed system is corn as it serves as best fodder for cattle. It was observed that with proposed system up to 95% accuracy in estimating fodder produce was achieved. 2021 IEEE. -
A Novel Approach for Web Mining Taxonomy for High-Performance Computing
Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optical Character Recognition (OCR) based Vehicle's License Plate Recognition System Using Python and OpenCV
License Platform Detection is a computer technology that enables us to identify digital images on the platform automatically. Different operations are covered in this system, such as imaging, number pad locations, alphanumeric character truncation and OCR. The final objective of the system is to construct and create efficient image processing procedures and techniques to position a licensing platter on the Open Computer View Library picture. It was used and implemented the K-NN algorithm and python programming language. The technology can be used in different industries such as security, highway speed detection, lighting violations, manuscript documents, automatic charging system, etc. Auto plate recognition is an integrated technology which identifies the auto licence plate. Auto plate auto recognition. Multiple applications include complex safety systems, public spaces, parking and urban traffic control. Automatic Vehicle License Plate Recognition (AVLPR) has undesirable aspects because of many effects, such as light and speed. This work presents an alternative technique to leverage free software for the implementation of AVLPR systems including Python and the Open Computer Vision (openCV). 2021 IEEE. -
Comparison of Machine Learning-Based Intrusion Detection Systems Using UNSW-NB15 Dataset
Various machine learning classifiers have been employed recently to enhance network intrusion detection. In the literature, researchers have put forth a wide range of intrusion detection solutions. The accuracy of the machine learning classifiers intrusion detection is limited by the fact that they were trained on dated samples. Therefore, the most recent dataset must be used to train the machine learning classifiers. In this study, UNSW-NB15, machine learning classifiers are trained using the most recent dataset. A taxonomy of classifiers based on eager and lazy learners is used to train the chosen classifiers, such as K-Means (KNN), Polynomial Features, Random Forest (RF), and Naive Bayes (NB), Linear Regression. In order to decrease the redundant and unnecessary features in the UNSW-NB15 dataset, chi-Square, a filter-based feature selection technique, is used in this study. When comparing these machine learning classifiers, performance is measured in terms of accuracy, mean squared error (MSE), precision, recall, and F1-score with or without feature selection technique. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE. -
Design of pseudo stator generator
In today's energy generation scenario, the extensive use of conventional sources are causing lot of environmental issues. It is necessary that humankind should come up with a strategy to produce clean energy. Even though we cannot completely stop relying on non-renewable sources of energy, lot of research is happening to find the ideal substitute for conventional sources of energy and also for migrating towards renewable energy sources from conventional sources. Its gaining popularity because of the fact that availability of fossil fuels are reducing at an alarming rate. Thus, these research works will aid in producing clean energy and also make the existing systems more efficient. A better substitution would be to design a machine which would use less conventional sources of energy and gives required output. Thus it is necessary to come up with a new technology which would suffice the above stated requirements. The proposed project is an novel idea aimed at designing an alternator which has higher power output at lower RPM when compared to conventional alternators. This model finds application in automobiles, WECS, Aerospace, hybrid vehicles in the near future. 2016 IEEE. -
An Analysis of Grimms' Transmedia Storytelling in the Age of Technology
This research paper delves into an intersection of traditional literature and transmedia storytelling, with particular emphasis on Grimms' tales and its television series adaptation. Providing young audiences with engaging and dynamic experiences, transmedia storytelling involves delivering a single story across numerous platforms. Utilizing narrative analysis, this research seeks to uncover hidden themes, character growth, and story dynamics by breaking down the complex presentation and structure of stories in diverse media. Natural Language Processing (NLP) techniques like thematic analysis, sentiment analysis, keyword sentiment analysis have been employed to examine the differences between the presentation of these stories in varied formats as well as evaluating audience reception. It also assesses the degree to which transmedia adaptations support the resuscitation of beloved children's books in popular culture. By incorporating digital surrealism and aspects of technology, this paper enhances our understanding of how traditional stories captivate audiences across various media forms while maintaining their timeless quality. 2024 IEEE. -
A Textual Analysis of Panchatantra, Enhanced by Technology from the Psychological Perspective
This research paper offers a textual analysis of the portrayal of animals in the Panchatantra tales, leveraging technology, Natural Language Processing (NLP) for enhanced insights. The study focuses on the interplay of anthropomorphism and stereotypes within these narratives, delving into the diverse stereotypes associated with specific animals in the stories. This analysis enhances our understanding of animal portrayal in children's literature. Natural Language Processing (NLP) techniques like textual classification and thematic analysis have been employed to examine the underlying archetypes embedded within the tales to comprehend stereotypes. Through a close examination of literary examples employing AntConc, a corpus analysis software, this paper provides readers with a nuanced understanding of how anthropomorphism and stereotypes influence human perceptions of animals and contribute to our understanding of the natural world. 2024 IEEE. -
Optimized gateway oriented unicast and multicast routing for multi hop communication network
Networking and communication in an infrastructure less environment had brought interest in the development of mobile ad hoc networks (MANET). Growth of technologies in years has increased the size of the network and its applications. In larger MANET, it becomes important to maintain the quality of performance for increased overhead scenarios. Grouping of network optimizes the workload with reduced overhead for routing and maintenance in larger MANET. Mobile nodes are grouped by the use of clustering algorithms. Once the MANET environment is formed, the network needs appropriate architecture and methods to have efficient and effective transactions. Mobility, energy, selection of cluster head and gateway are parallel related with efficiency metrics of the network optimizing these parameter helps to increase performance of network in terms of higher packet delivery ratio, less energy consumption and jitter. This work proposes a routing architectural algorithm especially for very large network to get high-quality performance. The proposed method uses unicasting and multicasting approaches in an optimized way for routing and maintenance of the network. Analysis results prove that the proposed model has performed with higher packet delivery ration of 1.17% with a reduced jitter of 0.0014 s. Springer Nature Singapore Pte Ltd 2017. -
IoT based Smart Poultry to Produce a Healthy Environment
According to studies, there are approximately 850 million poultry birds across India, with an average of 30 million farmers working in the sector. In other words, a poultry farm is a trustworthy and long-term way to make money in India. However, managing a poultry farm is labour intensive due to the need for constant surveillance and control over a wide range of environmental factors. The actual implementation of this is significantly more complicated, expensive, and time-consuming. The paper suggested a smart poultry system that tries to provide the solution for all the issues. The health of poultry birds heavily relies on environmental parameters, so variables like temperature and humidity are measured and monitored continuously. The website was made so that poultry keepers may get reliable information about their birds' health and use that information to take the appropriate measures. Moreover, in the event of a crisis, such as a fire or the illness of a single bird, the owner will receive a notification. It is also possible to gather information about the poultry in the specified timespan. The Firebase cloud is used for wireless monitoring and managing the poultry system. The suggested automatic smart poultry system will make the birds healthy and it indirectly helps the owners to increase their profit with minimal human effort. 2022 IEEE. -
Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling
This paper is focused on the development of a novel system known as 'IngredEye.' It involves various approaches that can be grouped into categories, such as computer vision, including YOLOv8, a KNN prediction model, and a Flutter framework that hosts all of them in a mobile application environment. Previous studies have analyzed the application of computer vision and OpenCV recognition in cooking and proved that such approaches could enhance the level of convenience in the culinary field. This paper addresses issues like changes in lighting, occlusions, and other factors that have to be solved by the algorithms envisaged for real applications. The objective of this paper solely relies on integrating the OpenCV object detection method with comprehensive machine learning techniques specialized for the culinary field. Presenting the end-user with recipe recommendations based on the visual input they have given. 2024 IEEE. -
Detection of fake opinions on online products using decision tree and information gain
Online reviews are one of the major factors for the customers to purchase any product or to get service from many sources of information that can be used to determine the public opinion on the products. Fake reviews will be published intentionally to drive the web traffic towards the particular products. These fake reviewers mislead the customers to distract the purchasers mind. Reviewers behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. In this work the reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using decision tree classifier and Information Gain.Significance of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficacy of the proposed approach. 2019 IEEE -
Investigations on Compression Behaviour of Short Reinforced SCC Columns
The objective of this work is to predict the values of deformation and load at cracking point, yielding point and ultimate point of short reinforced self-Compacting Concrete columns which was subjected to axially compression in loading frame. An ANN tool by giving proper inputs like fresh properties of materials, spacing of stirrups and percentage of longitudinal reinforcement and keeping target values obtained from experiments, it is compared with the experimental values accompanied by marginal errors. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Workloads for Cloud Services
Capturing best quality datasets for a study is the first evidence for better outcomes of research. If the analysis are based on such datasets, then the metrics, the characteristics and few factors determines proof point for well proven theories. Hence it is obvious that we rely on the best possible ways to arrive at such data acquiring sources. It can be either based on historical techniques or from the innovations in application of it to industry. This paper introduces a mapping framework for analyzing, and characterizing data previously used by research community and how they are made to fit for Cloud systems, i.e. using 'workloads' and 'datasets' as the 'refined definitions'. It was contributed in the past two decades within the scientific community setting their own workflow analysis mechanisms. The framework thus is validated by acquiring a sample workload per layer of cloud. The sources are form the literature that are available from existing scientific theories. These workloads are then experimented against the three tiers of the cloud computing ie., IaaS(Infrastructure as a Service), PaaS(Platform as a Service), & SaaS(Software as a Service). The selected data is analyzed by the authors for an offline model presented here based on the Machine Learning tool-kits. There are future studies planned for and to be experimented in a cloud auto scaled environment with online model as well. 2022 IEEE. -
Cloudsim exploration: A knowledge framework for cloud computing researchers
This paper aims to help find solutions for questions an early researcher may have to set up experiments in their development environment. Simultaneously, while identifying the steps required for experimenting, the authors narrowed on an experimenting toolkit for Cloud Computing as an area of their study. Because of such simulators, the cloud computing environment itself is available easily at the comfort of ones desktop resources instead of visiting an actual physical data center to collect trace and log files as data sets for real workloads. This paper acts as an experience sharing to naive researchers who are interested in how to go about to start cloud computing setups. A new framework called Cloud Computing Simulation Environment (CCSE) is presented with inspiration from Procure Apply Consider and Transform (PACT) model to ease the learning process. The literature survey in this paper shares the path taken by researchers for understanding the architecture, technology, and tools required to set up a resilient test environment. This path also depicts the introduced framework CCSE. The parameters found out of the experiments were Virtual Machines (VMs), Cloudlets, Host, and Cores. The appropriate combination of the values of the parameters would be horizontal scaling of VMs. Increasing VMs does not influence the average execution time after a specific limit on the number of VMs allocated. Nevertheless, in vertical scaling, appropriate combinations of the cores and hosts yield better execution times. Thereby maintaining the optimal number of hosts is an ultimate saving of resources in case of VM allocations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
An Efficient Localized Route Recommendation Scheme using Fusion Algorithm for VANET based Applications
The evolution of vehicles has led to the need for improved and advanced techniques to solve traffic related problems. The improvement related to cooperative vehicles has been a recent focus in dealing with such difficulties. The most popular application of co-operative vehicles is the route planning for travelers. In this paper, an innovative module namely Localized Route Recommendation with Fusion Algorithm (LR2FA) is proposed to enumerate a localized route recommendation system to communicate to co-operative vehicles. Traffic parameters such as vehicle speed and density information collected from the centralized location and used as decision factor to provide suggestions of routes using a novel Fusion Algorithm (FA). To evaluate the factors for route suggestion, FA uses a combination of genetic and heuristic-based approaches. The performance of the proposed localized route references is analyzed using simulated values of vehicle speed and density. It is seen from the results that the proposed LR2FA provides top fitting routes compared to greedy based route suggestion. 2022 IEEE. -
Enhancing Human-Computer Interaction with a Low-Cost Air Mouse and Sign Language Recognition System
The purpose of this study is to investigate the development of assistive technologies that are designed to empower people with disabilities by increasing their level of freedom and accessibility. Voice assistants, air mice, and software that recognizes sign language are some of the topics that are specifically covered in this. Those who have impaired fine motor skills can benefit from using air mice since they allow controls to be made by hand gestures. Using machine learning algorithms, sign language recognition software is able to decipher signs with an accuracy rate of over 90 percent, making it easier for people who are deaf or hard of hearing to communicate themselves. By relying solely on vocal instructions, voice assistants like Alexa make it possible to control devices without using your hands. Not only do these technologies have the potential to be revolutionary, but they also confront obstacles in terms of improving identification accuracy and integrating them into common gadgets. In this study, the development and impact of voice assistants, sign language software, and air mice are discussed. More specifically, the paper highlights the potential for these technologies to help millions of people with disabilities all over the world. Additionally, it examines potential enhancements that could be made to these technologies in the future in order to further improve accessibility and inclusivity. This research integrates computer vision and machine learning to create a multimodal system blending air mouse functionality with real-time sign language translation. Achieving 95% accuracy in gesture recognition for air mouse control and 98% accuracy in sign language letter classification using a basic webcam, the system promotes accessible interaction without specialized hardware. Despite limitations in vocabulary and lighting sensitivity, future efforts aim to broaden data training and explore mobile deployment. These advancements hold promise for enhancing natural human-computer interaction, particularly for users with disabilities, by enabling intuitive, hands-free control and communication. 2024 IEEE. -
Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
One of the incurable diseases that affect women is cervical cancer. It is brought on by a protracted infection of the skin and the vaginal mucous membrane cells. The Human Papilloma Virus (HPV), is the main factor causing aberrant cell proliferation in the area around the cervix. There are no symptoms present when the illness first appears. Early detection of this malignancy may be used to prevent death. People in less developed countries cannot afford to periodically examine themselves due to a lack of awareness, poor medical infrastructure, and expensive medication. The EDA technique is applied to examine the data and understand its characteristics. Machine Learning algorithm has been used to diagnose cervical cancer. In order to spot the existence of cervical cancer, five machine learning classifiers are utilized, the algorithms to begin earlier. The Logistic Regression classifier's results validate the correct stage prediction. 2023 IEEE. -
Stalling angle predictions of symmetric aero foil by flow analysis
The computational examination of the 2D subsonic stream over a National Advisory Committee for Aeronautics (NACA) 0012 airfoil at different approaches is done in an ANSYS R14.5 software. The stream elements are acquired by computing the fluid stream conditions of coherence and energy, momentum conservation with the K- model. The present investigation is fundamentally significant, ascribed to the way that the considered aero foil creates lift by moving rapidly through the air, and the examination of the flow over aero foil helps in execution assessment of the flight of the aero foil. In the current work, for a given stream speed with the aero foil set at an approach to the airstream, a pressure gradient among upper and lower wing surfaces exists, which involves the generation of lift, and the lift generated increases up to a certain angle of attack, beyond which the lift decreases, this particular point gives rise to stalling. The distinction in these pressing factor is very important to understand the effect of angle of attack on the lift of the wing. Consequently lift is a significant aspect to be evaluated for stalling angle predictions. The steps associated with assessment of the lift includes forecast of stalling angle at different flow conditions over a symmetric aero foil by modeling the NACA0012 aero foil using the co-ordinates provided by the NACA database and henceforth bringing in it to the ANSYS work bench for meshing, preprocessing followed by the interaction of post processing for different angles of attack. 2022 Author(s).