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Problem-Based Learning for Critical Reflections on Skill-based Courses Using DEAL Model
Higher education institutions focus on i mpr ovi ng bot h sof t ski l l s and engi neer i ng proficiencies among students. The learning progress requires a systematic assessment to know the areas of improvement to meet global competitiveness. Self-reflections and critical reflections on knowledge, skill, and behavior are crucial for an industry-ready graduate. Our work deals with conceptualization, course design, and rubrics design to achieve critical reflections on the graduate outcomes of the students. We have designed the rubrics to assess the behavioral and engineering skills needed to solve complex engineering problems that can be solved better as a team for life-long learning and developing ethical interpersonal skills. Our assessment patterns also helped students achieve higher-order thinking skills through experiential learning. 2024, Rajarambapu Institute Of Technology. All rights reserved. -
Kubernetes for Fog Computing - Limitations and Research Scope
With the advances in communications, Internet of Everything has become the order of the day. Every application and its services are connected to the internet and the latency aware applications are greatly dependent on Fog Infrastructure with the cloud as a backbone. With these technologies, orchestration plays an important role in coordinating the services of an application. With multiple services contributing to a single application, the services may be deployed distributed in multiple server. Proper coordination with effective communication between the modules can improve the performance of the application. This paper deals with the need for orchestration, challenges, and tools with respect to edge/fog computing. Our proposed research solution in the area of intelligent pod scheduling is highlighted with the possible areas of research in Microservices for Fog infrastructure. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
3D CNN-Based Classification of Severity in COVID-19 Using CT Images
With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Role of Humanoid Robots in Enhancing Communication and Social Skills among Students with Mild Autism
Introduction Children with Autism Spectrum Disorder (ASD) face challenges in social newlineinteraction. This is a growing concern and needs a due redressal for their wellbeing. Failing to address these challenges can cause further isolation resulting in displaying antisocial behaviours. Children with Autism require the intervention of technology. Over the years, special schools have been using newlineAssistive Technologies (AT) that bridge the gap in learning for students with newlinedisabilities in general and Autism in particular. As a result of this initiation newlineearlier studies have deciphered that the end result of drawing AT into teaching and learning have been promising and optimistic. Available literature has shed light on the positive impact of emerging and assistive technologies on the learning of students with disabilities. However, the use of emerging newlinetechnologies by special tutors to build vital skills amongst children with autism has not been sufficiently explored. The lack of adequate exploration in the aforementioned domain paves the way for this research in particular. Objectives This study aims to explore the benefits of using humanoid robots which is an emerging technology in recent years with specific reference to inclusive newlineclassroom setup. Inclusive classrooms use humanoid robots to develop social and- communication skills amongst students with mild autism. This study sheds light by using both qualitative and quantitative methods and identify whether robots can effectively address children s social and communication skills which contributes to children s speech and behavioural development. Methods As mentioned above this research has been undertaken with a qualitative research design a combination of tools to explore how humanoid robots enable in the development of social and- communication skills amongst students with mild autism. The researcher has conducted semi-structured interviews newlinewith special tutors and counsellors to gain insights into the positive benefits of newlinechild - robot interaction. -
Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE. -
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved. 2023 The Authors -
Cybersecurity Disclosure and Corporate Reputation: Rising Popularity of Cybersecurity in the Business World
This chapter emphasizes the importance of cybersecurity for a corporation as todays organizations are more vulnerable than ever and their enemies are in the form of viruses and malware. The work provides evidence that cybersecurity can have an impact on brand value, market value, and overall corporate reputation. It focuses on depicting the global scenario with reference to cybersecurity disclosures by corporations and how it is important in todays digitized era where data is the most valuable and vulnerable asset. With rapid digitalization, cybersecurity has become a major concern for all businesses, especially when there is financial and reputational damage to cybersecurity breaches and incidents. Even in the absence of clear cybersecurity laws and regulations, corporations are opting for voluntary disclosure. Existing literature explains this as an attempt to mitigate any potential risk or occurred risk through increased transparency which will build the trust of all stakeholders. 2023 by IGI Global. All rights reserved. -
Relationship between financial inclusion and financial development in India: Is there any link?
A dynamic chain of financial activities and services can be served from debtors to creditors in the international economy through an efficient and effective financial sector. The motivation behind this study is to investigate the linkages between financial inclusion and financial development in India during the period (19802017). For this, the study employ principal component analysis (PCA) to construct both financial inclusion index and financial development index which measures financial access and financial depth position respectively. Using a set of determinants related to financial inclusion and financial development, the present study estimates there is a unidirectional relationship between financial inclusion and financial development in India. So, it reveals that financial inclusion is an essential element for financial sector development especially in a developing country like India. 2021 John Wiley & Sons Ltd. -
Light-induced advanced oxidation processes as pfas remediation methods: A review
PFAS substances, which have been under investigation in recent years, are certainly some of the most critical emerging contaminants. Their presence in drinking water, correlated with diseases, is consistently being confirmed by scientific studies in the academic and health sectors. With the aim of developing new technologies to mitigate the water contamination problem, research activity based on advanced oxidation processes for PFAS dealkylation and subsequent mineralization is active. While UV radiation could be directly employed for decontamination, there are nevertheless considerable problems regarding its use, even from a large-scale perspective. In contrast, the use of cheap, robust, and green photocatalytic materials active under near UV-visible radiation shows interesting prospects. In this paper we take stock of the health problems related to PFAS, and then provide an update on strategies based on the use of photocatalysts and the latest findings regarding reaction mechanisms. Finally, we detail some brief considerations in relation to the economic aspects of possible solutions. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Smart Portable Neonatal Intensive Care for Rural Regions
Every year, an increasingly large number of neonatal deaths occur in India. Premature birth and asphyxia are being two of the leading causes of these neonatal deaths. A well-regulated thermal environment is critical for neonatal survival. In the current scenario, it is impossible for the health centers in the rural areas of India to afford a neonatal incubator for every newborn due to its price and transportability. The successful delivery of neonates is hampered in India due to its increasing population along with limited technology and resources. Thus, a prototype of an incubator has been designed that is affordable, transportable, and energy saving for the health centers in the rural regions, with an AI-based decision support system. Springer Nature Singapore Pte Ltd 2020. -
Thermal and solutal stratified Heimanz flow of AA7072-deionized water over a wedge in the presence of bioconvection
The bioconvective Heimanz flow of nanofluid across a wedge with thermal stratification is analyzed in this article. The wedges are often seen in glider aircraft, rocket climbing frames, etc. The nanofluid considered in this study is composed of aluminum alloys of AA7072 and deionized water. The AA7072 alloys are specially manufactured materials composed of Aluminum and Zinc in the ratio of (Formula presented.) along with metals like silicon, ferrous, and copper so that they possess enhanced heat transfer features. The mathematical model is formed using the modified Buongiornos model that includes the discussions related to slip mechanisms and volumetric analysis in terms of the weight of the nanoparticle. The model is in the form of partial differential equations and is later converted to ordinary differential equations with the assistance of similarity transformation. This set of equations is solved by the Differential Transformation Method (DTM) and the outcomes are discussed through graphs.,. 2024 Taylor & Francis Group, LLC. -
Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet
In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely TiO 2, Cu and Ag into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid Cu-TiO 2/ H 2O. Thus, forming the ternary nanofluid Ag-Cu-TiO 2/ H 2O. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing. 2023, The Author(s). -
Effects of activation energy and chemical reaction on unsteady MHD dissipative DarcyForchheimer squeezed flow of Casson fluid over horizontal channel
The impact of chemical reaction and activation energy plays a vital role in the analysis of fluid dynamics and its thermal properties. The application of the flow of fluid is significantly considered in nuclear reactors, automobiles, manufacturing setups, electronic appliances etc. This study explores the impacts of activation energy and chemical reaction on the magnetohydrodynamic DarcyForchheimer squeezed Casson fluid flow through a porous material across the horizontal channel where the two parallel plates are assumed to be in motion. By using similarity variables, partial differential equations are converted to ordinary differential equations. Numerical method is applied using MATLAB to solve the problems and acquire the data for velocity field, thermal distribution, and concentration distribution. The graphs indicate that fluid velocity and temperature increases as the plates are brought closer. In addition, there was a correlation between a rise in the Hartmann number and a decrease in the fluid's velocity because of the existence of strong Lorentz forces. The temperature and the concentration of the liquid will increase due to the Brownian motion. When the DarcyForchheimer and activation energy parameters are both increased, the velocity and concentration decreases. 2023, The Author(s). -
Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication
The dissertation titled ???Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication??? is the compilation of all efforts taken and tasks completed in order to implement an optimal handoff method in Vehicular Ad-hoc Network communication.Wireless communication technologies have been improving exponentially. Ad-hoc networks can form a network of wireless nodes anywhere and they are not bound by the limitations of a static infrastructure. This enhances the ability of mobile nodes to communicate with each other even in situations where a defined architecture is absent. Vehicular Ad-hoc Networks (VANETs) has its applications in dynamic environments that involve nodes with high mobility. The nodes frequently move between the coverage areas of different access points. This increases the chance of link breakage and new link formation in communication network. Handoff is a process that helps in transferring the session details between one access point to another whenever the node is about to move away from a currently serving access point. Many handoff methods have been proposed but a majority of them utilize just a particular attribute of a network to employ the channel selection process. This process of network selection would be skewed as other attributes of a network play important roles in improving its overall efficiency. Multiple Attributes Decision Making (MADM) methods make use of different attributes in order to perform the network selection process. Use of MADM methods help in selecting optimal access points that can provide services to the nodes for a longer duration. In the proposed system, MADM methods have been utilized to modify existing protocols in order to optimize their approach for handoff operations. Various scenarios involving vehicular nodes and different access points have been considered in order to improve the efficiency of the proposed system across applications. The proposed handoff mechanism follows a proactive approach where the target access points are selected before the mobile node reaches the edge of its coverage area. This leads to a seamless transition of the communication channels. Based on the client/access point information stored in the data log, optimal access points which are situated along the direction of the node???s movement can be selected. NS2 and SUMO have been implemented to simulate mobile environments that accommodate handoff operations. -
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LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors. -
Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE. -
A Potential Review on Self-healing Material Bacterial Concrete Methods and Its Benefits
Building plays an important role for survival of human being in a safe place to live and store basic requirements. The building can be constructed for any purpose and the architecture of each building (official, residential) differs according to the plan. Beyond the plan for a building, it is also significant in designing plans for the construction of bridges, dams, canals, etc. In all the construction, the key goal is the strength of a building which completely depends on the materials that are chosen for each work. Hence, it is essential to prefer high quality materials for the construction of a building and the major materials are such as cement, concrete, steel, bricks, and sand. Among these materials, the concrete is often used for construction which enables to harden the building by combining cement, sand, and water. The concrete looks like a paste that reinforce to prolong life of the building. In this paper, we discuss a review on the use of bacteria in concrete that has the ability of self-healing cracks in the building. The procedural process behind the activation and reaction of bacteria into concrete is studied with the benefits of this process. This bacterial concrete usage assures to enhance the property of durability and but still it is yet to be introduced in the industries. Hereby, we review the recent research works undergone in concrete using bacteria. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.