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Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits. 2022 by the authors. -
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Design of decision support system to identify crop water need
Crop Water Need (ET crop) is referred to as the amount of water needed by a crop to grow. ET crop has high significance to identify the adequate amount of irrigation need. In this paper, a decision support system is proposed to identify Crop Water Need. The proposed decision support system is implemented through sensors and android based smartphone. Internet of Things (IoT) based temperature sensor (DHT11) is used to acquire the real time environmental factors that affect the ET crop. The sensor will communicate with android based smartphone application using Bluetooth Technology (BT-HC05). This proposed system has been compared with available evapotranspiration and existing manual method of evapotranspiration and it was found that proposed system is more correlated than existing manual method of evapotranspiration. The correlation coefficient obtained between proposed system and available evapotranspiration is 0.9783. The proposed decision support system is beneficial for farmers, agriculture researchers and professionals. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models. 2024 -
Cyber-Physical Systems: AI and COVID-19
Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. 2022 Elsevier Inc. All rights reserved. -
An Intelligent Model forPost Covid Hearing Loss
Several viral infections tend to cause Sudden Sensorineural Hearing Loss (SSNHL) in humans. Covid-19 being a viral disease could also cause hearing deficiencies in people as a side effect. There have been pieces of evidence from various case studies wherein covid infected patients have reported to be suffering from sudden sensorineural hearing loss. The main objective of this study is to inspect the phenomenon and treatment of SSNHL in post-COVID-19 patients. This study proposes a mathematical model of hearing loss as a consequence of covid-19 infection using ordinary differential equations. The solutions obtained for the model are established to be non-negative and bounded. The disease-free equilibrium, endemic equilibrium and basic reproductive number have been obtained for the model which helps analyse the models trend through stability analysis. Moreover, numerical simulations have been performedfor validating the obtained theoretical results. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
The Role of Imposter Phenomenon on Self-Handicapping and Psychological Distress among Young Adults
The Imposter Phenomenon (IP), characterized by persistent self-doubt and a fear of being exposed as a fraud despite objective success, is a growing concern, particularly among young adults. This study explores the intricate relationships between the Imposter Phenomenon, Self-handicapping, and Psychological Distress in a sample of 242 young adults aged 1825. The data is analysed using descriptive statistics, correlation, and regression. Findings from a comprehensive survey, utilizing the Clance Impostor Phenomenon Scale, the Self-Handicapping Scale, and the Mental Health Inventory reveal a significant positive correlation and prediction between the Imposter Phenomenon and self-handicapping and a positive relationship between the Imposter phenomenon and psychological distress. These findings contribute to a deeper understanding of how the Imposter Phenomenon influences self-handicapping behaviours in young adults, shedding light on the psychological distress associated with these experiences. The study underscores the need for targeted interventions to address imposter feelings and their potential consequences on mental well-being in this vulnerable population, ultimately aiming to foster a healthier and more resilient generation. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Prediction of software defects using object-oriented metrics
In recent years, many of the object-oriented software metrics were proposed for increasing the quality of software design such as prediction of defects and the maintainability of classes and methods. As the word metrics is frequently used for specific measurements taken on a particular process or item and in object-oriented metrics the metrics are the unit of measurements that is used to characterize the data.The fundamental point of this research is to identify the significance difference between software metrics which observes defect prediction and also study about their relation involving in the object oriented metrics that is named as "Chidamber and Kemerer metric suite" which is also known as "CK metrics suite", the number of defects and then finally decide the differences of the metrics in ordering to Eclipse classes as defective and selected with regard to defect prediction. IAEME Publication. -
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
The efficiency of any cryptosystem not only depends on the speed of the encryption and decryption processes but also on its ability to produce different ciphertexts for the same plaintext. RSA, the public key cryptosystem, is the most famous and widely accepted cryptosystem, but it has some security vulnerabilities because it produces the same ciphertext for identical plaintexts occurring in several places. To enhance the security of RSA, magic square-based encoding models have been proposed in the literature. Although magic square-based encoding models have been proposed, they are static. Thus, this paper introduces a dynamic-based magic square with RSA, where encryption and decryption are performed using numbers generated from the magic square instead of ASCII values. Unlike the static magic square, the proposed dynamic magic square allows users to specify the starting and ending numbers in any position rather than fixed positions. In the proposed dynamic magic square generation, different 4 4 magic square templates are created, and 16 16 magic squares are generated from them. Experimental results clearly demonstrate the improved security of RSA. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Employing bioactive compounds derived from Ipomoea obscura (L.) to evaluate potential inhibitor for SARS-CoV-2 main protease and ACE2 protein
Angiotensin converting enzyme 2 (ACE2) and main protease (MPro) are significant target proteins, mainly involved in the attachment of viral genome to host cells and aid in replication of severe acute respiratory syndrome-coronaviruses or SARS-CoV genome. In the present study, we identified 11 potent bioactive compounds from ethanolic leaf extract of Ipomoea obscura (L.) by using GC-MS analysis. These potential bioactive compounds were considered for molecular docking studies against ACE2 and MPro target proteins to determine the antiviral effects against SARS-COV. Results exhibits that among 11 compounds from I. obscura (L.), urso-deoxycholic acid, demeclocycline, tetracycline, chlorotetracycline, and ethyl iso-allocholate had potential viral inhibitory activity. Hence, the present findings suggested that chemical constitution present in I. obscura (L.) will address inhibition of corona viral replication in host cells. 2020 The Authors. Food Frontiers published by NCU, NWU, JSU, ZJU & FAFU and John Wiley & Sons Australia, Ltd. -
Carbon dots as an effective material in enzyme immobilization for sensing applications
In carbon dots (CDs), both graphene quantum dots and carbon quantum dots were the latest entrants to the carbon family, all of which are spherical carbon nanoparticles of size <10nm. CDs have found their way in the various applications in the field of chemical sensing, biosensing, bioimaging, photocatalysis, nanomedicine, and electrocatalysis ever since their discovery. CDs provide interesting attributes to electrochemical and optical biosensing using enzyme biosensor due to they have desired advantages of biocompatibility, excellent physicochemical properties, high resistance to photo bleaching, intrinsic non/low-toxicity, high solubility, large specific surface area for the binding of enzymes, and low quantum yields, as well as their ability for modification with the attractive surface area. Surface active functional groups such as epoxide, hydroxyl(OH), and carboxylic acid (COOH) groups can be used for the immobilizing biomolecules on CDs. The enzyme immobilization is a process which is generally carried out by ionic/covalent interaction, encapsulation, and adsorption. The process of adsorption is considered to be a simple, effective, and economical method for enzyme immobilization. Thus enzymes immobilized on CDs have shown significant improvement in both activity and stability. This chapter aims to throw light on the progress and development of enzyme immobilization (e.g., laccase, bovine serum albumin, and horseradish peroxidase) in the CDs, which acts as a probe for sensing application, with laying emphasis on their synthesis along with the challenges faced in this exciting and promising field. 2023 Elsevier Inc. All rights reserved. -
5G-UFMC System For PAPR Reduction Using SRC-Precoding With Different Numerologies
Universal Filtered Multicarrier (UFMC) has been incorporated in 5G and is likely to be considered in future generations (B5G). The prominent limitation of UFMC manifests as a high Peak-to-Average Power Ratio (PAPR). Our suggested approach to address the Peak-to-Average Power Ratio (PAPR) issue in UFMC signals involves the application of diverse precoding matrices, including Square Root Raised Cosine Function (SRC), Discrete Cosine Transform (DCT), and Discrete Hartley Transform (DHT).This technique reduces the PAPR performance of UFMC signals over current state of the art methods. In square root raised cosine (SRC) precoding techniques, a novel precoding matrix is adapted for minimizing PAPR and improvement of BER respectively. Results show that the different subcarrier was applied and surpasses all existing techniques in reduction of PAPR and BER improvement. A novel SRC-Precoding technique reduces PAPR by 5dB for considering 512 sample points with QAM modulation as compared to 10dB for the conventional technique. Additionally, the Bit Error Rate Performance is maintaining 14dB when compared to conventional technique. Furthermore, the evaluation of Bit Error Rate (BER) performance and Peak-to-Average Power Ratio (PAPR) in the UFMC system reveals superior results compared to conventional technique. 2024 IEEE. -
Tomato Plant Disease Classification Using Transfer Learning
Detecting and categorizing diseases in tomato plants poses a significant hurdle for farmers, resulting in considerable agricultural losses and economic harm. The prompt underscores the significance of promptly identifying and classifying diseases to enact successful management strategies. Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in tasks involving image classification, notably in categorizing diseases that impact tomato plants. However, CNN models can be computationally expensive to train and require large datasets of labeled images. Utilizing advanced CNN models can enhance the efficacy of classification models for tomato plant diseases, simultaneously decreasing computational expenses and the demand for extensive training data. Enhanced CNN models can be developed using a variety of techniques, such as transfer learning, data augmentation, and residual networks. This project aims to implement a tomato plant disease classification model using an enhanced convolution neural network. This work uses the lifelong learning method which is the model that allows one to learn new tasks without forgetting previous knowledge. Leveraging sophisticated CNN models can improve the effectiveness of classification models for tomato plant diseases, while also reducing computational costs and the need for extensive training data. It is beneficial for tasks where there is limited data available to train a model from scratch. 2024 IEEE. -
A Comprehensive Study on E-learning Environments for Deaf or Hard of Hearing Learners
Quality education is the fundamental right of every individual regardless of the disabilities they have. For the Deaf or Hard of Hearing (d/DHH) people, e-learning is the most promising way to access the educational materials referred to as digital learning objects (LO) at any time and space which increase their autonomous learning skills. This form of instruction delivery was widely accepted during the outbreak of Covid-19. Hence a background study has been conducted to investigate the challenges in teaching the d/DHH learners during the pandemic. This research work aims at providing a personalized e-learning environment to the d/DHH student community belonging to St. Clare Oral Higher Secondary School for The Deaf, situated in Kerala. To build personalized systems, the primary step is to review the existing e-learning solutions available in the literature and the adaptation techniques implemented by them to offer personalization in line with the components of traditional adaptive e-learning systems. The study carried out in this paper illuminates the need of personalized e-learning platforms that adapt the basic needs, abilities and disabilities of deaf learners which will find the 'best learning solutions' in the form of learning objects. 2023 IEEE. -
Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
As e-Learning emerges as a promising tool for instruction delivery, personalizing the e-Learning platform for DHH learners will benefit them to improve their learning engagement and educational attainment. This study aims to collect and analyze the different features unique to DHH learners and analyze the significant features among them. This study highlights the importance of addressing the diversity among DHH learners, while creating a personalized learning environment for them. With this focus, we employ the K-Means clustering algorithm to group the learners based on similar needs and preferences and identified that distinguishing clusters can be formed within the DHH group. We also tried to understand the significant features contributing to forming well separated groups. These results provide valuable insights into the diverse preferences and requirements when they interact with the learning materials. These findings emphasize the significance of personalized approach for DHH learners in educational settings and serve as the stepping stone to develop a personalized learning environment for them. 2024 IEEE. -
Discovering the Micro-Clusters from a group of DHH learners: An approach using machine learning techniques
The e-learning environment is essentially helpful for improving the autonomous learning skills of the DHH learners. Facing numerous resources online, DHH learners need support to choose the right learning materials. This can be done by recommending suitable learning objects to similar types of learners. Hence, this research attempts to explore the possibilities of forming micro clusters from the group of DHH learners to improve the recommendation. As a result of k-means, three different micro clusters are formed. So, from the initial analysis, it is identified that the formation of micro clusters is possible, and features such as communication and learning ways play an important role in forming the well-defined micro clusters. This will definitely help the teachers in traditional classrooms and recommendation engines in e-learning to explore the micro clusters of learners with same learning patterns and communication preferences to appropriately stream the right pedagogical methods. 2024, IGI Global. All rights reserved. -
Predicting nitrous oxide contaminants in Cauvery basin using region-based convolutional neural network
Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N2O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N2O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N2O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N2O is a matter of environment. The outcomes encompass the specialized N2O contaminant model for riverbeds and the implementation of RCNN achieves precise N2O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N2O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N2O pollutants in river sediments. 2024 Elsevier B.V.