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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 and Implementation of Machine Learning-Based Hybrid Model for Face Recognition System
Face recognition technologies must be able to recognize users faces in a chaotic environment. Facial detection is a different issue from facial recognition in that it requires reporting the position and size of every face in an image, whereas facial recognition does not allow for this. Due to their general similarity in look, the photographs of the same face have several alterations, which makes it a challenging challenge to solve. Face recognition is an extremely challenging process to do in an uncontrolled environment because the lighting, perspective, and quality of the image to be identified all have a significant impact on the process's output. The paper proposed a hybrid model for the face recognition using machine learning. Their performance is calculated on the basis of value derived for the FAR, FRR, TSR, ERR. At the same time their performance is compared with some existing machine learning model. It was found that the proposed hybrid model achieved the accuracy of almost 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect
Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work. 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies
Conducting an in-depth study on algorithms addressing the interaction problem in the fields of machine learning and IoT security involves a meticulous evaluation of performance measures to ensure global reliability. The study examines key metrics such as accuracy, precision, recall, and F1 scores across ten scenarios. The highly competitive algorithms showcase accuracy rates ranging from 95.5 to 98.2%, demonstrating their ability to perform accurately in various situations. Precision and recall measurements yield similar information about the model's capabilities. The achieved balance between accuracy and recovery, as determined by the F1 tests ranging from 95.2 to 98.0%, emphasizes the practical importance of data transfer in the proposed method. Numerical evaluation, in addition to an analysis of overall performance metrics, provides a comprehensive understanding of the algorithm's performance and identifies potential areas for improvement. This research leads to advancements in the theoretical vision of machine learning for IoT protection. It offers real-world insights into the practical use of robust models in dynamically changing situations. As the Internet of Things environment continues to evolve, the study's results serve as crucial guides, laying the foundation for developing strong and effective security systems in the realm of interaction between virtual and material reality. The Author(s) 2024. -
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method
In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images. 2023 IEEE. -
Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
In order to reduce the expense of radiologists, deep learning algorithms have recently been used in the mammograms screening field. Deep learning-based methods, like a Convolutional Neural Network (CNN), are now being used to categorize breast lumps. When it involves classifying mammogram imagery, CNN-based systems clearly outperform machine learning-based systems, but they do have certain disadvantages as well. Additional challenges include a dearth of knowledge on feature engineering and the impossibility of feature analysis for the existing patches of pictures, which are challenging to distinguish in low-contrast mammograms. Inaccurate patch assessments, higher calculation costs, inaccurate patch examinations, and non-recovered patched intensity variation are all results of mammogram image patches. This led to evidence that a CNN-based technique for identifying breast masses had poor classification accuracy. Deep Learning-Based Featured Reconstruction is a novel breast mass classification technique that boosts precision on low-contrast pictures (DFN). This system uses random forest boosting techniques together with CNN architectures like VGG 16 and Resnet 50 to characterize breast masses. Using two publicly accessible datasets of mammographic images, the suggested DFN approach is also contrasted with modern classification methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Finding Real-Time Crime Detections during Video Surveillance by Live CCTV Streaming Using the Deep Learning Models
Nowadays, securing people in public places is an emerging social issue in the research of real-Time crime detection (RCD) by video surveillance, in which initial automatic recognition of suspicious objects is considered a prime problem in RCD. Dynamic live CCTV monitoring and finding real-Time crime activities by detecting suspicious objects is required to prevent unusual activities in public places. Continuous live CCTV video surveillance of objects and classification of suspicious activities are essential for real-Time crime detection. Deep training models have greatly succeeded in image and video classifications. Thus, this paper focuses on the use of trustworthy deep learning models to intelligently classify suspicious objects to detect real-Time crimes during live video surveillance by CCTV. In the experimental study, various convolutional neural network (CNN) models are trained using real-Time crime and non-crime videos. Three performance parameters, accuracy, loss, and computational time, are estimated for three variants of CNN models for the real-Time crime classifications. Three categories of videos, i.e., crime video (CV), non-crime video (NCV), and weapon-crime video (WCV), are used in the training of three deep models, CNN, 3D CNN, and Convolutional Long short-Term memory (ConvLSTM). The ConvLSTM scored higher accuracy, lower loss values, and runtime efficiency than CNN and 3D CNN when detecting real-Time crimes. 2024 ACM. -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Additively Composite Model Objective Function for Routing Protocol for Low-Power and Lossy Network Protocol
The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. 2024 Totem Publisher, Inc. All rights reserved. -
A comprehensive investigation of the effect of mineral additives to bituminous concrete
Research efforts to employ sustainable materials for road construction have been on the rise in recent years. In particular, the use of polymers as additives in asphalt mix has been actively explored by several researchers. Bituminous pavementsnormally constructed in India, have increasing number of premature failures, due to increase in traffic density and noteworthy variations in road temperatures. The modified binders have proven to improve numerous properties of bituminous surfaces such as temperature susceptibility, fatigue life, creep, resistance to permanent deformation and rutting. The present study has focussed on the experimental investigations conducted to evaluate the influence of mineral additives, such as wollostonite and Rice Husk Ash (RHA) on Indirect Tensile Strength (ITS) and Tensile Strength Ratio (TSR) of bituminous concrete (BC)maintaining uniformity of aggregate properties.The results establish that the bituminous concrete blends modified using rice husk ash at 20% and wollostonite at 8%, with hydrated lime are most suitable for practical applications. 2021 Elsevier Ltd. All rights reserved. -
Solid-state fermentation of pigment producing endophytic fungus Fusarium solani from Madiwala lake and its toxicity studies
Several consumer products look enticing due to colors and there has been a demand for colors for various applications ever since human civilization started. Although in the primitive days, humans had used natural colors, the wake of the industrial revolution saw the excessive use of diverse types of synthetic colors. Although it looked very fancy initially, slowly scientists discovered the dangers of large-scale use of these colorants. The current demand is for natural colors, and hence, there is a scope for sources of natural colors from biosources. The present study involved the isolation of an endophytic fungus, Fusarium solani producing a red pigment from the polluted waters of Madiwala lake in Bangalore. The fungal extract showed good antimicrobial and moderate antioxidant properties. Cytotoxicity assays using brine shrimps proved negligible toxicity which is a positive trait for natural colorants for safer applications in industries. Media optimization and solid state fermentation were carried out to improve the yield of the fungal pigment and also to formulate a cheaper media for fungal multiplication and pigment production. Green synthesis of silver nanoparticles was also carried out with the fungal extract and the nanoparticles were characterized. Thus, the present study provides an option for the extraction of environment friendly natural colorant from the fungus F. solani for potential industrial applications. 2024 Bhoomika Prakash Poornamath, et al. -
Template based speech enhancement of disordered speech
In this paper, we have taken Electro-Larynx (EL) speech and have improved the speech quality, electro-larynx speech was improved in terms of naturalness and intelligibility by introducing variations in the F0-contour and template matching with correlation coefficient. Initially, we introduced two different speech signals, the first speech signal introduced was healthy speech signal and the second speech signal introduced was disordered speech signal. Here, the second speech signal, the disordered speech is taken as the EL speech. The fundamental frequency or pitch was extracted first from the two inputed speech signals, then the contour of each fundamental frequency was extracted from the two input speech signals. Using these extracted features of fundamental frequency the gender classification by K-means algorithm was instigated. The same process was implemented with F0 contour features which was extracted using K-NN algorithm. EL speech contains directly radiated electrolarynx noise (DREL). The noise was filtered out using spectral subtraction algorithm. Once DREL noise is removed from EL speech, the quality of the speech was greatly improved. Then EL enhanced speech signal is compared and mapped with healthy speech signal using template matching algorithm with the help of correlation coefficient, this improves the overall quality, that is the naturalness and intelligibity of the introduced disordered speech signal. This technique helps solve the major problem of speech faced by differently abled persons with larynx disorder. 2016 IEEE. -
Modelling Networks withAttached Storage Using Perfect Italian Domination
Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
On Some Graphs Whose Domination Number Is thePerfect Italian Domination Number
Perfect Italian Domination (PID) is a vertex labelling of a graph G by numbers from the set such that a vertex in G labelled 0 has a neighbourhood where the summation of the labels of the vertices in it is precisely 2. The summation of labels on the vertices of the graph which satisfy the PID labelling is known as its PID number, and is the minimum possible PID number of a graph G. We find some characterization of graphs for which . We also find a lower bound for |V(G)|, which satisfies the same. Further, we discuss the graphs that satisfies or . A realisation problem is used to prove that PID cannot be bounded by a scalar multiple of the Domination number. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.