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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. -
AI Enhanced Global Economic Resilience: Predicting and Mitigating Financial Crises
Global economic resilience relies on our ability to predict and mitigate financial crises, especially for small and medium-sized enterprises (SMEs)vital drivers of economic growth. These SMEs are particularly susceptible to market fluctuations in business-to-business or consumer-focused sectors. Organizations integration of big data technologies has revolutionized global financial data management, enhancing our resilience. In our interconnected world, the timely identification of impending financial crises is crucial. It's the linchpin to prevent catastrophic collapses that could send shockwaves through the global economy and societies. To address this challenge, we introduce the Nature-inspired Red-optimized Stochastic Artificial Neural Network (NRFO-SANN), a powerful instrument for detecting global financial crises and anomalies. Our approach leverages a diverse array of financial data collected worldwide. Employing Minmax normalization, we meticulously pre-process the data, ensuring its readiness for analysis. Principal Component Analysis (PCA) extracts the core features crucial for crisis identification. These insights fuel the implementation of the NRFO-SANN method, unlocking the potential of AI-driven prediction. The results are remarkable. Our NRFO-SANN model not only outperforms its peers but does so resoundingly. With an impressive 96% accuracy rate, it operates efficiently, taking just 1s for computations. It boasts an F-score of 96.5%, a sensitivity of 94% and a specificity of 95%. This model equips us with a robust tool for anticipating and responding to global financial crises, ultimately reinforcing the stability and resilience of the global economy and societies. In this era of AI-empowered global economic resilience, we possess enhanced capabilities to navigate the intricacies of our interconnected world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Machine Learning Ensemble Approach for Corrupt Data Packet Identification
In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software Systems
Software defect prediction (SDP) is a critical method in modern software development, saving costs while ensuring the delivery of high-quality software systems. This study investigates the vital importance of SDP, focusing on its function in detecting and correcting software faults that might lead to system failures. SDP forecasts defect-proneness and optimizes software-testing processes by using software metrics such as lines of code and change information. The chapter examines the progress of SDP research since the turn of the century, emphasizing the academic emphasis on refining static characteristics and establishing efficient learning methods for building high-performance defect predictors. Recognizing the economic consequences of software flaws, particularly in major engineering projects, this chapter emphasizes the importance of SDP in limiting project failures and economic losses in the twenty-first century. Several defect prediction methods are investigated in the context of software quality, with an emphasis on ongoing attempts to prevent and discover errors early in the software development lifecycle. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.