Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately

A Novel IoT-based Framework for Urine Infection Detection and Prediction using Ensemble Bagging Decision Tree Classifier

One of the most common conditions treated in adult primary care medicine is Urinary Tract Infection (UTI), which accounts for a sizeable portion of antibiotic prescriptions. A high degree of diagnostic accuracy is necessary because this issue is so prevalent and important in everyday clinical practice. Particularly in light of the rising prevalence of antibiotic resistance, excessive antibiotic prescriptions should be avoided. To examine the machine learning approach and Internet of Things (IoT) for urinary tract infections, this research proposes an Ensemble Bagging Decision Tree Classifier (EBDTC). In our study, to learn more about UTI, we conducted a study in which we collected the physiological data of 399 patients and preprocessed them using the min-max scalar normalization. Feature extraction using Principle Component Analysis (PCA) and classification using Ensemble Bagging Decision Tree Classifier (EBDTC). The performance outcomes of accuracy (96.25%), precision(96.22%), recall (98.07%), and f-1 measure(97.17%) demonstrate the proposed strategy's significantly improved performance in comparison to other baseline existing techniques

Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion. This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC). We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements. We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT). Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions

An Optimised Shortest Path Algorithm for Network Rotuting & SDN: Improvement on Bellman-Ford Algorithm

Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands

Deep Residual Adaptive Neural Network Based Feature Extraction for Cognitive Computing with Multimodal Sentiment Sensing and Emotion Recognition Process

For the healthcare framework, automatic recognition of patients’ emotions is considered to be a good facilitator. Feedback about the status of patients and satisfaction levels can be provided automatically to the stakeholders of the healthcare industry. Multimodal sentiment analysis of human is considered as the attractive and hot topic of research in artificial intelligence (AI) and is the much finer classification issue which differs from other classification issues. In cognitive science, as emotional processing procedure has inspired more, the abilities of both binary and multi-classification tasks are enhanced by splitting complex issues to simpler ones which can be handled more easily. This article proposes an automated audio-visual emotional recognition model for a healthcare industry. The model uses Deep Residual Adaptive Neural Network (DeepResANNet) for feature extraction where the scores are computed based on the differences between feature and class values of adjacent instances. Based on the output of feature extraction, positive and negative sub-nets are trained separately by the fusion module thereby improving accuracy. The proposed method is extensively evaluated using eNTERFACE’05, BAUM-2 and MOSI databases by comparing with three standard methods in terms of various parameters. As a result, DeepResANNet method achieves 97.9% of accuracy, 51.5% of RMSE, 42.5% of RAE and 44.9%of MAE in 78.9sec for eNTERFACE’05 dataset.  For BAUM-2 dataset, this model achieves 94.5% of accuracy, 46.9% of RMSE, 42.9%of RAE and 30.2% MAE in 78.9 sec. By utilizing MOSI dataset, this model achieves 82.9% of accuracy, 51.2% of RMSE, 40.1% of RAE and 37.6% of MAE in 69.2sec. By analysing all these three databases, eNTERFACE’05 is best in terms of accuracy achieving 97.9%. BAUM-2 is best in terms of error rate as it achieved 30.2 % of MAE and 46.9% of RMSE. Finally MOSI is best in terms of RAE and minimal response time by achieving 40.1% of RAE in 69.2 sec

Pathology for gastrointestinal and hepatobiliary cancers using artificial intelligence

From visual data, artificial intelligence (AI) can extract complicated information. Histopathology pictures of gastrointestinal (GI) and liver cancer provide a large quantity of data that human observers can only decipher in part. AI permits the in-depth study of digitized histological slides of GI and liver cancer, complementing human observers, and has a wide variety of clinically useful applications. First, AI can recognize tumor tissue automatically, alleviating pathologists' ever-increasing labor. Furthermore, AI can capture prognostically significant tissue characteristics and so predict clinical prognosis across GI and liver cancer types, perhaps surpassing pathologists' capabilities

Pathology for Gastrointestinal and Hepatobiliary Cancers Using Artificial Intelligence

From visual data, artificial intelligence (AI) can extract complicated information. Histopathology pictures of gastrointestinal (GI) and liver cancer provide a large quantity of data that human observers can only decipher in part. AI permits the in-depth study of digitized histological slides of GI and liver cancer, complementing human observers, and has a wide variety of clinically useful applications. First, AI can recognize tumor tissue automatically, alleviating pathologists' ever-increasing labor. Furthermore, AI can capture prognostically significant tissue characteristics and so predict clinical prognosis across GI and liver cancer types, perhaps surpassing pathologists' capabilities

A Disaster Management Framework Using Internet of Things-Based Interconnected Devices

The advanced technology Internet of Things (IoT) visualizes a worldwide, that is, internally connected, networks of smart physical entities. IoT is a promising technology used in several applications including disaster management. In disaster management, the role of IoT is so important and ubiquitous and could be life-saving. This article describes the role of IoT in disaster management. More precisely, it presents IoT-based disaster management for different kind of disasters with a comparison between some solutions that are available in the market. It shows an implementation of some examples of the application of IoT such as early-warning system for fire detection and earthquake and represents some approaches talking about the application, IoT architecture, and focusing of the study on different disasters. This study could be a good guide to stakeholder about the use of IoT technology to secure their smart cities’ infrastructure and to manage disaster and reduce risks