Incorporating Machine Learning Algorithms Research Articles

Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning.

The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.

Smart Irrigation with Intrusion Detection

This paper proposes a comprehensive approach to smart irrigation by integrating intrusion detection mechanisms. By combining the functionalities of smart irrigation systems with intrusion detection systems (IDS), the proposed framework offers enhanced security and reliability in agricultural water management. The system employs a network of sensors weather, and soil moisture levels patterns, and other relevant parameters to optimize irrigation scheduling. Concurrently, it utilizes intrusion detection algorithms to identify and respond to unauthorized access attempts or anomalous behaviors within the irrigation infrastructure. The proposed approach represents a noteworthy advancement in the direction of sustainable and secure management of water in agriculture, contributing to improved crop yields, resource conservation, and overall perseverance in the face of emerging challenges. Additionally, by incorporating machine learning algorithms into the intrusion detection system, the framework is able to change and grow over time, enhancing its capacity to identify and neutralize possible threats. Furthermore, the system can more accurately predict when irrigation is needed by utilizing real-time data analysis and predictive modeling approaches. This maximizes water usage efficiency while reducing waste. This all-encompassing strategy encourages a more ecologically sustainable method of managing water resources while also strengthening the resilience of farming operations. Furthermore, the framework enables farmers to take educated decisions in real-time, maximizing productivity and lowering risks, by giving them actionable insights and alerts about security breaches and irrigation needs.

Advanced Traffic Clearance System for Emergency Vehicles

This paper elaborates development of Traffic Clearance System using camera and mobile Net. In urban areas, navigating through traffic congestion poses a significant challenge for emergency vehicles, often leading to delays that can impact response times and potentially endanger lives. This paper presents an innovative traffic control system leveraging camera technology to prioritize the passage of emergency vehicles. The proposed system utilizes a network of cameras strategically placed at intersections to detect emergency vehicle sirens and flashing lights. Upon detection, the system dynamically adjusts traffic signal timing to create a green corridor, facilitating the swift and safe passage of emergency vehicles. Additionally, the system incorporates machine learning algorithms to optimize traffic flow, minimize congestion, and ensure efficient utilization of road space. Through simulation studies and real-world testing, the effectiveness and reliability of the proposed system are evaluated, demonstrating its potential to enhance emergency response capabilities and improve overall traffic management in urban environments.

Efficient Disease Prediction Framework to Suggest Early Treatment Decisions in Healthcare

This study proposes a novel framework for disease prediction intended to aid healthcare providers in making early treatment decisions. In order to efficiently identify early disease indicators and provide prompt suggestions for treatment interventions, the framework incorporates machine learning algorithms, clinical data analysis, and predictive modeling techniques (Smith, Johnson, & Davis, 2023). For the purpose of creating the framework, a comprehensive dataset made up of clinical records, demographic data, and test results from a large patient cohort was gathered. The dataset was analyzed using a variety of machine learning methods, and predictive models for various diseases were created. As evaluation criteria, accuracy, precision, recall, and F1-score were used to gauge how well these models performed. The experimental findings show that the suggested framework has a high degree of disease prediction accuracy. Additionally, by recommending early treatment selections. The approach offers the potential to improve patient outcomes and lower healthcare expenditures based on anticipated illness outcomes. The study benefits healthcare professionals, policymakers, and academics looking to use data-driven strategies for better disease management and patient care by providing an effective method for disease prediction and early treatment decisions.

Adaptive Multi-Layer Security Framework (AMLSF) for real-time applications in smart city networks

<p>This study introduces the Adaptive Multi-Layer Security Framework (AMLSF), a novel approach designed for real-time applications in smart city networks, addressing the current challenges in security systems. AMLSF innovatively incorporates machine learning algorithms for dynamic adjustment of security protocols based on real-time threat analysis and device behavior patterns. This approach marks a significant shift from static security measures, offering an adaptive encryption mechanism that scales according to application criticality and device mobility. Our methodology integrates hierarchical key management with real-time adaptability, further enhanced by an advanced rekeying strategy sensitive to device mobility and communication overhead. The paper’s findings reveal a substantial improvement in security efficiency. AMLSF outperforms existing models in encryption strength, rekeying time, communication overhead, and computational time by significant margins. Notably, AMLSF demonstrates an adaptability increase of over 30% compared to traditional models, with encryption strength and computational time efficiency improving by approximately 25%. These results underscore AMLSF’s capability in delivering robust, dynamic security without sacrificing performance. The achievements of AMLSF are significant, indicating a promising direction for smart city security frameworks. Its ability to adapt in real-time to various security needs, coupled with its performance efficiency, positions AMLSF as a superior choice for smart city networks facing diverse and evolving security threats. This framework sets a new benchmark in smart city security, paving the way for future developments in this rapidly advancing field.</p>

Expense Tracker App

Abstract: The expense tracker application presents a comprehensive solution for efficient financial management and savings optimization. With a user-friendly home page showcasing transaction summaries and interactive graphs, users gain immediate insights into their spending patterns. The dedicated savings page facilitates goal-setting and progress tracking, enhancing financial discipline. Additionally, the app features an expense updating mechanism for real-time data accuracy. Future iterations will incorporate machine learning algorithms to provide personalized recommendations and predictive insights, further empowering users to make informed financial decisions. With its intuitive interface and future-proof design, the application emerges as a pivotal tool in navigating the complexities of personal finance, fostering proactive savings habits and financial well-being.

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.

An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction. We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation. Both SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort. The ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.

Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.

Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach.

The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, motor signals recorded from the patients' median nerve were transformed into time-frequency spectrograms using the short-time Fourier transform (STFT). These spectrograms were then used as input to a deep two-dimensional convolutional neural network (CONV2D) for classification into two categories: patients and controls. In the second methodology, sensory signals from the patients' median and ulnar nerves were subjected to multilevel wavelet decomposition (MWD), and statistical and non-statistical features were extracted from the decomposed signals. These features were utilized to train and test classifiers. The classification target was set to three categories: normal subjects (controls), patients with mild CTS, and patients with moderate to severe CTS based on conventional electrodiagnosis results. The results of the classification analysis demonstrated that both methodologies surpassed previous attempts at automatic CTS diagnosis. The classification models utilizing the motor signals transformed into time-frequency spectrograms exhibited excellent performance, with average accuracy of 94%. Similarly, the classifiers based on the sensory signals and the extracted features from multilevel wavelet decomposition showed significant accuracy in distinguishing between controls, patients with mild CTS, and patients with moderate to severe CTS, with accuracy of 97.1%. The findings highlight the efficacy of incorporating machine learning algorithms into the diagnostic processes of NCS, providing a valuable tool for clinicians in the diagnosis and management of neuropathies such as CTS.

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

This paper aims to present a critical review of the published scientific papers that have addressed the issue of forest health in Bangladesh using remote sensing techniques. A systematic review approach has been followed in this study where all the available papers on the application of remote sensing to assess the forest health vegetation condition of Bangladesh were considered for review. That search resulted in the selection of 48 papers. The findings indicate that remote-sensing-based studies have focused mostly on forest cover mapping and change, and landcover change detection rather than assessing the overall health condition of those forests. Also, among the major forests of the country, most studies have been conducted on Mangrove (Sundarban) forests whereas the least number of studies were found for the forests in the Chittagong Hill Tracts Forests areas and those studies were mostly conducted after the Rohingya crisis. Landsat satellite products have been most extensively used for their broader temporal resolution and availability while a few studies have worked with other products like MODIS, Sentinel, SPOT, etc. The application of advanced classification approaches incorporating machine learning algorithms and ground validation has shown effectiveness for investigating the forest or overall ecosystem health in a more detailed way. Although the RS techniques are increasingly used to study the forests of Bangladesh, forest health-specific and indicator-based research is yet to be done which can ensure sustainable forest management.&#x0D; The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 107-121

Employing Collective Intelligence at the IoT Edge for Spatial Decisions

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments.

An applied study of a technique incorporating machine learning algorithms to optimize water demand prediction

Abstract In water resource management, accurate water demand prediction is essential for developing effective water supply strategies and optimizing resource allocation. This study aims to investigate machine learning algorithms, particularly echo state network (ESN) models, to improve the accuracy and efficiency of water demand prediction. ESN models are selected for their excellent nonlinear time series processing capabilities, which address the challenges of traditional prediction methods when dealing with complex water resource systems. By optimizing the parameters of the ESN model, the study hopes to provide a more scientific and efficient method for residential domestic and agricultural water demand forecasting, thus supporting more refined water resources planning and management decisions. Residential water demand prediction and crop water demand prediction are the two parts of this study. In the prediction of residential water demand, based on the actual data of City Z, the optimized ESN model predicts the water demand in 2025, and the total water demand in the baseline scenario is 790.9 million m3, and the expected values of water demand in different scenarios combined with the economic growth rate and the change of water price range from 659.4708 million m3 to 730.448 million m3. The article’s accuracy analysis of crop water demand prediction indicates that the model’s relative errors in predicting the water demand of the three major crops are limited to 10%. The ESN model optimized using the machine learning algorithm in this paper has good potential for water demand prediction and is an efficient and accurate prediction tool for managing water resources.

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

Enhancing precision in chemical experiments

In this article, the integration of chemistry and information technology is explored to enhance precision in chemical experiments. This fusion involves leveraging electronic data and employing intelligent analysis to improve the accuracy of experiment results and reduce errors. The paper delves into methods and tools that utilize the combination of chemical data with information technology to enhance experiment performance. The innovative synergy between chemistry and information technology contributes to improved efficiency and precision in the field of chemistry. This intelligent approach to managing chemical experiment data utilizes algorithms and machine learning models for more accurate chemical data analysis. Furthermore, it enhances predictive capabilities for experiment outcomes and provides optimal solutions for conducting new experiments. This integration not only aids in the improvement of chemical experiment performance but also plays a significant role in various industries, including pharmaceuticals and biochemistry. The innovative combination empowers scientists to swiftly and more accurately address complex challenges in the field of chemistry. This amalgamation serves as an intelligent strategy for data management in chemical experiments. By incorporating machine learning algorithms, the analysis of chemical data becomes more precise. The integration not only focuses on retrospective analysis but also enhances the ability to predict future experiment outcomes. This predictive capability opens avenues for optimizing experimental procedures and resource utilization. The article emphasizes the transformative impact of this interdisciplinary collaboration, not only in traditional laboratory settings but also in diverse industries where chemical processes play a crucial role. Moreover, the intelligent synergy between chemistry and information technology proves instrumental in accelerating research and development processes, particularly in pharmaceuticals and biotechnology. The article concludes by highlighting the profound implications of this innovative approach, offering scientists a powerful tool to navigate and overcome the complexities of modern chemistry with increased speed and accuracy.

Enhancing dynamic security assessment in converter dominated distribution networks through digital twin-based small signal stability analysis

Abstract As renewable energy sources are increasingly integrated and distributed in the power system, the number of converters in the grid is growing. This leads to more and more dynamics in the distribution networks and may result in more frequent disturbances such as voltage fluctuations, power quality problems, etc. To prevent possible threats to the security and stability of the network due to the above issues, Dynamic Security Assessment (DSA) is necessary for a distribution network to ensure its safe and reliable operation. In this work, an innovative approach to DSA using Digital Twin (DT) technology, with a primary focus on small signal stability (SSS) analysis using an eigenvalue method is proposed. The DT of the distribution network is first constructed by incorporating machine learning algorithms and updated in real-time using data from sensors and SCADA systems to accurately represent the behaviour of the physical system. The DT will be used to continuously monitor and assess the stability of the distribution network characterised by the presence of power electronic converters. The DSA then uses SSS to analyse network behaviour under different operating conditions and identify potential risks or disturbances. This can include assessing the impact of sudden changes in load or renewable energy output, detecting potential faults or equipment failures, and identifying potential instabilities in the power system. Numerical case studies are used to verify the viability of the proposed approach. As a result, by integrating a DT into the DSA process for converter-based distribution networks, operators can improve the accuracy and efficiency of their security measures, reduce the risk of outages, and increase the overall reliability of the system.

A MATHEMATICAL MODEL FOR AN AUTOMATED SYSTEM OF MEDICAL DIAGNOSTICS

One of the primary focuses of the Republic of Kazakhstan concerning sustainable and stable improvements in the well-being of its population is the advancement of the healthcare sector. A mathematical model for an automated medical diagnostics system integrates machine learning algorithms, statistical models, and decision trees to analyze patient data and facilitate accurate diagnoses. This model enables healthcare professionals to enhance the efficiency and reliability of medical diagnostics by leveraging advanced computational techniques.In diagnosing diseases, the differential diagnosis between primary biliary cirrhosis of the liver and active hepatitis with cholestatic syndrome poses a challenge due to their overlapping symptoms. Both conditions exhibit jaundice, pruritus, fatigue, and hepatomegaly. However, distinguishing features based on clinical and laboratory observations can aid in accurate differentiation between the two states. These distinguishing features can be incorporated by developing a mathematical model for diagnosing diseases, enabling precise identification and guiding appropriate treatment strategies.&#x0D; Predicting the progression of diseases is a crucial aspect of healthcare, enabling personalized interventions and improved patient outcomes. A mathematical approach can facilitate this prediction by monitoring changes in diagnostic results aligned with the severity of symptoms, which inherently vary over the observation period. By employing mathematical modeling techniques, healthcare professionals gain valuable insights into disease progression, supporting informed decision-making and tailored treatments.&#x0D; In conclusion, developing a mathematical model for an automated medical diagnostics system, incorporating machine learning algorithms, statistical models, and decision trees, significantly contributes to healthcare. These models enhance the accuracy, efficiency, and personalization of medical diagnoses. Additionally, mathematical models aid in the differential diagnosis of challenging conditions and provide predictions regarding disease progression, ultimately benefiting patient care and treatment outcomes.

From YouTube Comments to Insights: A Sentiment Analysis of Opinions on Productivity Tools

Abstract: YouTube, a diverse content hub, acts as a prominent platform for both viewing videos and engaging through comments that express opinions. By carefully analyzing these comments, sentiments associated with products or topics become evident. This research delves into sentiment analysis of YouTube comments, specifically focusing on sentiments towards productivity tools and technologies. Through this examination of conveyed sentiments, it deepens understanding of public perceptions and attitudes toward these tools. Utilizing YouTube's reach and engagement, the study systematically collects comment data using the YouTube API and employs the established VADER sentiment analysis framework. Employing a Bag-of-Words approach, the research incorporates machine learning algorithms like Naive Bayes and Random Forest, achieving accuracy rates of 84.01% and 91.34%, respectively. In-depth temporal analysis of user engagement patterns uncovers trends, with heightened engagement followed by decline, correlating with external events, notably the Covid-19 pandemic. These insights enhance sentiment analysis and illuminate dynamics between societal occurrences, user sentiments, and digital dialogues, offering perspectives on evolving opinions about productivity tools and technologies

LEVERAGING MACHINE LEARNING AND REMOTE SENSING FOR WILDLIFE CONSERVATION: A COMPREHENSIVE REVIEW

In recent years, the application of machine learning and remote sensing technologies in wildlife conservation has demonstrated tremendous promise. This article provides a comprehensive overview of the advancements in these fields and the impact they have had on various aspects of wildlife conservation. These technologies contribute to more efficient and effective conservation strategies by automating species identification, mapping and monitoring habitats, tracking population dynamics, detecting wildlife crime, and analysing animal vocalisations. This article talks about the development of machine learning algorithms capable of classifying bird and amphibian calls, differentiating fish species, and identifying plant species such as orchids and cacti has made automated species identification possible. Incorporating machine learning algorithms with remote sensing techniquesprovides significant advantages for mapping and monitoring habitats. This article discusses that images captured by camera trapswhen combined with acoustic analysis, can help in monitoring population by enabling automated detection and tracking. These technologies provide efficient and non-invasive approaches to monitor and manage animal populations, from estimating animal density using passive acoustics to identifying and tracking endangered species such as tigers, cheetahs, and sea turtles using camera trap images. The analysis of animal vocalisations using machine learning techniques has revealed information about species behaviour, population dynamics, and habitat quality. Examining the acoustic communication of species such as black rhinoceros and Yangtze finless porpoise has been the focus of research. Acoustic analysis provides a non-invasive method for comprehending the communication patterns of animals and their implications for conservation efforts. By utilising artificial intelligence and remote sensing data, conservationists can make informed decisions and take targeted actions to protect and preserve endangered species and their habitats. These technologies continued development and application hold great promise for the future of wildlife conservation.

Obstructive Sleep Apnea Prediction Using Machine Learning

Abstract: A common sleep problem called obstructive sleep apnea (OSA) is characterized by periodic breathing pauses while you're asleep. For OSA to be effectively treated and related health concerns to be avoided, an early and precise diagnosis is essential. Machine learning approaches have become effective tools in recent years for managing and diagnosing OSA. The research that used machine learning techniques for OSA detection and prediction are reviewed in detail in this publication. A large dataset made up of polysomnography recordings and clinical data from OSA sufferers was gathered in order to achieve this. Using this dataset, many machine learning models were trained to categorize the severity levels of OSA or predict the occurrence of OSA, including support vector machines, random forests, and deep neural networks. The findings demonstrated the powerful ability of machine learning algorithms to recognize OSA and distinguish between various severity levels. These models outperformed conventional diagnostic techniques in terms of high sensitivity and specificity rates. To further shed insight on the underlying patterns and processes of the illness, feature selection approaches were used to determine the most pertinent physiological parameters for OSA detection. The effectiveness and precision of screening and therapy strategies may be improved by incorporating machine learning algorithms into OSA diagnosis. This study advances our understanding of the use of machine learning in sleep medicine and paves the door for the creation of automated and individualized OSA diagnostic tools.