Incorporating Machine Learning Algorithms Research Articles

Single-cell Raman spectroscopy for rapid detection of bacteria in ballast water and UV254 treatment evaluation

The increasing global trade has facilitated the transfer of ship ballast water, which has emerged as a primary pathway for alien species invasion into marine ecosystems, posing significant threats to marine biodiversity. Addressing the technical challenges in rapid microorganism detection and treatment efficiency assessment, this study developed a confocal Raman microscopic imaging (CRMI) system integrated with a metal-insulator-metal (MIM) broadband surface-enhanced Raman scattering (SERS) chip, enabling efficient acquisition of single-cell Raman spectroscopy (SCRS). By incorporating machine learning algorithms, the system achieved precise identification of up to 10 bacterial types in ballast water, exhibiting remarkable performance metrics with average accuracy, sensitivity, specificity, and precision above 95.5 %, 95.5 %, 99.5 %, and 95.5 %, respectively. To evaluate the efficacy of ultraviolet (UV) treatment, a Raman spectroscopy-based approach combined with heavy water labeling was introduced to characterize the changes in bacterial single-cell metabolic activity under UV254 irradiation. Experimental results demonstrated that a 10-min UV254 exposure at an effective intensity of 2 mW/cm2 was sufficient to achieve complete bacterial sterilization for the specific ballast water used in our experiment. This study not only established an efficient and accurate method for rapid detection of mixed bacteria but also provided a novel perspective for assessing UV treatment effects. It holds significance and practical value for optimizing ship ballast water management strategies and safeguarding the safety of marine ecosystems.

DDDR-33. TRANSCRIPTOMICS-BASED NOVEL DRUG DISCOVERY IN DIFFUSE MIDLINE GLIOMA

Abstract Introduction. Pediatric high-grade gliomas are the most common cause of cancer-related death in children, of which the most malignant and devastating tumors include diffuse midline glioma (DMG). Recent availability of tumor samples and advances in next-generation sequencing enable the profiling of thousands of molecular features in DMG. We employ a system-based approach that uses gene expression as a representation of the molecular state, providing an avenue for therapeutic discovery. Previously, we used this approach to identify repurposed candidates for DMG; however, the final candidate had poor brain penetration, prompting us to discover novel compounds with better brain penetration. To apply it to novel compound discovery, we need the expression profiles of all library compounds, which is impractical for millions of compounds. To overcome this, we have developed a platform, Gene expression profiles Predictor on chemical Structures (GPS), that incorporates machine learning algorithms which leverage existing drug-induced gene expression profiles to predict gene expression based on compound structure. methods. DMG RNA-Seq samples were acquired from St. Jude Children’s Research Hospital and Children’s Brain Tumor Network (CBTN). We developed deep learning autoencoder to search reference normal tissues from Genotype-Tissue Expression (GTEx). A DMG meta-signature was created and fed into GPS to predict drug candidates from the Enamine CNS library, seven of which were experimentally tested in vitro and the most promising one subsequently validated in vivo. Results. Cell proliferation analyzed by MTS assay showed three compounds with IC50< 50 µM in K27M-mutant DMG cell lines (SF8628, DIPG007, SU-DIPG36). Toxicity study showed no adverse effects in the mice treated with 100-200 mg/kg of the most promising compound (IC50 = 1.9 µM in DMG cells). Conclusion. Our novel computational framework leverages deep learning to fill the gap in drug discovery in DMG. Work is underway to determine antitumor activity, brain penetration (via HPLC) and survival benefit in the mice bearing DMG PDX treated with the lead compound.

Magnetic 3D macroporous MOF oriented urinary exosome metabolomics for early diagnosis of bladder cancer

Bladder cancer (BCa) exhibits the escalating incidence and mortality due to the untimely and inaccurate early diagnosis. Urinary exosome metabolites, carrying critical tumor cell information and directly related to bladder, emerge as promising non-invasive diagnostic biomarkers of BCa. Herein, the magnetic 3D ordered macroporous zeolitic imidazolate framework-8 (magMZIF-8) is synthesized and used for efficient urinary exosome isolation. Notably, beyond retaining the single crystals and micropores of conventional ZIF-8, MZIF-8 is further enhanced with highly oriented and ordered macropores (150 nm) and the large specific surface area (973 m2·g–1), which could enable the high purity and yield separation of exosomes via leveraging the combination of size exclusion, affinity, and electrostatic interactions between magMZIF-8 and the surfaces of exosome. Furthermore, the magnetic and hydrophilic properties of magMZIF-8 will further simplify the process and enhance the efficiency of separation. After conditional optimization, a 50 mL of urine is sufficient for exosome metabolomics analysis, and the time for isolating exosomes from 42 urine samples was 2 hours only. Incorporating machine learning algorithms with LC-MS/MS analysis of the metabolic patterns obtained from isolated exosomes, early-stage BCa patients were differentiated from healthy controls, with area under the curve (AUC) value of 0.844–0.9970 in the training set and 0.875-1.00 in the test set, signifying its potential as a reliable diagnostic tool. This study offers a promising approach for the non-invasive and efficient diagnosis of BCa on a large scale via exosome metabolomics.Graphical

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

Analysis of signals from air conditioner compressors with ordinal patterns and machine learning

Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment.

Robust chemical analysis with graphene chemosensors and machine learning.

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated bynon-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

A novel Real-time Control System for next generation gravitational-wave detectors

This contribution presents the INFN Pisa group research and development efforts aimed at defining and implementing a novel Real-time Control System (RCS) for next-generation interferometers, focusing on the initial phase of the Einstein Telescope (ET) gravitational wave detector. The RCS handles complex closed-loop systems, managing data collection from sensing elements, processing and actuator control within well defined time constraints. It integrates multiple spatially distributed control units, facilitating communications among them, with external systems as well as with users. Two distinct hardware approaches are explored: “Katane” and “Zancle”. These systems primarily differ in their core processing elements. The “Katane” system is built on powerful DSP processor boards, based on the Super-Attenuator control system, developed at INFN Pisa for Advanced Virgo detector. It uses the MicroTCA.4 architecture standard, ensuring flexibility, easy maintenance, and fast data exchange among the various custom modules, including several types of Front-End data converter boards, FPGA-based pre-processing boards, DSPs and standard processors. On the other hand, ”Zancle” explores GPU-based processing, aiming to use cost-effective consumer market systems and to leverage substantial computational power of these devices, also incorporating machine learning algorithms.

Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm

BackgroundCerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach. MethodsThe assessment encompassed demographic, clinical, biochemical, and MRI-derived parameters. Correlation analysis, logistic regression, receiver operating characteristic (ROC) curve analysis, and nnet neural network algorithm were employed to evaluate the predictive value of machine learning algorithms and MRI parameters for stroke occurrence within 3 years in patients with CSVD. ResultsMRI-derived parameters, including average WMH volume, perfusion deficit volume, ischemic core volume, microbleed count, and perivascular spaces, exhibited strong correlations with stroke occurrence (P < 0.001). MRI-derived parameters demonstrated high sensitivities (0.719 to 0.906), specificities (0.704 to 0.877), and AUC values (0.815 to 0.871). The combined model of machine learning algorithms and MRI parameters yielded an AUC value of 0.925, indicating significantly high predictive accuracy for identifying the risk of stroke within three years in CSVD patients. ConclusionThe integrated risk stratification model, incorporating machine learning algorithms and MRI parameters, demonstrated strong predictive potential for stroke within three years in patients with CSVD. This model offered valuable insights for personalized interventions and clinical decision-making in the management of CSVD.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

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, &amp; 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

&lt;p&gt;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.&lt;/p&gt;

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