Advanced Machine Learning Algorithms Research Articles

A Delve into the Implementation of Data Quality Framework on Very Large Databases

In today’s data-driven landscape, the reliability and accuracy of information are paramount for informed decision-making and operational efficiency. A robust data quality framework provides a structured methodology for managing and maintaining the quality of data as-sets throughout their lifecycle. It in-troduces a comprehensive data qual-ity framework encompassing four key pillars: data governance, data profil-ing, data cleansing, and data monitor-ing. The foundation of the framework lies in establishing clear data governance policies and procedures, defining owner-ship, responsibilities, and accountability for data quality across the organization. Data profiling techniques are then em-ployed to assess the quality of data, iden-tifying anomalies, inconsistencies, and inaccuracies. Subsequently, data cleans-ing processes are implemented to rec-tify these issues, employing techniques such as deduplication, standardization, and validation to ensure data integrity. Continuous data monitoring is integral to the framework, enabling proactive de-tection of data quality issues and facil-itating timely corrective actions. Ad-vanced analytics and machine learn-ing algorithms may be leveraged to au-tomate monitoring processes, flagging anomalies and deviations from prede-fined quality thresholds.

Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and β-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics.

Data-Driven Decision-Making: Accurate Customer Churn Prediction with Cat-Boost

In the cutthroat telecommunications industry, customer churn poses a significant threat to profitability and brand reputation. To combat this, we propose a data-driven approach to predict churn, leveraging advanced Machine Learning (ML) algorithms on a comprehensive dataset of 7043 customer records with 21 features sourced from the IEEE website. By applying one-hot encoding to transform categorical data, we optimize ML algorithm performance. Our study pits three ML algorithms against each other: Cat Boost, Stochastic Gradient Boost, and Extreme Gradient Boost. The results are striking, with Cat Boost incipient as the top performer, boasting an impressive 94% accuracy, 90% precision, 97% recall, 94% F1-score, and an AUC of 0.94. These discoveries emphasize Cat Boost's extraordinary ability to predict churn, empowering telecom companies to proactively prevent attrition, target high-risk customers, and make data-driven decisions to make the most of profits. In today's speedily developing technical landscape, correctly predicting churn is critical to meeting the ever-changing potential of customers. Our research marks a noteworthy innovation in addressing churn trials, flooring the way for long-term accomplishment in the telecom industry.

Fraud Detection in Bank Loans with KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results

Identifying mining-induced chromium contamination in soil through visible-near infrared spectroscopy and machine learning

The extraction, purification, and utilization of mineral resources have been among the largest anthropogenic sources of chromium (Cr) in soil. Determining Cr contamination in soil is a key issue prior to its appropriate remediation. Nevertheless, the efficient identification of large-scale soil Cr contamination requires continuous research. The present study proposes a continental-scale method to rapidly identify soil Cr contamination using visible-near infrared spectroscopy (vis-NIR) and machine learning (ML). A large dataset containing 18,675 topsoil samples from the Land Use/Land Cover Area Frame Survey 2009 projects across Europe was compiled. Five advanced ML algorithms were compared, and hyperparameter optimization was conducted using the grid search method. Permutation importance was employed to calculate the rank of each spectral wavelength, shedding light on the most sensitive spectral wavelength for Cr contamination. Results indicate that hyperparameter optimization had the most significant performance improvement on support vector machine (SVM), exhibiting an increase in training performance from 0.795 to 0.868. The achieved optimal SVM accuracy, area under the receiver operating feature curve, sensitivity, and specificity of 0.78, 0.85, 0.85, and 0.66, respectively, indicating excellent predictive performance on the Cr contamination classification. The optimal SVM model revealed that the most important spectral band for classifying Cr contamination was 1430–1433 nm. This finding implies that the adsorption of molecular water was closely related to the classification of Cr contamination. The current study introduces the first continental-scale identification of Cr contamination using vis-NIR, which has excellent guiding significance for Cr remediation and the identification of other heavy metals using vis-NIR.

Exploring Nvidia's evolution, innovations, and future stock trends

This paper undertakes a thorough examination of Nvidia's stock market performance, intertwining historical analysis with forward-looking projections to illuminate the dynamic trajectory of this semiconductor industry giant. Commencing with a retrospective review, the authors delve into pivotal milestones, technological innovations, and strategic maneuvers that have shaped Nvidia's stock evolution. Utilizing advanced machine learning algorithms, including Random Forest and Support Vector Regression (SVR), alongside traditional statistical forecasting methods, we forecast future patterns. Through the incorporation of emerging industry dynamics, technological advancements, and market forecasts, our goal is to furnish stakeholders with insights pivotal for strategic decision-making. This dual perspective, encompassing both historical retrospection and future outlook, weaves a holistic narrative capturing the essence of Nvidia's stock market journey. It serves as a valuable resource for avid readers navigating the ever-changing landscape of the semiconductor market, especially considering the expanding role of Nvidia's GPUs in Artificial Intelligence (AI) and deep learning applications.

Unvealing the Super App for Tourism with QR and Machine Learning

Abstract: In today’s dynamic world of travel and tourism, the demand for innovative solutions that improve user experience and simplify booking processes is constantly increasing. In response to this need, our research introduces an innovative travel super- app poised to revolutionize the way travelers explore and interactwith travel destinations. This comprehensive application uses cutting-edge technologies such as QR code and Bluetooth Low Energy (BLE) technology, as well as advanced machine learning algorithms, to provide users with a seamless and personalized booking experience. By combining various tourist attractions such as cafes, water parks, monuments and museums into one platform, Super App eliminates the need for multiple apps, offering unparalleled convenience and efficiency. With person- alized recommendations, dynamic price predictions and preview tools, users can receive personalized recommendations and makeinformed decisions about their travel plans. Integrating BLE technology into check-in and check-out processes ensures a frictionless and secure authentication experience that increases operational efficiency and visitor satisfaction in tourism desti- nations. Our research aims not only to answer the immediate challenges that tourists face, but also to pave the way for the further development of the tourism industry. With its innovative features and user-centric design, Super App is a significant step towards passenger navigation and communication in today’s digital age.

Drive Alert Solutions Synopsis

Abstract: In today's technological landscape, "Safety First: Drive Alert" pioneers a novel approach to enhance road safety by integrating AI and real-time data analysis. Developed with Flutter and advanced machine learning algorithms, this solution serves as a proactive tool for tracking driver locations and identifying high-risk accident-prone areas. With two-level alerts at 500m and 200m, it offers early warnings to drivers, mitigating potential hazards and preventing accidents. This innovation not only delivers critical real-time information to drivers but also signifies technology's transformative potential in addressing safety issues. By promoting a safer driving environment, it embodies innovation's ethos and underscores technology's role in protecting lives on the road

Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies

This research optimizes wastewater treatment plant (WWTP) operational performance by integrating advanced control strategies and predictive modeling. Emphasizing the significance of machine learning (ML), feature extraction techniques (filter, wrapper, and embedded methods) were employed to develop robust prediction models. The random forest (RF) model was applied to predict target variables, effluent ammonia, and nitrogen concentrations. Integrating these predictive models into the WWTP’s control system is necessary for enhanced efficiency and pollution regulation. Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. The two tested control strategies included a hybrid approach, combining feedforward and feedback control, resulting in an improved effluent quality index (EQI), a marginal increase in aeration energy (AE) and the operational cost index (OCI), and a significant decrease in effluent ammonia concentration. The second strategy utilized self-organizing fuzzy inference system (SOFIS) control, resulting in promising outcomes with improvements in EQI, ammonia, and nitrogen concentrations, with negligible increases in AE and OCI. The findings highlight the pivotal role of predicting effluent quality parameters and integrating the prediction into WWTP control systems. This integrated approach proves effective in optimizing pollutant regulation and overall system performance. The research provides insights into the practical implementation of ML-based control strategies in wastewater treatment. It offers future scope for exploring advanced ML algorithms and their real-time application in operational WWTPs. This research introduces a novel approach by integrating machine learning with the BSM1 weather dataset and sensor data for feature selection to predict effluent concentrations in a WWTP. Through the comparative analysis with the default proportional-integral (PI) control configuration, the research highlights the importance of integrating machine learning techniques into WWTP control systems.

Multiple Disease Detection System Using Machine Learning

Abstract: In recent times, there has been a surge of interest in employing machine learning techniques for the early and accurate detection of various diseases. This research introduces a holistic approach to constructing a robust multi-disease detection system that utilizes advanced machine learning algorithms. Our proposed system integrates diverse datasets encompassing a range of medical conditions, enabling the simultaneous detection of multiple diseases within a unified framework. We leverage cutting-edge machine learning models, including, but not limited to, [specify the models used], to analyze and interpret intricate patterns within the data.

Enhancing liver fibrosis diagnosis and treatment assessment: a novel biomechanical markers-based machine learning approach

Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine learning (ML) framework, which integrates light gradient boosting machine and multivariate imputation by chained equations to enhance liver status assessment using biomechanical markers. Building upon our previously established multiscale mechanical characteristics in fibrotic and treated livers, this framework employs Gaussian Bayesian optimization for post-imputation, significantly improving classification performance. Our findings indicate a marked increase in the precision of liver fibrosis diagnosis and provide a novel, quantitative approach for assessing fibrosis treatment. This innovative combination of multiscale biomechanical markers with advanced ML algorithms represents a transformative step in liver disease diagnostics and treatment evaluation, with potential implications for other areas in medical diagnostics.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Social media-based implosion of Silicon Valley Bank and its domino effect on bank stock indices: evidence from advanced machine and deep learning algorithms

Social media-driven speculations play a crucial role in triggering the collapse of the banking system and stock markets. In this paper, we investigate the effect of Twitter-based investor sentiment on the collapse of Silicon Valley Bank (SVB), the 16th largest bank in the US. Additionally, we examine the spillover effect of the social media-based investor sentiment and SVB collapse on the bank stock indices from twelve countries where Global Systemically Important Banks (G-SIBs) operate. Advanced machine and deep learning models are employed to model the social media-based investors’ sentiment regarding SVB implosion and its spillover effect on the G-SIBs’ bank stock indices. Our results reveal that social media-based negative investors’ sentiment played an important role in SVB implosion. Our results further show that the negative investors’ sentiment persisted, and its systemic shock was transmitted to the G-SIBs bank stock indices. Importantly, our results provide a lead and lag relationship between investors’ sentiment and returns of G-SIBs bank stock indices. The findings of this study offer crucial insights for policymakers to consider the external shocks associated with social media-based investors’ sentiment when devising policies related to bank runs, thus helping to prevent future financial crises and cross-border contagion.

Electricity Theft Detection in Smart Grids Using Sarimax and OCR

This study proposes a novel approach to detect electricity theft in smart grids by combining SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Factors) models with Optical Character Recognition (OCR) techniques. Electricity theft remains a significant challenge in smart grid systems, leading to revenue losses and operational inefficiencies. Traditional methods often fall short in accurately identifying and addressing such illicit activities. The integration of SARIMAX models leverages time-series data, capturing patterns and anomalies indicative of electricity theft. These models incorporate exogenous factors such as weather conditions, economic indicators, and historical consumption patterns, enhancing the detection accuracy. Additionally, OCR technology is applied to analyze meter readings and identify discrepancies or irregularities that may signal potential theft instances. The synergy between SARIMAX and OCR offers a comprehensive solution for electricity theft detection in smart grids. By harnessing advanced analytics and machine learning algorithms, this approach enables utility providers to proactively identify suspicious activities, mitigate revenue losses, and ensure the integrity of their grid infrastructure

Fundus- Based Glaucoma Detection- Machine Learning A Design Approach

Glaucoma is a leading cause of irreversible blindness worldwide, characterized by the progressive degeneration of the optic nerve, often associated with increased intraocular pressure. This paper proposes a machine learning-based design approach for the detection of glaucoma using fundus images. Our approach leverages advanced image processing techniques and machine learning algorithms to identify glaucomatous features in fundus images. The system comprises several key components: pre-processing of fundus images to enhance quality, feature extraction to identify relevant glaucomatous markers, and classification using machine learning models to differentiate between healthy and glaucomatous eyes. The proposed model achieved high accuracy, sensitivity, and specificity in detecting glaucoma, demonstrating its potential as a reliable and non-invasive diagnostic tool. Our findings highlight the efficacy of machine learning in medical imaging applications and underscore the importance of automated systems in assisting ophthalmologists in early glaucoma detection

Exploring the Frontier of Data Science: Innovations, Challenges, and Future Directions

Data science, an interdisciplinary field, has profoundly transformed our understanding and utilization of data across diverse sectors such as healthcare, finance, marketing, and transportation. With the rapid advancements in computational power and the exponential growth of data from digital sources, sophisticated methodologies and tools have emerged, enabling deeper insights and more informed decision-making. This paper explores the latest innovations in data science, focusing on advancements in machine learning algorithms, big data technologies, and data visualization tools. It highlights the development of cutting-edge techniques that enhance predictive accuracy, optimize resource allocation, and improve operational efficiencies. Additionally, we address the key challenges faced by practitioners, including ensuring data quality and management, navigating ethical and privacy concerns, and bridging the skill gap within the workforce. By examining these aspects, the paper provides a comprehensive overview of the current state of data science and its implications for future research and application. The insights gathered aim to guide researchers and professionals in leveraging data science advancements while addressing the inherent challenges to maximize the potential benefits across various industries.

ML Assisted Foot Step Power Generation using Piezoelectric Sensors

In our rapidly evolving world, the escalating demand for energy coupled with the finite nature of traditional resources underscores the imperative for sustainable, pollution-free, and inexhaustible energy solutions. This paper introduces a pioneering approach to harnessing the kinetic energy expended during human locomotion through the innovative use of piezoelectric sensors. Leveraging the piezoelectric effect, these sensors efficiently convert mechanical energy generated by footstep pressure into electrical energy, thereby mitigating wastage and addressing the increasing energy needs. Our model advocates for the deployment of an extensive sensor network along footpaths, complemented by an RFID-based mobile charging system for enhanced convenience and functionality. Moreover, we introduce an innovative method integrating Machine Learning (ML) techniques to enhance power generation efficiency through intelligent modulation of piezoelectric element resistance. Additionally, we leverage ML algorithms to enhance the requisite daily footstep count necessary to fulfill the energy demands of specific areas. This proactive approach ensures optimal deployment of footstep power generation devices based on actual foot traffic patterns. Our research underscores the significance of this technology in the context of urban energy sustainability, particularly in densely populated regions like China and India, where foot traffic is abundant. By harnessing mechanical energy and leveraging advanced ML algorithms, our approach promises to revolutionize energy harvesting paradigms, paving the way for greener and more efficient power generation systems

#2067 Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning techniques

Abstract Background and Aims INitiativeS on advancing Patients’ outcomes In REnal disease (INSPIRE) is an academia and industry collaboration set forth to identify critical investigations needed to advance the practice of medicine in nephrology. Gastrointestinal bleeding (GIB) is one of the most common types of bleeding events in the kidney dialysis population [1]. We aimed to develop a model to predict GIB hospitalization risk in a hemodialysis (HD) patient within 180 days. We evaluated advanced machine learning algorithm (XGBoost) and compared it to traditional machine learning algorithm (logistic regression) modeling techniques. Method We used data from a kidney care network from 2017 through 2020. We included data from adult dialysis patients (age ≥18 years). GIB related hospitalization was defined based on international classification of diseases (ICD) diagnosis codes recorded as the primary, secondary, or tertiary discharge reason for hospitalization. Two distinct models were created using XGBoost and logistic regression algorithms and using the same dataset. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Missing data was imputed using mean for quantitative data and mode for qualitative data. The dataset then was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising unseen patients and data was used to evaluate the performance of the model. This means that the model had never encountered the test data during its learning phase, which included training the model and then validating the model. Results The incidence of 180-day GIB hospitalization was 1.12% in the HD population (n = 5 116 with GIB hospitalization/ n = 451 653 without GIB hospitalization), and consistent in the unseen test dataset (n = 465 with GIB hospitalization/ n = 38586 without GIB hospitalization). The XGBoost model showed higher predictive performance compared to logistic regression (Table). The AUROC was 0.72 (95% confidence interval (CI) 0.69, 0.74) for the XGBoost model. In comparison, the AUROC was 0.615 (95% CI 0.60, 0.64) for the logistic regression model. Specificity was 67% versus 58% for the XGBoost model versus logistic regression model respectively. However, sensitivity for both the models was 65%. The top predictors for major GIB were consistent in both models for many factors, yet some factors showed different importance on outcome prediction (Fig. 1). Higher risk of GIB hospitalization was associated with older age, lower ferritin levels, and recent all-cause hospitalizations in both models. The XGBoost showed high importance on outcome prediction for lower hemoglobin and higher serum 25 hydroxy (25OH) vitamin D values, and the logistic regression model showed high importance for higher amounts of saline delivered during an HD treatment and lower intact parathyroid hormone (iPTH) levels. Conclusion We found advanced machine learning prediction modeling (XGBoost) appears suitable for identifying a HD patient at risk for a GIB hospitalization in the next 180 days, and outperforms traditional machine learning (logistic regression) modeling techniques. Although both models showed identical sensitivity, the XGBoost model had higher specificity. Prospective testing is needed to confirm the model's performance. The association between bone mineral metabolism markers and GIB hospitalization risk is unexpected and warrants further investigation.

Dubify: Multilingual Video Translation Platform

In an increasingly globalized society, effective cross-linguistic communication is essential. This brief presents a new approach. Dubify is a feature-rich application created to facilitate smooth translation of video information between languages. Dubify uses advanced machine learning algorithms to provide real-time translation services, ensuring that video content is understandable and accessible to viewers around the world. Strong speech recognition, accurate translation, and natural language generation are some of Dubify's main strengths. Thanks to the platform's extensive language support, users can easily translate video content into multiple languages. In addition, Dubify offers customization tools that allow users to adjust translations to suit their particular taste and dialect. Dubify's easy-to-use interface and advanced translation skills make it a flexible choice for individuals, businesses, and organizations looking to translate

Internet of things and deep learning based digital twins for diagnosis of brain tumor by analyzing MRI images

Although brain tumours are few, they have one of the highest mortality rates among all types of cancer due to their abnormal growth and proliferation. Brain tumours develop due to the accumulation of abnormal tissues in the brain. Various forms of abnormal tissue exist, however, in the majority of cases, they develop in a regular manner and perish without creating any detrimental effects. Digital twins are occasionally known as digital mirrors, digital mapping, and digital replicas. All of these are synonymous terms for the identical entity. It is a technique for transferring digital or physical information from one realm to another. Image processing involves enhancing or eliminating data from a photograph to achieve a certain objective. Convolutional neural networks are a specific type of neural network that take signals from images as input and produce the image itself or a subset of its elements as output. This research presents a technique for identifying brain cancers using digital replicas and advanced machine learning algorithms by analysing MRI images. Images obtained from MRI machines are stored in a centralised cloud using Internet of Things (IoT) digital devices. The input pictures and other health-related data are then retrieved from cloud storage. The Particle Swarm Optimization approach chooses features. Brain tumor images are classified using machine learning techniques such as convolutional neural networks, support vector machines, and extreme learning machines. The CNN algorithm demonstrates greater accuracy when assessing MRI images for the purpose of identifying brain tumours.