Advanced Machine Learning Algorithms Research Articles

Credit Card Fraud Detection System

Globally, credit card fraud is a serious threat to people, businesses, and financial institutions. With the rise of online transactions, fraudsters have developed clever ways to take advantage of loopholes in payment systems. Traditional fraud detection methods based on manual inspections and rules-based systems are unable to counteract this new and evolving risk. As a result, the use of data analytics and machine learning has become a viable option for real-time detection and prevention of credit card fraud. The paper looks at using machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, etc. to detect fraudulent transactions We go over the importance of data sources and components, analytical metrics, and how fraud detection on the effectiveness of examples. In addition, we list the current challenges and directions in which credit card fraud detection is likely to continue, including the use of blockchain technology and sophisticated AI techniques. Overall, this study highlights the importance of credit card theft detection and the promise of machine learning in mitigating this ubiquitous problem financial institutions use advanced machine learning algorithms and analytics function to detect fraudulent behaviour, protect customer interests, and maintain payment environment integrity to improve their capabilities.

Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.

Personalized Nutrition Analyzer using Artificial Intelligence

This project aims to develop an AI-powered nutrition analyzer tailored for fitness enthusiasts to regulate and achieve their overall fitness goals. Leveraging advanced machine learning algorithms, the system will analyze dietary patterns, nutritional intake, and individual health objectives. The user-friendly interface allows individuals to input their dietary information and exercise routines, receiving real-time, personalized recommendations. Beyond basic caloric assessment, the system considers comprehensive nutritional compositions, ensuring a balanced diet aligned with fitness targets. Continuous learning mechanisms enable the system to adapt and improve recommendations over time based on user feedback. This project seeks to empower users in making informed nutritional choices, fostering a personalized and effective approach to fitness management for enhanced overall well-being

Health Monitoring System for CNC Machine

This paper introduces a novel Health Monitoring System (HMS) tailored for CNC machines, addressing the critical need for maintaining their optimal performance and preventing unexpected breakdowns in modern manufacturing settings. The system integrates various sensors and data acquisition methods to continuously monitor key parameters like temperature, vibration, and tool wear. By employing advanced data analytics and machine learning algorithms, the HMS can swiftly identify anomalies in real-time, facilitating proactive maintenance and minimizing operational downtime. Additionally, the system features a user- friendly interface for visualizing machine health status and creating predictive maintenance schedules. Experimental validation conducted on a CNC machining center validates the efficacy and reliability of the developed HMS in enhancing machine efficiency, prolonging equipment lifespan, and curbing maintenance expenses. In summary, this Health Monitoring System offers a robust solution for ensuring the seamless operation and longevity of CNC machines in modern manufacturing environments.

Obstacle Detection For Visually Impaired

The paper examines innovative obstacle-detection solutions designed for visually impaired people who do not rely on traditional sensors and recognize the challenges they face when navigating their environment independently. It delves into techniques leveraging alternative sensory modalities such as auditory and tactile feedback, prioritizing user experience and safety. Evaluation criteria encompass effectiveness, ease of use, and affordability, during discussions involve adapting existing infrastructure for tactile navigation and collaboration with guide animal organizations to devise comprehensive solutions. In addition to case studies showing the successful implementation of sensor-free obstacle detection in a wide range of environments, i.e., cities and indoors, challenges related to training and social acceptance of alternative methods will be addressed. In particular, the paper offers beneficial insight into novel approaches to detecting obstacles aimed at improving the mobility and independence of those with visual impairment. Our method uses advanced machine learning algorithms to interpret and predict potential obstacles in real-time. Our system can identify and classify barriers with a high degree of accuracy through analysis of user's sound feedback from their surroundings as well as the use of sophisticated learning techniques. In a wide range of real-world scenarios, we demonstrate the effectiveness of our approach through extensive testing with visually impaired participants. The results show that compared to conventional sensor-based methods, the performance of obstacle detection is significantly improved. In addition, our solution is designed to improve the flexibility, affordability, and ease of integration with existing technologies to create an environment more conducive for people with visual impairment.

РЕЙТИНГОВАЯ СИСТЕМА ОЦЕНКИ ТРАНСПОРТНО-ЛОГИСТИЧЕСКИХ КОМПАНИЙ

The paper presents an approach to intelligent transport systems based on advanced machine learning algorithms, the purpose of which is to estimate the traffic flow throughout the entire road network

AI-Powered Innovations in Electrical Engineering: Enhancing Efficiency, Reliability, and Sustainability

Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.

Data driven machine learning prognostics of buckling failure modes in ballasted railway track

This study explores the development and application of a machine learning (ML) approach to predict buckling failure modes in ballasted railway tracks. With the growing demand for safer and more reliable railway systems, the ability to foresee and mitigate track failures is of paramount importance. Our study focuses on harnessing advanced ML algorithms to analyse and interpret complex data sets, aiming to identify potential buckling failures before they occur. The methodology employed involves collecting extensive data from previous advanced numerical studies. Faced with the inadequacy of field data collection on track buckling and the limited availability of data related to track conditions, our study has relied on simulation data for insight and analysis. This data is then processed and analysed using sophisticated ML models, trained to recognise patterns and anomalies indicative of potential buckling failures. A novel aspect of our approach is the integration of environmental factors, acknowledging their significant influence on the likelihood of both snap-through and progressive buckling in railway tracks. We compare the effectiveness of various ML algorithms in accurately predicting these failure modes, evaluating their performance in simulated and real-world scenarios. The findings demonstrate the models' proficiency in identifying early signs of both snap-through and progressive buckling, leading to timely interventions. This capability not only improves railway safety but also aids in efficient maintenance scheduling and asset management. Additionally, a case study in Thailand's railway system demonstrates the model's effectiveness in predicting buckling failures under tropical environmental conditions. This paper contributes a novel perspective to the field of railway infrastructure maintenance. By providing a reliable method for predicting specific buckling failure modes, it paves the way for enhanced operational safety and efficiency in railway networks, particularly in the face of dynamic environmental conditions.

Sea-surface pCO2 maps for the Bay of Bengal based on advanced machine learning algorithms

Lack of sufficient observations has been an impediment for understanding the spatial and temporal variability of sea-surface pCO2 for the Bay of Bengal (BoB). The limited number of observations into existing machine learning (ML) products from BoB often results in high prediction errors. This study develops climatological sea-surface pCO2 maps using a significant number of open and coastal ocean observations of pCO2 and associated variables regulating pCO2 variability in BoB. We employ four advanced ML algorithms to predict pCO2. We use the best ML model to produce a high-resolution climatological product (INCOIS-ReML). The comparison of INCOIS-ReML pCO2 with RAMA buoy-based sea-surface pCO2 observations indicates INCOIS-ReML’s satisfactory performance. Further, the comparison of INCOIS-ReML pCO2 with existing ML products establishes the superiority of INCOIS-ReML. The high-resolution INCOIS-ReML greatly captures the spatial variability of pCO2 and associated air-sea CO2 flux compared to other ML products in the coastal BoB and the northern BoB.

The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Enhancing Workplace Efficiency and Security Through Intelligent Employee Surveillance

This project aims to develop an innovative system for accurately tracking employees' working hours based on their presence within designated workspace areas, particularly their work cabins. Leveraging advanced technologies such as image annotation, preprocessing, and augmentation, as well as robust object detection models, this study addresses the need for efficient employee surveillance and time management solutions in contemporary workplaces. The methodology involved detailed annotation and enhancement of image data, enabling precise representation of cabin areas and identification of individual employees within images. Subsequently, a state-of-the-art object detection model, YOLOv8, was utilized to train using this annotated dataset, achieving an impressive accuracy of more than 90% in recognizing and tracking employee presence within the specified cabin regions. Through small incremental changes based on previous insights and optimization, the project achieved high levels of accuracy in inferring employees' working hours based on their occupancy within the designated workspace. By differentiating between time spent inside cabins (considered as working time) and time spent outside these areas (considered as non-working time), the system offers an automated and objective approach to time tracking, eliminating the need for manual input or subjective assessments. Future scopes for this research include exploring the integration of additional sensors or data sources to further enhance the accuracy and granularity of employee activity tracking. Additionally, advancements in machine learning algorithms and hardware may enable real-time processing and analysis of surveillance data, leading to more proactive management of employee productivity and well-being. Moreover, the application of this technology could extend beyond traditional office settings to various industries, such as manufacturing, retail, and healthcare, where precise monitoring of employee activities is crucial for optimizing operations and ensuring compliance with regulations. This research underscores the potential of advanced computer vision techniques, particularly YOLOv8, in revolutionizing employee surveillance and time management practices. By providing real-time monitoring capabilities and ensuring compliance with work regulations, this approach holds promise for enhancing workplace productivity, transparency, and accountability.

PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY

This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks.
 Keywords: Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

A Novel Approach Integrating IoT and WSN with Predictive Modeling and Optimization for Enhancing Efficiency and Sustainability in Smart Cities

The development of smart city solutions is necessary for traffic management and environmental sustainability to meet the challenges of global urbanization. Based on this idea, the paper presents a new way to amalgamate IoT and Wireless Sensor Networks with predictive modeling and optimization methods for use in smart city management. To this end, we have installed traffic sensors (infrared, ultrasonic) and environmental sensors (air quality, humidity, gas) at strategic points throughout the city where they can collect real-time data on traffic and pollution. Then, this data is used together with machine learning models such as ANN (Artificial Neural Network), DT or Decision Trees, KNN (K-Nearest Neighbors), and RF or Random Forests. In practical terms, it is through iterative training processes that our models have achieved ever greater accuracy over time. Now they can actually learn and adapt to changing urban dynamics. The holistic solution of this approach pertains to informed decision-making in smart city infrastructure management. Urban stakeholders can make data-driven decisions by leveraging advanced sensor technologies and machine learning algorithms to deal with traffic congestion, reduce pollution and enhance the quality of life for smart cities. Urban planners, policymakers and technologists working on smart city solutions to improve urban mobility and environmental quality will all be interested in the outcomes of this study.

Powder BED Additive Manufacturing using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook

Abstract: Additive manufacturing overcomes the limitations associated with conventional processes, such as fabricating complex parts, material wastage, and a number of sequential operations. Powder-bed additives fall under the category of additive manufacturing process, which, in recent years, has captured the attention of researchers and scientists working in various fields of science and engineering. Production of powder bed additive manufacturing (PBAM) parts with consistent and predictable properties of powders used during the manufacturing process plays an important role in deciding printed parts' reliability in aeronautical, automobile, biomedical, and healthcare applications. In the PBAM process, the most commonly used powders are polymer, metal, and ceramic, which cannot be effectively used without understanding powder particles' physical, mechanical, and chemical properties. Several metallic powders like titanium, steel, copper, aluminum, and nickel, several polymer polyamides (nylon), polylactide, polycarbonate, glass-filled nylon, epoxy resins, etc., and the most commonly used ceramic powders like aluminum oxide (Al2O) and zirconium oxide (ZrO2) can be utilized depending upon the method being adopted during PBAM process. Adoption of some post-processing techniques for powder, such as grain refinement can also be employed to improve the physical or mechanical properties of powders used for the PBAM process. In this paper, the effect of powder parameters, such as particle size, shape, density, and reusing of powder, etc., on printed parts have been reviewed in detail using characterization techniques such as X-ray computed tomography, scanning electron microscopy, and X-ray photoelectron spectroscopy. This helps to understand the effect of particle size, shape, density, virgin and reused powders, etc., used during the PBAM process. This article has reviewed the selection of appropriate process parameters like laser power, scanning speed, hatch spacing, and layer thickness and their effects on various mechanical or physical properties, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. One of the drawbacks of additive manufacturing is the variability in the quality of printed parts, which can be eliminated by monitoring the process using machine learning techniques. Also, the prediction of the best combination of process parameters using some advanced machine learning algorithms (MLA), like random forest, k nearest neighbors, and support vector machine, can be effectively utilized to quantify the performance parameters in the PBAM process. Thus, implementing machine learning in the additive manufacturing process not only helps to learn the fundamentals but helps to identify, predict and help to make actionable recommendations that help optimize printed parts quality. The performance of various MLAs has been evaluated and compared for projecting future research directions and suggestions. In the last part of this article, multidisciplinary applications of the PBAM process have been reviewed in detail. Additive manufacturing processes carried out by using conventional machines, called hybrid additive manufacturing, have also been reviewed by discussing their methods and arrangements in detail.

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.

The Role of X-ray Images for Early Detection of Covid-19 using Machine Learning

The principal aim of the Covid Detection Using X-ray project is to develop a robust platform for accurate and efficient detection of COVID-19 through the analysis of X-ray scans. The proposed system aims to enhance the diagnostic process by leveraging artificial intelligence (AI) algorithms to identify specific patterns associated with COVID-19 in X-ray images. The platform will facilitate timely and precise diagnosis, enabling healthcare professionals to make informed decisions and optimize patient care. This project integrates advanced image processing techniques and machine learning algorithms to discern subtle patterns indicative of COVID-19 in X-ray scans. The system will provide a user-friendly interface for healthcare professionals, allowing seamless uploading and analysis of X-ray images. Additionally, the platform incorporates a comprehensive database of COVID-19-related patterns for accurate comparison and classification. The primary objective of the project is to contribute to the ongoing efforts in combating the COVID-19 pandemic by providing a reliable and efficient tool for early detection. The utilization of cutting-edge technologies, including deep learning and image analysis, ensures a high level of accuracy in identifying potential cases. The project aims to be a valuable resource for healthcare practitioners, aiding in the prompt and accurate diagnosis of COVID-19 through the analysis of X-ray imagery.

Corporate culture, corporate governance, and independent directors: evidence from textual analysis

Purpose Taking advantage of a unique measure of corporate culture obtained from advanced machine learning algorithms, this study aims to explore how corporate culture strength is influenced by board independence, which is one of the most crucial aspects of the board of directors. Because of their independence from the corporation, outside independent directors are more likely to be unbiased. As a result, board independence is commonly used as a proxy for board quality. Design/methodology/approach In addition to the standard regression analysis, the authors execute a variety of additional tests, i.e. propensity score matching, an instrumental variable analysis, Lewbel’s (2012) heteroscedastic identification and Oster’s (2019) testing for coefficient stability. Findings The results show that stronger board independence, measured by a higher proportion of independent directors, is significantly associated with corporate culture. In particular, a rise in board independence by one standard deviation results in an improvement in corporate culture by 32.8%. Originality/value Conducting empirical research on corporate culture is incredibly difficult due to the inherent difficulties in recognizing and assessing corporate culture, resulting in a lack of empirical research on corporate culture in the literature. The authors fill this important void in the literature. Exploiting a novel measure of corporate culture based on textual analysis, to the best of the authors’ knowledge, this study is the first to link corporate culture to corporate governance with a specific focus on board independence.

A Comparative Study on Machine Learning Models for Fault Detection and Classification in Manufacturing Integration of IoT and Artificial Intelligence in Industry 4.0 management

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH4 levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH4 occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH4 dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH4, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH4 distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH4 modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing greenhouse gas emissions and mitigating climate change impacts.

Hand Gesture Controller

Abstract: The rapid evolution of human-computer interaction has spurred significant progress in gesture recognition technologies, placing a specific emphasis on diverse applications. This paper highlights key advancements in machine learning algorithms tailored for gesture recognition, including deep learning approaches that have notably improved the accuracy and robustness of hand tracking systems. Furthermore, the integration of hand gesture control into wearable devices and its implications for everyday technology usage are thoroughly examined. This paper relies on the formidable capabilities of Deep Learning, specifically Convolutional Neural Networks and Recurrent Neural Networks, to decipher and translate hand gestures into actionable directives. The implementation of the Hand Gesture Controller necessitates the integration of a camera or sensor module capable of capturing intricate hand movements. Real-time image processing and feature extraction are essential components, facilitating the provision of input data to the Deep Learning model. As technology progresses, this interface emerges as a versatile tool capable of enhancing productivity and inclusivity across various domains. The paper concludes with a discussion on future directions in hand gesture controller development, exploring anticipated technological advancements, novel use cases, and the potential for increased accessibility. In summary, the "Hand Gesture Controller using Deep Learning" project signifies a substantive stride forward in human-computer interaction, introducing an innovative interface that promises a future where devices seamlessly respond to innate gestures, thereby rendering technology more accessible and user-focused.