Advanced Machine Learning Algorithms Research Articles

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.

Automated Water Monitoring & Managing System for Aquaculture Using IOT

In recent years, the Internet of Things (IoT) has emerged as a transformative technology with profound implications for various sectors, including environmental monitoring and management. This paper presents an innovative IoT-based automated water quality monitoring and management system (AWQMS) designed to revolutionize the way we monitor and safeguard water resources.The AWQMS leverages a network of IoT-enabled sensors deployed at strategic locations throughout water bodies, continuously measuring crucial parameters such as pH, dissolved oxygen, turbidity, temperature, and conductivity. These sensors are equipped with wireless communication capabilities, enabling real-time data transmission to a centralized data acquisition and processing platform. At the heart of the AWQMS lies advanced data analytics and machine learning algorithms, which analyze incoming data streams to detect anomalies and deviations from predefined water quality thresholds. Key Word: Automated Water Monitoring and Managing system, IoT based solution, sensor-based monitoring, Real time monitoring system, cost-effectiveness.

Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market

An intelligent talent recommendation algorithm is a sophisticated system that utilizes advanced data analysis techniques and machine learning algorithms to match individuals with suitable job opportunities or educational programs. By analyzing a wide range of data points such as skills, qualifications, work experience, and preferences, these algorithms can generate personalized recommendations tailored to each individual's unique profile and career goals. Moreover, intelligent talent recommendation algorithms continuously learn and improve over time, refining their recommendations based on user feedback and performance data. This paper presents an intelligent talent recommendation algorithm tailored for college students preparing for the future job market, utilizing Ranking Hidden Chain Deep Learning (RHC-DL). The algorithm aims to provide personalized recommendations by analysing students' skills, qualifications, interests, and career aspirations. Through simulated experiments and empirical validations, the efficacy of the RHC-DL-enhanced recommendation algorithm is evaluated. Results demonstrate significant improvements in recommendation accuracy and relevance compared to traditional methods. For instance, students using the RHC-DL algorithm reported a 40% increase in job offer acceptance rates and a 30% improvement in job satisfaction levels. Additionally, the algorithm adapts and learns from user interactions, continuously refining its recommendations based on real-time feedback.

Parkinson’s Disease Detection

Abstract: Parkinson’s Disease is a disorder that affects the nervous system. Parkinson’s disease does not directly cause people to die but can make some people more vulnerable to serious and life-threatening infections. This research addresses the limitations of traditional clinical diagnosis by harnessing the potential of advanced data analysis techniques and machine learning algorithms. The project’s primary objectives include dataset compilation, feature extraction, model development, multimodal fusion, model validation, and considerations for clinical applicability. The dataset will encompass a diverse range of participants diagnosed with PD as well as healthy controls, ensuring the representation of various demographic and clinical factors. By extracting distinctive features from voice recordings, handwriting dynamics, and gait patterns, the project aims to capture unique biomarkers associated with PD. Machine learning models, tailored for each modality, will be developed to classify individuals as PD-positive or PD-negative

POPRAWA ZDROWIA UPRAW DZIĘKI CYFROWEMU BLIŹNIAKOWI DO MONITOROWANIA CHORÓB I RÓWNOWAGI SKŁADNIKÓW ODŻYWCZYCH

Digital Twins is a digital replica of a physical object to observe its real-time performance, gather data, and recommend corrective actions if required to enhance its performance. This fascinating technological idea is now reaching the agriculture fields to transform farming, by creating digital twins of entire farms. This initiative presents an innovative strategy to enhance crop health and yield by creating a digital twin for paddy fields. The aim is to enable early detection of nutrient deficiencies and leaf blast disease, leading to a transformation in agriculture. Creating virtual replicas of plants and fields, the digital twin harnesses real-time data and advanced analytics to transform the way agricultural systems are managed. By integrating remote sensing, data analytics, and various Internet of Things devices like pH, nitrous, potassium, and phosphorus sensors, coupled with a gateway system, the digital twin provides real-time monitoring and analysis of crop health and nutrient levels. Employing advanced machine learning algorithms, notably Convolutional Neural Networks ensures precise and early detection of nutrient deficiencies and crop diseases. This ground-breaking technology provides timely alerts and actionable insights to farmers, enabling proactive decision-making for optimal crop management. This farmland digital twin represents a transformative approach towards agricultural sustainability and enhancing productivity.

Enhancing Credit Card Fraud Detection: A Novel Approach with Random Forest and Behavioral Biometrics

Abstract: In an era marked by heightened fraudulent activities targeting credit card transactions, this research delves into the efficacy of advanced machine learning algorithms in combating such threats. The performance of logistic regression, decision trees, and the novel random forest approach in identifying fraudulent transactions is scrutinized. Additionally, behavioral biometrics are incorporated as an innovative authentication factor to enhance fraud detection accuracy. Incorporating behavioral biometrics as an additional authentication factor represents a cutting-edge approach to fraud detection, leveraging the unique behavioral patterns exhibited by individuals during transactional interactions.

The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

IoT and Machine Learning in Smart Kitchen Monitoring for Enhanced Worker Health

The significance of occupational health in culinary settings, particularly kitchens, is paramount due to the inherent health risks associated with these environments. This study addresses the necessity of maintaining optimal environmental conditions, such as temperature, humidity, and air quality, in kitchens to safeguard worker health. To achieve this, the study advocates for the implementation of sophisticated ventilation and air conditioning systems. The core focus of the research is the integration of Internet of Things (IoT) technology and advanced machine learning algorithms for the real-time monitoring and assessment of kitchen environments. Specifically, the study fine-tunes and evaluates several classification algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), aiming to accurately predict and manage kitchen conditions. The comparative analysis reveals that the DT algorithm outperforms others, demonstrating exceptional accuracy (97.41%), precision (95.35%), and proficiency in identifying relevant scenarios (88.57%). In contrast, the KNN algorithm registers the lowest accuracy (75.12%), while the SVM algorithm, despite being the least precise (86.55%), shows a relatively higher capability in recognizing pertinent cases (86.55%) compared to KNN (72.33%). This study underscores the potential of integrating IoT and machine learning in enhancing occupational health standards in kitchen settings.

Multimodal feature fusion in deep learning for comprehensive dental condition classification.

Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need. The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification. A dataset of 11,653 clinically sourced images representing six prevalent dental conditions-caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia-was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines (SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index. The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847. The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes.

Fixed Point Label Attribution for Real-Time Bidding

Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. In particular, we develop a fixed point algorithm that allows for large-scale implementation and showcase our solution using a large-scale publicly available data set from Criteo, a large demand-side platform. We dub our approach the fixed point label attribution algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser’s signal into display labeling. Demand Side Platforms providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.