Integration Of Machine Learning Algorithms Research Articles

Driving Efficiency in Industry IoT: A Framework Powered by Intelligent Computing and Machine Learning

The Industry Internet of Things (IIoT) represents a transformative paradigm in industrial settings, promising enhanced efficiency, productivity, and predictive maintenance capabilities. However, the effective utilization of IIoT requires robust frameworks that can handle vast amounts of data and extract actionable insights. In this paper, we propose a comprehensive framework for IIoT leveraging intelligent computing techniques, particularly machine learning (ML). Our framework aims to address key challenges in IIoT deployments, including data management, real-time analytics, and predictive maintenance. Through the integration of ML algorithms, we enable intelligent decision-making processes based on data-driven insights. We present a detailed discussion of the components and architecture of our proposed framework, highlighting its capabilities and advantages. Additionally, we provide case studies and experimental results to demonstrate the effectiveness and applicability of the framework in various industrial scenarios. Overall, our framework offers a scalable and adaptable solution for harnessing the full potential of IIoT in industrial environments

Stress Detection System Using Machine Learning

Abstract: Stress is a pervasive aspect of modern life, posing significant health risks if left unmanaged. Early detection of stress is crucial for preventing adverse health outcomes and promoting well-being. This paper presents a novel approach to stress monitoring and management using machine learning (ML) techniques and wearable physiological sensors. By analyzing multimodal datasets, including electrocardiogram (ECG) signals and other physiological parameters, our proposed model aims to accurately detect stress levels in individuals. Leveraging low-cost wearable sensors and IoT technology, our system provides real-time feedback and alerts individuals to their stress levels, enabling proactive intervention to mitigate health risks. Through a comprehensive review of existing stress detection approaches and integration of ML algorithms, our study contributes to the development of more efficient and effective stress monitoring systems. This research holds promise for improving health outcomes and enhancing quality of life in individuals facing stress-related challenges.

Automating Grievance Portal by Automating Central Grievance Redressal System using Artificial Intelligence

This research paper is introduces as a centralized grievance portal based on Artificial Intelligence (AI) for streamlined and effective complaint resolution. The proposed system manipulates advanced AI techniques to enhance the complaint handling process, providing a user-friendly interface for complainants & optimizing resource utilization for resolution authorities. The integration of machine learning algorithms ensures intelligent analysis of complaints, prioritization, and swift solution, ultimately contributing to an efficient and transparent grievance redressal mechanism

Machine learning for power outage prediction during hurricanes: An extensive review

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN.

Data-driven approach for sustainable geopolymer mortar production with treated sewage sludge

ABSTRACT This paper highlights an innovative approach to address the environmental challenges associated with waste management and construction materials. The study presents a new technique for utilising treated sewage sludge in the production of eco-friendly geopolymer mortar. Sewage sludge is a byproduct generated from wastewater treatment plants that poses significant environmental issues due to its disposal problems. Geopolymer mortar, on the other hand, is a sustainable construction material that can be produced by activating industrial waste materials, such as fly ash, using alkaline activators. By integrating machine learning algorithms and data visualisation techniques, this study optimises the mix parameters of geopolymer mortar while simultaneously reducing the negative impact of sewage sludge disposal. The experimental results show that sewage sludge ash can replace fly ash and fine aggregate in 1:3 geopolymer mortar by up to 30% and 20%, respectively, without any toxic or heavy metal residues. The compressive and flexural strength of the sludge-based mortar were comparable to ordinary geopolymer mortar, while water absorption and drying shrinkage were similar to control specimens. This research offers a unique solution to promote sustainability in waste management and construction industry by utilising waste materials, including fly ash and treated sewage sludge, for geopolymer production.

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

A COMPARATIVE REVIEW OF AI-GENERATED IMAGE DETECTION ACROSS SOCIAL MEDIA PLATFORMS

The proliferation of images generated by artificial intelligence (AI) has significantly impacted the digital landscape, especially on social media platforms where the distinction between natural and synthetic content is increasingly blurred. This study embarks on a comparative review of the strategies used by major social media platforms—Facebook/Instagram, Twitter, TikTok, and YouTube—to detect AI-generated images. Employing a comprehensive methodology that includes a systematic review of academic literature, analysis of platform policies, and expert interviews, this research assesses the effectiveness of various detection methods, ranging from sophisticated AI tools to user reporting mechanisms. The findings reveal diverse approaches: Facebook and Instagram utilise a blend of AI detection and human moderation; Twitter integrates machine learning algorithms with user reports; TikTok emphasises AI tools within moderation workflows and educational initiatives; and YouTube relies on its Content ID system alongside AI analysis. The study highlights the critical role of effective detection systems in maintaining content authenticity and user trust, underscoring the importance of balancing automated detection with human oversight. The ongoing development and refinement of these technologies, alongside collaborative efforts and evolving regulatory frameworks, are identified as essential for ensuring a trustworthy digital environment. This research contributes to the discourse on digital integrity, offering insights into the complexities of safeguarding social media ecosystems against the challenges posed by AI-generated content.

Trusting AI made decisions in healthcare by making them explainable.

In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The aim of this paper is to provide a transparency layer to black-box machine learning algorithms and empower mHealth applications to maximize their efficiency. Using a machine learning testing framework, we present the process of knowledge transfer between a white-box model and a black-box model and the evaluation process to validate the success of the knowledge transfer. The presentation layer of the final output of the base white-box model and the knowledge-infused white-box model shows clear differences in reasoning. The correlation between the base black-box model and the new knowledge-infused model is very high, indicating that the knowledge transfer was successful. There is a clear need for transparency in digital health and health in general. Adding solutions to the toolbox of explainable artificial intelligence is one way to gradually decrease the obscurity of black-box models.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Evaluation of machine learning algorithms' performance in digital transformations: a comparative analysis

In the realm of digital transformations, the role of machine learning algorithms stands as a critical catalyst for innovation and progress. The integration of machine learning algorithms has become integral in driving digital transformations across various industries. This research paper embarks on a comparative analysis, meticulously evaluating the performance of various machine learning algorithms in the context of driving and shaping digital transformations. Leveraging diverse datasets and real-world case studies, this study delves into the efficacy, adaptability, and limitations of prominent machine learning techniques. Through methodical experimentation and rigorous assessment, this research endeavors to offer valuable insights into the selection and optimization of these algorithms. By shedding light on their strengths and weaknesses, this analysis aims to empower organizations and decision-makers, enabling them to make informed choices when integrating machine learning into their digital transformation strategies. Ultimately, this study seeks to provide a comprehensive understanding of the landscape, assisting in maximizing the potential and impact of machine learning in advancing digital transformations across various industries.

Sustainable Energy Management using Machine Learning

The global energy sector is encountering escalating difficulties, including rising demands for efficiency, shifts in supply and demand patterns, and a lack of optimal management analysis. Utilizing machine learning (ML) to process energy sector data can gradually address these issues. ML algorithms have the capability to analyze equipment data, construct predictive models, and address sustainability-related problems. In smart cities, the integration of machine learning algorithms enables automatic responses to fluctuations in electricity prices, facilitating effective control of energy consumption. Systems employing machine learning can assist energy suppliers in adapting to variable renewable energy supplies. Worldwide, there is a growing emphasis on low-emission energy sources, leading to increased installed capacities of solar photovoltaic, wind farms, and marine energy systems. Consequently, artificial intelligence and machine learning are poised to play a vital role in effectively managing the challenges of the energy sector. The implementation of micro-grids presents significant challenges that necessitate advanced control techniques like model predictive control (MPC). This paper focuses on employing MPC for energy management in micro-grids and aims to provide an up-to-date overview of the development of MPC methods for sustainable energy management.

Mechanisms of non-stationary influence of urban form on the diurnal thermal environment based on machine learning and MGWR analysis

The non-stationary impact of urban form at block scale on the diurnal thermal environment has not been extensively studied. To fill the gap, a comprehensive multi-scale research framework was constructed, integrating machine learning algorithms and multiscale geographically weighted regression (MGWR) analysis. The urban form types were extracted by machine learning algorithms from 2282 blocks of Dalian, a coastal city, and the contribution to the diurnal land surface temperature (LST) was evaluated. Based on MGWR, the non-stationary effects of urban form, human activity and spatial location on the diurnal LST were quantified, which indicated the contributing factors to the diurnal thermal differences among the form types. The result was as follows: 1) Blocks characterized by low vegetation and mid/low-rise buildings had the highest warming contribution for diurnal LST. 2) The impact of sky view factor (SVF) on diurnal temperature amplitude exhibited no significant spatial-temporal heterogeneity. 3) Building density had a prominent effect on diurnal temperature amplitude. 4) High-vegetation and open (SVF > 0.5) mid/mid-high/low-rise (15–50 m) buildings were recommended. This study provides a more precise basis for policymakers to develop climate adaptation strategies throughout day and night, particularly for coastal cities.

Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.

Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals. The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity. A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94-0.99) and 97% (95% CI: 0.95-0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets. ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.

Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm.

The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.

Enhancing Business Decision Making through a New Corporate Reputation Measurement Model: Practical Application in a Supplier Selection Process

The importance of corporate reputation is a critical issue for business growth, sustainability, and success, as it represents a key intangible asset for the management of all companies. This business importance has its correlation in the academic and research field, where corporate reputation has a high number of publications in the literature. However, despite the importance of this concept, one of the great challenges of recent decades, and one that is still evident today, is how to measure corporate reputation quantitatively and how it affects sustainability. Following an in-depth exploration of the available literature, this manuscript aims to demonstrate the effective application of fuzzy models in enhancing decision-making processes within the realm of corporate reputation management for companies. To achieve this goal, this paper proposes a new corporate reputation measuring model based on the fuzzy 2-tuple linguistic and AHP (Analytic Hierarchy Process) methodologies. The proposed model promotes the computation of corporate reputation for companies based on three widely cited and universally recognized criteria outlined in the literature, drawing inspiration from a well-established framework in the field. This approach ensures a comprehensive and widely accepted foundation for evaluating corporate reputation: Capability, Benevolence, and Integrity and adding the Net Promote Score variable. To integrate sustainability into this equation, our model suggests the inclusion of variables related to sustainable practices in the measurement of corporate reputation. Recognizing the growing importance of sustainability in the public perception of companies, factors such as social responsibility, environmental management, and business ethics are recommended for consideration in the assessment of corporate reputation. The model proposed in this paper is tested and validated on a real business case, based on the selection of several companies selected for an empirical study in the selection of suppliers. For future research endeavors, the authors suggest expanding the model to encompass various decision-making processes. Additionally, they recommend exploring the integration of machine learning algorithms and data analysis techniques to identify patterns and provide recommendations for enhancing corporate reputation.

Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications.

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

This research introduces a novel approach to inventory control, leveraging machine learning techniques to optimize inventory decisions for deteriorating items while accounting for carbon emissions and trade credit policies. In contemporary supply chain management, sustainability considerations have become increasingly vital. The carbon footprint associated with inventory management practices necessitates innovative solutions to minimize environmental impact. Moreover, trade credit offers financial flexibility but requires careful management to maintain profitability. To address these complex challenges, our proposed framework integrates machine learning algorithms into inventory control to enhance decision-making precision. The model incorporates dynamic learning and forgetting effects, allowing the system to adapt to changing demand patterns over time. This adaptability is particularly critical when dealing with deteriorating items that exhibit non-constant demand rates. Carbon emissions are assessed throughout the supply chain, and environmentally conscious decisions are made to minimize the carbon footprint. Additionally, trade credit terms are optimized to strike a balance between financial constraints and inventory performance. Our approach demonstrates superior performance in terms of minimizing costs, reducing carbon emissions, and enhancing supply chain resilience compared to traditional inventory management methods. Real-world case studies and simulations validate the effectiveness of our machine learning-enabled inventory control system, showcasing its practical applicability. This research contributes to the advancement of sustainable supply chain management by providing a comprehensive framework that combines AI-driven inventory control, carbon emission reduction, and trade credit optimization, ultimately fostering environmentally responsible and financially viable inventory decisions.

On the use of machine learning for fragility analysis of multi-span girder bridges

Companies and enterprises managing the transportation networks are in charge of performing seismic risk assessments for a sensible number of bridges designed, over the past decades, without fulfilling requirements of anti-seismic codes. Recent research studies have focused on leveraging machine learning strategies to enhance the efficiency of probabilistic seismic assessments. This study tackles the integration of machine learning algorithms in the case of bridge-specific applications to predict probabilistic seismic demand models for substructure components such as piers, accounting for knowledge-based uncertainties.The machine-learning-based methodology employed builds on the results of nonlinear time history analyses and considers features related to seismic excitation and structural parameters. Random Forest algorithm is employed to investigate the methodology for a multi-span simply supported girder bridge, which is a representative example of the most prevalent bridge class in Europe. Aiming to reduce the need for extensive nonlinear time history analyses in the risk assessment of bridges belonging to this specific class, the methodology proposed shows promising potential highlighted in the results.

Machine learning-based prediction of coronary care unit readmission: A multihospital validation study.

Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals. Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk. The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model's robustness with consistent high AUCs, ranging from 0.852 to 0.879. The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.

AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.

Oral cancer retains one of the lowest survival rates worldwide, despite recent therapeutic advancements signifying a tenacious challenge in healthcare. Artificial intelligence exhibits noteworthy potential in escalating diagnostic and treatment procedures, offering promising advancements in healthcare. This review entails the traditional imaging techniques for the oral cancer treatment. The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. The review also encompasses the utilization of artificial intelligence such as automated image analysis, computer-aided detection and diagnosis integration of machine learning algorithms for oral cancer diagnosis and treatment. The customizing treatment approaches for oral cancer through artificial intelligence based personalized medicine is also part of this review. See also the graphical abstract(Fig. 1).