Utilizing Machine Learning Techniques Research Articles

Influence of fibre direction on the mechanical properties and artificial intelligence-based performance prediction of hybrid abaca-jute amino composites

In this study, the Hot-press method was used to create hybrid laminates using an equal amount of jute and abaca fibers with various fiber orientations [0/0/0/0/0/0, 0/45/0/0/45/0, 0/90/0/0/90/0, and +45/−45/+45/+45/−45/+45] embedded in amino resin. To assess the impact of fiber orientation on natural fiber hybrid eco-fiber composites, as per ASTM standard the tensile, flexural, impact, and ILSS tests were carried out. Additionally, conducting an in-depth statistical examination through the utilization of Machine Learning techniques like Artificial Neural Networks (ANN) to make predictions. The experimental results revealed that the voids and defects have a significant impact on composite density. (0/45/0/0/45/0)The fiber-oriented composite (Composite B) had a higher experimental density and void content than the other composites. The Composite A (0/0/0/0/0/0) outperformed other composites in terms of tensile strength, flexural load-bearing capacity, and impact strength by 5% to 29%. When compared to other composites, the Composite A fiber-oriented composite had the highest ILSS value (3.35 MPa). SEM analysis revealed a good interface between abaca-jute ecofibers and amino resin in the composite. ANN effectively predicts strengths with high R2 scores of 1, 0.98, and 0.99 for tensile, flexural, and impact strengths. The model’s interaction coefficients are close to 1, indicating strong correlative and predictive value.

Efficient Model for Waste Load and RouteOptimization

Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enterthe centralwater bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process consideredthe estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sitesto reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle'sorigin and destination, ensuring effective allocation of resources and prompt garbage removal. Installingthis approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modelingand route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment.Subsequently, we conductednumerous trials involving various waste disposalfrequencies. The findings suggest that waste disposalevery four(4) days is the most advantageous approach. Still, itperforms similarlyto waste disposalevery three (3)days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three(3) days, as it provides exceptional performancewhen consideringthe influence of natural pollution.

Learning locality-sensitive bucketing functions.

Many tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem-to recognize all pairs of sequences within a small edit distance-turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. In this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. The code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn.

Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review

The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.

Indexing Topological Numbers on Images by Transferring Chiral Magnetic Textures

AbstractTopological analysis is widely adopted in various research fields to unveil intricate features and structural relationships implied in geometrical objects. Especially, in the fields of data analysis, exploring the topological properties of various images offers rich insights into the intrinsic geometrical information within them. In this study, a novel approach is proposed to investigate the topological properties of arbitrary grayscale images by employing a straightforward procedure used in 2D magnetism studies to calculate topological numbers. This method utilizes machine learning techniques to transfer chiral magnetic textures onto the images. Then, the topological number is then computed directly from the converted images by integrating the solid angles formed by adjacent spin vectors. The method successfully identifies the topological numbers of various grayscale images, showing stable performances against small noises. Furthermore, two applications of the method: are demonstrated topological analysis of the Modified National Institute of Standards and Technology (MNIST) dataset and the counting of blood cells in microscopic images.

Understanding urban perception with visual data: A systematic review

Visual characteristics of the built environment affect how people perceive and experience cities. For a long time, many studies have examined visual perception in cities. Such efforts have accelerated in recent years due to advancements in technologies and the proliferation of relevant data (e.g., street view imagery, geo-tagged photos, videos, virtual reality, and aerial imagery). There has not been a comprehensive systematic review paper on this topic to reveal an overarching set of research trends, limitations, and future research opportunities. Such omission is plausibly due to the difficulty in reviewing a large number of relevant papers on this popular topic. In this study, we utilized machine learning techniques (i.e., natural language processing and large language models) to semi-automate the review process and reviewed 393 relevant papers. Through the review, we found that these papers can be categorized into the physical aspects of cities: greenery and water, street design, building design, landscape, public space, and the city as a whole. We also revealed that many studies conducted quantitative analyses with a recent trend of increasingly utilizing big data and advanced technologies, such as combinations of street view imagery and deep learning models. Limitations and research gaps were also identified as follows: (1) a limited scope in terms of study areas, sample size, and attributes; (2) low quality of subjective and visual data; and (3) the need for more controlled and sophisticated methods to infer more closely examined impacts of visual features on human perceptions. We suggest that future studies utilize and contribute to open data and take advantage of existing data and technologies to examine the causality of visual features on human perception. The approach developed to accelerate this review proved to be accurate, efficient, and insightful. Considering its novelty, we also describe it to enable replications in the future.

Cluster analysis for systematic database extension to improve machine learning performance in double-sided incremental sheet forming

Incremental sheet forming is a process for the production of sheet metal parts in small batch sizes. Due to the relatively low geometrical accuracy and the lack of precise and fast finite element analysis simulations of the process, industrial use cases are rare. Recently, a vast amount of scientific approaches simulated the process by utilizing machine learning techniques. Their success is limited by the quantity and quality of the used process data. Research institutes are struggling to gather enough data without industrial cooperations. For maximizing the distribution of process data in an experimental series and therefore their applicability for machine learning, the authors present a novel cluster analysis approach to systematically extend an existing database. The whole established process database consisting of 70 forming experiments and their toolpaths and digitizations is published to be used as a foundation for similar research.

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

A machine learning-based simplified collision model for granular flows

This study aims to create an efficient, rapid, and reliable particle collision model utilizing machine learning techniques for granular flow simulations. A simplified surrogate collision model developed in the framework of a Hybrid Euler–Lagrange (HEL) technique was successfully applied to model particle interactions for flows with a low fraction of the granular phase. The precision of the simplified collision model was evaluated using experimental data obtained from the in-house, two-stream particle collision test rig, focusing on solid phase velocity profiles. The implemented model demonstrates strong concordance with the experimental results. The simulations carried out highlight the relation between the simulation time step and the collision rate, which affects the cost of the numerical simulation. The execution time for both the conventional Discrete Element Method (DEM) on a CPU and the streamlined collision HEL model saw a reduction exceeding 70%.

Utilizing Machine Learning Techniques for Power Estimation in Residential Electricity Consumption

Electricity consumption analysis and estimation play a vital role in various contexts, ranging from business planning to household financial management. In an era where electrical energy is a primary source of human activity, understanding power usage patterns and estimating them becomes crucial. This research enables an understanding of energy consumption from individual to national scales and supports efficient energy policy planning. Power estimation provides insights into the expenditures that consumers or companies will incur, enabling effective financial planning. Research and development of accurate predictive models using machine learning techniques are essential to providing useful information for the energy industry and the general public. This study uses machine learning to compare the performance of different power prediction models. The decision tree (DT) method, with an MAE of 0.3613, an MSE of 0.4184, and an RMSE of 0.6469, gives the best results. The results show that the model can provide accurate predictions of electricity consumption, potentially becoming a reliable tool in power management and financial management. Its contribution is crucial in the energy industry and daily life, providing insights needed to enhance energy efficiency and budget management. As a result, this research provides relevant and beneficial solutions to energy and financial management for society and industry. Keywords: Electricity consumption analysis, Estimation, Machine learning, Power management, financial management

Preservative contact allergy in occupational dermatitis: a machine learning analysis.

Occupational dermatoses impose a significant socioeconomic burden. Allergic contact dermatitis related to occupation is prevalent among healthcare workers, cleaning service personnel, individuals in the beauty industry and industrial workers. Among risk factors, the exposure to preservatives is frequent, since they are extensively added in products for occupational use. The goal of this study is to investigate the contact allergy patterns in order to understand the linkage among hypersensitivity to preservatives, occupational profiles, patients' clinical and demographic characteristics. Patch test results were collected from monosensitized patients to Formaldehyde 2%, KATHON 0.02%, thimerosal 0.1%, and MDBGN 0.5%; information was also collected for an extended MOAHLFA (Male-Occupational-Atopic-Hand-Leg-Face-Age) index. To assess the relationship between allergen group and occupational-related ACD, the chi-square test for independence was utilized. To uncover underlying relationships in the data, multiple correspondence analysis (MCA) and categorical principal components analysis (CATPCA), which are machine learning approaches, were applied. Significant relationships were found between allergen group and: occupation class, atopy, hand, leg, facial, trunk, neck, head dermatitis, clinical characteristics, ICDRG 48h and ICDRG 72h clinical evaluation. MCA and CATPCA findings revealed a link among allergen group, occupation class, patients' demographic and clinical characteristics, the MOAHLFA index, and the ICDRG scores. Significant relationships were identified between the allergen group and various manifestations of dermatitis. The utilization of machine learning techniques facilitated the discernment of meaningful patterns in the data.

GNSS spoofing detection for UAVs using Doppler frequency and Carrier-to-Noise Density Ratio

Unmanned Aerial Vehicles (UAVs) are typical real-time embedded systems, which require precise locations for completing flight missions. The Global Navigation Satellite System (GNSS) plays a crucial role in navigation and positioning for UAVs. However, GNSS spoofing attacks pose an increasing threat to GNSS-dependent UAVs. Existing spoofing detection methods primarily rely on simulated data, perception data from multiple UAVs, or various control parameters. This paper proposes SigFeaDet, a signal feature-based GNSS spoofing detection approach for UAVs utilizing machine learning techniques. The core concept revolves around identifying anomalies in signal features arising from differences between authentic and spoofing signals. Key signal features, including Carrier-to-Noise Density Ratio (CN0) and Doppler frequency crucial for GNSS positioning, are employed to discern spoofing signals. Various machine learning algorithms are leveraged to train on GNSS signal data, determining the most effective classifier. TEXBAT GNSS dataset is processed to extract spoofing signal data, and flight experiments are conducted to gather GNSS data, augmenting the authentic GNSS signal dataset. The detection accuracy exceeds 95%. Equal Error Rate (EER) is approximately 5%. We evaluate various impact factors on SigFeaDet to show its robustness, including differences in velocities, altitudes, and experimental locations (10 kilometers apart), and the accuracy consistently surpasses 99%.

Applying machine learning to construct an association model for lung cancer and environmental hormone high-risk factors and nursing assessment reconstruction.

To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung cancer. This study is exploratory in nature. In Stage 1, data were sourced from a biological database, and machine learning methods, including logistic regression and neural-like networks, were employed to construct an association model. Results indicate significant associations between lung cancer and blood cadmium, urine cadmium, urine cadmium/creatinine, and di(2-ethylhexyl) phthalate. In Stage 2, 128 lung adenocarcinoma patients were recruited through convenience sampling, and the model was validated using a questionnaire assessing daily living habits and exposure to environmental hormones. Analysis reveals correlations between the living habits of patients with lung adenocarcinoma and exposure to blood cadmium, urine cadmium, urine cadmium/creatinine, polyaromatic hydrocarbons, diethyl phthalate, and di(2-ethylhexyl) phthalate. According to the World Health Organization's global statistics, lung cancer claims approximately 1.8 million lives annually, with more than 50% of patients having no history of smoking or non-traditional risk factors. Environmental hormones have garnered significant attention in recent years in pathogen exploration. However, current nursing assessments for lung cancer risk have not incorporated environmental hormone-related factors. This study proposes reconstructing existing lung cancer nursing assessments with a comprehensive evaluation of lung cancer risks. The findings underscore the importance of future studies advocating for public screening of environmental hormone toxins to increase the sample size and validate the model externally. The developed association model lays the groundwork for advancing cancer risk nursing assessments.

Acoustic design evaluation in educational buildings using artificial intelligence

Speech intelligibility is a critical aspect of building science, particularly in educational buildings where poor sound quality may have a detrimental impact on students' learning and teachers’ health. However, considering the numerous building regulations proposing varying definitions and ranges of acoustic comfort, calculating the necessary acoustic indicators can be challenging for designers. Speech intelligibility is a crucial component of indoor acoustics and acoustic comfort and can be calculated using formulas, simulation software, and data-based web tools. While formulas are fast, they lack details; acoustic simulation software is highly accurate but time-consuming and expensive. Data-based web tools, including machine learning algorithms, offer both speed and accuracy and are widely accessible. In this study, we present a system utilizing machine learning techniques to predict acoustic indicators, numeric and heatmap, in an educational building. The Pachyderm plugin in the Grasshopper was utilized to conduct extensive simulations in a single educational space, focusing on acoustic indicators in six different frequencies and general modes. Using Catboost and the pix2pix algorithm, the prediction models provide numerical and image indices on the developed dataset. Also, SHAP values were employed to interpret the Catboost model, analyzing the significance of each feature. The results showed remarkable accuracy, (i.e., between 89 % and 99 %) in the numerical portion, and PSNR index ranging from 0.817 to 0.970, and an SSIM index ranging from 15.56 to 31.57 in the image section. By utilizing data-driven methods, the system provides an efficient and accurate approach to calculating acoustic indicators, helping to ensure optimal acoustic environment in educational buildings.

Review Paper on Machine Learning Algorithms

This paper comprehensively reviews widely used machine learning algorithms across supervised, unsupervised, and reinforcement learning paradigms. It covers linear models, decision trees, support vector machines, neural networks, clustering techniques, dimensionality reduction methods, and ensemble approaches. For each algorithm, theoretical foundations, mathematical formulations, practical considerations like parameter tuning and computational complexity, and real-world applications across domains like computer vision and finance are discussed. Challenges and limitations such as overfitting and scalability are explored. Recent advancements like deep learning and transfer learning are highlighted. Finally, a comparative analysis evaluating strengths, weaknesses, and suitable problem domains for the algorithms is provided, serving as a guide for effective utilization of machine learning techniques. Keywords:- Machine learning · Deep learning, Gradient Descent, Logistic Regression, Support Vector Machine, K Nearest Neighbor, Predictive analytics,

Holistic exploration of reading comprehension skills, technology and socioeconomic factors in Spanish teenagers

The intricate relationship between teenagers' literacy and technology underscores the need for a comprehensive understanding, particularly in the Spanish context. This study employs explainable artificial intelligence (AI) to delve into this complex interplay, focusing on the pivotal role of reading comprehension skills in the personal and career development of Spanish teenagers. With a sample of 22,400 15-year-olds from the PISA dataset, we investigate the impact of socioeconomic factors, technology habits, parental education, residential location, and school type on reading comprehension skills. Utilizing machine learning techniques, our analysis reveals a nuanced connection between autonomy, technological proficiency, and academic performance. Notably, family oversight of technology use emerges as a crucial factor in managing the impact of digital technology and the Internet on reading comprehension skills. The study emphasizes the necessity for a balanced and supervised introduction to technology from an early age. Contrary to current trends, our findings indicate that online gaming may not contribute positively to reading comprehension skills, while moderate daily Internet use (1–4 h) proves beneficial. Furthermore, the study underscores the ongoing nature of acquiring reading comprehension and technological skills, emphasizing the need for continuous attention and guidance from childhood. Parental education levels are identified as partial predictors of children's performance, emphasizing the importance of a holistic educational approach that considers autonomy and technological literacy. This study advocates for addressing socio-economic and gender inequalities in education and highlights the crucial role of cooperation between schools and families, particularly those with lower educational levels.

Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT.

e13639 Background: Automated Machine Learning (Auto-ML) in medical imaging is a process that allows non-experts to utilize machine learning techniques, opening the door for non-coder physician-driven exploitation of the technology. Auto-ML was applied for opportunistic detection of thyroid nodules in the context of low-dose lung cancer screening chest CT, facilitated by an innovative platform integration. By leveraging scans originally intended for lung cancer screening, suspicious appearing asymptomatic thyroid nodules can also be screened for where technically feasible. Methods: CT scans from the National Lung Screening Trial (NLST) dataset were utilized. This dataset contains low-dose chest CT examinations initially used for lung cancer screening. Annotation was carried out within a no-code web-based platform, Gesund.ai. A board-certified diagnostic radiologist annotated 100 CT examinations. A case was labeled "suspicious" if it presented a thyroid nodule of 1 cm or greater in diameter, while cases with no such nodules were deemed "not suspicious." The Auto-ML feature was then engaged within the software platform for parameter adjustments to refine the model without the need for manual coding. This phase was characterized by an iterative exploration of model performance, facilitated by the platform’s robust validation tools. Results: Upon training and validation, the Auto-ML model, cultivated from 100 annotated cases and assessed against a separate set of 130 cases, demonstrated a discernment accuracy of 0.51 in identifying "suspicious" thyroid nodules with a sensitivity and precision of 0.51 and 0.62 respectively. The AUC for this class stands at 0.69, indicating a moderate ability to distinguish between the presence and absence of thyroid nodules. This metric, while foundational, offers insight into the model's potential efficacy in real-world diagnostic scenarios, reflecting the initial capabilities of the platform's Auto-ML integration in enhancing diagnostic processes. Conclusions: The findings from this study highlight the potential of Auto-ML in revolutionizing opportunistic screening for thyroid nodules via lung cancer CT. The initial accuracy rate underscores the necessity for further refinement and validation of the model, but also demonstrates the promising potential of Auto-ML. Institutional Auto-ML would include extraction of appropriate studies through the no-code software platform integrated directly within the PACS framework. This process would ensure data immobility and foster a secure environment for dataset creation. Annotation would be carried out by multiple physician imagers within the same facility, enhancing the data’s accuracy and comprehensiveness specific for the institution’s patient population. This exploration highlights the strategic role of Auto-ML in advancing cancer patient care through innovative technological integration.

P‐55: An Optimizing Finger Separation Method with Machine Learning Algorithm used In‐Cell Capacitive Touch Panel

In this paper, we utilized machine learning techniques to perform finger separation recognition. The research results show that using the machine learning method proposed in this article, in combination with Novatek TDDI NT36523 tablet TDDI IC, it can largely improve the resolution of finger enumeration, especially when two fingers are very close together.

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Car Price Prediction Using Machine Learning

Abstract: The objective of the "Car Price Prediction Using Machine Learning" project is to anticipate car prices based on pertinent variables by utilizing predictive modelling and advanced data analytics. The rising need for precise and active pricing systems in the automobile sector is addressed by this initiative. The system will evaluate past vehicle data, including brand, model, manufacturing year, the mileage, type of fuel, and other details, by utilizing machine learning techniques. In order to produce specific predictions, the suggested model will be trained on an extensive dataset, noticing patterns and parallels within the data. In order to create a reliable prediction model, the research focuses on using regression techniques, such as ensemble approaches or linear regression. System of measurement like as absolute mean error and R-squared coefficient will be used to evaluate the predicted accuracy of the system in order to regulate its usefulness. If this resourcefulness is employed successfully, it will have a big impact on the automobile business for clients and venders alike by giving them a tool to evaluate fair market prices and helping them make decisions. This study enhances to the body of knowledge in price analysis using machine learning and lays the footing for future improvements in the prognostication of dynamic market trends in the automobile sector.