Machine Learning Technology Research Articles

The role of students’ higher-order thinking skills in the relationship between academic achievements and machine learning using generative AI chatbots

Students’ perspectives on using generative artificial intelligence (AI) chatbots and machine learning are crucial in shaping the design, development, and implementation of their learning projects across various disciplines. Cognitive thinking, a key aspect of AI-related machine learning, aims to replicate human intelligence and behavior. However, the relation between cognitive thinking and knowledge acquisition is often overlooked. This cross-sectional study empirically examines the relationship between academic achievement and students’ attitudes toward machine learning, particularly through the use of generative AI chatbots. It specifically focuses on the role of higher-order thinking skills—such as problem-solving, critical thinking, and creativity—as both mediators and moderators in this relationship. A total of four hundred sixteen undergraduate students (n=416) from diverse academic backgrounds voluntarily took part in a project, in which they designed and developed generative AI chatbots in media and information literacy courses. The findings indicate that creativity mediated the relationship between academic achievements and attitudes toward machine learning, but its moderating impact was not significant. Problem-solving and critical thinking did not show significant mediating effects on attitudes toward machine learning, while they showed significant moderating effects in the connection between academic performance and attitudes toward machine learning. This study contributes by elucidating the interrelationships between students’ higher-order thinking skills, academic performance, and attitudes on the use of AI and machine learning technologies. By highlighting the mediating role of creativity and the moderating effects of problem-solving and critical thinking, this study offers a deeper understanding of how these skills shape students’ perceptions of AI.

Feature mining for thermoelectric materials based on interpretable machine learning.

In previous laboratory preparation processes, the selection and proportioning of specific experimental parameters often stemmed from the empirical experience of predecessors, necessitating the derivation of the optimal scheme for the target experiment through extensive trial and error. This process typically required the consumption of substantial resources and time. Thanks to the advancement of machine learning technologies, these have gradually become a powerful tool for addressing complex functional problems in material optimization. This study is based on a collected database of thermoelectric materials, aiming to clarify the correspondence between physical characteristics and the thermoelectric figure of merit, zT. The key features that can directly affect the experimental results are identified, and the features that indirectly affect the experimental results are also analyzed. By employing interpretable machine learning methods, this research analyzes critical molecular features in the characterized data, utilizing feature engineering techniques to construct and optimize machine learning models for the selected key features. Furthermore, in the subsequent model fitting and analysis phase, by comparing the efficiency of different feature combinations, the optimal feature descriptors for the current experimental system were determined. The adoption of the aforementioned improved methods endowed the related models with the potential for high-throughput screening, thereby further enhancing the efficiency of experimental optimization.

Real-time rainfall projection in hilly areas using ARLSTM model: a case of Dharamshala, India

Abstract Rainfall forecasting is essential in many industries, including agriculture, hydrology, urban planning, disaster management, and renewable energy. However, typical forecasting approaches frequently fail to reflect the complex spatiotemporal dynamics inherent in rainfall patterns, particularly in hilly areas. Since, the mountainous terrain influences wind patterns, moisture flow, and temperature inversions which affect precipitation patterns, adding challenge to meteorologists. So, designing a predictive forecasting model is essential for people and governments to make preventive measures in time. In recent years, the introduction of Internet of Things (IoT) technology and advances in machine learning (ML) techniques have provided a promising route for improving rainfall prediction accuracy. Real-time data on environmental elements such as temperature, wind speed, air pressure, and humidity can be collected using IoT sensor networks. This research paper investigates the efficacy of the Auto-Regressive Integrated Moving Average - Long Short-Term Memory (ARIMA-LSTM) model for real-time rainfall prediction in hilly towns like Dharamshala. Utilizing real-time data collected from sensors and using edge to preprocess it before sending it to the cloud the study explores the integration of traditional time series analysis techniques with deep learning architectures to enhance predictive accuracy. The propounded model is paralleled against alternative forecasting approaches and shows a significant reduction in RMSE, RRMSE, and MSE. Furthermore, it had a 0% Absolute Percentage Bias (APB), a high Nash-Sutcliffe Efficiency (NSE) of 0.99, and a low Theil's coefficient (U2) of 0.01. These results demonstrate that the ARLSTM model outperforms other forecasting models in terms of rainfall prediction.

Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning

The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant’s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal component regression (PCR), random forest (RF), and partial least squares regression (PLSR) were established with leaf chlorophyll under different nitrogen concentrations. The CARS_PLSR algorithm has the highest prediction accuracy with accuracy, precision, RMSEC, and RMSEP of 0.8790, 0.9187, 2.9581, and 2.5578, respectively. The study examined the impact of various nitrogen concentrations on the chlorophyll content of cherry tomato leaves, and it was found that the high concentration of nitrogen inhibited the SPAD value of cherry tomato leaves more than that of the low concentration, and the optimal concentration of nitrogen fertilization for tomatoes was 300 mg·L−1. Finally, a regression model was established by using CARS-PLSR combined with the pseudo-color map technology, and a distribution map of chlorophyll content in different SPAD value gradients of cherry tomato leaves was obtained, which could visualize the distribution of chlorophyll and its distribution sites in the leaves and understand the growth status of cherry tomatoes. The distribution of chlorophyll content in different SPAD values of cherry tomato leaves was obtained by using the CARS-PLSR regression model combined with pseudo-color map technology, which can visualize the distribution of chlorophyll in the leaves and the parts of distribution and understand the growth condition of cherry tomatoes. Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.

Security and Compliance in Cloud ERP Systems: A Deep Dive into Workday's Framework

This paper provides a comprehensive analysis of the security and compliance framework within cloud- based ERP systems, with a particular focus on Workday, a leader in the market for enterprise resource planning (ERP) solutions. As businesses increasingly transition to cloud ERP systems, ensuring robust security and regulatory compliance has become critical to safeguarding sensitive data, maintaining operational integrity, and meeting industry-specific regulations. The paper begins by introducing the essential concepts of cloud ERP security, highlighting the challenges of data breaches, data integrity, and access control. It further explores Workday's advanced security infrastructure, including its encryption protocols, secure data storage, and real-time monitoring systems, as well as its adherence to global compliance standards such as GDPR, SOC 2, and ISO 27001. The analysis also emphasizes Workday’s tailored security features for specific industries like finance, healthcare, and government, demonstrating how its platform fosters customer trust and drives adoption across diverse sectors. Looking to the future, the paper discusses emerging trends, including the integration of Artificial Intelligence (AI), Machine Learning (ML), and blockchain technology, which hold potential for enhancing data security and compliance in cloud ERP environments. This deep dive into Workday’s security and compliance framework underscores its role in mitigating risks, ensuring regulatory adherence, and positioning itself as a trusted solution in an evolving cloud landscape. Keywords: AI/ML, Cloud ERP Security, Workday Security Framework, Cloud ERP Compliance, Workday Compliance Standards, Data Security, Workday Data Encryption, Regulatory Compliance, SOC 2 Compliance, Identity and Access Management, GDPR Compliance, Risk Mitigation.

Machine Learning-based Dissolved Oxygen Prediction Modeling and Evaluation in the Yangtze River Estuary

Dissolved oxygen （DO） serves as a pivotal indicator, mirroring the intrinsic self-purification capacity of aquatic ecosystems and the overarching quality of the water environment. In the context of the Yangtze Estuary, a crucial hub for biodiversity and economic activities in China, understanding and forecasting levels of DO is instrumental for effective environmental stewardship and management strategies. Considering this, the introduction of sophisticated machine learning algorithms into the monitoring and predictive analytics of dissolved oxygen levels represents an important stride toward leveraging the power of data-driven insights for environmental sustainability. The Yangtze Estuary, characterized by its dynamic and complex hydrological and ecological systems, demands an insightful and nuanced approach to monitoring water quality parameters. To this end, six key monitoring stations were chosen across the estuary, including Xuliujing, Nantong Port, Qidong Port, Qinglong Port, South Port, and North Port, acting as sentinel sites for gauging the health of the water body. Leveraging three cutting-edge modeling techniques-particle swarm optimization-support vector regression （PSO-SVR）, artificial neural network （ANN）, and random forest （RF）-the research unraveled and forecasted the patterns of dissolved oxygen levels using monthly average water quality data spanning from 2004 to 2020. These models embodied the forefront of machine learning technology, each bringing distinct analytical strengths and perspectives to the table, from the nuanced, non-linear pattern recognition capabilities of ANN to the robustness and interpretability of RF. The meticulous evaluation conducted via the RF model underscored the paramount importance of three water quality variables, namely temperature, five-day biochemical oxygen demand, and ammonia nitrogen, in influencing the spatial-temporal dynamics of dissolved oxygen in the estuary. Comparative analysis of the prediction results yielded by the PSO-SVR, ANN, and RF models illuminated the superior performance of the RF model across the six monitoring stations, with an overall average error margin of 0.19, a testament to its efficacy and reliability. In comparison, the PSO-SVR and ANN models exhibited higher error rates of 0.38 and 0.47, respectively, albeit still contributing valuable insights into the complex dissolved oxygen dynamics in the Yangtze Estuary. The prediction performance of the machine learning models was evaluated, and the overall prediction performance ranking on the training set was RF （R2=0.971； RMSE=0.341 mg·L-1） > PSO-SVR （R2=0.884； RMSE=0.707 mg·L-1） > ANN （R2=0.792； RMSE=0.967 mg·L-1）. The overall prediction performance ranking on the test set was RF （R2 = 0.986； RMSE=0.165 mg·L-1） > PSO-SVR （R2=0.951； RMSE=0.332 mg·L-1） > ANN （R2=0.800； RMSE=0.633 mg·L-1）. Therefore, the RF model exhibited the best predictive ability on all monitoring sections, showing excellent performance and generalization ability both on the training and the test sets. The PSO-SVR model also performed well on most monitored profiles, with slightly lower predictive performance than that of the RF model though with better stability and generalization ability. However, the ANN model did not perform as perfectly as the other two models in some monitoring profiles and its network structure or parameters may need to be further optimized to improve the prediction accuracy and stability.

Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study

Branding has emerged as a multidiscipline with complication, conveyed by integrated digital technologies, AI, and rich linguistic approaches. This paper depicts the research landscape at the juncture of branding and linguistics, supported by VOSviewer, Biblioshiny, and CiteSpace in pinpointing key trends, themes, and collaboration networks. Drawing on data collected from Scopus, this study investigates the shifts in traditional branding strategies caused by technologies of machine learning, sentiment analysis, and social media platforms. It highlights the contributions made by leading authors, major publication sources, and patterns of international collaboration in influential works. Finally, thematic and co-occurrence analysis shows significant movement into data-based and computational approaches. This also points out some research gaps in the field of digital branding’s long- term effects, ethical considerations of AI-photo strategies, and multilingual branding approaches that provide a useful basis for future research and practical application innovation in the field of branding.

Health Monitoring Technology for Hydraulic Engineering Gates Based on Multi-Sensor Data Fusion

An essential part of the hydraulic engineering systems, hydraulic gates regulate water flow in dams and other structures. To avoid structural problems, they must ensured to be consistent. Multi-sensor data fusion has become a key tool for health monitoring with advances in machine learning (ML) and sensor technologies. This study aims to provide a novel multi-sensor data fusion health monitoring system for hydraulic engineering gates. To provide more effective and precise real-time monitoring of hydraulic engineering gates, a unique Dynamic Satin Bowerbird search-driven Bidirectional Long Short-Term Memory (DSB-BiLSTM) model were proposed. The hydraulic monitoring dataset was collected from Kaggle website, comprising information and some attributes obtained from hydraulic engineering gates. To improve data quality, noise reduction, and data normalization are used. Time-series analysis techniques extract important information such as vibration patterns and pressure variations. The DSB-BiLSTM model leverages bidirectional LSTM to progress time-dependent data, and the Satin Bowerbird search optimizes sensor selection. The method shows better accuracy and efficiency in identifying potential faults compared to traditional models. When compared to four attributes such as pump, cooling, valve and accumulator with the metrics outcomes of 0.9901, 0.9925, 0.9950, and 0.9890 in accuracy, 0.9930, 0.9960, 0.9902, and 0.9889 in precision, 0.9920, 0.9960, 0.9911, and 0.9890 in recall, and 0.9915, 0.9955, 0.9905, and 0.9889 in F1-score, the DSB-BiLSTM model performs better than all other approaches that were examined. This suggests better overall hydraulic engineering gate health monitoring performance. The proposed DSB-BiLSTM model is a show potential solution for real-time health monitoring of hydraulic gates, provided that it enhances accuracy and reduces computational complexity.

The impact of artificial intelligence on economic development: ethical aspects of technology implementation and development

Relevance. Digitalization is one of the main priorities of the economy. Machine learning and artificial intelligence technologies are introduced by advanced companies into production and management processes, and change the nature of their activities and the way they do business. New issues related to the legislative regulation of technologies, the distribution of areas of responsibility between humans and robotic systems arise, and legal relations that are not provided for by the regulatory framework appear. Technological development also leads to a profound transformation of society. New mechanisms of interaction raise a number of ethical issues. In this regard, the topic of research on the introduction of artificial intelligence is becoming increasingly popular in the scientific community.The purpose of the article is to study the features of the introduction of digital systems, including those which are based on artificial intelligence, as well as to systematize the ethical problems of using such technologies.Objectives: analyze the regulatory framework for the regulation of artificial intelligence systems, determine the basic principles of its development and implementation, consider the main problems arising in connection with the expansion of the scope of artificial intelligence, as well as options for their solutions.Methodology. To achieve the goal in the research are used methods of system analysis and synthesis, bibliographic analysis. The works of domestic and foreign scientists devoted to AI, the regulatory framework of the federal and regional levels, materials of scientific journals and conferences have become the information base of the study.Results. A literary review of modern scientific research on ethical issues of implementation is presented in the article, the main normative acts used both in international practice and developed in Russia are considered.Conclusions. The systematization of the principles and ethical problems of the introduction of artificial intelligence systems based on the analysis are presented. The use of new technology has advantages, but it is necessary to create a legislative and institutional framework that will minimize risks and achieve sustainable development goals.

AI-Driven Optimization of Last-Mile Delivery

The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics, consuming a substantial portion of total shipping costs. This comprehensive technical article examines how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery operations through advanced route optimization, demand forecasting, and resource allocation. This article synthesizes findings from recent implementations across major logistics providers, demonstrating that AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption across diverse operational environments. Analysis of machine learning models deployed by leading e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving unprecedented precision in estimated delivery windows. This article examines key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems, while also addressing implementation challenges such as data quality and system integration. Case studies from major logistics providers demonstrate substantial ROI improvements following AI implementation, with particular emphasis on neural network architectures optimized for urban delivery scenarios. This article provides a technical framework for understanding how AI technologies transform last-mile logistics while offering insights into future developments, including autonomous delivery systems and smart city integration.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

Deepmol: an automated machine and deep learning framework for computational chemistry

The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. Addressing these issues head-on, DeepMol stands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline. DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. As one of the first AutoML tools specifically developed for the computational chemistry domain, DeepMol stands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available at https://github.com/BioSystemsUM/DeepMol and https://deepmol.readthedocs.io/en/latest/. By introducing AutoML as a groundbreaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field.Scientific contributionDeepMol aims to provide an integrated framework of AutoML for computational chemistry. DeepMol provides a more robust alternative to other tools with its integrated pipeline serialization, enabling seamless deployment using the fit, transform, and predict paradigms. It uniquely supports both conventional and deep learning models for regression, classification and multi-task, offering unmatched flexibility compared to other AutoML tools. DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. Benchmarking on diverse datasets demonstrated its ability to deliver optimized pipelines and superior performance across various molecular machine-learning tasks.

Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the risk of misdiagnosis. To solve this problem, we aimed to develop a novel method for identifying patients with immunoglobulin A nephropathy from billing data using machine learning. The medical records and bills of 3,743 patients who consulted nephrologists at a single center were extracted. Patients were labeled to have been diagnosed with immunoglobulin A nephropathy through a review of medical records. A manual analysis of the diagnostic accuracy and machine learning was performed. For machine learning, the datasets were preprocessed in three patterns and assigned to the XGBoost program using five-fold cross-validation. Of all the participants, 437 were labeled as having been diagnosed with immunoglobulin A nephropathy. Bill codes for immunoglobulin A nephropathy were provided to approximately half of them. The manually created criteria consisting of the recommended examinations and treatments in the Japanese guidelines for immunoglobulin A nephropathy showed both specificity and sensitivity < 0.8. In contrast, with the receiver operating characteristic curve analysis, the machine learning process yielded area under the curve values over 0.9 with preprocessing from the clinical viewpoint. Applying machine learning technology to a dataset preprocessed from a clinical viewpoint achieved a high performance in detecting patients with immunoglobulin A nephropathy. This methodology contributes to the construction of a disease-specific cohort using big bill data.

ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS

Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This study analyzes CCP for a specific telecom company’s customer dataset using ML methods such as logistic regression (L.R.), -nearest neighbor (-NN), decision tree (D.T.), and random forest (R.F.). The UCI Iranian telecom churn dataset was utilized, and the influence of potential factors leading to customer churn was also considered. Results show that the tuned RF method yielded the best outcomes, with churn tendency analysis achieving a higher AUC score at 0.9042 with the accuracy of 0.9562. The most important feature of the dataset affecting the customer churn was identified as complains whereas the least important feature happened to be tariff plan.

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical staining image features using machine learning methods. Initially, CRC disease-specific genes were identified through bioinformatics analysis, SVM-RFE and Random Forest algorithm utilizing RNA-seq data from both GEO and TCGA databases. Subsequently, verification of these genes was performed using proteomics data from CPTAC and HPA database, resulting in identification of target proteins (AKR1B10, CA2, DHRS9, and ZG16) for further investigation. SVM and CNN were then employed to analyze and integrate the characteristics of immunohistochemical images to construct a reliable CRC diagnostic model. During the training and validation process of this model, cross-validation along with external validation methods were implemented to ensure accuracy and reliability. The results demonstrate that the established diagnostic model exhibits excellent performance in distinguishing between CRC and normal controls (accuracy rate: 0.999), thereby presenting potential prospects for clinical application. These findings are expected to provide innovative perspectives as well as methodologies for personalized diagnosis of CRC while offering more precise references for promising treatment.

Enhancing Information Technology Governance in Universities: A Smart Chatbot System based on Information Technology Infrastructure Library

The rapid evolution of information and communication technologies has created a pressing need for higher education institutions to modernize their Information Technology (IT) governance practices. This article proposes an innovative solution to enhance the governance and efficiency of IT services while optimizing and personalizing user experience. The proposed solution consists of a chatbot using Artificial Intelligence (AI) and Natural Language Processing (NLP) combined with the Information Technology Infrastructure Library (ITIL) standard to automate the management of IT services in the digital work environment (ENT). Intended for students, teachers, and administrators, this chatbot provides reactive support by responding to requests, reducing waiting times, and improving satisfaction. It also helps decrease operational costs and the workload of support teams by autonomously handling recurring requests. Beyond efficiency improvements, the chatbot contributes significantly to IT governance by providing structured service management, improving decision-making through real-time data, and supporting compliance with governance frameworks. An online survey conducted among 120 students revealed slow processing of requests and unavailability of services, justifying the need for this chatbot. The chatbot was designed with advanced NLP and Machine Learning (ML) technologies. Preliminary tests demonstrate the chatbot’s response reliability, with an accuracy rate of 96% and a response time decrease to an average of 4.17 seconds. The use of chatbots has considerable potential for universities to improve the efficiency of digital services offered to students.

Knowledge management and technology for enhanced cybersecurity effectiveness

Cybersecurity continues to be a major concern and presents significant challenges for businesses, governments, nonprofits, and individuals. Organizations are working diligently to mitigate threats to these entities while the rate of cybercrime continues to increase in sophistication and reach. Current models in cybersecurity are becoming increasingly ineffective against the velocity, agility, and persistence of cyber adversaries. This challenge presents an opportunity to consider concepts and methods in knowledge management to strategically evaluate and prioritize adversarial cyber threat activities. This research aims to explore a conceptual framework designed to provide cybersecurity professionals with new models for knowledge management to increase the effectiveness of the detection, mitigation, and attribution of these threats. This conceptual approach draws from the literature on knowledge management and cybersecurity to integrate key concepts into a new theoretical framework. This paper presents a new hybrid model for cybersecurity that integrates core concepts of the classic Nonaka and Takeuchi Knowledge Spiral and the National Institute of Standards and Technology (NIST) Cybersecurity Framework augmented with emerging artificial intelligence and machine learning technologies. The development of a hybrid cybersecurity model combined with proven knowledge management strategies for continual knowledge creation and innovation represents an integrative model designed to address emerging cybersecurity challenges.

Aqua-stream: an IoT based smart water management system for sustainable living

&lt;p class="03IJISAEAbstract"&gt;Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.&lt;/p&gt;

Emerging trends and hotspots in lung cancer-prediction models research.

In recent years, lung cancer-prediction models have become popular. However, few bibliometric analyses have been performed in this field. This study aimed to reveal the scientific output and trends in lung cancer-prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends. A marked increase in the number of publications related to lung cancer-prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer-prediction models. Over the last two decades, research on risk-prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer-prediction models and reduce the global burden of lung cancer.

Identification and characterisation of earthquake clusters from seismic historical data

New approaches and methods based on machine learning technologies make it possible to identify not only the spread of earthquakes, but also to establish hidden patterns that allow further assessment of any risks associated with their occurrence. In this article, the clustering algorithms of K-means and K-medoids are applied for the analysis of seismic data recorded on the territory of the Republic of Kazakhstan. Using the Elbow and Silhouette methods, the optimal value of K clusters was determined, which was later used in classifying a data set using cluster analysis methods. The results of seismic data classification by clustering algorithms are in line with expectations. However, when measuring the quality of clustering, the accuracy of the model by the K-means method exceeded the accuracy of the K-medoids model, and the scoring value by the K-means method is ahead of the value by the K-medoids method. In addition, the presented results of descriptive statistics allowed to carry out a more in-depth analysis of the characteristics of each cluster.