Self-organizing Map Research Articles

Predicting UPDRS in Parkinson’s disease using ensembles of self-organizing map and neuro-fuzzy

Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process.

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

AbstractIntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

Data science integrated with computational fluid dynamics for particle collision modeling in fluidized bed

Fluidized beds are critical in numerous industrial applications, including chemical processing and energy production. Accurate modeling of solid particles and fluids within these beds is essential for process optimization and improved efficiency. In this study, we developed a machine learning model to predict the coefficient of restitution (COR) of solid particles in fluidized beds. Four variables—collision velocity, effective temperature, effective mass, and effective elastic modulus—were used in the model. Our dataset comprised 2446 data points from previous literature. We employed self-organizing map (SOM) and artificial neural network (ANN) approaches for data analysis. The initial ANN model, which did not incorporate data clustering, exhibited an impressive coefficient of determination (R-squared) of 0.989, indicating its high accuracy. To enhance the model further, we clustered the data into four groups using SOM and developed separate ANN models for each group. All four models achieved R-squared greater than 0.99, illustrating the effectiveness of data clustering. The resulting model can be integrated into simulation software, such as MFIX, to provide a more precise representation of fluidized bed behavior across various industrial settings. The findings emphasize the potential of machine learning models to enhance fluidized bed simulations, leading to increased efficiency and cost-effectiveness in industrial processes. Future studies should explore the inclusion of additional variables and extend the model's application to different industrial processes. Additionally, incorporating recommendations for optimizing fluidized bed behavior based on the model's predictions would provide valuable insights for process engineers.

Identification of hydrogeological and geoelectrical parameters influencing the productivity of boreholes in the commune of Daloa (Central-Western of Côte d'Ivoire)

This study was to determine the hydrogeological and geoelectrical parameters affecting the productivity of water boreholes in the Daloa commune. Using the Schlumberger configuration, we carried out 15 parallel and perpendicular electrical profiling and 40 Vertical Electrical Soundings (VES). We also performed Electrical Resistivity Tomography (ERT) with a pole-dipole configuration at one site. The data processing allowed the identification of the different layers of the subsurface and their geoelectric properties that influence the productivity of the boreholes in the study area. These geoelectric data have shown that the productivity of the boreholes is more related to the conductive anomalies (W and U) than to the vertical electrical sounding. The processing of the hydrogeological data has shown that borehole productivity in the study area using Self-Organising Map (SOM) is a function of the hydraulic conductivity but also the thickness and density of the fracturing. Sixteen new boreholes on the main campus of Jean Lorougnon Guede University, Daloa, validate these results. The yield of these productive boreholes varies between 7.5 and 22 m3/h, with an average of 15.2 m3/h, four times higher than the values usually obtained in the study area. The success rate of these boreholes is about 70 %. This study has shown how effectively integrating hydrogeological and geoelectrical parameters provides information on the lithology and structural conditions in the subsurface necessary for productive drilling.

TESS Photometry of AM Her and AR UMa: Binary Parameters, Cyclotron Emission Modeling, and Mass Transfer Duty Cycles

Transiting Exoplanet Survey Satellite (TESS) photometry of the polars AM Herculis (AM Her) and AR Ursae Majoris (AR UMa) is presented, along with high-speed photometry. AM Her shows a variety of high states with frequent transitions between them. TESS photometry of AR UMa in the low state reveals no evidence of accretion, while the McDonald 2.1 m telescope caught AR UMa in its high accretion state. Roche-lobe overflow is shut off during low states of AR UMa, while accretion often still takes place during low states of AM Her. We derive inclinations of 50° and 70° for AM Her and AR UMa respectively. To model the high-state light curves of AM Her, we employ a self-organized map light-curve classification scheme to establish common accretion configurations. The cyclotron radiation properties then allow the production of emission region maps on the surface of the white dwarf. The accretion geometry of AM Her is most consistent with a multipolar field structure. The high-state photometry of AR UMa has stochastic accretion flaring, which we attribute to magnetically buffeted mass transfer through the inner Lagrangian point L1. To consider this possibility, we examine the magnetism of both stars and argue that the local magnetic field near L1 can initiate short-lived accretion events and affect transitions between high and low accretion states in both AM Her and AR UMa. In particular, AR UMa has the low state as its default, while AM Her and most other active polars are in the high state by default.

Analysis of driving style using self-organizing maps to analyze driver behavior

<span>Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.</span>

Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.

Joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation

It is hypothesized that air pollution and stress impact the central nervous system through neuroinflammatory pathways Despite this, the association between prenatal exposure to indoor air pollution and psychosocial factors on inflammatory markers in infancy has been underexplored in epidemiology studies. This study investigates the individual and joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation (interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)). We analyzed data from the South African Drakenstein Child Health Study (N = 225). Indoor air pollution and psychosocial factor measurements were taken in the 2nd trimester of pregnancy. Circulating inflammatory markers (IL-1β, Il-6, and TNF-α) were measured in serum in the infants at 6 weeks postnatal. Linear regression models were used to investigate associations between individual exposures and inflammatory markers. To investigate joint effects of environmental and psychosocial factors, Self-Organizing Maps (SOM) were used to create exposure profile clusters. These clusters were added to linear regression models to investigate the associations between exposure profiles and inflammatory markers. All models were adjusted for maternal age, maternal HIV status, and ancestry to control for confounding. Most indoor air pollutants were positively associated with inflammatory markers, particularly benzene and TNF-α in single pollutant models. No consistent patterns were found for psychosocial factors in single-exposure linear regression models. In joint effects analyses, the SOM profile with high indoor air pollution, low SES, and high maternal depressive symptoms were associated with higher inflammation. Indoor air pollutants were consistently associated with increased inflammation in both individual and joint effects models, particularly in combination with low SES and maternal depressive symptoms. The trend for individual psychosocial factors was not as clear, with mainly null associations. As we have observed pro- and anti-inflammatory effects, future research should investigate joint effects of these exposures on inflammation and their health effects.

Oral contraceptives in adolescents: a retrospective population-based study on blood pressure and metabolic dysregulation.

This study aimed to explore the relationship between oral contraceptive use and blood pressure values and in a national cohort of women adolescents and to investigate the level of coexistence of the high blood pressure levels, dyslipidemia or insulin resistance. This is a retrospective cohort study that evaluated data form 14,299 adolescents aged 14 to 17years. Crude and race-and age-adjusted analyses were performed using Poisson regression to estimate the prevalence ratios. Data clustering analysis was performed using machine learning approaches supported by an unsupervised neural network of self-organizing maps. We found that 14.5% (n = 2076) of the women adolescents use oral contraceptives. Moreover, an increased prevalence of high blood pressure, dyslipidemia, and insulin resistance (all P < 0.001) was observed among adolescents who use oral contraceptives as compared to those who do not. Our analysis also showed that 2.3% of adolescents using oral contraceptives had both high blood pressure levels and dyslipidemia, whereas 3.2% had high blood pressure levels combined with insulin resistance (all P < 0.001). The algorithmic investigative approach demonstrated that total cholesterol, LDLc, HDLc, insulin, and HOMA-IR were the most predicted variables to assist classificatory association in the context of oral contraceptive use among women adolescents with high blood pressure. These findings suggest that oral contraceptives were associated with an increased prevalence of high blood pressure, dyslipidemia, and insulin resistance among women adolescents. Although the indication of this therapy is adequate to avoid unintended pregnancies, their use must be based on rigorous individual evaluation and under constant control of the cardiometabolic risk factors.

An interpretable operating condition partitioning approach based on global spatial structure compensation-local temporal information aggregation self-organizing map for complex industrial processes

Effective and interpretable partitioning of operating conditions in complex industrial processes is crucial for ensuring the efficiency and reliability of emerging integrated production processes. However, contemporary operation–condition partitioning models fall short in this domain for two main reasons. First, the parameters related to purely data-driven methods fail to involve physically meaningful semantics, resulting in suboptimal accuracy and interpretability. Second, there exists a spatial–temporal coupling interaction among process variables that reflects physically meaningful spatial–temporal correlations, further impeding effective interpretation. To address these challenges, an interpretable operation–condition partitioning method based on a Global Spatial Structure Compensation-Local Temporal Information Aggregation Self-Organizing Map is proposed. This approach formulates the operation–condition partitioning problem as a spatio-temporal feature extraction based on the operation–condition partitioning problem. First, a local separable aggregation (LSA) model based on the informer backbone is introduced to partition industrial variables into interpretable time-local groups. This ensures that the model can learn the underlying factors of the condition’s occurrence, thereby providing a reliable basis for interpretable root cause analysis. Next, a Global Spatial Feature Compensator (GSFC) is introduced that leverages the process mechanism knowledge and graph network to establish explicit spatial interrelationships among time-local groups in the LSA algorithm, thereby enhancing the model's inferential capabilities and security assurance. Subsequently, a spatiotemporal self-organizing map is designed to integrate the aforementioned spatial and temporal features, facilitating precise operation–condition partitioning decisions. Finally, the effectiveness and understandability of the proposed method are validated through comprehensive experiments in a real industrial application case study.

Electric bus arrival and charging station placement assessment using machine learning techniques

ABSTRACT The goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.

Chorda Dorsalis System as a Paragon for Soft Medical Robots to Design Echocardiography Probes with a New SOM-Based Steering Control.

Continuum robots play the role of end effectors in various surgical robots and endoscopic devices. While soft continuum robots (SCRs) have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot, considering the concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as transesophageal echocardiography (TEE). The feasibility of fabrication of a soft endoscopic device derived from the Chorda dorsalis paragon was shown empirically by producing a three-segment pneumatic SCR. The main novelties include bioinspired design, modeling, and a navigation control strategy presented as a novel algorithm to maintain a kinematic similarity between the soft robot and the rigid counterpart. The kinematic model was derived based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) network was developed and applied to realize kinematic similarity. The simulation results indicate that the control method forces the soft robot tip to follow the path of the rigid probe within the prescribed distance error (5 mm). The solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.

A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing map (SOM) networks, this paper constructs the dynamic entropy weighted minimum quantization error (DEWMQE) as the initial health indicator (HI). Subsequently, employing windowed inertia smoothing and scale correction mechanisms, this paper filters anomalies in HI, ensuring trend smoothing and scale consistency. Considering the degradation process and characteristics of rotating machinery systems, this study utilizes linear multi-fractional Lévy stable motion (LMLSM) to build a degradation model. Building upon this, to effectively utilize historical data and overcome the shortcomings of random process theory requiring predefined degradation paths, this study further integrates bidirectional gated recurrent units (BGRU) and similarity transfer learning methods to devise the BGTLLM hybrid model, enabling adaptive fitting of the degradation trend. Finally, the Monte Carlo (MC) method is employed to evaluate the uncertainty in RUL prediction. The effectiveness and accuracy of the proposed hybrid prediction model in RUL forecasting are validated using heavy-duty truck axle data and the PHM2012 dataset.

Gaussian cross-entropy and organizing intelligence for design optimization of the outrigger system with inclined belt truss in real-size tall buildings

This research explores the optimal structural design for tall buildings with an outrigger and belt truss system. The study employs Gaussian Cross-Entropy with Organizing Intelligence (GCE-OI), a novel optimization approach that utilizes a self-organizing map as a machine learning algorithm, and Gaussian probability distribution in Cross-Entropy optimization. This approach is used to predict promising solutions and to guide the search process for swift convergence. The optimization encompasses member sizing (weight) and outrigger placement (topology) while introducing inclined belt trusses alongside traditional horizontal trusses for enhanced performance. The process involves optimizing a 25-story real-size model subjected to wind load, and the results are compared against multiple well-known algorithms. The results show that the proposed optimizer, supported by machine learning, outperforms alternative algorithms, offering superior solutions with enhanced convergence. Considering the efficiency of the inclined belt trusses and the proposed robust optimization method (GCE-OI), the optimally-placed outrigger system minimizes the constructional cost and enhances structural stability by limiting the responses.

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers.

Gut-derived metabolites, products of microbial and host co-metabolism, may inform mechanisms underlying children's neurodevelopment. We investigated whether infant fecal metabolites were related to toddler social behavior. Stool samples collected from 6-week-olds (n = 86) and 1-year-olds (n = 209) in the New Hampshire Birth Cohort Study (NHBCS) were analyzed using nuclear magnetic resonance spectroscopy metabolomics. Autism-related behavior in 3-year-olds was assessed by caregivers using the Social Responsiveness Scale (SRS-2). To assess the association between metabolites and SRS-2 scores, we used a traditional single-metabolite approach, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs). Using a single-metabolite approach and QEA, no individual fecal metabolite or metabolite set at either age was associated with SRS-2 scores. Using the SOM method, fecal metabolites of six-week-olds organized into four profiles, which were unrelated to SRS-2 scores. In 1-year-olds, one of twelve fecal metabolite profiles was associated with fewer autism-related behaviors, with SRS-2 scores 3.4 (95%CI: -7, 0.2) points lower than the referent group. This profile had higher concentrations of lactate and lower concentrations of short chain fatty acids than the reference. We uncovered metabolic profiles in infant stool associated with subsequent social behavior, highlighting one potential mechanism by which gut bacteria may influence neurobehavior. Differences in host and microbial metabolism may explain variability in neurobehavioral phenotypes, but prior studies do not have consistent results. We applied three statistical techniques to explore fecal metabolite differences related to social behavior, including self-organizing maps (SOMs), a novel machine learning algorithm. A 1-year-old fecal metabolite pattern characterized by high lactate and low short-chain fatty acid concentrations, identified using SOMs, was associated with social behavior less indicative of autism spectrum disorder. Our findings suggest that social behavior may be related to metabolite profiles and that future studies may uncover novel findings by applying the SOM algorithm.

An AI-Enabled Blockchain Algorithm: A Novel Approach to Counteract Blockchain Network Security Attacks

INTRODUCTION: In this research, we present a novel method for strengthening the security of blockchain networks through the use of AI-driven technology. Blockchain has emerged as a game-changing technology across industries, but its security flaws, particularly in relation to Sybil and Distributed Denial of Service (DDoS) attacks, are a major cause for worry. To defend the blockchain from these sophisticated attacks, our research centres on creating a strong security solution that combines networks of Long Short-Term Memory (LSTM) and Self-Organizing Maps (SOM).&#x0D; OBJECTIVES: The main goal of this project is to create and test an AI-driven blockchain algorithm that enhances blockchain security by utilising LSTM and SOM networks. These are the objectives that the research hopes to achieve: In order to assess the shortcomings and weaknesses of existing blockchain security mechanisms. The goal is to create a new approach that uses LSTM sequence learning and SOM pattern recognition to anticipate and stop security breaches. In order to see how well this integrated strategy works in a simulated blockchain setting against different types of security risks.&#x0D; METHODS: The methods used in our study are based on social network analysis. A combination of support vector machines (SOM) for pattern recognition and long short-term memory (LSTM) networks for learning and event sequence prediction using historical data constitutes the methodology. The steps involved in conducting research are: The current state of blockchain security mechanisms is examined in detail. Creating a virtual blockchain and incorporating the SOM+LSTM algorithm.&#x0D; Putting the algorithm through its paces in order to see how well it detects and defends against different security risks.&#x0D; RESULTS: Significant enhancements to blockchain network security are the primary outcomes of this study. Important results consist of: Using the SOM+LSTM technique, we were able to increase the detection rates of possible security risks, such as Sybil and DDoS attacks. Enhanced reaction times when compared to conventional security techniques for attack prediction and prevention. Demonstrated ability of the algorithm to adapt and learn from new patterns of attacks, assuring long-term sustainability.&#x0D; CONCLUSION: This paper's findings highlight the efficacy of enhancing blockchain security through the integration of artificial intelligence technologies such as LSTM and SOM networks. In addition to improving blockchain technology's detection and forecasting capabilities, the SOM+LSTM algorithm helps advance the platform toward greater security and reliability. This study provides a solid answer to the increasing worries about cyber dangers in the modern era and opens the door to more sophisticated AI uses in blockchain security.

Regionalization of the Onset and Offset of the Rainy Season in Senegal Using Kohonen Self-Organizing Maps

This study explores the spatiotemporal variability of the onset, end, and duration of the rainy season in Senegal. These phenological parameters, crucial for agricultural planning in West Africa, exhibit high interannual and spatial variability linked to precipitation. The objective is to detect and spatially classify these indices across Senegal using different approaches. Daily precipitation data and ERA5 reanalyses from 1981 to 2018 were utilized. The employed method enables the detection of key dates. Subsequently, the Kohonen algorithm spatially classifies these indices on topological maps. The results indicate a meridional gradient of the onset, progressively later from the southeast to the northwest, whereas the end follows a north–south gradient. The duration varies from 45 days in the north to 150 days in the south. The use of self-organizing maps allows for classifying the onset, end, and duration of the season into four zones for the onset and end, and three zones for the duration of the season. They highlight the interannual irregularity of transitions, with both early and late years. The dynamic analysis underscores the complex influence of atmospheric circulation fields, notably emphasizing the importance of low-level monsoon flux. These findings have tangible implications for improving seasonal forecasts and agricultural activity planning in Senegal. They provide information on the onset, end, and duration classes for each specific zone, which can be valuable for planning crops adapted to each region.

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

The accuracy of photovoltaic (PV) power forecasting techniques relies not only on high-quality spatiotemporal data but also on an efficient feature-mining methodology. In this study, a spatiotemporal power forecasting model based on the broad learning system (BLS) and the improved backtracking search optimization algorithm (IBSOA) is proposed. The objective is to enhance the accuracy of PV power predictions while reducing the time-intensive training process associated with an extensive set of broad learning system parameters. The spatiotemporal attributes of historical data from multiple PV sites are clustered using a self-organizing map. The clustering analysis explores the spatiotemporal correlation among five photovoltaic (PV) power stations for each season between 2017 and 2018. Subsequently, the IBSOA is employed to optimize the hyperparameters of the BLS model, particularly the mapping and enhancement nodes. By utilizing hyperparameter optimization, a BSOA-based broad learning model is introduced to achieve superior accuracy. The results are assessed using the proposed method in comparison with three popular optimization algorithms: 1) genetic algorithm (GA), 2) bird swarm algorithm (BSA), and 3) backtracking search optimization algorithm (BSOA). All scenarios are validated and compared using PV plant data from the DKA center in Australia. The root-mean-square error (RMSE) indicators of the proposed prediction method are consistently lower than the worst-case scenario in each season, decreasing by 3.2283 kW in spring, 3.9159 kW in summer, 1.3425 kW in autumn, and 1.4058 kW in winter. Similarly, the mean absolute percentage error (MAPE) exhibits a reduction compared to the worst case, with a decreases of 0.882% in spring, 1.2399% in summer, 1.803% in autumn, and 1.087% in winter. The comprehensive results affirm that the proposed method surpasses alternative optimization techniques, delivering high-quality power forecasts for the given case study.