Model Performance Metrics Research Articles

ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions is challenging due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts in which an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal is implemented and assessed in a laboratory context, in which the model performance and QoS metrics are analyzed. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% accuracy. Also, the obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions. Graph neural networks (GNNs) have proven effective in modeling complex interactions and predicting links within this network. The proposed model has successfully predicted tumor-gene associations by leveraging sampling-based GNNs and link layer embedding. The model's performance metrics, such as AUC-ROC scores, reached approximately 94%, demonstrating the potential of this heterogeneous network in predicting tissue-specific tumor sample-gene links. This paper's findings highlight the importance of tissue-specific associations in cancer research.

Component Identification and Depth Estimation for Structural Images Based on Multi-Scale Task Interaction Network

Component identification and depth estimation are important for detecting the integrity of post-disaster structures. However, traditional manual methods might be time-consuming, labor-intensive, and influenced by subjective judgments of inspectors. Deep-learning-based image visual inspection is a new approach to overcome these problems, but repeated modeling is required for different inspection tasks, which limits inspection accuracy and practical deployment efficiency. In this study, it is observed that the matched ratios of pixel pairs between component identification and depth estimation reach a high value, which indicates the dual tasks are highly related. Therefore, the Multi-Scale Task Interaction Network (MTI-Net) is proposed for structural images to simultaneously accomplish both tasks for accurate and efficient structural inspection. It propagates distilled task information from lower to higher scales. Then, it aggregates the refined task features from all scales to produce the final per-task predictions. Experiments show that MTI-Net delivers the full potential of multi-task learning, with a smaller memory footprint and higher efficiency compared to single-task learning. For the evaluation metrics of model performance, the mean Intersection over Union (mIoU) of component identification improves by 2.30, and root mean square error (RMSE) drops by 0.36 m with the aid of the multi-task strategy. The multi-task deep learning framework has great potential value in engineering applications.

Assessing Machine Learning Techniques for Predicting Banking Crises in India

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies.

Advanced Heart Disease Prediction: Deep Learning-Enhanced Convolutional Neural Network in the Internet of Medical Things Environment

Using the large dataset from the UCI Machine Learning Repository, this study presents a state-of-the-art Hybrid Convolutional Neural Network (HCNN) for predicting heart disease. The HCNN design has convolutional layers, leftover blocks, and attention methods. These help pull more features from cardiovascular health data, even when the patterns are very complex. Utilizing these deep learning parts, the HCNN shows better prediction skills, getting higher accuracy, strong classification (AUC), and a fair F1 Score. Because the model can change to find complicated connections in the information, it could be used to make medical diagnosis better. The HCNN is unique because it can instantly learn hierarchical structures from raw data. This lets it find hidden features that are important for accurately predicting heart disease. The convolutional layers help the model find local patterns, and the residue blocks stop problems with disappearing gradients, which makes training for deep designs more efficient. Attention processes make the network even more focused on important traits, which adds to its amazing ability to tell them apart. This work opens the door for researchers, doctors, and data scientists to use deep learning, and especially HCNN, to improve cardiovascular health analytics. This study takes a big step toward more accurate and efficient heart disease prognosis by giving a complete overview of the model's architecture and performance metrics. This opens the door for more research and use of advanced neural networks in the field of predictive medical diagnostics.

Abstract 4921: A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning

Abstract Understanding the genetic vulnerabilities (i.e., genetic dependencies) of cancer cells is crucial for developing novel anti-cancer treatments. The Cancer Dependency Map (DepMap) projects, conducted by the Broad Institute and Wellcome Sanger Institute, have performed comprehensive genome-wide CRISPR-Cas9 knockout screens across diverse cancer cell lines. These data resources have enabled computational biologists to glean insights into cancer genetic dependencies using sophisticated computational models. As different genomic mechanisms govern tumorigenesis and genetic vulnerability, the relationship between cancer genomics and genetic dependencies is nonlinear, making the prediction of genetic dependencies challenging. Addressing this challenge, we previously developed a deep learning model, namely DeepDEP, to predict cancer genetic dependencies using multi-omic profiles (Chiu et al. DOI:10.1126/sciadv.abh1275). DeepDEP has a transfer learning scheme that integrates genomic representations captured from unlabeled tumor samples of the Cancer Genome Atlas (TCGA) and genomic features that are predictive of genetic dependencies in cell lines. Such a design enables the application of DeepDEP to both cell lines and tumors. The model demonstrated superior performance compared to various conventional machine learning methods. Prediction results were validated using independent cell-line datasets and patient clinical data. Here, we developed an intuitive web application for DeepDEP which allows users to easily utilize the model on their in-house data. Users can simply indicate whether the query sample is derived from a cancer cell line or a tumor, upload any combination of mutation, gene expression, DNA methylation, and copy number alteration datasets, and view the predicted dependencies of 1,298 cancer-relevant genes. Along with the genetic dependency predictions, users are provided with model performance metrics, detailed gene annotations, and links to useful resources such as Ensembl and COSMIC. Our web application features a variety of interactive visualizations to guide users to interpret the results at the levels of individual genes and pathways. The tool has two supplemental modules to enhance its functionality. One allows users to search for similar cell lines to the query sample within the DepMap project or for similar tumors within the TCGA project. The other supplemental module provides data exploration tools for pre-calculated predictions across all TCGA tumors. In summary, this user-friendly web application provides researchers with access to deep learning predictions of cancer genetic dependencies with various interactive visualization and analysis tools. It is anticipated that this application will facilitate the study of cancer genetic dependencies and enhance the efficiency of anti-cancer drug discovery. Citation Format: Michael J. Kasper, Li-Ju Wang, Michael Ning, Yufei Huang, Yu-Chiao Chiu. A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4921.

Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies

This study presents a comprehensive research methodology integrating computational approaches, statistical analysis, and visualization techniques to predict biocompatible materials for medical implants and evaluate predictive model performance (Whiting K. 2020, October). The initial phase involves data acquisition and preprocessing, organizing a representative dataset into a pandas data frame. Visualization of the dataset through bar, pie, and line charts provides insights into relationships between materials and functional attributes. The subsequent phase focuses on evaluating a predictive model using simulated datasets and key metrics such as accuracy, precision, recall, F1 score, and the receiver operating characteristics (ROC) curve with an area under the curve (AUC) value. Performance metrics are visually represented through bar charts and ROC curves, aiding stakeholders in understanding the model’s strengths and areas for improvement. The confusion matrix offers a granular examination of the model’s classification performance. The results and discussion section delves into graphical representations, emphasizing the material vs. strength/conductance/resistance/function chart, elucidating the diverse functional profiles of materials. The distribution of material functionality pie chart succinctly illustrates the proportional contribution of each material, aiding informed decision-making in material selection. The materials performance graph provides a nuanced understanding of material characteristics, guiding the development of personalized healthcare solutions. Model performance metrics and receiver operating characteristics graphs comprehensively assess the predictive model, while the confusion matrix details classification outcomes. This methodology and its visualizations contribute to predicting biocompatible materials, emphasizing the significance of advanced computational approaches for efficiently navigating the complex material space. The study’s outcomes inform both material scientists and healthcare professionals, guiding the development of personalized healthcare solutions tailored to specific patient needs.

Heart rate characteristics predict risk of mortality in preterm infants in low and high target oxygen saturation ranges.

The Neonatal Oxygenation Prospective Meta-analysis found that in infants <28 weeks gestational age, targeting an oxygen saturation (S pO2 ) range of 85-89% versus 91-95% resulted in lower rates of retinopathy of prematurity but increased mortality. We aimed to evaluate the accuracy of the heart rate characteristics index (HRCi) in assessing the dynamic risk of mortality among infants managed with low and high target S pO2 ranges. We linked the SUPPORT and HRCi datasets from one centre in which the randomised controlled trials overlapped. We examined the maximum daily HRCi (MaxHRCi24) to predict mortality among patients randomised to the lower and higher target S pO2 groups by generating predictiveness curves and calculating model performance metrics, including area under the receiver operating characteristics curve (AUROC) at prediction windows from 1-60 days. Cox proportional hazards models tested whether MaxHRCi24 was an independent predictor of mortality. We also conducted a moderation analysis. There were 84 infants in the merged dataset. MaxHRCi24 predicted mortality in infants randomised to the lower target S pO2 (AUROC of 0.79-0.89 depending upon the prediction window) and higher target S pO2 (AUROC 0.82-0.91). MaxHRCi24 was an important additional predictor of mortality in multivariable modelling. In moderation analysis, in a model that also included demographic predictor variables, the individual terms and the interaction term between MaxHRCi24 and target S pO2 range all predicted mortality. Associations between HRCi and mortality, at low and high S pO2 target ranges, suggest that future research may find HRCi metrics helpful to individually optimise target oxygen saturation ranges for hospitalised preterm infants.

Data-centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog-bit.

Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications. We built SDMs for the invasive aquatic plant European frog-bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large-scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence-background or presence-absence) of occurrence data. Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat. Our findings relative to European frog-bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data-centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Confidence and consistency in discrimination: A new family of evaluation metrics for potential distribution models

Potential distribution models (PDMs) are widely applied to understand and predict biogeographic patterns. PDM evaluation, however, presents major challenges, including (1) matches of predictions with observed absences and presences being treated similarly and (2) treatment of predicted presences falling outside the observations as errors, while a major motivation of PDMs is to identify such locations. Our aim was to construct a family of model performance metrics to measure the reliability and transferability of PDMs while providing solutions to the problems mentioned above.Instead of binarisation, predictions are reclassified into three categories for model evaluation: certain negative, uncertain and certain positive predictions. Model performance is tested solely within known presences to reduce the effect of unoccupied but suitable sites registered as observed absences. Metrics were developed for both cases: (1) when the target of the modelling is the identification of potential presence locations and (2) when the target is the evaluation of potential presences and absences equally.The new measures offer evaluation optimised for PDM models. On the one hand, (1) the proposed metrics concentrate on matches within observed presences. Thus, matches within the typically large amount of observed absences do not inflate the metric values. On the other hand, (2) the proposed metrics do not treat all mismatches within observed presences as errors and thus allow exploitation of information in the mismatches, too. Besides the theoretical background, we also provide a new R package for calculating the measures.We tested the metrics both on field and simulation data. Both field-based and simulation-based case studies underlined that the new metrics capture a different aspect of model performance than the traditional metrics, such as AUC, TSS, sensitivity and specificity.We conclude that our metrics help to identify whether the modelling process could capture the preferences of the object well enough to reliably find further suitable sites or stay reliable when transferred in space and time.

What Aspect of Model Performance is the Most Relevant to Skillful Future Projection on a Regional Scale?

Abstract Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model’s performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China.

EXAMINING RISK FACTORS OF ANEMIA IN PREGNANCY USING HYBRID LOGISTIC REGRESSION MODEL AND ROUGH SET THEORY

Anemia in pregnancy is a potential danger to the mother and child. Therefore, the risk of anemia in pregnant women requires serious attention from all relevant parties. Considering the numerous negative effects caused by anemia in pregnant women, efforts must be made to prevent and treat anemia in pregnant women by understanding the factors that influence it. This study assesses the risk factors for anemia in pregnant women at Tegal Rejo Community Health Center, Yogyakarta Province. In this paper, a new integrated classification approach with binary logistic regression (LR) analysis and Rough Set Theory (RST) is proposed, in order to examine factors on the incidence of anemia in pregnancy. The proposed model is called the Logistic Regression and Reduction Rough Set (LR3S). In LR3S model, the RST technique is used to detect inconsistent sample and removing inconsistent sample that have probability less than 0.5 before doing LR modelling. To evaluate the development of the resulting model, a comparison was made of the performance of Original Logistic Regression (OLR), LR model after removed outlier namely as Remove Outlier Original Logistic Regression (RO2LR) and LR3S. Using a number of model performance metrics, it is found that LR3S has the best performance for the three models used. Using LR3S model, it is found that CED status, educational level, parity and gestational are significant variable impact on the incidence of anemia.

Hierarchy of demographic and social determinants of mental health: analysis of cross-sectional survey data from the Global Mind Project

ObjectivesTo understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative...

MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY

As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.&#x0D; Keywords: Machine Learning, Stock Market, Forecasting, Models, Review.

The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review

BackgroundArtificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. MethodsNine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. ResultsAuthors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. ConclusionsWhile AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. Level of Evidence2A.

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

The Compliance Model as an Instigation for Post-Financial Crisis Efficiency in Corporations

This paper examines how the compliance model improves corporate governance and efficiency post-financial crisis. This paper focuses on the goals of the compliance model, the strategies for its integration into corporate governance structures, as well as the results and conclusions. The compliance model prioritizes legal and regulatory compliance, ethical standards, and best practices in corporate governance. This methodology helps organizations prevent wrongdoing, enhance transparency, and build stakeholder confidence. Mixed methods study evaluates the compliance model. First, it reviews the theoretical and practical consequences of the compliance model. Next, a quantitative analysis compares pre- and post-compliance model performance metrics in a sample of firms. Finally, qualitative interviews with key stakeholders assess the impact of the model. This study shows that the compliance approach boosts post-financial crisis corporate efficiency. The approach improves corporate governance, malfeasance, and efficiency. Organizations with a culture of compliance, honesty, and responsibility gain stakeholder trust. The results show that the compliance model can help companies revive their corporate governance practices following financial crises. This methodology reduces risks, ensures legal and ethical compliance, and boosts efficiency and effectiveness. This study emphasizes the role of the compliance model in post-financial crisis corporate efficiency. It highlights the favorable effects of the model on corporate governance and organizational success. The report adds to the corporate governance literature and offers practical advice for companies trying to improve their governance in a complicated business environment.

Global and local interpretability techniques of supervised machine learning black box models for numerical medical data

The most effective machine learning classification techniques, such as artificial neural networks, are not easily interpretable, which limits their usefulness in critical areas, such as medicine, where errors can have severe consequences. Researchers have been working to balance the trade-off between the model performance and interpretability. In this study, seven interpretability techniques (global surrogate, accumulated local effects, local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP), model agnostic post hoc local explanations (MAPLE), local rule-based explanation (LORE), and Contextual Importance and Utility (CIU)) were evaluated to interpret five medical classifiers (multilayer perceptron, support vector machines, random forests, extreme gradient boosting, and naïve bayes) using six model performance metrics and three interpretability technique metrics across six medical numerical datasets. The results confirmed the effectiveness of integrating global and local interpretability techniques, and highlighted the superior performance of global SHAP explainer and local CIU explanations. The quantitative evaluations of explanations emphasised the importance of assessing these interpretability techniques before employing them to interpret black box models.

Analysis of solar energy potentials of five selected south-east cities in nigeria using deep learning algorithms

This study presents a meticulous examination of the solar energy potential of five selected metropolitan cities (Abakaliki, Awka, Enugu, Owerri, and Umuahia) in Eastern part of Nigeria using deep learning algorithm, specifically the Long Short-Term Memory (LSTM) model. These cities, despite being characterized by extended rainy seasons and a high level of cloudiness, are suitable environment for solar power generation and investment opportunities. The employed methodology capitalized on the LSTM deep learning approach to analyze and predict energy generation, utilizing comprehensive hourly weather data from the National Airspace Agency (NASA). The data set comprised various parameters, such as date/time, solar azimuth angle, temperature, humidity, wind speed, wind direction, cloud cover, and power, enabling a thorough analysis of each city. To ensure accuracy, energy prediction capabilities were benchmarked against real-time datasets from a solar power plant in Ulsan, South Korea, thereby training and fine-tuning the model for precision. The LSTM model's performance metrics were maintained at a learning rate of 0.07, a batch size of 150, and a train-test split ratio of 0.8 to 0.2. Data validation exhibited a mean square error (MSE) of 0.01, demonstrating the model’s reliability. Results showed Enugu as having the highest solar energy potential, averaging 6.25 kWh/day, while Awka registered the most substantial electricity demand across various sectors. These findings highlight the substantial potential for photovoltaic (PV) power systems and advocate for the immediate implementation of renewable energy policy in the selected cities. These are expected to bring about significant implications for future renewable energy environmentally friendly investments in Nigeria and globally.

Estimation of Paddy leaves Chlorophyll Content Using Convolutional Neural Network (CNN)

In contemporary agriculture, the demand for cutting-edge machine-learning techniques to elevate crop assessment and management is paramount. This study introduces an innovative approach employing a Convolutional Neural Network (CNN) with Inception_v3 as its base model to estimate chlorophyll levels in paddy leaves. The primary aim is to craft a robust, precise model capable of non-destructively predicting chlorophyll content, promising substantial improvements in the efficiency of evaluating paddy crop health and nutritional status. The dataset comprises 566 images of paddy leaves, spanning 122 unique chlorophyll content levels. A meticulous data partitioning strategy allocates 244 images for model training, with 122 and 180 images for validation and testing, respectively. Model performance metrics include a test loss of 1.19 and a test accuracy of 0.81. Leveraging the Inception_v3 architecture empowers the CNN model to extract intricate, distinguishing features from paddy leaf images. This capability enables the model to discern subtle variations in chlorophyll content across different classes, underpinning its promising predictive prowess. Future research directions may explore potential model enhancements and dataset expansion, marking significant progress toward revolutionizing crop health assessment in modern agriculture.