Essential Metrics Research Articles

FRACTAL-INSPIRED SENTIMENT ANALYSIS: EVALUATION OF LARGE LANGUAGE MODELS AND DEEP LEARNING METHODS

Sentiment analysis is a vital task in natural language processing (NLP) that aims to identify and extract the emotional states and opinions of text. In this study, we conduct a comprehensive comparison of large language models (LLMs), such as ChatGPT and Google Bard, with conventional methods in sentiment analysis. We employ a rigorous evaluation framework that covers four essential metrics: accuracy, precision, recall, and the [Formula: see text]1-score. Our results reveal that TextBlob outperforms other methods, achieving an impressive accuracy of 69% and precision of 83%. On the other hand, Bard shows a relatively poor performance, with only 39% accuracy and 46% precision. This study offers valuable insights into the diverse capabilities of AI models in sentiment analysis. A key finding of this study is the importance of model selection according to the specific requirements of the task. Each model has its own strengths and weaknesses, which are reflected in their performance profiles. Moreover, the context in which these models operate is crucial. For instance, ChatGPT generates varied responses, Bard struggles with multiple sentences, and Robustly Optimized BERT Pretraining Approach (RoBERTa) balances precision and recall. This study also reveals the performance gap between LLMs and state-of-the-art deep learning methods. We believe this work will inspire future research and applications of ChatGPT and similar AI models in sentiment analysis and related tasks.

Scaling Considerations for the Commercialization of the Electrochemical CO2 Reduction Reaction

The Electrochemical CO2 Reduction Reaction (ECO2RR) is an attractive method to produce CO2-based chemicals. Formate is an ideal target compound due to its high atom efficiency, low electrical demand, and potential as a building block for carbon-negative products. In recent years, low temperature ECO2RR electrocatalyst research has developed rapidly, reporting impressive efficiencies (>90 % FE) at industrial relevant current densities (>200 mA/cm2). While these are essential metrics, there is less focus on the commercialization of these systems, such as electrode stability and process scalability. For industrial feasibility, ECO2RR electrolyzers must be >1 meter high, stable for 5 years, and operate under elevated temperatures and fluctuating pressures. This presents challenges to typical gas diffusion electrodes used in ECO2RR, which rely on a fine pressure gradients for efficient gas transport to the catalyst layer. Furthermore, high product concentrations and high CO2 utilization are essential for achieving an overall, energy efficient process. At Avantium’s Volta group, we industrialize ECO2RR processes. This presentation will demonstrate the scaling potential of these processes with examples from our pre-pilot units. Overall, we believe that ECO2RR will be a key technology to de-fossilize the chemical industry and valorize CO2 as the feedstock of the future. Figure 1

Categorizing Dermatological Malignancies Via Computational Methods

In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics. Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy

Global Trends in Key Hospital Performance Indicators: A Decade of Research Insights (2014-2023)

Background: Hospital Key Performance Indicators (KPIs) are essential metrics used to evaluate the quality of healthcare services, assess organizational efficiency, and support sustainable hospital operations. Despite the importance of KPIs in enhancing patient care and hospital management, there remain gaps in the comprehensive assessment and application of these indicators across various healthcare settings. Methods: This study employs a qualitative methodology with a bibliometric analysis to examine the trajectory of research on hospital KPIs from 2014 to 2023. A comprehensive literature review was conducted using the Scopus database, selecting 111 relevant documents based on specific keywords and criteria. The data was analyzed using VOSviewer software, focusing on Network Analysis, Overlay Visualization, and Density Visualization to map the development of KPIs in hospital research, identify key themes, and explore the evolution of these studies over the years. Results: Results from network and density analysis using VOSviewer identified four main clusters of research themes, including hospital management, quality of care, emergency services, and hospital discharge processes. Keywords such as "quality indicator" and "hospital management" were identified as emerging areas with potential for future research. Furthermore, citation analysis revealed that the most referenced studies focused on developing KPIs to enhance clinical pharmacy practice and holistic hospital management. Conclusion: The study underscores the growing global interest in hospital KPIs, with a notable concentration of research in specific areas like hospital efficiency, quality of care, and operational metrics. The analysis highlights key trends and identifies emerging research opportunities, particularly in the areas of quality indicators and hospital management practices. These insights are crucial for guiding future research and improving the implementation of KPIs in hospital settings to enhance patient care and operational efficiency.

Cross-Validation Visualized: A Narrative Guide to Advanced Methods

This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field.

Quality indicators in respiratory therapy

Quality indicators in healthcare refer to measurable and quantifiable parameters used to assess and monitor the performance, effectiveness, and safety of healthcare services. These indicators provide a systematic way to evaluate the quality of care offered, and thereby to identify areas for improvement and to ensure that patient care meets established standards and best practices. Respiratory therapists play a vital role in areas of clinical administration such as infection control practices and quality improvement initiatives. Quality indicators serve as essential metrics for respiratory therapy departments to assess and enhance the overall quality of care. By systematically tracking and analyzing indicators related to infection control, treatment effectiveness, and adherence to protocols, respiratory care practitioners can identify areas to improve and implement evidence-based changes. This article reviewed how to identify, implement, and monitor quality indicators specific to the respiratory therapy departments to set benchmarks and enhance patient outcomes.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Tell your story: Metrics of success for academic data science collaboration and consulting programs

Measuring success plays a central role in justifying and advocating for a statistical or data science consulting or collaboration program (SDSP) within an academic institution. We present several specific metrics to report to targeted audiences to tell the story for success of a robust and sustainable program. While gathering such metrics includes challenges, we discuss potential data sources and possible practices for SDSPs to inform their own approaches. Emphasizing essential metrics for reporting, we also share the metric gathering and reporting practices of two programs in greater detail. New or existing SDSPs should evaluate their local environments and tailor their practice to gathering, analysing and reporting success metrics accordingly. This approach provides a strong foundation to use success metrics to tell compelling stories about the SDSP and enhance program sustainability. The area of success metrics provides ample opportunity for future research projects that leverage qualitative methods and consider mechanisms for adapting to the changing landscape of data science.

Compact design of MIMO antenna with split ring resonators for UWB applications

In recent times, the Multiple-Input Multiple-Output (MIMO) system has played a vital role in wireless communication. MIMO antenna uses multiple antennas on both the sides of the transmitter and receiver. Mutual coupling occurs between two antenna elements to reduce the performance. The passage of current in the same direction on both neighbouring sides of the antennas increases the rate of mutual coupling. To decrease the mutual coupling, Split Ring Resonators (SRR) are located on the MIMO antenna. In this research paper, a MIMO antenna with SRR for Ultra Wide Band (UWB) applications with different frequency notches is proposed. Also, bandwidth and isolation are considered essential metrics for MIMO antennas. In the proposed 4 × 4 MIMO antenna design, SRR is located at the antenna's two sides, and the defected ground structure is on the bottom portion with a permittivity of 4.4 (FR4 substrate). The defected ground structure is proposed with stubs to enhance the characteristics of bandwidth and create notch bands in the frequency range of 3.1 GHz to 10.6 GHz. Placing SRRs adjacent to the feed line enhances the gain performance. The experimental results show that the ECC of the proposed antenna is less than 0.25, DG is higher than 9.8 dB, and the peak realized gain for 9.38 GHz is 11.3 dB, which is more reliable than other antenna designs. Therefore, experimental outcomes of the proposed antenna design enhance the applicability of band notching for wideband communication.

A deterministic compartment model for analyzing tuberculosis dynamics considering vaccination and reinfection

Tuberculosis is a pressing global health concern, particularly pervasive in many developing nations. This study investigates the influence of treatment failure on tuberculosis control strategies, incorporating vaccination interventions using a deterministic compartmental epidemiological model. Mathematical analysis unveils disease-free and endemic equilibrium points, with the control reproduction number determined using next-generation methods. Identifying endemic equilibrium points and determining the control reproduction number provide essential metrics for assessing the effectiveness of control strategies and guiding policy decisions. The model exhibits a backward bifurcation phenomenon, leading to multiple endemic equilibria despite a reproduction number below one due to reinfection. Sensitivity analysis using Latin Hypercube Sampling/Partial Rank Correlation Coefficient elucidates parameter impacts on the control reproduction number. Vaccination efficacy is crucial for quality and validity, with superior quality and longer validity yielding more significant effects. While reinfection may not directly affect the reproduction number, its influence is pivotal in determining tuberculosis persistence or extinction. This study underscores the intricate interplay of factors in tuberculosis control strategies, providing insights vital for effective interventions and policy formulation.

Pagadian Diocesan Schools Information Monitoring System through Data Visualization

The Pagadian Diocesan Schools Information Monitoring System through Data Visualization is a web-based application meticulously developed to address the challenges of managing educational data across multiple schools within the Pagadian Diocese. Utilizing HTML, MySQL, PHP, CSS, and Bootstrap, the system streamlines the login process for administrators, enhances data monitoring, and provides a centralized view for superintendents. Through advanced data visualization tools, the platform presents essential metrics such as student population, income, and expenses, contributing significantly to the Pagadian government’s education initiatives while adhering to ethical data usage practices. Continuous user feedback ensures the refinement of the system, guaranteeing efficient and effective data management within the Pagadian Diocese. The study also encompasses the design, development, and testing phases of the system, following the Waterfall Model. Through rigorous testing, the system demonstrated commendable performance, stability, and security measures, with high user-friendliness and compatibility. The ISO/Logical Summary Report identified strengths in Functional Stability, Portability, Reliability, Security, Usability, and Maintainability, leading to a Total ISO/Logical Flow score of 90.94%. Overall, the system promises a smooth, effective, and dependable user experience, with room for improvement in UI design and error handling to enhance its performance further.

Enhancing Multi-Channel Consumer Behavior Analysis: A Data-Driven Approach using the Optimized Apriori Algorithm

Purpose –In the dynamic arena of retail and e-commerce, understanding customer behavior is crucial. This research paper investigates the strategies customers use to balance convenience and cost. It notes that consumers often divide their purchases, choosing online platforms for items they perceive as reasonably priced, while preferring physical stores for products associated with high shipping costs, lack of express shipping, or higher online prices. Traditional retail analysis methods, such as identifying frequently purchased and co-occurring items, are critical to the sector. However, these insights may be skewed if the analysis is solely confined to the online shopping cart, neglecting related purchases made at physical stores either before or after the online transaction. This paper addresses this analytical gap by looking into the complex interplay between online and offline purchases. The objective is to provide a more comprehensive, detailed understanding of multi-channel shopping behaviors, allowing retailers to better cater to their customers' needs and preferences while optimizing their strategies for a more effective market presence. This comprehensive approach attempts to uncover deeper insights into the interactions between online and offline purchases, thereby contributing to a more comprehensive understanding of consumer behavior in the retail and e-commerce industries.&#x0D; Design/methodology/approach- A detailed survey questionnaire was administered, obtaining 2465 responses. The questionnaire was specifically designed to capture the complexities of online and offline purchases, with an emphasis on Apple's purchase channels. The Apriori algorithm was used twice to find frequently co-occurring items that were purchased online and then online clubbed with offline. Essential metrics like support and confidence were calculated for both online and offline purchases. To determine any significant differences between the groups, an independent-samples t-test was used. This comprehensive methodology ensures a thorough examination of multi-channel shopping behaviors, providing valuable insights into consumer behavior in the retail and e-commerce sectors.&#x0D; Findings–We find that cross channel switching in a multi-channel distribution environment occurs due to price differences, convenience and flexibility. The paper illustrates the flaws of drawing inferences with data from a single channel emphasizing the need for a more holistic, multi-channel approach to data analysis in order to capture the full range of consumer behavior.&#x0D; Research limitations/implications –&#x0D; This study assumes that self-reported purchases or acquisitions of products accurately reflect actual consumer behavior. The insights derived from this research have significant implications for various marketing strategies, including pricing, product bundling, cross-selling, and promotional activities. However, it’s important to note that the reliance on self-reported data may introduce certain limitations, as it may not fully capture the complexity of actual consumer behavior. Despite these limitations, the insights gained from the study will add value for several marketing decisions like pricing, bundling, cross-selling and promotions.&#x0D; Originality value– While there is an abundance of literature on the application of Association Rule Mining for Market Basket Analysis, the existing body of work exploring its application within a multi-channel context is notably sparse. This study aims to address this gap in the literature, offering a unique perspective and making a substantial contribution to the existing body of knowledge in this area.

Nexus between digital financial inclusion and economic growth: a panel data investigation of Asian economies

PurposeThis study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.Design/methodology/approachThe study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.FindingsThe research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).Originality/valueThis study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Background and purpose&#x0D; Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost.&#x0D; Materials and Methods&#x0D; Data collection centered on Indian One Day International cricket statistics, encompassing matches played, batting and bowling averages, catches taken, and performance predictions. We sourced this data from reputable platforms such as ESPNcricinfo and the International Cricket Council website. Our performance prediction utilized of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Comparative analysis was conducted, evaluating these models through essential metrics including Mean Squared Error, Root Mean Squared Error, Mean absolute Error, and R-squared.&#x0D; Results&#x0D; The comparative analysis revealed that the XGBoost model consistently outperformed the others. It exhibited lower errors with the lowest Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, signifying greater predictive accuracy. XGBoost achieved the highest R-squared value, indicating a robust relationship between predictions and actual performance probabilities. Random Forest produced satisfactory results but fell short of XGBoost's accuracy, while Support Vector Regression displayed less accurate predictions across all metrics.&#x0D; Conclusions&#x0D; This research demonstrates the superior predictive ability of the XGBoost model in the performance probabilities of Indian cricket players in the ODI Cricket World Cup. The practical implications underscore the significance of data-driven insights for team selection and strategy.

Auto-BCS: A Hybrid System for Real-Time Breast Cancer Screening from Pathological Images.

Breast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, rendering them unsuitable for deployment on low-processing-power mobile devices. In this paper, a real-time automated system "Auto-BCS" is introduced that significantly enhances the efficiency of early breast cancer screening. The system is structured into three distinct phases. In the initial phase, images undergo a pre-processing stage aimed at noise reduction. Subsequently, feature extraction is carried out using a lightweight and optimized deep learning model followed by extreme gradient boosting classifier, strategically employed to optimize the overall performance and prevent overfitting in the deep learning model. The system's performance is gauged through essential metrics, including accuracy, precision, recall, F1 score, and inference time. Comparative evaluations against state-of-the-art algorithms affirm that Auto-BCS outperforms existing models, excelling in both efficiency and processing speed. Computational efficiency is prioritized by Auto-BCS, making it particularly adaptable to low-processing-power mobile devices. Comparative assessments confirm the superior performance of Auto-BCS, signifying its potential to advance breast cancer screening technology.

Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites

In this study, the compliance of the Weibull Distribution Function (WDF) and actual wind data (WD) from three different locations were investigated. The coefficients of the WDF were calculated using the Maximum Likelihood Method (MLM) in the Adana, Osmaniye, and Hatay regions. The main purpose of this study is to observe the performances of the MLM in determining the coefficients of the WDF in different regions in different years and to examine the success of this method in estimating the mean wind power and speed of the determined regions. The performance of the indicated approach in all three selected locations was evaluated using the Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Percentage Error (MPE). Also wind power densities were estimated for all three regions, which are one of the most essential metrics for estimating a region's wind energy (WE) potential. WDF power densities were estimated and compared to real wind power densities generated from measured WD for three different places. The performance of the method described in this paper was investigated in depth in various places with varying geographic characteristics. In addition, in the same years, the performance of the chosen method was evaluated in detail in three distinct places, and it was seen how geographical factors affected the method's performance.

Using Support Vector Regression Kernel Models for Cricket Performance Prediction in the Womens Premier League 2024

Background. The interest in women’s premier league cricket has caused the need for advanced analytics to understand the multifaceted dynamics of the sport. Study Purpose. This study aimed to contribute to sports analytics by assessing the efficacy of Support Vector Regression (SVR) kernel models in predicting the most valuable player. Such research methods as ANOVA, Bessel function, and Inverse MultiQuadratic kernel application have been deliberately chosen for their diverse mathematical approaches, aligning with the nuanced intricacies of women's premier league cricket. Materials and methods. Player performance was analyzed by using the following study methods: ANOVA, Bessel function and Inverse MultiQuadratic kernel application. The data, sourced from espncricinfo.com and the International Cricket Council, includes essential metrics for five teams. Rigorous preprocessing techniques, such as imputation and outlier removal, enhance data reliability, ensuring robust predictive models. Results. The application of the Inverse MultiQuadratic kernel exhibits exceptional predictive performance, surpassing ANOVA and Bessel function models. The kernels radial basis function proves effective in capturing the intricate dynamics of women’s premier league cricket. The findings underscore the suitability of kernel method for predicting standout performers in the Womenʼs Premier League 2024 season. Conclusions. The study revealed the dynamic interplay between sports analytics and machine learning in women's premier league cricket. The application of the Inverse MultiQuadratic kernel stands out as the most effective model, providing key insights into player predictions. This emphasizes the continual integration of advanced analytical techniques to enhance our understanding of the evolving landscape of women’s premier league cricket. As the sport gains prominence on the global stage, such analytical endeavors become imperative for strategic decision-making and sustained growth.

Quantum correlations and entanglement in coupled optomechanical resonators with photon hopping via Gaussian interferometric power analysis

Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation. Essentially, for quantum systems prepared in pure states, it is difficult to differentiate between quantum entanglement and quantum correlation. Nonetheless, this indistinguishability is no longer holds for mixed states. To contribute to a better understanding of this differentiation, we have explored a simple model for both generating and measuring these quantum correlations. Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities, coupled through the photon hopping process. this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes. The key ingredient in analyzing quantum correlation in this system is the global covariance matrix. It forms the basis for computing two essential metrics: the logarithmic negativity ( ENm ) and the Gaussian interferometric power ( PGm ). These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations, respectively. Our study reveals that the Gaussian interferometric power ( PGm ) proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system, particularly in scenarios featuring resilient photon hopping.

A Validation of the Phenomenon of Linearly Many Faults on Burnt Pancake Graphs with Its Applications

“Linearly many faults” is a phenomenon observed by Cheng and Lipták in which a specific structure emerges when a graph is disconnected and often occurs in various interconnection networks. This phenomenon means that if a certain number of vertices or edges are deleted from a graph, the remaining part either stays connected or breaks into one large component along with smaller components with just a few vertices. This phenomenon can be observed in many types of graphs and has important implications for network analysis and optimization. In this paper, we first validate the phenomenon of linearly many faults for surviving graph of a burnt pancake graph BPn when removing any edge subset with a size of approximately six times λ(BPn). For graph G, the ℓ-component edge connectivity denoted as λℓ(G) (resp., the ℓ-extra edge connectivity denoted as λ(ℓ)(G)) is the cardinality of a minimum edge subset S such that G−S is disconnected and has at least ℓ components (resp., each component of G−S has at least ℓ+1 vertices). Both λℓ(G) and eλ(ℓ)(G) are essential metrics for network reliability assessment. Specifically, from the property of “linearly many faults”, we may further prove that λ5(BPn)=λ(3)(BPn)+3=4n−3 for n⩾5; λ6(BPn)=λ(4)(BPn)+4=5n−4 and λ7(BPn)=λ(5)(BPn)+5=6n−5 for n⩾6.

Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.