Enhancing Classification Algorithm Accuracy through Hybrid Pre-Processing Strategies

The article addresses the relevant scientific and practical issue of developing innovative approaches to forecasting in enterprise financial management in the context of economic digital transformation. The necessity of using intelligent forecasting tools based on modern technologies of artificial intelligence, machine learning, big data analytics, and neural networks is substantiated. The main advantages of implementing such tools are identified, including increased accuracy and speed of financial analysis, enhanced adaptability of strategic planning, and reduced subjectivity in decision-making processes. The study systematizes key models of intelligent forecasting: ARIMA, GARCH, LSTM, CNN, decision trees, clustering algorithms, hybrid approaches, and fuzzy logic. Practical examples of their application in financial management are provided, including cash flow forecasting, investment attractiveness evaluation, risk analysis, enterprise development scenario modeling, and fraud detection. It is shown that intelligent technologies play a leading role in building flexible and resilient financial management systems capable of self-learning, self-regulation, and dynamic restructuring under uncertainty. At the same time, the main limitations of implementing intelligent systems in enterprise financial operations are outlined: high requirements for the quality and structure of input data, complexity of interpreting forecast results, shortage of qualified personnel, and the need for significant computational resources. The study emphasizes the importance of developing appropriate digital infrastructure, organizational transformation, and interdisciplinary cooperation for the successful integration of such tools. International experience in the application of intelligent forecasting by leading companies in digital transformation is generalized. A classification of major categories of forecasting tools by functional purpose is proposed, and practical recommendations for their adaptation to the Ukrainian context are developed. The results obtained can be used as an analytical and methodological basis for modernizing enterprise financial management systems under the new economic reality.

Remote sensing is one of the very important parts in the advance image processing which is used to acquiring satellite image to know about geo-spatial information. Remote sensing is widely used to monitor different resources available on earth surface, out of which the land used area monitoring is one of very important part. There are many techniques and methods are proposed for satellite image classification based on different theme. Here, in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image. LISS-III satellite image have been classified in different area covered by water, forest, mangroves and developments by selecting the pixel based classification. Here multiple linear regressions is used on initial training data set, after evaluation the new features data set is applied to artificial neural network to check the impact of best training data set on the classification accuracy. The accuracy of classification algorithm is calculated using confusion matrix and Kappa coefficient. It is observed with help of confusion matrix and Kappa coefficient that the accuracy of ANN classifier without evaluated training data set is 90.19% and Kappa Coefficient is 0.8533 and when training data set evaluated with the help of multiple Linear regression then ANN accuracy is 98.74% and the Kappa Coefficient is 0.9766 which is improved and very good. So with the proposed method classification accuracy of ANN classifier is improvement for classification of LISS-III satellite image.

In this digital era, the recommendation system is an important tool in helping users to make decisions, including choosing tourist destinations. This study aims to implement the K-Nearest Neighbors (k-NN) algorithm in determining the recommendation pattern for tourist destinations. The k-NN algorithm was chosen because of its ability to classify based on the closest neighbor of a point, so that it can obtain recommendation patterns that are more personal and relevant to user preferences. This implementation method involves data collection, data preparation, model building, classification process, model evaluation, and finally implementation of the model in a recommendation system. The results of this study indicate that a recommendation system based on the k-NN algorithm is able to provide suggestions for tourist destinations that are more in line with user preferences, thus potentially increasing user satisfaction and making a positive contribution to the tourism industry. However, the effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens opportunities for further research and development in the field of tourist destination recommendation systems using other algorithms or combining several algorithms to obtain more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is essential in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results.

The goal of this paper discusses about different types of data mining classification algorithms accuracies that are widely used to extract significant knowledge from huge amounts of data. Here illustrate 20 classifications of supervised data mining algorithms base on type-2 diabetes disease dataset perspective to Bangladeshi populations. In this paper we compare 20 classification algorithms by measuring accuracies, speed and robustness of those algorithms using WEKA toolkit version 3.6.5. Accuracies of classification algorithms are measured in 3 cases like Total Training data set, 10 fold Cross Validation and Percentage Split (66% taken). Speed (CPU Execution Time) and error rate also measured as like as accuracy. Firstly checked top perform algorithms that have best outcome for different cases and then ranked top outcomes algorithms. Finally ranked best 5 algorithms among 20 algorithms based on their accuracies.

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

Feature selection is an important part of data mining, image recognition and other fields. The efficiency and accuracy of classification algorithm can be improved by selecting the best feature subset. The classical feature selection technology has some limitations, and heuristic optimization algorithm for feature selection is an alternative method to solve these limitations and find the optimal solution. In this paper, we proposed a Multi Strategy Grey Wolf Optimizer algorithm (MSGWO) based on random guidance, local search and subgroup cooperation strategies for feature selection, which solves the problem that the traditional grey wolf optimizer algorithm (GWO) is easy to fall into local optimization with a single search strategy. Among them, the random guidance strategy can make full use of the random characteristics to enhance the global search ability of the population, and the local search strategy makes grey wolf individuals make full use of the search space around the current best solution, and the subgroup cooperation strategy is very important to balance the global search and local search of the algorithm in the iterative process. MSGWO algorithm cooperates with each other in three strategies to update the location of grey wolf individuals, and enhances the global and local search ability of grey wolf individuals. Experimental results show that MSGWO can quickly find the optimal feature combination and effectively improve the performance of the classification model.

Educational data mining (EDM) evaluates and predicts students' performance that assists to discover important factors affecting students' academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying feature selection algorithms. Feature selection algorithms help in selecting robots and meaningful features for predicting students' performance with high accuracy. This paper presents different EDM approaches for forecasting students' performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in educational data mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.

Using the neural network architecture of back propagation applied to speech recognition, a new input data structure is developed which improves shift invariance when recognizing signal data in the form such as a formant. The preliminary development of the data structure, piecewise linear regression vectors, is reported. The new input data structure reduces the amount of data presented to the network by as much as an order of magnitude, giving a computational advantage in execution speed.

Data preparation is crucial for achieving good data management following the four foundational FAIR principles—Findability, Accessibility, Interoperability, and Reusability. Processing datasets to achieve high data (and metadata) quality is mandatory in modern applications. However, the data preparation activities that are needed to reach such levels may easily become unsustainable due to, for example, resource intensity or scalability challenges. Moreover, some preparation efforts may become unnecessary if they result in negligible improvements or duplicate actions. This article examines the sustainability aspects of data preparation through the lens of a circular economy. Within the data landscape, this perspective encourages practices that minimize waste, extend the data life cycle, and maximize reuse in alignment with the FAIR principles. We explore these practices and their impact on selecting and configuring effective data preparation strategies to design sustainable, high-quality pipelines. To this end, we propose an evaluation model that integrates data quality metrics with sustainability parameters for human and computational tasks. Finally, we apply the model in a comparative analysis of key data preparation methods, demonstrating its effectiveness in assessing sustainability and quality tradeoffs.

Many ice rinks are facing renovations which require sound financial decision-making to improve their overall performance. Hence the concept of life-cycle cost (LCC) analysis is increasingly applied to estimate costs. However, a practical way to incorporate the concept when comparing the economic performances of refrigeration systems seems to be missing. This study addresses this issue by developing an LCC analysis model that is effective and capable of producing reliable results when comparing refrigeration systems in ice rinks. The LCC model is developed to automatically perform economic analysis based on simulated energy performance together with investment and service cost data gathered from the field. Different tools are included in the model to critically evaluate the input data. The model is applied to real cases in Sweden, where the LCC analysis often concludes that installing a CO2-based system with a heat export function would yield the best financial results. This paper further discusses the potential reasons behind these recurring findings. Results indicate that the sensitivity and scenario analyses play a crucial role when evaluating the quality of input data. The reliability and robustness of calculated results can therefore be assessed, which demonstrates the applicability of the developed model in comparative LCC analysis of refrigeration systems in ice rinks.

Pulse wave velocity (PWV) has been shown to be a good predictor of cardiovascular risk. Measuring PWV normally constitutes quantifying the transit time (TT) between proximally and distally measured blood pressure or velocity waveforms. However various factors affect our ability to measure PWV accurately making it a topic of continued attention. Primarily, the selection of the algorithm used to measure TT has been shown to have a remarkable effect upon accuracy. Other factors pertain to the quality and nature of the waveform data. The quality of the data (assuming good sensor function) worsens with increasing signal noise and decreasing sampling frequency, and the nature of the waveform data refers to waveform shape (e.g. pressure vs. velocity) or shape mismatch between the distal and proximal waveforms. Papaioannou et al.’s letter reflects upon an article our group published in the Annals of Biomedical Engineering (2013 Dec; 41(12):2617-29) which assessed current algorithms’ accuracies and variabilities due to waveform signal noise, temporal resolution, shape and shape mismatch. We used waveform data generated by a one-dimensional (1-D) arterial model for which the theoretical TT was known a priori, thus algorithm accuracy could be explicitly measured. Furthermore, the same algorithms were applied to beat-to-beat clinical data to investigate beat-to-beat variability in PWV measurement. Based on this study we proposed a set of recommendations for choosing the most accurate algorithm for a given waveform set. The algorithms tested included two variations of the traditional foot-to-foot method, a least squares fit of the systolic rise, and cross correlation of the entire waveform. The software developed for this study containing all algorithms tested was made available to the public in the interest of continued discussion upon this topic. We agree with the Papainnou et al.’s assertion that pressure and velocity waveforms may need to be addressed differently. Certainly the results we presented in our article (Fig. 4) explicitly show differences between algorithm accuracies when applied to pairs of pressure or velocity waveforms. Furthermore, these differences were outlined as a warning in Point 4 of the algorithm selection recommendations. We found wave shape mismatch between the proximal and distal wave pairs to be a crucial factor in determining which algorithm to use. For example, cross correlation is by definition a comparison of wave shapes weighted by local magnitude. In our article we measured waveshape similarity using a normalised cross-correlation coefficient (CCCoefficient) where a value of 1.0 would indicate identical shapes. Papainnou et al. were interested to know the CCCoefficient for the carotid and femoral arterial waveforms as these are the standard measurements made by clinical devices such as the SphygmoCor system. The CCCoefficient of the carotid-and-femoral-artery pressure waves was 0.78 for the numerical data and 0.84 ± 0.10 for our patient cohort (n = 20). These results indicate substantial wave shape mismatch in clinical assessment. Measuring beat-to-beat variation in carotid femoral PWV in our patient cohort was done retrospectively using 10 s tonometric signals measured sequentially at the carotid, and then the femoral artery. As each signal was measured concurrently with the ECG, individual waveforms could be dissected from the 10 s signal and aligned in time with reference to the R-wave of the ECG. Beat-to-beat variation was therefore assessed as the variation in measured PWV from as many waveAddress correspondence to Nicholas R. Gaddum, King’s College London, British Heart Foundation Centre, St. Thomas’ Hospital, 4th Floor Lambeth Wing, Westminster Bridge Road, London SE1 7EH, UK. Electronic mail: nickgaddum@gmail.com Annals of Biomedical Engineering, Vol. 42, No. 6, June 2014 ( 2014) pp. 1145–1147 DOI: 10.1007/s10439-014-1007-7

Data-parallel co-processors have the potential to improve performance in highly parallel regions of code when coupled to a general-purpose CPU. However, applications often have to be modified in non-intuitive and complicated ways to mitigate the cost of data marshalling between the CPU and the co-processor. In some applications the overheads cannot be amortized and co-processors are unable to provide benefit. The additional effort and complexity of incorporating co-processors makes it difficult, if not impossible, to effectively utilize co-processors in large applications.This paper presents CUBA, an architecture model where co-processors encapsulated as function calls can efficiently access their input and output data structures through pointer parameters. The key idea is to map the data structures required by the co-processor to the co-processor local memory as opposed to the CPU's main memory. The mapping in CUBA preserves the original layout of the shared data structures hosted in the co-processor local memory. The mapping renders the data marshalling process unnecessary and reduces the need for code changes in order to use the co-processors. CUBA allows the CPU to cache hosted data structures with a selective write-through cache policy, allowing the CPU to access hosted data structures while supporting efficient communication with the co-processors. Benchmark simulation results show that a CUBA-based system can approach optimal transfer rates while requiring few changes to the code that executes on the CPU.

This thesis presents a methodology of data preparation with probabilistic record linkage and information fusion for improving and enriching information visualizations of biomedical citation data. The problem of record linkage of citation databases where only non-unique identifiers such as author names and document titles are available as common identifiers to be linked was investigated. This problem in citation data parallels problems in clinical data and Knowledge Discovery in Databases (KDD) methods from clinical data mining are evaluated. Probabilistic and deterministic (exact-match) record linkage models were developed and compared through the use of a gold standard or truth dataset. Empirical comparison with ROC analysis of record linkage models showed a significant difference (p=.000) in performance of a probabilistic model over deterministic models. The methodology was evaluated with probabilistic linkage of records from the Web of Science, Medline, and CINAHL citation databases in the knowledge domains of medical informatics, HIV/AIDS, and nursing informatics. Data quality metrics for datasets prepared with probabilistic record linkage and information fusion showed improvement in completeness of key variables and reduction in sample bias. The resulting visualizations offered a richer information space for users through an increase in terms entering the visualization. The significant contributions of this work include the development of a novel model of probabilistic record linkage for biomedical citation databases which improves upon existing deterministic models. In addition a methodology for improving and enriching knowledge domain visualizations though a data preparation approach has been validated with analyses of multiple citation databases and knowledge domains. The data preparation methodology of probabilistic record linkage with information fusion offers a remedy for data quality problems, and the opportunity to enrich visualizations with added content for user exploration, which in turn improves the utility of knowledge domain visualizations as a medium for assessing available evidence and forming hypotheses.

We compared the application of ordinary linear regression, Deming regression, standardized principal component analysis, and Passing-Bablok regression to real-life method comparison studies to investigate whether the statistical model of regression or the analytical input data have more influence on the validity of the regression estimates. We took measurements of serum potassium as an example for comparisons that cover a narrow data range and measurements of serum estradiol-17beta as an example for comparisons that cover a wide data range. We demonstrate that, in practice, it is not the statistical model but the quality of the analytical input data that is crucial for interpretation of method comparison studies. We show the usefulness of ordinary linear regression, in particular, because it gives a better estimate of the standard deviation of the residuals than the other procedures. The latter is important for distinguishing whether the observed spread across the regression line is caused by the analytical imprecision alone or whether sample-related effects also contribute. We further demonstrate the usefulness of linear correlation analysis as a first screening test for the validity of linear regression data. When ordinary linear regression (in combination with correlation analysis) gives poor estimates, we recommend investigating the analytical reason for the poor performance instead of assuming that other linear regression procedures add substantial value to the interpretation of the study. This investigation should address whether (a) the x and y data are linearly related; (b) the total analytical imprecision (s(a,tot)) is responsible for the poor correlation; (c) sample-related effects are present (standard deviation of the residuals >> s(a,tot)); (d) the samples are adequately distributed over the investigated range; and (e) the number of samples used for the comparison is adequate.

Estimating antimicrobial resistance burden in Europe—what are the next steps?