This paper describes a comparative study where several regression and artificial intelligence (AI)-based methods are used to assess properties in Louisville, Kentucky. Four regressionbased [traditional multiple analysis (MRA), three non-traditional regression-based methods, Support Vector Machines using sequential minimal optimization (SVM-SMO), additive regression, M5P trees], AI-based [neural networks (NNs), radial basis function neural network (RBFNN), memory-based reasoning (MBR)]...

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The utilizes radial basis function (RBF) approximate the inverse of mixing mapping which is assumed exist and able be approximated using an RBF network. A contrast consists mutual information partial moments outputs system, defined separate minimization results independence with desirable such that original sources are separated properly. Two learning algorithms for parametric network...

Incident reporting and investigation are components of safety management systems. Timely accurate identification risk factors is crucial to effective prevention strategies. However, factor often hampered by size, complexity, the need for human involvement in categorizing incident data. We present a data-mining approach analysis using data from Aviation Safety Reporting System, which part Federal Administration. Our an attempt overcome obstacles related labor intensive manual as well...

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system-based approach estimate prices for residential properties. The data set consists of historic sales houses in market the Midwest region United States it contains parameters describing typical property features actual sale price. study explores use fuzzy systems assess real estate values neural networks creating fine-tuning rules used system. results are compared with those obtained using...

There is a lot of evidence in the research literature that Information Technologies can play crucial role achieving competitive advantage, improving decision-making, and organizational success. Unfortunately, on exploring issues using Disruptive (DT) still limited, especially studies into relationship between use DT The main contribution this study to investigate issue DT's impact success, particular identifying benefits organizations, as well examining what extent culture be factor...

This paper presents a new approach to fuzzy rule-based modeling of nonlinear systems from numerical data. The novelty the lies in way input partitioning and syntax rules. introduces interpretable relational antecedents that incorporate local linear interactions between variables into inference process. modification improves approximation quality allows for limiting number Additionally, resulting linguistic description better captures system characteristics by exposing variables.

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.6in 0pt 0.5in;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">The healthcare industry, specifically hospitals and clinical organizations, are often plagued by unpaid bills collection agency fees. These contribute significantly to the rising cost of healthcare. Unlike financial institutions, health care providers typically do not collect information about their patients.<span...

Abstract Drawing useful predictions from vast accumulations of data is becoming critical to the success an enterprise. Organizations' databases grow exponentially transactions with external stakeholders in addition their own internal activities. An important organizational computing issue that, as they grow, become potentially more valuable and also difficult analyze. One example predicting value residential real estate based on past comparable sales transactions. This several sectors US...

The performance of text classification methods has improved greatly over the last decade for instances less than 512 tokens. This limit been adopted by most state-of-the-research transformer models due to high computational cost analyzing longer instances. To mitigate this problem and improve texts, researchers have sought resolve underlying causes proposed optimizations attention mechanism, which is key element every model. In our study, we are not pursuing ultimate goal long...

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Lack of precision is common in property value assessment. Recently non-conventional methods, such as neural networks based have been introduced assessment an attempt to better address this lack and uncertainty. Although fuzzy logic has suggested another possible solution, no other artificial intelligence methods applied real...

The study investigates an issue of Big Data (BD) and elements shaping BD-based business value creation. outcome this research is to build verify a framework provide based on BD. contains three components: dynamic capabilities organizations, integrated process BD resource exploration exploitation, identification measurement has been subjected initial verification by conducting the survey among 25 organizations.

Infrastructure-as-code enables cloud architects to automate IT service delivery by specifying services through machine-readable definition files. To allow for a reusability of the infrastructure-as-code specifications, specify as compositions sub-processes. As AI planning agents automated composition proposed prior research fall short in context, we design search-based problem-solving agent named YUMA according science process fill this gap. holds search tree reflecting state space and...

Robot safety is a critical and largely unsolved problem involving the interaction of man machine. The paper presents new approach to robot which uses an integrated sensing architecture for monitoring workspace, detection decision logic regulating safe operation robot. Sensory information fused through trained neural network produce map hazards. Using this combined map, about robot's current position velocity, set fuzzy rules has been implemented regulate activity. Simulation results...

The paper compares the classification performance rate of eight models: logistic regression (LR), neural network (NN), radial basis function (RBFNN), support vector machine (SVM), case-base reasoning (CBR), and three decision trees (DTs). We build models test their accuracy rates on a historical data set provided by German financial institution. contains 21 attributes 1000 customers. Though at time loan application all individuals deemed to institution be qualified obtain loan, 300 them...

This paper introduces a novel technique for sequential blind extraction of singularly mixed sources. First, neural-network model and an adaptive algorithm single-source are introduced. Next, extractability analysis is presented singular mixing matrix, two sets necessary sufficient conditions derived. The then presented. stability the discussed. Simulation results to illustrate validity analysis. proposed suitable case nonsingular matrix as well matrix.

The K distribution is an accurate model for ultrasonic backscatter. A neural approach developed to estimate parameters. Accuracy and consistency of the estimates from simulated envelope data compare favorably with other techniques. Neural networks can potentially be used as a complementary technique tissue characterization.

<span>Neural networks are designed to detect complex relationships among variables better than traditional statistical methods. Our study examined whether the complexity of response measure impacts logistic regression or a neural network produces highest classification accuracy for financially distressed firms. We compared results obtained from two methods four state variable and dichotomous variable. suggest that not superior models variable, but more financial distress variable.</span>