varunr14/newsummary huggingface dataset:
Analytic Hierarchy Process is increasingly applied to healthcare and medical research and applications . However knowledge representation of pairwise reciprocal matrix is still dubious . This research discusses the related drawbacks and recommends pairwise opposite matrix as the ideal alternative . Pairwise opposite matrix is the key foundation of Primitive Cognitive Network Process which revises the AHP approach with practical changes . A medical decision treatment evaluation using AHP is revised by P CNP with a step by step tutorial . Comparisons with AHP have been discussed . The proposed method could be a promising decision tool to replace AHP to share information among patients or and doctors and to evaluate therapies medical treatments health care technologies medical resources and healthcare policies . 


Summary: Indicating the rating scale problems of the Analytic Hierarchy Process AHP and proposing the paired interval scale addressing the limitations. Introducing Primitive Cognitive Network Process P CNP to medical treatment decision making and showing how to use it from laymen perspective. Demonstrating how the current AHP data to medical decision can be converted to P CNP data which is further processed by the P CNP. Applications with the AHP data can be revised by P CNP to explore the more reliable research findings or make more reliable decisions. P CNP can be a promising decision making approach to evaluate medical and healthcare decisions.
This paper proposes a novel multi objective model for an unrelated parallel machine scheduling problem considering inherent uncertainty in processing times and due dates . The problem is characterized by non zero ready times sequence and machine dependent setup times and secondary resource constraints for jobs . Each job can be processed only if its required machine and secondary resource are available at the same time . Finding optimal solution for this complex problem in a reasonable time using exact optimization tools is prohibitive . This paper presents an effective multi objective particle swarm optimization algorithm to find a good approximation of Pareto frontier where total weighted flow time total weighted tardiness and total machine load variation are to be minimized simultaneously . The proposed MOPSO exploits new selection regimes for preserving global as well as personal best solutions . Moreover a generalized dominance concept in a fuzzy environment is employed to find locally Pareto optimal frontier . Performance of the proposed MOPSO is compared against a conventional multi objective particle swarm optimization algorithm over a number of randomly generated test problems . Statistical analyses based on the effect of each algorithm on each objective space show that the proposed MOPSO outperforms the CMOPSO in terms of quality diversity and spacing metrics . 


Summary: Proposing a fuzzy multi objective model for unrelated parallel machine scheduling. Presenting an effective multi objective particle swarm optimization solution method. Comparing the proposed MOPSO algorithm against a conventional MOPSO algorithm.
Merging sustainable development with the business and taking goals into account from its three dimensions which are derived from customer and stakeholder requirements have been a potential source of competitive differentiation for firms . Academic and corporate interest in sustainable supply chain management has also risen considerably in recent years . This paper examines the components and elements of SSC management and how they serve as a foundation for an evaluation framework . By using quality function deployment as a product system planning and improvement tool an effective SSC structure can be obtained . QFD uses a matrix called the House of Quality and constructing the HoQ is a critical step in the application of QFD as it translates customer requirements into engineering characteristics . However participants of HoQ construction sessions tend to provide information about their individual judgments in multiple formats such as numerically or linguistically depending on their different knowledge experience culture and circumstance . Furthermore they can generate incomplete preferences which are challenging to assess in a consistent way . Therefore the objective of this study is to apply an extended QFD methodology in SSC by introducing a new group decision making approach that takes multiple preference formats and incomplete information into account and fusions different formats of expressions into one uniform group decision by means of the fuzzy set theory . To assess the validity of the proposed approach a case study conducted at HAVI Logistics Turkey is also presented in the paper . 


Summary: The objective of the paper is to apply an extended QFD methodology in sustainable supply chain SSC . The paper examines the main elements of SSC and how they serve as a foundation for an evaluation framework. A new GDM approach that takes multiple preference formats and incomplete information into account is introduced. To assess the validity of the proposed approach a case study conducted at HAVI Logistics Turkey is presented.
Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling . Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data . This paper proposes a hybrid multi layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables . The multi layered artificial immune system creates and stores antibodies that bind to and annihilate an antigen . The genetic algorithm optimises the learning process of a stimulated antibody . The evaluation of the imputation is performed using the RIPPER k nearest neighbour na ve Bayes and logistic discriminant classifiers . The effect of the imputation on the classifiers is compared with that of the mean mode and hot deck imputation methods . The results demonstrate that when missing data imputation is performed using the proposed hybrid method the classification improves and the robustness to the amount of missing data is increased relative to the mean mode method for data missing completely at random missing at random and not missing at random .The imputation performance is similar to or marginally better than that of the hot deck imputation . 


Summary: Genetic algorithm optimization is effective for partial imputation using MAIS. The hybrid MAIS and genetic algorithm improves performance of classifiers. Increased strength and resilience in the presence of escalating missing data.
This paper presents a novel soft computing based solution to a complex optimal control or dynamic optimization problem that requires the solution to be available in real time . The complexities in this problem of optimal guidance of interceptors launched with high initial heading errors include the more involved physics of a three dimensional missile target engagement and those posed by the assumption of a realistic dynamic model such as time varying missile speed thrust drag and mass besides gravity and upper bound on the lateral acceleration . The classic pure proportional navigation law is augmented with a polynomial function of the heading error and the values of the coefficients of the polynomial are determined using differential evolution . The performance of the proposed DE enhanced guidance law is compared against the existing conventional laws in the literature on the criteria of time and energy optimality peak lateral acceleration demanded terminal speed and robustness to unanticipated target maneuvers to illustrate the superiority of the proposed law . 


Summary: This paper solves the real world problem of tactical missile guidance in which optimality is sought to be achieved using the evolutionary computing method of differential evolution. A valid criticism against optimization by evolutionary computing methods is that they are computationally intensive as compared to gradient based methods. By posing the problem as that of finding the coefficients of a third order polynomial the dimensionality of the problem is so greatly reduced that online implementation in real time is shown to be possible. The results so obtained are compared against conventional methods in the guidance literature.
A methodology for designing semi physical fuzzy models is proposed . Prior physical knowledge about the dynamics of the system is modeled with continuous time differential equations . Fuzzy knowledge bases are embedded in these equations as nonlinear constructive blocks . Rules comprising the knowledge bases are fitted to interval valued data with metaheuristics . A possibilistic filter is proposed that is able to gradually evolve an initial estimation of the latent variables of the model on the basis of successive prediction errors . This methodology has been applied to the prediction of voltage and state of charge of LiFePO4 batteries . An empirical study has been carried over data gathered in experiments at the Battery Laboratory at Oviedo University . Fitting between the proposed model and actual measurements is studied for four different manufacturers and different charge discharge patterns . Predictions of the evolution of the voltage during charge discharge and inactivity compare favorably to different models in the literature . The possibilistic filter allows to estimate the state of charge of batteries after an arbitrary path that may include partial charges and discharges . It is shown that the accuracy of the open loop model improves that of other approaches in the literature and at the same time the observer based online model is able to approximate the effective remnant charge of the battery after a reasonably short time . 


Summary: Methodology for obtaining fuzzy rule based semi physical models. Possibilistic maximally specific observer of the state of a nonlinear system. Mathematical model of open circuit voltage and state of charge of LiFePO4 batteries.
This paper proposes a method for finding solutions of arbitrarily nonlinear systems of functional equations through stochastic global optimization . The original problem is transformed into a global optimization one by synthesizing objective functions whose global minima if they exist are also solutions to the original system . The global minimization task is carried out by the stochastic method known as fuzzy adaptive simulated annealing triggered from different starting points aiming at finding as many solutions as possible . To demonstrate the efficiency of the proposed method solutions for several examples of nonlinear systems are presented and compared with results obtained by other approaches . We consider systems composed of n equations on Euclidean spaces . 


Summary: Efficient and accurate approach to solve nonlinear systems of functional equation. Optimization is carried out by stochastic fuzzy adaptive simulated annealing. Several examples presented and compared with results obtained by other approaches.
In the bacteria foraging optimization algorithm the chemotactic process is randomly set imposing that the bacteria swarm together and keep a safe distance from each other . In hybrid bacteria foraging optimization algorithm and particle swarm optimization algorithm the principle of swarming is introduced in the framework of BFAO . The hBFOA PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment . In this paper the effectiveness of the hBFOA PSO algorithm has been tested for automatic generation control of an interconnected power system . A widely used linear model of two area non reheat thermal system equipped with proportional integral controller is considered initially for the design and analysis purpose . At first a conventional integral time multiply absolute error based objective function is considered and the performance of hBFOA PSO algorithm is compared with PSO BFOA and GA. Further a modified objective function using ITAE damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller . Further robustness analysis is carried out by varying the operating load condition and time constants of speed governor turbine tie line power in the range of 50 to 50 as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA PSO optimized PI controller . The proposed approach is also extended to a non linear power system model by considering the effect of governor dead band non linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization approach for the identical interconnected power system . Finally the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided . 


Summary: Suitable objective function selection is very important for controller design. An objective function using ITAE damping ratio and settling times is proposed. The concept is applied to design an hBFOA PSO based PI controller for AGC system. Linear and nonlinear interconnected power system models are considered. Simulation results show better performance than PSO BFOA GA CRAZYPSO and ANFIS approaches.
The ability of artificial neural networks to model the rainfall discharge relationships of karstic aquifers has been studied in the Terminio massif which supplies the Naples area with a yearly mean discharge of approximately 1 3.5m3 s. The Mediterranean climate causes a rapid increase in evapotranspiration and a decrease in rainfall towards spring summer . Especially during drought and in combination with highly sensitive climatic parameters there are dramatic changes in the discharge amount especially during the July and August months . A neural network model was developed based on MLP network to forecast of water resources three and six month before the main stress months of July and August . Example data were extracted on an ultra centenarian hydrological serial . The training and validation phases confirmed by a ten fold cross validation methodology led to a very satisfactory calibration of the ANN model with errors in forecasting discharge values of just 5 and 10 . 


Summary: Ability of artificial neural networks to model the rainfall discharge relationships of karstic aquifers. Three month before forecast of water resources. Six month before forecast of water resources. Error in forecasting discharge values of just 5 three months before . Error in forecasting discharge values of just 10 six months before .
Stress is a major health problem in our world today . For this reason it is important to gain an objective understanding of how average individuals respond to real life events they observe in environments they encounter . Our aim is to estimate an objective stress signal for an observer of a real world environment stimulated by meditation . A computational stress signal predictor system is proposed which was developed based on a support vector machine genetic algorithm and an artificial neural network to predict the stress signal from a real world data set . The data set comprised of physiological and physical sensor response signals for stress over the time of the meditation activity . A support vector machine based individual independent classification model was developed to determine the overall shape of the stress signal and results suggested that it matched the curves formed by a linear function a symmetric saturating linear function and a hyperbolic tangent function . Using this information of the shape of the stress signal an artificial neural network based stress signal predictor was developed . Compared to the curves formed from a linear function symmetric saturating linear function and hyperbolic tangent function the stress signal produced by the stress signal predictor for the observers was the most similar to the curve formed by a hyperbolic tangent function with p 0.01 according to statistical analysis . The research presented in this paper is a new dimension in stress research it investigates developing an objective stress measure that is dependent on time . 


Summary: Proposed a computational stress signal predictor system to estimate a stress signal. System based on support vector machine genetic algorithm and neural network. An experiment was conducted to acquire real world stress data. Features extracted from the stress data were provided as input to the system. The stress signal was most similar to a hyperbolic tangent curve.
Models based on data mining and machine learning techniques have been developed to detect the disease early or assist in clinical breast cancer diagnoses . Feature selection is commonly applied to improve the performance of models . There are numerous studies on feature selection in the literature and most of the studies focus on feature selection in supervised learning . When class labels are absent feature selection methods in unsupervised learning are required . However there are few studies on these methods in the literature . Our paper aims to present a hybrid intelligence model that uses the cluster analysis techniques with feature selection for analyzing clinical breast cancer diagnoses . Our model provides an option of selecting a subset of salient features for performing clustering and comprehensively considers the use of most existing models that use all the features to perform clustering . In particular we study the methods by selecting salient features to identify clusters using a comparison of coincident quantitative measurements . When applied to benchmark breast cancer datasets experimental results indicate that our method outperforms several benchmark filter and wrapper based methods in selecting features used to discover natural clusters maximizing the between cluster scatter and minimizing the within cluster scatter toward a satisfactory clustering quality . 


Summary: Our hybrid intelligent model considers the use of filter and wrapper based feature selection methods. Three qualitative principles are highlighted. The usefulness of our model is demonstrated using relative cluster validities. Better use a subset of salient features for analyzing clinical diagnoses in performing clustering.
Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide . Maintaining a good control of the disease is critical to avoid severe long term complications . In recent years several artificial pancreas systems have been proposed and developed which are increasingly advanced . However there is still a lot of research to do . One of the main problems that arises in the automatic control of diabetes is to get a model explaining how glycemia varies with insulin food intakes and other factors fitting the characteristics of each individual or patient . This paper proposes the application of evolutionary computation techniques to obtain customized models of patients unlike most of previous approaches which obtain averaged models . The proposal is based on a kind of genetic programming based on grammars known as Grammatical Evolution . The proposal has been tested with in silico patient data and results are clearly positive . We present also a study of four different grammars and five objective functions . In the test phase the models characterized the glucose with a mean percentage average error of 13.69 modeling well also both hyper and hypoglycemic situations . 


Summary: We propose a method based on Grammatical Evolution to obtain individualized and customized glycemia models in humans. We have tested this proposal with five in silico patients taken from AIDA simulator. We present a study of four different grammars and five objective functions. In the test phase GE models characterized glucose levels with a mean percentage average error of 13.69 . Models obtained with our method reflect also a good representation of both hyper and hypoglycemic situations.
Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system . In this paper a modified incremental conduction maximum power point tracking algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions . An adaptive hysteresis current controller is proposed to control the inverter . The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller . The modeling and simulation of PV system along with the proposed controllers are done using MATLAB SIMSCAPE software . Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state . The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran 3 FPGA is also presented . The experimental results show satisfactory performance of the proposed approaches . 


Summary: A modified incremental conduction MPPT algorithm is proposed for solar PV system under partial shading conditions. An adaptive fuzzy modulator is developed to provide PWM pluses to the DC DC converter. An adaptive hysteresis current control algorithm is proposed for DC AC inverter in the PV system. The hardware implementation of proposed algorithms using Xilinx spartran 3 FPGA is presented.
Brushless DC machines are found increasing use in applications that demand high and rugged performance . In some critical circumstance such as aerospace the motor must be highly reliable . In this context a novel model based fault diagnosis system is developed for brushless DC motor speed control system . Under the consideration of the complexity of characterizing the dynamic of BLDC motor control system with analytic expression a LRGF neural network with pole assignment technique is carried out for modeling the system . During the diagnosis process fault signal of the motor is isolated with LRGFNN online . Meanwhile adaptive lifting scheme and adaptive threshold method are presented for detecting the faults from the isolated fault signal under the existence of mechanical error and electrical error . The effectiveness of the diagnosis system is demonstrated in the simulation of electrical and mechanical fault in the motor . The detection of the incipient fault is also given . 


Summary: An LRGF neural network with pole assignment technique is proposed to model the dynamic system. An adaptive lifting scheme is used for the improvement of the mechanical fault detection. An adaptive threshold scheme is proposed for the detection of several kinds of faults.