varunr14/newsummary huggingface dataset:
Hill climbing constitutes one of the simplest way to produce approximate solutions of a combinatorial optimization problem and is a central component of most advanced metaheuristics . This paper focuses on evaluating climbing techniques in a context where deteriorating moves are not allowed in order to isolate the intensification aspect of metaheuristics . We aim at providing guidelines to choose the most adequate method for climbing efficiently fitness landscapes with respect to their size and some ruggedness and neutrality measures . To achieve this we compare best and first improvement strategies as well as different neutral move policies on a large set of combinatorial fitness landscapes derived from academic optimization problems including NK landscapes . The conclusions highlight that first improvement is globally more efficient to explore most landscapes while best improvement superiority is observed only on smooth landscapes and on some particular structured landscapes . The empirical analysis realized on neutral move policies shows that a stochastic hill climbing reaches in average better configurations and requires fewer evaluations than other climbing techniques . Results indicate that accepting neutral moves at each step of the search should be useful on all landscapes especially those having a significant rate of neutrality . Last we point out that reducing adequately the precision of a fitness function makes the climbing more efficient and helps to solve combinatorial optimization problems . 


Summary: Comparison of the behavior of classical basic local search techniques and their ability to reach high local optima in combinatorial fitness landscapes. Two particular aspects of local search are considered pivoting rules first and best improvement strategies neutral move policies how neutral moves are considered during the search . Experimental analysis on four different landscape models non neutral and neutral NK landscapes flow shop QAP MAXSAT .
The classification of imbalanced data is a major challenge for machine learning . In this paper we presented a fuzzy total margin based support vector machine method to handle the class imbalance learning problem in the presence of outliers and noise . The proposed method incorporates total margin algorithm different cost functions and the proper approach of fuzzification of the penalty into FTM SVM and formulates them in nonlinear case . We considered an excellent type of fuzzy membership functions to assign fuzzy membership values and got six FTM SVM settings . We evaluated the proposed FTM SVM method on two artificial data sets and 16 real world imbalanced data sets . Experimental results show that the proposed FTM SVM method has higher G Mean and F Measure values than some existing CIL methods . Based on the overall results we can conclude that the proposed FTM SVM method is effective for CIL problem especially in the presence of outliers and noise in data sets . 


Summary: A fuzzy total margin based support vector machine FTM SVM method to handle the class imbalance learning CIL problem in the presence of outliers and noise was presented. The proposed method incorporates total margin algorithm different cost functions and the proper approach of fuzzification of the penalty into FTM SVM and formulates them in nonlinear case. We thoroughly evaluated the proposed FTM SVM method on two artificial data sets and sixteen real world imbalanced data sets
Support vector machine is sensitive to the outliers which reduces its generalization ability . This paper presents a novel support vector regression together with fuzzification theory inconsistency matrix and neighbors match operator to address this critical issue . Fuzzification method is exploited to assign similarities on the input space and on the output response to each pair of training samples respectively . The inconsistency matrix is used to calculate the weights of input variables followed by searching outliers through a novel neighborhood matching algorithm and then eliminating them . Finally the processed data is sent to the original SVR and the prediction results are acquired . A simulation example and three real world applications demonstrate the proposed method for data set with outliers . 


Summary: Support vector machine is sensitive to the outliers. A novel support vector regression together with fuzzification theory inconsistency matrix and neighbors match operator is presented. The objective of this novel support vector regression is to increase the generalization ability for data set with outliers.
In this study a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder from healthy controls . The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions . Therefore the potential use of such abnormality for autism screening is investigated . The connectivity patterns are estimated from electroencephalogram data collected from 8 brain regions under various mental states . The EEG data of 12 healthy controls and 6 autistic children were collected during eyes open and eyes close resting states as well as when subjects were exposed to affective faces . Subsequently the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques . Performance of the proposed system in each mental state is separately evaluated . The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods . 


Summary: A dynamic strategy for screening of ASD is proposed based on patterns of information flow between 8 brain regions. The EEG data is collected from 12 healthy and 6 autistic children in the age of 7 to10 years old. The subjects are then classified as autistic or healthy based on the connectivity features. The connectivity features are also compared with other established methods. The promising recognition rates of 93 were achieved in this study. This study shows that patterns of functional and effective connectivity in ASD subjects are different from healthy subjects.
In this paper a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed . Instead of numerals words are used in the evaluation process in order to provide greater flexibility . The proposed methodology is a synthesis of perceptual computing and a fuzzy ranking algorithm . Per C is adopted because it allows uncertainties of words to be considered in the evaluation process . Meanwhile the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student s contribution in a group and subsequently rank the student accordingly . A case study to demonstrate the effectiveness of the proposed methodology is described . Implications of the results are analyzed and discussed . The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students . 


Summary: A new fuzzy peer assessment methodology is proposed. A synthesis of perceptual computing and a fuzzy ranking algorithm. Providing both crisp scores and recommendations. A case study in Universiti Malaysia Sarawak is reported. An illustrative example is adopted to detect possible free riders.
Image quality assessment of distorted or decompressed images without any reference to the original image is challenging from computational point of view . Quality of an image is best judged by human observers without any reference image and evaluated using subjective measures . The paper aims at designing a generic no reference image quality assessment method by incorporating human visual perception in assigning quality class labels to the images . Using fuzzy logic approach we consider information theoretic entropies of visually salient regions of images as features and assess quality of the images using linguistic values . The features are transformed into fuzzy feature space by designing an algorithm based on interval type 2 fuzzy sets . The algorithm measures uncertainty present in the input output feature space to predict image quality accurately as close to human observations . We have taken a set of training images belonging to five different pre assigned quality class labels for calculating foot print of uncertainty corresponding to each class . To assess the quality class label of the test images maximum of T conorm applied on the lower and upper membership functions of the test images belonging to different classes is calculated . Our proposed image quality metric is compared with other no reference quality metrics demonstrating more accurate results and compatible with subjective mean opinion score metric . 


Summary: Human visual perception in assessing image quality using type 2 fuzzy sets. Entropy of visually salient regions measure uncertainty in feature space. Transformation of features to interval type 2 fuzzy feature space. Free from type reduction and defuzzification computation. Promising results compared to prominent subjective and objective image quality metrics.
The job shop scheduling problem with operators is an extension of the classic job shop problem in which an operation must be assisted by one of a limited set of human operators so it models many real life situations . In this paper we tackle the JSO by means of memetic algorithms with the objective of minimizing the makespan . We define and analyze a neighborhood structure which is then exploited in local search and tabu search algorithms . These algorithms are combined with a conventional genetic algorithm to improve a fraction of the chromosomes in each generation . We also consider two different schedule builders for chromosome decoding . All these elements are combined to obtain memetic algorithms which are evaluated over an extensive set of instances . The results of the experimental study show that they reach high quality solutions in very short time comparing favorably with the state of the art methods . 


Summary: New neighborhood structures for the job shop scheduling problem with operators JSO are proposed and analyzed. A new memetic algorithm is proposed for the JSO that incorporates the neighborhood structures in the local search procedure. An experimental study was conducted showing that the memetic algorithm compares favorably with the state of the art.
A new disturbance detection and classification technique based on modified Adaline and adaptive neuro fuzzy information system is proposed for a distributed generation system comprising a wind power generating system and a photovoltaic array . The proposed technique is based on a fast Gauss Newton parameter updating rule rather than the conventional Widrow Hoff delta rule for the Adaline network . The voltage and current signals near the target distributed generation particularly the DFIG whose speed varies from minimum to the maximum cut off speed are processed through the modified Adaline network to yield the features like the negative sequence power harmonic amplification factor total harmonic distortion etc . These features are then used as training sets for the ANFIS which employs a gradient descent algorithm to update its parameters . The proposed technique distinguishes the islanding condition of the distributed generation system with some other disturbances such as switching faults capacitor bank switching voltage swell voltage sag distorted grid voltage unbalanced load switching etc . which are referred to as non islanding cases in this paper . 


Summary: A modified Adaline and ANFIS are used for disturbance detection in distribution generation. Training of the Adaline is done using a robust decoupled Gauss Newton algorithm. Impact of wind velocity of the wind farm on islanding and non islanding cases is studied. Power quality indices are used to classify disturbances. Comparison with other techniques is shown to validate the superiority.
Classifying walking patterns helps the diagnosis of health status disease progression and the effect of interventions . In this paper we develop previous research on human gait to extract a meaningful set of parameters that allow us to design a highly interpretable system capable of identifying different gait styles with linguistic fuzzy if then rules . The model easily discriminates among five different walking patterns namely normal walk on tiptoes dragging left limb dragging right limb and dragging both limbs . We have carried out a complete experimentation to test the performance of the extracted parameters to correctly classify these five chosen gait styles . 


Summary: Based on previous research in the field of computing with perceptions. Highly interpretable and efficient linguistic model used to recognize the gait phases. Set of parameters that characterize relevant aspects of the gait. Fuzzy rule based classifier that discriminates among different walking patterns. Parameters capable of recognizing among five walking patterns with an accuracy of 84 .
Recent research revealed that model assisted parameter tuning can improve the quality of supervised machine learning models . The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches . However the advantages of the tuning often came along with high computation times meaning a real burden for employing tuning algorithms . While the training with a reduced number of patterns can be a solution to this it is often connected with decreasing model accuracies and increasing instabilities and noise . Hence we propose a novel approach defined by a two criteria optimization task where both the runtime and the quality of ML models are optimized . Because the budgets for this optimization task are usually very restricted in ML the surrogate assisted Efficient Global Optimization algorithm is adapted . In order to cope with noisy experiments we apply two hypervolume indicator based EGO algorithms with smoothing and re interpolation of the surrogate models . The techniques do not need replicates . We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling as well as EGO variants with replicates . 


Summary: The Kriging based EGO techniques performed better than the baseline LHS approach. The use of re interpolation is crucial to cope with noise. Repeats can be necessary but also decrease the number of possible infill points.
In this paper speed control of Brushless DC motor using Bat algorithm optimized online Adaptive Neuro Fuzzy Inference System is presented . Learning parameters of the online ANFIS controller i.e . Learning Rate Forgetting Factor and Steepest Descent Momentum Constant are optimized for different operating conditions of Brushless DC motor using Genetic Algorithm Particle Swarm Optimization and Bat algorithm . In addition tuning of the gains of the Proportional Integral Derivative Fuzzy PID and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm Particle Swarm Optimization and Bat Algorithm . Time domain specification of the speed response such as rise time peak overshoot undershoot recovery time settling time and steady state error is obtained and compared for the considered controllers . Also performance indices such as Root Mean Squared Error Integral of Absolute Error Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers . In order to validate the effectiveness of the proposed controller simulation is performed under constant load condition varying load condition and varying set speed conditions of the Brushless DC motor . The real time experimental verification of the proposed controller is verified using an advanced DSP processor . The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions . 


Summary: Bat algorithm optimized online ANFIS based speed controller presented for Brushless DC motor. The speed response of Brushless DC motor is analyzed for different operating conditions. The proposed controller eliminates the uncertainty problem due to load disturbance and set speed variations. The proposed controller enhances the time domain specifications and performance indices in all operating conditions.
The single machine scheduling problem with sequence dependent setup times with the objective of minimizing the total weighted tardiness is a challenging problem due to its complexity and has a huge number of applications in real production environments . In this paper we propose a memetic algorithm that combines and extends several ideas from the literature including a crossover operator that respects both the absolute and relative position of the tasks a replacement strategy that improves the diversity of the population and an effective but computationally expensive neighborhood structure . We propose a new decomposition of this neighborhood that can be used by a variable neighborhood descent framework and also some speed up methods for evaluating the neighbors . In this way we can obtain competitive running times . We conduct an experimental study to analyze the proposed algorithm and prove that it is significantly better than the state of the art in standard benchmarks . 


Summary: A replacement strategy that improves the diversity of the population is proposed. A decomposition of a computationally expensive neighborhood is defined. Some methods to speed up the evaluation of the neighbors are proposed and extended. The resulting hybrid algorithm is significantly better than the state of the art.
The traditional visual and acoustic embolic signal detection methods based on the expert analysis of individual spectral recordings and Doppler shift sounds are the gold standards . However these types of detection methods are high cost subjective and can only be applied by experts . In order to overcome these drawbacks computer based automated embolic detection systems which employ spectral properties of emboli speckle and artifact using Fourier and Wavelet Transforms have been proposed . In this study we propose a fast accurate and robust automated emboli detection system based on the Dual Tree Complex Wavelet Transform . Employing the DTCWT which does not suffer from the lack of shift invariance property of ordinary Discrete Wavelet Transform increases the robustness of the coefficients extracted from the Doppler ultrasound signals . In this study a Doppler ultrasound dataset including 100 samples from each embolic Doppler speckle and artifact signal is used . Each sample obtained from forward and reverse blood flow directions is represented by 1024 points . In our method we first extract the forward and reverse blood flow coefficients separately using DTCWT from the samples . Then dimensionality reduction is applied to each set of coefficients and both of the reduced set of coefficients are fed to classifiers individually . Subsequently in the view that the forward and reverse blood flow coefficients carry different characteristics the individual predictors of these classifiers are combined using ensemble stacking method . We compare the obtained results with Fast Fourier Transform and DWT based emboli detection systems and show that the features extracted using DTCWT give the highest accuracy and emboli detection rate . It is also observed that combining forward and reverse coefficients using stacking ensemble method improves the emboli and artifact detection rates and overall accuracy . 


Summary: Embolic signals are used for the identification of active embolic sources in stroke prone individuals. Dual Tree Complex Wavelet Transform DTCWT is used as a new feature extractor from forward and reverse Doppler ultrasound signals. The features acquired from forward and reverse flow directions of the blood are fed into k NN and SVMs. The individual predictions of classifiers are combined using ensemble stacking method considering that the forward and reverse blood flow coefficients carry different characteristics. The results show that the DTCWT is superior to the DWT and FFT.
This paper presents a biologically inspired sequential learning spiking neural classifier for pattern classification problems . It consists of a two layered neural network and a separate decision block which estimates the predicted class label . Inspired by observations in the neuroscience literature the input layer employs a new neuron model which converts real valued stimuli into spikes with varying amplitudes and firing times . The intermediate layer neurons are modeled as integrate and fire spiking neurons . The decision block identifies that intermediate neuron which fires first and returns the class label associated with that neuron as the predicted class label . The sequential learning algorithm for the spiking neural network automatically determines the network structure from the training samples and adapts its synaptic weights by long term potentiation and long term depression . Performance of SLSNC has been evaluated using a number of benchmark classification problems and the results have been compared with other well known spiking neural network classifiers in the literature as well as with the standard support vector machine with a Gaussian kernel and the fast learning Extreme Learning Machine classifiers . The results clearly indicate that the described spiking neural network produces similar or better generalization performance with a smaller network . 


Summary: LSNC automatically evolves the architecture. Real valued data is encoded using a 2 D encoding having spike amplitude and time. Sequential learning algorithm developed for SLSNC. Learning algorithm relies on computationally inexpensive operations.
The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications . Generally immunity based robotic applications map local instantaneous sensory information into either an antigen or a co stimulatory signal according to the choice of representation schema . Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells in terms of actuation signals . It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead resulting in an ad hoc manner these applications are reported . On the other hand the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications . Authors therefore present a detailed review of immuno inspired robotic applications in an attempt to identify the possible areas to explore . Moreover the literature has been categorized according to the underlying immuno definitions . Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary real or hybrid approaches . Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory . As a result of this study authors suggest a renewed focus on innate immunity action contextualization prior to B T cell invocation and behavior evolution instead of arbitration . In this context a multi tier immunological framework for robotics research combining innate and adaptive components together is also suggested and skeletonized . 


Summary: Immunity based robotic applications are reviewed according to immunological models old and new. Mathematical details of reported literature are tabulated genealogically. Issues pertaining to validity of immunological models are raised. We have suggested immunological equivalents of various support functions in these applications. Modern trends in robotics are emphasized in conjunction with those in immunology.