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This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduces estimation error. A new recovery order based on confidence is established to resolve the error propagation problem. Compared to other state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations. It also has significantly lower complexity.

Learning from examples has a number of distinct algebraic forms, depending on what is to be learned from the available information. One of these forms is , where the input-output tuple (x, y) is the available information, and G represents the process determining the mapping from x to y. Various models, y = f(x), of G can be constructed using the information from the (x, y) tuples. In general, and for real-world problems, it is not reasonable to expect the exact representation of G to be found (i.e. a formula that is correct for all possible (x, y)). The modeling procedure involves finding a satisfactory set of basis functions, their combination, a coding for (x, y) and then to adjust all free parameters in an approximation process, to construct a final model. The approximation process can bring the accuracy of the model to a certain level, after which it becomes increasingly expensive to improve further. Further improvement may be gained through constructing a number of agents {α}, each of which develops its own model f_α. These may then be combined in a second modeling phase to synthesize a team model. If each agent has the ability for internal reflection the combination in a team framework becomes more profitable. We describe reflection and the generation of a confidence function: the agent's estimate of the correctness of each of its predictions. The presence of reflective information is shown to increase significantly the performance of a team.

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald’s as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, $f$-measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique.

Traditionally the beta distribution has been used due to its ease for computing the mean of tasks, times, cash-flows, etc. because, in this case, the expected value is a weighted average of optimistic, most likely and pessimistic values. One of the critiques of PERT has been the difficulty of introducing the level of confidence that the expert has in his own estimate of the modal value. The Belief in Fuzzy Probability Estimations of Time (BIFPET) model uses human judgement instead of stochastic assumptions to determine the duration of a project. Thus, each supervisor responsible of an activity specifies the three values with their respective probabilities. The manager accepts these probabilities or may extend them within a certain range, according to his belief in these likelihoods or the degree of control exercised by himself in achieving the lower bound of the expected value. However, this approach can be implemented in the PERT methodology. Thus, in this paper, we introduce the confidence that the manager has in the most likely value supplied by the supervisor in order to determine the specific beta distribution to model the variable, showing the advantages of this intuitive procedure which moreover can be easily implemented.

This paper intends to establish the importance of expectations and confidence of Indian investors on the financial market in India. Previous research of investors in other countries has shown that both of these attitudes manifest clear tendencies to change through time and strongly influence the behavior of the pragmatic markets. The field of behavioral finance — an emerging field in financial analysis that takes explicit account of psychological factors — is the driving force of this research. This research is based on information obtained through a survey process in India.

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

This paper characterises the joint impacts of intra- and extra-organisational contexts on innovation development during the socio-economic crisis in Portugal. The characterisation of these contexts in 309 firms of the Information and Communication Technology services sector allowed for the identification of two profiles via a cluster analysis. These were mostly discriminated by financial resources and clients and not by science and technology activities. Subsequently, these profiles were related to innovation, top managers’ perceptions and expectations for the future. The data shows that under favourable contexts, innovation increases, the firm is perceived to drive innovation and confidence in the companies’ future emerges. In more unfavourable scenarios, innovation is compromised, the environment is perceived to block innovation and confidence is halted. This paper establishes companies’ profiles for the first time in Portugal and suggests that intra- and extra-organisational contexts have to be jointly tackled to foster present innovation and promote future activities.

In this chapter, we study the role of individuals’ confidence in healthcare institutions on curative and preventive demand for healthcare during the COVID-19 pandemic in India. Specifically, we collate data on pre-pandemic self-reported measures of confidence in healthcare institutions in the private and public sectors and correlate these measures to aspects of demand for healthcare during the pandemic. We find that confidence in healthcare is positively correlated with demand for healthcare, which implies that policymakers must seek to establish credible health care institutions for COVID-19 care to tackle this major public health problem. This is because, in the absence of such credible institutions, which are perceived to be better service providers, the demand for health care is likely to be low, which can mask the actual infection rates leading to underreporting of infections and low vaccine take-up. We present a conceptual framework based on a simple microeconomic theoretical model that can help understand such behavior and use historical data on self-reported confidence measures from a large nationally representative household survey, along with rapidly incoming COVID-19 data, to perform our empirical analysis.

This chapter introduces the concept of association rules, a form of local-pattern discovery in an unsupervised learning system. Association rules are used to uncover relationships between inherently unrelated data items. The terminology, notation, and the processes used with association rules are discussed, and a brief overview of three basic rule inference algorithms is given.

The study dealt with the practical skills possessed by technical teachers and their level of confidence in applying ICT for teaching in polytechnics in the North-East Geo-political Zone of Nigeria. It answered two research questions and tested two hypotheses. The study employed descriptive survey research design. Questionnaire was used to collect data from 100 certified and 114 uncertified technical teachers. The questionnaire which was divided into sections was validated by two experts in Industrial Technology Education from the academia. The validated copy of the questionnaire was trial tested on 25 technical teachers chosen from Plateau State Polytechnic, Barkin Ladi, Jos, Nigeria. Using the Crombach Alpha formula, the trial test gave the reliability coefficient of the instrument as 0.91. Six weeks were devoted to the administration of the questionnaire, at the end of which 209 valid copies were obtained, upon which analysis of the results was carried out. Mean and standard deviation were used to answer the research questions, while the Z-test tested the hypotheses at 0.05 level of significance. The study found that practical skills possessed by technical teachers’ in using ICT for teaching in polytechnics was moderate. It found that technical teachers had high confidence and motivation in applying ICT facilities for teaching. The study also found that a significant difference did not exist between the mean scores of certified and un-certified technical teachers on the technical teachers’ level of practical skills in using ICT for teaching in polytechnics. Similarly, a significant difference did not exist between the mean scores of certified and un-certified technical teachers on the level of confidence in applying ICT facilities for teaching in polytechnics. The study implied that for teachers’ level practical skills in applying of ICT in teaching to fall in line with expected standards, teacher training and professional development, and as well funding and provision of ICT facilities, must be given a serious consideration. This has become necessary since the teachers’ level of confidence and morale in applying ICT facilities in teaching is high. In this connection, the study suggested that there is need to train and retrain technical teachers on ICT use in order for them to effectively apply the facilities in teaching.