Robust fuzzy clustering for multiple instance regression.

Multiple instance regression (MIR) operates on a collection of bags, where each bag contains multiple instances sharing an identical real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining instances are noise and outliers observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this thesis, we introduce an algorithm that uses robust fuzzy clustering with an appropriate distance to learn multiple linear models from a noisy feature space simultaneously. We show that fuzzy memberships are useful in allowing instances to belong to multiple models, while possibilistic memberships allow identification of the primary instances of each bag with respect to each model. We also use possibilistic memberships to identify and ignore noisy instances and determine the optimal number of regression models. We evaluate our approach on a series of synthetic data sets, remote sensing data to predict the yearly average yield of a crop and application to drug activity prediction. We show that our approach achieves higher accuracy than existing methods

What is the impact of social well-being factors on happiness?

Purpose – The purpose of this study is to examine the effect of social support, healthy life expectancy, freedom to make life choices, generosity, corruption perception, real gross domestic product per capita and the Gini index on happiness. Design/methodology/approach – In this study, the sample consists of 137 countries observed over the period 2017–2019. A multidimensional approach is used consisting of a principal component analysis and an econometric linear regression model. Findings – The findings indicate that perception, taking care of other people, corruption perception, freedom to make life choices and healthy life expectancy are the most determining factors of social well-being. Practical implications – Well-being benefits countries by improving living standards. Indeed, taking care of other people, corruption perception, freedom to make life choices and healthy life expectancy directly and positively correlate with social well-being. Originality/value – This study contributes to the previous literature in three ways. First, this paper provides fresh and recent data on social well-being. Second, the author introduced a multidimensional approach using a principal component analysis of the different social well-being factors to detect correlation between these indicators and to determine homogeneous clusters. Third, through these indicators, a country’s leaders can formulate policies to enhance social well-being because it is closely linked to the improvement of the standard of living, good governance and therefore an increase in GDP.info:eu-repo/semantics/publishedVersio

The impact of liquidity risk determinants on profitability: An empirical study on islamic banks in the Kingdom of Bahrain

The sustainability of the banking system depends on the profitability and capital adequacy. Practically, profitability and liquidity are effective indicators of the corporate health and performance of not only the Islamic commercial banks but all profit-oriented ventures. Therefore, liquidity risk is considered as one of the serious concerns and challenges for modern era banks. As the global financial crisis spread, Islamic banks in Kingdom of Bahrain began to be affected; all of a sudden, some of the biggest Islamic banks, such as the Bahrain Islamic Bank, the Gulf Finance House and the Ithmar Bank, ended up with net losses. The aim of this study is to investigate the impact of the significant determinants of liquidity risk on the profitability of Islamic commercial banks in Bahrain during the 2007-2013 periods as well as to assess the impact of the global financial crisis on the profitability of these banks during the recovery period. Multiple regressions analysis was applied. By using Ordinary Least Squares (OLS) the results revealed that all the independent variables are significant with both models ROA and ROE except financial leverage and deposits have a statistically insignificant impact on ROA- Capital adequacy, financial leverage, deposits and GDP have a positive and significant impact; whereas bank size and the global financial crisis have a negative impact and are statistically significant. From these results, it is recommended that these banks control and manage properly these variables in order to create a high level of liquidity in the banks which would achieve a good profitability, leading to the sustainability of the financial banking syste

The Euro-Zone; is it the crisis ahead!

The turmoil affecting capital markets since summer 2007 and its intensification since mid-September 2008 inflicted noticeable blows to the world economy. Although the high-risk real estate American market is believed to be the immediate source of such turmoil, these last years the euro-zone capital markets and financial institutions seem to absorb a continued credit cycle phenomenon and are seriously hit by aggravating tensions. It is the first financial crisis the Eurozone witnesses. Today, the priority for member states is to quickly find and implement solutions. In this paper, we analyse the recent developments in the Eurozone, mainly the Greek and Irish financial crises and the threats the Eurozone risks. Finally, we propose some solutions for the crises

Friction and wear behaviors of Fe-14Mn-9Cr-5Ni-6Si and Fe-13Cr-12Co-8Mn-6Ni-6Si shape memory alloys

363-368Iron-based shape memory alloys are the materials that can be especially used in civil engineering structures and pipe coupling because of their good mechanical properties and their low price compared with Ni-Ti and cooper-based alloys. However, their applications have been remained limited so it is very important to determine the wear behaviours of this family of materials. This paper presents friction and wears behaviours of Fe-14Mn-9Cr-5Ni-6Si and Fe-13Cr-12Co-8Mn-6Ni-6Si under both dry and lubricated conditions by means of reciprocating ball-on-flat tribometer. The aim of this study is to determine the influence of chemical composition of iron-based shape memory alloys on tribological behaviours of material and compare their behaviours with that of Fe-32Mn-6Si which is considered as a basis reference. Morphologies and microstructures of specimens have been characterized by optical microscope. The wear tests and the optical micrograph observations of studied alloys have shown that the tribological behaviors of shape memory alloys depend on the chemical composition of material which is mainly influenced by the presence of nickel, chromium and cobalt in material. The results have shown that friction coefficients of Fe-14Mn-9Cr-5Ni-6Si and Fe-13Cr-12Co-8Mn-6Ni-6Si are almost equal and less than of Fe-32Mn-6Si. Moreover, the addition of cobalt in the chemical composition of shape memory alloys reduces considerably the stick-slip phenomenon. It has been also shown that wear resistance of alloys in oily friction is higher than that in dry friction

Robust Synchronization of the Unified Chaotic System

The paper investigates the synchronization problem of the unified chaotic system. The case of identical, but unknown master and slave unified chaotic systems is considered. Based on compound matrices formalism, a unified synchronization control scheme is proposed independently of the unknown system parameter. Simulation results are provided to show the effectiveness of the presented scheme

Overreaction on the Tunisian stock market: an empirical test

The financial market interest several researchers, especially in the domain of assessment of the financial assets and their performances. The previous research identified several anomalies of the market, as size, Monday, January, PER effects, etc. putting in question the notion of market efficiency and thereafter the predictability of assets returns. In the same context, W.F.M. De Bondt and R. Thaler [1985] disclosed one stock course overreaction: assets having recorded bad performances in the past in stock market would know performances subsequently superior to the average and vice-versa for assets having recorded excellent performances. In this paper we study the overreaction effect on the Tunisian stock market and we show that the hypothesis of basis that consists at exploiting the negative dependence of returns is a necessary condition but not sufficient so that a market reacts giving an explanation thus to the results contradictory of the different authors on the overreaction effect.Assets pricing anomalies, portfolio selection, efficiency, performance, overreaction, momentum strategies.

Three-Phase Two-Leg T-Type Converter based Active Power Filter

In this work, a three-phase two-leg T-type active power filter topology is investigated when a three-level hysteresis band current modulation technique is applied. The three-phase two-leg T-type configuration which utilizes eight switching devices together split DC link capacitor banks is able to improve the reliability of the system as well as effectively reduces the power losses. The compensation of the current harmonics at each phase are achieved successfully with the control of phase-a and phase-b. Using this technique, the phase-c is directly connected to the midpoint of the split capacitors, which eliminates the necessity of additional control on the phase-c. The performance of the proposed topology and control method is demonstrated through the simulation results

LogGPT: Log Anomaly Detection via GPT

Detecting system anomalies based on log data is important for ensuring the security and reliability of computer systems. Recently, deep learning models have been widely used for log anomaly detection. The core idea is to model the log sequences as natural language and adopt deep sequential models, such as LSTM or Transformer, to encode the normal patterns in log sequences via language modeling. However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection. To fill up the gap, we propose LogGPT, a novel framework that employs GPT for log anomaly detection. LogGPT is first trained to predict the next log entry based on the preceding sequence. To further enhance the performance of LogGPT, we propose a novel reinforcement learning strategy to finetune the model specifically for the log anomaly detection task. The experimental results on three datasets show that LogGPT significantly outperforms existing state-of-the-art approaches