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An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.

This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.

The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.

The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

The purpose of this paper is to contribute to international tourism market segmentation research by proposing a comprehensive framework that examines behavioral, benefits and lifestyle segmentations. The moderating roles of geographic segmentation (nationality) and advertising media types are also discussed.

Tourists volunteered to participate in a self-administered survey at random during peak seasons. Total number of collected questionnaires was 966. The authors used WarpPLS 6.0 software to analyze data.

Results from a sample of 919 tourists show that tourists in the benefit segmentation cluster had intentions to revisit the destination but they were unlikely to recommend it to others. Another finding indicates that marketing campaigns on different advertising media types might have different results when targeting different activities.

Leaning on the foundations of the marketing literature and the market segmentation theory, this research attempts to create a theoretical contribution that can be used to segment international tourists based on their travel motivations. Additionally, this study highlights the power of conditional probability approach, as it could be of more value than the predominant path coefficient approach.

The purpose of this paper is to employ lessons learned from the industrial accidents in Skikda refinery during the period from 2005 to 2016 as input data for the numerical simulation of risk consequences to identify the exposed areas to the various effects of industrial accidents.

In order to assess how the lessons learned can contribute to modeling the accidents effects in the refining activities, this paper presents a combined statistical/dynamic approach that combines two main tools, namely, lessons learned from petroleum refining in Algeria and Areal Locations of Hazardous Atmospheres software.

The results showed that fire is the most frequent accident at Skikda refinery that is mainly caused by equipment failures with a frequent involvement of crude oil and LNG. The NO₂ toxic effects are unacceptable. This means that in the case of a similar accident, the entire population will be exposed to an intolerable concentration of NO₂. Therefore, people must be relocated to a safer place. The results indicate that the concentration threshold can be met beyond the distance of 1 km.

Due to the economic importance of Skikda refinery and the absence of data related to the accidents in the refineries of Algiers, Arzew and Hassi Messaoud, this study is limited to the statistical analysis of accidents related to Skikda refinery.

This approach makes the risk assessment more practical and effective for the appropriate utilization of safety barriers and for the whole decision-making process.

This work presents a review paper of accidents that occurred in the oil-refining sector in Algeria, whose objective is learning lessons from past accidents history, by identifying their immediate causes and effects on personnel, equipment and environment in order to propose prevention measures. The novelty of this work is highlighted by the fact that this statistical analysis of oil and gas refining accident is realized for the first time in Algeria. This is due to the difficulty of obtaining data on accidents in the Algerian refining sector; for this reason, the authors have limited the study to the Skikda refinery.

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.

Camouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.

There are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.

Even though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.

Camouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.

Social problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.

The proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.

Most epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.

Two probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.

The managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.

Very few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.

For over 60 years, Lerner's (1944) probabilistic approach to the welfare evaluation of income distributions has aroused controversy. Lerner's famous theorem is that, under ignorance regarding who has which utility function, the optimal distribution of income is completely equal. However, Lerner's probabilistic approach can only be applied to compare distributions with equal means when the number of possible utility functions equals the number of individuals in the population. Lerner's most controversial assumption that each assignment of utility functions to individuals is equally likely. This paper generalizes Lerner's probabilistic approach to the welfare analysis of income distributions by weakening the restrictions of utilitarian welfare, equal means, equal numbers, and equal probabilities and a homogeneous population. We show there is a tradeoff between invariance (measurability and comparability) and the information about the assignment of utility functions to individuals required to evaluate expected social welfare.

This paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system management in ubiquitous computing systems.

This paper proposes an approach to problem localization for learning the knowledge of dynamic environment using probabilistic dependency analysis to automatically determine problems. This approach is based on Bayesian learning to describe a system as a hierarchical dependency network, determining root causes of problems via inductive and deductive inferences on the network. An algorithm of preprocessing is performed to create ordering parameters that have close relationships with problems.

The findings show that using ordering parameters as input of network learning, it reduces learning time and maintains accuracy in diverse domains especially in the case of including large number of parameters, hence improving efficiency and accuracy of problem localization.

An evaluation of the work is presented through performance measurements. Various comparisons and evaluations prove that the proposed approach is effective on problem localization and it can achieve significant cost savings.

This study contributes to research into the application of probabilistic dependency analysis in localizing the root cause of problems and predicting potential problems at run time after probabilities propagation throughout a network, particularly in relation to fault management in self‐managing systems.

One of the most important dimensions of search engine user information seeking behaviour is content‐based behaviour. One of the main elements in developing a personalised intelligent search engine is new topic identification. The purpose of this study is to perform automatic new topic identification in search engine transaction logs using conditional probabilities of new topic arrivals.

Sample data logs from FAST (currently owned by Yahoo!) and Excite (currently owned by IAC Search & Media) are used in the study. Conditional probabilities of new topic arrivals and topic continuations given query category are used to estimate new topic arrivals.

The findings of this study show that the conditional probability approach reduced overestimation of topic shifts, increasing some performance measures to their highest ever value compared to previous studies. A straightforward procedure such as the conditional probability approach can be as successful as, and for some measures more successful than, more complex methods applied in previous automatic new topic identification studies.

A straightforward procedure that can enable fast automatic new topic identification, a problem not yet solved, and an important step towards personalised search engines.

The paper aims to answer the question “can the bipolar Negative‐Neutral‐Positive logic be extended and motivated in some probabilistic framework?”

Using the context of cognitive map interpretation of the conjunction and disjunction connectives in bipolar logic, three probabilistic causal reasoning have been put forward. The first one is based on the infinitesimal representation of material implication while the second one relies on the qualitative representation developed by Suppes and Cartwright. In both cases special conditions for transitivity of inference and multiple inputs scenarios are examined. The third developed approach implicitly omits the cognitive interpretation and rather relies on the idea that the causal independence structure can be substituted by some functional that combines independent inputs in such a way to force the output to be in full agreement with results expected through the conjunctive and disjunctive connectives.

The paper reports several theoretical findings regarding the different conditions ensuring the agreement and equivalence between the bipolar logic connectives and their probabilistic counterparts for each proposal. The paper also provides useful insights to link the finding to probabilistic argumentation system where pro and con arguments are considered simultaneously.

The paper offers theoretical basis for researchers investigating different categories of logics and contribute to the discussion linking logic to probability.