Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options

An information fusion system with local sensors sometimes requires the capability to represent the temporal changes of uncertain sensory information in dynamic and uncertain situation to access to a hypothesis node which cannot be observed directly. One of the central issue and challenging problem is the decision of what combination and order of sensors allocation should be selected between sensors, in order to maximize the global gain in the flow of information, when the data association is limited. In this area, Bayesian Networks (BNs) can constitute a coherent fusion structure and introduce different options (the combination of sensors allocation) for achieving to the hypothesis node through a number of intermediate nodes that are interrelated by cause and effect. BNs can rank the options in terms of their probabilities from Bayes’ theorem calculation. But, decision making based on probabilities and numerical representations might not be appropriate. Thus, re-ranking the set of options based on multiple criteria such as those of multi-criteria decision aid (MCDA) should be ideally considered. Re-ranking and selecting the appropriate options are considered as a multi-attribute decision making (MADM) problem by user interaction as semi-automatically decision support. In this paper, Multi Attribute Decision Making (MADM) techniques as TOPSIS, SAW, and Mixed (Rank Average) for decision-making as well as AHP and Entropy for obtaining the weights of attributes have been used. Since MADM techniques give most probably different results according to different approaches and assumptions in the same problem, statistical analysis done on them. According to the results, the correlation between compared techniques for re-ranking BN options is strong and positive because of the close proximity of weights suggested by AHP and Entropy. Mixed method as compared to TOPSIS and SAW is the preferred technique when there is no historical (real) decision-making case; moreover, AHP is more acceptable than Entropy for weighting

Modeling and Selection of Software Service Variants

Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases

Development of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory

Paper no. 046Building Information Modeling (BIM) could support different activities throughout the life cycle of a building and has been widely applied in design and construction phases nowadays. However, BIM has not been widely implemented in the operation and maintenance (O&M) phase. As-is information for the majority of existing buildings is not complete and even outdated or incorrect. Lack of accurate and complete as-is information is still one of the key reasons leading to the low-level efficiency in O&M. BIM performs as an intelligent platform and a database that stores, links, extracts and exchanges information in construction projects. It has shown promising opportunities and advantages in BIM applications for the improvement in O&M. Hence, an effective and convenient approach to record as-is conditions of the existing buildings and create as-is BIM objects would be the essential step for improving efficiency and effectiveness of O&M, and furthermore possibly refurbishment of the building. Many researchers have paid attention to different systems and approaches for automated and real-time object recognition in past decades. This paper summarizes state-of-the-art statistical matching-based object recognition methods and then presents our image-based building object recognition application, which extracts object information by simply conducting point-and-click operations. Furthermore, the object recognition research system is introduced, including recognizing structure object types and their corresponding materials. In this paper, we combine the Multi-Attribute Utility Theory (MAUT) with the fuzzy set theory to be Fuzzy-MAUT, since the MAUT allows complex and powerful combinations of various criteria and fuzzy set theory assists improving the performance of this system. With the goal of creating as-is BIM objects equipped with the semi-automatic object recognition system, our image-based object recognition system and its recognition process are validated and tested. Key challenges and promising opportunities are also addressed.postprin

CASE ID DETECTION IN UNLABEL LED EVENT LOGS FOR PROCESS MINING

In the realm of data science, event logs serve as valuable sources of information, capturing sequences of events or activities in various processes. However, when dealing with unlabelled event logs, the absence of a designated Case ID column poses a critical challenge, hindering the understanding of relationships and dependencies among events within a case or process. Motivated by the increasing adoption of data-driven decision-making and the need for efficient data analysis techniques, this master’s project presents the "Case ID Column Identification Library" project. This library aims to streamline data preprocessing and enhance the efficiency of subsequent data analysis tasks by automatically identifying the Case ID column in unlabelled event logs. The project’s objective is to develop a versatile and user-friendly library that incorporates multiple methods, including a Convolutional Neural Network (CNN) and a parameterizable heuristic approach, to accurately identify the Case ID column. By offering flexibility to users, they can choose individual methods or a combination of methods based on their specific requirements, along with adjusting heuristic-based formula coefficients and settings for fine-tuning the identification process. This report presents a comprehensive exploration of related work, methodology, data understanding, methods for Case ID column identification, software library development, and experimental results. The results demonstrate the effectiveness of the proposed methods and their implications for decision support systems

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability

A Novel Semantic Statistical Model for Automatic Image Annotation Using the Relationship between the Regions Based on Multi-Criteria Decision Making

Automatic image annotation has emerged as an important research topic due to the existence of the semantic gap and in addition to its potential application on image retrieval and management.  In this paper we present an approach which combines regional contexts and visual topics to automatic image annotation. Regional contexts model the relationship between the regions, whereas visual topics provide the global distribution of topics over an image. Conventional image annotation methods neglected the relationship between the regions in an image, while these regions are exactly explanation of the image semantics, therefore considering the relationship between them are helpful to annotate the images. The proposed model extracts regional contexts and visual topics from the image, and incorporates them by MCDM (Multi Criteria Decision Making) approach based on TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method. Regional contexts and visual topics are learned by PLSA (Probability Latent Semantic Analysis) from the training data. The experiments on 5k Corel images show that integrating these two kinds of information is beneficial to image annotation.DOI:http://dx.doi.org/10.11591/ijece.v4i1.459

Modeling and Selection of Software Service Variants

Providers and consumers have to deal with variants of software services, which are alternative instances of a services design, implementation, deployment, or operation. This work develops the service feature modeling language to represent software service variants and a suite of methods to select variants for development or delivery. An evaluation describes the systems implemented to make use of service feature modeling and its application to two real-world use cases