<abstract xmlns="http://www.w3.org/1999/xhtml">

Criteria weighting is a widely used and also an important feature of multi criteria decision making problems specially in engineering, computer science and management investigations. In particular in many studies related to complex systems there would be usually two main groups of cause and effect criteria. In this research it is intended to make an hybrid objective model comprising DEMATEL and SWARA techniques to assign classified weights to the subgroup of cause and effect criteria. As a main goal, the proposed hybrid model in this presented paper can afford to assign greater values for criteria who belong to cause group. In this regard we apply the objective information which derived from the parameters of (R, equal to sum of direct and indirect influence of a criteria), (R/C, named as net influence power of a criteria) and (R-C, named as net effect of a criteria) related to the final total influence matrix T in DEMATEL methodology. The main contribution in this work lies in utilizing the SWARA methodology and making us of its revision where the relatively Comparative Importance <italic>Sj</italic>, applied in SWARA technique is reconfigured by some aggregation operators including <italic>max</italic>, <italic>Einstein</italic> and <italic>Hamacher</italic> operators for obtaining more uniformed weights of cause and effect criteria relatively to SWARA basic methodology. Finally results shows that the (R/C) and (R-C)would transfer more clear and refined data and numeric information achieving better and highly reliable weights of criteria categorized into two groups of cause and effect group.
</abstract>

A Modified Hybrid Objective Model to Calculate the Weights of Cause and Effect Criteria in a System: DEMATEL and DEVELOPED SWARA (D-DS) Based Model

<abstract xmlns="http://www.w3.org/1999/xhtml">

Smart cities are included in the literature as a technology-based concept that has been on the agenda in recent years and whose framework is constantly changing with the changes in technology. There are different frameworks and indexes to define the smartness of a city. Smart City Index 2021 published by Institute for Management Development (IMD) and Singapore University of Technology and Design (SUTD) is one of the accepted studies in the world. In the report of Smart City Index 2021, 118 cities are evaluated in five criteria namely health &amp; safety, mobility, activities, opportunities (work &amp; school) and governance. To re-evaluate the cities and compare the results, a Multi-Criteria Decision Making (MCDM) process including Entropy based Complex Proportional Assessment (COPRAS) and Addivite Ratio Assessment (ARAS) methodology is applied in this paper. To prioritize the criteria, entropy weight method is used. 118 cities are ranked both technologically and structurally using the COPRAS and ARAS method. As a result of the analyses, according to these methods, the rankings of the smart cities are the same. Also, when technologically smart cities are listed, it is determined that the first three countries are Zhuhai, Shenzhen, Nanjing, and at the same time, Abu Dhabi, Chongqing, Hangzhou in terms of structurally.
</abstract>

Evaluation of the Smart Cities Listed in Smart City Index 2021 by Using Entropy Based Copras and Aras Methodology

<abstract xmlns="http://www.w3.org/1999/xhtml">

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.
</abstract>

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

<abstract xmlns="http://www.w3.org/1999/xhtml">

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.
</abstract>

Department of Electrical and Computer Engineering

Department of Management Science and Technology

Content-Based Recommender Systems Taxonomy

<abstract xmlns="http://www.w3.org/1999/xhtml">

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (<italic>F</italic> ) and crossover rate (<italic>CR</italic>). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the probabilities associated with the partition values of <italic>F</italic> and <italic>CR</italic>. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (<italic>ns</italic>) for partitioning the control parameter ranges and the reset period (<italic>rs</italic>) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable <italic>ns</italic> and <italic>rs</italic> against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.
</abstract>

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

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Foundations of Computing and Decision Sciences

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international journal published since 1975. One of its main aims is its focus on the links between Computing (understood in the sense defined in the report of the ACM Task Force on the Core of Computer Science chaired by Peter J. Denning: Computing as a Discipline, CACM, Vol. 32, No. 1, 1989) and broadly understood Decision Sciences.  Scope of topics  The journal provides a forum for exchange of ideas for researchers in the fields of computing and decision sciences. The scope of the journal covers various areas of computing and decision sciences, particularly:    approximate reasoning,  artificial intelligence,  combinatorial optimization,  computational complexity theory,  databases and data warehouses,  intelligent decision support,  knowledge engineering,  machine learning and data mining,  metaheuristics,  multiple criteria decision analysis,  networking and distributed systems,  parallel computing and concurrency,  production and project scheduling,  scheduling theory,  soft and granular computing,  software engineering.     Why submit   International editorial board composed of distinguished scholars highly representative of the field of computing and decision sciences,  Quick turnaround,  Comprehensive peer review with at least two highly competent referees assessing each manuscript,  Promotion of published papers in the vast scientific community of researchers working in the field.   Rejection Rate   ca. 60%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process (iThenticate, https://www.ithenticate.com/ ). 