DBLP

Modern multi-criteria decision-making (MCDM) approaches must handle ambiguous and imprecise initial information about decision-making phenomena in an efficient way. Application of the Dempster-Shafer theory (DST) provides a strong mathematical basis for processing uncertain information. Due to the close relationship between fuzzy sets and DST, various fuzzy set environments are applied to model the DST system to solve multi-criteria decision-making problems. To work on this challenge, a picture fuzzy set (PFS) can be applied to model the vague information. This paper presents a novel MCDM approach, namely the hybrid SWARA DS and CoCoSo DS method, developed within Dempster-Shafer’s framework. The benefits of the new proposed approach in handling MCDM problems involving uncertain criteria and expert weightings are demonstrated through its application in selecting the optimal roof shape for a renovated single-family house. Sensitivity and comparative analyses validate the method’s reliability and effectiveness.

In this research, facial features are identified from face templates using deep neural network models. Facial templates are widely used in modern biometric systems, enabling efficient and reliable individual recognition. Such templates are compact, easy to process, and are often used in real-time systems. During the research, a dataset with 115,023 items (face templates), used by the Neurotechnology company to identify persons, was prepared as a new dataset. The dataset has been prepared to study three facial features from face templates: gender, race, and age. The facial templates are lighter-weight than real photos and could be reused to estimate the facial features. However, it is not yet known how effective facial feature identification will be, as some data are lost when converting a photo into a face template. Several methods have been proposed for classifying original image attributes solely from face templates. Both deep neural networks and classical machine learning algorithms were used in the experiments. The experiments revealed that gender is the most accurately predictable attribute, with the best model achieving 93 % accuracy. Classification of people’s race and age from face templates is more challenging, likely because face template generation models are designed to eliminate information unrelated to identity. A comparison of machine learning methods showed that deep neural networks are better suited to this task than classical classification algorithms.

The Computer-Assisted Qualitative Data Analysis Software (CAQDAS) and mixed methods research integration propose a potential for advancing knowledge management and decision support processes. Therefore, this systematic literature review focuses on Web of Science (WoS) results related to the mixed methods and MAXQDA® software symbiosis. During the research period (at the end of 2024), 140 related articles were reduced to 16 most suitable based on the inclusion and exclusion criteria. However, the article faces some limitations, such as focusing solely on WoS data—articles in English, which could be solved in future studies. Based on the results, another potential research gap is evident, as few articles are related to the exploratory sequential mixed methods design and analysis of drawbacks using CAQDAS within mixed methods. Future studies could empirically analyze it.

Multi-criteria decision-making (MCDM) methods provide structured approaches for evaluating alternatives based on multiple conflicting criteria. However, many existing MCDM techniques assess alternatives independently, either by aggregating weighted performance scores or by measuring deviations from reference points without a comparative basis. These approaches often fail to account for the relative nature of evaluations within the decision matrix, potentially leading to less reliable rankings. This research introduces a novel Deviation-Based Pairwise Assessment Ratio Technique (DEPART) for MCDM. Unlike conventional methods, DEPART evaluates alternatives through pairwise deviation ratios, ensuring a relative and holistic assessment. By incorporating all available decision data, this approach enhances the reliability and interpretability of rankings. To validate its effectiveness, a simulation-aided analysis is conducted. The evaluation process includes a benchmark example, a simulation analysis with varying criteria weights, and tests on randomly generated datasets with different problem sizes and performance ranges. Correlation coefficients are computed to measure consistency, while robustness is assessed using Analysis of Means (ANOM) and confidence intervals. The results demonstrate that DEPART provides stable and reliable rankings, making it a promising approach for solving MCDM problems.

The maintenance of visual appeal and coherence in the procedural game scene generation is still a difficult problem. Traditional procedural game scene generation algorithms produce samples that show a noticeable resemblance to each other. The proposed algorithm allows us to add diverse game object compositions and increase creativity value in that way. Result diversity is formed by the proposed genetic algorithm modification and MCDM method based on the fitness function. Video game immersion is reached by aesthetic game element pattern composition, and one of the solutions for this issue is to apply automated aesthetic modelling of the generated game levels. In this research, the construction of fitness function was extended by the modelling of aesthetic principles, which were reverse-engineered from Gestalt principles. All rules were implemented by construction of a focal function with a square zone for each matrix cell of the single game scene. Five types of Gestalt rules were modelled and combined into a Pythagorean neutrosophic WASPAS method and the final score calculation algorithm was proposed. The proposed approach to generating game scenes strikes a balance between functionality and aesthetics to provide players with an engaging and immersive gaming experience.

Malware detection is a quintessential task for every security for securing work stations, mobile devices, servers etc. This detection is mainly used for identifying malware that are causing malicious problems. The traditional detection system has a much lesser rate of detection rate and the chances of getting an error is higher as well. As the emerging technology revolutionized day by day, the usage of Deep Learning (DL) is highly influenced in these detection fields. So, this paper brings an effective DL based detection of malware in which the following are the stages: a) Data collection being carried from Malimg dataset, b) Pre-processing carried out to eliminate the unwanted noise from the dataset and passed to c) Feature extraction, where Principal Component Analysis (PCA) used for extracting required features, d) Feature selection where Particle Swarm Optimization (PSO) used for dimensionality reduction and finally passed for e) Classification where Convolutional Neural Network (CNN) used as a classifier for effective classification. These models are evaluated under measures like Accuracy, sensitivity, specificity, precision, recall, f1-score, TPR, FPR and detection rate over models like VGG16, VGG19, Densenet, Alexnent, Ensemble learning. The proposed system (D-WARE) gives much higher performance with a 96% accuracy.

In this preliminary research, we try to understand the major characteristics of the news coverage of the Covid-19 pandemics in Spanish and Portuguese digital media, during the first period of lockdown of 2020. Following the trend previously observed in traditional media and about other issues, digital news coverage of Covid-19 assumed a single-dimension point of view, a patronising and moralist tone, an emotional alarmist discourse, and a strongly polarised interpretation. Journalist coverage also opted to highlight the official speeches and the nearby dramatic situations, and disregard alternative speeches and different dimensions of this social phenomenon.

Nowadays fuzzy approaches gain popularity to model multi-criteria decision making (MCDM) problems emerging in real-life applications. Modern modelling trends in this field include eval- uation of the criteria information uncertainty and vagueness. Traditional neutrosophic sets are considered as the effective tool to express uncertainty of the information. However, in some cases, it cannot cover all recently proposed cases of the fuzzy sets. The m-generalized q-neutrosophic sets (mGqNNs) can effectively deal with this situation. The novel MCDM methodology CoCoSomGqNN is presented in this paper. An illustrative example presents the analysis of the effectiveness of dif- ferent retrofit strategy selection decisions for the application in the civil engineering industry.

The article presents the data of qualitative research and analyses factors of successful learning of the study subject of Engineering Graphics mainly focusing on external and internal motives. The article is grounded on the conception of constructivism and student-centred studies, which embrace the whole study system from the description of study subject to the student-teacher relation. The research results show that seeking for the success of student learning, it is firstly necessary to think over the formal requirements of the study subject of Engineering Graphics: the description of the study subject, the number of assignments, their content as well as the content of independent and contact work. Striving for successful process of teaching and learning, the teacher has to consider the specifics and tendencies of generation Z students. However, internal motives, which are related to the students’ competence of learning to learn, and the teacher’s didactic abilities are seen as the most important factors.

Road irregularities are the main effect influencing driving quality, due to its unpredictability driver can not find the best driving parameters easily and fast. Identification of road profile lets us control the vehicle’s damping characteristics and increase driving quality as fast as possible. Power Spectral Density (PSD) could be easily found from the road profile. Deduction of road waviness and amplitudes from PSD could be used to provide the mandatory information required for controllable damping system. Using our developed dynamic model of the car, we performed a simulation of the vehicle passing artificially generated road profiles with different waviness. Obtained results show that the straight-line approximation of PSD of the road profile as recommended by ISO standard doesn’t provide suitable information required for the control of car suspension. We define that, two split approximations of PSD spectra is a better solution for this purpose. It provides reliable results and allows to choose optimal damping coefficient value with a small delay for long distances.