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A Decision Modeling Approach for Data Acquisition Systems of the Vehicle Industry Based on Interval-Valued Linear Diophantine Fuzzy Set
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Modeling data acquisition systems (DASs) can support the vehicle industry in the development and design of sophisticated driver assistance systems. Modeling DASs on the basis of multiple criteria is considered as a multicriteria decision-making (MCDM) problem. Although literature reviews have provided models for DASs, the issue of imprecise, unclear, and ambiguous information remains unresolved. Compared with existing MCDM methods, the robustness of the fuzzy decision by opinion score method II (FDOSM II) and fuzzy weighted with zero inconsistency II (FWZIC II) is demonstrated for modeling the DASs. However, these methods are implemented in an intuitionistic fuzzy set environment that restricts the ability of experts to provide membership and nonmembership degrees freely, simulate real-world ambiguity efficiently, utilize a narrow fuzzy number space, and deal with interval data. Thus, this study used a more efficient fuzzy environment interval-valued linear Diophantine fuzzy set (IVLDF) with FWZIC II for criterion weighting and IVLDF with FDOSM for DAS modeling to address the issues and support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves adapting a decision matrix that intersects DAS alternatives and criteria. The second phase (development phase) proposes a decision modeling approach based on formulation of IVLD-FWZIC II and IVLD-FDOSM II to model DASs. A total of 14 DASs were modeled on the basis of 15 DAS criteria, including seven subcriteria for “comprehensive complexity assessment” and eight subcriteria for “design and implementation,” which had a remarkable effect on the DAS design when implemented by industrial communities. Systematic ranking, sensitivity analysis, and modeling checklists were conducted to demonstrate that the modeling results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criterion weight values, supporting the most important research points, and proposing a value-adding process in modeling the most desirable DAS.

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Publication Date
Tue Sep 16 2025
Journal Name
Construction Materials
Molasses-Modified Mortars: A Sustainable Approach to Improve Cement Mortar Performance
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The utilization of sugarcane molasses (SCM), a byproduct of sugar refining, offers a promising bio-based alternative to conventional chemical admixtures in cementitious systems. This study investigates the effects of SCM at five dosage levels, 0.25%, 0.50%, 0.75%, 1.00%, and 1.25% by weight of cement, on cement mortar performance across fresh, mechanical, thermal, durability, and density criteria. A comprehensive experimental methodology was employed, including flow table testing, compressive strength (7, 14, and 28 days) and flexural strength measurements, embedded thermal sensors for real-time hydration monitoring, water absorption and chloride ion penetration tests, as well as 28-day density determination. Results revealed clear

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study
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     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

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Publication Date
Tue Aug 06 2013
Journal Name
Robotica
Function approximation technique-based adaptive virtual decomposition control for a serial-chain manipulator
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SUMMARY<p>The virtual decomposition control (VDC) is an efficient tool suitable to deal with the full-dynamics-based control problem of complex robots. However, the regressor-based adaptive control used by VDC to control every subsystem and to estimate the unknown parameters demands specific knowledge about the system physics. Therefore, in this paper, we focus on reorganizing the equation of the VDC for a serial chain manipulator using the adaptive function approximation technique (FAT) without needing specific system physics. The dynamic matrices of the dynamic equation of every subsystem (e.g. link and joint) are approximated by orthogonal functions due to the minimum approximation errors produced. The contr</p> ... Show More
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Publication Date
Sat Aug 01 2020
Journal Name
Computational Biology And Chemistry
A graph-based multi-sample test for identifying pathways associated with cancer progression
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Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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Publication Date
Tue Aug 01 2023
Journal Name
Biomedical Signal Processing And Control
Decoding transient sEMG data for intent motion recognition in transhumeral amputees
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Publication Date
Sun Dec 01 2019
Journal Name
Baghdad Science Journal
Histological and Physiological Studies on the Long-term Effect of Different Concentrations of Energy Drink (Tiger) on the Renal and Hepatic Systems of Young Mice
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The present study aims to investigate the long-term histopathological, and physiological effects of different concentrations of a commercially available energy drink (Tiger) on liver and kidney of young mice. Sixteen Balb/c male mice,6 -week old, were divided into 4 groups (n=4). Two groups consumed the energy drink at a concentration of 28µl energy drink/ml water. One group were killed after 10 days (T1), another group were killed after 20 days (T2). Other group of mice consumed the energy drink at a final concentration of 14µl/ml for 20 days (T3). The last group was provided only with water and served as control. Mice of all groups drank around 3 ml per day. The histopathological study on liver of treated groups showed many changes s

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Publication Date
Mon Mar 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
The study of the effect of the use of programming in the linear programming model (applied study)
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The parametric programming considered as type of sensitivity analysis. In this research concerning to study the effect of the variations on linear programming model (objective function coefficients and right hand side) on the optimal solution. To determine the parameter (θ) value (-5≤ θ ≤5).Whereas the result، the objective function equal  zero and the decision variables are non basic، when the parameter (θ = -5).The objective function value increases when the parameter (θ= 5) and the decision variables are basic، with the except of X24, X34.Whenever the parameter value increase, the objectiv

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Publication Date
Sat Apr 01 2023
Journal Name
Al-qadisiyah Journal Of Pure Science
Weakly and Strongly Forms of Fibrewise Fuzzy ω-Topological Spaces
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This paper is devoted to introduce weak and strong forms of fibrewise fuzzy ω-topological spaces, namely the fibrewise fuzzy -ω-topological spaces, weakly fibrewise fuzzy -ω-topological spaces and strongly fibrewise fuzzy -ω- topological spaces. Also, Several characterizations and properties of this class are also given as well. Finally, we focused on studying the relationship between weakly fibrewise fuzzy -ω-topological spaces and strongly fibrewise fuzzy -ω-topological spaces.

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Publication Date
Sun Jan 27 2019
Journal Name
Civil Engineering Journal
Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques
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Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

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