Anastasios Dounis^*, Ioannis Palaiothodoros, Anna Panagiotou

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 759-811, 2025, DOI:10.32604/cmes.2024.057888 - 17 December 2024

Abstract Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and retrieval. Advanced fuzzy set theories have emerged as effective tools to address these challenges. In this paper, new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets (q-ROFS) and interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). Three aggregation operators are proposed in our methodologies: the q-ROF weighted averaging (q-ROFWA), the q-ROF weighted geometric (q-ROFWG), and the q-ROF weighted neutrality averaging (q-ROFWNA), which enhance decision-making under uncertainty. These operators are paired More > Graphic Abstract

Alya Penta Agharid¹, Indra Permana², Nitesh Singh¹, Fujen Wang^2,*, Susan Gustiyana²

Energy Engineering, Vol.121, No.12, pp. 3549-3571, 2024, DOI:10.32604/ee.2024.056398 - 22 November 2024

Abstract Hotel buildings are currently among the largest energy consumers in the world. Heating, ventilation, and air conditioning are the most energy-intensive building systems, accounting for more than half of total energy consumption. An energy audit is used to predict the weak points of a building’s energy use system. Various factors influence building energy consumption, which can be modified to achieve more energy-efficient strategies. In this study, an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year. Energy modeling is conducted by using Autodesk Revit 2025. It… More >

Inna Bilous¹, Dmytro Biriukov¹, Dmytro Karpenko², Tatiana Eutukhova², Oleksandr Novoseltsev^2,*, Volodymyr Voloshchuk¹

Energy Engineering, Vol.121, No.12, pp. 3617-3634, 2024, DOI:10.32604/ee.2024.051684 - 22 November 2024

Abstract This article focuses on the challenges of modeling energy supply systems for buildings, encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings. Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material, such as for thermal upgrades, which consequently incurs additional economic costs. It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions, considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in… More > Graphic Abstract

Haemin Jung¹, Heesung Park², Hae Sun Jung³, Kwangyon Lee^4,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2261-2279, 2024, DOI:10.32604/cmc.2024.057118 - 18 November 2024

Abstract The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to… More >

Soha A. Shaban^*, Amira A. Atta, Dalia L. Elsheweikh

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1213-1250, 2024, DOI:10.32604/csse.2024.052603 - 13 September 2024

Abstract Field training is the backbone of the teacher-preparation process. Its importance stems from the goals that colleges of education aim to achieve, which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training. Currently, trainee students attendance in field training is recorded manually through signatures on attendance sheets. However, this method is prone to impersonation, time wastage, and misplacement. Additionally, traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes. Field training also lacks modern technology that the supervisor can… More >

Jing Wang^1,2,*, Tianwen Lin¹, Chen Zhang¹, Jun Peng^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4621-4641, 2024, DOI:10.32604/cmc.2024.053206 - 12 September 2024

Abstract In this paper, a feature interactive bi-temporal change detection network (FIBTNet) is designed to solve the problem of pseudo change in remote sensing image building change detection. The network improves the accuracy of change detection through bi-temporal feature interaction. FIBTNet designs a bi-temporal feature exchange architecture (EXA) and a bi-temporal difference extraction architecture (DFA). EXA improves the feature exchange ability of the model encoding process through multiple space, channel or hybrid feature exchange methods, while DFA uses the change residual (CR) module to improve the ability of the model decoding process to extract different features More >

Saroj Lamichhane¹, Roseline Mostafa², Bhaskaran Gopalakrishnan^2,*, Dayakar G. Devaru³

Energy Engineering, Vol.121, No.10, pp. 2743-2767, 2024, DOI:10.32604/ee.2024.051035 - 11 September 2024

Abstract Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool (eQUEST). The model is calibrated using the Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by… More > Graphic Abstract

Ahmed Mustaffa Saleem, Abdullah A. Badr, Bahjat Hassan Alyas, Omar Rafae Alomar^*

Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1231-1244, 2024, DOI:10.32604/fhmt.2024.053770 - 30 August 2024

Abstract This study numerically involves the performance of thermal insulation of different types of composite walls and roofs to demonstrate the best model that can be used for energy-efficient building construction in Iraq. The mathematical model is solved by building its code using the Transmission Matrix Method in MATLAB software. The weather data of 21st July 2022 in Baghdad City/Iraq is selected as a test day. The wall types are selected: the first type consists of cement mortar, brick, and gypsum, the second type consists of cement mortar, brick, gypsum, and plaster and the third type… More >

Rittick Maity^1,2, Muhammad Khairul Imran bin Ahmad Shuhaimi³, Kumarasamy Sudhakar^3,4,5,*, Amir Abdul Razak³

Energy Engineering, Vol.121, No.9, pp. 2331-2361, 2024, DOI:10.32604/ee.2024.051576 - 19 August 2024

Abstract The United Nations’ Sustainable Development Goals (SDGs) highlight the importance of affordable and clean energy sources. Solar energy is a perfect example, being both renewable and abundant. Its popularity shows no signs of slowing down, with solar photovoltaic (PV) panels being the primary technology for converting sunlight into electricity. Advancements are continuously being made to ensure cost-effectiveness, high-performing cells, extended lifespans, and minimal maintenance requirements. This study focuses on identifying suitable locations for implementing solar PV systems at the University Malaysia Pahang Al Sultan Abdullah (UMPSA), Pekan campus including buildings, water bodies, and forest areas.… More > Graphic Abstract

Muhammad Ayoub Kamal^1,3, Muhammad Mansoor Alam^1,2,4,6, Aznida Abu Bakar Sajak¹, Mazliham Mohd Su’ud^2,5,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1927-1945, 2024, DOI:10.32604/cmc.2024.052169 - 15 August 2024

Abstract In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The More >