The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine

This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this paper proposes a new K–means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU’s threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini–MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score

Oversized Electrical Appliance Impacts on Condominium Energy Efficiency and Cost-Effectiveness Management: Experts’ Perspectives

A direct use approach incorporating a cost approach assumed that replacing oversized electrical appliances with those better fit to actual energy consumption can reduce energy consumption, optimizing capacities of the new appliances to the maximum while reducing electricity costs. This study aimed to verify the assumption that the size of appliances has impacts on energy consumption and cost effectiveness. A mixed-method approach included these instruments for data elicitations (i.e., a questionnaire, data records of 485 transformers, two assessments of condominium technical caretakers, and two in-depth interviews of electrical engineering experts). The findings revealed that most condominiums installed electric appliances that are too large for their actual energy usage, which lies between 5.4% and 7.1% of the capacity. This study therefore proposed a total cost reduction of 54% by downsizing these appliances (i.e., MV Switchgear 2 sets, dry type transformer 2 sets 80,000, LV Cable 10 m. (XLPE), main distribution board, Busduct (MDB-DB), generator (20% of Tr.), and generator installation). Even though this analysis is limited to Bangkok, Thailand, this case may contribute decision-making on electrical appliance selection at early stage of investment or to downsize the currently installed appliances for the more energy efficient and cost-effective management of condominiums around the world

The Relationship between Government Procurement of Procurement Administration Effciency at Mahasarakham University

The purposes of this research were to, - 1) Study the opinions of executives concerning government procurement at Mahasarakham University. 2) Examine the relationship between government procurement and procurement administration efficiency at the University. 3) Study the opinions of executives on the procurement administration efficiency at the University 4) Examine the effects of government procurement and procurement administration efficiency at Mahasarakham University. This study used a questionnaire as an instrument for collecting the data from a sample of 92 executives at Mahasarakham University. The statistics used for the analyses were mean, standard deviation, multiple correlations, and multiple regressions analyses. The results showed 1) Executives at Mahasarakham University had perceived overall opinions at a high level for government procurement and at high level for each aspect of procurement; procurement planning, purchasing, and contract administration. 2) Government procurement in terms of purchasing and contract administration was positively related to procurement administration efficiency. 3) The executive at Mahasarakham University perceived the overall opinions at a high level for procurement administration efficiency and high level for each of its aspects; worthiness, transparency, efficiency and effectiveness, and inspection. 4) Government procurement in terms of purchasing and contract administration positively affected the procurement administration efficiency with statistical significance

Prediction of Depression for Undergraduate Students Based on Imbalanced Data by Using Data Mining Techniques

Depression is becoming one of the most prevalent mental disorders. This study looked at five different classification techniques to predict the risk of students’ depression based on their socio-demographics, internet addiction, alcohol use disorder, and stress levels to see if they were at risk for depression. We propose a combined sampling technique to improve the performance of the imbalanced classification of university student depression data. In addition, three different feature selection methods, Correlation, Gain ratio, and Relief feature selection algorithms, were used for extracting the most relevant features from the dataset. In our experimental results, we discovered that combining the bootstrapping technique with the Relief selection technique under sampling methods enabled the generation of a relatively well-balanced dataset on depression without significant loss of information. The results show that the overall accuracy in the risk of depression prediction data was 93.16%, outperforming the individual sampling technique. In addition, other evaluation metrics, including precision, recall, and area under the curve (AUC), were calculated for various models to determine the most effective model for predicting risk of depression

Expert Opinions on the Intranet-Based Security System in Industrial Electrical Switchboards

The management and operation of an electrical switchboard originally was processed by an inspector so only tangible malfunctions could be identified while other intangible ones that can cause severe damages to the switchboard were overlooked. To solve this serious deprivation, this investigation, therefore, implemented an intranet sensors system in the electrical switchboard to create a new channel of communication via smart devices to operate and access it remotely, which will eventually lead to increased safety and efficiency of managing electrical switchboards, as well as manufacturing reliability and stability. All these will also increase competitiveness in business. The findings of this research indicate that the application could solve the deprivation by signaling all security malfunctions, both tangible and intangible, remotely via smartphones and laptops in the real-time operating system, which helps reduce severe damages to the switchboard, on-site inspection, and loss of service time to fix malfunctions and human and related risks, as well as increase manufacturing reliability and stability of the operation. The implemented intranet sensors system was also compatible with the current existing security system. This increased security, therefore, verifies the efficiency and business competitiveness of the intranet sensors system

Expert Opinions on the Intranet-Based Security System in Industrial Electrical Switchboards

The management and operation of an electrical switchboard originally was processed by an inspector so only tangible malfunctions could be identified while other intangible ones that can cause severe damages to the switchboard were overlooked. To solve this serious deprivation, this investigation, therefore, implemented an intranet sensors system in the electrical switchboard to create a new channel of communication via smart devices to operate and access it remotely, which will eventually lead to increased safety and efficiency of managing electrical switchboards, as well as manufacturing reliability and stability. All these will also increase competitiveness in business. The findings of this research indicate that the application could solve the deprivation by signaling all security malfunctions, both tangible and intangible, remotely via smartphones and laptops in the real-time operating system, which helps reduce severe damages to the switchboard, on-site inspection, and loss of service time to fix malfunctions and human and related risks, as well as increase manufacturing reliability and stability of the operation. The implemented intranet sensors system was also compatible with the current existing security system. This increased security, therefore, verifies the efficiency and business competitiveness of the intranet sensors system

IoT-Based Discomfort Monitoring and a Precise Point Positioning Technique System for Smart Wheelchairs

The Internet is becoming increasingly important in our daily lives, allowing people to exchange and receive a wide variety of data. It can be utilized in a variety of ways for maximum benefit. For example, the concept of the Internet of Things (IoT) suggests that objects can be linked to the Internet. Based on this concept, in this paper, we describe the creation of modern smart-wheelchair accessories. These make the wheelchair simple to use, suitable for the elderly, and foldable. A health monitoring accessory is one of the critical functions. The Internet of Things is central to the concept of an electric-powered smart wheelchair. Residential communication networks connect electrical appliances and services, enable monitoring, and provide access from which to control various devices. The controls of a smart wheelchair comprise three essential components: a smart device that connects to the wheelchair, an Internet network, and a microcontroller. The results of our tests enable remote operation of the electric-powered wheelchair; command and control are excellent. Most significantly, our method provides consumers with an extra stage of security