In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.

When driving, the most crucial factor to consider is your own safety. Driver’s must be kept under observation for any potential harmful act, whether intentional or inadvertent, in order to ensure a safe navigation for a driver. As a result, a real-time emotion detection system for a driver has been developed to detect, exploit, and evaluate the driver's emotional state. This paper discusses how to recognize emotions using facial expressions for application in active security systems for drivers. We discuss our research and development of a Convolutional Neural Network-based intelligent system for face image-based expression classification in this paper.

Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity. In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production. The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set. The training and test results of the suggested approach are presented visually on a tensor board. With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use. According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.

Osteoporosis is a common systemic bone disease with insidious onset and low treatment efficiency. Once it occurs, it will increase bone fragility and lead to fractures. Computed tomography (CT) is a non-invasive medical examination method that can identify the bone condition of patients. In this paper, we propose a novel channel attention module, which is subsequently integrated into the supervised deep convolutional neural network (DCNN) termed DSNet, which can perform feature fusion from two different scales, and use the method of quadratic weight calculation to enhance the interconnection among feature map channels and improve the detection and classification performance for the bone condition in lumbar spine CT images. To train and test the proposed framework, we retrospectively collect 4805 CT images of 133 patients, using DXA as the gold standard. According to the T-value diagnostic criteria defined by WHO, the vertebral bodies of L1 - L4 in CT images are labeled and classified into osteoporosis, osteopenia and normal bone mineral density. Meanwhile, the training set and test set are constructed in the ratio of 4:1. As a result, the DSNet achieves a prediction accuracy of 83.4% and a recall rate of 90.0% on the test set, indicating that the proposed model has the potential to assist clinicians in diagnosing individuals with abnormal BMD and may alert patients at high risk of osteoporosis for timely treatment.

The Semantic Web provides approaches and tools that allow for the processing and analysis of online content, including multimedia resources. Multimedia resources like videos, audios, and photos are increasingly common in contemporary Web content. Cinematographic works (also known as film contents) stand out among these resources as one of the most recent attractions on the Internet. An important tool employed recently in the semantic indexation of digital resources and film content is ontological annotation. This paper studies the current multimedia ontologies related to the film contents on the web. The relevant indicators were discussed comparatively, and some open issues were reviewed in details. In this way, the authors managed to integrate the metadata related to online films practically into the web of data.

An estimated 9.6 million deaths, or one in every six deaths, were attributed to cancer in 2018, making it the second highest cause of death worldwide. Men are more likely to develop lung, prostate, colorectal, stomach, and liver cancer than women, who are more likely to develop breast, colorectal, lung, cervical, and thyroid cancer. The primary goals of medical image segmentation include studying anatomical structure, identifying regions of interest (RoI), and measuring tissue volume to track tumor growth. It is crucial to diagnose and treat liver lesions quickly in order to stop the tumor from spreading further. Deep learning model-based liver segmentation has become very popular in the field of medical image analysis. This study explores various deep learning-based liver lesion segmentation algorithms and methodologies. Based on the developed models, the performance, and their limitations of these methodologies are contrasted. In the end, it was concluded that small size lesion segmentation, in particular, is still an open research subject for computer-aided systems of liver lesion segmentation, for there are still a number of technical issues that need to be resolved.

The global child mortality rate, which is steadily declining, will be around 26 fatalities per 1000 live births in 2022. Numerous Sustainable Development Goals of the United Nations take into account the declining child mortality rate, which illustrates how far humanity has come. Cardiotocograms (CTGs) are a simple and affordable tool that most professionals choose to reduce infant and mother mortality. Three of the most cutting-edge methodologies are utilized in this research to classify the data, and their results are compared. All three classifiers outperformed the random forest, whose accuracy was 94.3%.

Due to a number of circumstances, grain depots will emit exhaust gases that are harmful to the environment and the health of the surrounding population in addition to being complex in composition and challenging to manage. In order to cope with environmental exhaust gases, this work integrates microbial spray filtering with an exhaust gas treatment equipment. The authors ran simulations of the mixture of exhaust gases and the microbial solution using COMSOL Multiphysics at various pipe diameters, initial nozzle distances, nozzle number, and nozzle intervals. The findings indicate that the pipe diameter should be 300mm, the starting nozzle distance should be 290mm, there should be five nozzles, and the nozzle interval should be 200mm to obtain the optimal mixing of exhaust gases and the microbial solution. The study offers a useful guide for microbial deodorization.

The overuse of natural resources by humanity in recent decades has resulted in noticeable changes environment quality. Global environmental research is particularly interested in the topics of land use change and land cover. The Republic of Serbia has a diverse spectrum of landforms, with agricultural use taking up the largest portions, followed by forestry, water, and building land. Significant anthropogenic pressures (such as mining, deforestation, urbanization, and uncontrolled land use, among other things) have harmed Serbia's natural resources over the past two decades. This study examines the causes of specific trends in land-use change in Serbia, utilizing the CORINE Land Cover (CLC) database to track temporal and spatial changes in the major categories of land use and land cover from 1990 to 2018. The authors explained that focusing on the rational use of natural resources is the only way to promote sustainable development, legal alignment with EU law, and prompt adoption of harmonized laws and planning documents across all sectors.

This work completes the thorough petrophysical interpretation of 46 wells, as well as a technical feasibility analysis. Even though the acoustic logging was of very poor quality, work was done to get it ready for use in creating synthetic seismograms that accurately represented the section. The sle.28 Karagie Severny, which was drilled in 2012 and has significantly better GIS quality, was used to control this operation. Through a dynamic analysis, the shooting system's (footprint) influence on the distribution of the amplitudes at the Karagie Severny site was not eliminated during data processing, but it was removed during the re-processing. As a result, the findings for Karagie Severny should be taken with a grain of salt because the initial data's quality was not considered when choosing the sites for the suggested wells. However, the seismic facies analysis in two forms—classical and cluster—showed the presence of at least three primary facies complexes, which are reflected in both formed, with a more precise distribution in accordance with cluster analysis.

The primary way to design building structures refers to the stationary loads specified by the governing laws. However, the load pattern does not guarantee the appropriateness of the seismic design. To make matters worse, old or ancient structures are traditionally reinforced for gravitational loads. This study reveals that the traditional reinforcement, in most cases, harms the seismic performance of buildings. The authors introduced the approach of most computer programs for seismic design, along with their limitations. Then, the ancient Roman approach was explained, and the reasons for the survival of many of these ancient structures were exposed thoroughly. After that, classical advices were summarized briefly for good seismic design of structures and reinforcement. Finally, a few classical mistakes were identified in reinforcement design.

In view of the limitations of traditional InSAR technology in selecting stable target point for orbit refining and surface subsidence inversion in complicated mining area, this paper proposes a time-series InSAR mining area subsidence monitoring method based on the fusion of multi threshold targets. On the basis of the traditional technology, the deviation threshold parameters, the regional window threshold parameters and the coherence threshold parameters are set to extract the relatively stable target points on the ground. Applying this method and traditional InSAR method to practical cases, the monitoring results of surface subsidence in the study area are obtained and verified. The results show that: (1) there are three mining subsidence areas in the mining area, the maximum annual average subsidence rate is -156 mm/a, and the maximum subsidence is -376 mm. Compared with the optical image data, the location of the mining subsidence area is consistent with the mining work area of the coal mine; (2) The absolute average difference of subsidence in the mining area using the two methods shall not exceed 12 mm. It shows that the InSAR method of fusing multi threshold targets can not only effectively overcome the limitations of traditional InSAR, but also ensure high accuracy, and has more advantages in the monitoring of surface subsidence in mining areas.

The development of railways brings many positive externalities, such as the expansion of built environment, the growth of feeder roads, the rise of passenger mobility, and the creation of economic opportunities for locals. In the meantime, the railway transport system exerts some negative externalities on environmental sustainability, which intensifies climate change. This paper assesses the negative externalities of railway transport through the changing dynamics of the normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC). The spatial regression model was calibrated to understand the degree of these externalities. In addition, a prediction model was constructed based on machine learning techniques like cellular automata and Markov chain. The study reveals that the development of railway stations in Tripura, India has significant negative externalities on the environment.

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen.

Road maintenance is essential to the growth of the transportation infrastructure and, thereby, has a big impact on a nation's overall economic stability and prosperity. It is impossible to simultaneously monitor and maintain the entire network. As a result, transportation authorities are eager to develop scientific foundations for assessing the importance of maintenance tasks within the network of roads. Hence, pavement assessment methods are needed to establish the priorities and achieving the most convenient level of service. In this study, a road stretch was assessed using the sixteen criteria in the Distress Identification Manual for pavement defects, using pavement condition index (PCI) and multi-criteria decision-making models (MCDM). The two methods were compared to determine the possibility of using MCDM. The study came to the conclusion that MCDM is reliable in assessing pavement performance because both methods indicated that the road pavement is deteriorating.

Thanks to its superb curve negotiation characteristics, the side-suspended high-temperature superconducting (SS-HTS) maglev system boasts a great potential for high-speed transportation. The SS-HTS maglev system, however, significantly differs in suspension features from the conventional maglev system because of its unique side-suspended structure. To improve suspension performance, the field-cooling technique of superconducting bulks in the SS-HTS system was investigated through a number of experiments. To fit the experimental data, the authors proposed the mathematical models of the levitation and guidance forces as well as the optimal field-cooling position. Furthermore, a dynamic model was developed for the SS-HTS maglev vehicle operating on a curve line, and the curve negotiation characteristics were simulated for the maglev vehicle. Finally, the stability of the curve negotiation for the SS-HTS system was assessed using the Sperling index. The results show that the SS-HTS maglev vehicle can pass over bends at a certain speed. The authors also recommended the suspension parameters the maglev vehicle.