bluuebunny/crossref_metadata_2025 huggingface dataset:
EVALUATION OF ENVIRONMENTAL FACTORS IN SMART HOMES IMPLEMENTATION (CASE STUDY: CITIES OF MAZANDARAN PROVINCE IN IRAN)
Authors: Motevalli S. , Seyedian S. A.
Published: January 2023

Abstract. The growing trend of IT technologies has continued at a faster pace in recent decades. The efforts of experts in this field have always been reducing the problems and challenges existing in urban communities and improving the quality of citizens' lives. They have concentrated on using the artificial intelligence and Internet of Things in a way which leads to maximize self-actualization of citizens. Among these technologies, IoT has played a significant role. Since it can improve the urban infrastructures, and also can be implemented in the scale of home facilities and equipment, can improve the quality of life in smart homes. However, it seems that the implementation of smart residential buildings in the context of smart cities is not simple as been defined in theory and may be accompanied by challenges. In this paper, an attempt has been made to evaluate the environmental factors affecting this field by SWOT analysis tool in a case study in cities of Mazandaran Province in Iran. As a result, strategy and tactics to emphasize strengths, to make the most of existing opportunities, to eliminate the weaknesses, and to reduce the risk of perceived threats, have been identified and been introduced. Based on the results, OS strategy should be held to obtain the most valuable goals of a smart city; since the points of opportunities and strengths are relatively more than others.
EVALUATION OF DIFFERENT PARAMETERS FOR PLANT CLASSIFICATION BY PRE-TRAINED DEEP LEARNING MODELS WITH BIGEARTHNET DATASET
Authors: Naali F. , Alipour-Fard T. , Arefi H.
Published: January 2023

Abstract. Vegetation monitoring and mapping are essential for a diverse range of environmental problems such as forest management, food resources, and climate change assessment. Several methods have been developed to classify different vegetation types based on remote sensing (RS) data. Land use classification has been revolutionized with the advent of neural networks. Various vegetation types were classified using multispectral Sentinel-2 satellite images due to their high spatial resolution and spectral information. Deep Convolutional Neural Network is considered a promising method for classifying remote sensing images with high spatial resolution due to its powerful feature extraction capabilities. However, large labeled datasets are required for better classification performance, so we have used pre-trained ResNet networks with 152 layers, 50 layers, and 101 layers trained on Big Earth Net (BEN). In order to obtain the best network performance and evaluate the sensitivity of the parameters in this study, we have performed two experiments: 1) the effect of different patch sizes and 2) increasing the number of images. The results demonstrate that ResNet 152 shows the highest accuracy with patches of 120 × 120 pixels, with an accuracy of 76.62%, and ResNet 50 is the best with an accuracy of 76.2% since the process of this network does not take much time.
IMPROVED INDOOR POSITIONING USING FINGERPRINT TECHNIQUE AND WEIGHTED K-NEAREST NEIGHBOUR
Authors: Naderi Salim S. , Alizadeh M. M. , Chamankar S. , Schuh H.
Published: January 2023

Abstract. Global Navigation Satellite Systems are not effective when there is no direct line of sight between the user and the satellites, such as indoor environments and dense urban areas. Today, location-based services are used significantly due to their utility and ease of access. The fingerprint method is one of the common methods of determining the location in indoor environments. In this research, the indoor positioning system based on the fingerprint algorithm with a wireless network has been implemented. The positioning system based on the method of nearest neighbour and weighted K-nearest neighbour with two access points has been implemented in two different scenarios. The output accuracy of each technique has been compared to each other. The main goal of this article is to compare the accuracy of positioning with the fingerprint method using the mentioned algorithms and to find the most suitable mode and algorithm for determining the indoor position in most places. The improved weighted nearest neighbour method will have an almost acceptable result in all scenarios and also in the first scenario with dense and regular reference points the weighted K-nearest neighbour method with RMSE=0.2812(m) has provided the best result. In the second scenario with scattered and irregular reference points the weighted K-nearest neighbour with RMSE=0.6735(m) has given lower accuracy result.
MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Najaf M. , Arefi H. , Amini Amirkolaee H. , Farajelahi B.
Published: January 2023

Abstract. Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth from a single image, which act as encoders and decoders. In this article, we use one of these networks, which has performed well for depth estimation, in order to extract height from aerial and satellite images. Our main goal is to investigate the performance of Google Earth satellite data in order to produce a digital elevation model. At first, we extracted the digital model of the target area using ISPRS benchmark data, then we did the same thing using Google Earth satellite images. The paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB Google Earth image. The results show the proper performance of Google Earth satellite images for height extraction. We achieved values of 2.07 m and 0.36 m for the RMS and REL metrics, respectively, which are very comparable and acceptable to the values of 2.04 m and 0.39 m obtained from the ISPRS benchmark images.
DETECTING MULTIPATH EFFECTS ON SMARTPHONE GNSS MEASUREMENTS USING CMCD AND ELEVATION-DEPENDENT SNR SELECTION TECHNIQUE
Authors: Nasr-Azadani S. , Alizadeh M.M. , Schuh H.
Published: January 2023

Abstract. Regarding increasing smartphone receivers' usage in science and industry, they must improve their positioning algorithms to increase positioning accuracy and location-based software's productivity. For this goal, various studies have been presented to remove or adjust the errors in the GNSS signal received by smartphones. Nevertheless, so far, no study has been conducted to investigate the effect of the multipath effect on smartphone observations. Various algorithms have been performed to study the effect of multipath, from detecting and removing this effect by correcting errors in the signal processing step to weighting the measurements to reduce the effect of multipath on the observations in the positioning step.In this article, an attempt has been made to compare the results of two different algorithms, CMCD and Selection Signal, based on SNR elevation-dependent in detecting the effect of multipath on smartphones to check their performance for detecting measurements contaminated with multipath.
FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING
Authors: Noroozi H. , Hasanlou M.
Published: January 2023

Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.
MEASURING LAND USE CHANGES AND QUANTIFYING URBAN EXPANSION USING REMOTE SENSING AND GIS TECHNIQUES – A CASE STUDY OF QOM
Authors: Norouzi Y.
Published: January 2023

Abstract. Rapid urban sprawl necessitates solid urban planning tactics, which requires assessing and quantifying the sustainable or unsustainable encroachment of urban settings towards the urban periphery. Like many cities in developing countries, Iranian cities have witnessed tremendous changes in recent decades. The process of urbanization following economic and social developments has caused the intractable and unrestrained growth of cities with a national and regional role. Remote sensing and geospatial information system technology give urban planners the appropriate and pertinent information they need to guarantee the sustainable management of urban environments. This study explored the changes in Qom metropolis, Iran during four time periods, from 1985, 2000, 2010, and 2021, and assessed how the city expanded using Shannon's entropy model and the Urban Expansion Intensity Index. In this study, Landsat satellite images from the selected years were employed, and three land use/land cover classification types, including agricultural, built-up, and others, were derived using the maximum likelihood classification approach. The relative Shannon’s entropy result for the study years (1985, 2000, 2010, and 2021) are 0.66, 0.68, 0.69, and 0.86 respectively, which demonstrate a dispersed expansion pattern, with the maximum value in 2021. Also, the Urban Expansion Intensity Index, with the values 0.33, 0.33, and 0.51 for three periods (1985–2000, 2000–2010, 2010–2021), indicates that the city's expansion rate was low-speed throughout the chosen periods, despite having reached its peak between 2010 and 2021.
BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS
Authors: Omati M. , Omati M. M. , Bastani M. H.
Published: January 2023

Abstract. Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in the 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when a large number of SAR observations. In this study, with a limited number of SAR images, we have assessed the efficiency of the nonparametric spectral estimation methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) in the reconstruction of the third dimension of urban buildings. The experiments are conducted on both simulated and TerraSAR-X stripmap images to indicate the effectiveness of the LP proposed estimation algorithm. The analysis of the results proves that by minimizing the average output signal power over the antenna array elements, the LP spectral estimation achieves the discrimination of distinct scatterers inside an image pixel. In addition, this low computational estimator improves the sidelobe suppression and the height estimates of the scatterers in the complex multiple-scattering urban environment. Compared to SVD, maximum entropy, Capon, minimum norm, and beamforming, the height of the Eskan tower in Tehran, Iran, obtained with the LP technique, is considerably near to field-based measurement.
MEASURING SPATIAL ACCESSIBILITY TO HEALTHCARE FACILITIES IN ISFAHAN METROPOLITAN AREA IN IRAN
Authors: Rabiei-Dastjerdi H. , Mohammadi S. , Samouei R. , Kazemi M. , Matthews S. , McArdle G. , Homayouni S. , Kiani B. , Sadeghi R.
Published: January 2023

Abstract. Appropriate spatial accessibility to healthcare facilities is an important component of the efficient delivery of healthcare. This study aims to measure spatial accessibility to healthcare facilities in Isfahan Metropolitan Area, a rapidly growing megacity in Iran. We used two methods of population-weighted fuzzy analytic hierarchy process and the two-step floating catchment area (2SFCA) to measure spatial accessibility to urban healthcare facilities, including hospitals, pharmacies, clinics, and laboratories. The results of these two methods were compared. Not surprisingly, the center of Isfahan has the highest accessibility scores for healthcare facilities, and the peripheral areas of the city have the lowest levels. Despite the existence of higher numbers of healthcare facilities in northern parts of Isfahan, accessibility to healthcare facilities is low because of the large population. Both methods show that healthcare services are not evenly distributed in Isfahan. Some areas with a high population have low accessibility to health facilities. Ideally, these areas would be prioritized for future health infrastructure investment. The methods used here can help urban healthcare policymakers identify spatial inequalities in access to care and thus target the areas in need.
CRUSTAL DEFORMATION IN NW IRAN: INSIGHTS FROM DIFFERENT INVARIANT AND VARIANT COMPONENTS OF GEODETIC STRAIN RATE TENSORS
Authors: Rahmani M. , Nafisi V. , Asgari J. , Nadimi A.
Published: January 2023

Abstract. Northwest of Iran, as a tectonically active region, has experienced numerous devastating earthquakes. That is why it is so important to study the earth deformation in this area and to provide more precise insights. So far, most researchers have had the preference of using the invariant component of strain rate tensor for investigating the Earth's shape deformation in the region. However, to examine the efficiency of the variant components of the geodesic strain rate tensor in interpreting deformations of north-western Iran, we have in this article maps of variant components of the geodetic strain rate tensor (normal strain rate along north and eastbound). Using the velocity field gathered from a previous article, and also using a simple and straightforward method, the strain rate tensors were calculated. The obtained contraction along the north direction (from the normal strain along this axis) confirms the Eurasia-Arabia collision. Besides, the obtained extension along the east direction and the derived expansion of the dilatation, show the effect of Anatolian motion to the west and eastward movement of the central Iran plateau on the tectonic structure of the studied area. These two results showed that the variant component of strain rate tensor also provides us with useful information about a region shape deformation.
ENSEMBLING OF DECISION TREES, KNN, AND LOGISTIC REGRESSION WITH SOFT-VOTING METHOD FOR WILDFIRE SUSCEPTIBILITY MAPPING
Authors: Rezaei Barzani A. , Pahlavani P. , Ghorbanzadeh O.
Published: January 2023

Abstract. As a result of climate change, climatic catastrophes, such as wildfires, are likely to increase. Wildfires continue to occur frequently and spread with greater intensity due to extreme weather conditions. In recent years, explosive fire growths have been reported in the United States, Australia, and other parts of the world. A combination of climate change and human activity has caused the semi-arid forestry areas in Iran's northern provinces to become more desiccated, leading to an increase in wildfires. The accuracy of the resulting fire susceptibility maps (FSMs) will directly be related to the performance of the method classifier. In this study, we use an ensemble classifier to model the FSM for a selected forestry case study area in one of the northern provinces of this country. Therefore, FSM is generated based on established criteria using the ensemble model. With Decision Trees, K nearest neighbor, and Logistic Regression, the ensemble model was created using the soft-voting method. A forest fire inventory data is created based on data collected over five years using GPS and the MODIS thermal anomalies product for training and testing the applied approach. The K-fold method was used for validation, and the resulting FSM was validated using five accuracy assessment metrics. The best result from the area under the curve (AUC) yields 93% for fold 9, and the mean AUC for ten folds yields 88%.
SINGLE IMAGE SUPER RESOLUTION VIA COUPLED SPARSE AND LOW RANK DICTIONARY LEARNING
Authors: Sahebkheir S. , Esmaeily A. , Saba M.
Published: January 2023

Abstract. Limitations in imaging systems and the effects of changes in sensing have caused limitation in acquiring high resolution images such as satellite images and magnetic resonance imaging (MRI). Sparsity can reduce the noises and improve the resolution. Super resolution in medical and satellite imagery is essential because low resolution image analysis is very difficult. Sparsity techniques have significant influence on computer vision specially when the main objective is extracting the meaningful information. The success of sparsity is related to the nature of signals such as image and sound which are naturally sparse because they were founded based on Wavelet and Fourier equations. In this research, we proposed a method for restoring a clear image from the related low-resolution parts of both MRI and satellite images. First, we proposed a widespread structure for learning the couple low rank and sparse main characteristic representation. Combined optimization of the nuclear and L1 norms extracts the total low rank formation and the local patterns lodged in the image. In that case the reconstructed image will be more informative and matrix decomposition problem can recover a noisy observation matrix into an approximation of low rank matrix and a second matrix which contains some low dimensional structure. We assumed that by removing the blur and noise from these images, they would be reconstructed in the highest quality. The proposed method was compared with a variety dictionary learning approaches which addressed super resolution problem, such as tensor sparsity, Generative Bayesian and TV based methods. We demonstrated the results of applied method on MRI and satellite images, showing both visual and psnr improvements. Dealing with complex data in best manner shows the robustness of the proposed method.
IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING
Authors: Sahraei R. , Ghorbanian A. , Kanani-Sadat Y. , Jamali S. , Homayouni S.
Published: January 2023

Abstract. Mangroves provide numerous environmental benefits, such as carbon sequestration, water purification, climate change mitigation, and flood and Tsunami impact reduction. Despite these unique advantages, mangroves are threatened by the combined adverse impacts of human activities and climate change. Therefore, it is essential to implement reasonable practices to avoid further degradation of mangroves and provide efficient workflows to increase their extent. Accordingly, better plantation policies are principally required for their conservation and rehabilitation. In this study, we desired to detect suitable locations for mangrove plantation in coastal areas of Hormozgan Province, Iran. We considered a relatively new Multi Criteria Decision Making (MCDM) technique to combine ten criteria derived from remote sensing in a GIS environment. The Best Worst Method (BWM), as an MDCM technique, was implemented to determine the relative importance of each criterion. Afterward, all criteria were aggregated using the Weighted Linear Combination (WLC) method to produce a mangrove plantation suitability map. Statistical measures, including Overall Accuracy (OA = 95%), Kappa Coefficient (KC = 87.9%), and Area Under Curve (AUC = 98.79%), indicated the high applicability of the implemented method for mangrove plantation site allocation. The produced map could give managers a profound insight into finding optimal spots to plant mangroves.
FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS
Authors: Sahraei R. , Kanani-Sadat Y. , Safari A. , Homayouni S.
Published: January 2023

Abstract. Flood is one of the most hazardous natural disasters that cause damages and poses a major threat to human lives and infrastructures worldwide, and its prevention is almost unfeasible. Thus, the detection of flood susceptible areas can be a key to lessen the amount of destruction and mortality. This study aims to implement a framework to identify flood potential zones in an ungauged large-scale area with frequent flood events in recent years. We used two Multi-Criteria Decision Making (MCDM) approaches combined with geospatial analysis, and remote sensing observations for this susceptibility analysis. Nine geomorphological and environmental factors that have an impact on flood behaviour were selected and used for susceptibility modelling. At first, the criteria’s weights were estimated using two MCDM approaches and based on experts’ knowledge. The resultant weights revealed that Flow Accumulation, Topographic wetness index, and Distance to River were the most influential flood susceptibility criteria. After calculating these weights, the criteria’s layers were aggregated through geospatial analysis, which resulted in generating flood susceptibility map. The area under the curve (AUC) and statistical measures such as the Kappa index were used to evaluate the proposed method's efficiency. The validation results illustrate that hybrid FAHP, with AUC= 96.68 and Kappa = 81.36 performed more efficiently than standard AHP, with AUC= 94.53 and Kappa=76.35. Overlaying these maps with the historical flood inventory dataset revealed that 86.43% of flooded areas were categorized as “high” and “very high”. Therefore, the flood susceptibility maps generated through the proposed approach can help the decision-makers and managers allocate the mitigation equipment and facility in data-scarce and ungauged large-scale areas.
AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY
Authors: Samadzadegan F. , Dadrass Javan F. , Hasanlou M. , Gholamshahi M. , Ashtari Mahini F.
Published: January 2023

Abstract. Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition.
DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION
Authors: Samadzadegan F. , Toosi A. , Dadrass Javan F. , Stein A.
Published: January 2023

Abstract. Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images.
A QUICK SEASONAL DETECTION AND ASSESSMENT OF INTERNATIONAL SHADEGAN WETLAND WATER BODY EXTENT USING GOOGLE EARTH ENGINE CLOUD PLATFORM
Authors: Seyed Mousavi S. M. , Akhoondzadeh M.
Published: January 2023

Abstract. Understanding the variation of Water Extent (WE) can provide insights into Wetland conservation and management. In this study, and-inter inner-annual variations of WE were analyzed during 2019–2021 to understand the spatiotemporal changes of the International Shadegan Wetland, Iran. We utilized a thresholding process on Modified Normalized Difference Water Index (MNDWI) to extract the WE quickly and accurately using the Google Earth Engine (GEE) platform. The water surface analysis showed that: (1) WE had a downward trend from 2019 to 2021, with the overall average WE being 1405.23 km2; (2) the water area reached its peak due to the water supply to International Shadegan Wetland through the Jarahi River and upstream reservoirs at the end of 2019 and the beginning of 2020, and the largest water body appeared in Winter 2019, reaching 1953.31 km2. In contrast, the smallest water body appeared in Autumn 2021, reaching 563.56 km2; (3) The WE of the wetland showed predictable seasonal characteristics. The water area in Winter was the largest, with an average value of 1829.1 km2, while it was the smallest in Summer, with an average value of 1100.3 km2; (4) The average water area in 2019 was 1490.5 km2 whereas in 2020 and 2021 decreased by 9% and 25%, respectively, and reached 968.6 km2 and 811.9 km2. Finally, to evaluate the proposed model, its results were compared with the Random Forest (RF) classification results. Accordingly, Histogram Analysis (HA) classification achieved 94.6% of the average overall accuracy and the average Kappa coefficient of 0.93, but the RF method obtained 95.38% of the average overall accuracy and an average Kappa coefficient of 0.94.
POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS
Authors: Shokri D. , Zaboli M. , Dolati F. , Homayouni S.
Published: January 2023

Abstract. Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations.
ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH
Authors: Torres R. A. B. , Ramos R. V. , Recto B. A. B. , Tamondong A. M. , Jiao B. J. D. , Cayetano M. G.
Published: January 2023

Abstract. The generation of air quality concentration data is imperative for the health and environment of highly urbanized regions. Through remote sensing, air pollutant concentrations can be obtained over large areas for a long time. In this study, particulate matter (PM2.5 and PM10) concentrations were estimated using satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) values observed in the National Capital Region (NCR), Philippines. Models were generated using multiple linear regression (MLR) and gradient boosting regression to determine the best models for the whole data from 2017 to 2020, dry season, and wet season with a 70–30 split for the train-test sets. Initial models resulted with the best coefficient of determination R2 values of 2.6% and 1.2% using MLR and 2.0% using gradient boosting regression. The results for PM2.5 and PM10 showed the lowest Root Mean Square Error (RMSE) values of 8.79 μg/m3 and 18.99 μg/m3 using MLR and 8.08 μg/m3 and 16.85 μg/m3 using gradient boosting, respectively. The preliminary results indicate the relatively poor performance of models in estimating particulate matter using satellite-derived AOD images. Improvements in the models will include the integration of more in-situ data from air quality monitoring stations and the addition of additional variables and features such as meteorological parameters and geographical layers.
Democratic education and dialogic pedagogy: synergies and dissonances
Authors: Kullenberg Tina , Marjanovic-Shane Ana
Published: January 2023

This paper is a republication of an interview between Tina Kullenberg and Ana Marjanovic-Shane, published in the EARLI SIG 25 Interview Series: ”The role of theory and philosophy in Educational Science” (Kullenberg & Marjanovic-Shane, 2020)[2]. In the interview, the authors discuss the reasons democratic schools sometimes support but other times do not support or even limit dialogic pedagogy. In the interview conducted by Tina Kullenberg, Marjanovic-Shane makes a distinction between schooling and education. Finally, in the face of global development, while sharing her comprehensive experiences over time and cultures, Marjanovic-Shane critically reflects on predominant approaches to education and the organization of schooling.
Küresel Eğitim Şirketlerinde İnsan Kaynakları Yönetimi Çalışmalarının İncelenmesi
Authors: Denli Saniye
Published: January 2023

The purpose of this study is to examine the human resource management studies in global education companies. In parallel with the managerial processes of global education companies, the most important reserve of wealth and qualification criteria are the human resources management owned by these companies. The correct application of human resources management, which is one of the most important elements affecting the performance of global education companies, is very important in terms of ensuring the sustainability of the company. It is seen that the main purpose of almost all global education companies is to provide institutionalization in this way. On this basis, institutionalization is an obligatory justification for global education companies to become a system and to create an efficient working environment. Human resources management is also an important criterion as a system in which global training companies are guided, recruited and controlled. Human resources within the framework of Corporate Governance, long-term strategies of the company and a training company managers and increase the efficiency of business operations and supervisory arrangements of a facilitation effect. Key Words: Training Company, Human Resources, Corporate Governance
Ortaokulda Çalışan Öğretmenlerin Stres Kaynaklarının İncelenmesi
Authors: Orhan Özcan
Published: January 2023

The purpose of this research is to examine the stress sources of teachers working in secondary schools, which are the second tier of educational institutions. Within this framework, it was aimed to reveal the sources of stress of a total of 54 teachers working at a secondary school located in Başakşehir district of Istanbul province and to determine whether this situation differs according to various variables. The universe of the research consists of teachers working at a secondary school located in Başakşehir district. In the research, the obtained data were collected with scale forms. The first part of the scale table contains individual information, and the second part contains an evaluation table related to the stress coping scale. In the evaluation of the data, descriptive statistical methods as well as SPSS for statistical analyses of scaling data.22.0 examination program, frequency, percentage (%), t test and one-sided ANOVA were used. As a result of the analysis, it was determined that the stress levels of the participating teachers were slightly higher than the general average, unlike the results obtained from research conducted in the literature. Keywords: Secondary School, Teacher, Sources of Stress
Okul Binalarının Aydınlatılmasında Günışığını Yönlendiren Sistemlerin İncelenmesi
Authors: Güvenç Mahmut , Selvi Selma , Özdemir Orhan
Published: January 2023

The main purpose of this study is to investigate the effects of effective use of sunlight on building forms by using existing developable techniques in parallel with the methods that can be renewed, resorting to control systems related to smart lighting in schools, and to examine the performance of the methods that maximize the competent use of the sun's rays in this regard in terms of system conditions that direct the sun's rays. Using daylight effectively by using intelligent lighting control systems in schools is a common area related to many strategies, especially energy conservation. The efficient use of daylight in schools ensures the efficient use of energy at the same time. The effective use of daylight in schools depends on the effective use of economical and natural lighting systems in parallel with the developing technology. This situation also appears as a mechanism that develops in direct proportion to the rate of progress of increasing technologies related to systems that direct sunlight. Key Words: School, Daylight, Lighting, Router Systems
Eğitim Yönetiminde Ar-Ge Çalışmalarının Öneminin İncelenmesi
Authors: Denli Burhan
Published: January 2023

Abstract In this study, it is aimed to examine the activities carried out in educational institutions on R&Dec Dec studies and entrepreneurship perspectives for the future by focusing on the importance of R&D studies in educational management. Study R Dec Dec Dec Dec Dec Dec dec con-sideration of R&D studies, Characteristics of R&D studies, entrepreneurship in R&D studies, Characteristics of the role of entrepreneurship in R&D studies, approaches in R&D studies and the importance of R&D studies will be discussed. R&D studies as a process are very related to the Decisional structures of education managers. With this behavioral process, a manager tries to influence other people in the institution to do their jobs in line with achieving their educational goals. Since R &D studies are a dynamic process, it becomes difficult to Decipher a role, method and technique that will best affect the process. There is a diverse and different set of R&D studies methods and techniques and each of them is seen as the most appropriate form in different situations and conditions. Dec. R&D studies are in a very important position in terms of progressive, entrepreneurial and leading education managers in general Decisively and expect continuous improvement. Key Words: Education Management, R&Dec, Development, Entrepreneurship
Eğitim Kurumlarında Teknik Uygulama Çalışmalarının İncelenmesi
Authors: Çelik Yasin
Published: January 2023

In this study, it is aimed to examine the technical application studies in educational institutions. In the study, which is shaped within the framework of the concepts of education, technique, educational institutions and educational technology, the stages of development of educational technologies in the historical process in general are also briefly examined. The phenomenon of globalization is progressing at an inconceivable speed in our age. The advanced inventions and developments in science and technology accelerate the globalization process and affect societies in every field, as well as changing and improving education systems. The educational and technical concept in globalization represents a process that allows us to constantly learn, acquire knowledge, become knowledgeable people, produce knowledge, and live with knowledge. From this point of view, education and technology occupy an important place in the globalization process. In this process, it is to keep up with the technical development expected from the personnel working in educational institutions. In addition, education administrators are expected to use technology and science effectively in the process of influencing educators in order to achieve the goals of education. Key Words: Education, Educational Institutions, Technology
Özel Eğitim Öğretmenlerinin Okula Bağlılık Düzeylerinin İş Motivasyonlarıyla İlişkisinin İncelenmesi
Authors: Yanık Feyzullah
Published: January 2023

In this research, it was aimed to investigate the relationship of special education teachers' commitment levels to school with their work motivations. In order to determine the effect of special education teachers' perceptions of organizational commitment on job motivation in educational institutions, regression analysis was carried out in parallel with this. In the types of motivation, intrinsic motivation and extrinsic motivation sources were examined by considering them together in the said research. According to the results obtained as a result of the analysis,; it has been understood that only emotional attachment is a significant predictor of intrinsic motivation from organizational commitment components. In addition, it has been determined that attendance commitment and emotional commitment to the institution are significant predictors of extrinsic motivation together. Emotional attachment, which stands out as an important concept here, expresses the individual's attachment as a feeling, identification with the organization he is in, preference to stay in the organization and desire to be a part of the organization. Key Words: Educational Institution, Special Education, Commitment, Work Motivation
Eğitim Yönetiminde Uygulanan Ar-Ge Modellerinin İncelenmesi
Authors: Denli Burhan
Published: January 2023

In this study, the applicability of R&D models in educational institutions and which models will increase development and progressivism more will be examined by focusing on the R&D models applied in educational management. Dec.Dec. There are some modeling methods in the process and stage Deciencies of R&D studies applied in education management. In this study, it is aimed to examine the activities carried out in relation to the mentioned models and the studies related to their application phases. There is a consensus that the needs for development and progress are effective by using scientific research techniques and methods in general in order to conduct the study in question at the R & Dec stage. In the R&D process, the interaction and governance of these studies necessitates a more effective and competent perspective in the context of human Decency, time and administrative process, as it will cover a more inclusive and long-term time period. he has undergone a change inside. In this study, the topic of entrepreneurship will be focused on. R & D modeling, these ideas get ideas and dial to the product or service, then this product or service which will offer the benefit of society or business venture, setting up an economic institution of surplus value in the value chain of a high contribution in the form of a developer can be explained while; On the other hand, an R & D modeler can be explained as a person who takes authority and initiative, takes responsibility for conducting successful economic work for the benefit of society and has this awareness, is open to development and can innovate, can manage risks and possible crises related to processes, and even when faced with obstacles or difficulties, can focus on the end of developing and progressing events and situations, and can best and Decently direct and manage the process. Key Words: Educational Institutions, R&D, R&D Models Deciently Deciently
Eğitim Kurumlarında Teknik Araç ve Gereçlerin Kullanımı Çalışmalarının İncelenmesi
Authors: Çelik Yasin
Published: January 2023

In this study, it is aimed to examine the use of technical tools and equipment in educational institutions. Although the concept of education has a deep-rooted concept, the concept of educational technology appears as a new descriptive framework. In parallel, the concept of educational technology should be applicable at all levels of education in a broad conceptual framework, it should produce solutions to the problems of teachers and students, it should be used on the spot and in the decision. The necessary support should be given to educational institutions in accessing technical tools and equipment in education, and recommendations and requests should not be ignored. Dec. Decisions should be taken in the direction of these requests and demands in order to reach the desired level of education. In order for the decisions taken to be binding, official inspections should be carried out, financial funds should be created by the state for regions deprived of educational technology. In order for students to have access to certain competencies and status in professional and school life, students, teachers and parents should be informed through seminars and conferences organized by the state. The programs and materials related to education that are being implemented in educational institutions, that is, technical tools and equipment, should be supervised by the units that will be created in order to show positive development, and should not be misused. Key Words: Education, Technology, Tools and Equipment, Educational Institutions
Interview with Jasser Auda
Authors: Mohadi Mawloud
Published: January 2023

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The Fundamentals (Usul) of Maqasidi Tafsir
Authors: Zomorod Faridah
Published: January 2023

This article discusses the topic of maqasid-based tafsir (exegesis) by investigating its origin based on the founding studies of early exegetes, and the renewed insights of the modern and contemporary maqasidi scholars and exegetes at the level of conceptualisation and applications. This paper aims to bring to light that the maqasid-based exegesis is not completely different from the early heritage of exegesis, nor is it different from the renewed exegesis in every era and place. Rather, it deserved such depiction because of the extra focus on maqasid and profound consideration of its implementation and activation. Moreover, this paper deliberates the rules, regulations, and conditions related to maqasid-based exegesis which are not exclusive to it, but they are part of the general rules and fundamentals of exegesis that guide the exegetes in dealing with the maqasid of the Holy Qur’an, which we consider as rational fundamentals of exegesis because they are related to the spectrum of reflection and independent reasoning.
Maqasid-based Tafsir of the Holy Qur’an between Activation and Disruption
Authors: Chokri Farid
Published: January 2023

The maqasid-based exegesis is one of the most important categories of Qur’anic commentary that has not been taken into consideration in terms of theory and application. This category of tafsir was never singled out in any of the classical or contemporary literature, except for the presence of its precursors in some commentaries and contemporary theoretical studies. This paper demonstrates that maqasid-based exegesis is generally classified into two parts according to two basic levels. In terms of premise, there is the guided maqasid-based exegesis and the conclusive maqasid-based exegesis. In terms of purpose and objective, we have the availing, activating, and complementing maqasid-based exegesis, and reversing, disruptive, and obstructive maqasid-based exegesis. This classification would steadily rectify the contemporary maqasidi engagement without excessiveness, negligence, alteration, or disruption, especially after many studies have sought to disrupt the Qur’anic provisions or dilute them in the name of maqasid, as is the case with many contemporary recitations of the Holy Qur’an. Accordingly, the maqasid-based exegesis project should lay a set of guidelines that regulate every exegetical or interpretative approach to guidance, rectification, and evaluation.
Book Review: Al-Ta’sis al-Itimani li ‘Ilm al-Maqasid
Authors: Hassani Ismail
Published: January 2023

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The Role of Context in Interpreting and Translating the Qur’an
Authors: Abdul Haleem M. , Mohadi Mawloud
Published: January 2023

Quoting out of context can become especially serious when dealing with religious texts. As will be demonstrated in this article, attention to context (siyāq) is essential to proper understanding and translation of the Qur'an. However, in much tafsīr writing, and in most of the translations of the Qur'an into English, as well as more general discussion of the Qur'an, we come across examples where insufficient regard to the context seriously mars understanding and results in misrepresentation of the Qur'an's message. The study of context has a central place in rhetoric (balāgha) and in Qur'anic studies in Arabic, but is hardly mentioned in Qur'anic studies undertaken in English. This article explores the significance of context in reading the Qur'an. In the introduction, I outline the various types of context, and then move on to examine its role in determining the meaning of words and sentences, and in determining the inclusion and omission of material, as well as the order and amount of material used in any given situation in the Qur'an. The discussion will also explore which linguistic features can cause difficulties in determining meaning, and what clues are given in the Qur'an to help identify the context and appropriate meanings of specific passages. Examples will be given from translations of the Qur'an, tafsīr, and Qur'anic studies.