Support Vector Machine Classification Technique Research Articles

“Intelligent tutoring effects on induced emotions and cognitive load of learning-disabled learners”

Purpose This study addresses the learning requirements of learners with learning difficulties by monitoring their learning experience in an Intelligent Tutoring System. Intelligent Tutoring Systems were developed to enrich the teaching-learning process. Materials and Methods In the present work, the interface is designed and developed utilizing the potential of Artificial Intelligence to meet their individual needs. Designing an online learning platform for a learners with learning difficulties requires consideration of their learning problems and preferences. The interface was developed focusing on all the requirements of the LD learners. The objective of the present study is to monitor the learning experience in the form of induced emotions and cognitive load of the learners to determine the impact of learning. Results 83 learners were observed during various stage of learning. The results obtained through the Support Vector machine (SVM) classification technique showed the positive attitude towards intelligent tutoring. The analysis revealed that a total of 0.23% of learners were positively induced. Their learning experience was positive and effective. The cognition load on learners was minimum with single-mode instruction and least disturbed. Conclusions The system was improved based on preference feedback on design features. This helps in improving content design and creating device independent and responsive visual design. The fatigue effect analysis on cognitive load implied that multiple modes of instruction increased drowsiness. Single mode of instruction have a positive impact on the learning process and it reduces the cognitive load of the learners. Implications for Rehabilitation The user interface designed and developed for learners with Dyslexia, Dysgraphia and Dyscalculia has learning disabled-friendly features. These can be used to create a device-independent and responsive design. Learning experience is monitored along with the impact on cognitive load of the learners. The research helps in understanding the stimulation and response of learners with learning disability for different learning conditions. Most existing learning systems are limited to non-learning-disabled learners. The ITS developed during research presents a Universal learning design helpful for all learners with and without learning disability.

Optimizing Doctor Availability and Appointment Allocation in Hospitals through Digital Technology and AI Integration

Many patients miss their appointments all around the world and many of them don't even cancel at all or don't do so in time due to several reasons. In order to address the widespread issue of medical no-shows, this paper proposes a solution that involves building a machine learning model utilizing patient datasets that are already available. This model will identify patterns and links between various patient factors and the patients' propensity to miss appointments. As a result, based on their information, it is possible to anticipate the chance of a patient appearing. Based on the Support Vector Machines classification technique, the machine learning model created the solution predictive model. Effective healthcare services are vital in today's fast-paced environment. This strategy aims to reduce the distance between patients and medical professionals by offering a workable and friendly solution. For certain medical institutions, such as clinics and hospitals, this initiative makes it easier for patients and customers to schedule doctor appointments online. Using this technology, patients may easily browse a database of doctors' biographies, specializations, and availability. Even the day and time of their choosing can be chosen for appointments. Each patient's appointment request will be scheduled by this doctor's appointment system and forwarded to the physician. The system administrator will update the list of doctors, including their specialties, personal information, and system access credentials. Patients will look for a physician who specializes in their requirements by exploring the doctor's appointment system online. Before making their request, the patient can browse the doctor's weekly schedule to choose a day and time that works best for them. Following that, the physicians have access to all of their appointments as well as the patients' appointment requests, which are prioritized according to their availability. It gives medical professionals a strong tool for successfully managing the schedules, which reduces administrative strain and ensures a positive patient experience.

Assessing urban growth in Greater Surabaya using Google Earth Engine: An evaluation of built-up area expansion in Indonesian secondary cities

Urbanization in Indonesia's cities is increasing, leading to various impacts, including negative consequences due to insufficient investment in local public infrastructure. Urbanization assessment primarily relies on examining changes in built-up areas over the past decade. These changes serve as an indicator that can be effectively derived from remote sensing data. In our study, we applied remote sensing data from the Google Earth Engine (GEE) catalog to delve into the urbanization dynamics within Greater Surabaya area, Indonesia. We employed satellite imagery from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI TIRS) for 2012 and 2022. We used Support Vector Machine (SVM) classification techniques to construct precise urban expansion models. Our analysis revealed distinct urban expansion trends in Mojokerto and Sidoarjo, which contrast with the relatively stable urban development trends in northern Surabaya due to the construction of toll roads. The findings provide valuable inputs for urban management, necessitating targeted interventions and strategies to address the urbanization disparities between these two areas. It underscores the critical importance of resource allocation, infrastructure development, and urban planning initiatives, with a specific focus on Gresik, to ensure sustainable urban growth and mitigate potential challenges associated with rapid expansion.

Comparative Study of K-NN, Naive Bayes and SVM for Face Expression Classification Techniques

Abstract Classification is a data mining technique used to predict group membership for data instances within a given dataset. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. This comparative study focuses on the application of K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machines (SVM) classification techniques in the context of face expression recognition. The study aims to evaluate and compare the performance of these techniques in accurately classifying facial expressions, their advantages and disadvantages. Facial expression recognition is an important area in computer vision and emotion analysis. It involves identifying and classifying different facial expressions such as happiness, sadness, anger, fear, surprise and disgust. Accurate classification of facial expressions is crucial for various applications, including human-computer interaction, affective computing and psychological research. K-NN is a non-parametric algorithm that classifies data based on the majority class of its K-nearest neighbors. Naive Bayes is a probabilistic algorithm that makes classification decisions based on the Bayes theorem and assumes independence between features. SVM is a machine learning algorithm that constructs a hyperplane to separate different classes of data. Dataset such as Affectnet is used to conduct the study including labeled facial expression images. Three algorithms are tested on this dataset to evaluate their performance in correctly classifying facial expressions. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the classification performance of each technique. Software used for conducting the experiments is Python.

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System

Health care Management System (HMS) is a key to successful management of any health care industry. Health care management systems have so many research dimensions such as identifying disease and diagnostic, drug discovery manufacturing, Bioinformatics’ problem, personalized treatments, Patient image analysis and so on. Heart Disease Prediction (HDP) is a process of identifying heart disease in advance and recognizes patient health condition by applying techniques on patient heart related symptoms. Now a day’s the problem of identifying heart diseases is solved by machine learning techniques. In this paper we construct a heart disease prediction method using combined feature selection and classification machine learning techniques. According to the existing study the one of the main difficult in heart disease prediction system is that the available data in open sources are not properly recorded the necessary characteristics and there is some lagging in finding the useful features from the available features. The process of removing inappropriate features from an available feature set while preserving sufficient classification accuracy is known as feature selection. A methodology is proposed in this paper that consists of two phases: Phase one employs two broad categories of feature selection techniques to identify the efficient feature sets and it is given to the input of our second phase such as classification. In this work we will concentrate on filter-based method for feature selection such as Chi-square, Fast Correlation Based Filter (FCBF), Gini Index (GI), RelifeF, and wrapper-based method for feature selection such as Backward Feature Elimination (BFE), Exhaustive Feature Selection (EFS), Forward Feature Selection (FFS), and Recursive Feature Elimination (RFE). The UCI heart disease data set is used to evaluate the output in this study. Finally, the proposed system's performance is validated by various experiments setups.

Remote photoplethysmography-based human vital sign prediction using cyclical algorithm.

This article aims to predict vital signs like heart rate (HR), respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the cascade residual CNN-FPNR technique for preprocessing and SNR enhancement using energy variance maximization. The image cascade network (ICNet) facilitates segmentation, achieving strong segmentation in low-light ambient videos. Remote photoplethysmography (iPPG) enables noncontact vital sign monitoring, predicting HR and respiratory rate (RR). An innovative noninvasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time-warping-based optimization method is used for precise region selection. The study introduces an intensity variance-based threshold analysis for arterial oxygen saturation level determination. Ultimately, the support vector machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.

Multi-Objective Models for Sparse Optimization in Linear Support Vector Machine Classification

The design of linear Support Vector Machine (SVM) classification techniques is generally a Multi-objective Optimization Problem (MOP). These classification techniques require finding appropriate trade-offs between two objectives, such as the amount of misclassified training data (classification error) and the number of non-zero elements of the separator hyperplane. In this article, we review several linear SVM classification models in the form of multi-objective optimization. We put particular emphasis on applying sparse optimization (in terms of minimization of the number of non-zero elements of the separator hyperplane) to Feature Selection (FS) for multi-objective optimization linear SVM. Our primary purpose is to demonstrate the advantages of considering linear SVM classification techniques as MOPs. In multi-objective cases, we can obtain a set of Pareto optimal solutions instead of one optimal solution in single-objective cases. The results of these linear SVMs are reported on some classification datasets. The test problems are specifically designed to challenge the number of non-zero components of the normal vector of the separator hyperplane. We used these datasets for multi-objective and single-objective models.

Spatio-temporal dynamics of Acacia decurrens plantations in Awi Zone Highlands, Northwest Ethiopia

This study aimed to assess the Spatio-temporal Acacia decurrens plantation (ADP) cover dynamics and its drivers in the Awi highlands of Northwestern Ethiopia. Landsat imageries (1993, 2007, and 2022) with Support vector machine classification techniques and Normalized difference vegetation index were used to analyze the ADP cover changes from 1993 to 2022. Besides, questionnaires, focused-group discussions, and interviews were employed to identify the drivers of ADP cover changes. The findings revealed that ADP cover witnessed substantial changes between 1993 and 2022 in the study area. ADP cover dramatically increased from 0.05 percent in 1993 to 24.53 percent in 2022, with a total gain of 40,788.97 ha. In contrast, cropland, grassland, and shrubland significantly decreased from 1993 to 2022, with a net loss of 23,111.63 ha, 18,479.23 ha, and 7942.44 ha, respectively. Hence, ADP was expanded considerably in the study area at the expense of cropland, grassland, and shrubland in the past 29 years. Soil infertility/acidity; land degradation; attractive benefits of ADP; increasing fuelwood demand; living condition improvement of ADP growers; and suitability of ADP with other land uses were identified as the top six drivers for ADP expansion. The innovative environmental rehabilitation and tree plantation experiences of the Awi-community should be upgraded and expanded to other areas.

EEG Neuro-markers to Enhance BCI-based Stroke Patients Rehabilitation

Stroke is the second largest cause of death worldwide and one of the most common causes of disability. However, several approaches have been proposed to deal with stroke patient rehabilitation like robotic devices and virtual reality systems, researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor imagery (MI)-based BCI therapy by investigating sensorimotor areas using frequency and time-domain features and to select particular methods that help in enhancing the MI-based BCI systems for stroke patients using EEG signal processing. Therefore, to detect the imagined movements that are typically required within conventional rehabilitation therapy with good identification accuracies, the conventional filters and wavelet transform (WT) denoising technique was used in the first stage. Next, attributes from frequency and entropy domains were computed. Finally, support vector machine (SVM) classification techniques were utilized to test the motor imagery (MI)-based BCI rehabilitation. The results demonstrate the capability of the WT denoising technique together with the used features and SVM classifier to discriminate the tested classes of the left hand, right hand and foot MI-based BCI rehabilitation. This study will help medical doctors, clinicians, physicians and technicians to introduce a good rehabilitation program for post-stroke patients.

Evaluation of Feelings of Smart Phone Product Review Using SVM Technique of Sentiment Analysis

Abstract: Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts.. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.

Smart Phone Product Review Using SVM Technique of Sentiment Analysis

Abstract: Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.

Investigating land use land cover changes and their effects on land surface temperature and urban heat islands in Sharqiyah Governorate, Egypt

The Urban Heat Island (UHI) issue is a result of the undesirable effects of urban growth on the environment, such as temperature rises and landscape changes that cause environmental dangers. Thus, the purpose of this research is to investigate the effect of Land Use Land Cover (LULC)change on Land Surface Temperature (LST) and then study UHI in Sharqiyah from 2001 to 2022 using remote sensing data.This data was collected from the Landsat satellite and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A Mono-Window Algorithm was used on Landsat 8 and 9 data to estimate the LST. To determine the LST and UHI, the thermal band was utilized. LULC maps were created using the Support Vector Machine (SVM) classification technique. To evaluate various LULC indicesin the Sharqiyah and find their correlation with LST, the spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Bare LandIndex (NDBaI), and Normalized Difference Built-up Index (NDBI) were obtained from the processing of multispectral Landsat data. To checkdata sources, air temperature measurements for Sharqiyah werealso acquired. The results show that urban expansion has increased in a noticeable trend. The built-up area increased by 18.9% during the research phase, and the region's mean LST increased within 3.98℃. The UHI threshold temperature increased by 4.27℃. This research is critical for Planning Engineers and environmental scientists to realize LULC variations effects on LST and to suggest suitable political steps to regulate urbanization in Sharqiyah Governorate.

An intelligent algorithm for lung cancer diagnosis using extracted features from Computerized Tomography images

According to the World Health Organization, lung cancer is a leading cause of death worldwide. This research aims to process the Computerized Tomography (CT) images of lung cancer patients for the early diagnosis of the disease. The images are processed using Convolutional Neural Network (CNN) in the first approach, where Artificial Neural Network (ANN) is employed to classify the images. In the second approach, the images are pre-processed and segmented before utilizing CNN and ANN. In the third method, all the pre-processed and segmented images are converted to numerical data via specific feature extraction algorithms in the last step. Besides, dimensional reduction and feature selection algorithms are employed to classify with three machine learning techniques, i.e., Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM). An extensive comparative analysis is made to come up with the best technique. The comparisons are made by evaluating the methodologies on a set of lung CT scan images collected from a medical center. The results show that when either SVM or RF classification techniques are used, a 95% accuracy is obtained in diagnosing lung cancer.

Archaeological prospection using WorldView‐3 short‐wave infrared (SWIR) satellite imagery: Case studies from the Fertile Crescent

AbstractGeologists have long valued satellite imagery in the short‐wave infrared (SWIR) part of the electromagnetic spectrum (1100–2500 nm) because it can reveal subtle differences in minerology and soil moisture that are otherwise invisible, but the low spatial resolution (20‐30 m) of publicly available SWIR imagery has limited its utility for archaeological investigations. As part of a NASA‐funded research project, this paper presents results of an effort to evaluate the potential of higher resolution (3.7 m), 8‐band SWIR imagery from the WorldView‐3 satellite programme to aid in the identification and mapping of archaeological sites and landscape features in the Fertile Crescent. With case studies in the Amuq Plain of southern Turkey, the Khabur Plain of eastern Syria, and the Diyala Plain of southern Iraq, we utilize several methods including experiments with numerous band combinations, production of band ratios designed for crop cover analysis and support vector machine (SVM) classification techniques to enhance site visibility in multispectral SWIR imagery. Results reveal some of the seasonal, land use/cover and other factors that can impact the visibility of archaeological sites and features, demonstrating the potential and pitfalls of this emerging remote sensing resource.

Classify the Feelings of Smart Phone Product Review Using SVM Technique of Sentiment Analysis

bstract: Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts.. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone

Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC.

Land use and land cover (LULC) both define the earth's surface both anthropogenically and naturally. It helps maintain global balance but changes in land use create inequality. The LULC modification adversely affects physical parameters such as infiltration, groundwater recharge, surface runoff, ground temperature, and air quality. It is high time to monitor land use changes globally. Remote sensing and GIS techniques help to monitor these changes with a low budget and time. Various types of LULC classifiers have been invented to classify the LULC types. Maximum likelihood is a popular LULC classifier, but nowadays, support vector machine (SVM) classifier is gaining popularity because it provides a more accurate LULC than the maximum likelihood classifier. Therefore, in this study, the SVM classification technique has been applied to produce good accuracy LULC maps. Using the SVM classifier, six LULC maps are produced from 1995 to 2020 for the Shali reservoir area in India which is a medium irrigation project irrigating ~ 3211 hectares of land per year. It plays an important role in the agricultural production of the region by providing irrigation water in monsoon and post-monsoon seasons. The impact of LULC change on the environment is also studied. The LULC forecast maps are also created using the cellular automata (CA) model and MOLUSCE plugin. Kappa coefficient and validation methods are used to validate the LULC and simulated maps. Both maps produce high accuracy with a kappa coefficient of 0.9. Secondary data, collected from the governmental gazette, such as population, crop production, and water level is also used to justify the results. The simulated map shows that 4% of agricultural land and built-up area may increase from 2020 to 2030. Overall, it has been proven that the SVM and CA models can produce accurate classified results.

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.

Detection of Cardiovascular Disease Using Ensemble Feature Engineering With Decision Tree

Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which are becoming a leading cause of human death across the globe. Hence, an essential framework is demanded for the early detection of CVDs which can help to prevent premature death. The application of Artificial Intelligence (AI) in healthcare has opted for this challenge and makes it easier to detect CVDs using a computational model. In this study, the authors built a reduced dataset using ensemble feature selection methods and got five features as per their weight values. Support Vector Machine, Logistic Regression, and Decision Tree classification techniques are utilized to check the effectiveness of newly designed datasets through different validation approaches. The authors also worked on data processing and visualization techniques, including Principal Component Analysis (PCA), and T-sne for understanding the data structure. From the findings, it was possible to conclude that DT has achieved an optimal accuracy and AUC of 98.9% and 0.99 ROC with leave one out Cross Validation (CV).

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand for rapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a better representation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), SwiRed Index (SwiRed), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and other remote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager) satellite data.