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Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.

Data used in particle physics analyses have an imbalanced nature in which the events of interest are rare due to the broad background. These events can be identified from bulk by intensive computational studies including application of sophisticated analysis techniques. Classification algorithms provided by supervised machine learning (ML) approaches can be utilized to interpret skewed particle dataset as an alternative to the classic techniques even for multi particle state analysis. In this study, the ground state of the bottomonium ( Υ (1 S)) and its excited states ( Υ (2 S) and Υ (3 S)) were studied by application of multiclass classification approach based on random forest classifier (RFC) which is a novel ML approach example in particle analysis with implementation of resampling techniques for preprocessing dataset and modification of the weighting strategy. For this purpose, five widely used oversampling and two hybrid strategies, using over and under resampling together, were adjusted to RFC. Moreover, class weights applied RFC, weighted random forest (WRF), was used in the analysis. Due to the data structure, performance of the applied models was evaluated by the derivatives of confusion matrix. It is revealed that hybrid techniques implemented in RFC is suitable for handling highly imbalanced classes. G-mean and BAcc scores of upsilon states presented that with SMOTETomek strategy the model exhibited highest classification achievement, around 90 % , with high sensitivity implying the success of the application on multiclass classification.

Assessing the dynamics and patterns of Land Use and Land Cover (LULC) and its transformation is an important practice of urban planners and environmentalists for a variety of applications, including land management, urban climate modeling, and sustainability of any urban region. Monitoring changes in LULC using geospatial techniques can help to identify areas at risk for indefensible land use, low-grade environment, and especially for sustainable urban planning. This study aims to analyze the changing pattern, dynamics, and alteration of LULC using Google Earth Engine (GEE) and Machine Learning Applications for the years 1991, 2001, 2011, and 2022 in the Varanasi City Development Region (VCDR). The LULC classification was divided into seven classes using random forest classification, and Landsat-5(TM) and 9(OLI-2) satellite data were used. Saga GIS has been utilized for the detection of LULC change during the 1991-2022 period. For validation of classification results, accuracy assessment was estimated using error matrices and through user, producer, and overall accuracy estimation. The Kappa statistics were applied for the reliability of the accuracy assessment result. As a result, the built-up area increased by 507.8 percent, and other classes like agricultural, barren, fallow land, and vegetation cover rapidly declined and altered into concrete areas over the period. Water bodies and river sand classes have been slightly converted into different classes. The finding explains that 114.8 km2 of fertile agricultural land, 14.81 km2 barren land, and 12.93 km2 of vegetation cover transformed into impervious surface, which is unsustainable and causes various problems like food scarcity, environmental degradation, and low quality of urban life. This study can be a useful guide for urban planners, academicians, and policymakers by providing a scientific background for sustainable urban planning and management of VCDR and other cities as well.

The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni’s classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject’s gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.

Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988–2001 and 2001–2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

Clinical depression is a neurological disease identifiable by the analysis of the electroencephalography signals (EEG). The electroencephalographic signals (EEG) are often polluted by many artifacts. Deep study models have been employed in recent years to denoise electroencephalography. The main difficulty in medical analysis is the extraction of true brain signals from the polluted EEG data. Noise reduction from recorded EEG data is very important for better brain disorder investigation. This paper proposed an effective EEG signal estimation model for the process of EEG signals. The proposed model uss the Morelette wavelet transformation model for the pre-processing of the EEG signal. With the pre-processed EEG signal model feature extraction is performed with the Convolutional Neural Network (CNN) for the EEG signal. With the pre-processed EEG signal model training and testing are estimated for the classification of the EEG signal. The EEG signal categorization was carried out utilizing characteristics derived from EEG data. Many characteristics have proven sufficiently distinctive for usage in all applications linked to the brain. The EEG may be categorized using a range of functions such as autoregression, energy spectrum density, energy entropy and linear complexity. However, various characteristics indicate varying strength of discrimination for different individuals or trials. Two characteristics are utilized in this study to enhance the performance of EEG signals. Techniques based on the neural network are used for the extraction of EEG signal. Classification methods include the Random Forest Classification. The model was tested using a random splitting method and 93.4 percent of the EEG signals were received accordingly.

In contemporary society, with advancements in science and technology, many global activities, ranging from financial transactions to information transfers, are conducted through the Internet via dedicated websites and applications. Unfortunately, the prevalence of online platforms has increased the proliferation of fake websites aimed at exploiting sensitive data, such as bank card information and personal details. It addresses the problem of cybersecurity w.r.t. the categorization of a set of 1353 websites by a machine learning algorithm into three categories, namely phishing, suspicious, and legitimate URLs. The dataset was gathered from published papers and divided into 70-30 in the training and testing phases. This will help keep members' banking and personal data much safer online. This paper uses the RFC model with two optimization schemes, Sea Horse Optimizer (SHO) and Jellyfish Search Optimization Algorithm (JSOA), to improve performance. After that, optimized versions of the schemes are tagged as RFSH and RFJS, respectively. After extensive training and testing on these three schemes, the best model was identified by comparing the performances of the three on the database in hand. The RFSH model performed better predicting, achieving 0.952 for all the data. It outperformed the RFJS model with a precision of 0.932 and the RFC single framework with an accuracy of 0.9106. Hence, it emerged as the best-predicting model.

The internet of things (IoT) is an internet among things through advanced communication without human’s operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.

Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g., silent commands during task-force training) or simply not possible (e.g., if the user has hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing hand shapes, such as those used in non-verbal communication (e.g., thumbs up, okay gesture, …) and in sign language. In this paper, we present a hand-shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an outlier detection and a feature selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial data augmentation, i.e., we created new artificial data from the recorded and filtered data to augment the training data set. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. The voting meta-classifier (VL2) proved to be the most accurate, albeit slowest, classifier. A good alternative is random forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. outlier detection was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.

Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16). Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75–0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=−46.0 to 25.4min/d). The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure.