Supervised Classification Techniques Research Articles

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

Measurement of tyre-based microplastics using traditional and quantum cascade laser-based infrared spectrometry

BackgroundDespite the potential environmental impact of TWPs (tyre wear particles), there is a lack of reliable analytical methodologies suitable for their routineidentification and characterization. The number of papers dealing with this topic is, so far, very reduced and, therefore, there is a need for addressing it, mostly because traditional transmittance-based IR techniques are suboptimal due to scattering caused by black carbon in tyres. ResultsThis study aims to evaluate the most appropriate infrared (IR) spectrometric technique for monitoring TWPs. Macro attenuated total reflectance (ATR), reflectance microscopy, and quantum cascade laser-based micro transflectance (QCL-LDIR) were employed to analyse samples from used car and truck tyres in two sample configurations: small tyre fragments (∼1 cm2) and TWPs (< 1 mm). ATR yielded well-defined spectra with good signal-to-noise ratios, allowing for a straightforward interpretation of the major functional moieties. Despite reflectance measurements on tyre fragments provided good results, those on TWPs offered limited information due to noise and scattering. Transflectance offered clear peaks and enhanced resolution in the fingerprint region –compared to the other techniques-, much faster analysis times and the ability to effectively measure particles down to 20–10 µm, thus, emerging as the most effective technique for TWPs analysis. However, spectral interpretation is not immediate. Further, a proof-of-concept study was done to evaluate whether the analytical techniques contain information to differentiate types of tyres. An unsupervised pattern recognition and a supervised classification technique (principal components analysis and classification trees, CART) were used, which were able to differentiate among the tyres, notably the truck tyre from the cars tyres. SignificanceThe study presents first time the use of micro transflectance IR to study tyre particles down to 20 µm. Traditional total attenuated reflectance is demonstrated as a suitable way to analyse bigger microplastics. These two options open pathways to monitor this important emerging contaminant in environmental matrices

Assessing Land-Cover Changes in the Natural Park ‘Fragas do Eume’ over the Last 25 Years: Insights from Remote Sensing and Machine Learning

The ‘Fragas do Eume’ Natural Park includes one of the best-preserved Atlantic forests in Europe. These forests are part of the Natura 2000 Network. This scientific study focuses on analysing land-cover changes in the ‘Fragas do Eume’ Natural Park (NW Spain) over a 25-year period, from 1997 to 2022, using machine learning techniques for the classification of satellite images. Several image processing operations were carried out to correct radiometry, followed by supervised classification techniques with previously defined training areas. Five multispectral indices were used to improve classification accuracy, and their correlation was evaluated. Land-cover changes were analysed, with special attention to the transitions between eucalyptus plantations and native deciduous forests. A significant increase in eucalyptus plantations (48.2%) (Eucalyptus globulus Labill.) was observed, while native deciduous forests experienced a decrease in their extent (17.6%). This transformation of the landscape affected not only these two habitats, but also cropland and scrubland areas, both of which increased. Our results suggest that the lack of effective conservation policies and the economic interest of fast-growing tree plantations could explain the loss of native deciduous forests. The results highlight the need to implement pro-active and sustainable management measures to protect these natural forest ecosystems in the ‘Fragas do Eume’ Natural Park.

Evaluating land use and climate change impacts on Ravi river flows using GIS and hydrological modeling approach

The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R2, PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.

Exoplanet Detection Using Machine Learning : A Comparative Study Using Kepler Mission Data

The search for habitable planets outside our solar system has captivated scientists throughout the centuries. Discovery and characterization of exoplanets have been one of the most important endeavors of modern astronomy. With various space missions, we have significantly expanded our observational capacity, resulting in an abundance of information about the universe. The influx of more data necessitates the development of techniques that can aid astronomers in processing all the information more efficiently and in an automated manner. Machine learning in recent years has become an indispensable paradigm to automate complex tasks that are possible only by humans. This work explores the application of machine learning to detect exoplanets from NASA’s Kepler mission. Our dataset comprises Kepler Objects of Interest (KOIs), encompassing their characteristic features and confirmed exoplanet status. We experiment with multiple supervised classification techniques including classical, tree-based, and neural methods. The best-performing model Histogram Gradient Boosting achieves a strong performance of 94.6% precision and 94.1% recall on a held-out dataset demonstrating the strong potential of integrating machine learning techniques into astronomy, potentially leading to new insights into planetary systems outside the solar system.

Evaluating pasture cover density mapping: a comparative analysis of Sentinel-2 and Spot-5 multispectral sensor images

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

Retraction Note: A novel method for prediction of skin disease through supervised classification techniques

Retraction Note: A novel method for prediction of skin disease through supervised classification techniques

Land use and land cover changes, and woody vegetation status of the Tsimur Gebriel Monastery in Northern Ethiopia

Ethiopian Orthodox churches are significant habitats for endemic and threatened plant species, yet their vegetation status and the land use systems impacting them, are little known. Therefore, this study assessed the land use and land cover changes (LULCC) within a 3 km buffer area and the woody vegetation status of the Tsimur Gebriel Monastery in the Tigray region, Northern Ethiopia. The United States Geological Survey's multi-dated (1986, 1999, and 2018) Landsat imagery was used for LULCC analysis. A supervised classification technique was employed for image classification using a maximum likelihood algorithm. Systematic sampling techniques were used to collect vegetation data (tree species, height, and DBH), using 20 sample plots (20 × 20 m) distanced 100 m apart. The results highlighted that among the five identified LULCC types in the buffer zone of the monastery, the farmland area has expanded from 56 to 78 % at the expense of shrublands between 1986 and 2018. At the monastery, 19 woody tree species from 13 families were identified, with an evenness of 0.5 and a Shannon diversity index of 2.4. The stem density was 336 stems per hectare, and the forest cover was approximately 65 %. Olea europaea was the dominant tree species, while Juniperus procera showed a lack of regeneration at the monastery. Despite the fair natural regeneration, the monastery exhibited lower species diversity, richness, and evenness. However, the monastery remains an important habitat for rare and threatened tree species and may supply seeds for the restoration of degraded lands. Therefore, establishing exclosures in the buffer zone, strengthening stone walls and enrichment planting of degraded tree species should be implemented to ensure the sustainable conservation of valuable tree species.

Discrimination of internal crack for rice seeds using near infrared spectroscopy

It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy and chemometrics is proposed. Principal component analysis (PCA) was used to analyze the rice seeds spectra. Four supervised classification techniques(partial least squares discriminate analysis (PLS-DA), support vector machines (SVM), k-nearest neighbors (KNN) and random forest (RF)) with four different pre-processing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative with Savitzky-Golay (SG) smoothing) were applied. The best results (Sn = 0.8824, Sp = 0.9429, Acc = 0.913) were achieved by PLS-DA with the raw spectral data. The performance of the best SVM model was inferior to that of PLS-DA, but superior to that of RF and KNN. Except for PLS-DA, four different preprocessing techniques were improved the performance of the developed models. The important variables for discriminating internal cracks in rice seeds were related to the amylose. Overall, the all results demonstrated the feasibility of non-destructive discrimination of internal crack for rice seeds (Oryza sativa L.) using near infrared spectroscopy and chemometrics.

Predicting Global Education Quality: A Comprehensive Machine Learning Approach Using World Bank Data

This paper introduces an innovative approach to predicting the quality of education on a global scale. It leverages a comprehensive dataset spanning multiple years and countries from the World Bank. Our methodology involves two key steps: first, unsupervised clustering using the K-means model to categorize countries based on their educational quality levels; and second, employing supervised classification techniques to develop a predictive model. Through training and optimizing various machine learning (ML) algorithms, we aim to identify the most accurate model for predicting education quality. The outcomes highlight the efficacy of our approach, with the KNN algorithm demonstrating superior performance after hyperparameter optimization. It achieved precision, recall, accuracy, and AUC values of 0.9740, 0.9721, 0.9711, and 0.9959, respectively. These findings provide valuable insights for policymakers, educational institutions, and researchers, helping to identify areas that require attention and to design targeted interventions.

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

Urban growth assessment using machine learning algorithms, GIS techniques, and its impact on biodiversity: The case of Sululta sub-city, Central Oromia, Ethiopia

Ecological services in metropolitan areas are degrading more quickly due to changes in LULC brought about by urban expansion. To make a sustainable choice about the ideal location, however, merging the existing simulation approach with LULC optimization approaches involves several intricate procedures. Therefore, the goal of this study is to develop a unique technique that can forecast urban expansion over an extended period and to link with optimization of LULC techniques so as to make meaningful decisions on the impacts of urbanization on loss of biodiversity. In this study, three primary procedures were used: (1) an SVM-based supervised classification technique for LULC classification; (2) a Markov chain and cross-tabulation method for the examination of LULC trends in space and time, (3) utilizing the CA-Markov approach to forecast urban growth. By using Landsat imagery of 2008, 2015, and 2023, the study determined how urban cover changed over time, and what kind of LULC-to-urban transition occurred. The study revealed that uncontrolled and haphazard urban expansion was observed in the Sululta sub-city, which could have disastrous repercussions on physical, biological and urban ecosystem. The %age of urban area increased from 9.04% in 2008 to 13.07% in 2015. However, because of the internally displaced people from the Ethio-Somali Region, who have been resettled there since 2017, the ratio of urban areas grew from 13.7% in 2015 to 24.65% in 2023. Furthermore, by 2040, the sub-city will have grown by 27.69 %. The kappa coefficient statistics of the three classified images of the years 2008, 2015, and 2023 were 93.3 %, 97.5%, and 97.5 %, respectively. To identify the areas that would be covered by future city growth, it is advised that this innovative technique be integrated with optimizing land use strategies.

Using Feature Selection Enhancement to Evaluate Attack Detection in the Internet of Things Environment

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects of supervised classification, including feature selection, model training, and evaluation methodologies, to comprehensively evaluate their impact on attack detection effectiveness. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. The experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and has a low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 s.

Assessing the impact of land use land cover changes on soil moisture and vegetation cover in Southern Punjab, Pakistan using multi-temporal satellite data

ABSTRACT Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Human intervention caused massive destruction of the second largest mangrove forest, Chakaria Sundarbans, Bangladesh.

Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering areaa total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest areadegradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

Breast cancer classification based on hybrid machine learning model

This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.

Multispectral remote sensing approach of predicting the potential distribution and evaluating the current spread of water hyacinth (Eichhornia crassipes)

The water hyacinth is categorized among the world’s top ten worst invasive plant species of aquatic ecosystems. This study assessed changes in the spatiotemporal distributions of the water hyacinth in Lake Koka and Ziway of the Upper Awash River basin during the peak growth season of the plant. Household questionnaires and key informant interviews along with Landsat images for 2013, 2017, and 2021 were collected to identify the past, present, and future potential distributions of the invasive plant in the two lakes. Household surveys and key informant interviews were prepared using the Kobo Toolbox which monitors data collection online. A total number of 413 households were sampled and the data were analyzed through descriptive statistics. For Landsat images, a supervised classification technique was applied to classify the land use classes using the maximum likelihood algorithm. The survey results showed increased water hyacinth expansion in the area since the year 2011. The water hyacinth expansion affected 285 households’ livelihoods by invading 69.0% of their farmlands which caused 97.6% food scarcity in the study districts. The results of the Landsat image indicated that the water hyacinth invasion in Lake Koka occupied 1.48% in 2013, and this increased to 7.13% in 2021, while the water body decreased from 75.94 to 69.90%, respectively. However, the area of the other vegetation is nearly identical between the years 2013–2021. Likewise, water hyacinth covered 4.66% of Lake Ziway in 2013, and this was raised to 8.42% in 2021. At Lake Ziway water hyacinth invasion affected the area of other vegetation coverage as it decreased from 16.19 to 10.67% but the area of the water body remained almost the same during the years. Between 2013 and 2025, the amount of water hyacinths increased in both Lake Koka and Lake Ziway. According to LULC data, the water hyacinth's rate of spread was 0.56% in Lake Koka and 0.95% in Lake Ziway. The results of this study revealed the signals of LULC change due to water hyacinth invasion in the Upper Awash Basin which is considered an important aspect for future water resources planning and management.

Hydrological changes in Serra da Mantiqueira Range watersheds (Southeast Brazil)

The Serra da Mantiqueira Environmental Protection Area is one of the most important Atlantic Forest biodiversity hotspots in the World, and one of the main water sources for the Brazilian Southeast Region. In this study, we analyze the existence and characteristics of streamflow changes in two watersheds (Sapucaí-Mirim and Sapucaí-Guaçu River watersheds) of the Serra da Mantiqueira Range, and if these changes are due to climate change, alterations of land cover and/or external factors. Mann-Kendall and Pettit tests were used to detect changes in streamflow, rainfall and evapotranspiration data series, at different time scales. The characteristics and magnitude of land cover changes were evaluated from multi-temporal maps, produced from satellite images and the supervised classification technique. The results show an increase in winter rainfall at the two watersheds studied. No changes were detected in the streamflows of the Sapucaí-Mirim River Basin. The summer streamflow of Sapucaí-Guaçu River watershed reduced at a rate of −0.035 m³.s−1. year−1. In addition, there were sudden streamflow changes in the summer (from the year 1967) and autumn (from the year 1988) in Sapucaí-Guaçu watershed. Furthermore, the reference summer evapotranspiration of the Sapucaí-Guaçu watershed showed an increasing trend and an abrupt increase (in 1988), besides a sudden reduction of actual evapotranspiration in 2006. The integrated analysis of these results, together with the changes in land cover and population, allow us to conclude that the reductions in summer and autumn streamflows at the Sapucaí-Guaçu watershed were probably due to the increase in the water withdrawal for public supply, added to the effects of urbanization on the hydrology of the watershed.