Combination of density-clustering and supervised classification for event identification in single-molecule force spectroscopy data

Single-molecule force spectroscopy(SMFS)measurements of the dynamics of biomolecules typically require identifying massive events and states from large data sets,such as extracting rupture forces from force-extension curves(FECs)in pulling experiments and identifying states from extension-time trajectories(ETTs)in force-clamp experiments.The former is often accomplished manually and hence is time-consuming and laborious while the latter is always impeded by the presence of baseline drift.In this study,we attempt to accurately and automatically identify the events and states from SMFS experiments with a machine learning approach,which combines clustering and classification for event identification of SMFS(ACCESS).As demonstrated by analysis of a series of data sets,ACCESS can extract the rupture forces from FECs containing multiple unfolding steps and classify the rupture forces into the corresponding conformational transitions.Moreover,ACCESS successfully identifies the unfolded and folded states even though the ETTs display severe nonmonotonic baseline drift.Besides,ACCESS is straightforward in use as it requires only three easy-to-interpret parameters.As such,we anticipate that ACCESS will be a useful,easy-to-implement and high-performance tool for event and state identification across a range of single-molecule experiments.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

An Analysis of Land Use and Land Cover Changes, and Implications for Conservation in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, 2002-2022

Understanding trends of land use land cover (LULC) changes is important for biodiversity monitoring and conservation planning, and identifying the areas affected by change and designing sustainable solutions to reduce the changes. The study aims to evaluate and quantify the historical changes in land use and land cover in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, from 2002 to 2022. The objective of the study was to analyse the LULC changes in Ward 2 (Mukumbura), Mt Darwin, Northern Zimbabwe, for a period of 20 years using geospatial techniques. Landsat satellite images were processed using Google Earth Engine (GEE) and the supervised classification with maximum likelihood algorithm was employed to generate LULC maps between 2002 and 2022 with a five (5) year interval, investigating the following variables, forest cover, barren land, water cover and the fields. Findings revealed a substantial reduction in forest cover by 38.8%, water bodies (wetlands, ponds, and rivers) declined by 55.6%, whilst fields (crop/agricultural fields) increased by 93.3% and the barren land cover increased by 26.3% from 2002 to 2022. These findings point to substantial changes in LULC over the observed years. LULC changes have resulted in habitat fragmentation, reduced biodiversity, and the disruption of ecosystem functions. The study concludes that if these deforestation trends, cultivation, and settlement land expansion continue, the ward will have limited indigenous fruit trees. Therefore, the causes for LULC changes must be controlled, sustainable forest resources use practiced, hence the need to domesticate the indigenous fruit trees in arborloo toilets.

Migration and Spatiotemporal Land Cover Change: A Case of Bosomtwe Lake Basin, Ghana

Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.

Computer vision-based limestone rock-type classification using probabilistic neural network

Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

Classification of features selected through Optimum Index Factor (OIF) for improving classification accuracy

The present investigation was performed to determine if the features selected through Optimum Index Factor （OIF） could provide improved classification accuracy of the various categories on the satellite images of the individual years as well as stacked images of two different years as compared to all the features considered together. Further, in order to determine if there occurs increase in the classification accuracy of the different categories with corresponding increase in the OIF values of the features extracted from both the individual years＇ and stacked images, we performed linear regression between the producer＇s accuracy （PA） of the various categories with the OIF values of the different combinations of the features. The investigations demonstrated that there occurs significant improvement in the PA of two impervious categories viz. moderate built-up and low density built-up determined from the classification of the bands and principal components associated with the highest OIF value as compared to all the bands and principal components for both the individual years＇ and stacked images respectively. Regression analyses exhibited positive trends between the regression coeffi- cients and OIF values for the various categories determined for the individual years＇ and stacked images respectively signifying the prevalence of direct relationship between the increase in the information content with corresponding increase in the OIF values. The research proved that features extracted through OIF from both the individual years＇ and stacked images are capable of providing significantly improved PA as compared to all the features pooled together.

Exploring the Sample Quality Using Rough Sets Theory for the Supervised Classification of Remotely Sensed Imagery

In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.

Supervised polarimetric SAR classification method based on Fisher linear discriminant

A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant. The feature parameters used in this classification method could be se- lected flexibly according to land covers to be classified. Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be com- bined together for classification use, without consideration of the dimension difference of each fea- ture parameter and the joint probability density function of those parameters. Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94. 33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data, demonstrating the effectiveness of this method.

Lithological Mapping Using Landsat 8 OLI in the Meso-Cenozoic Tarfaya Laayoune Basin (South of Morocco): Comparison between ANN and SID Classification

In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.

A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

Detection of Epilepsy Cases in Newborns

Epilepsy is a very common worldwide neurological disorder that can affect a person’s quality of life at any age. People with epilepsy typically have recurrent seizures that can lead to injury or in some cases even death. Curing epilepsy requires risky surgery. If not, the patient may be subjected to a long drug treatment associated with lifestyle advice without guarantee of total recovery. However, regardless of the type of treatment performed, late treatment necessarily creates psychological instability in the patient. It is therefore important to be able to diagnose the disease as early as possible if we desire that the patient does not suffer from its consequences on their mental health. That is why the study aims to propose a model for detecting epilepsy in order to be able to identify it as early as possible, especially in newborns. The objective of the article is to propose a model for detecting epilepsy using data from electroencephalogram signals from 10 newborns. This model developed using the extra trees classifier technique offers the possibility of predicting epilepsy in infants with an accuracy of around 99.4%.

Assessment of Spatial Expansion of Rift Valley Lakes Using Satellite Data

The present work assessed the expansion and fluctuation of Lake Nakuru in Kenya by using satellite data and information. Surface water magnitude was measured from optical sensors, such as Landsat. ENVI software was used to process and analyze data from the satellite images. The data was then used to create shapefile to get the area of the lake only. The shapefiles were classified using both Supervised and Unsupervised classification, and the area of the lake was obtained in hectares. The obtained area in hectares was recorded in a table and graphs were plotted to show the trend of the lake in the years 1972-2019. Furthermore, correlation was done by assuming the area of the shapefile before any classification is more accurate, therefore it was compared with the other results obtained by using different methods. Maximum likelihood gave the best correlation values. For R2 it gave 0.8627 and R was 0.9312.

Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.During evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

Using multispectral landsat and sentinel-2 satellite data to investigate vegetation change at Mount St. Helens since the great volcanic eruption in 1980

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.

Analysis of forest cover change at Khadimnagar National Park, Sylhet,Bangladesh, using Landsat TM and GIS data

We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.

Land cover changes in the rural-urban interaction of Xi’an region using Landsat TM/ETM data

Landsat ETM/TM data and an artificial neural network （ANN） were applied to analyse the expansion of the city of Xi＇an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index （NDBI） were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km^2 in 2000 to 358.60 km^2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi＇an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of “returning farmland to woodland”, some farmland was converted into economic woodland, such as Chinese goosebeerv garden, vineyard etc.

Land use change detection in Solan Forest Division,Himachal Pradesh,India

Background： Monitoring the changing pattern of vegetation across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment. Land use pattern i the state of Himachal Pradesh in the Indian Western Himalayas has been undergoing rapid modifications due to changing cropping patterns, rising anthropogenic pressure on forests and government policies. We studied land use change in Solan Forest Division of Himachal Pradesh to assess species wise area changes in the forests of the region. Methods： The supervised classification （Maximum likelihood） on two dates of IRS （LISS III） satellite data was performed to assess land use change over the period 1998-2010. Results： Seven land use categories were identified namely, chir pine （Pinus roxburghii） forest, broadleaved forest, bamboo （Dendrocolamus strictus） forest, ban oak （Quercus leucotrichophora） forest, khair （Acacia catechu） forest, culturable blank and cultivation. The area under chir pine, cultivation and khair forests increased by 191 ha （4.55 %）, 129 ha （13.81%） and 77 ha （23.40 %）, whereas the area under ban oak, broadleaved, culturable blank and bamboo decreased by 181 ha （16.58 %）, 152 ha （6.30 %）, 71 ha （2.72 %） and 7 ha （0.47 %）, respectively. Conclusions： The study revealed a decrease in the area under forest and culturable blank categories and a simultaneous increase in the area under cultivation primarily due to the large scale introduction of horticultural cash crops in the state. The composition of forests also exhibited some major changes, with an increase in the area of commercially important monoculture plantation species such as pine and khair, and a decline in the area of oak, broadleaved and bamboo which are facing a high anthropogenic pressure in meeting the livelihood demands of forest dependent communities. In time deforestation, forest degradation and ecological imbalances due to the changing forest species composition may inflict irreversible damages upon unstable and fragile mountain zones such as the Indian Himalayas. The associated common property externalities involved at local, regional and global scales, necessitate the monitoring of land use dynamics across forested landscapes in developing future strategies and policies concerning agricultural diversification, natural forest conservation and monoculture tree plantations.

A PRACTICAL METHOD FOR RICE ACREAGE STIMATION WTTH REMOTE SENSING

The crop area estimaton is one of the main fields in application of remotesensing. The paper focuses on the operational method for rice planting areaestimation, in which TM datu is used to ertract base rice area in a given year of1992. The NOAA AVHRR data is used to prwhct the changing tendency of the nceplanting area. The base area data needs to be updated for every rice growth penodupon the availability of TM data. Three methods can be used to extract the base riceplanting area. They are (1) visual interpretation with interaedve adjustmant on thescreen, (2) iflteraCtive automatic classification with manual elinunating of the non-rice pixels on the screen, and (3) automatic dassification with GIS spatial analysis.These methods can be combined to increase reliability and accuracy. The currentpaper is only concemed with the description of the second method. MultitemporalNOAA AVHRR SAVI data are combined as multiband image and are classifiedusing supetwsed makimum likelihood classifier on ERDAS to prediCt the changingtendency of rice planting area. The method has been successfully used in extraCtingearly nce area in Hubei Province in 1994 and acceptable result was obtained.

Studying distribution of rare earth elements by classifiers,Se-Chahun iron ore,Central Iran

The increased production and price of rare earth elements(REEs) are indicative of their importance and of growing global attention. More accurate and practical exploration procedures are needed for REEs, and for other geochemical resources. One such procedure is a multivariate approach. In this study, five classifiers, including multilayer perceptron(MLP), Bayesian, k-Nearest Neighbors(KNN), Parzen, and support vector machine(SVM),were applied in supervised pattern classification of bulk geochemical samples based on REEs, P, and Fe in the Kiruna type magnetite-apatite deposit of Se-Chahun,Central Iran. This deposit is composed of four rock types:(1) High anomaly(phosphorus iron ore),(2) Low anomaly(metasomatized tuff),(3) Low anomaly(iron ore), and(4)Background(iron ore and others). The proposed methods help to predict the proper classes for new samples from the study area without the need for costly and time-consuming additional studies. In addition, this paper provides a performance comparison of the five models. Results show that all five classifiers have appropriate and acceptable performance. Therefore, pattern classification can be used for evaluation of REE distribution. However, MLP and KNN classifiers show the same results and have the highest CCRs in comparison to Bayesian, Parzen, and SVM classifiers. MLP is more generalizable than KNN and seems to be an applicable approach for classification and predictionof the classes. We hope the predictability of the proposed methods will encourage geochemists to expand the use of numerical models in future work.