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.

High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated highdimensional data describing non-independent observations to identify SNPs related to a phenotype.We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection and population structure adjustment in highdimensional prediction models.Specifically,the model simultaneously selects variables and estimates their effects,taking into account correlations between individuals.Single nucleotide polymorphisms(SNPs)are a type of genetic variation and each SNP represents a difference in a single DNA building block,namely a nucleotide.Previous research has shown that SNPs can be used to identify the correct source population of an individual and can act in isolation or simultaneously to impact a phenotype.In this regard,the study of the contribution of genetics in infectious disease phenotypes is of great importance.In this study,we used uncorrelated variables from the construction of blocks of correlated variables done in a previous work to describe the most related observations of the dataset.The model was trained with 90%of the observations and tested with the remaining 10%.The best model obtained with the generalized information criterion(GIC)identified the SNP named rs2493311 located on the first chromosome of the gene called PRDM16((PR/SET domain 16))as the most decisive factor in malaria attacks.

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 selected 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 combined together for classification use,without consideration of the dimension difference of each feature 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.

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.

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.

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.

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 in 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(Dendrocalamus 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.

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

Supervised image classification has been widely utilized in a variety of remote sensing applications.When large volume of satellite imagery data and aerial photos are increasingly available,high-performance image processing solutions are required to handle large scale of data.This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data.The solution is scalable and satisfies the need of change detection,object identification,and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.

A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

Real-time recognition of sows in video: A supervised approach

This paper proposes a supervised classification approach for the real-time pattern recognition of sows in an animal supervision system(asup).Our approach offers the possibility of the foreground subtraction in an asup’s image processing module where there is lack of statistical information regarding the background.A set of 7 farrowing sessions of sows,during day and night,have been captured(approximately 7 days/sow),which is used for this study.The frames of these recordings have been grabbed with a time shift of 20 s.A collection of 215 frames of 7 different sows with the same lighting condition have been marked and used as the training set.Based on small neighborhoods around a point,a number of image local features are defined,and their separability and performance metrics are compared.For the classification task,a feed-forward neural network(NN)is studied and a realistic configuration in terms of an acceptable level of accuracy and computation time is chosen.The results show that the dense neighborhood feature(d.3×3)is the smallest local set of features with an acceptable level of separability,while it has no negative effect on the complexity of NN.The results also confirm that a significant amount of the desired pattern is accurately detected,even in situations where a portion of the body of a sow is covered by the crate’s elements.The performance of the proposed feature set coupled with our chosen configuration reached the rate of 8.5 fps.The true positive rate(TPR)of the classifier is 84.6%,while the false negative rate(FNR)is only about 3%.A comparison between linear logistic regression and NN shows the highly non-linear nature of our proposed set of features.

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.

Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks

This paper poses a question:How many types of social relations can be categorized in the Chinese context?In social networks,the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes,rather than just indicating whether the link exists or not.Previou research suggests that Granovetter measures tie strength so as to distinguish strong ties from weak ties,and the Dunbar circle theory may offer a plausible approach to calculating 5 types of relations according to interaction frequency via unsupervised learning(e.g.,clustering interactive data between users in Facebook and Twitter).In this paper,we differentiate the layers of an ego-centered network by measuring the different dimensions of user's online interaction data based on the Dunbar circle theory.To label the types of Chinese guanxi,we conduct a survey to collect the ground truth from the real world and link this survey data to big data collected from a widely used social network platform in China.After repeating the Dunbar experiments,we modify our computing methods and indicators computed from big data in order to have a model best fit for the ground truth.At the same time,a comprehensive set of effective predictors are selected to have a dialogue with existing theories of tie strength.Eventually,by combining Guanxi theory with Dunbar circle studies,four types of guanxi are found to represent a four-layer model of a Chinese ego-centered network.