Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit field-based ...ice thickness observations in the Himalaya. Remote-sensing applications, together with mathematical models, provide alternative techniques for glacier ice thickness and volume estimation. The objective of the present research is to assess the application of artificial neural network (ANN) modelling coupled with remote-sensing techniques to estimate ice thickness on individual glaciers with direct field measurements. We have developed two ANN models and estimated the ice thickness of Chhota Shigri Glacier (western Himalaya) on ten transverse cross sections and two longitudinal sections. The ANN model estimates agree well with ice thickness measurements from a ground-penetrating radar, available for five transverse cross sections on Chhota Shigri Glacier. The overall root mean square errors of the two ANN models are 24 and 13 m and the mean bias errors are ±13 and ±6 m, respectively, which are significantly lower than for other available models. The estimated mean ice thickness and volume for Chhota Shigri Glacier are 109 ± 17 m and 1.69 ± 0.26 km3, respectively.
Applications of unmanned aerial vehicles (UAVs) based remote sensing is increasing rapidly due to their advanced accessibility, capability for fast and easy deployment, capability for miniaturization ...of sensors and efficient collection of remotely-sensed data from relatively low altitudes. Recently, UAV data sets have been found to be quite useful for forest feature identification due to their relatively high spatial resolution. Several machine learning algorithms have been broadly used for remotely-sensed image classification. In remote sensing image classification, deep learning based methods can be considered quite effective techniques as they have achieved promising results. In this study, we have used deep learning based supervised image classification algorithm and images collected using UAV for classification of forest areas. The deep learning algorithm stacked Auto-encoder has been found to have tremendous potential regarding image classification and the assessment of forest coverage area. Our experimental results show that deep learning method provides better accuracy compared to other machine learning algorithms. Cross-validation showed that the overall accuracy of the deep learning method is about 93%. This study highlights the essential role that UAV observations and deep learning could play in the planning and management of forest areas which are often under the threat of deforestation and forest encroachment.
Malignant cancer angiogenesis has historically attracted enormous scientific attention. Although angiogenesis is requisite for a child's development and conducive to tissue homeostasis, it is ...deleterious when cancer lurks. Today, anti-angiogenic biomolecular receptor tyrosine kinase inhibitors (RTKIs) to target angiogenesis have been prolific in treating various carcinomas. Angiogenesis is a pivotal component in malignant transformation, oncogenesis, and metastasis that can be activated by a multiplicity of factors (e.g., VEGF (Vascular endothelial growth factor), (FGF) Fibroblast growth factor, (PDGF) Platelet-derived growth factor and others). The advent of RTKIs, which primarily target members of the VEGFR (VEGF Receptor) family of angiogenic receptors has greatly ameliorated the outlook for some cancer forms, including hepatocellular carcinoma, malignant tumors, and gastrointestinal carcinoma. Cancer therapeutics have evolved steadily with active metabolites and strong multi-targeted RTK inhibitors such as E7080, CHIR-258, SU 5402, etc. This research intends to determine the efficacious anti-angiogenesis inhibitors and rank them by using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE- II) decision-making algorithm. The PROMETHEE-II approach assesses the influence of growth factors (GFs) in relation to the anti-angiogenesis inhibitors. Due to their capacity to cope with the frequently present vagueness while ranking alternatives, fuzzy models constitute the most suitable tools for producing results for analyzing qualitative information. This research's quantitative methodology focuses on ranking the inhibitors according to their significance concerning criteria. The evaluation findings indicate the most efficacious and idle alternative for inhibiting angiogenesis in cancer.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of ...regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.
Availability and diversity of cloud service providers (CSPs) had put the users into confusion for its selection of the appropriate service providers. Some cloud service providers are good at some ...services while others are good at offering other services. The selection of an appropriate cloud service is one of the multi-criteria decision analysis (MCDA) problems that became a critical issue of public concern in the uncertain cloud industry. Based on multiple criteria, various multi-criteria decision-making (MCDM) methods can be used for the selection of the best CSPs. Researchers considered MCDM techniques as the best methodology for deciding cloud rank. The paper presents a set of decision criteria and their sub-criteria required for evaluating CSPs. The main goal of this paper is to present a review of various MCDM methods for decision-making. Furthermore, the strengths and weaknesses of various MDCM techniques are discussed to help the researchers about the current trends in the field of decision making. An overview of MCDM techniques used for Cloud service selection (CSS) is presented. Several methods used for deriving priority vectors from a Pairwise Consistency Matrix (PCM) in Analytic Hierarchy Process (AHP) technique, used in recent years are discussed in this research paper.
Abstract
The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important ...issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.
In this study, the domain of Android malware detection was explored with a specific focus on leveraging the potential of Machine Learning (ML). At the time of this study, Android had firmly ...established its dominance in the mobile landscape and IoT devices, necessitating a concerted effort to fortify its security against emerging malware threats. Static analysis methods were scrutinized as vital sources of feature extraction for ML, while dynamic analysis methods were employed to analyze the behavior of applications in real or simulated environments. Additionally, a hybrid method, combining both static and dynamic analyses, was investigated. The study evaluated four ML models: XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), revealing compelling insights into their performance metrics. Notably, RF achieved the highest accuracy of 0.99, closely followed by SVM with an accuracy of 0.96. These results underscore the potential effectiveness of ML techniques in bolstering Android malware detection and mitigating security risks. As the research progressed, it underscored the latent power of integrating ML into the framework of Android malware analysis. With an eye towards the future, the overarching goal was to empower enhanced security measures and foster a resilient mobile ecosystem through the insights gleaned from this investigation.
Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the ...detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.
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