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Evaluation of machine learning algorithms for surface water delineation using landsat 8 images
Surface water detection and delineation is an important and necessary step in change detection studies on water bodies using multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral un mixing based methods. This paper presents a comparative study of commonly used machine learning algorithms viz. ANN, SVM, Decision Tree, Random Forest and K-means clustering for their suitability and effectiveness when applied on Landsat 8 images for surface water detection and delineation. The algorithms are compared for their classification accuracy and execution time. While all the above mentioned algorithms exhibited their usefulness in water detection, Decision Tree and Random Forest algorithms were found be faster in both training phase and testing phase and also yielded better accuracy with fewer miss-classifications. Though K-means clustering with more than four clusters yielded results comparable to that of supervised classification methods, it requires appropriate post-processing to obtain the output image with only two clusters; corresponding to water pixels and non-water pixels. Pierson's correlation co-efficient and Structural similarity Index (SSIM) are computed to compare the correlation and similarity of the output images yielded by the algorithms being studied. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Geospatial crime analysis to determine crime density using kernel density estimation for the indian context
Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime. 2020 American Scientific Publishers. -
A comparative study on the adaptability of the different varieties of solanum lycopersicum L. (tomato) in salt stress condition
The objective of the present study was to study the levels of antioxidant and oxidant metabolites such as total protein, proline, peroxidase, lipid peroxidase, catalase,and anthocyanin and phenol contents in nine varieties of tomato plants (Cerasiforme (Cherry tomato), Indamrohini, Marglobe, Ns 538, Sacchariya, San Marzano 3, Suhyana, Tomato Oblate Yellow, Vanda) treated under various NaCl concentrations (50, 100, 200 and 250 Mm). Salinity is one of the significant abiotic stresses, which affects plant cell metabolism and reduces plant productivity. Plants tolerant to NaCl implement a series of adaptations to acclimate to salinity, including morphological, physiological and biochemical changes. Under saline conditions, plants have to activate different physiological and biochemical mechanisms in order to cope with the resulting stress. Such mechanisms inclu de changes in morphology, anatomy, water relations, photosynthesis, the hormonal profile, toxic ion distribution and biochemical adaptation (such as the antioxidative metabolism response). An updated discussion on salt-induced oxidative stress and its effect on the antioxidant machinery in both salt-tolerant and salt-sensitive plants is the major part of this study. The aim of the present study is to extend our understanding of how salinity may affect the physiological characteristics of plants. 2020 IJSTR. -
Friction and wear behaviour of copper reinforced acrylonitrile butadiene styrene based polymer composite developed by fused deposition modelling process
This paper focuses on the development of copper filled Acrylonitrile Butadiene Styrene (ABS) composites by fused deposition modelling (FDM) and to characterize its friction and wear behaviour. Twin screw extrusion technique was employed to extract copper-ABS composite filament. Three different materials were tested, i.e. pure ABS, ABS+2.5wt% Cu and ABS+5wt% Cu. Friction and wear characteristics of pure ABS and copper filled ABS composites were tested under various loads and sliding velocities. Addition of Copper powder has significantly improved the friction and wear properties of the developed composites. Further, it is also observed that friction and wear behaviour increased with increase in copper content in ABS. Worn out surfaces were subjected to scanning electron microscopy studies to analyse and identify the possible wear mechanisms involved. Faculty of Mechanical Engineering, Belgrade. -
A Signature-Based Mutual Authentication Protocol for Remote Health Monitoring
Remote health monitoring can offer a lot of advantage to all the players in healthcare industry and it can contribute to reduced healthcare expenses. Wireless medical sensor networks capable of accumulating and transferring vital parameters of patients play a crucial role in remote health monitoring. Security and privacy are major concerns preventing the patients from adopting this technology with an open mind. This paper presents a signature-based authentication protocol for remote health monitoring. The work also discusses an authentication protocol for the mutual authentication of users and medical server. The protocol does not require the server to maintain a password table. The proposed algorithms are resistant to various attacks such as replay attack, stolen verifier attack, and privileged insider attack. The work includes the informal and formal security analysis of the proposed protocols. Scyther tool is used for formal security analysis and the results show that the protocol is resistant to various common and automated attacks. 2019, Springer Nature Singapore Pte Ltd. -
Markov based genetic algorithm (M-GA): To mine frequent sub components from molecular structures
Processing the molecular compounds to identify the internal chemical structure is a challenging task in bio-chemical research. Popular approaches, mine the frequent subcomponents from the molecules with chemical and biological properties represented in the form of feature vector histogram. Though this helps to identify the absence or presence of mined feature, calculating the frequency of every frequent substructure involves sub graph isomorphism test which is an NP-Complete process. To overcome the above mentioned bottleneck we proposed Markov based Genetic algorithm (M-GA) in which the chemical descriptors were considered from two-dimensional representations of molecules that classify chemical compounds using mining significant substructure and generates the binary vector that generate pure active classes, singleton reactors, descriptor sets. This method scales down the process of mining substructures that are statistically significant from huge chemical databases. The results shows that the performance of proposed algorithm is improved compared to the existing algorithms. 2020, Research Trend. All rights reserved. -
Performance of pradhan mantri fasal bima yojana: Perception of farmers in rural bangalore
Crop insurance is an agricultural development program supporting the sustainability of farmers. PradhanMantriFasalBimaYojana crop insurance scheme was introduced to provide insurance cover, financial stability, innovative and modern methods of agricultural practice. The study primarily focuses on the reasons for enrollment, benefits, challenges and suggestions regarding the PradhanMantriFasalBimaYojana with respect to farmers of Rural Bengaluru. A qualitative thematic analysis using a primary study reveals PMFBY as a source of financial security and financial stability with reduced premium that increases the confidence level among the farmers. 2019 SERSC. -
Cuda implementation of non-local means algorithm for GPU processors
Non-Local Means algorithm (NLM) is a prominent image denoising algorithm. One of the major limitations of NLM algorithm and its variants is the time requirement. In this era of high performance computing, an efficient alternative to reduce the time complexity of any algorithm is its parallelization. In this paper, a parallelized version of basic NLM algorithm using CUDA architecture is proposed. The algorithm is developed on NVIDIA GeForce 940M GPU which follows Maxwell architecture with 3 SMs and 384 CUDA cores. Experiments are carried out using selected set of natural and medical images of various sizes. Our proposed parallelized version of NLM algorithm reduces the time requirement approximately by 50% in comparison to its basic version and also achieves comparable denoising performance in terms of PSNR, SSIM and FSIM evaluation metrics. The proposal is a model which can be customized for newer GPU architectures. 2020, Engg Journals Publications. All rights reserved. -
Random forest application on cognitive level classification of E-learning content
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through E-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with random forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level. The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Some new results on proper colouring of edge-set graphs
In this paper, we present a foundation study for proper colouring of edge-set graphs. The authors consider that a detailed study of the colouring of edge-set graphs corresponding to the family of paths is best suitable for such foundation study. The main result is deriving the chromatic number of the edge-set graph of a path, Pn+1, n ? 1. It is also shown that edge-set graphs for paths are perfect graphs. 2020 the author(s). -
Canopy removal on satellite images using classification and contrast enhancement
The increasing the usage of satellite remote sensing for a civilian purpose has proved to be the most cost-effective mapping environmental changes with regard to natural resources, particularly in developing countries. Forests as one part of the wildlife of human societies in economic growth and permanency of natural resources in the countries of the world. But because of various details such as the growth of population, progressively varying forest to the other unfitting applications such as agriculture, providing energy and fuel, millions of hectares from the natural means are destroyed every year, and the remainder of the surface changes quantitatively and qualitatively. For better management of the forests, the evolution of forest area and rate of forest concentration should be examined. It is achievable that, there isnt any change in the field of the forest during the time, but the density of forest canopy is changed. Estimation of forest canopy cover has recently become an essential part of the forest. Therefore, the research study is to develop Forest Canopy Remover, which is used to get an accurate result of Forest and deforested area from the satellite earth images. It is used to calculate forest density using vegetation. Then, the changes in area and forest density during a particular period can be distinguished. 2020 IJSTR. -
Perception of information and communication technology tools among small and medium enterprises in Bengaluru
The Small and Medium Enterprises (SMEs) sector is a critically important sector. Despite its large contribution to the economy of the country, SMEs are not in a good position in terms of finance, technology and markets at present. The major problem faced by SME?s in India is the adoption of technology. The basic aim of this study is to evaluate the Information and Communication Tools (ICT) adoption by SME?s in India. For the study, a survey consisting of a self-administered questionnaire was conducted. The study utilized correlation and regression analysis. The findings prove that the institutional pressures have no significant influence on the advantages of ICT adoption, Challenges of ICT adoption and Awareness of different government schemes. Benefits of ICT adoption has moderate influence on Challenges of ICT adoption. The study showcases the factors that motivate entrepreneurs, firm owners to adopt ICT, and the challenges that an SME will face for ICT adoption. 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Polyhydroxyalkanoate biosynthesis and characterization from optimized medium utilizing distillery effluent using Bacillus endophyticus MTCC 9021: a statistical approach
Use of expensive raw material and inefficient extraction method has limited the production of biopolymers at pilot scale compared to commercial plastics. Hence, this research work spotlights on production of polyhydroxyalkanoate (PHA) using Bacillus endophyticus MTCC 9021 in different dilutions of untreated distillery effluent (1025%) among which 10% dilutions exhibited maximum PHA production. Statistical optimization through Response surface methodology yielded a maximum PHA of 6.45 0.07 g/L. The obtained PHA was analysed by FTIR and NMR which confirms the polymer to be polyhydroxybutyrate. The extracted PHA was reduced to nanoscale to develop PHA nanosheet which exposed higher oil absorption and retention ability than normal PHA sheet. Further the rapid degradation of PHA nanosheet with synthetic plastics in garden soil emphasises their unique application in wide sectors. 2020, 2020 Informa UK Limited, trading as Taylor & Francis Group. -
Acute Toxicity of Leaf Extracts of Enydra fluctuans Lour in Zebrafish (Danio rerio Hamilton)
The present study was focused on the concentration-dependent changes in oral acute toxicity of leaf extracts of E. fluctuans in zebrafish. The study was also aimed at the details of histopathological changes in the gill, liver, brain, and intestine of zebrafish exposed to the leaf extracts of the plant E. fluctuans. Enydra fluctuans Lour is an edible semiaquatic herbaceous plant used widely for the alleviation of the different diseases. Since there were no toxicity studies conducted on this plant, the present study was an attempt to look into the elements of toxicity of the plants. Two types of experiments are conducted in the present study. First, the acute oral toxicity study was conducted as per the OECD guidelines 203. Second, histopathological changes were observed in the fishes exposed to the lethal concentrations of plant extract. The oral acute toxicity studies conducted on Zebrafish have revealed that the leave extracts of E. fluctuans were toxic to the tested fish at the concentration of 200 mg/kg body weight. The histopathological studies conducted on the intestine of treated fishes showed that treatment has induced rupturing of the villi structure and fusion of villi the membrane and detachment of the villi structure from the basal membrane of the intestine. The histology of the liver also showed severe vacuolization in the cells while it is not affected in control. The studies on gills showed the detachment of the basal epithelial membrane in the gills compared to control which might have led to death of the fish. The histopathological observations of brain tissues treated with test samples also revealed the marked impingement in the brain parenchyma while the control is normal without impingement of the brain. 2020 Jobi Xavier and Kshetrimayum Kripasana. -
Connected k-forcing sets of graphs and splitting graphs
The notion of k-forcing number of a graph was introduced by Amos et al. For a given graph G and a given subset I of the vertices of the graph G, the vertices in I are known as initially colored black vertices and the vertices in V (G) ? I are known as not initially colored black vertices or white vertices. The set I is a k-forcing set of a graph G if all vertices in G eventually colored black after applying the following color changing rule: If a black colored vertex is adjacent to at most k-white vertices, then the white vertices change to be colored black. The cardinality of a smallest k-forcing set is known as the k-forcing number Zk (G) of the graph G. If the sub graph induced by the vertices in I are connected, then I is called the connected k-forcing set. The minimum cardinality of such a set is called the connected k-forcing number of G and is denoted by Zck (G). This manuscript is intended to study the connected k-forcing number of graphs and the splitting graphs. 2020 the author(s). -
Optimized handwritten character recognition using artificial neural network
Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. -
Determinants of bank profitability in India: Applications of count data models
This paper employs count data models, namely Poisson and negative binomial regression to investigate whether macroeconomic factors increase or decrease the count of number of 18 Indian public sector banks in losses. The analysis is based on quarterly data from Q3 2009 to Q4 2019. This paper also considers one and two lagged macroeconomic factors. The results provide a new perspective for understanding the determinants of bank profitability. The contemporary, one and two lagged gross domestic product (GDP) growth rate and inflation increase the count of number of banks in losses. Further, the count of number of banks in losses surges with increase in contemporary and one lagged index of industrial production (IIP). However, one and two lagged exchange rates are significant to shrink the count of number of banks in losses. This study enables banks and policy makers to deliberate on the macroeconomic determinants considered for this study. 2020 Inderscience Enterprises Ltd. -
An enhanced biometric attendance monitoring system using queuing petri nets in private cloud computing with playfair cipher
Every educational institutions needs to analyse and monitor participation. Educationists believe that there should be a fair number of students available in the majority of their classes. In colleges participation is used a measure of consistency. To deal with this kind of a challenging situation, biometric based participation monitoring framework is being proposed. This proposed method with the assistance of face recognition will help in maintaining every detail about the present students in a classroom save the same in the class database. The camera captures the image of students and compares them with the existing visual data available in the database. In case, the software is not able to find a match for the captured data in the student database, the particular student is marked as absent. Queuing Petri nets help in fulfilling customised demands of various institutions along with providing better performance in terms of hold up time. With the application of this technology, classroom participation is recorded and saved every hour. The database is accessed and maintained using cloud services and necessary security measured are incorporated as provided by major private cloud service providers with playfair cipher technique. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Securing cloud data against cyber-attacks using hybrid aes with MHT algorithm
Cloud computing is dealing with large amount of data during data communication. This data processing is named as big data. The big data is growth of the demand in accessing the storage, computation and communication. This big data has the major defects. A raising issue in emerging big data is cost minimization. The architecture of big data ranges over multiple machines and cluster which have sub system. The major challenge of this big data is preprocessing and analysing the data patterns. This research article is dealing with different data pre-processing and secure data storage. There are many research challenges during this data process. The possible gap and drawbacks in the technology are identified through this survey and the efficient big data service is provided through MHT and AES algorithm. The main aim of this proposed method is to provide better data security during larger data process. The proposed hybrid MHT with AES algorithm is to minimize the encryption and decryption time apart from that it reduces the attacker ratio. All these parameters automatically increase the Quality of Service. Copyright Research Institute for Intelligent Computer Systems, 2020. All rights reserved.