Huge Amount Of Datasets Research Articles

Compressive-sensing model reconstruction of nonlinear systems with multiple attractors

In this study, facing the challenges on model reconstruction for multi-attractor nonlinear systems, the data generation and sparse regression processes in sparse identification method are generalized, referring to compressive sensing, to obtain the accurate description under the least volume of test data set. In the data generation process, we introduce compressive sensing process by an arbitrary initial condition and arbitrary perturbations applied on time domain to transcend the local attractors. In the sparse regression process, the optimum sparse parameter is obtained by bi-optimization criterion according to accuracy and interpretability. Then, accuracy criterion is proposed, and when it discovers the unperceived dynamic behaviors, more dynamic data could be recorded and added into the modeling reconstruction data set by perturbations. It requires less dynamic signals by dynamical compressive sensing with perturbations compared to the previous method with uniform point fetching on the state space. Several numerical cases and two experiments of nonlinear systems with different kinds of multi-attractors are proposed to illustrate the effectiveness of the reconstruction method. In experiment, for the dynamic systems with multi-steady states phenomenon, the most obvious problem is the calibration of the equilibrium at the symmetrical configuration, which cannot be obtained for local dynamic behaviors due to its instability. In applications, this generalized modeling reconstruction method can continuously and compressively sense the dynamic behaviors and the stability of multiple attractors to figure out the accurate governing equations under a small quantity of data. In summary, different from the previous sparse regression algorithm for nonlinear systems with multiple attractors under huge amount of data set assembled offline filling a large enough state space, the proposed model reconstruction process can intelligently sense the dynamic behaviors and give the accurate prediction.

KIPEs3: Automatic annotation of biosynthesis pathways

Flavonoids and carotenoids are pigments involved in stress mitigation and numerous other processes. Both pigment classes can contribute to flower and fruit coloration. Flavonoid aglycones and carotenoids are produced by a pathway that is largely conserved across land plants. Glycosylations, acylations, and methylations of the flavonoid aglycones can be species-specific and lead to a plethora of biochemically diverse flavonoids. We previously developed KIPEs for the automatic annotation of biosynthesis pathways and presented an application on the flavonoid aglycone biosynthesis. KIPEs3 is an improved version with additional features and the potential to identify not just the core biosynthesis players, but also candidates involved in the decoration steps and in the transport of flavonoids. Functionality of KIPEs3 is demonstrated through the analysis of the flavonoid biosynthesis in Arabidopsis thaliana Nd-1, Capsella grandiflora, and Dioscorea dumetorum. We demonstrate the applicability of KIPEs to other pathways by adding the carotenoid biosynthesis to the repertoire. As a technical proof of concept, the carotenoid biosynthesis was analyzed in the same species and Daucus carota. KIPEs3 is available as an online service to enable access without prior bioinformatics experience. KIPEs3 facilitates the automatic annotation and analysis of biosynthesis pathways with a consistent and high quality in a large number of plant species. Numerous genome sequencing projects are generating a huge amount of data sets that can be analyzed to identify evolutionary patterns and promising candidate genes for biotechnological and breeding applications.

ASSESSING LONG-TERM IMPACTS OF DISASTER USING PREDICTIVE DATA ANALYTICSFOR EFFECTIVE DECISION SUPPORT

Disaster is a big issue that seriously disrupts and affects the community or society. The impact of a disaster causes a short and long period of time. To analyze the impacts of disasters there are lots of available related datasets. Data analytics methods have the potential to assess the impacts of different types of disasters. Collectively data analytics and machine learning techniques play an important role in transforming and being able to make decisions about our social, economic, mental, and psychological things. The objective of this paper is to assess the impacts of disasters from immediate term to long-term, provide crucial help to the emergency management workforce, and policy decisions making based on the latest available datasets. With the help of the various data agencies, extraction of information and activities carried out, we can determine the effects on disaster victims, their community and impacts on society in general. The analysis provides the statistics that can guide our emergency service about the status of facilities that can further support the survivors, and other related information. Detailed assessment i.e., structural survey and hazard mapping provide specific information about reconstruction and mitigation to monitor the situation, needs of the victims, and supporting entities. The assessment is based on the type of disaster that happened and its impact after a few years. In the current technological advancement of data analytics and machine learning algorithms, the prediction of the long-term effects of a disaster can be performed. Analyzing the impacts over a long period of time is also dependent on the growth of actively cared datasets gathering bodies like agencies, government, NGOs, media, etc., where prediction of short-term and long-term impacts is dependent on the available datasets. Available datasets are preprocessed using data analytics tools and implementation of training and testing for the purpose of predictions and recommendations. As a huge amount of data sets are available through different sources the classification of the datasets can also be performed resulting fast and accurate processing. Model validation techniques play an important role to check the validation, test result, and related outcomes. In this paper advanced machine learning and data analytics tools i.e., XG boost, modified SVM, and modified RF are used for better prediction. The analysis of the short-term effects of disasters has already been suggested and recommended by the various conventional approaches. Here the focus is to analyze and detect the long-term effects of a disaster along with recommendations and models preparing for good decision-making. Therefore, planning should be focused on assessing the impacts from short-term to long-term. The findings of the paper would be helpful to the agencies, local & national authorities, and the government by recommending action plans and their future effects for a longer period in case of disaster.

Novel Approach on sustainable Topic Modelling paradigms for Recognition of health-related topics in a social platform through Distributed file system

social media is one of the most sustainable and prominent features of the Internet and has a vast amount of influence in today’s world. All the latest information regarding every topic is available on social media i.e., social media serves as a sustainable repository of the latest information, particularly Twitter where discourse on every major trending topic takes place and is shown to the user hence it is utilized by many individuals all over the world to keep themselves updated with the latest news occurring everywhere in the world. In Current time due to the pandemic, Twitter also played a key role in relaying updated and accurate information regarding many topics related to health care. Millions of people everyday share their ideas and opinions regarding health-related concepts. To analyze these many tweets which contain billions of users is a challenging task and so to understand the key features or topics discussed by the user’s artificial intelligence has a branch called Natural language processing also called as NLP which can help in analyzing texts from a huge amount of dataset. Topic modeling, which is a part of NLP helps in analyzing healthcare data extracted from twitter which would provide a description of current trending healthcare topics and development of most significant tweets and their content.

Self-Supervised Learning for Medical Imaging

Abstract: The role of deep learning is growing quite effectively. As the created models are producing promising accuracy and the early detection and mitigation of diseases is becoming quite easy. As a result, the deep learning algorithm is receiving a variety of interest nowadays for fixing several problems within side the area of scientific imaging. In ophthalmology, one instance is detecting disorder or anomalies by using photos and classifying them into diverse disorder types or severity levels. This sort of project has been finished the use of quite a few machine learning algorithms which have been optimized, in addition to theoretical and empirical approaches. Diabetic Retinopathy is such a disease where in early detection plays a severe role as it could result in imaginative and prescient loss. Diabetic Retinopathy disease recognition has been one of the active and challenging research areas in the field of image processing. Deep learning technique as well as hinders to work with disease recognition and find the accuracy of the model. To create a model in a supervised manner, we need a huge amount of dataset which is very costly. So, as to overcome this problem, we have implemented a self - supervised model for the detection of diabetic retinopathy which works with a very limited dataset. This model is implemented using one of the pretext/proxy task image rotations developed on Dense NET architecture. The model is fine-tuned with the various quantities of subsets of the original dataset and compared internally.

Crime Pattern Analysis, Visualization and Pridiction Using Data Mining

Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analyzing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyze crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyze, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various types of crimes against women taking place in different states and cities of India. Crime mapping will help the administration to plan strategies for prevention of crime, further using data mining technique data can be predicted and visualized in various form in order to provide better understanding of crime patterns.

GRADE: A Novel Gravitational Density-Based Clustering Approach in the Multi-Objective Framework

Background: Clustering analysis plays a vital role in obtaining knowledgeable data from the huge amount of data sets in knowledge discovery. Most of the traditional clustering algorithms do not work well with high dimensional data. The objective of effective clustering is to obtain well connected, compact, and separated clusters. Density-Based Clustering (DBSCAN) is one of the popular clustering algorithms that use local density information of data points to detect clusters with arbitrary shapes. The Gravitational Search Algorithm (GSA) is one of the effective approaches inspired by Newton’s law of gravitational force where every particle in the universe attracts every other particle with a force. Objectives: The primary aim of this paper is to design and develop a novel multi-objective clustering approach to produce the desired number of valid clusters. Further, these resulting clusters are to be optimized to obtain an optimal solution. Methods: In the proposed approach, a hybrid clustering algorithm based on GSA along with DBSCAN is recommended to group the data into the desired number of clusters, and in the next phase of the algorithm, Particle swarm optimization technique is applied in order to optimize the solutions using the fitness functions. Results: In the analysis of the result, we employed two objective functions namely quantization error and inter–cluster distance on four real-life data sets such as Iris, Wine, Wisconsin, and Yeast to evaluate the performance of our algorithm. Conclusion: The effectiveness of the GRADE algorithm is comprehensively demonstrated by comparing it with the well-known traditional K-mean algorithm in terms of accuracy and computational time.

Flawless Identification of Fusarium Oxysporum in Tomato Plant Leaves by Machine Learning Algorithm

In the olden days, plant diseases could be measured by visual observation and based on the level and severity of the symptoms on plant leaves. Over the day, it became a high-level degree of complexity due to the huge volume of cultivated plants. Now a day, the diseases are very different due to diverted manure procedures, and its diagnosis will be very tough even experienced farmers and agronomists too. Even though, after diagnosis, there is a lack of perfect remedy or mistaken treatment for that. The plants are affecting by many vascular fungal diseases which are widespread in many crops. Fusarium wilt (FW) is one of the fungal diseases in many plants. Mostly the tomato, sweet potatoes, tobacco, legumes, cucurbits plants are affected by this Fusarium oxysporum (FO) disease often due to its soil. The main goal of this research article is used to determine FO disease in the tomato plant leaves. Besides, the proposed algorithm constructs model with two times classifying and identifying the disease for better accuracy. The open database consists of 87k images with 60% affected leaves images, 40% healthy plant leaves too. Our proposed hybrid algorithm is found the disease with 96% accuracy with the huge amount of dataset.

COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION

There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.

Application of Parameter Estimation: A Big Data Analytics Perspective

At the present time there are various ways to human interaction like human to machine and machine to machine interaction. Today the medium of human interaction is social media which is very popular. The social media interaction can be a sharing ideas, sending text messages, video, and audio call and machine to machine interaction such as automatic door opening, traffic control, telemedicine, weather forecast etc. the machine learning are using different devices for a particular purpose. Those devices are generating a huge amount of data in a various format like numerical, text, audio, video, images in every second such types of data is known as big data. Big data can be defined based on three parameters, volume, variety, and velocity. If the data has volume (large data sets), variety (text, images, audio, video) and velocity (speed of data). For the store and process those complex data we need a reliable and appropriate algorithm. In this paper, we have proposed a framework based on big data parameter estimation. The proposed framework will predict the useful information from the heterogeneous with huge amount of data sets through suitable sampling techniques.

Robust Iris Classification Based on Deep Neural Network (DNN) and Stationary Wavelet Transform (SWT)

The eye is a vital part of our body. It consists of several layers like sclera, retina, tunica, and iris. Among these several layers, Iris plays a vital role in human visionary. There are various infections which affect the Iris functioning. The sign, symptoms, and diagnosis of this is still a challenge for doctors. To overcome this many techniques and technologies have been introduced. But still, the existing system has several drawbacks in recognition like a huge amount of dataset, classification, extraction, etc. To overcome this we propose a system where Deep Neural Network plays a major part. It classifies the iris disease in our eyes in a more clear and precise manner. In additional to Deep Neural Network several other algorithms have been used like Stationary Wavelet Transform, for image selection and recognition, Local Binary Pattern, for Feature extraction and at a final stage Deep Neural Network for classification of Iris images.

How to Improve Total Data Quality Management (TDQM) Using the Semantic Technology

Total data quality management is considered one of the most popular research areas in the last decade, where improving the quality plays the main role in the targeted achievement for any enterprise. Also, there is a lot of emerging information technologies which aid in improving the quality in different aspects. Through this research we will focus on how to improve the quality of data using the most recent technology in IT fields which is named Semantic Web technology by conducting set of hypotheses which aim to prove whether there is a the relation between the improving of web data and the usage of semantic web techniques or not. Also, we will focus on how to improve the performance of data query processing specially when having huge amount of dataset using the big data technologies

Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.

SSLSS: Semi-Supervised Learning-Based Steganalysis Scheme for Instant Voice Communication Network

In the field of instant voice communication (IVC) steganalysis, the traditional detecting methods are mainly based on supervised learning scheme that results in a large amount of complex manual pre-processing training data set. The accuracy of supervised learning scheme can be easily destroyed by the difference between the distribution of training and testing data set in the actual voice application. The disadvantages of this method are obvious in the big data environment. In this regard, this paper initially introduced a novel semi-supervised hybrid learning detection model for the IVC network. This provides the progress of manually annotating training data set that has been removed to solve the problem of complex operations and poor applicability in classifier. Therefore, this model has a simpler structure and more extensive detection scopes with the huge amount of data. Then, we designed a multi-criteria fusion module that can automatically generate the pseudo-label set from testing data set to train the classifier model. Thus, our scheme will not be affected by the distribution shift. In this module, we defined the confidence level and representative level to judge the feature vector for pseudo-labeled. Through the experimental analysis, the low bit-rate speech coding steganalysis (G.723.1/G.729/iLBC speech codecs) is analyzed on quantization index modulation that are common codecs in the IVC network. The results show that our method has higher accuracy than un-supervised method. The proposed approach is less affected and more accurate than the previous supervised methods through the distribution of different training and testing data sets. The experiments also proved that our method can be deployed in the different kinds of the IVC codec by considering huge amount of data set.

A Review on K-Mode Clustering Algorithm

The main purpose of the process of data mining is to extract useful information from a huge amount of dataset. As one of the most important tasks in data mining, clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. It is a form of unsupervised learning that means how data should be grouped the data objects (similar types) together will be not known in advance. The algorithms used for clustering are k-means algorithm, k-medoid algorithm, k-nearest neighbour algorithm, k-mode algorithm etc. The K-Mode Algorithm is an eminent algorithm which is an extension of the K-Means Algorithm for clustering data set with categorical attributes and is famous for its simplicity and speed. The ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Mode’ of clusters is used instead of ‘Means’. In this paper, review on the K-Mode Algorithm is done.

A Binary Particle Swarm Algorithm with A Simple Mutation Operator for Solving Phylogenies Problems

Manipulating a huge amount of data sets makes the mechanism of searching very difficult to find the bestfeatures combinations in phylogenetic tree construction. This article has proposed a binary particle swarm optimizationalgorithm that randomly selects chloroplast genes to construct phylogenetic trees. Also, a simple mutationoperator the article has suggested to speed up the search and facilitate the search process to find the best chloroplast genescombinations. These combinations can be evaluated according to the bootstrap values of all the nodes of theconstructed tree.

A prediction-based dynamic replication strategy for data-intensive applications

Data-intensive applications produce huge amount of data sets which need to be analyzed among geographically distributed nodes in grid computing environment. Data replication is essential in this environment to reduce the data access latency and to improve the data availability across several grid sites. In this work, an Intelligent Replica Manager (IRM) is designed and incorporated in the middleware of the grid for scheduling data-intensive applications. IRM uses a Multi-criteria based replication algorithm which considers multiple parameters like storage capacity, bandwidth and communication cost of the neighboring sites before taking decisions for the selection and placement of replica. Additionally, future needs of the grid site are predicted in advance using modified apriori algorithm, which is an association rule based mining technique. This IRM based strategy reduces the data availability time, data access time and make span. The simulation results prove that the proposed strategy outperforms the existing strategies.

Nature Inspired Feature Selection Approach for Effective Intrusion Detection

Objectives: To reduce the dimensionality of network traffic dataset by selecting the relevant and irredundant features for accurate and quick intrusion detection. To achieve the target, we proposed a new feature selection approach based on the nature. Methods/Statistical Analysis: The proposed Modified CuttleFish Algorithm (MCFA) approach plays a crucial role in intrusion detection by selecting appropriate subset of most relevant features from huge amount of dataset. Griewank fitness function is used to calculate the fitness of the modified cuttlefish algorithm. Naive bayes classifier is employed at the generated subset of features from benchmark KDD 99 dataset in WEKA data mining tool Compare the results of proposed approach with the existing approaches of WEKA and Improved Cuttlefish Algorithm (ICFA), with different performance metrics. Findings: As per the outcomes are obtained by the WEKA Experimenter with the 9 feature selection approaches on KDD 99 10% training dataset, it has been observed that the Consistency Subset feature selection approach with Greedy stepwise search method gives higher accurate results than other approaches and from the literature survey has been found that the ICFA performs well, but still there is problem of low True positive (TP) rate and False negative (FN) rate. These problems are addressed by the proposed feature selection approach which outer perform best from others in accuracy rate (91.79%), True positive rate (0.947), false positive rate (0.025) and ROC area (0.9982) with the minimum amount of time at 19 relevant subset of features instead 41 features. As the consequence, the proposed approach is novel from existing approaches to increase the intrusion detection rate and discard the redundant and irrelevant subset of features. Application/Improvements: MCFA has improved intrusion detection rate by increasing the TP rate and decreasing the FP rate, so MCFA can be used for the real time applications of intrusion detection system. Keywords: Accuracy, Dimensionality, Feature Selection Approach, FP Rate, Intrusion Detection, Modified Cuttlefish Algorithm, TP Rate

Challenges with Big Data Analytics

The current computer science and industry approach to the Big Data Analytics emphasizes on the importance of Graph processing, how to scale up the capability to process big graph and many algorithms are developed in this line of thinking. However, the more fundamental issue to deal with huge amount of Data objects with many attributes cannot be avoided. Huge amount of datasets from various complex systems are flourishing in the last few years, thus, the exploration of these datasets are supposed to lead the discovery of the unexpected new Data Laws. This paper will examine the challenges of big data, the solution of handling big data and the work has been done in the ASGC.

Analysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer

Big data analysis plays a crucial role in the health care for early diagnosis of fatal disease. The data mining techniques are widely used for data analysis problem to discover valuable knowledge from a large amount of data. This paper uses the data mining methods such as feature selection and classification to provide a predictive model for ovarian cancer detection. A huge amount of dataset is gathered to build knowledge based system. Rough set theory is utilized to find the data reliance and reduce the feature set contained in the data set. The Hybrid Particle Genetic Swarm Optimization (PGSO) is used to optimize the selected features to efficiently classify the ovarian cancer, either normal or early or different stages of ovarian cancer. Multi class SVM is adopted as the classifier to classify normal or different stages of ovarian cancer using the optimized feature set. The experiment is done on different ovarian cancer dataset and the proposed system has obtained better results for all datasets.