Precise Approach for Modified 2 Stage Algorithm to Find Control Points of Cubic Bezier Curve

Pande, Sandeep Dwarkanath; Patil, Urmila; Chinchore, Rahul; Chetty, Manna Sheela Rani

doi:10.1109/iccubea47591.2019.9128550

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Gupta et al [16] worked on user emotions. Pande et al [17][18][19] worked on the spline curve etc. The mathematical model for the classification using deep neural network approach is very important to get higher accuracy [20][21][22].…”

Section: Figure 2 Generic Model For Language Processingmentioning

confidence: 99%

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Shelke¹,

Vanjale²

2022

ISI

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NER assumes a key part in Information Extraction from reports (for example email), conversational information, and so forth. Many tongue handling applications, for example, data recovery, question responding to, and machine interpretation, depend on NER. It tends to be challenging to determine the ambiguities of lexical components utilized in a text arrangement. There is too much work has been already done in English language but there is a need to improve accuracy for the NER in Hindi language. In this research researcher are minimize chances of misclassification by using different classifier namely location, name, weather etc. BiLSTM Development of a NER framework for Indian languages is a similarly troublesome task. In this paper, Researcher have done the different research to contrast the aftereffects of NER and typical implanting and quick text implanting layers to examinations the exhibition of word installing with various bunch sizes to prepare the profound learning models. In this paper, Researcher have done the different examinations to contrast the consequences of NER and typical implanting and quick text installing layers to investigations the presentation of word inserting with various group sizes to prepare the profound learning models. The value of the precision of proposed system architecture is 76.13% which is way more than other system architectures. Also, the value of recall and F1-score of proposed system architecture is 71.49 and 74.26 respectively. So, by comparing proposed system architecture with existing SpaCy, CoreNLP and NLTK it is easy to conclude that proposed system architecture is reliable in all the sense.

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Section: Figure 2 Generic Model For Language Processingmentioning

confidence: 99%

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Shelke¹,

Vanjale²

2022

ISI

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“…Shelke et al [23] and Gupta et al [24] also worked on the similar domain of research. Pande et al [25][26][27] worked on the spline curve etc. Basic concept of staggler and ML are referred from the papers [28][29][30][31].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments

Gaykar¹,

Khanaa²,

Joshi³

2022

ISI

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Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.

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“…Apichanukul et al [25] using coding, clustering, as well as adaptive selection, researchers investigated the trade-off between wallclock duration, networking, and processing needs for gradient-based dispersed training. Bangare et al [26][27][28][29], Shelke et al [30], Gupta et al [31], Awate et al [32] and Pande et al [33][34][35] have worked in the area of the machine learning and IOT issues etc. Stragglers benefit both from coding as well as clustering, whereas adaptive selection aims to minimize computational and communication demands.…”

Section: Literaturementioning

confidence: 99%

Faulty Node Detection in HDFS Using Machine Learning Techniques

Gaykar¹,

Khanaa²,

Joshi³

2022

RIA

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The design of Hadoop has ability to elimination of fault tolerance, which consists of rescheduling the task on the defective nodes to run on other devices in the system. However, this strategy is ineffective if an error arises after most of the task has been completed. As a result, it is essential to make an early detection of the problem at the node to ensure that the resumption of the work will not result in a significant loss of both time and productivity. The ability to predict these problems provides us with the required time to move the workload onto different nodes, which helps to avoid data loss or processing time. In this paper, we propose an identification of faulty nodes from a large Hadoop distributed environment using machine learning techniques. Initially, we deployed one controller node and numerous data nodes as virtual machines in a distributed manner. The execution performs when the end-user submits a specific job to the controller node. The master node is the middleware controller that continuously communicates with other data nodes and assigns a task to each data node accordingly. However, this conventional process of HDFS that can generate data leakage or high computation whenever the specific node is heated or straggler. In our approach, we initially collect the log history of each data node and apply some statistical and a few machine learning algorithms to identify nodes' status. According to the achieved outcome of each node, we can decide to eliminate the specific node for task execution. We applied five machine learning algorithms in the extensive experimental analysis, including a Support Vector Machine (SVM). The SVM obtains 96.7% higher accuracy over the conventional machine learning classifiers for the entire execution.

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Precise Approach for Modified 2 Stage Algorithm to Find Control Points of Cubic Bezier Curve

Cited by 8 publications

References 10 publications

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments

Faulty Node Detection in HDFS Using Machine Learning Techniques

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