Regenerative pixel mode and tumour locus algorithm development for brain tumour analysis: a new computational technique for precise medical imaging

Bangare, Sunil L.; Pradeepini, G.; Patil, S. T.

doi:10.1504/ijbet.2018.10014305

Cited by 12 publications

(6 citation statements)

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“…The core component of this model is LFLSeg, which is a label-free and weakly supervised segmentation module. In this model, it uses feature maps to extract the segmentation information [6], and this in return helps the model to learn about dense and interior regions of the leaf images implicitly. The output of the segmented image is projected as a heat map so that it calculates the probability of each pixel in the final decision.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Govathoti¹,

Reddy²,

Kamidi³

et al. 2022

IJACSA

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Designing an automation system for the agriculture sector is difficult using machine learning approach. So many researchers proposed deep learning system which requires huge amount of data for training the system. The proposed system suggests that geometric transformations on the original dataset help the system to generate more images that can replicate the physical circumstances. This process is known as "Image Augmentation". This enhancement of data helps the system to produce more accurate systems in terms of all metrics. In olden days when researchers work with machine learning techniques they used to implement traditional approaches which are a time consuming and expensive process. In deep learning, most of the operations are automatically taken care by the system. So, the proposed system applies neural style and to classify the images it uses the concept of transfer learning. The system utilizes the images available in the open source repository known as "Kaggle", this majorly consists of images related to chilly, tomato and potato. But this system majorly focuses on chilly plants because it is most productive plant in the South Indian regions. Image augmentation creates new images in different scenarios using the existing images and by applying popular deep learning techniques. The model has chosen ResNet-50, which is a pretrained model for transfer learning. The advantage of using pretrained model lies in not to develop the model from scratch. This pre-trained model gives more accuracy with less number of epochs. The model has achieved an accuracy of "100%".

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Section: Literature Reviewmentioning

confidence: 99%

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Govathoti¹,

Reddy²,

Kamidi³

et al. 2022

IJACSA

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“…They may considerably boost the efficacy of "straggler detection" when used simultaneously. Bangare et al [18][19][20][21][22] have contributed Machine learning projects for medical images. Shelke et al [23] and Gupta et al [24] also worked on the similar domain of research.…”

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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Regenerative pixel mode and tumour locus algorithm development for brain tumour analysis: a new computational technique for precise medical imaging

Cited by 12 publications

References 0 publications

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

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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