A Traditional Analysis for Efficient Data Mining with Integrated Association Mining into Regression Techniques

Narayana, G. Surya; Kolli, Kamakshaiah; Ansari, Mohd Dilshad; Gunjan, Vinit Kumar

doi:10.1007/978-981-15-7961-5_127

Cited by 33 publications

(14 citation statements)

References 9 publications

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“…Because of limitations such as parameter selection or the need for prior knowledge of the images as well as significant calculation times, skull stripping is a common preparation stage in classical discriminative approaches [ 15 ]. Deep learning was utilized to construct a classification method for multigrade brain tumors, according to the authors of [ 16 , 17 ]. The CNN model is used to segment the tumor in this strategy, although the results are limited in accuracy and sensitivity because of the limitations of the CNN model [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Kolla

Mishra

Huq

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

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Section: Related Workmentioning

confidence: 99%

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Kolla

Mishra

Huq

et al. 2022

Computational Intelligence and Neuroscience

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“…AOCT-NET [ 23 ] is an 18-layer transfer learning architecture developed by Suryanarayana et al [ 24 ]. The authors of this paper compared the performance metrics of AOCT-NET to those of contemporary architectures in the literature.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Transfer Learning for Malaria Detection

Jameela

Athotha

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.

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“…ey applied two-phase algorithms: (a) clustering the user-item rating database to N partitions and (b) using memory-based CF algorithms to estimate recommendations for every user within the clusters based on only preferences from cluster members. An experimental study showed that making recommendations predictions within smaller clusters, using k-means algorithms, improves scalability in clustering techniques when compared with classical CF techniques; they reduce the number of neighborhoods to be tested due to the static precomputed clusters, and, as a result, the online prediction process becomes much faster [57]. e experiment in Sarwar et al's clustering methods presented two observations.…”

Section: Model-based Collaborativementioning

confidence: 99%

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Ahmed

Ansari

Singh³

et al. 2022

Mobile Information Systems

Self Cite

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Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

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A Traditional Analysis for Efficient Data Mining with Integrated Association Mining into Regression Techniques

Cited by 33 publications

References 9 publications

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Deep Learning and Transfer Learning for Malaria Detection

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

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