A DeepLSTM Model for Personality Traits Classification Using EEG Signals

Bhardwaj, Harshit; Tomar, Pradeep; Sakalle, Aditi; Acharya, Divya; Badal, Tapas; Bhardwaj, Arpit

doi:10.1080/03772063.2021.2012278

Cited by 13 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research [ 9 ] has demonstrated that combining genetic data with pathology images to diagnose tumors is very effective. For predicting breast cancer outcome, researchers [ 10 ] combined pathology images and genetic data. To connect the heterogeneous data of two modalities, a multiple kernel learning method was used.…”

Section: Literature Surveymentioning

confidence: 99%

“…The image data itself is used to produce high-level feature representation. The deep learning approach has gained huge profits and success in different applications like image recognition, objection detection, speech processing, and many others [ 10 ] with the addition and support of considerable parallel architecture and GPU. Physicians classify patients' symptoms into one of several illness classes based on their understanding.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

Hema

Ramachandran

Alhomrani

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Ovarian cancer is a serious sickness for elderly women. According to data, it is the seventh leading cause of death in women as well as the fifth most frequent disease worldwide. Many researchers classified ovarian cancer using Artificial Neural Networks (ANNs). Doctors consider classification accuracy to be an important aspect of making decisions. Doctors consider improved classification accuracy for providing proper treatment. Early and precise diagnosis lowers mortality rates and saves lives. On basis of ROI (region of interest) segmentation, this research presents a novel annotated ovarian image classification utilizing FaRe-ConvNN (rapid region-based Convolutional neural network). The input photos were divided into three categories: epithelial, germ, and stroma cells. This image is segmented as well as preprocessed. After that, FaRe-ConvNN is used to perform the annotation procedure. For region-based classification, the method compares manually annotated features as well as trained feature in FaRe-ConvNN. This will aid in the analysis of higher accuracy in disease identification, as human annotation has lesser accuracy in previous studies; therefore, this effort will empirically prove that ML classification will provide higher accuracy. Classification is done using a combination of SVC and Gaussian NB classifiers after the region-based training in FaRe-ConvNN. The ensemble technique was employed in feature classification due to better data indexing. To diagnose ovarian cancer, the simulation provides an accurate portion of the input image. FaRe-ConvNN has a precision value of more than 95%, SVC has a precision value of 95.96%, and Gaussian NB has a precision value of 97.7%, with FR-CNN enhancing precision in Gaussian NB. For recall/sensitivity, SVC is 94.31 percent and Gaussian NB is 97.7 percent, while for specificity, SVC is 97.39 percent and Gaussian NB is 98.69 percent using FaRe-ConvNN.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

Hema

Ramachandran

Alhomrani

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…Due to the exponential growth of social networking services, people tend to utilize these available services online. Similarly, on OTT platforms, users have plenty of choices, and hence, it is difficult for any user to decide what recommendation to follow [4][5][6].…”

Section: Evolution and Significance Of Recommendation Systemmentioning

confidence: 99%

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

Khalique

Rahmani

Saquib

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience.

show abstract

“…Despite this, the global tumour burden is expected to reach 28.40 million cases in 2040, which is a 48 percent increase from 2020. is will be further exacerbated by an increase in risk factors such as smoking. Standard challenges on recognising lung tumours in patients from decade include zero symptoms that are not related to age factor, patients who have breathing problems, patients who have smoked for 30-40 years, and patients who have no symptoms [3,4]. Numerous researchers have used a wide array of methodologies, including segmentation, detection, and classification techniques; in an effort to circumvent the challenges that are associated with the diagnosis of lung cancer [5,6], artificial intelligence has played an increasingly since it was first introduced.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

Joshi

Pandit²,

Shukla

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients’ courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name “computer-aided diagnostics” is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.

show abstract

A DeepLSTM Model for Personality Traits Classification Using EEG Signals

Cited by 13 publications

References 31 publications

Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

Contact Info

Product

Resources

About