SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network

Banbhrani, Santosh Kumar; Xu, Bo; Liu, Haifeng; Lin, Hongfei

doi:10.3390/info12100389

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The methods like deep LSTM + RNN, NN + Deep convolutional neural network (DCNN), NN + SVM, Sine Cosine Political Optimization (SCPO)-based deep LSTM + RideNN (Banbhrani et al. , 2021), Improved Whale Optimization Algorithm (IWOA)-based deep LSTM + RideNN (Vedavathi and Anil Kumar, 2021) and proposed RiderSSA-based deep LSTM + RideNN are considered.…”

Section: Resultsmentioning

confidence: 99%

Course complexity in engineering education using E-learner's affective-state prediction

Rathi¹,

Deshpande²

2022

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PurposeAffective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.Design/methodology/approachA new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.FindingsThe proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.Originality/valueThe proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

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Section: Resultsmentioning

confidence: 99%

Course complexity in engineering education using E-learner's affective-state prediction

Rathi¹,

Deshpande²

2022

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“…Social media has emerged as a prominent platform for online social interaction and sharing opinions, which has led to an overwhelming volume of text information, leading to the problem of social media text information overload. The keyword generation technology in natural language processing (NLP) can automatically extract text features and generate the central words or phrases that best reflect the theme of the text, which not only helps us quickly acquire essential information and better understand the content, but also can be applied to various downstream NLP tasks, such as document classification [1,2], recommendation systems [3,4], information retrieval [5,6], text summarization [7,8], text classification [9], and knowledge graph [10]. Therefore, it is a vital method to alleviate the problem of text information overload.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Keyword Generation Method for Low-Resource Social Media Text

Guan,

Long

2023

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The exponential growth of social media text information presents a challenging issue in terms of retrieving valuable information efficiently. Utilizing deep learning models, we can automatically generate keywords that express core content and topics of social media text, thereby facilitating the retrieval of critical information. However, the performance of deep learning models is limited by the labeled text data in the social media domain. To address this problem, this paper presents a hierarchical keyword generation method for low-resource social media text. Specifically, the text segment is introduced as a hierarchical unit of social media text to construct a hierarchical model structure and design a text segment recovery task for self-supervised training of the model, which not only improves the ability of the model to extract features from social media text, but also reduces the dependence of the keyword generation model on the labeled data in the social media domain. Experimental results from publicly available social media datasets demonstrate that the proposed method can effectively improve the keyword generation performance even given limited social media labeled data. Further discussions demonstrate that the self-supervised training stage based on the text segment recovery task indeed benefits the model in adapting to the social media text domain.

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Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network

Cited by 5 publications

References 24 publications

Course complexity in engineering education using E-learner's affective-state prediction

Course complexity in engineering education using E-learner's affective-state prediction

Hierarchical Keyword Generation Method for Low-Resource Social Media Text

Socio-inspired evolutionary algorithms: a unified framework and survey

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