Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data

Alam, M. M. Gowthul; Baulkani, S.

doi:10.1007/s00500-018-3124-y

Cited by 75 publications

(21 citation statements)

References 38 publications

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“…But this approach failed to apply in various machine learning algorithms. In recent years, many soft computing approaches has been acheived better performance in engineering applications 27–38.…”

Section: Review Of Related Workmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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Section: Review Of Related Workmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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“…In the present generation, the way of cheering the consumers in writing a review regarding the product has become a better scheme for marketing the product via the audience's real voice. 10 In recent years, machine learning [11][12][13][14] and optimization methods [15][16][17][18][19][20][21][22][23] has been achieved better results in engineering applications. [24][25][26][27][28][29] Over the past few years, detecting fake review has been attracted a great deal of attention from research and business communities.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

Jacob¹,

Rajendran²

2021

Concurrency and Computation

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In recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money-spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre-processing, feature extraction, feature selection, and classification. The features obtained in the pre-processing phase are extracted and selected using chi-squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM-FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.

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“…During the communication operation, it consumes the maximum amount of energy. While every network node act as a data packet transmitter and router during communication 3–16 …”

Section: Introductionmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

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Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

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Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data

Cited by 75 publications

References 38 publications

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

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