Extreme Learning Machine for Automatic Language Identification Utilizing Emotion Speech Data

Albadr, Musatafa Abbas Abbood; Tiun, Sabrina; Ayob, Masri; AL‐Dhief, Fahad Taha; Abdali, Taj-Aldeen Naser; Abbas, Aymen Fadhil

doi:10.1109/icecce52056.2021.9514107

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…Therefore, the total number of implemented experiments is 11 times. Besides, there were various of the evaluation measures have used to evaluate the performance of the proposed system in the detection of human facial emotions such as accuracy, precision, recall (i.e., sensitivity), F-measure, and G-mean, as shown in ( 7)-( 11) [28]. The experiments of this proposed system have carried out by using MATLAB.…”

Section: Resultsmentioning

confidence: 99%

Emotions recognition from human facial images based on fast learning network

Alsemawi

Mutar

Ahmed

et al. 2023

IJEECS

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Systems of facial emotion recognition have witnessed a high significance in the research field. The face emotions are based on human facial expressions which play a crucial role in silent communication. Machine learning algorithms have widely used in systems of human facial emotion detection from images. However, many systems suffer from low accuracy rate. In this paper, we present a system of facial emotion recognition by using images. In this proposed system, the samples of facial emotions have taken from Yale Face database. In addition, the histograms of oriented gradients (HOG) is used to extract features from the images. The extracted features will feed the fast learning network (FLN) algorithm for the classification part to identify the images of facial emotions with respect to their subjects. Many evaluation measurements have used to evaluate the performance of the proposed system. Based on the results of the experiment, the proposed system achieves 95.04% for the highest accuracy, 72.73% precision. Also, the results of the proposed system in terms of recall, f-measure, and G-main are all equal to 72.73%, respectively.

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Section: Resultsmentioning

confidence: 99%

Emotions recognition from human facial images based on fast learning network

Alsemawi

Mutar

Ahmed

et al. 2023

IJEECS

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“…In eqn (16), randomly selected scaling parameters are denoted as 𝛼; 𝛼 ∈ (0, 𝑑 𝑥 , 𝑥 . The 𝛼 value is selected at every iteration.…”

Section: 𝐶 = Tanh (𝑊 °[ℎ 𝑥 ] + 𝑏mentioning

confidence: 99%

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Chassab,

Zakaria,

Tiun

2024

IEEE Access

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The computer-based Automated Essay Scoring (AES) system automatically marks or scores student replies by considering relevant criteria. The methodology, which systematically categorizes writing quality, can increase operational effectiveness in academic and major commercial institutions. To study the projected score, AES relies on extracting numerous aspects from the student's response, including grammatical and textural information. However, the recovered features may result in dimensionality reduction and a challenging-to-understand feature selection procedure. As the number of parameters rises, the model also demands a large cost for processing and training the data. However, these problems worsen the accuracy of score prediction as a whole and widen the gap between actual and anticipated results. This study suggested the Fox-optimized Long Short-Term Memory-based Augmented Language Model (FLSTM-ALM) as a solution to these problems for giving successful training to text features; the model uses an augmented learning paradigm. The retrieval score was then analyzed and generated using a neural knowledge encoder and retriever. The neural model successfully classifies the output based on this score. The best features are then chosen using the fox optimization algorithm based on the food-searching category. This choice of parameters solves the exploration and optimization issue with document classification. The performance of the optimized AES system was assessed using the two datasets, ASAP and ETS, and it demonstrated a high accuracy of 98.92% and a low error rate of 0.096%. Dimensionality reduction can thus be fixed by optimizing the FLSTM-ALM model with an appropriate meta-heuristic method, such as the FOX algorithm, which raises the predicted accuracy, recall, and f1 score for the AES model.

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“…This study suggests using an ELM instead of the fully connected layer to provide a real-time processor [11][12][13]. Utilizing a DCNN automated feature, in conjunction with ELMs, can effectively tackle the problems associated with manual feature extraction and the elongation of training time in the proposed two-phase technique.…”

Section: Introductionmentioning

confidence: 99%

“…The Random Vector Functional Link (RVFL) [14] is utilized to establish the foundation of the ELM, resulting in a highly efficient and adaptable system [12,13]. Research shows that engineering applications use ELM regularly [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Enhanced PRIM recognition using PRI sound and deep learning techniques

Hasani Azhdari,

Mahmoodzadeh,

Khishe

et al. 2024

PLoS ONE

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Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network’s robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.

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Extreme Learning Machine for Automatic Language Identification Utilizing Emotion Speech Data

Cited by 12 publications

References 23 publications

Emotions recognition from human facial images based on fast learning network

Emotions recognition from human facial images based on fast learning network

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Enhanced PRIM recognition using PRI sound and deep learning techniques

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