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The Arab nation is seriously affected by computational propaganda. The detection of Arab computational propaganda has become a hot research topic in social networking platforms. Propaganda campaigns endeavor to influence people’s mindsets to improve a particular agenda. They automatically employ the anonymity of the Internet, the micro-profiling capability of social network platforms, and the ease of managing and creating coordinated networks to reach masses of social network users with persuasive messages, mainly aimed at topics each user is sensitive to, and ultimately affecting the outcomes on the targeted problem. Using computation techniques and methods, analysts and researchers can better understand the scope, scale, and impact of propaganda efforts in Arabic-speaking communities and develop strategies to counter them. In recent times, deep learning (DL) approaches targeted explicitly at analyzing, detecting, or countering propaganda within online platforms or Arabic-speaking communities. DL is a subset of machine learning (ML), which includes training artificial neural networks (ANNs) with multiple layers for learning data representation. This paper designs an improved fractal walrus optimization algorithm with DL-based Arab computation propaganda detection (IWOADL-ACPD) technique. The IWOADL-ACPD method mainly focuses on the recognition and classification of propaganda in the Arabic language. The IWOADL-ACPD method begins with a preprocessing step to standardize and clean raw Arabic text data. Consequently, BERT word embedding encodes meaningful data, capturing contextual nuances vital for accurately detecting propaganda. In addition, the stacked sparse autoencoder (SSAE) detection technique is employed to discern subtle patterns indicative of propaganda content. To improve the performance of the SSAE method, the IWOADL-ACPD method uses IWOA to fine-tune the hyperparameter effectively. The proposed IWOADL-ACPD method contributes to Arabic computation propaganda detection by providing an adaptive and comprehensive technique for the complexity of cultural, digital, and linguistic landscapes specific to the Arabic-speaking context. The robustness and efficacy of the IWOADL-ACPD technique are demonstrated through stimulation analysis on the Arabic dataset, which showcases its capability to perform better than other existing methods. The IWOADL-ACPD technique exhibited a superior accuracy value of 95.25% over existing methods. [ABSTRACT FROM AUTHOR]

Arabic dialect identification (ADI) is a specific task of natural language processing (NLP) that intends to forecast the Arabic language dialect of the input text automatically. ADI is the preliminary step toward establishing many NLP applications, including cross-language text generation, multilingual text-to-speech synthesis, and machine translation. The automatic classification of the Arabic dialect is the first step in various dialect-sensitive Arabic NLP tasks. ADI includes predicting the dialects related to the textual input and classifying them on their respective labels. As a result, increased interest has been gained in the last few decades to address the problems of ADI through deep learning (DL) and machine learning (ML) algorithms. The study develops an Arabic multi-class dialect recognition using fast random opposition-based fractals learning aquila optimizer with DL (FROBLAO-DL) technique. The FROBLAO-DL technique utilizes the optimal DL model to identify distinct types of Arabic dialects. In the FROBLAO-DL technique, data preprocessing is involved in cleaning the input Arabic dialect dataset. In addition, the ROBERTa word embedding process is used to generate word embedding. The FROBLAO-DL technique uses attention bidirectional long short-term memory (ABiLSTM) network to identify distinct Arabic dialects. Moreover, the ABiLSTM model’s hyperparameter tuning is implemented using the FROBLOA method. The performance evaluation of the FROBLAO-DL method is tested under the Arabic dialect dataset. The empirical analysis implies the supremacy of the FROBLAO-DL technique over recent approaches under various measures. [ABSTRACT FROM AUTHOR]

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification. [ABSTRACT FROM AUTHOR]

This study explores the integration of Industry 5.0 innovations into the healthcare sector to address prevalent issues related to prescription errors, drug side effects, and healthcare accessibility. It introduces a novel system that enables healthcare professionals to create prescriptions efficiently through voice commands and facilitates secure delivery to patients via email or SMS. Additionally, the research discusses the potential of digitizing the medical sector to reduce paper usage. By providing an accessible alternative to self-medication, this system aims to enhance patient safety and mitigate the risks associated with misconceptions regarding self-treatment. This paper seeks to introduce and assess an innovative fractal electronic prescription system that uses Healthcare 5.0 technologies within the framework of teleconsultation. It aims to tackle the constraints and drawbacks associated with conventional prescription approaches by putting forth cutting-edge technologies and telecommunication functionalities to elevate the effectiveness, precision, and patient-centric nature of the prescription procedure. [ABSTRACT FROM AUTHOR]

Currently, Text-to-Speech (TTS) or speech synthesis, the ability of the complex system to generate a human-like sounding voice from the written text, is becoming increasingly popular in speech processing in various complex systems. TTS is the artificial generation of human speech. A classical TTS system translates a language text into a waveform. Several English TTS systems produce human-like, mature, and natural speech synthesizers. On the other hand, other languages, such as Arabic, have just been considered. The present Arabic speech synthesis solution is of low quality and slow, and the naturalness of synthesized speech is lower than that of English synthesizers. Also, they lack crucial primary speech factors, including rhythm, intonation, and stress. Several studies have been proposed to resolve these problems, integrating using concatenative techniques like parametric or unit selection methods. This paper proposes an Applied Linguistics with Artificial Intelligence-Enabled Arabic Text-to-Speech Synthesizer (ALAI-ATTS) model. This ALAI-ATTS technique includes three essential components: data preprocessing through phonetization and diacritization, Extreme Learning Machine (ELM)-based speech synthesis, and Grey Wolf Fractals Optimization (GWO)-based parameter tuning. Initially, the data preprocessing step includes diacritization, where diacritics are restored to unvoweled text to ensure correct pronunciation, followed by phonetization, translating the text into its phonetic representation. Then, the ELM-based speech synthesis model uses the processed dataset for speech generation. ELMs, well known for their excellent generalization performance and fast learning speed, are especially suitable for real-time TTS applications, balancing high-quality speech output and computational efficiency. Lastly, the GWO methodology is employed to tune the parameters of the ELM. The simulation outcomes validate that the ALAI-ATTS technique considerably enhances the intelligibility and naturalness of Arabic synthesized speech compared to existing approaches. The experimental results of the ALAI-ATTS technique portrayed a lesser value of 3.48, 0.15 and 1.37, 0.25 under WER and DER. [ABSTRACT FROM AUTHOR]

The field of Applied Linguistics, which deals with language and its practical uses, connects with technology in interesting methods, especially in the advancement of text-to-speech (TTS) synthesizers. TTS synthesizers change written text into spoken words, deploying ethics from phonology, phonetics, and syntax to create natural-sounding speech. Within linguistics use, these methods are invaluable purposes such as increasing communication in fractal human–computer interactions (HCIs), language-learning tools, and providing accessibility solutions for visually impaired individuals. TTS purposes at synthesizing understandable and natural speech from text, and it has advanced quickly in recent times because of the progress of artificial intelligence (AI). During past years, deep learning (DL)-based TTS techniques have been established quickly, enabling the generation of natural speech with a high-quality narrator that matches human levels. Creating TTS methods at the quality of the human level has always been the aspiration of speech synthesis practitioners. Although current TTS techniques achieve impressive voice quality, there remains an evident gap in quality compared to human recordings. In this paper, we present an Applied Linguistics with Deep Learning-based Data-Driven Text-to-Speech Synthesizer (ALDL-DDTTS) technique for Arabic corpus. The ALDL-DDTTS technique mainly aims to detect the text and convert it into speech signals on Arabic corpora. In the ALDL-DDTTS technique, a multi-head attention bi-directional long short-term memory (MHA-BiLSTM) approach can be employed with fractal optimization methods to predict the diacritic and gemination signs. Additionally, the Buckwalter code has been deployed for capturing, storing, and displaying the Arabic texts. To boost the efficiency of the ALDL-DDTTS technique, the hyperparameter selection process uses the fractal ant lion optimization (ALO) algorithm. For examining the boost performance of the ALDL-DDTTS methodology, a wide range of simulations is involved. The experimental outcomes illustrated that the ALDL-DDTTS technique reaches better performance than other models. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches. [ABSTRACT FROM AUTHOR]

With technological development, human–computer interaction (HCI) has improved, and spoken communication among machines and humans is one solution to enhance and expedite this process. Researchers have recently explored several systems to improve speech and speaker recognition performance in recent decades. A crucial threat in HCI is developing models that can effectually listen and respond like humans. It resulted in the development of the automated speech emotion recognition (SER) method, which can recognize various emotional classes by electing and extracting effectual features from speech signals. The fundamental problem of automated speech detection is the considerable variation in speech signals because of distinct speakers, language differences, speech differences, contents and acoustic conditions, voice modulation differences based on age and gender. With enhancements in deep learning (DL) and the affordability of computational resources, specifically graphical processing units (GPUs), research underwent a paradigm shift. Therefore, this study develops a multi-class automated speech language recognition using natural language processing with optimal deep learning (MASLR-NLPODL) technique. The MASLR-NLPODL technique intends to accomplish the efficient identification of different spoken languages. In the MASLR-NLPODL technique, the initial preprocessing technique involves windowing, frame blocking, and pre-emphasis block. Next, an adaptive time-frequency feature extractor approach utilizing the discrete fractional Fourier transform (DFrFT) was applied, which can be attained by extending the discrete Fourier transform (DFT) with eigenvectors. An improved Harris hawks optimization (IHHO) technique can be employed to select effectual features. Moreover, the classification of spoken languages can be performed by the gated recurrent unit (GRU) model. Finally, the salp swarm algorithm (SSA)-based hyperparameter selection process is involved in enhancing the performance of the GRU model. The design of the IHHO-based feature selection and SSA-based hyperparameter tuning process demonstrates the novelty of the work. The performance evaluation of the MASLR-NLPODL technique takes place under the VoxForge Dataset. The experimental validation of the MASLR-NLPODL technique exhibited a superior accuracy outcome of 96.40% over existing techniques. [ABSTRACT FROM AUTHOR]

Meter classification in Arabic poetry is a crucial factor that describes the rhythmic structure of poems. Classical Arabic poetry relies on explicit meters, referred to as “Arud”, to create a structured and harmonious flow. Arabic meter is based on the pattern of short and long syllables, and each meter has a particular combination of feet (taf’ilah) that defines its unique rhythmic structure. Poets use diverse Arabic meters to evoke aesthetic or emotional qualities in their poetry. The mastery of meter is considered a sophisticated and skillful aspect of traditional Arabic poetry, which reflects the rich heritage of Arabic literature. The meter provides poets with unique opportunities and constraints, influencing the style and tone of their verses. Using deep learning (DL) for the meter classification of Arabic poems includes leveraging a neural network to automatically learn the features and patterns that discriminate between various meters. This paper presents a Fractal Harris Hawks Optimization with DL-based Meter Classification of Arabic Poems (HHODL-MCAP) technique. The HHODL-MCAP technique exploits the optimal DL model for the identification of distinct classes of meters of Arabic poems. The HHODL-MCAP technique involves a three-layered process. Primarily, the HHODL-MCAP technique performs data preprocessing to transform the data into a beneficial format. Second, the HHODL-MCAP technique applies long short-term memory (LSTM) with a Bidirectional Temporal Convolutional Networks (BiTCNs) model for the automated identification of various Arabic meter classes. At last, the HHO algorithm can be exploited to choose the hyperparameter values of the LSTM-BiTCN model optimally. A series of experiments were conducted to ensure the improved detection outcomes of the HHODL-MCAP technique. The extensive simulation results underline the supremacy of the HHODL-MCAP technique in the meter classification process. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is an essential application of machine learning (ML) and natural language processing (NLP) that comprises the automatic extraction of opinions or sentiments presented in textual data. By leveraging methods to distinguish the expressive nature conveyed in written content, SA permits businesses and research workers to gain valuable insights into social media discourse, customer feedback, and public reviews. In the field of SA, the synergy of Applied Linguistics and Artificial Intelligence (AI) has led to a robust method that goes beyond conventional methods. By incorporating linguistic principles into AI methods, this interdisciplinary collaboration allows a more nuanced perception of human sentiments expressed in language. Applied Linguistics offers the theoretical basis for understanding the details of pragmatics, semantics, and linguistic structures, while AI algorithms leverage this knowledge for analyzing large datasets with notable accuracy. This study presents an Applied Linguistics-driven Artificial intelligence Approach for SA and Classification (ALAIA-SAC) system in social media. The primary intention of the ALAIA-SAC technique is to apply an attention mechanism with a fractal hyperparameter-tuned deep learning (DL) method for identifying sentiments. In the ALAIA-SAC technique, data preprocessing takes place in several stages to convert the input data into a compatible format. In addition, the TF-IDF model could be employed for the word embedding method. The self-attention directional long short-term memory (SBiLSTM) model is used for sentiment classification. Finally, the SBiLSTM model’s hyperparameter selection is performed using a Fractal Pelican optimization algorithm (FPOA). The experimentation results of the ALAIA-SAC method are assessed under two benchmark datasets. The comparative study of the ALAIA-SAC technique exhibited a superior accuracy value of 99.17% and 99.39% under Twitter US Airlines and IMDB datasets. [ABSTRACT FROM AUTHOR]