Fractals

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models.

Fast and robust vehicle recognition from remote sensing images (RSIs) has excellent economic analysis, emergency management, and traffic surveillance applications. Additionally, vehicle density and location data are vital for intelligent transportation systems. However, correct and robust vehicle recognition in RSIs has been complex. Conventional vehicle recognition approaches depend on handcrafted extracted features in sliding windows with distinct scales. Recently, the convolutional neural network can be executed for aerial image object recognition, and it has accomplished promising outcomes. This research projects an automatic vehicle detection and classification utilizing an imperialist competitive algorithm with a deep convolutional neural network (VDC-ICADCNN) technique. The primary purpose of the VDC-ICADCNN technique is to develop the RSI and apply deep learning (DL) models for the recognition and identification of vehicles. Three main procedures were involved in the presented VDC-ICADCNN technique. At the primary stage, the VDC-ICADCNN technique employs the EfficientNetB7 approach for the feature extractor. Then, the hyperparameter fine-tuning of the EfficientNet approach takes place utilizing ICA, which aids in attaining improved performance in the classification process. The VDC-ICADCNN technique utilizes a variational autoencoder (VAE) model for the vehicle recognition method. Extensive experiments can be implemented to establish the superior solution of the VDC-ICADCNN technique. The obtained outcomes of the VDC-ICADCNN technique highlighted a superior accuracy value of 96.77% and 98.59% with other DL approaches under Vehicle Detection in Aerial Imagery (VEDAI) and Potsdam datasets.

One of the most advanced waveforms available in the beyond-fifth-generation radio (B5GR) framework is nonorthogonal multiple access (NOMA). The power amplifier (PA) of the NOMA structure performs less efficiently when the signal is strong and the peak-to-average power ratio (PAPR) is high. This study applies a hybrid algorithm to the NOMA structure, combining fractal partial transmit sequence (PTS), A-law companding, and the bacterial foraging algorithm (BFA). Fractals are known for their self-repeating structures at different scales, allowing for efficient coverage and exploration of space. Fractals enhance the bacteria foraging algorithm (BFA) by improving search efficiency, enabling better exploration of complex, multi-dimensional optimization landscapes. Similarly, BFA balances exploring the search in promising areas. We utilized BFA to achieve optimal phase factors for the PTS algorithm and applied A-Law companding to the NOMA symbols to further enhance the structure’s performance. The intermediate computational complexity in the Rician and Rayleigh channels improves PAPR, bit error rate (BER), and power spectral density performance. Simulation results reveal that the performance of the proposed hybrid method is superior to that of existing PAPR reduction algorithms and substantially enhances the efficiency of NOMA.

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.

Young children can enhance their movements and develop motor abilities through play and education with guidance and practice. Understanding young children’s physical development and identifying potential growth areas require an analysis of their motor skills. In this study, we assess young children’s motor skills using a unique deep residual technique on the cloud-to-thing continuum. Seven kids were used to identify autistic movements. The dataset was gathered from the Tamimi Centre for Autism in Saudi Arabia. The data preprocessing to standardize the data’s scale using min–max normalization and removing noise-relevant features is extracted using principal component analysis (PCA). This step is crucial for ensuring the quality and reliability of the data used for subsequent analysis. The bumble bees mating optimization with deep residual algorithm (BBMO-DRN) is designed to handle the complexities of motor skill assessment. Finally, we explore the potential of cloud–fog–edge computing in data storing and processing young children’s motor skill development. The results showed that the proposed method performs better compared to the existing methods. This will make it possible to evaluate how well the suggested solution works in terms of accuracy, precision, recall, $F$1-score, and scalability. As a result, this proposed approach in evaluating young children’s motor skills is to improve data storage in the cloud to the thing continuum.

The surge in applications necessitates a more intelligent and automated Higher Education Admission Management System (HEAMS). This research proposes a novel Deep Learning (DL)-based HEAMS utilizing a Cloud-Edge Architecture. The first step collects applicant data like transcripts, test scores, essays, and recommendations. Edge devices perform initial cleaning and preprocessing on these data to ensure quality and privacy. These preprocessed data using normalization and feature extraction using the Latent Dirichlet Allocation (LDA) are then transferred to the cloud where DL models, such as Convolutional Neural Networks (CNNs) for essays or Recurrent Neural Networks (RNNs) for transcripts, are trained. These models learn complex patterns from historical labeled data (admitted/not admitted) to predict an applicant’s success probability. During application evaluation, new data are fed through the trained models on the edge, generating probabilities for predefined classifications — high-potential, moderate, or low-potential. The cloud receives these probabilities and combines them with predefined admission criteria like minimum GPA. This combined analysis leads to a final classification using Novel Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO) for each applicant — admitted, waitlisted, or rejected and admission management system by refining admission decisions for admitted, waitlisted, and rejected applicants based on institutional priorities and constraints. The system not only generates classifications but also provides detailed model score breakdowns for transparency. This Cloud-Edge HEAMS offers improved efficiency, reduced workload for admissions staff, and potentially fairer decisions by mitigating bias through data-driven analysis.

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise in improving the wide-ranging applications of the Internet of Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making in a variety of sectors. IoT devices collect information in real time, while remote sensing captures data afar without direct contact. UAVs equipped with sensors offer high-quality images for classification tasks. DL techniques, especially the convolutional neural networks (CNNs), analyze data streams, extracting complicated features for the accurate classification of objects or environmental features. This synergy enables applications including urban planning and precision agriculture, fostering smarter disaster response, decision support systems, and efficient resource management. This paper introduces a novel Pyramid Channel-based Feature Attention Network with an Ensemble Learning-based UAV Image Classification (PCFAN-ELUAVIC) technique in an assisted remote sensing environment. The PCFAN-ELUAVIC technique begins with the contrast enhancement of the UAV images using the CLAHE technique. Following that, the feature vectors are derived by the use of the PCFAN model. Meanwhile, the hyperparameter tuning procedure is executed by the inclusion of a vortex search algorithm (VSA). For image classification, the PCFAN-ELUAVIC technique comprises an ensemble of three classifiers like long short-term memory (LSTM), graph convolutional networks (GCNs), and Hermite neural network (HNN). To exhibit the improved detection results of the PCFAN-ELUAVIC system, an extensive range of experiments are carried out. The experimental values confirmed the enhanced performance of the PCFAN-ELUAVIC model when compared to other techniques.

We are aware that autonomous vehicle handles camera and LiDAR data pipelines and uses the sensor pictures to provide an autonomous object identification solution. While current research yields reasonable results, it falls short of offering practical solutions. For example, lane markings and traffic signs may become obscured by accumulation on roads, making it unsafe for a self-driving car to navigate. Moreover, the car’s sensors may be severely hindered by intense rain, snow, fog, or dust storms, which could endanger human safety. So, this research introduced Multi-Sensor Fusion and Segmentation for Deep $Q$-Network (DQN)-based Multi-Object Tracking in Autonomous Vehicles. Improved Adaptive Extended Kalman Filter (IAEKF) for noise reduction, Normalized Gamma Transformation-based CLAHE (NGT-CLAHE) for contrast enhancement, and Improved Adaptive Weighted Mean Filter (IAWMF) for adaptive thresholding have been used. A novel multi-segmentation using several segmentation methods and degrees dependent on the orientation of images has been used. DenseNet (D Net)-based multi-image fusion provides faster processing speeds and increased efficiency. The grid map-based pathways and lanes are chosen using the Energy Valley Optimizer (EVO) technique. This method easily achieves flexibility, robustness, and scalability by simplifying the complex activities. Furthermore, the YOLOv7 model is used for classification and detection. Metrics like velocity, accuracy rate, success rate, success ratio, and mean-squared error are used to assess the proposed method.

Peak-to-average power ratio (PAPR) in orthogonal time frequency space (OTFS) is vital for optimizing signal efficiency, minimizing distortion, and enhancing the performance of a beyond fifth-generation (B5G) system. The paper focuses on lowering the PAPR while retaining the bit error rate (BER) and power spectral density (PSD) for 64 and 256 sub-carriers. The PAPR is minimized by using a fractal optimization process known as selective mapping (SLM) and partial transmission sequence (PTS) at the transmitting block of the OTFS framework. The combination of SLM and PTS reduces peak power by selectively modifying the phase of transmitted symbols. SLM generates multiple versions of the signal, while PTS allocates power to these versions optimally, collectively mitigating amplitude peaks and minimizing PAPR. According to the simulation results, the suggested SLM+PTS works better than the traditional SLM and PTS, achieving low spectrum leakage and PAPR improvements of 5.5 dB and 4.9dB as well as SNR gains of 2.4dB and 2.1dB. In the future, SLM+PTS may work on lowering PAPR by making computers faster, researching adaptive algorithms, and combining machine learning methods for real-time optimization in various communication settings. Complex modeling, including fractal-based SLM and PTS approaches, provides a detailed understanding of waveform behavior and interference patterns, enabling more accurate and effective hybrid PAPR reduction techniques. This combination enhances the ability to manage to mitigate non-linear distortion across various sub-carriers, crucial for the performance and efficiency of 6G OTFS modulation.

The inability to communicate verbally is widely acknowledged as a significant disability. The primary objective of this research is to create a practical system aimed at individuals with hearing impairments, particularly those who depend on sign language as their primary means of communication. This system aims to enable deaf individuals to express themselves, communicate effectively and facilitate understanding of their language which would otherwise be challenging since most people are unfamiliar with sign language. By employing human gesture interpretation and motion capture, this technology can facilitate the translation of sign language into spoken language and vice versa. Despite the existence of various methods to convert sign language into voice, none of them currently provide an entirely intuitive user interface. Our objective is to create a system that not only translates sign language but also integrates a natural user interface, thus enhancing accessibility for individuals who are blind or have visual impairments. This system will achieve this by recognizing facial expressions and effectively conveying emotions behind words assisting visually impaired individuals in expressing themselves more effectively. Hence, in this project, we tentatively aim to build a system that can ease the lives of blind, deaf and dumb people to some extent. Normal communication like normal people do might never be possible for differently abled people but through our project, we try to provide them with a tool that can help them experience normalcy while communicating with normal people.

Nonorthogonal multiple access (NOMA) waveforms need to be tested for their performance and effectiveness using partial transmit sequence (PTS) techniques and peak-to-average power ratio (PAPR) analysis. This is an important part of the advanced radio framework. This paper examines the PAPR features of NOMA systems using PTS with different sub-carrier configurations (64, 256, and 512). We examine BER, PSD, and PAPR distributions by modeling NOMA waveforms with PTS to understand the impact of different sub-carrier counts on signal complexity and efficiency. The findings shed light on how sub-carrier quantity affects PAPR statistics and offer guidance on the best way to design NOMA waveforms for improved spectral efficiency and reduced signal distortion. The simulation results show that by lowering the PAPR while maintaining the BER performance, the suggested system performs better than the traditional PAPR algorithm.

The search complexity for partial transmit sequence (PTS) and selective mapping (SLM) techniques increases exponentially with the number of sub-blocks, necessitating a comprehensive search over all possible combinations of phase-weighting variables. This paper proposes a novel complex system modeling approach for PTS and SLM in an Orthogonal Time Frequency Space (OTFS) system, utilizing phase factors and a sub-block partition scheme. We describe an OTFS system that achieves low computational complexity in identifying optimal phase-weighting factors and reducing the peak-to-average power ratio (PAPR) using sub-optimal PTS and SLM based on the particle swarm optimization (PSO) algorithm. Parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) were analyzed for 64, 256, and 512 sub-carriers in Rayleigh and Rician channels. The experimental outcome reveals that the proposed approaches can effectively regulate the optimal phase-weighting factors, substantially lessening PAPR with modest complexity. Fractals enhance complex modeling by optimizing PAPR reduction in OTFS 6G waveforms using fractal-influenced PSO for sub-carrier efficiency. The proposed method incorporates fractal modeling to enhance the optimization process in complex environments. Fractals, known for their intricate patterns and self-similarity, provide a robust framework for exploring vast and complex search spaces, crucial in PSO. This approach improves the efficiency of the framework.

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

Exercise has long been known to improve cardiovascular health, energy metabolism, and well-being. However, myocardial cell responses to exercise are complex and multifaceted due to their molecular pathways. To understand cardiac physiology and path physiology, one must understand these pathways, including energy autophagy. In recent years, deep learning techniques, IoT devices, and cloud computing infrastructure have enabled real-time, large-scale biological data analysis. The objective of this work is to extract and analyze autophagy properties in exercise-induced cardiac cells in a cloud-IoT context using deep learning, more especially an autoencoder. The Shanghai University of Sport Ethics Committee for Science Research gave its approval for the data collection, which involved 150 male Sprague–Dawley (SD) rats that were eight weeks old and in good health. The $Z$-score normalization method was used to standardize the data. Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to analyze deep learning with Autoencoder, the autography energy of exercise myocardial cells within a cloud-IoT. To capture the intricate myocardial energy autophagy during exercise, we introduced the DMO-GCNN-Autoencoder, a Dwarf Mongoose Optimized Graph Convolutional Neural Network. The results showed that the proposed network’s performance matches that of the existing methods.

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.

Social media platforms have become vast repositories of user-generated content, offering an abundant data source for sentiment analysis (SA). SA is a natural language processing (NLP) algorithm that defines the sentiment or emotional tone expressed in the given text. It includes utilizing computational techniques to automatically detect and categorize the sentiment as negative, positive, or neutral. Aspect-based SA (ABSA) systems leverage machine learning (ML) approaches to discriminate nuanced opinions within the text, which break down sentiment through particular attributes or aspects of the subject matter. Businesses and researchers can gain deep insights into brand perception, public opinion, and product feedback by integrating social media data with ABSA methodologies. This enables the extraction of sentiment polarity and more actionable and targeted insights. By applying ML approaches trained on the abundance of social media data, organizations can identify areas for improvement, tailor their strategies to meet their audience’s evolving needs and preferences and better understand customer sentiments. In this view, this study develops a new Fractal Snow Ablation Optimizer with Bayesian Machine Learning for Aspect-Level Sentiment Analysis (SAOBML-ALSA) technique on social media. The SAOBML-ALSA approach examines social media content to identify sentiments into distinct classes. In the primary stage, the SAOBML-ALSA technique preprocesses the input social media content to transform it into a meaningful format. This is followed by a LeBERT-based word embedding process. The SAOBML-ALSA technique applies a Naïve Bayes (NB) classifier for ALSA. Eventually, the parameter selection of the NB classifier will be done using the SAO technique. The performance evaluation of the SAOBML-ALSA methodology was examined under the benchmark database. The experimental results stated that the SAOBML-ALSA technique exhibits promising performance compared to other models.

Sustainable agriculture in the Kingdom of Saudi Arabia (KSA) intends to develop farming practices that maintain productivity while promoting economic viability and preserving environmental resources. Moreover, the country invests in advanced technologies, which include vertical farming and hydroponics, to reduce environmental impact and enhance food security. Globally, the pepper crop is one of the leading agricultural products of human food security. However, it is vulnerable to various diseases such as gray leaf spots, powdery mildew symptoms on pepper leaf, blight leaf disease, common rust, fruit rot disease, etc. Usually, farmers identify the disease through visual inspection; however, this has its drawbacks as it is generally time-consuming and inaccurate. Several research workers have previously presented different pepper plant disease classification techniques, mainly deep learning (DL) and image processing approaches. This paper introduces a novel Pepper Leaf Disease Detection using the Optimal Deep Transfer Learning (PLDD-ODTL) technique for the sustainable agricultural sector in KSA. As a preliminary preprocessing stage, the PLDD-ODTL technique initially utilizes an adaptive window filtering (AWF) approach to eliminate the noise in the plant images. Next, the PLDD-ODTL approach involves a feature fusion process comprising three DL approaches: residual neural network (ResNet), VGG-19, and DenseNet models. To enhance the performance of the DL techniques in biological systems modeling, the hyperparameter selection process is done by a hybrid artificial ecosystem fractal optimizer using a chaos game optimization (ACGO) technique. Finally, a deep belief network (DBN) is applied to classify the disease. A series of experiments were conducted to illustrate the enhanced performance of the PLDD-ODTL technique. The experimental results of the PLDD-ODTL technique portrayed a superior accuracy outcome of 99.71% over existing approaches.

Humans prefer to convey information through speech utilizing similar language. Speech detection is the capability to recognize the spoken words of the speaking person. Recent work demonstrates the increased attention among researcher workers in this field specially in Brain-Like computing applications and emphasizes the real-world usability of speech for speaker recognition across different applications. Automatic speech recognition (ASR) is the method of identifying human speech and converting it into text. This study has gained much popularity in recent times. It is a crucial area of research for human-to-machine interaction. Pioneer methods are concerned with manual feature extraction and classical algorithms including Hidden Markov Models (HMM), Gaussian Mixture Model (GMM), and the Dynamic Time Warping (DTW) model. In recent years, neural networks, namely convolutional neural networks (CNN), recurrent neural networks (RNN), and Transformers, have been utilized in the context of ASR and reached outstanding performance over the past few years. This study introduces Intelligent Speech Recognition using the Fractal Amended Grasshopper Optimization Algorithm with Deep Learning (ISR-AGODL) approach. The presented ISR-AGODL technique correctly identifies and recognizes speech signals. In the ISR-AGODL technique, the speech signals are transformed into spectrograms. Besides, the features are derived using the deep convolutional neural networks (DCNN) model. Followed by the Fractals AGO technique is utilized for the choosing of hyperparameters. Finally, the recognition of speech signals is achieved using the extreme gradient boosting (XGBoost) model. The simulation outcomes of the ISR-AGODL method can be validated using a benchmark dataset. The experimental results of the ISR-AGODL method portrayed a superior accuracy outcome of 96.34% over other models.

Dialects are language variations that occur due to differences in social groups or geographical regions. Dialect speech recognition is the approach to accurately transcribe spoken language that involves regional variation in vocabulary, syntax, and pronunciation. Models need to be trained on various dialects to handle linguistic differences effectively. The latest advancements in automatic speech recognition (ASR) and complex systems methods are showing progress in recurrent neural networks (RNN), deep neural networks (DNN), and convolutional neural networks (CNN). Multi-dialect speech recognition remains a challenge, notwithstanding the progress of deep learning (DL) in speech recognition for many computing applications in environmental modeling and smart cities. Even though the dialect-specific acoustic model is known to perform well, it is not easier to maintain when the number of dialects for all the languages is large and dialect-specific data are limited. This paper offers an Automated Multi-Dialect Speech Recognition using the Stacked Attention-based Deep Learning (MDSR-SADL) technique in environmental modeling and smart cities. The MDSR-SADL technique primarily applies the DL model to identify various dialects. In the MDSR-SADL technique, stacked long short-term memory with attention-based autoencoder (SLSTM-AAE) model is used, which integrates stack modeling with LSTM and AE. Besides, the attention model enables dialect identification by offering dialect details for speech identification. The MDSR-SADL model uses the Fractals Harris Hawks Optimization (FHHO) model for hyperparameter selection. A sequence of simulations was implemented to illustrate the improved solution of the MDSR-SADL model. The experimental investigation of the MDSR-SADL technique exhibits superior accuracy values of 99.52% and 99.55% over other techniques under Tibetan and Chinese datasets.

Posting sarcastic comments on social media has become popular in the modern era. Sarcasm is a linguistic expression that typically conveys the contrary meaning of what has already been said, making it challenging for machines to find the literal meaning. It depends mainly on context, making it a tedious process for computational analysis. It is well known for its modulation with spoken words and an irony undertone. In addition, sarcasm conveys negative sentiment using positive words, which easily confuses sentiment analysis (SA) models. Sarcasm detection is a natural language processing (NLP) process and is prevalent in SA, human–machine dialogue, and other NLP applications due to sarcasm’s ambiguities and complex nature. Concurrently, the advancement of machine learning (ML) techniques makes it easier to develop robust sarcasm detection methods. This paper presents an automated sarcasm recognition using applied linguistics-driven deep learning with a large language model (ASR-ALDL3M) technique. The purpose of the ASR-ALDL3M technique is to focus on recognizing the sarcastic data using the DL model. In the ASR-ALDL3M technique, the initial data preprocessing phase is utilized, and glove word embedding is applied. Next, the sarcasm recognition procedure is applied using the long short-term memory (LSTM) model. Moreover, the hyperparameter selection of the LSTM model is performed using the fractals monarch butterfly optimization (MBO) technique. At last, a large language model (LLM) is utilized to enhance the sarcastic recognition process. A comprehensive result analysis is made to validate the outcomes of the ASR-ALDL3M technique. The performance evaluation outcomes stated that the ASR-ALDL3M method gains better performance over other models.

Consumer electronics like action underwater drones and cameras commonly include object detection abilities to automatically capture underwater images and videos by tracking and focusing objects of interest. Underwater object detection (UOD) in consumer electronics revolutionizes interactions with aquatic environments. Modern consumer gadgets are increasingly equipped with sophisticated object detection capabilities, from action cameras to underwater drones, which allow users to automatically capture clear videos and images underwater by tracking and identifying objects of interest. This technology contributes to user safety by enabling devices to avoid collisions with underwater obstacles and improving underwater videography and photography quality in complex systems simulation platforms. Classical approaches need a clear feature definition that suffers from uncertainty due to differing viewpoints, occlusion, illumination, and season. This paper focuses on developing Deep Transfer Learning with Imaging Technology for Underwater Object Detection and Tracking (DTLIT-UOBT) techniques in consumer electronics. The DTLIT-UOBT technique uses deep learning and imaging technologies to detect and track underwater objects. In the DTLIT-UOBT technique, the bilateral filtering (BF) approach is primarily used to improve the quality of the underwater images. Besides, an improved neural architectural search network (NASNet) model derives feature vectors from the preprocessed images. The DTLIT-UOBT technique uses the jellyfish search fractal optimization algorithm (JSOA) for the hyperparameter tuning process. Finally, the detection and tracking of the objects can be performed by an extreme learning machine (ELM). A sequence of simulations was used to authorize the performance of the DTLIT-UOBT model by utilizing an underwater object detection dataset. The experimental validation of the DTLIT-UOBT model exhibits a superior accuracy value of 95.71% over other techniques.

In this research, the goal is to formulate a mathematical model that predicts the transference mechanisms of Ebola virus (EBOV) and Nipah virus (NIV) infections. They utilize fractional-order derivatives to describe the behavior of the viruses, particularly focusing on the difference in disease manifestation between humans and fruit bats, the putative natural reservoirs. Additionally, they employ fixed-point theory to analyze the qualitative aspects of their model. Also, we investigate the stability of the model using Ulam-Hyers-type results. Furthermore, we utilize the fractional Atangana–Baleanu integral and the Adams–Milton numerical method using MATLAB to provide graphical representations and insights into the behavior of the viruses under various conditions of transference.

Optical orthogonal time frequency space (OTFS) is better at handling Doppler shifts and multipath fading, making signals more reliable and valuable in places with much movement beyond the fifth generation (B5G). Using practical power amplifiers at the transmitter causes power inefficiency and signal distortion because of the OTFS system’s high peak-to-average power ratio (PAPR), severely reducing system efficiency. Combining partial transmit sequence (PTS) and selective mapping (SLM), a technique known as PTS+SLM, reduces peak power. While SLM generates numerous phase-modulated signal candidates and chooses the one with the lowest PAPR, PTS separates the signal into sub-blocks and optimizes their phases to decrease peak power. With few changes to the signal structure, this dual strategy effectively reduces PAPR while improving power spectral density (PSD) efficiency. As a result, we ensure the accuracy and dependability of the transferred data by maintaining the bit error rate (BER). Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to explore the phase space more effectively or to discover new phase sequences that result in lower PAPR. According to the simulation findings, the suggested PTS+SLM method works better than the traditional PTS and SLM methods.

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches.

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.

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.

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Natural language processing (NLP) is a domain of artificial intelligence (AI) that concentrates on the communication between human and computer language. Detection of Arabic spam and ham tweets involves leveraging deep learning (DL) models, mainly NLP techniques such as brain-like computing and AI-driven tweets recognition, to mechanically differentiate between spam and ham messages dependent upon content semantics, linguistic patterns, and contextual data within the Arabic text. This study presents an optimal deep learning with natural language processing for Arabic spam and ham tweets recognition (ODLNLP-ASHTR) technique in various complex systems platforms. In the ODLNLP-ASHTR technique, the data pre-processing is initially performed to alter the input tweets into a compatible format, and a BERT word embedding process is used. For Arabic ham and spam tweet recognition, the ODLNLP-ASHTR technique makes use of the self-attention bidirectional gated recurrent unit (SA-BiGRU) model. At last, the detection performance of the SA-BiGRU model can be boosted by the design of an improved salp swarm algorithm (ISSA). The experimental evaluation of the ODLNLP-ASHTR technique takes place using the Arabic tweets dataset. The experimental results pointed out the improved performance of the ODLNLP-ASHTR model compared to recent approaches with a maximum accuracy of 98.11%.

Consumer electronics is a device designed to meet the everyday needs of individuals, including home appliances, smartphones, and laptops. The Internet of Things (IoT) environment is witnessing tremendous growth, driven by the simultaneous increase and continuous cyber-attack innovation. The imperative to fortify IoT devices against emerging threats became more prevalent as these threats became increasingly complex. In this context, threats include malware attacks, unauthorized access, and data breaches. Securing these devices requires robust encryption, regular software updates, and vigilant monitoring to protect sensitive information and ensure user safety. Threat detection using deep learning (DL) includes leveraging cutting-edge neural network models to detect and alleviate security threats within digital environments. DL, a branch of machine learning (ML), excels in processing abundant data to detect anomalies and patterns that might indicate possible threats. This technique improves classical threat detection techniques by reducing false positives and enhancing accuracy. By continuously analyzing data from various sources, such as network traffic and user behavior, DL techniques can quickly recognize and respond to emerging vulnerabilities, providing a proactive and adaptive security solution essential for protecting consumer electronics and IoT devices. This study develops a new Hybrid DL-based Threat Detection with Blockchain Technology (HDLTD-BCT) technique for secure IoT-based consumer electronics. The presented HDLTD-BCT technique can improve security among consumer electronics in the IoT environment. For this purpose, the HDLTD-BCT technique initially applies BC technology to monitor data flow in the IoT platform. The HDLTD-BCT technique scales the input data for threat detection using a min–max scalar. Besides, the detection process is performed by HDL, comprising a stacked LSTM-based denoising autoencoder (LSDAE) model. To enhance the HDL method’s performance, the Maritime Cyber Warfare Officers (MCWO) performs the hyperparameter selection process. The experimental results of the HDLTD-BCT technique can be validated utilizing the NSLKDD dataset. The simulation outcomes of the HDLTD-BCT technique exhibited a superior accuracy value of 99.68% compared to other DL models.

Spoken Language Identification (SLID) is the problem of categorizing the language spoken by a speaker in the audio clips. SLID is valuable in multi-language speech recognition systems, personalized voice assistants, and automated speech translation systems in call centers to automatically route calls to the language operator. A primary challenge is the language detection from audio with different noise levels and sampling rates, accurately and with a short delay. A further problem is to differentiate between short-duration languages. Previous research works have applied SLID’s lexical, phonetic, phonotactic, and prosodic features. Spoken language detection using deep learning (DL) usually includes training RNN or CNN approaches on audio features such as spectrograms or MFCCs to categorize the language spoken in audio samples. Pioneering methodologies, such as CNN–RNN transformers or hybrids, can capture the spatial and temporal features for better performance. This paper presents a Multi-Class Spoken Language Detection using Artificial Intelligence with Fractal Al-Biruni Earth Radius Optimization (MCSLD-AIBER) technique. The MCSLD-AIBER technique mainly aims to identify the various classes of spoken languages. In the MCSLD-AIBER technique, the Constant-$Q$ Transform (CQT) approach is applied to transform the speech signals. Additionally, the MCSLD-AIBER technique employs Inception with a Residual Network model for the feature extraction process. Moreover, the hyperparameters can be adjusted using the BER approach. A long short-term memory (LSTM) network can be utilized to identify multiple spoken languages. A set of experiments were involved to illustrate the efficient performance of the MCSLD-AIBER technique. The simulation outcomes indicated that the MCSLD-AIBER method performs optimally over other models.

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.