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Sign language (SL) recognition for individuals with hearing disabilities involves leveraging machine learning (ML) and computer vision (CV) approaches for interpreting and understanding SL gestures. By employing cameras and deep learning (DL) approaches, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), these models analyze facial expressions, hand movements, and body gestures connected with SL. The major challenges in SL recognition comprise the diversity of signs, differences in signing styles, and the need to recognize the context in which signs are utilized. Therefore, this manuscript develops an SL detection by Improved Coyote Optimization Algorithm with DL (SLR-ICOADL) technique for hearing disabled persons. The goal of the SLR-ICOADL technique is to accomplish an accurate detection model that enables communication for persons using SL as a primary case of expression. At the initial stage, the SLR-ICOADL technique applies a bilateral filtering (BF) approach for noise elimination. Following this, the SLR-ICOADL technique uses the Inception-ResNetv2 for feature extraction. Meanwhile, the ICOA is utilized to select the optimal hyperparameter values of the DL model. At last, the extreme learning machine (ELM) classification model can be utilized for the recognition of various kinds of signs. To exhibit the better performance of the SLR-ICOADL approach, a detailed set of experiments are performed. The experimental outcome emphasizes that the SLR-ICOADL technique gains promising performance in the SL detection process. [ABSTRACT FROM AUTHOR]

Fall detection (FD) for disabled persons in the Internet of Things (IoT) platform contains a combination of sensor technologies and data analytics for automatically identifying and responding to samples of falls. In this regard, IoT devices like wearable sensors or ambient sensors from the personal space role a vital play in always monitoring the user's movements. FD employs deep learning (DL) in an IoT platform using sensors, namely accelerometers or depth cameras, to capture data connected to human movements. DL approaches are frequently recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that have been trained on various databases for recognizing patterns connected with falls. The trained methods are then executed on edge devices or cloud environments for real-time investigation of incoming sensor data. This method differentiates normal activities and potential falls, triggering alerts and reports to caregivers or emergency numbers once a fall is identified. We designed an Artificial Rabbit Optimizer with a DL-based FD and classification (ARODL-FDC) system from the IoT environment. The ARODL-FDC approach proposes to detect and categorize fall events to assist elderly people and disabled people. The ARODL-FDC technique comprises a fourstage process. Initially, the preprocessing of input data is performed by Gaussian filtering (GF). The ARODL-FDC technique applies the residual network (ResNet) model for feature extraction purposes. Besides, the ARO algorithm has been utilized for better hyperparameter choice of the ResNet algorithm. At the final stage, the full Elman Neural Network (FENN) model has been utilized for the classification and recognition of fall events. The experimental results of the ARODL-FDC technique can be tested on the fall dataset. The simulation results inferred that the ARODL-FDC technique reaches promising performance over compared models concerning various measures. [ABSTRACT FROM AUTHOR]

Owing to the rapid increase in the interchange of text information through internet networks, the reliability and security of digital content are becoming a major research problem. Tampering detection, Content authentication, and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies. The authors' difficulties were tampering detection, authentication, and integrity verification of the digital contents. This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection (ADMDTW-TAD) via the Internet. The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document's appropriate characteristics to embed larger watermark information. The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely. Once the watermark is embedded with no damage to the original document, it is then shared with the destination. The watermark extraction process is performed to get the original document securely. The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes, and the outcomes were inspected in terms of different measures. The simulation values indicated that the ADMDTW-TAD technique improved performance over other models. [ABSTRACT FROM AUTHOR]

In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners' understanding of social media's importance for business development. However, the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns. Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies. In this background, the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection (CEHOML-HSD) model in the context of the Arabic language. The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal. To attain this, the CEHOML-HSD model follows different sub-processes as discussed herewith. At the initial stage, the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer. Secondly, the Support Vector Machine (SVM) model is utilized to detect and classify the hate speech texts made in the Arabic language. Lastly, the CEHO approach is employed for fine-tuning the parameters involved in SVM. This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm. The design of the CEHO algorithm for parameter tuning shows the novelty of the work. A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach. The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches. [ABSTRACT FROM AUTHOR]

Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites' finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a DeerHuntingOptimizationwithDeep BeliefNetwork Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-ECmodel aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%. [ABSTRACT FROM AUTHOR]

Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusionbased predictive tool for energy consumption prediction (MDLFM-ECP) of RE and NRE power sources. Actual data may influence the prediction performance of the results in prediction approaches. The proposed MDLFMECP technique involves pre-processing, fusion-based prediction, and hyperparameter optimization. In addition, the MDLFM-ECP technique involves the fusion of four deep learning (DL) models, namely long short-termmemory (LSTM), bidirectional LSTM (Bi-LSTM), deep belief network (DBN), and gated recurrent unit (GRU). Moreover, the chaotic cat swarm optimization (CCSO) algorithm is applied to tune the hyperparameters of the DL models. The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models, showing the novelty of the work. A series of simulations took place to validate the superior performance of the proposed method, and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural Network (CNN) model and conducted SA for Hindi movie reviews. The current study introduces an Effective Improved Metaheuristics with Deep Learning (DL)-Enabled Sentiment Analysis for Movie Reviews (IMDLSA-MR) model. The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format. Besides, the Term Frequency-Inverse Document Frequency (TF-IDF) model is exploited to generate the word vectors from the pre-processed data. The Deep Belief Network (DBN) model is utilized to analyse and classify the sentiments. Finally, the improved Jellyfish Search Optimization (IJSO) algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model, which shows the novelty of the work. Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model. The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%. [ABSTRACT FROM AUTHOR]