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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the "NUPT-FPV" dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average (ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE). [ABSTRACT FROM AUTHOR]