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The Internet of Things (IoT) introduces new technological advancements for the development of diverse significant applications. To address the need for protection against attacks, fraud and network intrusions, the Intrusion Detection System (IDS) has become a crucial component within organizations. However, due to the limited resources of IoT devices, classifying attacks and training the model on large datasets consumes more time. In this research, the proposed approach combines the Reptile Search Algorithm (RSA) and Whale Optimization Algorithm (WOA) with Multi-head attention with Long Short-Term Memory (MHA-LSTM) for effective and accurate IDS classification. Initially, IDS is obtained from CICIDS2017, CICIDS2018 and NSL-KDD. Pre-processing methods like Standard Scaling and Label Encoding are involved in transforming numerical values, segmenting features and adjusting them using mean and standard deviation to reduce sensitivity. The RSA with WOA is involved in enhancing intrusion detection by selecting relevant features and optimizing the detection process efficiently to solve complex optimization problems. The classification combination of MHA-LSTM allows the model to scale effectively to large datasets and maximum complex tasks without compromising the performance and accuracy. The proposed WOA-RSA – MHALSTM technique is evaluated on CICIDS 2017, CICIDS 2018 and NSL-KDD datasets, achieving higher accuracies of 99.997% on CICIDS2017, 99.99% on CICIDS2018 and 99.99% on NSL-KDD datasets, which is more effective than Deep Neural Network (DNN) and LSTM. [ABSTRACT FROM AUTHOR]

Introducing deep learning algorithms into the field of structural health monitoring (SHM) has contributed to the automatic extraction of damage-sensitive features, but the type and architecture of these algorithms are still in dispute. This paper proposes a hybrid deep learning framework entitled time-distributed one-dimensional convolutional neural network (1D CNN) long short-term memory (LSTM) model, which utilizes raw multisensor time histories to detect structural damages. Using a sliding window that moves along the temporal dimension, the multisensor data are first segmented into subsequences. The 1D CNN layers are simultaneously applied to each subsequence to extract damage-sensitive features from row data samples. These features are then fed into the LSTM layers to extract temporal features between subsequences. As the final step, these extracted features are classified using fully connected layers. In order to assess the performance of this model, a numerical model of a high-rise frame with nonlinear members is used. This hybrid model is assumed to identify the location of damages to this frame. In order to assess the proposed model with a real-world structure, a well-known benchmark building is employed to identify damage patterns by this deep hybrid neural network. A set of metrics related to the performance of the model is measured and evaluated. It is found that the model has an average accuracy of above 96.6% in localizing damage in the numerical structure and above 99.6% in detecting each damage pattern in the experimental building. The results indicate that the proposed model can be applied effectively to the SHM of different structural systems with different damage patterns. [ABSTRACT FROM AUTHOR]

The kidneys have an important role in keeping blood pressure, electrolyte sense, and acid-base sense of body balance to remove toxins from our body. Malfunction is responsible for irrelevant to life-threatening diseases, along with malfunction in the other functional organs. As a result, scholars worldwide have committed to finding methods for effectively diagnosing and accurately treating chronic kidney disease. As machine learning (ML) classifier is widely deployed in the healthcare field for diagnoses, also CKD is now involved in the collection of disorders that could be predicted through the ML classifier. Neutrosophic logic (NL) can be employed as a form of logic that expands classical, fuzzy, and intuitionistic fuzzy logic (IF) by integrating a third constituent: indeterminacy. It enables data handling and representation with three dissimilar membership functions: truth (T), indeterminacy (I), and false (F). The complete set is independent and may differ in the interval [0, 1], providing a convoluted strategy to handle, data incompleteness, vagueness and uncertainty. This makes NL especially relevant in complicated systems where data might be partially unknown, ambiguous, or inconsistent. This article employs a Rough Neutrosophic Attribute Reduction with Deep Learning based Enhanced Kidney Disease Diagnosis (RNSAR-DLKDD) technique. Initially, the RNSAR-DLKDD technique reduces the attributes via the RNSAR technique. Followed by, the detection and classification of kidney disease take place using long short-term memory (LSTM) model. Finally, the hyperparameter selection process is carried out via crow search algorithm (CSA). To highlight the performance of the RNSAR-DLKDD technique, a series of experiments were involved. The extensive results inferred the betterment of the RNSAR-DLKDD technique over other models [ABSTRACT FROM AUTHOR]

This study presents a natural language processing (NLP) approach to classify drugs based on compositional and usage descriptions. NLP techniques including text preprocessing, word embedding, and deep learning models were applied to our own collected data in Vietnam. Traditional machine learning models like Support Vector Machines (SVM) and deep models including Bidirectional Long Short-Term Memory (BiLSTM) and PhoBERT were evaluated. Besides, since there is a limitation in the information of the collected data, some data augmentation techniques were applied to increase the variation of the dataset. Results show PhoBERT achieving 95% accuracy, highlighting the benefits of transferring knowledge from large language models. Errors primarily occurred between similar drug categories, suggesting taxonomy refinement could improve performance. In summary, an automated drug classification framework was developed leveraging state-ofthe-art NLP, validating the feasibility of analyzing drug data at scale and aiding therapeutic understanding. This supports NLP’s potential in pharmacovigilance applications. [ABSTRACT FROM AUTHOR]

Landslides establish a main geologic threat of strong concern in many parts of the world. The vigor of soil, rocks, or other rubbish moving down a slope can destroy whatever in its track. Landslides happen in an extensive variety of geological and structural settings, geomechanical contexts, and as a response to numerous triggering and loading procedures. They are frequently related to other main natural disasters like floods, earthquakes, and volcanic waves. Landslides occasionally attack without noticeable warning. While only some cases have been examined the earlier, modern monitoring models are certain to deliver a wealth of novel quantitative observations based on SAR (synthetic aperture radar) and GPS technology for mapping the surface velocity area. This study emphasizes the latent of incorporating advanced machine learning (ML) models with geophysical data to improve prediction of landslides and risk management strategies. This study develops a Predicting Landslides with frictional-based Deep Learning using Spider Wasp Optimizer (PLFFDL-SWO) Method. The major intention of the PLFFDL-SWO technique lies in the robust frictional force based on predicting landslides. In the presented PLFFDL-SWO model, Z-score normalization is performed to transform the raw data into compatible format. Then, the long short-term memory (LSTM) model is utilized for the prediction of landslides. LSTM is a recurrent neural network (RNN) type, for predicting landslides based on frictional force data. Traditional landslide prediction methods often struggle with temporal dynamics and nonlinear relationships inherent in geophysical data. Finally, the spider wasp optimizer (SWO) algorithm is exploited for the optimal hyper parameter adjustment of the LSTM model to improve prediction accuracy. The experimentation result investigation of the PLFFDL-SWO technique can be examined by employing a benchmark dataset. The simulation outcomes reported the supremacy of the PLFFDL-SWO technique under different measures [ABSTRACT FROM AUTHOR]

Email types sorting is one of the most important tasks in current information systems with the purpose to improve the security of messages, allowing for their sorting into different types. This paper aims at studying the Convolution Neural Network and Long Short-Term Memory (CNN-LSTM), Convolution Neural Network and Gated Recurrent Unit (CNN-GRU) and Long Short-Term Memory (LSTM) deep learning models for the classification of emails into categories such as "Normal", "Fraudulent", "Harassment" and "Suspicious". The architecture of each model is discussed and the results of the models' performance by testing on labelled emails are presented. Evaluation outcomes show substantial gains in precision and throughput to conventional approaches hence inferring to the efficiency of these proposed models for automated email filtration and content evaluation. Last but not the least, the performance of the classification algorithms is evaluated with the help of parameters like Accuracy, precision, recall and F1-Score. From the experiment, the models found out that CNN-LSTM, together with the Term Frequency and Inverse Document Frequency (TF-IDF) feature extraction yielded the highest accuracy. The accuracy, precision, recall and f1-score values are 99. 348%, 99. 5%, 99. 3%, and 99. 2%, respectively. [ABSTRACT FROM AUTHOR]

The temperature control of molten steel is crucial in ladle furnace (LF) refining. In this paper, the factors influencing endpoint temperature were obtained by analyzing the LF refining process, and high-quality data sets were obtained by combining data cleaning and processing. Then, a black box model based on particle swarm optimization (PSO) and long short-term memory (LSTM) neural network machine learning algorithm was proposed to predict the endpoint temperature of LF, avoiding the inadequacy of manual parameter adjustment. The constructed PSO–LSTM model yielded the following results: The coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were 0.924, 4.064, and 5.109, and the prediction hit rates within the ranges of [−3,3], [−5,5], [−8,8], and [−10,10] were 43.16%, 71.12%, 88.15%, and 94.83%, respectively. The accuracy of multiple linear regression (MLR), backpropagation (BP) neural network, genetic algorithm back propagation (GA-BP) neural network, particle swarm optimization back propagation (PSO-BP) neural network, LSTM neural network, and the present model was compared from different dimensions, and the results demonstrated that the consistency and correlation between the PSO–LSTM model prediction values and the measured values were higher, the model prediction accuracy was superior, and the model effectively predicted molten steel temperature in the LF furnace. [ABSTRACT FROM AUTHOR]

Accurate and efficient prediction of wave energy flux (WEF) holds significant engineering value. However, the inherently non-stationary and nonlinear characteristics of WEF pose a formidable challenge to forecasting efforts. A novel hybrid model for WEF prediction was proposed, which integrated efficient modal decomposition and fusion strategies, employs long short-term memory neural networks (LSTM) for forecasting, and utilizes the sparrow search algorithm (SSA) for overall optimization. First, the WEF sequences were pre-processed using complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) to reduce their complexity. Second, the mid-frequency and low-frequency sequences after sample entropy (SE) reconstruction were used for prediction, while the high-frequency sequences needed to undergo quadratic decomposition before being applied to the prediction. Third, SSA was employed to optimize the prediction model for higher performance. At last, the three predictions obtained are merged into the final prediction of the WEF. Experiments were carried out for one-step, three-step, and six-step predictions using the two buoy station data in the eastern North Pacific Ocean. The results indicated that the proposed model has higher prediction accuracy, with the lowest MAE of 0.6348, 0.8694, 2.4579 and the highest ${R^2}$ R 2 of 0.9985, 0.9953, 0.9770, respectively. Therefore, this study provides an effective prediction tool for engineering applications of wave energy. [ABSTRACT FROM AUTHOR]

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long shortterm memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM's hyperparameters. In addition, the BC technology is utilized for securing the data transmission. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is considered a spectrum disorder. The availability of technology to identify the characteristics of ASD will have major implications for clinicians. In this article, we present a new autism diagnosis method based on attention mechanisms for behavior modelingbased feature embedding along with aspect-based analysis for a better classification of ASD. The hybrid model comprises a convolutional neural network (CNN) architecture that integrates two bidirectional long short-term memory (BiLSTM) blocks, together with additional propagation techniques, for the purpose of classification the origins of Autism Tweet dataset; the proposed work takes Autism Tweet dataset and preprocesses them to employ n-gram to extract features of which the features of the ASD behavior are fed to generate the significant behavior for classification. The model takes into account both behavior-guided features across every aspect of the Class/ASD to provide higher accuracy using Adam optimizer. The experimental values inferred that the n-BiLSTM technique reaches maximum accuracy with 98%. [ABSTRACT FROM AUTHOR]

This research paper monitors the patient's health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRTDLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches. [ABSTRACT FROM AUTHOR]

Over the last decade, web defacements and other types of web attacks have been considered serious security threats to web-based services and systems of many enterprises and organizations. A website defacement attack can bring severe repercussions to the website owner, such as immediate discontinuance of the website operations and damage to the owner's reputation, which may lead to enormous monetary losses. Several solutions and tools for monitoring and detecting web defacements have been designed and developed. Some solutions and tools are limited to static web pages, while others can handle dynamic ones but demand significant computational power. The existing proposals' other issues are relatively low detection rates and high false alarm rates because many crucial elements of web pages, including embedded code and images are not properly processed. This paper proposes a novel model for detecting web defacements to address these issues. The model is based on the bidirectional long-short term memory (Bi-LSTM) deep learning method using features of the plain text content extracted from web pages. Comprehensive testing on over 96,000 web pages dataset demonstrates that the proposed Bi-LSTM-based web defacement detection model outperforms earlier methods, achieving a 96.04% overall accuracy and a 2.03% false positive rate. [ABSTRACT FROM AUTHOR]

This article studies modern classification techniques in ECG signals through the transfer learning approach with CNN (Convolutional Neural Network). The proposed pre-trained network combines an Imagenet with huge labeled image datasets and a separate network composed of fully connected layers. This method uses the CWT (Continuous Wavelet Transform) to construct a time-frequency visualization of ECG signals, which are subsequently transformed into RGB images. The developed images are plugged into a pre-trained CNN to retrieve the desired features. We next employ supervised learning to train the neural network on the ECG labeled data using CNN features. To train a Deep Neural Network, three sets of PhysioNet databases are used: MIT-BIH (ARR) Arrhythmia, NSR (Normal Sinus Rhythm), and BIDMC CHF (Congestive Heart Failure). The classification Accuracy, Sensitivity, Specificity, F1-score, Precision, and Detection Error Rate of the CNN classifier are compared to AlexNet, GoogleNet, Vgg16, and SqueezeNet pre-trained networks. Among all these networks, SqueezeNet provides an Acc of 98.7%, Se of 99.1%, Sp of 99.20%, F1-score of 98.33%, Precision of 98.67%, and DER of 0.89%. For further investigation, the technique suggested can be implemented in addition to Bi-LSTM on some real ECG data. [ABSTRACT FROM AUTHOR]

The present investigation employs relevance vector machine (RVM) and long short-term memory (LSTM) models to predict the time-dependent bearing capacity of concrete piles. Each RVM model (SRVM) is configured by each linear, polynomial, gaussian, sigmoid, laplacian, and exponential kernel function. Each SRVM model has been optimized by each genetic (GA_SRVM) and particle swarm optimization (PSO_RVM) algorithm. Moreover, the double kernel-based RVM models (DRVM) have been employed and optimized by each GA (GA_DRVM) and PSO (PSO_DRVM) algorithm. Thus, an extensive comparison among 33 RVM (6SRVM + 6GA_RVM + 6PSO_RVM + 5DRVM + 5GA_DRVM + 5PSO_DRVM) has been carried out. Conversely, the Adam, root mean squared propagation and stochastic gradient descent with momentum algorithms have optimized the LSTM model. Each optimized RVM and LSTM model has been trained and tested by 100 and 26 datasets. In addition, the effect of structural and database multicollinearities has been analyzed on models' prediction capabilities. The performance index (PI), the variance accounted for (VAF), performance (R), mean absolute error (MAE), normalized mean bias error (NMBE), and root mean square error (RMSE) matrices have analyzed the prediction capabilities of each model. The comparison of 33 RVM and 3 LSTM models reveals that the genetic algorithm-optimized Gaussian kernel function-based SRVM model, i.e., UBC7, has been recognized as the optimal performance model with the RMSE = 146.3962 kPa, PI = 1.85, VAF = 94.60, NMBE = 30.1379 kPa, MAE = 105.7009 kPa, and R = 0.9727, close to the ideal values. Furthermore, the score (= 56), Wilcoxon (= 94.95% confidence), uncertainty (= 1st rank), generalizability (= close to ideal values), and Anderson Darling (= 9.435 ≈ 9.336) tests confirm the superiority of model UBC7. Still, structural and database multicollinearity has drastically impacted dual kernel-based RVM and stochastic gradient descent optimized LSTM models. [ABSTRACT FROM AUTHOR]

Precipitation forecasting is vital for managing disasters, urban traffic, and agriculture. This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). Using precipitation data from January 1, 2019, to December 31, 2022, as a sample, the model capitalizes on CEEMDAN's superior signal decomposition capabilities and GRU's ability to capture nonlinear dynamic patterns in time series. To assess the model's effectiveness, comparisons were conducted with 12 benchmark models, including CEEMDAN-LSTM, EMD-GRU, EMD-LSTM, BI-LSTM, GRU, LSTM, and TCN. The results demonstrate that the CEEMDAN-GRU model achieves higher accuracy and stability in short-term precipitation forecasting. Leveraging an Adam optimizer with adaptive learning rate reduction enhances convergence and ensures reliable predictions, achieving an R²of 0.7915, MAE of 0.05382, and MSE of 0.09081. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD. SEFRON explores the advantages of Spiking Neural Network (SNN) which is suitable for neuromorphic devices. To evaluate the performance of SEFRON, 2 publicly available standard datasets, namely (1) UCI: Oxford Parkinson's Disease Detection Dataset and (2) UCI: Parkinson Dataset with replicated acoustic features are used. The performance is compared with other well-known neural network models: Multilayer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). The experimental results demonstrate that the SEFRON classifier achieves a maximum accuracy of 100% and an average accuracy of 99.49% on dataset 1. For dataset 2, it attains a peak accuracy of 94% and an average accuracy of 91.94%, outperforming the other classifiers in both cases. From the performance, it is proved that the presented model can help to develop a robust automated PD detection device that can assist the physicians to diagnose the disease at its early stage. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To address the non-stationarity of sEMG signals, we employ a windowing strategy for signal augmentation within deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3–4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. To regulate identification, physicians entertain the children's activities and growing histories. The human face is employed as a biological signature as it has the potential reflections of the brain. It is utilized as a simpler and more helpful tool for early detection. Artificial intelligence (AI) algorithms in medicinal rehabilitation and diagnosis can help specialists identify various illnesses more successfully. However, owing to its particular heterogeneous symptoms and complex nature, diagnosis of ASD remains to be challenging for investigators. This work presents a Fusion of Transfer Learning (TL) with the Dandelion Algorithm for Accurate Autism Spectrum Disorder Detection and Classification (FTLDA-AASDDC) method. The FTLDA-AASDDC technique detects and classifies autism and non-autism samples using facial images. To accomplish this, the FTLDA-AASDDC technique utilizes a bilateral filter (BF) approach for noise elimination. Next, the FTLDA-AASDDC technique employs a fusion-based TL process comprising three models, namely MobileNetV2, DenseNet201, and ResNet50. Moreover, the attention-based bi-directional long short-term memory (A-BiLSTM) method is used to classify and recognize ASD. Finally, the Dandelion Algorithm (DA) is employed to optimize the parameter tuning process, improving the efficacy of the A-BiLSTM technique. A wide range of simulation analyses is performed to highlight the ASD classification performance of the FTLDA-AASDDC technique. The experimental validation of the FTLDA-AASDDC technique portrayed a superior accuracy value of 97.50% over existing techniques. [ABSTRACT FROM AUTHOR]

Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy's Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R2 score of 0.82. There is also the provision of novel well relation analysis through two method of explanation coming from SHapley Additive exPlanations (SHAP) and attention mechanisms within DA-LSTM. Extracting feature well importance from these two methods and using target wells as vehicles for pipeline demonstration, it is found that wells 199-D5-160, 199-D4-68, 199-D2-11, 199-D8-94, and 199-D2-10 have had the most year-long impact on Hexavalent Chromium (CrVI) concentration at the target during the testing year, while wells 199-D8-94, 199-D4-95, 199-D8-53, 199-D4-85, and 199-D4-99 are found to be strong candidates affecting target CrVI instantaneous change during a particular timeframe. [ABSTRACT FROM AUTHOR]

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), Fine Particles Matter (PM2.5), Coarse Particles Matter (PM10), and Ammonia (NH3). Hourly datasets were collected for air quality monitoring stations near Delhi, India, from November 25, 2020 to January 24, 2023. In this context, five intelligent models were developed, including Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). The modelling results revealed that Bi-LSTM model had the best predictability performance for forecasting CO with (R2 = 0.979), NO with (R2 = 0.961), NO2 with (R2 = 0.956), SO2 with (R2 = 0.955), PM10 with (R2 = 0.9751) and NH3 with (R2 = 0.971). Meanwhile, GRU and LSTM models performed better in forecasting O3 and PM2.5 with (R2 = 0.9624) and (R2 = 0.973), respectively. The current research provides illuminating visuals highlighting the potential of deep learning to comprehend air quality modeling, enabling improved environmental decisions. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is one of the most important segments of technology advancement in applications of smart devices, healthcare systems & fitness. HAR uses details from wearable sensors that capture the way human beings move or engage with their surrounding. Several researchers have thus presented different ways of modeling human motion, and some have been as follows: Many researchers have presented different methods of modeling human movements. Therefore, in this paper, we proposed the CNN BiLSTM model with undersampling to improve the recognition of human actions. The model is evaluated using state-of-the-art metrics, including accuracy, precision, recall, and F1-score, on two publicly available datasets: For instance, the MHEALTH and Actitracker. This will enable the team to attain test accuracies of up to 98.5% on the MHEALTH dataset. The proposed CNN-BiLSTM model outperforms the conventional deep learning methods, as reported in the Actitracker dataset, by about 5% improvement. HAR has many applications, one of which is used to keep vigil over elderly people who live alone to alert when one has fallen or when any strange movement is noticed which could be a sign that the individual is experiencing a medical Emergency. It can also be applied in physiotherapy, where the patient's development throughout rehabilitation exercises can be accessed. [ABSTRACT FROM AUTHOR]

Effective road terrain recognition is crucial for enhancing the driving safety, passability, and comfort of autonomous vehicles. This study addresses the challenges of accurately identifying diverse road surfaces using deep learning in complex environments. We introduce a novel end-to-end Tire Noise Recognition Residual Network (TNResNet) integrated with a time-frequency attention module, designed to capture and leverage time-frequency information from tire noise signals for road terrain classification. Our method was evaluated on five distinct road types: asphalt, cement, grass, mud, and sand. The performance of TNResNet was rigorously compared against traditional machine learning techniques, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines, as well as advanced deep learning models like Long Short-Term Memory and Convolutional Neural Networks. Experimental results demonstrate that TNResNet achieves superior classification accuracy of 99.48%, outperforming all comparative methods. This work not only establishes a robust framework for road terrain identification but also showcases the significant practical implications of TNResNet in the realm of autonomous vehicle navigation. [ABSTRACT FROM AUTHOR]

In today's competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer retention and ensuring the sustainable development of an organization. However, traditional machine learning algorithms and single deep learning models have limitations in extracting complex nonlinear and time-series features, resulting in unsatisfactory prediction results. To address this problem, this study proposes a hybrid neural network-based customer churn prediction model, CCP-Net. In the data preprocessing stage, the ADASYN sampling algorithm balances the sample sizes of churned and non-churned customers to eliminate the negative impact of sample imbalance on the model performance. In the feature extraction stage, CCP-Net uses Multi-Head Self-Attention to learn the global dependencies of the input sequences, combines with BiLSTM to capture the long-term dependencies in the sequential data, and uses CNN to extract the local features, and ultimately generates the prediction results. Experimental results of cross-validation on Telecom, Bank, Insurance, and News datasets show that CCP-Net outperforms the comparison algorithms in all performance metrics. For example, CCP-Net achieves a Precision of 92.19% on the Telecom dataset, 91.96% on the Bank dataset, 95.87% on the Insurance dataset, and 95.12% on the News dataset, which compares to other hybrid neural network models, the performance improvement of CCP-Net ranges from 1% to 3%. These results indicate that the design of the CCP-Net model effectively improves the accuracy and robustness of churn prediction, enabling it to be widely applied to different industries, especially in the financial, telecommunication, and media fields, to provide more comprehensive and effective churn management strategies for enterprises. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge. This paper proposes a Dinucleotide-Codon Fusion Feature encoding (DNCFF) and constructs an LPI prediction model based on deep learning, termed LPI-DNCFF. The Dual Nucleotide Visual Fusion Feature encoding (DNVFF) incorporates positional information of single nucleotides with subsequent nucleotide connections, while Codon Fusion Feature encoding (CFF) considers the specificity, molecular weight, and physicochemical properties of each amino acid. These encoding methods encapsulate rich and intuitive sequence information in limited encoding dimensions. The model comprehensively predicts LPIs by integrating global, local, and structural features, and inputs them into BiLSTM and attention layers to form a hybrid deep learning model. Experimental results demonstrate that LPI-DNCFF effectively predicts LPIs. The BiLSTM layer and attention mechanism can learn long-term dependencies and identify weighted key features, enhancing model performance. Compared to one-hot encoding, DNCFF more efficiently and thoroughly extracts potential sequence features. Compared to other existing methods, LPI-DNCFF achieved the best performance on the RPI1847 and ATH948 datasets, with MCC values of approximately 97.84% and 84.58%, respectively, outperforming the state-of-the-art method by about 1.44% and 3.48%. [ABSTRACT FROM AUTHOR]

As the primary power source for electric vehicles, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the reliable operation of the power system. Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. However, traditional LSTM-based SOH estimation methods do not account for the fact that the degradation sequence of battery SOH exhibits trend-like nonlinearity and significant dynamic variations between samples. Therefore, this paper proposes an LSTM-based lithium-ion SOH estimation method incorporating data characteristics and spatio-temporal attention. First, considering the trend-like nonlinearity of the degradation sequence, which is initially gradual and then rapid, input features are filtered and divided into trend and non-trend features. Then, to address the significant dynamic variations between samples, especially for capacity regeneration,a spatio-temporal attention mechanism is designed to extract spatio-temporal features from multidimensional non-trend features. Subsequently, an LSTM model is built with trend features, spatio-temporal features, and actual capacity as inputs to estimate capacity. Finally, the model is trained and tested on different datasets. Experimental results demonstrate that the proposed method outperforms traditional methods in terms of SOH estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

Sentiment Lexicon (SL) is utilized to extract feedback from a large amount of data, which encompass numerous words, in which a few words contain various semantic definitions in various fields, which is called domain-specific (DS) language, and every word under its domain is demonstrated very significantly as their definition varies from one another. Therefore, this research proposes a Self-configuring knowledge graph-based BiLSTM Classifier with Bat–Harris optimization to determine the accurate sentiment from the comment or text message as well as compute whether it is positive, negative, or neutral, by utilizing the feature extraction process which includes Term Frequency-Inverse Document Frequency (TF-IDF), as well as Hybrid word2vec features, and finally detect the sentiment polarity of text. The experimental results which are depending on the performance metrics show that the developed model is improved compared to the prior method, in which the rate of accuracy of the developed scheme on TP 90 is 93.40% and 94.29%, whereas the sensitivity attains the value of 93.31%, and 94.20%, and finally, the specificity acquires the value of 93.68%, and 94.66% based on datasets 1 and 2. [ABSTRACT FROM AUTHOR]

Background: Missing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing the distributions of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series affect the forecasting performance of time series models. We evaluated the prediction performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network models on imputed time series data using ten different imputation techniques. Methods: Missing values were generated under missing completely at random (MCAR) mechanism at 10%, 15%, 25%, and 35% rates of missingness using complete data of 24-h ambulatory diastolic blood pressure readings. The performance of the mean, Kalman filtering, linear, spline, and Stineman interpolations, exponentially weighted moving average (EWMA), simple moving average (SMA), k-nearest neighborhood (KNN), and last-observation-carried-forward (LOCF) imputation techniques on the time series structure and the prediction performance of the LSTM and ARIMA models were compared on imputed and original data. Results: All imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. The mean imputation, LOCF, KNN, Stineman, and cubic spline interpolations methods performed better for a small rate of missingness. Interpolation with EWMA and Kalman filtering yielded consistent performances across all scenarios of missingness. Disregarding the imputation methods, the LSTM resulted with a slightly better predictive accuracy among the best performing ARIMA and LSTM models; otherwise, the results varied. In our small sample, ARIMA tended to perform better on data with higher autocorrelation. Conclusions: We recommend to the researchers that they consider Kalman smoothing techniques, interpolation techniques (linear, spline, and Stineman), moving average techniques (SMA and EWMA) for imputing univariate time series data as they perform well on both data distribution and forecasting with ARIMA and LSTM models. The LSTM slightly outperforms ARIMA models, however, for small samples, ARIMA is simpler and faster to execute. [ABSTRACT FROM AUTHOR]

Spreading fake news on social media is regarded as a cybercrime that greatly affects society, government and people. Identifying this fake news by manual analysis is a complex task. Hence, various researchers carried out an analysis on exploiting intelligent approaches to detect fake news. This work aims to propose a hybrid deep learning model for fake news detection. In these works, the news is verified in a unidirectional manner. Hence, there is a demand to change the present scenario and a new model is required for increasing the accuracy of the fake news detection. In this work, the deep learning (DL) model Long Short Term Memory (LSTM) with 3 Parallel-concatenated Convolutional Neural Networks (PCCNN) is utilised for fake news detection. For the feature extraction process, the methods like Term Frequency- Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) are utilised. Finally, for optimising the weights of the neural network, the metaheuristic optimisation Enhanced Sun Flower Optimisation (ESFO) algorithm is used. The robustness of LSTM-PCCNN is compared with other deep learning models to verify its robustness. The experimentation is carried out on the two benchmark datasets and obtained better accuracies of 0.994 on the fake news and 0.997 on the Real and fake news datasets respectively. [ABSTRACT FROM AUTHOR]

Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human–machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional 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]

The conversion of handwritten text into machine-readable format is termed as Handwritten Character Recognition (HCR). The differences in size, design, and alignment angle of the Telugu and Kannada alphabets have difficulty in recognizing handwritten documents in these languages across various real-world applications. Newly developed machine learning and deep learning models provide a significant improvements in the handwritten text recognition. These innovative methods offer promising enhancements in the accuracy and efficiency of character recognition within handwritten documents. However, effective recognition of digits is not an easy task due to people’s varying writing styles in the input sample. To overcome such limitations, we explore a novel approach specifically designed to boost the performance of HCR in South Indian languages such as Kannada and Telugu. Initially, handwritten images are gathered using traditional data sources. These collected images are then given into the recognition phase. Here, an Adaptive Dilated convolution-based Deep Network (ADC-DeepNet) is developed for character identification purposes. In ADC-DeepNet, the ShuffleNetV2 blends with the Bidirectional Long Short-Term Memory (Bi-LSTM) to produce accurate results. This fusion provides effective character recognition. Here, the Iterative Concept of Lyrebird Optimization (ICLO) is newly proposed to optimize the variables from ADC-DeepNet to improve the character recognition efficacy. The efficiency of the HCR system is evaluated among several recent techniques with some performance measures. Finally, the outcome showed that the accuracy of the proposed approach is 95.6, and other models like CNN, ResNet, Convolutional Autoencoder, and DeepNet gave the accuracy of 88.8, 91.5, 90.6, and 93.3, respectively. Thus, the findings of the experiment show that the developed ADC-DeepNet model can effectively identify the handwritten characters in south Indian languages. [ABSTRACT FROM AUTHOR]

The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely, KDD Cup-99, NSL-KDD, WSN-DS, CICIDS 2017, and Drone, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries. [ABSTRACT FROM AUTHOR]

AbstractThis study explores the predictors of learner success in a MicroMasters focusing on behaviors across multiple courses that influence the final comprehensive exam outcome. MicroMasters, a term trademarked by edX, are a series of massive open online courses (MOOCs) that stack into a final credential that can translate into formal academic credit. Using a bi-directional Long Short-Term Memory (LSTM) model, we analyzed engagement data from 1,603 massive open online course (MOOC) learners, achieving 96.6% accuracy in predicting pass/fail outcomes. SHAP (SHapley Additive exPlanations) values identified key behavioral predictors, emphasizing the importance of consistent engagement, active forum participation, and interaction with assessments. Learners who regularly interacted with course materials, attempted questions, and engaged in discussions were more likely to pass the exam and earn the MicroMasters. Performance in foundational courses emerged as a critical predictor of success. These findings address gaps in understanding the temporal and behavioral dimensions of learner engagement and offer actionable strategies for course facilitators to implement targeted interventions that improve completion rates and support student success. This study highlights the potential of machine learning to enhance learner support and inform instructional design, ultimately advancing the quality and scalability of microcredential programs in online, competency-based education pathways. [ABSTRACT FROM AUTHOR]

This study proposes an AI-based framework to predict the quality of results from the deep drawing process without requiring considerable amounts of actual data. A few-shot learning method with a Siamese network was employed for training with limited experimental data and virtual simulation data. As the base of the Siamese network, three network architectures, fully connected network (FCN), convolutional neural network (CNN), and long short-term memory (LSTM), were considered. By increasing the ratio of the experimental data, the best accuracy for each network was determined. Finally, the K-nearest neighbor algorithm was trained to predict the drawing quality using the embedding vector from the Siamese network. In all networks, it was confirmed that the accuracy of the classification algorithm improved as more experimental data were added. At this time, the classification accuracy of the CNN was 100%, which was significantly superior to the 77.8% of the FCN and 88.9% of the LSTM. Finally, the AI-based framework proposed in this paper can be effectively applied to various press forming processes as a method for predicting the quality of deep drawing molded products. [ABSTRACT FROM AUTHOR]

Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grading. Mitotic count (MC) is a significant cancer detection and grading parameter. Conventionally, a pathologist examines the biopsy image physically by employing higher-power microscopy. The MC cells have been marked physically at every analysis, and total MC must be utilized as a major aspect for the cancer ranking and considered as the initiative of cancers. Numerous pattern recognition algorithms for cell-sized objects in HIs depend upon segmentation to assess features. The correct description of the segmentation has been difficult, and feature outcomes can be highly complex to the segmentation. The MC cells are an essential element in many cancer grading methods. Extraction of the MC cell from the HI is a highly challenging assignment. This manuscript proposes the Coati Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Segmentation and Classification (COADL-MNSC) technique. The major aim of the COADL-MNSC technique is to utilize the DL model to segment and classify the mitotic nuclei (MN). In the preliminary stage, the COADL-MNSC approach implements median filtering (MF) for pre-processing. Besides, the COADL-MNSC approach utilizes the Hybrid Attention Fusion U-Net (HAU-UNet) model to segment the MN. Moreover, the capsule network (CapsNet) model is employed for the feature extraction method, and its hyperparameters are adjusted by utilizing the COA model. At last, the classification procedure is performed using the bidirectional long short-term memory (BiLSTM) model. Extensive simulations are performed under the MN image dataset to exhibit the excellent performance of the COADL-MNSC methodology. The experimental validation of the COADL-MNSC methodology portrayed a superior accuracy value of 98.89% over existing techniques under diverse measures. [ABSTRACT FROM AUTHOR]

Accurate travel time information is essential for logistics vehicles to reserve the most suitable parking lot in logistics centers. The purpose of this study is to explore how the uncertainty of traffic time prediction affects parking lot reservation near logistics centers. A hybrid model integrating convolutional long short-term memory network and attention mechanism is proposed to provide the reliable information for travel time prediction intervals. Furthermore, a reliability-based parking lot reservation model is developed by explicitly considering logistics vehicles' time probability. Several benchmark models are compared with the proposed traffic speed prediction model. The performance of the parking lot reservation model is illustrated by travel behavior questionnaire data and global positioning system data of collected from Beijing, China. The results illustrate that the proposed prediction model exhibits a better accuracy than benchmark models. Moreover, it is found that travel time prediction interval can improve the reliability and stability of travel time, and provide a reliable time information for the parking lot reservation. [ABSTRACT FROM AUTHOR]

Informal education via social media plays a crucial role in modern learning, offering self-directed and community-driven opportunities to gain knowledge, skills, and attitudes beyond traditional educational settings. These platforms provide access to a broad range of learning materials, such as tutorials, blogs, forums, and interactive content, making education more accessible and tailored to individual interests and needs. However, challenges like information overload and the spread of misinformation highlight the importance of digital literacy in ensuring users can critically evaluate the credibility of information. Consequently, the significance of sentiment analysis has grown in contemporary times due to the widespread utilization of social media platforms as a means for individuals to articulate their viewpoints. Twitter (now X) is well recognized as a prominent social media platform that is predominantly utilized for microblogging. Individuals commonly engage in expressing their viewpoints regarding contemporary events, hence presenting a significant difficulty for scholars to categorize the sentiment associated with such expressions effectively. This research study introduces a highly effective technique for detecting misinformation related to the COVID-19 pandemic. The spread of fake news during the COVID-19 pandemic has created significant challenges for public health and safety because misinformation about the virus, its transmission, and treatments has led to confusion and distrust among the public. This research study introduce highly effective techniques for detecting misinformation related to the COVID-19 pandemic. The methodology of this work includes gathering a dataset comprising fabricated news articles sourced from a corpus and subjected to the natural language processing (NLP) cycle. After applying some filters, a total of five machine learning classifiers and three deep learning classifiers were employed to forecast the sentiment of news articles, distinguishing between those that are authentic and those that are fabricated. This research employs machine learning classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forest, to analyze and compare the obtained results. This research employs Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers, and afterwards compares the obtained results. The results indicate that the BiGRU deep learning classifier demonstrates high accuracy and efficiency, with the following indicators: accuracy of 0.91, precision of 0.90, recall of 0.93, and F1-score of 0.92. For the same algorithm, the true negatives, and true positives came out to be 555 and 580, respectively, whereas, the false negatives and false positives came out to be 81, and 68, respectively. In conclusion, this research highlights the effectiveness of the BiGRU deep learning classifier in detecting misinformation related to COVID-19, emphasizing its significance for fostering media literacy and resilience against fake news in contemporary society. The implications of this research are significant for higher education and lifelong learners as it highlights the potential for using advanced machine learning to help educators and institutions in the process of combating the spread of misinformation and promoting critical thinking skills among students. By applying these methods to analyze and classify news articles, educators can develop more effective tools and curricula for teaching media literacy and information validation, equipping students with the skills needed to discern between authentic and fabricated information in the context of the COVID-19 pandemic and beyond. The implications of this research extrapolate to the creation of a society that is resistant to the spread of fake news through social media platforms. [ABSTRACT FROM AUTHOR]

Background: Interactions between microRNAs and RNA-binding proteins are crucial for microRNA-mediated gene regulation and sorting. Despite their significance, the molecular mechanisms governing these interactions remain underexplored, apart from sequence motifs identified on microRNAs. To date, only a limited number of microRNA-binding proteins have been confirmed, typically through labor-intensive experimental procedures. Advanced bioinformatics tools are urgently needed to facilitate this research. Methods: We present DeepMiRBP, a novel hybrid deep learning model specifically designed to predict microRNA-binding proteins by modeling molecular interactions. This innovation approach is the first to target the direct interactions between small RNAs and proteins. DeepMiRBP consists of two main components. The first component employs bidirectional long short-term memory (Bi-LSTM) neural networks to capture sequential dependencies and context within RNA sequences, attention mechanisms to enhance the model's focus on the most relevant features and transfer learning to apply knowledge gained from a large dataset of RNA-protein binding sites to the specific task of predicting microRNA-protein interactions. Cosine similarity is applied to assess RNA similarities. The second component utilizes Convolutional Neural Networks (CNNs) to process the spatial data inherent in protein structures based on Position-Specific Scoring Matrices (PSSM) and contact maps to generate detailed and accurate representations of potential microRNA-binding sites and assess protein similarities. Results: DeepMiRBP achieved a prediction accuracy of 87.4% during training and 85.4% using testing, with an F score of 0.860. Additionally, we validated our method using three case studies, focusing on microRNAs such as miR-451, -19b, -23a, -21, -223, and -let-7d. DeepMiRBP successfully predicted known miRNA interactions with recently discovered RNA-binding proteins, including AGO, YBX1, and FXR2, identified in various exosomes. Conclusions: Our proposed DeepMiRBP strategy represents the first of its kind designed for microRNA-protein interaction prediction. Its promising performance underscores the model's potential to uncover novel interactions critical for small RNA sorting and packaging, as well as to infer new RNA transporter proteins. The methodologies and insights from DeepMiRBP offer a scalable template for future small RNA research, from mechanistic discovery to modeling disease-related cell-to-cell communication, emphasizing its adaptability and potential for developing novel small RNA-centric therapeutic interventions and personalized medicine. [ABSTRACT FROM AUTHOR]

Efficiently determining a price and optimal exercise boundary for an American option is a critical subject in the financial sector. This study introduces a novel application of long short-term memory neural networks to solve a relevant Volterra equation, enhancing the accuracy and efficiency of American option pricing. The proposed approach outperforms traditional numerical techniques, including finite difference methods, binomial trees, and Monte Carlo methods, delivering an impressive speed improvement by a factor of thousands while maintaining industry-accepted accuracy levels. It exhibits computational speeds up to about a hundred times faster than the state-of-the-art method used by Andersen et al. [High-performance American option pricing, J. Comput. Finance 20(1) (2016), pp. 39–87] when evaluating numerous options. The proposed network, trained on a reasonable range of parameters related to the Black–Scholes model, swiftly determines option prices and exercise boundaries, serving as a practical closed-form solution. [ABSTRACT FROM AUTHOR]

This study proposes a novel multi-agent system designed to detect Distributed Denial of Service (DDoS) attacks, addressing the increasing need for robust cybersecurity measures. The hypothesis posits that a structured multi-agent approach can enhance detection accuracy and response efficiency in DDoS attack scenarios. The methodology involves a five-stage detection model: (1) Preprocessing using a modified double sigmoid normalization technique to eliminate duplicate data; (2) Feature Extraction where raw data and improved correlation-based features, mutual information, and statistical features are identified; (3) Dimensionality Reduction conducted by a reducer agent to streamline the feature set; (4) Classification utilizing Deep Belief Networks (DBN), Bi-LSTM, and Deep Maxout models, with their weights optimally tuned using the hybrid optimization algorithm, WUJSO; and (5) Decision Making by the decision agent to ascertain the presence of attacks, followed by mitigation through modified entropy-based techniques. The results demonstrate that the proposed method achieves a detection accuracy of 0.953 at a learning rate of 90%, significantly outperforming other methods, including Bi-GRU (0.857), DEEP-MAXOUT (0.910), Bi-LSTM (0.865), RNN (0.814), NN (0.894), and DBN (0.761). This research underscores the effectiveness of the multi-agent approach in enhancing DDoS attack detection and mitigation. [ABSTRACT FROM AUTHOR]

In this study, we propose an innovative latent space-based machine learning model to predict coupled flame-flow fields across various combustion states. This model consists of two main components: a local convolutional autoencoder (LCAE) with an encoder to establish a latent space and a decoder to restore predicted features to their original physical dimensions, and a long short-term memory (LSTM) model to forecast the spatiotemporal behavior of features in this latent space. The model is trained and validated using flame-flow images simultaneously measured in a gas turbine model combustor, utilizing synchronized stereoscopic particle image velocimetry (S-PIV) and planar laser induced fluorescence of the hydroxyl radical (OH-PLIF). Our model effectively extracts large-scale structures from the coupled flame-flow measurements, even in the presence of aleatoric noise, and demonstrates satisfactory prediction and generalization capabilities, as verified through out-of-sample testing. By establishing a common latent space for the coupled flame-flow fields, we reveal inherent similarities between flame and flow fields in a low-dimensional feature space, suggesting the potential for holistic understanding and modeling of their behavior in such a space. Additionally, our model predicts rapidly and reliably, requiring only 403 ms for out-of-sample predictions, with no error accumulation across 499 predictions. This paves the way for early warning of impending undesirable combustion states in future applications. • A novel machine learning model for predicting coupled flame-flow fields is proposed. • The model framework enables rapid, stable predictions with a simple workflow. • The model shows strong generalization and robustness with low computational cost. [ABSTRACT FROM AUTHOR]

Reservoir pressure depletion is a critical aspect leading to declined oil production. Water flooding becomes mandatory to increase the recovery efficiency from certain reservoirs. Establishing well placements, injection rates, and injection schedules enables improved fluid displacement in such reservoirs. Standard reservoir simulators are widely used to provide near-optimal scenarios for maximizing oil recovery. Such reservoir simulations involved numerous iterations and required significant computational effort. Also, the conventional machine learning methods provide a relatively high uncertainty to predict the considered problem. In this context, a deep-learning surrogate model comprising long short-term memory networks and densely connected networks is proposed to predict cumulative oil production using various waterflood injection well patterns, injection rates, and injection period information. The relevance test clarifies that the oil recovery mainly controls by water injection duration and injection pattern. A total of 1485 data records are considered to validate the model's performance, demonstrating less than 3.5% absolute average relative error and a prediction versus actual value regression coefficient of 0.99989. In addition, the prediction accuracy of the proposed deep-learning model is also shown to outperform two well-known traditional machine-learning models (i.e. artificial neural networks and support vector regressions). [ABSTRACT FROM AUTHOR]

The growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybrid neural network. First, variational modal decomposition (VMD) was used for noise reduction to maximize retention of the original information and prevent capacity degradation. Second, the trend of capacity decline after noise reduction was modeled and predicted using the combination of bidirectional long short-term memory (BiLSTM) and Monte Carlo (MC) dropout. Finally, experiments were conducted to show that the new method based on the VMD-MC-BiLSTM network achieves good performance for predicting the remaining life of a lithium battery with sufficient confidence level, thereby providing a new approach for optimizing the management of lithium batteries. [ABSTRACT FROM AUTHOR]

In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production. [ABSTRACT FROM AUTHOR]

Power amplifiers in wireless communication systems can introduce nonlinear distortion, degrade signal transmission quality, and increase power consumption. The paper presents a BiLSTM-CNN-based model for modelling power amplifier behaviour to address this issue. The model uses BiLSTM layers to capture temporal information from the signal data and incorporates a multi-head attention mechanism to focus on different temporal features. Additionally, convolutional layers process global features and reduce model parameters through weight sharing. Using this model, a digital pre-distortion (DPD) model is proposed to linearise the power amplifier through an indirect learning approach. The results show that the BiLSTM-CNN model achieves a normalised mean square error (NMSE) of −40.3 dB, and the DPD model enhances the adjacent channel power ratio (ACPR) of the communication system by 18 dB, demonstrating the model’s feasibility. Comparative analysis with other network models indicates that BiLSTM-CNN outperforms traditional methods of fitting performance and convergence speed, showcasing its superiority. [ABSTRACT FROM AUTHOR]

Purpose: This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models. Design/methodology/approach: Repair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors. Findings: The LSTM model's learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models' learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects. Originality/value: Gathering a real-world high-quality data set of apartment's repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively. [ABSTRACT FROM AUTHOR]

Accurate air pollution predictions in urban areas facilitate the implementation of efficient actions to control air pollution and the formulation of strategies to mitigate contamination. This includes establishing an early warning system to notify the public. Creating precise estimates for PM2.5 air pollutants in large cities is a challenging task because of the numerous relevant factors and quick fluctuations. This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from decomposed components of PM2.5 and other feature variables, such as pollutants and meteorological variables. Bidirectional long-short-term memory (BILSTM) uses these features to extract temporal relationships, enabling the forecasting of daily PM2.5 levels at four locations in Delhi. This hybrid model uses attention mechanisms to extract the most significant information, as well as Bayesian optimization to tune the hyperparameters. The suggested model greatly improved performance in all four regions used in this study, as evidenced by the findings. We compared it with the CNN-BILSTM, BILSTM, LSTM, and CNN models, and the suggested model outperformed the state-of-the-art models by utilizing STL decomposition components and other features. The overall results show that the STL-CNN-BILSTM-AM is better at predicting air quality, especially the concentration of PM2.5 in cities when the data has a high seasonal trend and is complex. [ABSTRACT FROM AUTHOR]

In this paper, cyber-attacks in IOT-WSN are detected through proposed optimized-Neural Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN), (ii) Particle Swarm Optimization (PSO-NN), (iii) Single Candidate Optimizer Neural Network (SCO-NN) and (iv) Single Candidate Optimizer Long Short-Term Memory (SCO-LSTM) with different connecting, hidden neural network layers and threat intelligence data. The proposed algorithms detect the attacker node, which frequently changes the behaviour such as attacker node/ normal node. Existing IDS system detects the attacks in WSN and unable to detect the changing behavior attacker nodes in IOT-WSN. The behaviour of attacker node changes from normal behaviour to attacker behaviour due to nodes connected to internet continuously. The classification accuracy rates of proposed SCO-LSTM algorithm without and with threat intelligence are about 99.7% and 99.89%, respectively. [ABSTRACT FROM AUTHOR]

Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), aims to extract subjective information from textual data. With the proliferation of social media platforms like Twitter, accurately determining public sentiment has become increasingly important for businesses, policymakers, and researchers. This study introduces the Dual Joint Classifier (DJC), which integrates the strengths of RoBERTa and BERT architectures. The DJC model leverages Bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) layers to capture complex sequential dependencies and nuanced sentiment expressions. Advanced training techniques such as Focal Loss and Hard Sample Mining address class imbalance and improve model robustness. To further validate the DJC model's robustness, the larger TweetEval Sentiment dataset was also included, on which DJC outperformed conventional models despite increased training time. Evaluations were conducted on the Twitter US Airlines and Apple Twitter Sentiment datasets to verify experiments. The DJC model achieved 87.22% and 93.87% accuracies, respectively, and demonstrated improvement over other models like RoBERTa-GLG, BiLSTM(P), and SVM. These results highlight the DJC model's effectiveness in handling diverse sentiment analysis tasks and its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Technological advancements have significantly progressed from the inception of computers and the internet to the rise of artificial intelligence (AI), profoundly impacting various sectors such as business, education, and healthcare. Among these advancements, ChatGPT has emerged as a prominent conversational AI tool, facilitating tasks such as sentiment prediction and sector-specific decision-making. This study aims to evaluate ChatGPT's performance in analyzing public sentiment using data from X (formerly Twitter) and to propose an ethical framework for its sector-specific applications. By combining the BERT transformer model with BiLSTM, Random Forest, and K-Nearest Neighbor algorithms, the study achieves a hybrid approach to sentiment prediction, demonstrating superior performance with an accuracy of 86.7%. Furthermore, the study explores ethical considerations, such as fairness, accountability, and transparency, to address ChatGPT's adoption across industries. The findings offer insights into both the technical and ethical dimensions of ChatGPT's integration into business, education, and healthcare sectors, emphasizing its potential for sustainable and responsible use. [ABSTRACT FROM AUTHOR]