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A paper-based colourimetric assay for the detection of alanine transaminase has been developed. In the presence of alanine transaminase, 2,4-dinitrophenyl hydrazine changes to pyruvate hydrazone leading to a colour change from pale yellow to dark yellow. Reaction conditions were optimized using absorption spectroscopic studies. Hydrophobic patterns on the Whatman chromatographic paper were created by wax printing, and the reagents were drop cast at the reagent zone. On the paper device, the intensity of the yellow colour increases with ALT concentration in the range of 20–140 U/L in human serum. For the quantification of ALT, coloured images were captured using a digital camera and were processed with Image J software. The machine learning approach was also explored for the ALT analysis by training with colour images of the paper device and testing using a cross-validation procedure. The results obtained with real clinical samples on the paper device showed good accuracy of less than 5% relative error with the clinical lab results. Furthermore, the paper device shows high selectivity to ALT in the presence of various interfering species in blood serum with a sensitivity of 0.261 a.u/(U/L), a detection limit of 4.12 U/L, and precise results with an RSD of less than 7%. For the testing of whole blood, a plasma separation membrane was integrated with the patterned paper. [Display omitted] • Colourimetric detection of ALT on paper strip using 2,4-DNPH. • Negligible interference from other biomolecules. • Tested with serum and ALT spiked serum samples. • Plasma separation membrane incorporated strip used for testing of blood samples. • Quantification of ALT on paper strip using ImageJ and Machine learning approach. [ABSTRACT FROM AUTHOR]

The article reports that With the continued growth of IoT devices and their deployment, manually managing and connecting them is impractical and presents multiple challenges. To that end, Zero Touch Networks that rely on software-based modules instead of dedicated propriety hardware become a viable potential solution. The overall aim of zero-touch networks is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them.

In areas with high tuberculosis (TB) prevalence, high mortality rate has significantly increased over the past few decades. Even though tuberculosis can be treated, areas with high disease burden continue to have insufficient screening tools, leading to diagnostic delays and incorrect diagnoses. As a result of these challenges, a computer-aided diagnostics (CAD) system has been developed that can automatically detect tuberculosis. There are few different methods that can be used to screen for tuberculosis; however, chest X-ray (CXR) is most commonly used and strongly suggested because it is so effective in identifying lung irregularities. Over past ten years, we have seen a meteoric rise in amount of research conducted into application of machine learning strategies to examination of chest X-ray images for screening regarding pulmonary abnormalities. Particularly, we have also noticed significant interest in testing for TB. This attentiveness has increased in tandem with phenomenal progress that has been made in deep learning (DL), which is predominately founded on convolutional neural networks (CNNs). Because of these advancements, significant research contributions have been made in field of DL techniques for TB screening by utilizing CXR images. The main focus of this paper is to emphasize favorable methods and data collection, as well as methodological contributions, identify data collections, and identify challenges. [ABSTRACT FROM AUTHOR]

Reinforcement learning is a compelling area of research within machine learning because it enables the improvement of control strategies for future manipulation of dynamic systems, leveraging previous data even without a precise model of the system. It usually makes complex, model-free predictions from data alone, which is actually consistent with the purpose of control in that they both aim to design systems using richly structured perceptions to execute planning and control strategies that adequately adapt to changing environments. The robust trajectory tracking control of intricate mechanical systems presents a challenging problem that necessitates effective control methods. In this paper, we propose a novel nonlinear control strategy based on deep reinforcement learning to solve the trajectory tracking problem of a 3-degree-of-freedom (3-DOF) control moment gyroscope (CMG). First, dynamic modeling of the 3-DOF CMG is used as a policy solver for the reinforcement learning training environment, and transfer learning is employed to bridge the reality gap. Then, the hyperparameters and reward functions of the neural network are optimized using the asynchronous successive halving algorithm. Ultimately, the twin delay depth determination policy gradient algorithm is trained in simulation to yield an agent capable of tracking user-defined trajectory routes as a nonlinear controller for the system. Both simulation and experimental results show that the proposed method works well for both high-frequency and low-frequency varying trajectory tracking control, and that the proposed method has better response speed and robustness than classic linear parameter-varying control methods and the state-of-the-art nonlinear parameter-varying method and the neural network-based feedback linearization adaptive control method. [ABSTRACT FROM AUTHOR]

Deep learning integration in cancer diagnosis enhances accuracy and diagnosis speed which helps clinical decision-making and improves health outcomes. Despite all these benefits in cancer diagnosis, the present AI models in urology cancer diagnosis have not been sufficiently reviewed systematically. This paper reviews the artificial intelligence approaches used in cancer diagnosis, prediction, and treatment of urology cancer. AI models and their applications in urology subspecialties are evaluated and discussed. The Scopus, Microsoft Academic and PubMed/MEDLINE databases were searched in November 2022 using the terms "artificial intelligence", "neural network", "machine learning," or "deep learning" combined with the phrase "urology cancers". The search was limited to publications published within the previous 20 years to identify cutting-edge deep-learning applications published in English. Irrelevant review articles and publications were eliminated. The included research involves two kinds of research analysis: quantitative and qualitative. 48 articles were included in this survey. 25 studies proposed several approaches for prostate cancers, while 15 were for bladder cancers. 8 studies discussed renal cell carcinoma and kidney cancer. The models presented to detect urology cancers have achieved high detection accuracy (77–95%). Deep learning approaches that use convolutional neural networks have achieved the highest accuracy among other techniques. Although it is still progressing, the development of AI models for urology cancer detection, prediction, and therapy has shown significant promise. Additional research is required to employ more extensive, higher-quality, and more recent datasets to the clinical performance of the proposed AI models in urology cancer applications. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions. Scientific contribution: We aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances. [ABSTRACT FROM AUTHOR]

Accurate prediction of crude petroleum production in oil fields plays a crucial role in analyzing reservoir dynamics, formulating measures to increase production, and selecting ways to improve recovery factors. Current prediction methods mainly include reservoir engineering methods, numerical simulation methods, and deep learning methods, and the required prerequisite is a large amount of historical data. However, when the data used to train the model are insufficient, the prediction effect will be reduced dramatically. In this paper, a time series-related meta-learning (TsrML) method is proposed that can be applied to the prediction of petroleum time series containing small samples and can address the limitations of traditional deep learning methods for the few-shot problem, thereby supporting the development of production measures. The approach involves an architecture divided into meta-learner and base-learner, which learns initialization parameters from 89 time series datasets. It can be quickly adapted to achieve excellent and accurate predictions with small samples in the oil field. Three case studies were performed using time series from two actual oil fields. For objective evaluation, the proposed method is compared with several traditional methods. Compared to traditional deep learning methods, RMSE is decreased by 0.1766 on average, and MAPE is decreased by 4.8013 on average. The empirical results show that the proposed method outperforms the traditional deep learning methods. [ABSTRACT FROM AUTHOR]

This document provides summaries of various research papers published in the journal Sensors. The summaries cover a wide range of topics, including personalized medicine, laser ranging, memristive interfaces, face and person detection, IoT data visualization, monitoring elderly activities, auditory illusions, brain connectivity, corona discharges, thermal data analysis, vital signal detection, and eye-blink artifact removal. The document showcases the contributions and findings of each study, offering valuable insights for researchers and library patrons interested in these areas of study. [Extracted from the article]

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being played. This paper proposes a deep learning-based method for simultaneous HAR and musical note recognition in music performances. We conducted experiments on Morin khuur performances, a traditional Mongolian instrument. The proposed method consists of two stages. First, we created a new dataset of Morin khuur performances. We used motion capture systems and depth sensors to collect data that includes hand keypoints, instrument segmentation information, and detailed movement information. We then analyzed RGB images, depth images, and motion data to determine which type of data provides the most valuable features for recognizing actions and notes in music performances. The second stage utilizes a Spatial Temporal Attention Graph Convolutional Network (STA-GCN) to recognize musical notes as continuous gestures. The STA-GCN model is designed to learn the relationships between hand keypoints and instrument segmentation information, which are crucial for accurate recognition. Evaluation on our dataset demonstrates that our model outperforms the traditional ST-GCN model, achieving an accuracy of 81.4%. [ABSTRACT FROM AUTHOR]

This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems. The algorithm utilizes the Separation Index (SI) as a supervised complexity measure to evaluate and train each layer in a forward manner. The proposed method explains that gradually increasing the SI through layers reduces the input data's uncertainties and disturbances, achieving a better feature space representation. Hence, by approximating the SI with a variant of local triplet loss at each layer, a gradient-based learning algorithm is suggested to maximize it. Inspired by the NGRAD (Neural Gradient Representation by Activity Differences) hypothesis, the proposed algorithm operates in a forward manner without explicit error information from the last layer. The algorithm's performance is evaluated on image classification tasks using VGG16, VGG19, AlexNet, and LeNet architectures with CIFAR-10, CIFAR-100, Raabin-WBC, and Fashion-MNIST datasets. Additionally, the experiments are applied to text classification tasks using the DBPedia and AG's News datasets. The results demonstrate that the proposed layer-wise learning algorithm outperforms state-of-the-art methods in accuracy and time complexity. [ABSTRACT FROM AUTHOR]

To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model's predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning. [ABSTRACT FROM AUTHOR]

It is of great significance to accurately grasp ground-level ozone concentration on a wide scale in order to cope with the increasingly serious ground-level ozone pollution problem. Currently, satellite technology can be used to monitor ground-level ozone concentration on a wide scale, but there is the problem of insufficient spatial resolution. The use of ground monitoring stations to detect ground-level ozone concentration can obtain more accurate ozone concentration around the station, but there are problems of limited number of stations and sparse distribution. Therefore, it is still a challenge to accurately grasp the ground-level ozone concentration at a large scale and high spatial resolution. To address this issue, this paper proposes a ground-level ozone concentration inversion algorithm based on deep learning methods, incorporating data from remote sensing satellites, ground monitoring stations, meteorological conditions, geography, and land use. Taking Yunnan Province in China as the experimental region and utilizing five-fold cross-validation, the model's Mean Square Error (MSE) reached 340.45 and Root Mean Square Error (RMSE) reached 18.45. Compared to machine learning models, the error decreased by 14.53%, outperforming Random Forest models, Convolution Neural Networks, and Vision Transformers. This study offers a referential approach for accurately capturing ground-level ozone concentrations at a large scale with high spatial resolution. [ABSTRACT FROM AUTHOR]

Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM. [ABSTRACT FROM AUTHOR]

In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm. [ABSTRACT FROM AUTHOR]

Many science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed for multilabel classification task in the literature. Furthermore, several open-source tools implementing these approaches have also been developed. However, the characteristics of real-world multilabel patent and publication datasets are not completely in line with those of benchmark ones. Therefore, the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets. Three real-world datasets (Biological-Sciences, Health-Sciences, and USPTO) from SciGraph and USPTO database are constructed. Seven multilabel classification methods with tuned parameters (dependency-LDA, MLkNN, LabelPowerset, RAkEL, TextCNN, TexRNN, and TextRCNN) are comprehensively compared on these three real-world datasets. To evaluate the performance, the study adopts three classification-based metrics: Macro-F1, Micro-F1, and Hamming Loss. The TextCNN and TextRCNN models show obvious superiority on small-scale datasets with more complex hierarchical structure of labels and more balanced documentlabel distribution in terms of macro-F1, micro-F1 and Hamming Loss. The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution. Three real-world datasets differ in the following aspects: statement, data quality, and purposes. Additionally, open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection, which in turn impacts the performance of a multi-label classification approach. In the near future, we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings. The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets, underscoring the complexity of real-world multi-label classification tasks. Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels. With ongoing enhancements in deep learning algorithms and large-scale models, it is expected that the efficacy of multi-label classification tasks will be significantly improved, reaching a level of practical utility in the foreseeable future. (1) Seven multi-label classification methods are comprehensively compared on three real-world datasets. (2) The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution. (3) The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution. [ABSTRACT FROM AUTHOR]

One of the predominant health issues affecting Saudi Arabia and leading to many complications is Type 2 diabetes (T2D). Early detection and significant preventative measures lead to curbing and controlling the health issue. There are fewer datasets in the literature for the detection of T2D in the Saudi population. Past studies using Saudi data have favoured machine learning algorithms to classify T2D. Although the application of this data in machine learning is evident, no studies exist in the literature that compare this data, especially those related to deep learning algorithms. This study's objective is to use specific Saudi data to develop multiple deep learning models that could be used to detect T2D. The research uses a Deep Neural Network (DNN), an Autoencoder (AE), and a Convolutional Neural Network (CNN) to create predictive models and compare their performance with a traditional machine learning classifier used on the same dataset that outperformed other machine learning algorithms such as a Decision Forest (DF). Various metrics were used to evaluate the effectiveness of the models, such as accuracy, precision, recall, F1 score and area under the ROC curve (AUC) where the ROC acts as a receiver operating characteristic curve. There are two cases in this paper: (i) uses all features of the dataset and (ii) uses six of the ten features, such as DF. In case (i), the results were shown that AE outperformed other models with the highest accuracy for imbalanced and balanced data 81.12% and 79.16%, respectively. The results for case (ii) showed that AE scored the highest 81.01% accuracy with imbalanced data compared to DF and DF achieved the highest accuracy of 82.1% with balanced data. As a result, both cases explored in this study revealed that AE has a constant superior performance if imbalanced data is used. In contrast, DF demonstrated the highest accuracy when a balanced dataset was used with a feature set reduction. They help to identify the undiagnosed T2D, and they are essential for professionals in Saudi Arabia in the health sector to promote health connections, identify risks and contain or improve their diabetes management. [ABSTRACT FROM AUTHOR]

News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms. [ABSTRACT FROM AUTHOR]

Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer's disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%). [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer's disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. [ABSTRACT FROM AUTHOR]

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. [ABSTRACT FROM AUTHOR]

Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n-gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. [ABSTRACT FROM AUTHOR]

Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis. [ABSTRACT FROM AUTHOR]

The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed's features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined. [ABSTRACT FROM AUTHOR]

Several works report on overcoming the challenge of complex general-purpose signal processing algorithms that result in quick utilization of chip area resources (like FPGA in implementing system on chip or network on chip). However, with the advent of machine learning and deep learning algorithms, reducing this complexity and immense use of resources is a challenging task that can improve the system's performance in real time. One of the approaches is algorithm optimization, which consumes fewer resources and produces an equivalent performance. This approach may likely be accepted in less-sensitive applications, like voice or video processing. Recently, an optimized algorithm of correlation-less Wiener–Hopf-based adaptive channel equalizer is reported, in which the overall complexity of the system is reduced by employing a more compact way of weight optimization. This paper is the continuation of that work that reports the modification in the Steepest-Descent-based adaptive channel equalizer algorithm by keeping the autocorrelation matrix out of the algorithm. The proposed design is simulated and tested at equivalent spectral performance and produces comparable performance with fewer chip resources when translated on FPGA. The proposed design supports that algorithm-level optimization may be accepted extensively for the optimal hardware-based design of DSP systems. [ABSTRACT FROM AUTHOR]

The advanced development of communication technologies has made covert communications quite challenging to be recognized. By altering an entity's timing behavior during overt network communication, a covert timing channel (CTC) provides a way to leak sensitive data. This is becoming a serious threat due to the ability to transmit hidden messages without being detected by traditional security systems such as proxies and firewalls. In this paper, we explore the automatic classification and identification of covert timing channels using deep neural networks, namely, Long Short-Term Memory (LSTM), 1D-Convolutional Neural networks (1D-CNN), and a hybrid of LSTM and 1D-CNN. These classifiers have been trained and tested using sequence real inter-arrival times datasets labeled with covert or legitimate. The stream length differs for each dataset; (32, 64, 128, 256, and 512 bytes). Traditional machine-learning models have also been developed for comparisons and evaluation. The evaluation results showed that the hybrid of LSTM and CNN model outperforms other models either developed by deep neural networks or traditional machine learning with an accuracy of %97.5. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense mechanisms against adversarial attacks: (a) robust training and (b) adversarial training. The former suggests that training a DNN on a data set consisting solely of robust features should produce a model resistant to adversarial attacks. The latter creates an adversarially trained model that learns to minimise an expected training loss over a distribution of bounded adversarial perturbations. We reveal a significant lack in the transferability of these defense mechanisms and provide insight into the potential dangers posed by L ∞ -norm attacks previously underestimated by the research community. Such conclusions are based on extensive experiments involving (1) different model architectures, (2) the use of canonical correlation analysis, (3) visual and quantitative analysis of the neural network's latent representations, (4) an analysis of networks' decision boundaries and (5) the use of equivalence of L 2 and L ∞ perturbation norm theories. [ABSTRACT FROM AUTHOR]

The rapid expansion and pervasive reach of the internet in recent years have raised concerns about evolving and adaptable online threats, particularly with the extensive integration of Machine Learning (ML) systems into our daily routines. These systems are increasingly becoming targets of malicious attacks that seek to distort their functionality through the concept of poisoning. Such attacks aim to warp the intended operations of these services, deviating them from their true purpose. Poisoning renders systems susceptible to unauthorized access, enabling illicit users to masquerade as legitimate ones, compromising the integrity of smart technology-based systems like Network Intrusion Detection Systems (NIDSs). Therefore, it is necessary to continue working on studying the resilience of deep learning network systems while there are poisoning attacks, specifically interfering with the integrity of data conveyed over networks. This paper explores the resilience of deep learning (DL)—based NIDSs against untethered white-box attacks. More specifically, it introduces a designed poisoning attack technique geared especially for deep learning by adding various amounts of altered instances into training datasets at diverse rates and then investigating the attack's influence on model performance. We observe that increasing injection rates (from 1% to 50%) and random amplified distribution have slightly affected the overall performance of the system, which is represented by accuracy (0.93) at the end of the experiments. However, the rest of the results related to the other measures, such as PPV (0.082), FPR (0.29), and MSE (0.67), indicate that the data manipulation poisoning attacks impact the deep learning model. These findings shed light on the vulnerability of DL-based NIDS under poisoning attacks, emphasizing the significance of securing such systems against these sophisticated threats, for which defense techniques should be considered. Our analysis, supported by experimental results, shows that the generated poisoned data have significantly impacted the model performance and are hard to be detected. [ABSTRACT FROM AUTHOR]

Deep learning methods have shown significant potential in computer vision applications within the field of civil engineering. One important area of research is the detection and classification of construction machines using convolutional neural networks (CNNs). However, a major challenge in adopting machine learning models for vision systems lies in hyperparameter optimization. Therefore, the objective of this paper is to propose a meticulous statistical approach to hyperparameter tuning for deep learning in the visual classification of construction machines. It is noteworthy that the method for selecting hyperparameters utilizes statistical concepts, including analysis of variance and the Tukey test. Moreover, three research questions were formulated to address statistical differences in performance when varying the CNN architecture, optimizer and configuration (architecture and optimizer). Two additional research questions were posed to determine the recommended hyperparameters for the task and assess performance differences for non-selected hyperparameters. For this purpose, 18 hyperparameter combinations were analyzed, encompassing three CNN architectures (DenseNet, Mobilenet and VGG16) and six optimizers (adadelta, adagrad, rmsprop, adam, adamax and sgd). The Alberta Construction Image Dataset, consisting of 2850 images of three construction machines (excavator, dump truck and concrete mixer truck), was used in the experiments. The results demonstrate that the hyperparameter configurations exhibit statistically significant differences in image classification. Furthermore, the recommended combination (DenseNet × adagrad) yielded the highest average accuracy results in both binary tests (90.0%) and multiclass classification (77.8%). [ABSTRACT FROM AUTHOR]

The concept of geo-localization broadly refers to the process of determining an entity's geographical location, typically in the form of Global Positioning System (GPS) coordinates. The entity of interest may be an image, a sequence of images, a video, a satellite image, or even objects visible within the image. Recently, massive datasets of GPS-tagged media have become available due to smartphones and the internet, and deep learning has risen to prominence and enhanced the performance capabilities of machine learning models. These developments have enabled the rise of image and object geo-localization, which has impacted a wide range of applications such as augmented reality, robotics, self-driving vehicles, road maintenance, and 3D reconstruction. This paper provides a comprehensive survey of visual geo-localization, which may involve either determining the location at which an image has been captured (image geo-localization) or geolocating objects within an image (object geo-localization). We will provide an in-depth study of visual geo-localization including a summary of popular algorithms, a description of proposed datasets, and an analysis of performance results to illustrate the current state of the field. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-centric approach is proposed for solving the 3D mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each node in a 3D mesh graph. This makes the mesh data more compact and easier to process which is important for reducing computation costs. K-Means clustering is then used to cluster each node according to their node embedding information. This data-centric approach is more computationally efficient than other complex models such as CNN and RNN. The main contribution of this study is the development of a data-centric AI framework that combines node2vec embedding, machine learning, and deep learning techniques. The use of cosine similarity is also adapted to compare and evaluate the trained node embedding vectors with different hyperparameters. Additionally, a new algorithm is developed to determine the optimal cluster number using geodesic distance on the 3D mesh. Overall, this approach provides competitive results compared to existing mesh segmentation methods. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This article analyzes the publicly available literature on drone classification in the radio frequency domain, focusing on detection and identification. Drones are increasingly used for illegal purposes, making classification techniques crucial. This review paper covers passive radio frequency sensors, classification techniques, and datasets that highlight the challenges. Methods: Researchers are developing antidrone solutions because drones have become valuable tools and targets for illegal activities. Due to the scope of the subject matter, the review included only the classification of drones via passive radio frequency sensors with a description of the classification techniques (set of algorithms, methods, and procedures) and the datasets used for performance testing. This study introduces a new categorization and offers deeper insights into publicly available drone classification techniques. Results: Based on the results of this study, it is apparent that deep learning algorithms are presently the most effective approach to addressing the challenge of drone classification within the radio frequency domain. One of the primary obstacles is the absence of a comprehensive standard for classifying drones in the radio frequency domain, which should be based on end-user requirements. Additionally, the results of two ablative experiments highlight the preprocessing of raw I/Q radio signals as an essential step in drone classification. Conclusion: In summary, the proposed categorization provides a valuable tool for literature review. Deep learning is the most effective technique for drone classification, but publicly available datasets with drone radio signals are limited. The key strength of this study is that it represents the first review of publicly available datasets with drone radio signals. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- β (TGF- β ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid's stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method. [ABSTRACT FROM AUTHOR]

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant's exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human's micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are becoming more popular due to their superior performance like high power density, long lifespan, broad operating range of temperatures, quick charging capabilities, and low self-discharge. The implementation of a Battery Management System (BMS) is crucial in order to guarantee the secure and optimal functioning of electric vehicle batteries. BMS monitors, controls, and maintains the health and performance of rechargeable batteries by monitoring the State of Charge (SoC), State of Health (SoH), and State of Temperature (SoT). SoC quantifies the amount of energy stored in a battery at a certain moment, and it is employed to approximate the remaining distance that may be covered. SoH describe the overall health or state of a rechargeable battery. It reveals how well a battery performs in comparison to its original state. SoT estimation keeps the battery temperature within a safe range, allowing it to reach a higher age and safety, which is critical for battery reliability. In this paper SoC, SoH, SoT estimation models for a lithium-ion battery have been developed using an improved EP-based R110-BLSTM approach. The Emperor Penguin based Residual Network-110 incorporated Bidirectional Long-Short Term Memory (EP-based R110-BLSTM) is ideal for estimating SoC, SoH, and SoT and features great accuracy, a quick estimation speed, and strong generalization capabilities. However, Extreme Learning Machine performance is heavily reliant on proper feature extraction. In order to enhance estimate performance by extracting the best features, Simulated-Annealing-based Golden Eagle optimization is utilized. Electric vehicle drive cycles are used to test the model's resilience to temperature changes. MATLAB 2018b software is used in the execution of this research. The results demonstrate that the proposed model performs better in terms of accuracy and has lower SoC, SoH, and SoT error rates than existing models. A thorough comparison between the recommended model and existing methods is also made, further demonstrating the proposed model's superiority. [ABSTRACT FROM AUTHOR]

Applications for the stock market that can be relied upon to provide the information regular and professional investors need to make better-informed purchases are a boon to both. A well-thought-out sales approach may help buyers mitigate risk, zero in on the companies most likely to provide the highest returns, and increase their chances of making a purchase. Due to numerous interrelationships between various company values, executing stock market research utilizing batch processing methods provides an exceptionally tough task. Technology advancements, such as the licensing of global processes, usher in a new age for stock market forecasting. The present market climate has increased the significance of data applications. An interesting and new contribution made by this work is the proposal of a deep learning-based resilience time series model for forecasting future market values. By analyzing financial time series, this study aimed to build a sophisticated strategy for forecasting stock market prices. The use of artificial intelligence (AI) to forecast future market behavior is one of the most intriguing developments in this generation of technological breakthroughs. Particularly promising results have been found when applying deep learning techniques to the task of predicting the actions of market players. In this paper, we propose a method for forecasting the final prices of publicly traded firms like Tesla, Inc. and Apple, Inc. by merging a convolutional neural network with long short-term memory (LSTM). These two methods were both developed with the help of deep learning techniques. The time frame covered by statistics used in these projections is two years. To evaluate the performance of our deep learning models, we compared their market predictions using several different metrics, including mean squared error (MSE), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and Pearson's R. When compared to the options, the CNN-LSTM deep learning algorithm did the best. When comparing convolution neural networks CNN-LSTM model to the normal LSTM and a simpler deep learning variant, the latter showed to be more effective at forecasting stock market values. This was the case when examining all three variants together. [ABSTRACT FROM AUTHOR]

In the era of big data, with the continuous popularization of social media, various text data are growing in the network and in life. It is of great significance to analyze and manage text data using text classification technology. Text classification is a basic research field in the field of artificial intelligence natural language processing. Under the given criteria, it classifies text according to content. The application scenarios of text classification are very extensive, such as sentiment analysis, topic classification, relationship classification, etc. Deep learning is a method of representation learning based on data in machine learning, and it shows good classification effect in text data processing. Chinese text and English text have differences in form, sound, and image. Focusing on the uniqueness of Chinese text classification, this paper analyzes and expounds the deep learning methods used for Chinese text classification, and finally sorts out commonly used datasets for Chinese text classification. [ABSTRACT FROM AUTHOR]

Social networks are widely used platforms for sharing various information and content, including text, images, and videos.The main challenge in social networking today is the verification of data credibility and the identification of genuine social media profiles. Cybercriminals take advantage of this by utilizing fake profiles to disseminate false information. However, current research mainly concentrates on spam filtering and analyzing malicious behavior separately, disregarding the interrelated nature of these issues. We propose a more desirable global, hybrid solution that encompasses both malicious profile detection and spam detection to mitigate the spread of spam effectively. This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate profiles, we first provide the detection of fake profiles using an automated honeypot. Next, we detect those who make interactions as malicious profiles, and finally, we collect their shared content to find spam tweets using a convolution neural network algorithm. We suggest using collaborative filtering and content filtering algorithms from recommender systems to define accounts similar to harmful profiles and spam similar to spam material picked up by convolution neural networks. We get a highly compelling and intriguing outcome with higher accuracy (99.23%) and lesser loss than typical learning algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an incredibly growing technology. However, due to hardware inadequacy, IoT security is not improving to the same extent. For this reason, lightweight encryption algorithms have begun to be developed. This paper presents a method for assessing the security of Pseudorandom Number Generator (PRNG) generated binary sequences in a reasonable time using a pre-trained deep learning (DL) model. Due to their long execution times, Randomness Test Standards (RTSs) that include statistical tests that examine whether the sequences generated by PRNGs contain any patterns that cause cryptographic vulnerabilities are not suitable for running on edge devices with low processing capacities such as the IoT. We argue that every random sequence, even generated by a PRNGs that are classified as cryptographically secure, utilized in cryptographic applications should be used after successful results obtained from RTSs in every time. Therefore, an alternative method based on machine learning has been proposed to overcome the processing time problem of these test suites. The most utilized RTSs are NIST 800-22 Rev.1a, GB/T 32915-2016 and AIS 20/31. The 800-22 Rev.1a, which NIST has designated as a standard, has been observed to be the most referenced test standard in the literature. With this implementation, we sought to show that 15 statistical tests of the NIST 800-22 rev.1a environment can be modeled using DL. The application findings indicate that this modeling can serve as an alternative to the existing test environments. The average accuracy recorded throughout 15 tests was 98.64 percent. As a result, the trained model can be implemented even in edge computing devices with limited capability including IoTs. [ABSTRACT FROM AUTHOR]

Data traffic is rapidly growing due to e-commerce, digital communication, and the digital world. Optical networks must provide a better solution to enhance transmission and improve service with cost efficiency. Considerable investigations of the 64QAM (Quadrature Amplitude Modulation) model have been conducted on wireless and optical communication to improve transmission efficiency. The next generation 400Gb/s systems are now widely recognized to be commercially accessible. At the same time, optical communication networks need a superior method to provide the best Quality of Transmission with improved data transmission efficiency. This paper aims to improve the Quality of Transmission in Optical Communication Networks (OCN). Therefore, a simple and fast reconfigurable network model is essentially required. However, there is a problem primarily related to configuring the network parameters to improve the Quality of Transmission (QoT). Research has shown that deep learning algorithms can improve QoT. Recurrent Neural Networks and Long-Short-Term-Memory (RNN-LSTM) algorithms are employed to leverage the factors of QoT in OCN. The RNN model analyzes the physical network parameters, and LSTM analyzes the dynamic data parameters and data from OCN. The Gaussian processes and path computation elements are utilized to obtain a mean absolute Signal Noise Ratio error of merely 0.1dB as a better result by the Gaussian process. The proposed model can provide high flexibility in changing the network topologies, planning, and period with less complexity by including a dataset evolution, adjusting the parameters, and examining output. A real-time dataset is analyzed using RNN-LSTM, implemented in Python, and experimented with. The throughput, bandwidth, and modulation path following light paths are calculated in the experiment to estimate the QoT. The experimental results showed that the proposed RNN-LSTM model provides better QoT. From the comparison, the proposed model provides a better throughput of 1 to 2 Gbps and Under Provisioning Ratio of 0.5 Hz over the existing models. [ABSTRACT FROM AUTHOR]

The precise classification of kidneys and kidney tumors is of great guiding significance for the diagnosis and surgical planning of clinical doctors. Artificial segmentation of kidneys and renal tumors not only increases the workload of doctors, but also has a certain degree of subjectivity. Due to the varying morphology and pathological characteristics of various kidney tumors, conventional imaging analysis methods have certain limitations in their application. This study aims to propose a novel optical imaging device based on deep learning to achieve better results in kidney tumor image segmentation and recognition. The study collected a batch of image data on kidney tumors and trained the data using deep learning algorithms. After many experiments and optimization, a model with excellent performance in renal tumor segmentation and recognition was obtained. Compared with several existing methods, the method proposed in this paper has better segmentation accuracy for kidney tumors. This can not only avoid mutual interference between the network and the resolution network, but also improve the recognition efficiency between the two networks. In addition, the system can also provide reliable data feedback for clinical doctors, helping them continuously correct problems in subsequent visual examinations, thereby achieving higher diagnostic accuracy. The experimental results show that the neural network is effective in image segmentation and quantitative analysis, and can be applied in clinical practice. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. [ABSTRACT FROM AUTHOR]

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios. [ABSTRACT FROM AUTHOR]

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management. [ABSTRACT FROM AUTHOR]

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments. [ABSTRACT FROM AUTHOR]