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Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO). The experimental results show that the method is effective in fault diagnosis of asynchronous motors, with an accuracy rate close to 100%, and can still maintain a high accuracy rate under the condition of low noise ratio, with good robustness and generalization ability. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods. [ABSTRACT FROM AUTHOR]

This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms' performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead's superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are utilized in different industrial sectors such as manufacturing, transportation, transmission, and energy due to their ruggedness, low cost, and high efficiency. If IMs fail without advanced warning, unscheduled maintenance needs to be performed, leading to downtime and maintenance costs for asset owners. To avoid these, conducting prognostics and health management (PHM) for IMs is indispensable. There are different PHM methods (expert knowledge, physics-based, and machine learning) to analyze the health and estimate the remaining useful life (RUL) of IMs. It is essential to select appropriate methods and algorithms to solve practical engineering problems by comparing their pros and cons. This paper will systematically summarize the application of the PHM framework to IMs and comprehensively present how to select appropriate general methods as well as specific algorithms applied in the PHM for IMs to solve practical engineering problems, aiming to provide some guidance for future researchers and practitioners. [ABSTRACT FROM AUTHOR]

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals. [ABSTRACT FROM AUTHOR]

As immovable stone cultural heritage is kept in the open air, they are more susceptible to damage, and damage detection is very important for the protection and restoration of cultural heritage. This is especially true for gold-overlaid stone cultural heritage, which is usually more complicated than ordinary stone carvings. However, the detection of cultural heritage damages is mainly based on expert visual inspection, which is often subjective, time-consuming, and laborious. This paper uses the Mask R-CNN algorithm to rapidly and accurately detect the gold foil shedding of stone cultural heritage through two-dimensional images. The research data are from the high-precision images of the Dazu Thousand-Hand Bodhisattva Statue (World Heritage, UNESCO) in Chongqing, China. After cleaning and augmentation, 1900 images are input into Mask R-CNN model for training. Finally, the average precision value (AP) for detecting gold foil shedding is found to be 0.967. In order to test the performance of the model, the new images that do not participate in the training period are used, and it is found that the model can still accurately detect the gold foil shedding even if there are interference factors. This is the first attempt to detect the damages of gold-overlaid stone cultural heritage based on a deep learning algorithm, and it has achieved good results. [ABSTRACT FROM AUTHOR]

This paper contributes to the ongoing debate on the nature and characteristics of the volatility transmission channels of major crash events in international stock markets between 03 July 1997 and 09 March 2021. Using dynamic conditional correlations (DCC) for conditional correlations and volatility clustering, GARCH-BEKK for the direction of transmission of disturbances, and the Diebold-Yilmaz spillover index for the level of volatility contagion, the paper finds that the climbs in external shock transmissions have long-lasting impacts in domestic markets due to the contagion effect during crisis periods. The findings also reveal that the heavier magnitude of financial stress is transmitted between Asian countries via the Hong Kong stock market. Additionally, the degree of volatility spillovers between advanced and emerging equity markets is smaller compared to the pure spillovers between advanced markets or emerging markets, offering a window of opportunity for international market participants in terms of portfolio diversification and risk management applications. Furthermore, the study introduces a novel early warning system created by integrating DCC correlations with a state-of-the-art deep learning model to predict the global financial crisis and COVID-19 crisis. The experimental analysis of long short-term memory network finds evidence of contagion risk by verifying bursts in volatility spillovers and generating signals with high accuracy before the 12-month crisis period. This provides supplementary information that contributes to the decision-making process of practitioners, as well as offering indicative evidence that facilitates the assessment of market vulnerability for policymakers. [ABSTRACT FROM AUTHOR]

Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study. [ABSTRACT FROM AUTHOR]

Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security. Machine learning and deep learning have become mainstream methods to analyze traffic, and labeled dataset construction is the basis. Android occupies a huge share of the mobile operating system market. Instant Messaging (IM) applications are important tools for people communication. But such applications have complex functions which frequently switched, so it is difficult to obtain function-level labels. The existing function-level public datasets in Android are rare and noisy, leading to research stagnation. Most labeled samples are collected with WLAN devices, which cannot exclude the operating system background traffic. At the same time, other datasets need to obtain root permission or use scripts to simulate user behavior. These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained. Previous work (Chen et al. in Appl Sci 12(22):11731, 2022) proposed a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C, running at the application-level in Android. This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android, F3L. The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground. It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions. The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels. Experiments show that the method can match the correct label within 3 s after the user operation. [ABSTRACT FROM AUTHOR]

The tasks of few-shot, one-shot, and zero-shot learning—or collectively "low-shot learning" (LSL)—at first glance are quite similar to the long-standing task of class imbalanced learning; specifically, they aim to learn classes for which there is little labeled data available. Motivated by this similarity, we conduct a survey to review the recent literature for works which combine these fields in one of two ways, either addressing the obstacle of class imbalance within a LSL setting, or utilizing LSL techniques or frameworks in order to combat class imbalance within other settings. In our survey of over 60 papers in a wide range of applications from January 2020 to July 2023 (inclusive), we examine and report methodologies and experimental results, find that most works report performance at or above their respective state-of-the-art, and highlight current research gaps which hold potential for future work, especially those involving the use of LSL techniques in imbalanced tasks. To this end, we emphasize the lack of works utilizing LSL approaches based on large language models or semantic data, and works using LSL for big-data imbalanced tasks. [ABSTRACT FROM AUTHOR]

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Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. [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]

For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is constructed based on the random incremental algorithm of stochastic configuration networks. Leveraging the foundational structure of recurrent neural networks, our learning model commences with a modest-scale recurrent neural network featuring a single hidden layer and a solitary hidden node. Subsequently, the node parameters of the hidden layer undergo incremental augmentation through a random configuration process, with corresponding weights assigned structurally. This iterative expansion continues until the network satisfies predefined termination criteria. Noteworthy is the adaptability of this algorithm to handle time series data, exhibiting superior performance compared to traditional recurrent neural networks with similar architectures. The experimental results presented in this paper underscore the efficacy of the proposed RSCN for sequence data processing, showcasing its advantages over conventional recurrent neural networks in the context of the performed experiments. [ABSTRACT FROM AUTHOR]

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26–0.81, mean reader-AI kappa = 0.49, 95% CI 0.27—0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform. [ABSTRACT FROM AUTHOR]

In this study, the deep learning algorithm of Convolutional Neural Network long short-term memory (CNN–LSTM) is used to classify various jewelry rocks such as agate, turquoise, calcites, and azure from various historical periods and styles related to Shahr-e Sokhteh. Here, the CNN–LSTM architecture includes utilizing CNN layers for the extraction of features from input data mixed with LSTMs for supporting sequence forecasting. It should be mentioned that interpretable deep learning-assisted laser induced breakdown spectroscopy helped achieve excellent performance. For the first time, this paper interprets the Convolutional LSTM effectiveness layer by layer in self-adaptively obtaining LIBS features and the quantitative data of major chemical elements in jewelry rocks. Moreover, Lasso method is applied on data as a factor for investigation of interoperability. The results demonstrated that LIBS can be essentially combined with a deep learning algorithm for the classification of different jewelry songs. The proposed methodology yielded high accuracy, confirming the effectiveness and suitability of the approach in the discrimination process. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk. [ABSTRACT FROM AUTHOR]

This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities. [ABSTRACT FROM AUTHOR]

Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations. [ABSTRACT FROM AUTHOR]

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset's feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool. [ABSTRACT FROM AUTHOR]

Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China's water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results. [ABSTRACT FROM AUTHOR]

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based, utilizing satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in machine learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS, such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions. [ABSTRACT FROM AUTHOR]

While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks. [ABSTRACT FROM AUTHOR]

Gossen's First Law describes the law of diminishing marginal utility. This paper aims to further verify the proposed hypothesis that Gossen's First Law also holds in the modeling for Demand Side Management (DSM) with a thorough heat pump case study. The proposed hypothesis states that in general the complexity-utility relationship in the field of DSM modeling could be represented by a diminishing marginal utility curve. On the other hand, in data based modeling, when utilizing a large dataset for validation, the data integrity is critical to the reliability of the results. However, the absence of partial time series data may occur during the measurement due to missing sensors or IT related issues. In this work, an extensive real-world open dataset of a ground source heat pump is utilized for the case study. In the raw data, one key variable namely the flow rate is missing. Thus, three different algorithms based on machine learning and deep learning architectures namely Random Forest (RF), Long Short-Term Memory (LSTM) and Transformer are applied to predict the flow rate by utilizing an open loop forecasting. The raw data are first pre-processed with a time interval of one hour and then used for training, validation and forecast. Furthermore, a modified persistence model as the baseline is also defined. The predicted flow rate using LSTM yields the lowest error of 7.47 % nMAE and 10.56 % nRMSE respectively. The forecast results are then utilized in the following step of modeling of a heat pump use case. With the introduced quantification method for complexity and a modified version for utility, we further verify the proposed hypothesis with a longer time horizon of 7 days. [ABSTRACT FROM AUTHOR]

Audio classification related to military activities is a challenging task due to the high levels of background noise and the lack of suitable and publicly available datasets. To bridge this gap, this paper constructs and introduces a new military audio dataset, named MAD, which is suitable for training and evaluating audio classification systems. The proposed MAD dataset is extracted from various military videos and contains 8,075 sound samples from 7 classes corresponding to approximately 12 hours, exhibiting distinctive characteristics not presented in academic datasets typically used for machine learning research. We present a comprehensive description of the dataset, including its acoustic statistics and examples. We further conduct a comprehensive sound classification study of various deep learning algorithms on the MAD dataset. We are also releasing the source code to make it easy to build these systems. The presented dataset will be a valuable resource for evaluating the performance of existing algorithms and for advancing research in the field of acoustic-based hazardous situation surveillance systems. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries. [ABSTRACT FROM AUTHOR]

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

Twitter is a powerful platform for communication and information sharing but is also susceptible to spreading false information. This false information has adverse consequences for society and can significantly impact public perception, decision-making, and political outcomes. Therefore, there is an urgent need to build a fake news detection system that can accurately catch false information before it is disseminated. Building such a system requires the existence of good quality and trustworthy labeled datasets. The limitations of the existing datasets are undeniable. Most of them are not updated to reflect the advanced generation patterns of the new fake news creators. Thanks to Truth Seeker research team, who offered a large-scale fake news dataset that was labeled based on Amazon Mechanical Turk. The dataset was collected between 2009 and 2022 and then validated according to a robust procedure to ensure its quality and reliability. However, the credibility and trustability of this dataset is still questionable. In this paper, we study and analyze the feasibility of building a fake news detection model based on deep learning using Truth seeker dataset. Mainly we investigated the impact of different text representation techniques on the accuracy of deep learning models. Also, we investigated the importance of hand-crafted features associated with the dataset in the final results. The results have shown that using truth seeker dataset show potential to help social media platforms in detecting fake news. on the other hand, using deep contextualized text representation produced more accurate results compared to word2vec and TF-IDF techniques. The impact of hand-crafted features on the final performance of deep learning models is often negligible, and it is suggested to be excluded from the final models. [ABSTRACT FROM AUTHOR]

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or ATLAS experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used to reconstruct the energy and position of these particles from the showers they induce in the electromagnetic calorimeter. Despite their accuracy and efficiency, these methods still suffer from several limitations, such as low-energy background and limited capacity to reconstruct close-by particles. This paper introduces an innovative machine-learning technique to measure the energy and position of photons and electrons based on convolutional and graph neural networks, taking the geometry of the CMS electromagnetic calorimeter as an example. The developed network demonstrates a significant improvement in resolution both for photon energy and position predictions compared to the algorithm used in CMS. Notably, one of the main advantages of this new approach is its ability to better distinguish between multiple close-by electromagnetic showers. [ABSTRACT FROM AUTHOR]

Hardware such as multipliers and dividers is necessary for all electronic systems. This paper explores Vedic mathematics techniques for high-speed and low-area multiplication. In the study of multiplication algorithms, various bits-width ranges of the Anurupyena sutra are used. Parallelism is employed to address challenging problems in recent studies. Various designs have been developed for the Field Programmable Gate Array (FPGA) implementation employing Very Large-Scale integration (VLSI) design approaches and parallel computing technology. Signal processing, machine learning, and reconfigurable computing research should be closely monitored as artificial intelligence develops. Multipliers and adders are key components of deep learning algorithms. The multiplier is an energy-intensive component of signal processing in Arithmetic Logic Unit (ALU), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN). For the DNN, this method introduces the Booth multiplier blocks and the carry-save multiplier in the Anurupyena architecture. Traditional multiplication methods like the array multiplier, Wallace multiplier, and Booth multiplier are contrasted with the Vedic mathematics algorithms. On a specific hardware platform, Vedic algorithms perform faster, use less power, and take up less space. Implementations were carried out using Verilog HDL and Xilinx Vivado 2019.1 on Kintex-7. The area and propagation delay were reduced compared to other multiplier architectures. [ABSTRACT FROM AUTHOR]

Conventional photography can only provide a two-dimensional image of the scene, whereas emerging imaging modalities such as light field enable the representation of higher dimensional visual information by capturing light rays from different directions. Light fields provide immersive experiences, a sense of presence in the scene, and can enhance different vision tasks. Hence, research into light field processing methods has become increasingly popular. It does, however, come at the cost of higher data volume and computational complexity. With the growing deployment of machine-learning and deep architectures in image processing applications, a paradigm shift toward learning-based approaches has also been observed in the design of light field processing methods. Various learning-based approaches are developed to process the high volume of light field data efficiently for different vision tasks while improving performance. Taking into account the diversity of light field vision tasks and the deployed learning-based frameworks, it is necessary to survey the scattered learning-based works in the domain to gain insight into the current trends and challenges. This paper aims to review the existing learning-based solutions for light field imaging and to summarize the most promising frameworks. Moreover, evaluation methods and available light field datasets are highlighted. Lastly, the review concludes with a brief outlook for future research directions. [ABSTRACT FROM AUTHOR]

In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model's ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90%, 90.38%, 94.62% and 57%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid approach integrating the expert knowledge judgment approach using the Cognitive Pairwise Comparison (CPC) to the Deep Learning, a modern classification approach, for astronomic object classification. The astronomic data with ten thousand samples retrieved from Sloan Digital Sky Survey Sky Server Data Release 15 (SDSS SkyServer DR 15) are used for this study. The CPC is an approach to elicit and encode expert knowledge in the format of a Pairwise Opposite Matrix (POM) to evaluate expert preferences for the features. A forward feature selection algorithm taking the expert choices using CPC for the ordered features is used for the feature selection for the deep learning algorithm to build a heuristic training model based on the astronomic data. Whilst the accuracy of the case of improper feature selection is just 37.1%, the proposed hybrid approach can obtain a very high accuracy of 97.9% for the classification of the astronomic object using the eight scaled features (u, g, r, i, z redshift, ra, dec). To extend this research, the proposed CPC can be used as a human-centered tool to be applied to other areas of data sciences. [ABSTRACT FROM AUTHOR]

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%. [ABSTRACT FROM AUTHOR]

Water pollution is one of the most challenging environmental issues. A powerful tool for measuring the suitability of water for drinking is required. The Water Quality Index (WQI) is a widely used parameter for the assessment of water quality through mathematical formulas. In this paper, a Deep Neural Network (DNN) model is developed to forecast WQI based on parameters selected for the dry and wet seasons throughout the year. Statistical modeling and unsupervised machine learning techniques are used. These modelings include the Principal Component Analysis/Factor Analysis (PCA/FA) which is used to interpret seasonal changes and the sources of springs under study. The other modeling technique utilized in this study is the Hierarchical Cluster Analysis (HCA). The results of this study reveal that the developed DNN model has achieved a high accuracy of ***. The goodness of fit of the developed model using R-Squared (R2) is 0.98 which is deemed high. The Mean Square Error metric is close to zero. Furthermore, the PCA/FA revealed five major parameters that impact water quality which together account for 92% of the total variance of water quality in summer and 96% in winter. Moreover, results show that the average of the WQI for all springs is of poor water quality at 46.75% during the dry season and medium water quality at 55.5% during the wet season. [ABSTRACT FROM AUTHOR]

As the trendsetter of the digital currency market, Bitcoin fluctuates dramatically in a short period of time and has received increasing attention from investors. However, its high volatility has brought great uncertainty to the financial market. In this paper, we focus on forecasting the realized volatility of Bitcoin by using an optimized deep learning model. Firstly, we construct a more comprehensive system of factor indicators and employ different methods for feature selection, and find that the Random Forest-based feature selection fits better on the deep learning model. Then, we use the particle swarm optimization (PSO) algorithm to optimize the parameters of gated recurrent unit (GRU) model to improve the prediction accuracy, and the results show that the prediction accuracy of PSO–GRU model is 10.47%, 15.28%, 21.73%, 34.79% better than the GRU model, long-short term memory model, machine learning models and the generalized autoregressive conditional heteroscedasticity model on the mean absolute error, respectively. Finally, we establish an early risk warning scheme for Bitcoin volatility and a butterfly option arbitrage strategy, that provide investors with a reference for reasonable arrangement of trading strategies. [ABSTRACT FROM AUTHOR]

The exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms. [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]

The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data. [ABSTRACT FROM AUTHOR]

Detecting defects in welds used in critical or non-critical industrial applications is of intense interest. Several non-destructive inspection methods are available, each allowing the preservation of the integrity of the sample under analysis. However, visual-based inspection methods are the most well-assessed, which usually require human experts to inspect each sample, looking for shallow defects. This process often requires time and effort by the human operator, therefore not allowing to perform real-time defect identification, which may result in unexpected (and undesired) production costs. In recent years, several methods have been proposed to automatically deal with visual-based inspection, mainly through convolutional neural networks. However, while effective, these models require a lot of data and computational power to be trained, which is also time-consuming. This paper proposes a high-throughput data gathering and processing method using laser profilometry, along with an automatic defect detection method based on lightweight machine learning algorithms. Six different machine and deep learning approaches are compared, including SVMs, decision forests, and neural networks, achieving a top-1 accuracy of 99.79 % for defect identification and 99.71 % for defect categorization. Thanks to its effectiveness and the high data throughput achievable by data gathering, the whole method can be implemented in real production lines to minimize costs and perform real-time monitoring and defects assessment. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has always been used to solve high-dimensional complex sequential decision problems. However, one of the biggest challenges for reinforcement learning is sample efficiency, especially for high-dimensional complex problems. Model-based reinforcement learning can solve the problem with a learned world model, but the performance is limited by the imperfect world model, so it usually has worse approximate performance than model-free reinforcement learning. In this paper, we propose a novel model-based reinforcement learning algorithm called World Model with Trajectory Discrimination (WMTD). We learn the representation of temporal dynamics information by adding a trajectory discriminator to the world model, and then compute the weight of state value estimation based on the trajectory discriminator to optimize the policy. Specifically, we augment the trajectories to generate negative samples and train a trajectory discriminator that shares the feature extractor with the world model. Experimental results demonstrate that our method improves the sample efficiency and achieves state-of-the-art performance on DeepMind control tasks. [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]

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy. [ABSTRACT FROM AUTHOR]