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Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal Friction, focusing on the special issue of Artificial Intelligence (AI) and emerging computational approaches for tribology. Tribology research, which studies friction and wear, has traditionally relied on extensive experimentation. However, AI and computational approaches offer new opportunities to explore complex processes in tribology and push the boundaries of research. The special issue includes 15 papers covering various aspects of AI and machine learning in tribology, such as wear assessment, lubrication performance, contact wear prediction, and composite materials design. These articles highlight the transformative impact of AI in tribology research and provide valuable insights for future innovations. [Extracted from the article]

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology. [ABSTRACT FROM AUTHOR]

The infrared (IR) absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy. The Kohonen neural network algorithm was used to identify the type of the lubricating grease. The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands, which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases. The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease. [ABSTRACT FROM AUTHOR]

For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to "learn" the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm. [ABSTRACT FROM AUTHOR]

H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission, and has excellent performance in all aspects. Since the wear behavior of electrical contact pairs is particularly complex when they are in service, we evaluated the effects of load, sliding velocity, displacement amplitude, current intensity, and surface roughness on the changes in contact resistance. Machine learning (ML) algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance. Random forest (RF), support vector regression (SVR) and BP neural network (BPNN) algorithms were used to establish RF, SVR and BPNN models, respectively, and the experimental data were trained and tested. It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear. Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties. The wear behavior of electrical contacts is influenced by factors such as load, sliding speed, displacement amplitude, current intensity, and surface roughness during operation. Machine learning algorithms can predict the electrical contact performance after wear caused by these factors. Experimental results indicate that an increase in load, current, and surface roughness leads to a decrease in stable mean resistance, while an increase in displacement amplitude and frequency results in an increase in stable mean resistance, leading to a decline in electrical contact performance. To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors, three algorithms (random forest (RF), support vector regression (SVR), and BP neural network (BPNN)) were used to train and test experimental results, resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear. The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models. [ABSTRACT FROM AUTHOR]

It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance. [ABSTRACT FROM AUTHOR]

The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data. Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R2 of 0.9219 and 0.9243, respectively. Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients, and the normal load, the content of graphite, and the hardness of the matrix influence the wear rates the most. [ABSTRACT FROM AUTHOR]

Protein acetylation refers to a process of adding acetyl groups (CH3CO-) to lysine residues on protein chains. As one of the most commonly used protein post-translational modifications, lysine acetylation plays an important role in different organisms. In our study, we developed a human-specific method which uses a cascade classifier of complex-valued polynomial model (CVPM), combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative samples. Complex-valued gene expression programming and differential evolution are utilized to search the optimal CVPM model. We also made a systematic and comprehensive analysis of the acetylation data and the prediction results. The performances of our proposed method aie 79.15% in Sp, 78.17% in Sn, 78.66% in ACC 78.76% in F1, and 0.5733 in MCC, which performs better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Dissecting the molecular basis of adaptation remains elusive despite our ability to sequence genomes and transcriptomes. At present, most genomic research on selection focusses on signatures of selective sweeps in patterns of heterozygosity. Other research has studied changes in patterns of gene expression in evolving populations but has not usually identified the genetic changes causing these shifts in expression. Here we attempt to go beyond these approaches by using machine learning tools to explore interactions between the genome, transcriptome, and life-history phenotypes in two groups of 10 experimentally evolved Drosophila populations subjected to selection for opposing life history patterns. Our findings indicate that genomic and transcriptomic data have comparable power for predicting phenotypic characters. Looking at the relationships between the genome and the transcriptome, we find that the expression of individual transcripts is influenced by many sites across the genome that are differentiated between the two types of populations. We find that single-nucleotide polymorphisms (SNPs), transposable elements, and indels are powerful predictors of gene expression. Collectively, our results suggest that the genomic architecture of adaptation is highly polygenic with extensive pleiotropy. [ABSTRACT FROM AUTHOR]

One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently. [ABSTRACT FROM AUTHOR]

Automated inspection is vital in modern industrial manufacturing, optimizing production processes and ensuring product quality. Welding, a widely used joining technique, is susceptible to defects like porosity and cracks, compromising product reliability. Traditional nondestructive testing (NDT) methods suffer from inefficiency and limited accuracy. Many researchers have tried to apply deep learning for defect detection to address these limitations. This study compares traditional algorithms with deep learning methods, specifically evaluating the SwinUNet model for weld segmentation. The model achieves an impressive F1 score of 96.31, surpassing traditional algorithms. Feature analysis utilizing class activation maps confirms the model's robust recognition and generalization capabilities. Additionally, segmentation results for different welding defects were compared among various models, further substantiating the recognition capabilities of SwinUNet. The findings contribute to the automation of weld identification and segmentation, driving industrial production efficiency and enhancing defect detection. [ABSTRACT FROM AUTHOR]

The ship tracking and mapping capabilities that geospatial technology provides create an opportunity to observe fishing vessels as they move through established maritime boundaries. This paper connects data availability to ground-truthing research and explores the limits of vessel movement mapping in representing worker abuse at sea through three related themes. First, a conceptual background links the advancements in maritime remote sensing to critical GIS scholarship and provides a background on worker abuse aboard Taiwanese fishing vessels. Second, the paper examines the potential of machine learning algorithms to represent worker abuse at sea, arguing that more extensive ground-truthing research with workers could help address variations in the data and limited data sets. Third, I use remote sensing data to identify and unpack Taiwanese fishing across the three EEZs with the most concentrated Taiwanese fishing activity: starting with Taiwan, followed by the Falkland Islands, and Seychelles. I argue that fishing activity and the digital representation of vessel movements are governed by terrestrial geopolitics and subject to manipulation by ship captains. Finally, the conclusion offers recommendations for how future research can capitalize on the capabilities of AIS, particularly with respect to addressing problems of working conditions and abuse at sea. [ABSTRACT FROM AUTHOR]

This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase. [ABSTRACT FROM AUTHOR]

Uncertainty analysis is an inevitable attribute of location-allocation problems due to unforecastable parameter changes. Although the simultaneous variation of parameters is more realistic than their instinct variation, the simultaneous sensitivity analysis (SSA) of mixed-integer mathematical models’ outputs upon simultaneous changes of parameters’ value has taken no attention. In the metamodeling process using machine learning tools like regression models and artificial neural networks, several combinations of input values are used for metamodel creation. This paper employs this feature of metamodeling to propose an approach for global sensitivity analysis of mixed-integer mathematical models. The proposed approach is applied in analyzing a newly developed multi-period mixed integer mathematical model for cancer hospitals’ location-allocation problem in a case study. The comparison of results using SSA based on using Regression metamodel (RMM) and traditional one-factor-at-a-time (OFAT) methods showed different ranks of parameters. In addition to more adaptation of SSA to simultaneous changes of parameters in the real world, the created machine learning tools aim to forecast the corresponding outputs approximately when the mixed-integer mathematical model is infeasible or long-run upon some combinations of input parameters’ value. Finally, some applicable managerial insights and recommendations for future works are provided. [ABSTRACT FROM AUTHOR]

The effective use of time series data is crucial in business decision-making. Temporal data reveals temporal trends and patterns, enabling decision-makers to make informed decisions and prevent potential problems. However, missing values in time series data can interfere with the analysis and lead to inaccurate conclusions. Thus, our work proposes a Focalize K-NN method that leverages time series properties to perform missing data imputation. This approach shows the benefits of taking advantage of correlated features and temporal lags to improve the performance of the traditional K-NN imputer. A similar approach could be employed in other methods. We tested this approach with two datasets, various parameter and feature combinations, and observed that it is beneficial in scenarios with disjoint missing patterns. Our findings demonstrate the effectiveness of Focalize K-NN for imputing missing values in time series data. The more noticeable benefits of our methods occur when there is a high percentage of missing data. However, as the amount of missing data increases, so does the error. [ABSTRACT FROM AUTHOR]

The segmentation of pathological image is an indispensable content in the cancerous diagnosis and grading, which is provided to doctors for the location and quantitative analysis of pathologically altered tissue. However, pathological whole slide image (WSI) generally has gigapixel size and huge region-level objective to be segmented. Extracting patches from WSI can address the limitation of computer memory, but the integrity of target is hence affected. Moreover, supervised learning methods require manually annotated labels for training, which is laborious and time-consuming. Thus, we studied a novel weakly supervised learning (WSL)-based end-to-end framework for semantic segmentation of cancerous area in WSI. The proposed framework is based on the block-level segmentation of convolutional neural network (CNN), while CNN is required to integrate the global average pooling layer and single fully connected layer as WSL-CNN. Class activation map and dense conditional random field (DenseCRF) are adapted to realize pixel-level segmentation of the cancerous area in patch, which is incorporated into the classification process of WSL-CNN. The hierarchically double use of DenseCRF effectively improves the precision of semantic segmentation. A region-based annotation method and a flexible method of constructing training dataset are proposed to reduce the workload of annotation. Experiments show that the block-level segmentation of CNNs has better performance than the pixel-level segmentation of fully convolutional networks, ResNet50 is the best one that achieves F1 score of 0.87426, Jaccard score of 0.78079, Recall of 0.94251 and Precision of 0.82182. The proposed framework can effectively refine the block-level prediction as semantic segmentation without pixel-level label. The precision of all tested CNNs get improved in the experiments, with WSL-ResNet50 achieving F1 score of 0.90630, Jaccard score of 0.83230, Recall of 0.92051 and Precision of 0.89789. We propose a complete end-to-end framework, including the specific structure of neural network, the construction of training dataset, the prediction method using neural network and the post-processing. CNN-like architectures can be widely transplanted into this framework to realize semantic segmentation, solving the problem of insufficient label of large-scale medical image to a certain extent. [ABSTRACT FROM AUTHOR]

Machine learning, and more broadly artificial intelligence (AI), is a fascinating technology and can be considered as the closest approximation to the Cartesian “thinking thing” that humans have ever created. Just as the industrial revolution required a new ethos, the age of intelligent machines will create its own, challenging the established moral, economic, and political presuppositions. This paper discusses the relationship between AI and society; it presents several thought experiments to explore the complexity of the relationship and highlights the insufficiency of the current normative paradigm in addressing technological expansion. I argue that many of the externalities, such as security risks, loss of privacy, and economic instability will result from trying to fit the emerging technologies into the existing frame of efficiency and utility, by redefining the notions of human value, identity, autonomy, purity, and truth, among others. The age of the intelligent machine is elevating alienation to new levels, treating the individual as mere patterns in data—its primary commodity. I further argue that while the possibility of unintended consequences, due to the potential misuse of AI is ever present, the intelligent machine per se is unlikely to engage in a zero-sum game for power on its own initiative. I question whether singularity is at all attainable and argue that technology will forever remain a proxy for human interests. I conclude by posing questions for charting the path forward. Through this analysis, I aim to provide a more nuanced understanding of the complex relationship between the AI and humans. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of art history and AI technology. Special attention is paid to Generative Adversarial Networks (GANs), a machine learning technology widely used in AI art. This technology is particularly interesting to art history and the philosophy of art because it raises enduring questions about the creative process of artmaking, especially what constitutes a new and original work of art. While this is a relatively new area, it is possible to discern emerging directions where art and AI intersect. One pressing issue concerns the use (or misuse) of algorithmically enabled technologies and datasets to perpetuate longstanding art-historical biases. The critical position adopted in this paper shares this concern. I examine a notable example of GAN technology that claims to create original art styles. While such experiments in computational creativity are technically interesting, their artistic aims can be questionable. I argue a mechanical model of artistic development based solely on the autonomy of style is problematic for at least two reasons: 1) it uncritically reinstates a formalist view of art history; 2) it recreates a narrow style-centric ‘historical modernism.’ However, despite voicing these reservations, I also discuss the potential of this technology to engage with topics of critical relevance to art history and the philosophy of art. [ABSTRACT FROM AUTHOR]

This paper discusses some societal implications of the most recent and publicly discussed application of advanced machine learning techniques: generative AI models, such as ChatGPT (text generation) and DALL-E (text-to-image generation). The aim is to shift attention away from conceptual disputes, e.g. regarding their level of intelligence and similarities/differences with human performance, to focus instead on practical problems, pertaining the impact that these technologies might have (and already have) on human societies. After a preliminary clarification of how generative AI works (Sect. 1), the paper discusses what kind of transparency ought to be required for such technologies and for the business model behind their commercial exploitation (Sect. 2), what is the role of user-generated data in determining their performance and how it should inform the redistribution of the resulting benefits (Sect. 3), the best way of integrating generative AI systems in the creative job market and how to properly negotiate their role in it (Sect. 4), and what kind of “cognitive extension” offered by these technologies we ought to embrace, and what type we should instead resist and monitor (Sect. 5). The last part of the paper summarizes the main conclusions of this analysis, also marking its distance from other, more apocalyptic approaches to the dangers of AI for human society. [ABSTRACT FROM AUTHOR]

In this paper, a streak camera system is used for obtaining the spectral images in laser-induced breakdown spectroscopy. Streak images are time-resolved, thus enabling tracking of the temporal development of the atomic and ionic emission lines from the plasma plume. To make our study more comprehensive, we have used a machine-learning approach to data analysis. We propose how to reduce the level of noise on streak images by applying the principal component analysis technique. The usual method of improving the signal-to-noise ratio by taking a large number of expositions in streak camera photon counting mode is often not practical. [ABSTRACT FROM AUTHOR]

In the training process of machine learning, the minimization of the empirical risk loss function is often used to measure the difference between the model’s predicted value and the real value. Stochastic gradient descent is very popular for this type of optimization problem, but converges slowly in theoretical analysis. To solve this problem, there are already many algorithms with variance reduction techniques, such as SVRG, SAG, SAGA, etc. Some scholars apply the conjugate gradient method in traditional optimization to these algorithms, such as CGVR, SCGA, SCGN, etc., which can basically achieve linear convergence speed, but these conclusions often need to be established under some relatively strong assumptions. In traditional optimization, the conjugate gradient method often requires the use of line search techniques to achieve good experimental results. In a sense, line search embodies some properties of the conjugate methods. Taking inspiration from this, we apply the modified three-term conjugate gradient method and line search technique to machine learning. In our theoretical analysis, we obtain the same convergence rate as SCGA under weaker conditional assumptions. We also test the convergence of our algorithm using two non-convex machine learning models. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

Knowing accurate values of rock engineering parameters in hard rock condition is the key in estimating and adjusting TBM performance. In this study, TBM penetration per revolution (Pr) as driving parameter, TBM operational parameters including cutterhead torque (Tq), field penetration index (FPI), thrust force (Th), cutter load (Fn), cutterhead rotational speed revolution per minute (RPM), and also cutterhead diameter as a mechanical machine specification are used to predict geomechanical parameters including rock quality designation (RQD) and unconfined compressive strength (UCS) based on reverse analysis in three types of strong rocks (Tuff, Andesite, Diorite) along the Southern lot of Kerman water transfer tunnel; KrWCT. A soft computing method called least squares-support vector machine (Ls-SVM), is trained using operating and driving data as well as rock engineering parameters gathered from the KrWCT project which is excavated in hard rock condition using double shield TBM. Due to the lack of access to the excavation face in mechanized tunneling, the collection of geomechanical data on the rock mass is possible only through the examination of excavated material and limited inspections during cutterhead repairs. Therefore, this paper has tried to predict and classify the rock engineering factors using some indicators related to machine performance, mechanical specification, and TBM operational parameters. Among the data collected during the pre-construction and construction phases of the project, the dataset was classified into 42 zones, and the reasonable distribution intervals of the main tunneling factors corresponding to each zone were defined. New empirical equations are developed to estimate the properties of igneous rocks based on linear and non-linear multivariable regression (MR). Several loss functions were used to assess the performance and precision of the applied methods. The results of the proposed models indicate an acceptable and reliable accuracy. [ABSTRACT FROM AUTHOR]

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. [ABSTRACT FROM AUTHOR]

The paper studies the effectiveness of machine learning techniques in predicting the microhardness of composite materials manufactured from a mixture of iron-copper (Fe-Cu) nanopowders and hydroxyapatite (HA) particles. It is shown that the different proportion of Fe-Cu and HA powders and the polymer fraction in the composite significantly affect its hardness. A surrogate model based on the artificial neural network (ANN) and the analytical model, is proposed to quantitatively evaluate the microhardness, depending on the powder/polymer ratio. The ANN models show the probability distribution of indentation values after microhardness testing, which is the input data for the analytical model to compute the hardness. This approach can reduce the time and cost of the material research and minimizes the reliance on expensive materials and experimental equipment. [ABSTRACT FROM AUTHOR]

Due to favourable economic and environmental concerns, reduced power losses, and technological advancements in data science, large-scale renewable energy sources (RESs) are being inserted into smart microgrids (MGs). Generally, these resources are inserted at the distribution level and feed consumers locally with better reliability. The planning and stable operation of the power networks are complicated at numerous levels as a result of large-scale renewable integration, as the infeed from renewable sources like wind and solar is highly intermittent and uncertain, significantly affecting the resilient, intelligent, and optimal operation of the power system. The creation of scenarios is an essential step in the planning and stable operation of renewable-rich power systems. Scenarios can be generated based on seasonal events to mitigate growing complexity and intermittency. In this paper, time-series solar data is used for forecasting solar irradiance using long-short-term memory (LSTM). Besides, a cost-based optimisation model has been developed by using stochastic programming to obtain both the optimal sizing and optimal switching sequence of distributed units as well as the optimal number of required units. The stochastic nature of solar and load demand is considered in beta (β ) distribution. The objective function formulated covers different costs, like capital, operational, and switching costs associated with required units. The running cost incorporated with the capital cost gives us a broader design for planning the MG, thus making it both planning and operational problem. The entire proposed MG planning problem has been developed in a Python and MATLAB ® environment. [ABSTRACT FROM AUTHOR]

The heating of high-voltage isolating switches above 110 kV will cause serious safety accidents. To identify the heating faults of isolating switches accurately, this study uses natural language processing technology, knowledge graph (KG) technology and machine learning algorithm to mine the structured and unstructured mixed data. Firstly, based on bidirectional encoder representations (BERT) pre-training model, a bidirectional long short-term memory network and conditional random field model are introduced to identify entities related to high-voltage isolating switches and the thermal failures from the text data. Further, a convolutional neural network fusion attention mechanism model is leveraged to recognize the corresponding entity relationships from the text records. Then, the KGs of isolating switches and the heating faults are constructed, and a path ranking algorithm is proposed to deduce and identify the possible factors causing overheating on the KGs. Combined with the structured data, a support vector machine model based on focal loss function (FL-SVM) is proposed to recognize the heating faults of isolating switches. Experiments show that the performances of all six models used in this paper on the hybrid dataset are higher than those on the structured dataset. Such results suggest that joint text-structured data mining can compensate for insufficient structured data and thus, achieving a better heating failure recognition, especially for the infrequent malfunctions. Meanwhile, the proposed FL-SVM performs better than other models due to its excellent ability to solve the problem of imbalanced faults and normal samples. [ABSTRACT FROM AUTHOR]

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency. [ABSTRACT FROM AUTHOR]

The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration. [ABSTRACT FROM AUTHOR]

The Bayesian approach to solving inverse problems relies on the choice of a prior. This critical ingredient allows expert knowledge or physical constraints to be formulated in a probabilistic fashion and plays an important role for the success of the inference. Recently, Bayesian inverse problems were solved using generative models as highly informative priors. Generative models are a popular tool in machine learning to generate data whose properties closely resemble those of a given database. Typically, the generated distribution of data is embedded in a low-dimensional manifold. For the inverse problem, a generative model is trained on a database that reflects the properties of the sought solution, such as typical structures of the tissue in the human brain in magnetic resonance imaging. The inference is carried out in the low-dimensional manifold determined by the generative model that strongly reduces the dimensionality of the inverse problem. However, this procedure produces a posterior that does not admit a Lebesgue density in the actual variables and the accuracy attained can strongly depend on the quality of the generative model. For linear Gaussian models, we explore an alternative Bayesian inference based on probabilistic generative models; this inference is carried out in the original high-dimensional space. A Laplace approximation is employed to analytically derive the prior probability density function required, which is induced by the generative model. Properties of the resulting inference are investigated. Specifically, we show that derived Bayes estimates are consistent, in contrast to the approach in which the low-dimensional manifold of the generative model is employed. The MNIST data set is used to design numerical experiments that confirm our theoretical findings. It is shown that the approach proposed can be advantageous when the information contained in the data is high and a simple heuristic is considered for the detection of this case. Finally, the pros and cons of both approaches are discussed. [ABSTRACT FROM AUTHOR]

Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to choose from. However, there lacks a comprehensive comparison of these disparate approaches in terms of predictive performance and characteristics. This paper addresses that deficiency by examining the performance of 17 different methodologies in nowcasting US quarterly GDP growth, including all the methods most commonly employed in nowcasting, as well as some of the most popular traditional machine learning approaches. Performance was assessed over a 20-year period, from 2002 to 2022. This span encompassed two crises, the 2008 financial crisis and the COVID crisis, as well as extended tranquil periods. The two best-performing methodologies in the analysis were long short-term memory artificial neural networks (LSTM) and Bayesian vector autoregression (Bayesian VAR). To facilitate further application and testing of each of the examined methodologies, an open-source repository containing boilerplate code that can be applied to different datasets is published alongside the paper, available at: github.com/dhopp1/nowcasting_benchmark [ABSTRACT FROM AUTHOR]

Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability. [ABSTRACT FROM AUTHOR]

Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers have progressively focused on employing machine learning and mathematical algorithms to address the complexities and uncertainties of evaluating soil liquefaction potential. This paper investigates the performance of fuzzy c-means clustering of incomplete data for assessing liquefaction potential based on cone penetration test (CPT) and shear wave velocity (Vs) field data. The research was conducted using two approaches: (1) whole data strategy; (2) partial distance strategy. The used database contains 786 CPT and 846 Vs records, with specified liquefaction conditions in past earthquake events. We compared the effectiveness and success of this method with traditional deterministic and probabilistic liquefaction evaluation approaches. It was found that the fuzzy c-means clustering model had a comparable predictive ability with other methods and would be reliable when assessing the liquefaction possibility. [ABSTRACT FROM AUTHOR]

In order to achieve an optimal system performance, decision makers are continually faced with the responsibility of making choices that will enhance availability and reduce failures cost. To realize this goal, it is crucial to ensure the timely maintenance of equipment, which often poses a significant challenge. However, the adoption of predictive maintenance (PdM) technology can offer a solution by enabling real-time maintenance, resulting in various benefits such as reduced downtime, cost savings, and enhanced production quality. Machine learning (ML) techniques are increasingly being used in the field of predictive maintenance to predict failures and calculate estimated remaining useful life (RUL) of equipment. A case study is proposed in this research paper based on a maintenance dataset from the aerospace industry. It experiments and compare multiple combination of feature engineering techniques and advanced ML models with the aim to propose the most efficient techniques for prediction. Moreover, future research papers can focus on the challenge of validating this proposed model in different industrial environments. [ABSTRACT FROM AUTHOR]

By exploiting double-penalty terms for the primal subproblem, we develop a novel relaxed augmented Lagrangian method for solving a family of convex optimization problems subject to equality or inequality constraints. The method is then extended to solve a general multi-block separable convex optimization problem, and two related primal-dual hybrid gradient algorithms are also discussed. Convergence results about the sublinear and linear convergence rates are established by variational characterizations for both the saddle-point of the problem and the first-order optimality conditions of involved subproblems. A large number of experiments on testing the linear support vector machine problem and the robust principal component analysis problem arising from machine learning indicate that our proposed algorithms perform much better than several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This research paper focuses on the challenges associated with learning classifiers from large-scale, highly imbalanced datasets prevalent in many real-world applications. Traditional algorithms learning often need better performance and high computational efficiency when dealing with imbalanced data. Factors such as class imbalance, noise, and class overlap make it demanding to learn effective classifiers. In this study, we propose a novel self-paced ensemble framework for classifying imbalanced data. The framework employs under-sampling to self-harmonize data hardness and build a robust ensemble. Extensive experimental testing demonstrates promising results in handling overlapping classes and skewed distributions while maintaining computational efficiency. The self-paced ensemble method addresses the challenges of high imbalance ratios, class overlap, and noise presence in large-scale imbalanced classification problems. By incorporating the knowledge of these challenges into our learning framework, we establish the concept of classification hardness distribution, and the self-paced ensemble is a revolutionary learning paradigm for massive imbalance categorization, capable of improving the performance of existing learning algorithms on imbalanced data and providing better results for future applications. [ABSTRACT FROM AUTHOR]

Classifying imbalanced data presents a significant challenge, and many studies have proposed methodologies to address this issue. Among them, Convolutional Neural Networks have demonstrated superior performance for imbalanced image classification. This paper initially employs various data pre-processing methods such as over-sampling, under-sampling, and SMOTE to enhance the original dataset. Subsequently, an Ensemble CNN learning model is used to train and predict the data. In order to comprehensively evaluate models trained on imbalanced data, we used metrics such as Accuracy, Recall, Precision, F1-score, and G-mean. On the CIFAR-10 and Fashion-MNIST datasets, different samples from each category were extracted as imbalanced data for experimental research. Compared to the AdaBoost-DenseNet model, our proposed methodology increases the test accuracy on the CIFAR-10 dataset by 9%. Similarly, the F1-score and G-mean improved by 0.096 and 0.069, respectively. Compared to traditional methodologies, our proposed method significantly improves accuracy, recall, precision, and other performance indicators. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven framework for evaluating collaboration efficiency within scientific research teams. The framework introduces a team efficiency evaluation system consisting of 40 specific indicators, which are analyzed and modeled using statistical methods. The adaptive enhancement algorithm model achieves the highest accuracy, recall, and F1 values, with scores of 0.852, 0.530, and 0.620, respectively. These findings demonstrate the feasibility of the proposed data-driven research team collaboration model, offering theoretical support for enhancing the effectiveness of group collaboration. Moreover, the study is significant for further research on group collaboration in diverse fields. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

Distributed algorithms are receiving renewed attention across multiple disciplines due to the dramatically increasing demand of big data processing. We consider a class of consensus optimization problems over a static network system of multiple agents, where each of local cost functions is a sum of a smooth function and a non-Lipschitz regularization term. This kind of problems is widely found in scientific and engineering areas such as machine learning and data analysis. Inspired by Bian and Chen (SIAM J. Opt. 23(3), 1718–1741 2017), we propose a decentralized smoothing quadratic regularization algorithm (abbreviated as D-SQRA) for solving the composite consensus problem with non-Lipschitz singularities. To some extent, D-SQRA can be seen as an extension in the decentralized setup of the smoothing quadratic regularization algorithm (SQRA) proposed in (SIAM J. Opt. 23(3), 1718–1741 2017). Our main contribution is to show that D-SQRA can inherit the theoretical properties of its centralized counterpart, i.e., SQRA, in both sides of convergence and worst-case iteration complexity to achieve an ϵ scaled stationary point. We also present some numerical examples on sparse sensing problems based on synthetic and real datasets to corroborate the effectiveness of the proposed decentralized algorithm. [ABSTRACT FROM AUTHOR]

Water Scarcity is a very severe problem across the world, one of the main factors is improper distribution of water and careless use of water by people, this is only going to be more severe in future as population and needs of the world rises. Many countries have increased deployment of smart water meters to monitor water usage and tried convincing people to not use water in a careless manner but have not been successful yet. This research paper presents the development and implementation of a smart water meter (SWM) prototype for household water consumption measurement. The SWM utilizes Wi-Fi or Long Range (LoRa) technology to transmit data and is integrated with Citizen Id (SSN) to centralize water distribution, and help detect water theft. Additionally, the meter incorporates SARIMA forecasting to predict water consumption based on past usage trends on the edge. The water consumption data can be accessed through a web and Android application, and an integrated billing system has been developed to provide users with information about their current water usage. The machine learning model was trained and tested on the water consumption dataset by DAIAD. The DAIAD dataset consists of hourly water consumption time series for 1,007 randomly selected consumers from the AMAEM (Association of Energy and Water Management) utility in Alicante, Spain, spanning from January 2015 to May 2017, totaling 16,857,056 measurements. The whole system was tested by installing it in a house and the forecasting model achieved an accuracy of 74%. [ABSTRACT FROM AUTHOR]

Addressing the contemporary complexity inherent in anomaly detection within heterogeneous systems is paramount. This paper presents a novel methodology tailored to pinpoint appliance anomalies within the framework of Internet of Things (IoT) technology. By amalgamating the capabilities of IoT and Machine Learning (ML), this approach not only heightens the precision and dependability of anomaly detection but also serves as a practical solution for industrial applications. To better align with the current industrial landscape, we emphasize the practical implications of our work. Our methodology is designed to cater specifically to industrial needs, offering a solution that can be seamlessly integrated into existing systems, thereby enhancing operational efficiency and reliability. The core of our approach lies in employing a hybrid method, utilizing both the Facebook Prophet and Isolation Forest ML algorithms for robust and intelligent anomaly detection. This dual-layered strategy, integrating forecasting and classification objectives, ensures a comprehensive approach to anomaly detection tailored for industrial settings. Evaluation of our methodology involves rigorous testing against real-time and emulated datasets, as well as comparison with existing methods. MSE has also been calculated using DT, RF, SVM, NB and Logistic supervised regressor. The Facebook Prophet model’s accuracy, assessed using Root Mean Squared Error (RMSE), demonstrates its proficiency in forecasting values closely aligned with reference data points. Meanwhile, the IForest Unsupervised ML model excels in identifying anomalies, achieving high accuracy rates across various contamination levels. Through meticulous cross-validation, our proposed method exhibits significant accuracy, with rates of 93.60% and 95.72% on real-time and emulated datasets, respectively. The hybrid model (Fbprophet + iforest) has an average accuracy of 96.35% on (real-time + emulated). These results underscore the efficacy and reliability of our approach in industrial anomaly detection scenarios. In summary, by addressing the present-day challenges in anomaly detection and offering a tailored solution for industrial applications, our methodology stands poised to significantly enhance the efficiency and reliability of systems operating in diverse industrial contexts. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact. [ABSTRACT FROM AUTHOR]

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan. [ABSTRACT FROM AUTHOR]

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others. [ABSTRACT FROM AUTHOR]

With the rapid growth in the internet traffic, analysis and exploration of traffic classification has become more challenging task especially for dark net or dark web. Darknet, within the confines of deep web, has been witnessing illegitimate doings such as drug trafficking, terrorism, betting etc. Hence classification of darknet traffic is an important task. This paper presents intelligent framework for the darknet traffic categorization with proposed hybrid feature selector. The darknet traffic consists of data packet related information as input predictors for the model. Twenty-one machine learning models with and without feature selectors are presented to categorize the darknet traffic. We propose a Hybrid LASSO-Random Forest (HLRF) feature selector to reduce feature dimensionality. Classification of darknet traffic is evaluated with well-known KNN, Extra Tree and XGBoost classifiers. The performance of proposed models was assessed in terms of Accuracy, Precision, Recall, Harmonic Mean Value of True Positive Value (TPV) and Positive Predicted Value (PPV), Matthews Corelation Coefficients (MCC) and Jaccard Score. The experimental results approve that XGBoost with proposed HLRF feature selector outperforms in categorization of darknet traffic. The results revel that proposed XGBoost-HLRF model obtain an accuracy and recall value of 98.10% with precision of 98.12%. Comparison of XGBoost-HLRF model with other proposed state of art models are presented for performance assessment of our model. [ABSTRACT FROM AUTHOR]

The scattering effect of light and the presence of water organism deteriorates image quality captured in underwater environment. Researchers have made several proposals to improve the quality of these images using traditional image processing methods. Some of them are specific to underwater images while others are used for generalized purpose. Both these categories can deal with noise, such as explicit modeling, which usually leads to problems of low contrast and color deviation, but are unable to extract image features due to lack of prior knowledge of experts. Recently, machine learning approaches have gained popularity due to its ability to automate image processing task. Also, its efficiency and scalability is good. Therefore, this paper employ Conditional Generative Adversarial Network (CGAN) to synthesize unlabeled images and generate congruent data to original data. The proposed model consists of generator and discriminator. The former maps the features of input image to corresponding high-contrast image while the generated image and real image are passed to the later for the classification purpose. Result analysis illustrates that the proposed framework not only depicts the best Absolute Mean Brightness Error (AMBE), contrast, and Contrast Improvement Index (CII) parameters compared to available mechanisms in the literature but also shows Average Information Content (AIC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Degree of Entropy Un-preservation (DEU) are 99.84%, 99.63%, 97.61% and 98.33% similar to expected outcome. Also, when the technique is compared to the state of art techniques given in literature, the performance is quite good. [ABSTRACT FROM AUTHOR]

Dark web discussion forums have become one of the digital communication medium over the public infrastructure of Internet. Monitoring and analysing data from dark web discussion forums and social media platforms can unfold useful insights to the security intelligence.Through our research work we demonstrate the possibility of detecting online terrorist activities from dark web forums using machine learning based and deep learning based algorithms. In order to achieve desired objectives, we presented two specific use cases in this paper :(i) procurement of illegal weapons, (ii) online recruitment of terrorists. LSTM based deep learning classification approach is employed on annotated data for both use cases. We have also compared proposed approach with machine learning classifiers. It is seen that deep learning based text classification is possible with proposed LSTM architecture with acceptable accuracy measures. Automating the process of identifying violent activities over dark web discussion forums is a novel and fruitful contribution of our current work. Machine Learning and Deep Learning algorithms for detecting violent activities can be a powerful tool when developed and used responsibly. [ABSTRACT FROM AUTHOR]