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Indigo blue is a natural organic color from the Indigofera tinctoria plant. It is insoluble in water and does not require mordant to fix the color; therefore, due to its nature it can be considered as a pigment or dye. The identification of indigo in cultural heritage is very difficult due to the complexity of its chemical formula and the changes that it can undergo in certain conservation and exposition conditions. For this reason, characterization of possible degradation, transformation, and reaction processes is essential for its identification. In this study, multispectral imaging has been used for the first time as noninvasive technique to characterize the aging state of indigo blue using samples aged in a controlled climate chamber under extreme conditions of humidity, temperature, and light. The technique has proven to be very promising for this application, together with classification techniques based on machine learning approaches. [ABSTRACT FROM AUTHOR]

At present, in the age of computers and automation of services, deep learning (DL) technology, mainly the subset of machine learning (ML) and artificial intelligence (AI), is expressively used in innumerable domains of computer vision such as data analysis, image recognition, classification, natural language processing, and many more. It has become the foremost choice of researchers as of its effectiveness in producing decent results. This paper presents detailed and analytical literature starting from the very elementary level to the recent trends of this trending technology while focusing on the most used DL model, that is, convolutional neural network and its pretrained models for image classification and object detection. It also reviews diverse existing current literature based on this. Further, a brief introduction of AI, ML, and DL has also been presented, making the foundation for the readers. As pretrained models continuously give an upper edge to DL over ML and other technologies, 23 most popular pretrained models with their architectural diagrams have also been presented. This paper aims to summarize and analyze all the concepts used to formulate DL and its models. Also, we have emphasized more on the GoogleNet models and the entire Inception modules in detail. Finally, the fascinating applications and discussion on integral components of DL have been presented. This paper will definitely draw the attention of the students and researchers working in the area of DL and its models. [ABSTRACT FROM AUTHOR]

Abrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty‐two articles have been obtained using defined filters divided into four sections: hypervisor‐based IDS, network‐based IDS, machine learning‐based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost‐sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]

Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log data from two different Estonian schools. These data were used in different combinations to build collaboration estimation models and then assessed across different subjects, different types of activities (collaborative‐writing, group‐discussion) and different schools. Our results suggest that the automated collaboration model can generalize to the context of different schools but with a 25% degradation in balanced accuracy (from 82% to 57%). Moreover, the results also indicate that multimodality brings more performance improvement in the case of group‐discussion‐based activities than collaborative‐writing‐based activities. Further, our results suggest that the video data could be an alternative for understanding collaboration in authentic settings where higher‐quality audio data cannot be collected due to contextual factors. The findings have implications for building automated collaboration estimation systems to assist teachers with monitoring their collaborative classrooms. Practitioners notesWhat is already known about this topicMultimodal learning analytics researchers have established several features as potential indicators for collaboration quality, e.g., speaking time or joint visual attention.The current state of the art has shown the feasibility of building automated collaboration quality models.Recent research has provided preliminary evidence of the generalizability of developed automated models across contexts different in terms of given task and subject.What does this paper addThis paper offers collaboration indicators for different types of collaborative learning activities in authentic classroom settings.The paper includes a systematic investigation into collaboration quality automated model's generalizability across different tasks, types of tasks and schools.This paper also offers a comparison between different modalities' potential to estimate collaboration quality in authentic settings.Implications for practiceThe findings inform the development of automated collaboration monitoring systems for authentic classroom settings.This paper provides evidence on across‐school generalizability capabilities of collaboration quality estimation models. [ABSTRACT FROM AUTHOR]

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post‐operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most‐widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions. This paper reviews the latest developments of supervised machine learning (ML) techniques that have been used to analyse data related to post‐operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions. [ABSTRACT FROM AUTHOR]

Purpose: Since its release in November 2022, Chat Generative Pre‐Trained Transformer 3.5 (ChatGPT), a complex machine learning model, has garnered more than 100 million users worldwide. The aim of this study is to determine how well ChatGPT can generate novel systematic review ideas on topics within spine surgery. Methods: ChatGPT was instructed to give ten novel systematic review ideas for five popular topics in spine surgery literature: microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement. A comprehensive literature search was conducted in PubMed, CINAHL, EMBASE and Cochrane. The number of nonsystematic review articles and number of systematic review papers that had been published on each ChatGPT‐generated idea were recorded. Results: Overall, ChatGPT had a 68% accuracy rate in creating novel systematic review ideas. More specifically, the accuracy rates were 80%, 80%, 40%, 70% and 70% for microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement, respectively. However, there was a 32% rate of ChatGPT generating ideas for which there were 0 nonsystematic review articles published. There was a 71.4%, 50%, 22.2%, 50%, 62.5% and 51.2% success rate of generating novel systematic review ideas, for which there were also nonsystematic reviews published, for microdiscectomy, laminectomy, spinal fusion, kyphoplasty, disc replacement and overall, respectively. Conclusions: ChatGPT generated novel systematic review ideas at an overall rate of 68%. ChatGPT can help identify knowledge gaps in spine research that warrant further investigation, when used under supervision of an experienced spine specialist. This technology can be erroneous and lacks intrinsic logic; so, it should never be used in isolation. Level of Evidence: Not applicable. [ABSTRACT FROM AUTHOR]

Single image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers. [ABSTRACT FROM AUTHOR]

This paper examines the effect of drivers in the development of eco‐innovation from a system dynamics perspective. While previous literature has made important contributions in identifying factors that influence the development of eco‐innovations, there remains limited understanding of how these drivers act and interact in promoting its development. Therefore, there is a need to develop a framework of relationships and drivers that encourage and support eco‐innovation in companies. This paper develops an integrated framework encompassing key internal, market and governmental factors and their complex interactions using principles of system dynamics and machine learning to address this gap. The research questions how these drivers interact in a dynamic and non‐linear manner to influence the development of eco‐innovation in companies and how can these interactions be effectively modelled and understood, considering the complexities of sustainable business practices and the limitations of traditional linear approaches. We empirically test these questions by using the Spanish Technological Innovation Panel database. The findings demonstrate that eco‐innovation is not solely driven by isolated factors; instead, it emerges from the complex interplay between internal capabilities, governmental policies and market dynamics. By emphasising the synergistic effects of these drivers, the research offers a nuanced understanding of their systemic interactions. Furthermore, our analysis highlights the varying efficiency levels of different drivers, underscoring the pivotal role of environmental corporate policies and the strategic allocation of financial resources. In contrast, cooperation, market forces and regulations exhibit lower efficiency in driving eco‐innovation processes. These insights not only advance theoretical knowledge but also provide valuable guidance for businesses and policymakers, offering a more holistic approach to fostering sustainable innovation. [ABSTRACT FROM AUTHOR]

Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance. [ABSTRACT FROM AUTHOR]

Background Method Result Conclusion In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments.The objective of this study is to develop a new deep learning‐based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network‐based enhanced kernel extreme learning machine (DBN‐EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN‐EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge‐based optimization algorithm (NBGSK).The efficacy of the proposed DBN‐EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F‐measure.Experimental analyses demonstrate that the DBN‐EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Fiber‐reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta‐fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta‐fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta‐fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta‐fiber distribution. The well‐trained GAN is implemented to design meta‐fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics. [ABSTRACT FROM AUTHOR]

Summary: 5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods. [ABSTRACT FROM AUTHOR]

Summary: Autonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift. [ABSTRACT FROM AUTHOR]

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an "attention guided Grad‐CAM" that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

This paper introduces a shape‐based inflow scenarios reduction framework applied in long‐term hydro‐thermal scheduling. This scheduling problem involves strategically managing the limited stored hydro energy in coordination with the electricity system. The scenario fan problem, selected as a long‐term hydro‐thermal scheduling approach, can effectively represent the individual hydropower plants and their short‐term flexibility within the long‐term scheduling process, making it well‐suited for capturing the disaggregated physical characteristics of hydro stations. However, the computational burden of scenario fan problem increases due to the complexity of the cascaded system and the large number of inflow scenarios. To tackle these challenges, a machine learning clustering method is developed for extracting shape‐based features to capture the energy and shape attributes of hydro inflows. A comparative analysis was conducted to evaluate the effectiveness of the proposed method, comparing it with four established clustering methods. This evaluation was carried out on a hydro‐thermal test case, utilizing both in‐sample and out‐of‐sample data analysis. The findings demonstrate that the proposed framework outperforms other methods in terms of computational time by 42.6% and optimal reservoir trajectories by 12.1% considering K‐means using Euclidean distance as the benchmark. Additionally, this study showcases the feasibility of performing long‐term hydro‐thermal scheduling at a granular level, with detailed topological information and a shorter time scale. This expanded approach opens up new possibilities for addressing the integration of renewable energy resources within the realm of long‐term hydro‐thermal scheduling. [ABSTRACT FROM AUTHOR]

Autonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning‐based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion. Key Points: A machine learning method based on random forest regression algorithm is used to recover the relief of the magnetic basement interface by using high‐precision magnetic dataApplying potential field processing and transformation to feature extraction for machine learningThe proposed technique intelligently and efficiently restores the 3D relief of the magnetic basement interface, and it has been successfully implemented in the Datong Basin's Tianzhen‐Yanggao sag [ABSTRACT FROM AUTHOR]

Papertronics introduce a sustainable, cost‐effective revolution in electronics, especially for the Internet of Things. This research overcomes the traditional challenges of paper's porosity, which has impeded electronic component fabrication and performance. A novel approach that harnesses paper's natural capillary action, combined with hydrophobic wax patterning, to achieve precise vertical integration of electronic components is introduced. This method marks a significant departure from conventional surface deposition techniques. This study demonstrates the successful creation of tunable resistors, capacitors, and field‐effect transistors, embedded within a single sheet of paper. Contrary to previous assumptions that impeded the use of paper, its rough and porous texture as a strategic advantage, facilitating the precise fabrication of intricate electronic components is leveraged. Machine learning algorithms play an important role in predicting and enhancing the performance of these papertronic components. This innovation facilitates the development of compact printed circuit boards with increased circuit density, enabling the integration of diverse analog and digital circuits in either single or multi‐layer paper formats. The resulting papertronic systems exceed performance benchmarks, offering eco‐friendly disposal through biodegradability or incineration. These breakthroughs establish papertronics as a feasible, eco‐friendly alternative in the electronics industry, permitting widespread adoption and continuous innovation in sustainable electronic solutions. [ABSTRACT FROM AUTHOR]

In the modern display panel manufacturing industry, the Mura problem that occurs during initial shipment has a significant impact on improving image quality. The purpose of this paper is to effectively solve these Mura defects while reducing additional production costs. We propose an innovative method of storing and compressing the De‐Mura compensation data of the display panel in external memory. Two‐dimensional compensation data compression based on machine learning using surface fitting and vector quantization is presented as an effective response to the Mura problem. This approach, which achieves up to 8:1 compression ratio while minimizing image quality degradation through vector quantization, is expected to provide economic benefits to manufacturers. The paper also introduces a new surface prediction method that replaces the existing DC‐shift and uses residual vector quantization, which can reduce the implementation complexity of vector quantization. Through experimental results, this study demonstrates that the compensated data compression technique yields excellent results in post‐compression restoration performance. [ABSTRACT FROM AUTHOR]

In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, the drilling process is highly prone to the loss of drilling fluid. This not only affects drilling efficiency but can also lead to severe accidents, such as blowouts. Therefore, it is crucial to understand the distribution pattern of these fractures. However, the formation of carbonate rock fracture‐cavity system systems, being controlled by various factors, is difficult to precisely identify. This limitation hampers the efficient development of such types of oil and gas fields. This paper presents a case study of the M55 sub‐section carbonate gas reservoir in the Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data to identify response characteristics of fractures in the carbonate reservoirs. And its identification results have been confirmed to be accurate by various fracture data obtained through different means, such as the core samples, cast thin section photographs, imaging logging data, and seismic attributes. This method incorporates the Ghost convolution module to replace the Conv module in the backbone network of the YOLOv5s model, and modifies the C3 module into a Ghost Bottleneck module, effectively making the model more lightweight. Additionally, a Convolutional Block Attention Module is integrated into the Neck network, enhancing the model's feature extraction capabilities. Finally, the method employs the Efficient Intersection over Union Loss function instead of the Complete Intersection over Union Loss, reducing the network's regression loss. The validation results using actual data demonstrate that this method achieves an average recognition accuracy of 87.3% for the fracture‐cavity system, which is a 3% improvement over the baseline model (YOLOv5s). This enhancement is beneficial for precisely locating the drilling fluid loss positions in carbonate reservoirs. [ABSTRACT FROM AUTHOR]

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work. [ABSTRACT FROM AUTHOR]

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting. Plain Language Summary: Traditionally, weather forecasts are made by models that attempt to replicate the physical processes of the atmosphere. This has been very successful over the last few decades as better computers, better observations and model upgrades have lead to steadily improving weather forecasts. However, with rapid advances in artificial intelligence (AI), the question can be asked whether one can simply learn a weather model from past observations or reanalyzes. In the last couple of years, we have seen tremendous progress with state‐of‐the‐art AI models rivaling the best "traditional" weather models in skill. WeatherBench 2 is a benchmark data set designed to evaluate and compare the quality of AI and traditional models. By setting a standard for evaluation, alongside providing open‐source data and code, this project aims to accelerate this research direction and lead to better weather prediction. Key Points: WeatherBench 2 is a framework for evaluating and comparing data‐driven and traditional numerical weather forecasting modelsIt provides an evaluation framework, publicly available data sets and a website to assess the state‐of‐the‐art weather modelsThe evaluation protocol has been designed following best practices established in the operational weather forecasting community [ABSTRACT FROM AUTHOR]

Repeat photography offers distinctive insights into ecological change, with ground‐based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end‐to‐end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high‐resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root‐mean‐square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas. [ABSTRACT FROM AUTHOR]

Reliability engineering faces many of the same challenges today that it did at its inception in the 1950s. The fundamental issue remains uncertainty in system representation, specifically related to performance model structure and parameterization. Details of a design are unavailable early in the development process and therefore performance models must either account for the range of possibilities or be wrong. Increasing system complexity has compounded this uncertainty. In this work, we seek to understand how the reliability engineering literature has shifted over time. We exe cute a systematic literature review of 30,543 reliability engineering papers (covering roughly a third of the reliability papers indexed by Elsevier's Engineering Village. Topic modeling was performed on the abstracts of those papers to identify 279 topics. The hierarchical topic reduction resulted in the identification of eight top‐level method topics (prognostics, statistics, maintenance, quality control, management, physics of failure, modeling, and risk assessment) as well as three domain‐specific topics (nuclear, infrastructure, and software). We found that topics more associated with later phases in the development process (such as prognostics, maintenance, and quality control) have increased in popularity over time relative to other topics. We propose that this is a response to the challenges posed by model uncertainty and increasing complexity. [ABSTRACT FROM AUTHOR]

The asynchronous advantage actor-critic (A3C) algorithm is widely regarded as one of the most effective and powerful algorithms among various deep reinforcement learning algorithms. However, the distributed and asynchronous nature of the A3C algorithm brings increased algorithm complexity and computational requirements, which not only leads to an increased training cost but also amplifies the difficulty of deploying the algorithm on resource-limited field programmable gate array (FPGA) platforms. In addition, the resource wastage problem caused by the distributed training characteristics of A3C algorithms and the resource allocation problem affected by the imbalance between the computational amount of inference and training need to be carefully considered when designing accelerators. In this paper, we introduce a deployment strategy designed for distributed algorithms aimed at enhancing the resource utilization of hardware devices. Subsequently, a FPGA architecture is constructed specifically for accelerating the inference and training processes of the A3C algorithm. The experimental results show that our proposed deployment strategy reduces resource consumption by 62.5% and decreases the number of agents waiting for training by 32.2%, and the proposed A3C accelerator achieves 1.83× and 2.39× improvements in speedup compared to CPU (Intel i9-13900K) and GPU (NVIDIA RTX 4090) with less power consumption respectively. Furthermore, our design shows superior resource efficiency compared to existing works. [ABSTRACT FROM AUTHOR]

A cement‐based material that meets the general goals of mechanical properties, workability, and durability as well as the ever‐increasing demands of environmental sustainability is produced by varying the type and quantity of individual constituents in high‐performance concrete (HPC) and ultrahigh‐performance concrete (UHPC). Expensive and time‐consuming laboratory experiments can be used to estimate the properties of concrete mixtures and elements. As an alternative, these attributes can be approximated by means of predictive models created through the application of artificial intelligence (AI) methodologies. AI approaches are among the most effective ways to solve engineering problems due to their capacity for pattern recognition and knowledge processing. Machine learning (ML) and deep learning (DL) are a subfield of AI that is gaining popularity across many scientific domains as a result of its many benefits over statistical and experimental models. These include, but are not limited to, better accuracy, faster performance, greater responsiveness in complex environments, and lower economic costs. In order to assess the critical features of the literature, a comprehensive review of ML and DL applications for HPC and UHPC was conducted in this study. This paper offers a thorough explanation of the fundamental terms and ideas of ML and DL algorithms that are frequently used to predict mechanical properties of HPC and UHPC. Engineers and researchers working with construction materials will find this paper useful in helping them choose accurate and appropriate methods for their needs. [ABSTRACT FROM AUTHOR]

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper explores the use of Artificial Neural Networks (ANN) for the rocking problem. The paper adopts rigid rocking blocks of different sizes and slenderness, which undergo rocking motion without sliding and bouncing when subjected to recorded earthquakes. This research focuses on the cases where the blocks overturn or safely return to their initial (rest) position at the end of the ground shaking. An ANN model is trained to efficiently categorise the response into overturning or safe rocking using the structural parameters, ground motion characteristics, and the coefficient of restitution as input. The results show the substantial contribution of velocity and frequency characteristics of the ground motion to overturning. In addition, ANN is used to predict the response amplitude and identify the most critical input variables that govern safe rocking. The analysis reveals that rocking amplitude is governed by a combination of duration, frequency, and intensity characteristics of the ground excitation. Interestingly, the maximum incremental velocity (MIV), a novel intensity measure for the rocking literature, shows a substantial correlation with the rocking amplitude. In this context, this paper proposes closed‐form expressions using the most influential input variables to provide a quick, yet adequately accurate, response prediction. Finally, this study pays special attention to the contribution of the coefficient of restitution, which, in general, is less critical to the peak safe rocking response, while it becomes more important to the overturning response. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation. [ABSTRACT FROM AUTHOR]

In order to explore the international competitiveness of China's agricultural product supply chain, this paper combines machine learning algorithms to optimize the data processing process of agricultural product supply chain, and proposes a data processing method for agricultural product supply chain based on the intra‐column rules and inter‐column rules. According to the problems of my country's agricultural product supply chain, this paper establishes a new agricultural product supply chain management model based on the various modules of the supply chain link, and constructs a corresponding intelligent analysis model. Moreover, this paper combines machine learning algorithms to study the improvement path of the international competitiveness of China's agricultural product supply chain, and builds an intelligent model to improve the international competitiveness of China's agricultural products. Finally, after constructing the model, this paper obtains the path to improve the international competitiveness of China's agricultural products through the simulation operation model, which also verifies the effectiveness of the intelligent algorithm of this paper from the side. [ABSTRACT FROM AUTHOR]

Background: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. Aims: This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. Methods & Procedures: This is a cross‐sectional, online, self‐administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence‐Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi‐QA‐MPNet (Multi‐Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence‐Transformer. Outcomes & Results: The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. Conclusions & Implications: This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. WHAT THIS PAPER ADDS: What is already known on this subject: It is already known that speech‐ and language‐based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech‐ and language‐based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge: This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work?: The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure‐from‐motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability.This paper presents a new paradigm for mapping underwater environments from ego‐motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images.The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high‐precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer‐grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego‐motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes.Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning‐based Structure‐from‐Motion has broad implications for fast low‐cost mapping of underwater environments other than coral reefs. [ABSTRACT FROM AUTHOR]

This paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short‐term memory (LSTM) network architecture. Results from the LSTM model are compared with an object‐based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object‐based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application. [ABSTRACT FROM AUTHOR]

This article is a comprehensive review of the literature on smart technology in consumer studies from 1996 to 2023. While the paper provides information about the development of the field by identifying important publications and authors, it employs topic modeling to pinpoint key topics in papers published in marketing and business journals. These topics are then grouped into three research streams and evaluated concerning theoretical, contextual, and methodological perspectives. While doing so, specific gaps were identified. By revealing gaps in the literature, the study suggests promising avenues for further research. Finally, this article advances our comprehension of the smart technology literature in marketing and business journals and informs future inquiry in this rapidly evolving domain. [ABSTRACT FROM AUTHOR]

Background and Aims: The precise prediction of COVID‐19 prognosis remains a clinical challenge. In this regard, early identification of severe cases facilitates the triage and management of COVID‐19 cases. The present paper aims to explore the prognosis of COVID‐19 patients based on routine laboratory tests taken when patients are admitted. Methods: A data set including 1455 COVID‐19 patients (727 male, 728 female) and their routine laboratory tests conducted upon hospital admission, age, Intensive Care Unit (ICU) admission, and outcome were gathered. The data set was randomly split into the train (75% of the data) and test data set (25% of the data). The explainable boosting machine (EBM) and extreme gradient boosting (XGBoost) were used for predicting the mortality and ICU admission of COVID‐19 cases. Also, feature importance was extracted using EBM and XGBoost. Results: The EBM and XGBoost achieved 86.38% and 88.56% accuracy in the test data set, respectively. In addition, EBM and XGBoost predicted the ICU admission with an accuracy of 89.37%, and 79.29% in the test data set for COVID‐19 patients, respectively. Also, obtained models indicated that aspartate transaminase (AST), lymphocyte, blood urea nitrogen (BUN), and age are the most significant predictors of COVID‐19 mortality. Furthermore, the lymphocyte count, AST, and BUN level were the most significant ICU admission predictors of COVID‐19 patients. Conclusions: The current study indicated that both EBM and XGBoost could predict the ICU admission and mortality of COVID‐19 cases based on routine hematological and clinical chemistry evaluation at the time of admission. Also, based on the results, AST, lymphocyte count, and BUN levels could be used as early predictors of COVID‐19 prognosis. [ABSTRACT FROM AUTHOR]

High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of "Dual Tree Complex Wavelet Transform (DTCWT)," which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation. [ABSTRACT FROM AUTHOR]

Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However, the frequencies of macroeconomic variables are low and different from the stock market volatility, and few literature considers the low‐frequency macroeconomic variables as input indicators for deep learning models. In this paper, we forecast the stock market volatility incorporating low‐frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH‐MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH‐MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short‐term volatility and finally takes the predicted short‐term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally, in the comparison of the forecasting effects among different models, it is also found that the forecasting effect of the deep learning model is the best, the machine learning model is worse, and the traditional econometric model is the worst. [ABSTRACT FROM AUTHOR]

Background and Aim: Schizophrenia and bipolar disorder (BD) are critical and high‐risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods: A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results: In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full‐text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion: ML can precisely predict results and assist in making clinical decisions‐concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research. [ABSTRACT FROM AUTHOR]

Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data or incorporating human feedback into existing datasets and models. In these dynamic scenarios, data change over time and models must adapt. However, new data do not necessarily mean new patterns. The main goal of this paper is to devise a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth it to train it or not. That is, will the model hold significantly better results than the current one? To address this issue, we propose the use of meta‐learning. Specifically, we evaluate two different meta‐models, one built for a specific machine learning problem, and another built based on many different problems, meant to be a generic meta‐model, applicable to virtually any problem. In this paper, we focus only on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re‐training models between 60% and 98%, depending on the problem and on the threshold used. [ABSTRACT FROM AUTHOR]

As a transformative technology, connected and automated driving technology is expected to deal with the issues of traffic congestion and conflicts, and meanwhile to ensure the driving safety in intelligent transportation systems (ITS). Chen et al. presented a finite time disturbance observer (FTDO)-based super-twisting sliding mode control (SMC) for a vehicle platoon system against parameter uncertainties and external disturbances. Cai et al. designed a Platoon Sharing Deep Deterministic Policy Gradient Algorithm for a multi-vehicle network. [Extracted from the article]

In industries, it is difficult to measure physical quantities such as product quality. In order to estimate it, soft sensors have been proposed. In recent years, the Just‐In‐Time (JIT) method has attracted attention. According to this method, the output is estimated by extracting data from the database similar to the current situation. On the other hand, a database‐driven control system has been proposed in which the JIT is equipped with a learning algorithm. This method calculates the control parameters using a database and has the scheme to learn the control parameters to minimize the control error. This paper proposes a new design method for soft sensors which uses a database‐driven approach. Specifically, this paper proposes a method to improve the estimation accuracy without modifying the stored past data by introducing a new modifier c into the learning algorithm. Furthermore, a control system is designed in which the estimated value using the soft sensor is fed back. [ABSTRACT FROM AUTHOR]

This paper discusses the automatic detection of mura, non‐uniformity of brightness or color, which has been a long‐standing challenge in the display industries. Our purpose is to develop a method using machine learning, which automatically detects and classifies mura in the front‐end process. This will enable prompt feedback to the manufacturing process and contribute to improvement of the productivity. We propose "Progressive Hybrid model," which is based on the human visual perception and consists of a multiclass CNN (Convolutional Neural Network), a 2‐class residual neural network, and a 2‐class CNN. The two 2‐class models based on the subspace method to reproduce the boundary‐samples in the human visible test are for accurate classification between Normal displays and weak mura. To reproduce the appropriate test‐images used in the human visible test, we enhance contrast of 8000 images of the dataset obtained from an OLED (Organic Light Emitting Diode) manufacturing line. The evaluation results show improvement of the accuracy from 0.830 in the general application of CNN to 0.884 in the proposed model. The proposed model is particularly effective to improve classification of Normal displays, which is usually the most numerous in the manufacturing, and thus further beneficial in the practical use. [ABSTRACT FROM AUTHOR]

This paper illustrates the application of machine learning algorithms in predictive analytics for local governments using administrative data. The developed and tested machine learning predictive algorithm overcomes known limitations of the conventional ordinary least squares method. Such limitations include but not limited to imposed linearity, presumed causality with independent variables as presumed causes and dependent variables as presume result, likely high multicollinearity among features, and spatial autocorrelation. The study applies the algorithms to 311 non‐emergency service requests in the context of Miami‐Dade County. The algorithms are applied to predict the volume of 311 service requests and the community characteristics affecting the volume across Census tract neighborhoods. Four common families of algorithms and an ensemble of them are applied. They are random forest, support vector machines, lasso and elastic‐net regularized generalized linear models, and extreme gradient boosting. Two feature selection methods, namely Boruta and fscaret, are applied to identify the significant community characteristics. The results show that the machine learning algorithms capture spatial autocorrelation and clustering. The features generated by fscaret algorithms are parsimonious in predicting the 311 service request volume. [ABSTRACT FROM AUTHOR]

The reconfigurable capability of static random‐access memory (SRAM) field programmable gate array (FPGA) can be used for its fault self‐repair method. As a machine learning method, the genetic algorithm (GA) is an FPGA fault repair method that can be automatically executed on‐orbit without any ground support. However, the GA‐based fault repair method has disadvantages, such as the dependency on processors, the knowledge requirement for user designs in FPGAs, and the small size of repaired circuits. To address these issues, this paper presents a comprehensive analysis of the FPGA bitstream in the aerospace industry. An accurate on‐orbit fault location can be identified by bitstream copying and exhaustive test and the executed area of the GA can be reduced to one tile. In addition, the probability function of the algorithm is optimized, which converts floating‐point operations into integer arithmetic operations that are easily implemented in FPGAs without processors. The method is outstanding compared with existing ones, considering: (1) The size of repaired circuits is hundreds of times larger than those from other methods. (2) Its implementations are totally up to FPGAs' own logic, with no requirement for processors. (3) There is no knowledge requirement for user design. (4) It reaches the leading level with a success rate of 81%–93%. The method has been verified by various applications in XC7VX330T, which demonstrates its engineering practicability. [ABSTRACT FROM AUTHOR]

Fake news potentially causes serious problems in society. Therefore, it is necessary to detect such news, which is, of course, associated with some challenges such as events, verification and datasets. Reference datasets related to this area face various problems, like the lack of sufficient information about news samples, no subject diversity, etc. The present paper proposes a model using feature extraction and machine learning algorithms for dealing with some of these problems. In the feature extraction phase, two new features (named coherence and cohesion), along with other key features, were extracted from news samples. In the detection phase, initially, the news samples of each dataset were sorted based on a specific order (easier samples in the beginning and harder ones towards the end) using a hybrid method consisting of statistical descriptors and a k‐nearest neighbor algorithm. Then, inspired by the human learning principles, the sorted news samples, were sent to the Long‐Short‐Term Memory and classical machine learning algorithms for the detection of fake news. The obtained results indicated the higher performance of the proposed model in fake news detection compared to benchmark models. [ABSTRACT FROM AUTHOR]

Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non‐publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state‐of‐the‐art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques. [ABSTRACT FROM AUTHOR]

Aims: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. Methods: A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. Results: From 18 papers, 11 discrete GDM‐based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two‐thirds of the apps provided condition‐relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. Conclusions: There are limited mHealth apps for GDM that incorporate AI or AI‐based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community. [ABSTRACT FROM AUTHOR]

Summary: In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs). [ABSTRACT FROM AUTHOR]