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The applications and advantages of the Internet for real-time information sharing can never be over-emphasized. These great benefits are too numerous to mention but they are being seriously hampered and made vulnerable due to phishing that is ravaging cyberspace. This development is, undoubtedly, frustrating the efforts of the Global Cyber Alliance – an agency with a singular purpose of reducing cyber risk. Consequently, various researchers have attempted to proffer solutions to phishing. These solutions are considered inefficient and unreliable as evident in the conflicting claims by the authors. Against this backdrop, this work has attempted to find the best approach to solving the challenge of identifying suspicious uniform resource locators (URLs) on Reddit social networks. In an effort to handle this challenge, attempts have been made to address two major problems. The first is how can the suspicious URLs be identified on Reddit social networks with machine learning techniques? And the second is how can internet users be safeguarded from unreliable and fake URLs on the Reddit social network? This work adopted six machine learning algorithms – AdaBoost, Gradient Boost, Random Forest, Linear SVM, Decision Tree, and Naïve Bayes Classifier – for training using features obtained from Reddit social network and for additional processing. A total sum of 532,403 posts were analyzed. At the end of the analysis, only 87,083 posts were considered suitable for training the models. After the experimentation, the best performing algorithm was AdaBoost with an accuracy level of 95.5% and a precision of 97.57%. [ABSTRACT FROM AUTHOR]

In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions. [ABSTRACT FROM AUTHOR]

Cyber-attacks on the numerous parts of today's fast developing IoT are only going to increase in frequency and severity. A reliable method for detecting malicious attacks such as botnet in the IoT environment is critical for reducing security risks on IoT devices. Numerous existing methods exist for mining IoT networks for previously discovered patterns that may be exploited to improve security. This study used a hybrid deep learning approach, namely the CNN-LSTM technique, to detect botnet attacks. Any software that infiltrates a computer system or is installed there without the administrators' knowledge or permission is malicious. There is a wide range of viruses that cyber-criminals use to further their nefarious ends. A revolutionary deep learning system has been developed to counteract the increasing quantity of harmful programs. The system takes advantage of NLP methods as a baseline, mixes CNNs and LSTM neurons to capture local spatial correlations, and learns from successive long-term dependencies. Spatial invariance, often known as symmetry, is the property wherein the dataset size remains constant throughout iterations of an algorithm while undergoing various transformations. Therefore, automated extraction of high-level abstractions and representations aids in the malware categorization process. When compared to its predecessor research study, the current level of categorization accuracy is significantly greater than 0.81. The proposed CNN-LSTM method obtained an R2 = 99.19% in the dataset, with a correlation coefficient for the CNN-LSTM technique of R2 = 100% utilizing the provided dataset. The symmetry correlation of the CNN-LSTM, which illustrates that the CNN-LSTM method has the highest detection accuracy, at 99%, among the other malware detection methods such as the SVM and DT. The rest of classifiers had an accuracy of 98% for DT, and 95% for SVM. The accuracy of the LSTM model is 99%, the precision of the CNN-LSTM is 99%, recall is 99% and F1 score is 1. [ABSTRACT FROM AUTHOR]

With the maturity and popularity of Internet of Things (IoT), the notion of Social Internet of Things (SIoT) has been proposed to support novel applications and networking services for the IoT in more effective and efficient ways. Although there are many works for SIoT, they focus on designing the architectures and protocols for SIoT under the specific schemes. How to efficiently utilize the collaboration capability of SIoT to complete complex tasks remains unexplored. Therefore, we propose a new problem family, namely, Task-Optimized SIoT Selection (TOSS), to find the best group of IoT objects for a given set of tasks in the task pool. TOSS aims to select the target SIoT group such that the target SIoT group is able to easily communicate with each other while maximizing the accuracy of performing the given tasks. We propose two problem formulations, named Bounded Communication-loss TOSS (BC-TOSS) and Robustness Guaranteed TOSS (RG-TOSS), for different scenarios and prove that they are both NP-hard and inapproximable. We propose a polynomial-time algorithm with a performance guarantee for BC-TOSS, and an efficient polynomial-time algorithm to obtain good solutions for RG-TOSS. Moreover, as RG-TOSS is NP-hard and inapproximable within any factor, we further propose Structure-Aware Reinforcement Learning (SARL) to leverage the Graph Convolutional Networks (GCN) and Deep Reinforcement Learning (DRL) to effectively solve RG-TOSS. Further, since we use graph models to simulate the problem instance for DRL, which is different from the real ones, we propose Structure-Aware Meta Reinforcement Learning (SAMRL) for fast adapting to new domains. Experimental results on multiple real datasets indicate that our proposed algorithms outperform the other deterministic and learning-based baseline approaches. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

AutoML aims at best configuring learning systems automatically. It contains core subtasks of algorithm selection and hyper-parameter tuning. Previous approaches considered searching in the joint hyper-parameter space of all algorithms, which forms a huge but redundant space and causes an inefficient search. We tackle this issue in a cascaded algorithm selection way, which contains an upper-level process of algorithm selection and a lower-level process of hyper-parameter tuning for algorithms. While the lower-level process employs an anytime tuning approach, the upper-level process is naturally formulated as a multi-armed bandit, deciding which algorithm should be allocated one more piece of time for the lower-level tuning. To achieve the goal of finding the best configuration, we propose the Extreme-Region Upper Confidence Bound (ER-UCB) strategy. Unlike UCB bandits that maximize the mean of feedback distribution, ER-UCB maximizes the extreme-region of feedback distribution. We first consider stationary distributions and propose the ER-UCB-S algorithm that has $O(K\ln n)$ O (K ln n) regret upper bound with $K$ K arms and $n$ n trials. We then extend to non-stationary settings and propose the ER-UCB-N algorithm that has $O(Kn^\nu)$ O (K n ν) regret upper bound, where $\frac{2}{3}<\nu <1$ 2 3 < ν < 1 . Finally, empirical studies on synthetic and AutoML tasks verify the effectiveness of ER-UCB-S/N by their outperformance in corresponding settings. [ABSTRACT FROM AUTHOR]

In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use the square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the $\ell _1$ ℓ 1 -loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex or smooth. To alleviate this problem, we design an efficient algorithm based on majorization-minimization. The crux is on constructing a good optimization surrogate, and we show that this surrogate can be efficiently obtained by the alternating direction method of multipliers (ADMM). By properly monitoring ADMM's convergence, the proposed algorithm is empirically efficient and also theoretically guaranteed to converge to a critical point. Extensive experiments are performed on four machine learning applications using both synthetic and real-world data sets. Results show that the proposed algorithm is not only fast but also has better performance than the state-of-the-arts. [ABSTRACT FROM AUTHOR]

The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process. [ABSTRACT FROM AUTHOR]

Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. Methods: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. Results: Seven factors—erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)—were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. Conclusion: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies. Plain Language Summary: AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies. [ABSTRACT FROM AUTHOR]

Edge intelligence enables edge devices to collaboratively perform computations and generate knowledge where distributed computations act as the main innovative technologies. The existing solutions for distributed computations mainly focus on the designs of computations, such as distributed machine learning algorithms. By contrast, edge intelligence poses the design requirements for efficient networking technology, including one request multiple reply in a time Interval (ORMRI), computation aggregation, and chained computation, which have not been well addressed. To satisfy those requirements, we design a Computation-Oriented Network (CON) to enable the efficient discovery, task allocation, and collaborative computations for edge devices in this article. In CON, model and datasets are afforded with the names for direct resource discovery, and a hybrid computation oriented routing (HCOR) mechanism has been proposed, which combines proactive and reactive routing approaches. Through CON, computation requests and replies can be sent to and collected from multiple suitable edge devices, respectively; computation aggregation is enabled during the computation result replying process; and chained computation is supported through sequentially popping out model names. We examine the performance of CON through analysis and implementation, which shows that the proposed CON can greatly reduce the communication bandwidth consumption compared to the existing end-to-end approach. [ABSTRACT FROM AUTHOR]

Owing to the considerations of cell density and low static power consumption, nonvolatile random-access memory (NVRAM) has been a promising candidate for collaborating with a dynamic random-access memory (DRAM) as the main memory in modern computer systems. As NVRAM also brings technical challenges (e.g., limited endurance and high writing cost) to computer system developers, the concept of write reduction becomes the famous doctrine in NVRAM-based system design. Unfortunately, a well-known machine learning algorithm, random forest, will generate a massive amount of write traffic to the main memory space during its construction phase. In other words, a random forest hits the Achilles’ heel of NVRAM-based systems. For remedying this pain, our work proposes an NVRAM-friendly random forest algorithm, namely, Amine, for an NVRAM-based system. The design principle of Amine is to replace write operations with read accesses without raising the read complexity of the random forest algorithm. According to experimental results, Amine can effectively decrease the latency of random forest construction by 64%, compared with the original random forest algorithm. [ABSTRACT FROM AUTHOR]

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions. [ABSTRACT FROM AUTHOR]

A common theme emerging from nutrient omission trials conducted extensively across sub-Saharan Africa has been the large variability in yield response to applied nutrients. Yet, little is known about the factors associated with this variability. Therefore, the objectives of this review were to (1) synthesize the available data from nutrient omission trials of maize and rice and quantify spatial variability in yield responses and the probability of attaining specified yield targets; (2) identify the genotypic, environmental, and nutrient management factors associated with yield responses; and (3) provide insights and actionable information for setting priorities for future nutrient management research and development. Here, we provide distributions and expected values of yield response and agronomic efficiencies of nitrogen (N), phosphorus (P), and potassium (K) to serve as benchmarks for improving fertilizer recommendations. We also show that with the combined application of NPK fertilizer, rain-fed maize and rice yields can be raised from the current yields of ~2 Mg ha−1 to 4 Mg ha−1. Yield responses to N, P, and K were in the ratio of 11:6:1 in maize and 13:7:5 in rice, probably arising from differences in uptake requirements and growing environments. K yield responses were 2–3 times higher in rice than in maize. Explorative analysis using machine learning algorithms provided further insights into the possible interaction of agroecology, soil type, and exchangeable cations on the spatial variability in yield responses to N, P, and K in maize and rice. We recommend future research to address site-specific interactions between the applied and indigenous soil nutrients, focusing on optimizing application rates of K, other macronutrients and micronutrients for sustainable intensification of maize and rice production. This study highlights the critical need for balanced fertilization to optimize the productivity and nutrient use efficiencies in rice and maize production in smallholder farming systems in SSA. [ABSTRACT FROM AUTHOR]

This study was aimed at evaluating the effect of spontaneous lacto-fermentation of carrot slices on flesh structure using different machine learning approaches. The textures computed from digital images of lacto-fermented and fresh carrot slices were compared using neural networks and other algorithms from different groups. In the case of Multilayer Perceptron, accuracies for training, testing and validation were considered. For some of the networks, lacto-fermented and fresh samples were completely distinguished. The accuracies for training, testing and validation were equal to 100%. For models built using other algorithms (LDA (Linear Discriminant Analysis), Multi Class Classifier, LMT (Logistic Model Tree), KStar, Naive Bayes, PART), the following metrics were used for the evaluation of model effectiveness: accuracies, time taken to build model, Kappa statistic, mean absolute error, root mean squared error, PRC (Precision-Recall) Area, ROC (Receiver Operating Characteristic) Area, MCC (Matthews Correlation Coefficient), F-Measure, Recall, Precision, FP (False Positive) Rate and TP (True Positive) Rate. The most satisfactory results were obtained for the LDA. The lacto-fermented and fresh carrot slices were distinguished with an average accuracy of 99%, low values of errors (mean absolute error: 0.0117, root mean squared error: 0.1014) and FP Rate (0.010). The weighted averages of other metrics were greater than or equal to 0.98 (Kappa statistic: 0.98, PRC Area: 0.987, ROC Area: 0.991, MCC: 0.980, F-Measure: 0.990, Recall: 0.990, Precision: 0.990, TP Rate: 0.990). The obtained results demonstrated the usefulness of different machine learning approaches to the evaluation of the effect of fermentation on changes in the carrot flesh structure. [ABSTRACT FROM AUTHOR]

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research. [ABSTRACT FROM AUTHOR]

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies. [ABSTRACT FROM AUTHOR]

Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision and natural language processing etc. In this survey article, we comprehensively review CL from various aspects including motivations, definitions, theories, and applications. We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum. In particular, we summarize existing CL designs based on the general framework of Difficulty Measurer $+$ + Training Scheduler and further categorize the methodologies for automatic CL into four groups, i.e., Self-paced Learning, Transfer Teacher, RL Teacher, and Other Automatic CL. We also analyze principles to select different CL designs that may benefit practical applications. Finally, we present our insights on the relationships connecting CL and other machine learning concepts including transfer learning, meta-learning, continual learning and active learning, etc., then point out challenges in CL as well as potential future research directions deserving further investigations. [ABSTRACT FROM AUTHOR]

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where only subjective inconsistent rankings from multiple rankers are associated with each instance. Two ranking methods are proposed from the perspective of instances and rankers, respectively. The first method, Instance-oriented Preference Distribution Learning (IPDL), is to learn a latent preference distribution for each instance. IPDL generates a common preference distribution that is most compatible to all the personal rankings, and then learns a mapping from the instances to the preference distributions. The second method, Ranker-oriented Preference Distribution Learning (RPDL), is proposed by leveraging interpersonal inconsistency among rankers, to learn a unified model from personal preference distribution models of all rankers. These two methods are applied to natural scene images dataset and 3D facial expression dataset BU_3DFE. Experimental results show that IPDL and RPDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms. [ABSTRACT FROM AUTHOR]

Reliable water quality prediction can improve environmental flow monitoring and the sustainability of the stream ecosystem. In this study, we compared two machine learning methods to predict water quality parameters, such as total nitrogen (TN), total phosphorus (TP), and turbidity (TUR), for 97 watersheds located in the Southeast Atlantic region of the USA. The modeling framework incorporates multiple climate and watershed variables (characteristics) that often control the water quality indicators in different landscapes. Three techniques, such as stepwise regression (SR), Least Absolute Shrinkage and Selection Operator (LASSO), and genetic algorithm (GA), are implemented to identify appropriate predictors out of 28 climate and catchment-related variables. The selected predictors were then used to develop the Random Forest (RF) and Boosted regression tree (BRT) models for water quality predictions in selected watersheds. The results highlighted that while both algorithms provided reasonable results (based on statistical metrics), the RF algorithm was easier to train and robust to model overfitting. Partial dependence plots highlighted the complex and nonlinear relationships between the individual predictors and the water quality indicators. The thresholds obtained from partial dependence plots showed that the median values of total nitrogen (TN) and total phosphorus (TP) in streams increase significantly when the percentage of urban and agricultural lands is above 40% and 43% of the watershed area, respectively. Furthermore, when soil hydraulic conductivity increases, the reduction in runoff results in decreased Turbidity levels in streams. Therefore, identifying the key watershed characteristics and their critical thresholds can help watershed managers create appropriate regulations for managing and sustaining healthy stream ecosystems. Besides, the forecasting models can improve water quality predictions in ungauged watersheds. [ABSTRACT FROM AUTHOR]

Blood type identification is a method used for determining the specific blood type of a person. It is a requirement before blood transfusions or blood donations is undertaken especially during emergency situations. Presently, the tests are performed manually by medical technologists in the laboratories. Sometimes, manual blood typing is prone to human error, resulting to incorrect blood grouping and wrong typing in the report, leading to fatal transfusion reactions. The study was focused on the development of a device that is capable of identifying the blood type of an individual using an image processing and machine learning algorithms. The study covered the identification of eight blood types, specifically rhesus positive and negative, A, B, O, and AB, by developing a capturing box integrated with a web camera system that could effectively capture blood sample images. In this study, the methodologies utilized were image processing through segmentation, feature extraction by color and texture properties, and different machine learning algorithms. After training, the results showed that coarse tree DT has the best performance accuracy score of 97.77% using 70:30 holdout validation. The testing results showed that the system is 100% accurate as validated by a registered medical technologist. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate how machine-learning algorithms can be used to predict the detection and classification of faults in transmission lines. Voltages and currents in three phases have been considered as inputs. For accurate prediction of fault, Neural Network (NN), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machines (LightGBM) are used. The simulation results from this research indicate that LightGBM has superior prediction accuracy and exhibit enhanced performance in detecting and classifying transmission line faults. [ABSTRACT FROM AUTHOR]

In plants, it is one of the important factors to provide ecological factors for productivity. Plants perceive the effects of environmental factors outside as stress and create appropriate physiological responses in their metabolism. Water stress is a type of abiotic stress and is one of the important factors affecting the growth and development of plants. Irrigation water management is very important for sustainable use of water resources. Therefore, support tools are becoming increasingly important in deciding irrigation. With the developments the technology, computerized support applications are usually used in agricultural areas. In the study, it is aimed to determine the effects of irrigation applications performed in different periods on yield quality and spectral reflections of safflower plant and to create applicable irrigation programs in safflower cultivation. From the reflections detained from safflower leaves by using spectroradiometer device, the determination of safflower plant according to water ratio different situations is done. Reflections are taken as three groups where the plants are grown at wavelengths of 325 nm to 1075 nm. Water due diligence related with safflower is consist of two stages. The first stage is the provision of the feature vector, the second stage is the classification of the feature vector related to data. As classification methods, support vector machine, k-nearest neighbor and decision tree are used. In comparison of three groups, the average value of the performance is high. As a result, considering the fallowed method and the procedure used, it is evaluated that such studies can be used in agriculture and cultivation. [ABSTRACT FROM AUTHOR]

More program functions are no longer written in code but learned from a huge number of data samples using a machine learning (ML) algorithm. We present an overview of current techniques to manage complex software and discuss how this applies to ML models. [ABSTRACT FROM AUTHOR]

In the 2015 Canadian case of Ewert v. Canada, risk assessment tools were put on trial in Canada’s Federal Court and eventually at the Supreme Court of Canada, their efficacy was challenged, and their reliability was upended ,. Risk assessment tools are used by the justice system to present a calculated prediction of an offender’s risk of future criminal behavior. These tools are viewed by the legal community as precursors to machine learning and technological advancements in artificial intelligence (AI) in the criminal justice system, using algorithmic and data-driven decision-making to provide courts with predictions regarding an offender’s risk if released. [ABSTRACT FROM AUTHOR]

Speech disorder is a significant problem for people affected with Parkinson's disease (PD) leading to a substantial disability to communicate with others. PD affects the voice, including changes in pitch, intensity, articulation, and syllable rate.We aimed to study the current status of artificial intelligence (AI) using machine learning algorithms (MLAs) in the assessment of speech abnormalities in PD along with the generation of intelligible synthetic speech for voice rehabilitation. We searched the literature for studies focusing on speech/voice disorder in PD and rehabilitation techniques till June 18, 2022. We searched PubMed and Engineering Village (Compendex and Inspec combined) databases. After careful screening of the title and evaluation of abstracts, we used select articles describing the use of AI or its various forms in the management of speech abnormalities in PD to synthesize this review. MLAs classify PD and non-PD patients with an accuracy of more than 90% using only voice features. Non-acoustic sensors can rehabilitate PD patient by converting dysarthric speech to highly intelligible speech using MLAs. MLAs can automatically assess several speech features and quantify the progression of speech abnormalities in PD. PD speech rehabilitation using MLAs may prove superior to other available therapies. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data. [ABSTRACT FROM AUTHOR]

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability. [ABSTRACT FROM AUTHOR]

As complete elimination of porosity from the weld is very difficult, the next option available is to minimize this weld porosity, which is crucial for the safe performance of the welded components. However, this investigation through experiments alone is very tedious and time consuming. Additionally, very limited models are available in the literature for accurate prediction of different porosity attributes. The present study, thus, addressees both the experimental as well as modelling aspect on the study of micro-porosity during electron beam welding (EBW) of SS304 plates. Welding parameters are reported to have significant influence on the micro-porosity. Hence, the influences of these parameters on micro-porosity attributes, namely pores number, average diameter, and sphericity are extensively studied experimentally employing optical microscopy (OM), scanning electron microscopy (SEM), X-ray computed tomography (XCT), and Raman spectroscopy. This is followed by an elaborate modelling using seven popular and well-recognized machine learning algorithms (MLAs), namely multi-layer perceptron (MLP), support vector regression (SVR), M5P model trees regression, reduced error pruning tree (REPTree), random forest (RF), instance-based k-nearest neighbor algorithm (IBk), and locally weighted learning (LWL). These different techniques enhance the chance of obtaining the better predictions of the said micro-porosity attributes by overcoming the effect of data-dependence and other limitations of individual MLAs. The different model-predicted micro-porosity data are also validated through experimental data. Statistical tests and Monte-Carlo reliability analysis are additionally utilized to evaluate the performances of the employed algorithms. IBk and MLP are overall found to perform well. [ABSTRACT FROM AUTHOR]

This paper presented a review on the capabilities of machine learning algorithms toward Earth observation data modelling and information extraction. The main purpose was to identify new trends in the application of or research on machine learning and Earth observation—as well as to help researchers positioning new development in these domains, considering the latest peer-reviewed articles. A review of Earth observation concepts was presented, as well as current approaches and available data, followed by different machine learning applications and algorithms. Special attention was given to the contribution, potential and capabilities of Earth observation-machine learning approaches. The findings suggested that the combination of Earth observation and machine learning was successfully applied in several different fields across the world. Additionally, it was observed that all machine learning categories could be used to analyse Earth observation data or to improve acquisition processes and that RF, SVM, K-Means, NN (CNN and GAN) and A2C were among the most-used techniques. In conclusion, the combination of these technologies could prove to be crucial in a wide range of fields (e.g., agriculture, climate and biology) and should be further explored for each specific domain. [ABSTRACT FROM AUTHOR]

Although satellite remote sensing technology is intensively used for the monitoring of water quality, the inversion of coastal water bodies and non-optically active parameters is still a challenging issue. Few ongoing studies use remote sensing technology to analyze the driving forces of changes in water quality from multiple aspects based on inversion results. By the use of Landsat 5/8 imagery and measured in situ data of the total nitrogen (TN) and total phosphorus (TP) in the Shenzhen-Hong Kong Bay area from 1986 to 2020, this study evaluated the modeling effects of four machine learning methods named Tree Embedding (TE), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Back-propagation Neural Network (BPNN). The results show that the BPNN creates the most reliable and robust results. The values of the obtained correlation coefficients (r) are 0.83, 0.92, 0.84, and 0.90, and that of the coefficients of determination (R2) are 0.70, 0.84, 0.70, and 0.81. The calculated mean absolute errors (MAEs) are 0.41, 0.16, 0.06, and 0.02, while the root mean square errors (RMSEs) are 0.78, 0.29, 0.12, and 0.03. The concentrations of TN and TP (CTN, CTP) in the Shenzhen Bay, the Starling Inlet, and the Tolo Harbor were relatively high, fluctuated from 1986 to 2010, and decreased significantly after 2010. The CTN and CTP in the Mirs Bay kept continuously at a low level. We found that urbanization and polluted river discharges were the main drivers of spatial and inter-annual differences of CTN and CTP. Temperature, precipitation, and wind are further factors that influenced the intra-annual changes of CTN and CTP in the Shenzhen Bay, whilethe expansion of oyster rafts and mangroves had little effect. Our research confirms that machine learning algorithms are well suited for the inversion of non-optical activity parameters of coastal water bodies, and also shows the potential of remote sensing for large-scale, long-term monitoring of water quality and the subsequent comprehensive analysis of the driving forces. [ABSTRACT FROM AUTHOR]

Use of an Automated Driving System is expected to improve traffic safety by protecting drivers from drowsy driving. Previous studies on the use of Automated Driving Systems mainly focused on detecting a driver's level of drowsiness and protecting drivers from accidents by performing fallback maneuvers. However, maneuvers conducted in drowsy states are limited in their ability to achieve Minimal Risk Conditions because human drivers show a gradual degradation in their driving ability as they fall asleep and the probability of an accident increases greatly after a driver becomes drowsy. Thus, current Automated Driving Systems require algorithms to predict drowsiness and perform maneuvers before the driver becomes too drowsy. This paper suggests an algorithm that not only detects but also predicts driver drowsiness using 6 vehicle data points. Driver condition is classified into 4 states and Driver drowsiness can be predicted by detecting the severe fatigue state, which tends to occur one minute before the drowsy state. The vehicle driving data are collected using a simulator and features that can be used to distinguish between the 4 states are investigated through data analysis. Ultimately, an optimum machine learning algorithm that can predict driver drowsiness is developed using the investigated factors. [ABSTRACT FROM AUTHOR]

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM. [ABSTRACT FROM AUTHOR]

Lake Chad is facing critical environmental situations since the 1960s due to the effects of climate change and anthropogenic activities on its ecosystems. The statistical analyses of remote sensing climate variables (i.e., evapotranspiration, specific humidity, soil temperature, air temperature, precipitation, soil moisture) and remote sensing and ground-truth lake level applied to the period 1993-2012 reveal that remote sensing lake level data has a skewed distribution and positive significant association with only soil moisture, whereas ground-truth lake level has a symmetrical distribution and negative significant associations with all the climate variables. The regression of remote sensing and ground-truth lake level onto climate variables using Linear Regression (LR), Support Vector Regression (SVR), Regression Tree (RT), Random Forest Regression (RF), and Deep Learning (DL) methods show that (i) RF outperforms the other models with the highest coefficient of determination (R2) and explained variance score (EVS) values and (ii) SVR has the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and k-fold cross-validation (k-fold CV) values. The RF feature ranking function shows that soil temperature is the major driver of remote sensing lake level fluctuations, whereas precipitation is the first factor for ground-truth lake level. This study provides more in-depth knowledge of the factors influencing Lake Chad's level and perspectives for an integrated and forward-looking water management system for connecting climate change, vulnerability, human activities, and water balance research in the Lake Chad human-environment system. We cannot get the necessary ground truth data at this time because of the challenging security situations in the region. However, the development of the data analysis methodology reported here is of fundamental importance in understanding the water cycle dynamics in this important basin, even under challenging field conditions. Verification studies can be performed when more. [ABSTRACT FROM AUTHOR]

Adaptive gradient algorithms have recently become extremely popular because they have been applied successfully in training deep neural networks, such as Adam, AMSGrad, and AdaBound. Despite their success, however, the distributed variant of the adaptive method, which is expected to possess a rapid training speed at the beginning and a good generalization capacity at the end, is rarely studied. To fill the gap, a distributed adaptive subgradient algorithm is presented, called D-AdaBound, where the learning rates are dynamically bounded by clipping the learning rates. Moreover, we obtain the regret bound of D-AdaBound, in which the objective functions are convex. Finally, we confirm the effectiveness of D-AdaBound by simulation experiments on different datasets. The results show the performance improvement of D-AdaBound relative to existing distributed online learning algorithms. [ABSTRACT FROM AUTHOR]

Introduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender. [ABSTRACT FROM AUTHOR]

This article discusses the use of a handheld electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines, and ketones. Typical concentrations of these compounds (from 5 to 120 ng in the cork samples) have been measured. Two electronic nose prototypes have been developed as an instrumentation system comprise of eight commercial gas sensors to perform two sets of experiments. In the first experiment, a quantitative approach was used whist in the second experiment a qualitative one was used. Machine learning algorithms such as k-nearest neighbors and artificial neural networks have been used in order to test the performance of the system to detect cork defects. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels. We found this electronic nose to have near 100 % accuracy in the detection of these defects. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning use a wide variety of algorithms. More than 60 years of research have shown that intelligent behavior requires a variety of methods. We discuss the major classes of algorithms and their uses. [ABSTRACT FROM AUTHOR]

While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples. [ABSTRACT FROM AUTHOR]

Rainfall prediction is the most significant requirement nowadays due to the chaotic nature of climate. Climate has changed drastically over the last few years due to global warming and has become very unpredictable. Rainfall prediction is essential for decision-making in various sectors like agriculture, transportation, tours and travels, outdoor events, etc. In this study, machine learning algorithms are analysed and experimented on the dataset comprising various atmospheric parameters of Hyderabad city in Telangana. The work is carried out on individual popular classifiers, namely Naïve Byes, Decision Tree, Random Forest, K nearest neighbour, and Support Vector Machine. The performance is compared with techniques like voting classifiers and stacking ensemble. The experiment gives predictions on the rainfall intensity as No Rain, Low to Medium rain, or Heavy rain. The k-cross-fold validation technique is used as the evaluation metric, which is very effective and results in less biased estimations. The aim is to provide the decision-making capabilities based on the mentioned intensity of rainfall that can be very useful in managing the irrigation cycle in the agriculture field, deciding on an outdoor event, or any travelling plan based on the current atmospheric parameters. The platform used is python, which is portable, open to access, and available easily. [ABSTRACT FROM AUTHOR]

The problem of representative selection amounts to sampling few informative exemplars from large datasets. Existing approaches to data selection often fall short of simultaneously handling non-linear data structures, sampling concise and non-redundant subsets, rejecting outliers, and yielding interpretable outcomes. This paper presents a novel representative selection approach, dubbed MOSAIC, for drawing descriptive sketches of arbitrary manifold structures. Resting upon a novel quadratic formulation, MOSAIC advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures novelty of the samples by minimizing redundancy, and rejects disruptive information by effectively detecting outliers. Theoretical analyses shed light on geometrical characterization of the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC’s superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been flagged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a $(2+\epsilon)$ (2 + ε) -approximation algorithm for the $k$ k -center clustering problem with “small” amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while $k$ k and $\epsilon$ ε are constant. Furthermore, we significantly improve the memory requirement of our fully dynamic algorithm, although at the cost of a worse approximation ratio of $4 +\epsilon$ 4 + ε . Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Multi-view subspace clustering has attracted intensive attention to effectively fuse multi-view information by exploring appropriate graph structures. Although existing works have made impressive progress in clustering performance, most of them suffer from the cubic time complexity which could prevent them from being efficiently applied into large-scale applications. To improve the efficiency, anchor sampling mechanism has been proposed to select vital landmarks to represent the whole data. However, existing anchor selecting usually follows the heuristic sampling strategy, e.g. $k$ -means or uniform sampling. As a result, the procedures of anchor selecting and subsequent subspace graph construction are separated from each other which may adversely affect clustering performance. Moreover, the involved hyper-parameters further limit the application of traditional algorithms. To address these issues, we propose a novel subspace clustering method termed Fast Parameter-free Multi-view Subspace Clustering with Consensus Anchor Guidance (FPMVS-CAG). Firstly, we jointly conduct anchor selection and subspace graph construction into a unified optimization formulation. By this way, the two processes can be negotiated with each other to promote clustering quality. Moreover, our proposed FPMVS-CAG is proved to have linear time complexity with respect to the sample number. In addition, FPMVS-CAG can automatically learn an optimal anchor subspace graph without any extra hyper-parameters. Extensive experimental results on various benchmark datasets demonstrate the effectiveness and efficiency of the proposed method against the existing state-of-the-art multi-view subspace clustering competitors. These merits make FPMVS-CAG more suitable for large-scale subspace clustering. The code of FPMVS-CAG is publicly available at https://github.com/wangsiwei2010/FPMVS-CAG. [ABSTRACT FROM AUTHOR]

We present a novel method to detect underground cavities using cross-borehole ground-penetrating radar (GPR). We model the propagation of a GPR signal across a cavity and find that the spectrum of the signal will be distorted in a specific low-pass manner. Comparing the spectra of received signals with the predicted spectrum using a hypothesis-testing approach allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Our approach generally remains effective even when conventional methods fail; our method can also be combined with conventional methods to more successfully distinguish between cavities and geologic clutter. [ABSTRACT FROM AUTHOR]

[Display omitted] • An automatic sorting machine is designed for grading pomegranates. • Machine vision is utilized in order to detect sunburn on pomegranates. • Three different image processing algorithms are proposed for feature extraction. • ANN and SVM classification models are used to sort the fruits automatically. • The experimental results of algorithms and overall sorting machine are provided. Sunburn is a widespread problem in pomegranate marketing. About 30 % of the harvested pomegranates suffer from sunburn. In this research, an automatic sorting machine was designed, developed, and tested to separate the sunburn pomegranates. To detect sunburn on pomegranates, three novel image processing algorithms were proposed: skin spot detection, brightness intensity distribution, and statistical surface parameter. Two supervised machine learning algorithms comprising artificial neural networks and support vector machines were utilized to classify the fruits. The proposed algorithms were used to control the developed sorting machine to separate the sunburn pomegranates from the healthy ones. Finally, the performance of the proposed algorithms was evaluated by the accuracy and response time of the sunburn sorting function of the developed machine. The brightness intensity distribution algorithm with the accuracy and response time of 98 % and 0.88 s, respectively, is suggested as the fastest and the most accurate algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of the DTPA chelating agent on the rock/oil contact angle and rock surface charge was examined under varying concentrations, salinity, and potential-determining ions. • An experimental database comprising 240 sets of wettability data was acquired. • Two advanced machine-learning models, namely Random Forest and Boosted Regression Tree, were developed to accurately estimate the alteration in rock wettability. • Statistical analysis was conducted, demonstrating outstanding predictions for a wide range of input variables. Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of diethyl ether on the interfacial tension (IFT) of crude oil-brine was examined under varying salinity and temperature conditions. • An experimental database comprising 7,017 sets of crude oil/brine/DEE IFT data was acquired. • Six advanced machine-learning models were developed to accurately estimate the IFT of Crude oil–Brine-DEE. • Statistical analysis was conducted, demonstrating outstanding predictions for a wide range of input variables. Solvent injection, a well-established method for enhanced oil recovery (EOR), has demonstrated significant improvements in oil recovery when compared with conventional water flooding techniques. The interfacial tension (IFT) is pivotal in determining the displacement efficiency and overall performance of innovative techniques like dimethyl ether-enhanced waterflooding (DEW), which has gained substantial attention in recent years. In this study, following laboratory measurements of IFT, six advanced machine learning (ML) techniques were employed: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Boosted Regression Tree (BRT) to model the IFT in both oil-brine and oil-brine-diethyl ether (DEE) systems. The analysis is based on an extensive dataset comprising 7,017 data points for oil-brine and 6,949 data points for oil-brine-DEE systems obtained from experimental studies. The findings indicate that the developed RF model excels in predicting IFT, boasting a remarkable coefficient of determination (R2 = 0.99) along with the lowest root mean squared error (RMSE = 0.2), mean squared error (MSE = 0.04), and mean absolute error (MAE = 0.13). The study underscores the significance of optimizing salinity levels to achieve the most substantial reduction in IFT. This reduction is attributed to the enhanced migration of polar components, such as asphaltene molecules, to the interface of the oil-brine system. Moreover, the research highlights a synergistic decrease in IFT when both DEE and soluble ions are present, resulting in the lowest IFT at around 2 mN/m in 40,000 ppm salinity (S 2) at 70 °C (T 3). This indicates that the adsorption of DEE at the water–oil interface forms a layer capable of adsorbing ions, thereby enhancing the layer's thickness. As a result, the oil–solvent-ion layer becomes thicker compared to the oil-ion layer, leading to the maximum decrease in IFT. Additionally, with increasing temperature up to 70 °C, the IFT of both systems demonstrated a downward trend, as evidenced by all experiments. The outcomes of this study have the potential to enhance our comprehension of the underlying mechanisms involved in water-soluble solvent EOR techniques. [ABSTRACT FROM AUTHOR]

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology. [ABSTRACT FROM AUTHOR]

Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a novel real-time method to improve the detection of seizures using the spectral features of non-stationary electroencephalogram (EEG) signals. It is observed that the discrete wavelet transform (DWT)-based features do not consider the interrelationship among EEG signal components. This interrelationship has been well captured by the novel representation of EEG in the form of graph signals. Here, the spectral analysis of the graph signals is investigated by the graph-based Fourier transform (GFT). Then, GFT-based features have been selected and fed into different classifiers for analysis. The seizure detection rate in two publicly available EEG-based datasets, the University of Bonn (UB) and the Neurology Sleep Clinic New Delhi (NSC-ND), have been achieved with accuracy of 98.68% and 96.84%, respectively. The accuracy achieved is significantly better than the existing state-of-the-art techniques. This approach demonstrates the impact of utilizing the interrelationship among the EEG components, followed by enhanced feature selection based on GFT for the improved detection of seizures. [ABSTRACT FROM AUTHOR]

It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever ML-based models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scientists to select the appropriate tools in projecting water temperature, and other environmental factors, under changing climate. [Display omitted] • An unparalleled temperature database was collected to test performance of ML models. • ML models captured the seasonal temperature pattern from top to bottom water layers. • ML models accurately reproduced the temporal development of stratification structure. • Extrapolation in ML models should be cautious under climate warming conditions. [ABSTRACT FROM AUTHOR]