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

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hindered by patient confidentiality laws, and there is current interest in algorithms to generate synthetic tabular data from real data, for example via deep learning. We demonstrate a novel EM-based clustering algorithm, MMM ("Madras Mixture Model"), that outperforms standard algorithms in determining clusters in synthetic heterogeneous data, and recovers structure in real data. Based on this, we demonstrate a synthetic tabular data generation algorithm, MMMsynth, that pre-clusters the input data, and generates cluster-wise synthetic data assuming cluster-specific data distributions for the input columns. We benchmark this algorithm by testing the performance of standard ML algorithms when they are trained on synthetic data and tested on real published datasets. Our synthetic data generation algorithm outperforms other literature tabular-data generators, and approaches the performance of training purely with real data. [ABSTRACT FROM AUTHOR]

A wide range of issues including missing values, class noise, class imbalance, outliers, correlation and irrelevant variables have the potential to negatively affect the overall performance of disease diagnosis classification algorithms. This study proposes a new technique, alternative to the t-Score method, to increase the performance of ensemble learning classification algorithms by removing irrelevant variables. Therefore, three publicly available datasets from medical domain varying in their sample sizes, number of variables, and data preprocessing problems were selected and processed with our newly proposed feature selection method called Outliers and Correlation t-Score (OCtS). Afterwards, six widely used ensemble learning algorithms including Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting Machine, Light Gradient Boosting Machine, CatBoost, and Bagging were employed for disease diagnosis classification, and performance metrics were measured. Our results indicate that the classification performance of six ensemble learning algorithms significantly increased when the OCtS method was employed, and our feature selection method, OCtS, exhibited higher performance compared to the standard t-score method across all datasets (p = 0.0001). We conclude that, using data preprocessing methods with OCtS offers better algorithm performance when employing ensemble learning algorithms in disease diagnosis classification. [ABSTRACT FROM AUTHOR]

The long-time test of battery discharge time is the main problem for implementing the performance requirements of the standard IEC 60086. Prediction of battery discharge time can be a solution for the long-time test problem. The discharge test data conceive valuable information of the nonlinear characteristics of the battery to build battery models. An intelligent system with learning algorithm to update the capability of the model from a set of training test data is suitable to predict the battery discharge time. The well-known ANFIS (Adaptive Neuro Fuzzy Inference Systems) is a neuro-adaptive system that has adaptive input nodes and has been applied to many prediction problems through learning and validation of the network from data. In this paper, the empirical test data related to the discharge time are acquired from the laboratory tests and are used to build the persuasive ANFIS model for the discharge time prediction from inputs to 48 recombination output data by means of modified membership functions. [ABSTRACT FROM AUTHOR]

Background: Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper algorithm primarily focuses on shortening the path to find an optimal feature set. However, it underutilizes the capability of feature subset models, which impacts feature selection and its predictive performance. Method and Results: This study proposes a novel Artificial Intelligence based Wrapper (AIWrap) algorithm that integrates Artificial Intelligence (AI) with the existing wrapper algorithm. The algorithm develops a Performance Prediction Model using AI which predicts the model performance of any feature set and allows the wrapper algorithm to evaluate the feature subset performance in a model without building the model. The algorithm can make the wrapper algorithm more relevant for high-dimensional data. We evaluate the performance of this algorithm using simulated studies and real research studies. AIWrap shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms and wrapper algorithms. Conclusion: AIWrap approach provides an alternative algorithm to the existing algorithms for feature selection. The current study focuses on AIWrap application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data. [ABSTRACT FROM AUTHOR]

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL. [ABSTRACT FROM AUTHOR]

Rising stars are the researchers who are relatively new to the research area and have published fewer research articles, but their research work is of such standard that they have the potential to be top researchers in near future. Research work on the evaluation of researchers and prediction of rising stars is getting attention because it can be useful for selecting capable candidates for the jobs, hiring young faculty members for institutes, and seeking reviewers for journals and conferences and members for different committees. In this research study, the authors address the research problem of finding rising stars and propose novel features in diverse feature sets of three categories: article, author, and venue. The real-world data set has been extracted, preprocessed, and used from the Web of Science for empirical analysis. Several diverse supervised machine learning, ensemble learning algorithms, and deep learning are applied to the data set. The results, using classifiers, are compared based on standard performance evaluation measures to reveal the significance of the proposed as well as existing features. It also shows that the novel features play a significant role in finding rising stars. The ensemble-based machine learning classifier generalized linear model outperforms all other classifiers and gives the highest accuracy and F-measure compared to other models and the existing studies in the relevant literature. [ABSTRACT FROM AUTHOR]

This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning. GLocal-LS-SVM addresses the challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm is a double-layer learning approach that employs multiple local LS-SVM models in the first layer and one global LS-SVM model in the second layer. The key idea behind GLocal-LS-SVM is to extract the most informative data points, known as support vectors, from each local region in the input space. Local LS-SVM models are developed for each region to identify the most contributing data points with the highest support values. The local support vectors are then merged at the final layer to form a reduced training set used to train the global model. We evaluated the performance of GLocal-LS-SVM using both synthetic and real-world datasets. Our results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models. In addition, our experiments show that GLocal-LS-SVM outperforms standard LS-SVM in terms of computational efficiency. For instance, on a training dataset of 9, 000 instances, the average training time for GLocal-LS-SVM was only 2% of the time required to train the LS-SVM model while maintaining classification performance. In summary, the GLocal-LS-SVM algorithm offers a promising solution to address the challenges associated with decentralised data sources and large datasets while maintaining high classification performance. Furthermore, its computational efficiency makes it a valuable tool for practical applications in various domains. [ABSTRACT FROM AUTHOR]

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability. [ABSTRACT FROM AUTHOR]

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization. [ABSTRACT FROM AUTHOR]

A recommender system not only "gains users' confidence" but also helps them in other ways, such as reducing their time spent and effort. To gain users' confidence, one of the main goals of recommender systems in an e-commerce industry is to estimate the users' interest by tracking the users' transactional behavior to provide a fast and highly related set of top recommendations out of thousands of products. The standard ranking-based models, i.e., the denoising auto-encoder (DAE) and collaborative denoising auto-encoder (CDAE), exploit positive-only feedback without utilizing the ratings' ranks for the full set of observed ratings. To confirm the rank of observed ratings (either low or high), a confidence value for each rating is required. Hence, an improved, confidence-integrated DAE is proposed to enhance the performance of the standard DAE for solving recommender systems problems. The correctness of the proposed method is authenticated using two standard MovieLens datasets such as ML-1M and ML-100K. The proposed study acts as a vital contribution for the design of an efficient, robust, and accurate algorithm by learning prominent latent features used for fast and accurate recommendations. The proposed model outperforms the state-of-the-art methods by achieving improved P@10, R@10, NDCG@10, and MAP scores. [ABSTRACT FROM AUTHOR]

HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets and ice crystals. Cloud particle properties include position, diameter, and shape. In this work we evaluate the potential for processing HOLODEC data by leveraging a combination of GPU hardware and machine learning with the eventual goal of improving HOLODEC processing speed and performance. We present a hologram processing algorithm, HolodecML, which utilizes a neural network segmentation model and computational parallelization to achieve these goals. HolodecML is trained using synthetically generated holograms based on a model of the instrument, and it predicts masks around particles found within reconstructed images. From these masks, the position and size of the detected particles can be characterized in three dimensions. In order to successfully process real holograms, we find we must apply a series of image corrupting transformations and noise to the synthetic images used in training. In this evaluation, HolodecML had comparable position and size estimations performance to the standard processing method, but it improved particle detection by nearly 20 % on several thousand manually labeled HOLODEC images. However, the particle detection improvement only occurred when image corruption was performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. The trained model also learned to differentiate artifacts and other impurities in the HOLODEC images from the particles, even though no such objects were present in the training data set. By contrast, the standard processing method struggled to separate particles from artifacts. HolodecML also leverages GPUs and parallel computing that enables large processing speed gains over serial and CPU-only based evaluation. Our results demonstrate that the machine-learning based framework may be a possible path to both improving and accelerating hologram processing. The novelty of the training approach, which leveraged noise as a means for parameterizing non-ideal aspects of the HOLODEC detector, could be applied in other domains where the theoretical model is incapable of fully describing the real-world operation of the instrument and accurate truth data required for supervised learning cannot be obtained from real-world observations. [ABSTRACT FROM AUTHOR]

• We developed a ML model to recommend first FSH dosage for all types of patients. • The model performance surpassed the clinicians' in both development and validation. • The model can serve as quality check, second opinion or learning tool for trainees. Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model? Observational study (2011–2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011–2019) and 774 in the validation phase (2020–2021). Predictor variables included age, BMI, AMH, AFC and previous live births. Performance was measured with a proposed score based on the number of MII oocytes retrieved and dose received, recommended, or both. The included cycles were from women aged 37.7 ± 4.4 years (18–45 years), with a BMI of 23.5 ± 4.2 kg/m2, AMH of 2.4 ± 2.3 ng/ml, AFC of 11.3 ± 7.6, and an average number of MII obtained 6.9 ± 5.4. The model reached a mean performance score of 0.87 (95% CI 0.86 to 0.88) in the development phase, significantly better than for doses prescribed by clinicians for the same patients (0.83, 95% CI 0.82 to 0.84; P = 2.44 e-10). Mean performance score of the model recommendations was 0.89 (95% CI 0.88 to 0.90) in the validation phase, also significantly better than clinicians (0.84, 95% CI 0.82 to 0.86; P = 3.81 e-05). The model was shown to surpass the performance of standard practice. This machine learning model could be used as a training and learning tool for new clinicians, and as quality control for experienced clinicians. [ABSTRACT FROM AUTHOR]

Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay between the negative pairs is ignored as they do not put in place special mechanisms to treat negative pairs differently according to their specific differences and similarities. In this paper, we present Extended Momentum Contrast (XMoCo), a self-supervised representation learning method founded upon the legacy of the momentum-encoder unit proposed in the MoCo family configurations. To this end, we introduce a cross consistency regularization loss, with which we extend the transformation consistency to dissimilar images (negative pairs). Under the cross consistency regularization rule, we argue that semantic representations associated with any pair of images (positive or negative) should preserve their cross-similarity under pretext transformations. Moreover, we further regularize the training loss by enforcing a uniform distribution of similarity over the negative pairs across a batch. The proposed regularization can easily be added to existing self-supervised learning algorithms in a plug-and-play fashion. Empirically, we report a competitive performance on the standard Imagenet-1K linear head classification benchmark. In addition, by transferring the learned representations to common downstream tasks, we show that using XMoCo with the prevalently utilized augmentations can lead to improvements in the performance of such tasks. We hope the findings of this paper serve as a motivation for researchers to take into consideration the important interplay among the negative examples in self-supervised learning. [ABSTRACT FROM AUTHOR]

Foreign species can deteriorate the environment and the economy of a country. To automatically monitor biosecurity threats at country borders, this paper investigates compact deep networks for accurate and real-time object segmentation for hyperspectral images. To this end, knowledge distillation (KD) approaches compress the model by distilling the knowledge of a large teacher network to a compact student network. However, when the student is over-compressed, the performance of standard KD methods degrades significantly due to the large capacity gap between the teacher and the student. This gap can be addressed by adding medium-sized teacher assistants, but training them incurs significant computation and hence is impractical. To address this problem, this paper proposes a new framework called Knowledge Distillation from Multi-head Teacher (KDM), which distills the knowledge of a multi-head teacher to the student. By encapsulating multiple teachers in a single network, our proposed KDM assists the learning of a very compact student and significantly reduces the training time. We also introduce Bio-HSI, a new large benchmark hyperspectral image dataset of 3,125 high-resolution images with dense segmentation ground truth. This new, large dataset can be expected to advance research on deep models for hyperspectral image segmentation. Evaluated on this dataset, the student trained via our KDM has 762 times fewer parameters than the state-of-the-art segmentation model (i.e., HRNet), while achieving competitive accuracy. [ABSTRACT FROM AUTHOR]

For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD. • A method to detect multiple underlying subspaces in the brain associated with a trait. • Multimodal active subspaces fuse structural and functional features of the brain. • Active subspaces retain predictive information for brain disorders. • Experiments with Alzheimer's related scores show successful subspace detection. [ABSTRACT FROM AUTHOR]

HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets and ice crystals. Cloud particle properties include position, diameter, and shape. In this work we evaluate the potential for processing HOLODEC data by leveraging a combination of GPU hardware and machine learning with the eventual goal of improving HOLODEC processing speed and performance. We present a hologram processing algorithm, HolodecML, that utilizes a neural network segmentation model and computational parallelization to achieve these goals. HolodecML is trained using synthetically generated holograms based on a model of the instrument, and predicts masks around particles found within reconstructed images. From these masks, the position and size of the detected particles can be characterized in three dimensions. In order to successfully process real holograms, we find we must apply a series of image corrupting transformations and noise to the synthetic images used in training. In this evaluation, HolodecML had comparable position and size estimations performance to the standard processing method, but improved particle detection by nearly 20% on several thousand manually labeled HOLODEC images. However, the particle detection improvement only occurred when image corruption was performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. The trained model also learned to differentiate artifacts and other impurities in the HOLODEC images from the particles, even though no such objects were present in the training data set. By contrast, the standard processing method struggled to separate particles from artifacts. HolodecML also leverages GPUs and parallel computing that enables large processing speed gains over serial and CPU-only based evaluation. Our results demonstrate that the machine-learning based framework may be a possible path to both improving and accelerating hologram processing. The novelty of the training approach, which leveraged noise as a means for parameterizing non-ideal aspects of the HOLODEC detector, could be applied in other domains where the theoretical model is incapable of fully describing the real-world operation of the instrument and accurate truth data required for supervised learning cannot be obtained from real-world observations. [ABSTRACT FROM AUTHOR]

Foreign-material content determination in uncleaned peanuts based on dielectric properties and bulk density measurements by microwave techniques is presented in this paper. A microwave free-space transmission technique was used at 10 GHz. Two measurement systems for measuring the dielectric properties of cleaned unshelled peanuts (nine-peanut pods) and uncleaned unshelled peanuts placed in polycarbonate sample holder (12.1 cm × 21 cm × 20.5 cm) were developed and integrated in one single measuring unit. The nine-peanut-pods system provided the cleaned unshelled peanuts moisture content which was used in the algorithms for foreign material content determination. The dielectric properties and bulk density measurements of the uncleaned unshelled peanut sample were related to the foreign-material content. These parameters, namely bulk density and dielectric properties of uncleaned peanuts and cleaned unshelled moisture content were supplied to machine learning algorithms, linear regression technique and artificial neural network algorithms. Results obtained with the artificial neural network algorithm showed the best estimate of foreign material content with a standard error of performance of 1.36% compared to that obtained with the linear regression algorithm with a standard of performance of 2.39%. [ABSTRACT FROM AUTHOR]

We propose a novel spatial bagging workflow for predictive ensemble machine learning that improves on standard bagging models. Our proposed method integrates spatial bootstrap for bagging with the number of effective sample size, n e f f , for integration of the spatial context of the dataset. We benchmark the improved performance over standard machine learning bagging models with a large number of two-dimensional synthetic datasets with varying degrees of Gaussian noise. For noise free datasets, both methods demonstrate equivalent accuracy; however, spatial bagging achieves this with a significantly smaller sample size, showcasing its improved efficiency. As data noise increases, spatial bagging consistently outperforms standard bagging, displaying an improved Mean Squared Error (MSE) and robustness against overfitting. Our proposed spatial bagging method computes the optimal effective sample size for spatial data, reducing model overfitting. Furthermore, our proposed method requires only the additional step of variogram calculation and modeling, and can be implemented with any predictive machine learning bagging model with minimal code modification. i.e., specification of the number of bootstrap samples as the number of effective data. We recommend using spatial bagging for improved predictions for any spatial data setting across diverse scientific fields, e.g., atmospheric, agricultural, subsurface resources etc. • Ensemble learning methods employ standard bagging, which is prone to overfitting. • We introduce a novel spatial bagging technique utilizing effective sample size. • Spatial bagging mitigates overfitting, enhancing robustness against noise. • Spatial bagging provides a computational advantage by using smaller data. [ABSTRACT FROM AUTHOR]

Due to the enormous amount of user-generated content being generated on the web, labeling such data is a time-consuming and expensive endeavor. As a result, we have limited annotated data and the vast majority of data are unlabeled. Analysis reveals that extracting (external) knowledge from unlabeled data and integrating it with knowledge extracted from labeled data is a beneficial task for text information processing, in particular text classification. In this paper, we present a hybrid approach for classifying sentiments that employs external knowledge, which is categorized as either general-purpose sentiment knowledge or domain-related knowledge. General-purpose sentiment knowledge is extracted from sentiment lexicons, whereas domain-related knowledge is extracted from unlabeled data from the same or related domains. Similar domains for a given domain are identified based on their similarity score in terms of overlapping features. The proposed approach utilizes both forms of external knowledge and combines them with logistic regression to train an improved classification model. The classification model uses the conventional gradient descent algorithm for optimization, and its convergence analysis indicates that it is convex and converges to the global optimum. The proposed approach is empirically evaluated and compared to three baselines and one state-of-the-art method using standard performance evaluation metrics on a multi-domain sentiment dataset. The experiment results are encouraging, demonstrating that the proposed approach considerably outperforms the baseline approaches and outperforms the state-of-the-art approach by up to 2% in terms of both f-score and accuracy. • A hybrid approach for document-level sentiment classification that makes use of external knowledge. • A domain-specific knowledge extraction approach from unlabeled text documents. • A detailed convergence analysis of the proposed optimization model used in the proposed approach. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

Multi-dimensional classification (MDC) task can be considered the most inclusive description of all classifications tasks, as it joins multiple class spaces and their multiple class members into a single compound classification problem. The challenges in MDC arise from the possible class dependencies across different class spaces, as well as the imbalance of labels in training datasets due to lack of all possible combinations. In this paper, we propose a straightforward, yet efficient, MDC deep learning classifier, named Deep Self-Organizing Cube (DSOC) that can model dependencies among classes in multiple class spaces, while consolidating its ability to classify rare combinations of labels. DSOC is formed of two n -dimensional components, namely the Hypercube Classifier and the multiple DSOC Neural Networks connected to the hypercube. The multiple neural networks component is responsible for feature selection and segregation of classes, while the Hypercube classifier is responsible for creating the semantics among multiple class spaces and accommodate the model for rare sample classification. DSOC is a multiple-output learning algorithm that successfully classify samples across all class spaces simultaneously. To challenge the proposed DSOC model, we conducted an assessment on seventeen benchmark datasets in the four types of classification tasks, binary, multi-class, multi-label and multi-dimensional. The obtained results were compared to four standard classifiers and eight competitive state-of-the-art approaches reported in literature. The DSOC has achieved superior performance over standard classifiers as well as the state-of-the-art approaches in all the four classification tasks. Moreover, in terms of Exact Match accuracy metrics, DSOC has outperformed all state-of-the-art approaches in 77.8% of the cases, which reflects the superior ability of DSOC to model dependencies and successfully classify rare samples across all dimensions simultaneously. [ABSTRACT FROM AUTHOR]

The absorption and scattering properties of the water medium cause various types of distortion in underwater images, which seriously affects the accuracy and effectiveness of subsequent processing. The application of supervised learning algorithms in underwater image enhancement is limited by the difficulty of obtaining a large number of underwater paired images in practical applications. As a solution, we propose an unsupervised representation disentanglement based underwater image enhancement method (URD-UIE). URD-UIE disentangles content information (e.g., texture, semantics) and style information (e.g., chromatic aberration, blur, noise, and clarity) from underwater images and then employs the disentangled information to generate the target distortion-free image. Our proposed method URD-UIE adopts an unsupervised cycle-consistent adversarial translation architecture and combines multiple loss functions to impose specific constraints on the output results of each module to ensure the structural consistency of underwater images before and after enhancement. The experimental results demonstrate that the URD-UIE technique effectively enhances the quality of underwater images when training with unpaired data, resulting in a significant improvement in the performance of the standard model for underwater object detection and semantic segmentation. • URD-UIE accomplishes underwater image decomposition: domain-invariant content & domain-specific style. • We use disentangled cycle translation for structural consistency & diverse styles in recovered images. • Our method employs diverse loss functions ensuring realistic enhanced images via disentangled features. • URD-UIE trains unsupervised, providing valuable improvements for subsequent underwater tasks. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Tensile behaviour of coir-based composites was analysed and modelled. • Coir fibers enhanced the properties of composites at higher aspect ratio. • Neural network regression model predicted the tensile properties with high accuracy. • Predicted and experimental tensile properties were in good agreement. • The developed framework provides insights into designing eco-friendly composites. Coconut husk-derived coir fiber composites, an eco-friendly alternative to conventional polymer composites, are being considered as a viable substitute for non-structural applications. The current research endeavor aims to develop a framework for analyzing and predicting the tensile properties of short coir-based composites (SCBCs) using neural networks. The effect of parameters such as fiber treatment, fiber content, fiber aspect ratio, manufacturing process, and matrix type on the tensile properties of SCBCs has been considered. The standard performance metrics assessed the feasibility of the developed model. The coefficient of determination (R2) of 0.9661 and 0.9816 was obtained for the tensile strength and tensile modulus prediction model, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used. [ABSTRACT FROM AUTHOR]

There have been research attempts to automate the process of lithology identification from the well-logs using machine learning algorithms. However, a single algorithm cannot be applied on a universal scale due to the nonstationary nature of geological data sets. The notion of changes in the well-log signal must be considered to resolve the issue. This paper presents a multistage method using multivariate change detection and statistical machine learning to (a) identify the significant changes in the well-logs, (b) map multivariate change in log signal with the lithofacies classes, (c) enhance the data set by removing the highly populated class, (d) using statistical machine learning tools, i.e., Support Vector Machine (SVM) to classify target classes. We apply the algorithm to the data collected from a mostly shale-contained subsurface environment in the Krishna-Godavari (KG) basin, India. The primary objective of the research is to locate thin layers of hydrocarbon-contained sand in the subsurface while dealing with challenges such as intra-well and inter-well heterogeneity and a severe class imbalance. The algorithm satisfactorily performs in lithofacies classification with significantly better performance than the standard machine learning algorithm. • Lithofacies identification using a multi-stage change detection approach • Lithofacies identification in a non-stationary subsurface • Lithofacies classification in an unbalanced condition [ABSTRACT FROM AUTHOR]

• Face expression analysis suffers from insufficiently annotated data. • We introduce the timid semi-supervised strategy based on diversity for face expression analysis. • The solution produces superior results in both expression recognition and action unit estimation. • Simple solutions based on diversity perform better when biased unlabelled data is available. [Display omitted] In the last years, semi–supervised learning has been proposed as a strategy with high potential for improving machine learning capabilities. Face expression recognition may highly benefit from such a technique, as accurate labeling is both difficult and costly, whereas millions of unlabeled images with human faces are available on the Internet, but without annotations. In this paper we evaluate the benefits of semi–supervised learning in the practical scenarios of face expression analysis. Our conclusion is that better performance is indeed achievable, but by methods that put a distinct emphasis on the diversity of exploring patterns in the unlabeled data domain. The evaluation is carried on multiple tasks such as detecting Action Units on EmotioNet, assessing Action Units intensity on the spontaneous DISFA database and, respectively, recognizing expressions on static images acquired in the wild, from the RAF-DB and FER+ databases. We show that, in these scenarios, a so–called timid semi–supervised learner is more robust and achieves higher performance than standard, confident semi–supervised learners. [ABSTRACT FROM AUTHOR]

Obesity, termed as excessive body fat, is a major public health problem. Being able to accurately predict body fat percentage provides an opportunity for making appropriate prevention and diagnosis. Although various assessment techniques have been applied for body fat estimation, high-cost tests are required using special equipment, and the performance of standard machine learning models are typically impacted by noise data. In this paper, we propose a novel fuzzy-weighted approach for improving body fat prediction using easily accessed body measurements in a cost-effective manner. Given the noise data problem, we design a novel fuzzy weighted operation by extending the minimum and maximum values for calculating the fuzzy weights to cover more possibilities. The fuzzy weights are incorporated into the error constraints of a relative error support vector machine model to improve its robustness to noise data. In addition, the Whale Optimization Algorithm (WOA) is applied in our proposed model for parameter optimization. Our proposed fuzzy-weighted approach delivers excellent results for body fat prediction by outperforming other well-known machine learning models. Further, the statistical test based on the 20 runs of experiments not only confirms that our proposed model could significantly outperform the models under comparison, but also show the fuzzy weighted operation and WOA are effective to improve the proposed approach. This offers a viable alternative for medical decision makers to make appropriate prevention, early diagnosis, and early treatment of body fat related diseases when high-cost measurement techniques are not available. • A novel fuzzy-weighted approach is proposed for body fat prediction. • The whale optimization algorithm is applied for parameter optimization. • Evaluation metrics indicate the effectiveness of our proposed approach. • Our approach can be a useful tool for medical decision makers. [ABSTRACT FROM AUTHOR]

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy. [ABSTRACT FROM AUTHOR]

Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed by state-of-the-art optimization methods. To close this performance gap, we propose a novel large neighborhood search (LNS) framework for vehicle routing that integrates learned heuristics for generating new solutions. The learning mechanism is based on a deep neural network with an attention mechanism and has been especially designed to be integrated into an LNS search setting. We evaluate our approach on the capacitated vehicle routing problem (CVRP), the split delivery vehicle routing problem (SDVRP), and the capacitated team orienteering problem (CTOP). We show that the NLNS approach is able to outperform a handcrafted LNS on the CVRP and SDVRP and match the performance of a standard LNS on the CTOP. NLNS is thus able to quickly and effectively learn high performance heuristics to maneuver through the search space of difficult routing problems, coming close to the performance of state-of-the-art optimization approaches. [ABSTRACT FROM AUTHOR]

For an efficient detection of intrusion in the network, the system requires sensitive information. This information contains various features depending upon the network. The selection of useful features and relevant information from the dataset has always been difficult for the user. These features bound the performance of the system and also restrict them in making accurate decisions. This irrelevant feature plays a major role in making confusion between the normal data pattern against the attack data pattern. This article proposes a novel Maximum correlation-based mutual information technique for efficient feature selection (MCMIFS) in a data network. The proposed method is utilized with Kernel Extreme Learning Machine (KELM) based multiclass classifier for effective intrusion detection. The performance of the proposed scheme is evaluated using standard intrusion detection datasets with feature ranging from 42 to 155. The hybrid of MCMIFS with KELM reveals the effectiveness of the proposed scheme by improving the accuracy of detection, i.e., 99.97%, decreasing the FPR, i.e., 0.19, by decreasing the computational complexity. The proposed Intrusion Detection Scheme (IDS) results are analyzed and compared with the other existing techniques. • An optimized MCMIFS based feature selection scheme is proposed. • The proposed MCMIFS is utilized with Kernel ELM for multiclass intrusion detection. • The proposed IDS is tested using standard intrusion detection datasets. • Standard performance metrics i.e. Accuracy, Detection Rate and FPR are utilized. • Proposed technique outperforms the existing ones for all used cases. [ABSTRACT FROM AUTHOR]

Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develop efficient techniques to approximate, or draw samples from posterior distributions. Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for this issue. However, the vanilla SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable or too expensive to evaluate. In this paper we explore one way to address this challenge by the construction of a local surrogate for the target distribution in which the gradient can be obtained in a much more computationally feasible manner. More specifically, we approximate the forward model using a deep neural network (DNN) which is trained on a carefully chosen training set, which also determines the quality of the surrogate. To this end, we propose a general adaptation procedure to refine the local approximation online without destroying the convergence of the resulting SVGD. This significantly reduces the computational cost of SVGD and leads to a suite of algorithms that are straightforward to implement. The new algorithm is illustrated on a set of challenging Bayesian inverse problems, and numerical experiments demonstrate a clear improvement in performance and applicability of standard SVGD. [ABSTRACT FROM AUTHOR]

[Display omitted] • Strain-sensing ability of a nanoengineered self-sensing cementitious composite is assessed by integrating multiscale finite element analysis simulations and machine learning. • Hyperparameter-optimized feed-forward multilayer perceptron-based neural network showed excellent efficacy for the prediction of strain-sensing ability. • Shapley Additive Explanations algorithm reveals that coating thickness shows higher influence on the sensing ability than fraction of coated sand. • Comprehensive approach presented here can be used as starting point toward development of reliable performance standards for self-sensing cementitious composites. Prediction of in-situ strain sensing efficiency of self-sensing cementitious composites using machine learning (ML) requires a large, representative, consistent, and accurate dataset. However, such large experimental dataset is not readily available. Moreover, the success of the ML approach depends on its ability to abide by the fundamental laws of physics. To address these challenges this paper synergistically integrates a validated finite element analysis (FEA)-based multiscale simulation framework with ML to predict the strain-sensing ability of self-sensing cementitious composites enabled by incorporating nano-engineered interfaces. The multiscale simulation framework is leveraged to develop a balanced, representative, complete, and consistent dataset containing 3000 combinations of strain-dependent electromechanical responses. This large dataset is used to predict the strain-sensing ability of the nanoengineered cementitious composites using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. This paper also applies a Shapley Additive Explanations (SHAP) algorithm to interpret the NN predictions in light of the relative importance of different design parameters on the strain-sensing ability of the composite. Overall, the synergistic and comprehensive approach presented here can be used as a starting point toward the development of reliable performance standards to accelerate the acceptance of these self-sensing cementitious composites for large-scale applications. [ABSTRACT FROM AUTHOR]