Machine Learning Fusion Research Articles

Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition

The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.

Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection

Explainable artificial intelligence received great research attention in the past few years during the widespread of Black-Box techniques in sensitive fields such as medical care, self-driving cars, etc. Artificial intelligence needs explainable methods to discover model biases. Explainable artificial intelligence will lead to obtaining fairness and Transparency in the model. Making artificial intelligence models explainable and interpretable is challenging when implementing black-box models. Because of the inherent limitations of collecting data in its raw form, data fusion has become a popular method for dealing with such data and acquiring more trustworthy, helpful, and precise insights. Compared to other, more traditional-based data fusion methods, machine learning's capacity to automatically learn from experience with nonexplicit programming significantly improves fusion's computational and predictive power. This paper comprehensively studies the most explainable artificial intelligent methods based on anomaly detection. We proposed the required criteria of the transparency model to measure the data fusion analytics techniques. Also, define the different used evaluation metrics in explainable artificial intelligence. We provide some applications for explainable artificial intelligence. We provide a case study of anomaly detection with the fusion of machine learning. Finally, we discuss the key challenges and future directions in explainable artificial intelligence.

Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion.

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.

An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers’ behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business intelligence systems has produced potentially better results for business workers. This study proposes a fusion model of machine learning and business intelligence to facilitate the effective pricing mechanism of products. Three machine learning algorithms namely (1) Multiclass Random Forest, (2) Multiclass Logistic Regression and (3) Multiclass one-vs-all have been applied for product price prediction. This intelligent business model helps business workers to set product pricing and discounts based on customer’s buying behavior (i.e., customer’s average amount spent in buying items). The model also predicts the demand for the product in the market, which may help in making effective decisions about the product to gain a better productivity. The performance of the model has been evaluated using hold-out cross validation. Multiclass one-vs-all out performed the other two algorithms in terms of average log-loss and it produced better accuracy in prediction for both hold-out as well as cross validation. The outcomes of the proposed model may help businesses in exploring business data and to gain higher share in the market.

Fusion of Machine learning for Detection of Rumor and False Information in Social Network

In recent years, spreading social media platforms and mobile devices led to more social data, advertisements, political opinions, and celebrity news proliferating fake news. Fake news can cause harm to networks, communications, and users and cause trust issues toward government, healthcare, or social media platforms. This inspired many researchers to implement models to detect falsified information content. But there are still many issues that need to be discussed and explored. In our paper, we introduce categories of fake news detection methods and compare these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of social media. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in social networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks to aid the readers and researchers in explaining the motive and role of the different machine learning fusion paradigms to offer them a comprehensive realization of unexplored issues related to false information and other scenarios of social networks.

Deep Neural Network-based Fusion and Natural Language Processing in Additive Manufacturing for Customer Satisfaction

Modern Machine learning fusion approaches tend to extract features depending on two techniques (hand-crafted feature and representation learning). Hand-crafted features can waste time and are not sufficient for downstream tasks. Unlike representation learning, we automatically learn features with minimum time and effort and are suitable for downstream tasks. In our paper, we provide work on graph neural network methods with details on classical graph embedding approaches and the different methods in neural graph networks such as graph filtering, graph pooling, and the learning parameter for graph following each technique with a general framework or mathematical proof for customer satisfaction. To satisfy customer's feel, this research employs NLP techniques. We describe the adversarial attacks and defenses on graph representation approaches. Also, advanced application of neural graph networks is reviewed, such as combinational optimization, learning program representation, physical system modeling, and natural language processing. Finally, the challenges in geometric neural networks and future research work have been introduced.

Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

In this age, where cryptocurrencies are slowly creeping into the banking services and making a name for them, it is becoming crucially essential to figure out the security concerns when users make transactions. This paper investigates the untrusted users of cryptocurrency transaction services, which are connected using smartphones and computers. However, as technology is increasing, transaction frauds are growing, and there is a need to detect vulnerabilities in systems. A methodology is proposed to identify suspicious users based on their reputation score by collaborating centrality measures and machine learning techniques. The results are validated on two cryptocurrencies network datasets, Bitcoin-OTC, and Bitcoin-Alpha, which contain information of the system formed by the users and the user's trust score. Results found that the proposed approach provides improved and accurate results. Hence, the fusion of machine learning with centrality measures provides a highly robust system and can be adapted to prevent smart devices' financial services.

Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality

Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed leading to microangiopathic complications. To address this major shortcoming, we designed a contactless, non-invasive diagnostic point-of-care-device (POCD) consisting of a digital camera and a screen. Our solution relies on (1) recording videos of subject’s face (2) applying Eulerian video magnification to videos to reveal important subtle color changes in subject’s skin that fall outside human visual limits (3) obtaining spatio-temporal tensor expression profile of these variations (4) studying empirical spectral density (ESD) function of the largest eigenvalues of the tensors using random matrix theory (5) quantifying ESD functions by modeling the tails and decay rates using power law in systems exhibiting self-organized-criticality and (6) designing an optimal ensemble of learners to classify subjects into those with diabetic neuropathy and those of a control group. By analyzing a short video, we obtained a sensitivity of 100% in detecting subjects diagnosed with diabetic neuropathy. Our POCD paves the way towards the development of an inexpensive home-based solution for early detection of diabetic neuropathy and its associated complications.

Characterization of complex fluvio\u2013deltaic deposits in Northeast China using multi-modal machine learning fusion

Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from post-stack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio–deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.

Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe

An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe.

Machine Learning and OOP Integration

This abstract delves into the synergistic integration of Machine Learning (ML) and Object-Oriented Programming (OOP), exploring the symbiotic dating among these powerful paradigms. The fusion of ML and OOP brings forth a transformative technique, wherein the structured design standards of OOP seamlessly interface with the adaptive getting to know skills of ML algorithms, offering a complete framework for growing smart and dynamic applications. The integration of ML with OOP affords an established and modular foundation for designing smart systems. This summary examines how OOP's concepts of encapsulation, inheritance, and polymorphism facilitate the corporation of ML algorithms into reusable and scalable additives. The cohesive integration allows developers to encapsulate gadget mastering models inside items, fostering a modular layout that enhances code readability, reusability, and maintainability. Furthermore, the summary explores the function of OOP in addressing the interpretability and transparency challenges associated with ML models. By encapsulating complicated ML algorithms inside nicely-defined gadgets, developers can decorate the comprehensibility of the gadget, facilitating less complicated version inspection and debugging. OOP's hierarchical structure aids in developing interpretable relationships among exceptional machine studying additives, contributing to a more obvious and comprehensible machine. The adaptability of ML and the shape supplied by using OOP converge to create structures able to dynamic studying and evolution. This summary emphasizes how OOP's layout styles accommodate the incorporation of evolving ML fashions, allowing seamless updates and modifications. The integration gives a basis for adaptive structures which can examine from new statistics, adjust to changing environments, and constantly improve overall performance over time. In end, the abstract highlights the transformative effect of integrating ML and OOP, emphasizing the dependent design standards of OOP as a sturdy basis for organizing and encapsulating ML algorithms. The cohesive integration addresses demanding situations related to interpretability and flexibility, providing a framework that fosters sensible, obvious, and dynamic packages. As generation advances, the combination of ML and OOP provides a promising paradigm for growing state-of-the-art and adaptive systems throughout diverse domain names.

White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis

Deep learning as a data modeling tool is hard to be understood of how its predicted result came about from its inner working. It is generally known as a black box and is not interpretable. Often in medical applications, physicians need to understand why a model predicts a result. On the other hand, BN is a probabilistic graph with nodes representing the variables, and the arcs present the conditional dependences between the variables. In this article, a white learning framework is proposed, which advocates three levels of fusing the black-box deep learning and white box BN, that offers both predictive power and interpretability. A case of breast cancer classification is conducted in an experiment. From the results, it is observed that white learning, which combines black-box and white-box machine learning, has an edge in performance over individually BN alone or deep learning alone. The white learning framework has the benefits of interpretability and high predictive power, making it suitable for critical decision-making task where a reliable prediction is as important as knowing how the outcome is predicted. The predicted output, which is generated from white learning, can be traced back via the conditional probability at each node. It is, hence, anticipated that in the future, especially for medical domain, white learning, which has the benefits of both black -box and white-box learning, would be highly valued and raised in popularity.

Fusing multiple frequency-decomposed seismic attributes with machine learning for thickness prediction and sedimentary facies interpretation in fluvial reservoirs

Abstract Defining the boundaries, thicknesses and sedimentary facies of fluvial reservoirs (sand bodies) is critical for predicting hydrocarbon volumes, designing schemes for petroleum exploration and development and improving oil recovery. Most reservoirs contain thick and thin sand bodies at the same intervals, while the amplitude values of seismic data usually highlight sand bodies near the 1/4 wavelength for the tuning phenomena. Hence, the application of spectral decomposition to seismic attributes and the combination of multiple frequency-decomposed (spectral-decomposed) seismic attributes have gained increasing attention for the readjustment of tuning thickness to predict sand bodies of various thicknesses. However, the popular method of red-green-blue blending is a simple linear combination of three frequency-decomposed seismic attributes that qualitatively analyzes the sand thickness without well-log interpretation. This research proposes machine learning fusion as a new nonlinear method for fusing high-, middle-, and low-frequency seismic attributes. This method uses machine learning to link well-log interpretation and multiple-frequency seismic attributes for the quantitative prediction of sand thickness, which is important for development work in a mature field. Test results of the conceptual model and the real case indicate that the predicted sand thickness after fusing multiple frequency-decomposed seismic attributes is approximately in line with the actual thickness (correlations between 80 and 90%). Combined with the coherence attribute and the red-green-blue blending results, the distributions and histories of sedimentary facies are analyzed based on the predicted sand thickness and well data. The results suggest that the proposed method can effectively readjust the tuning thickness and improve the resolution of seismic interpretation. This method is a potentially effective technique to characterize the sand thickness and sedimentary facies in other fields using a similar geological setting and dataset.

Analysis of eHealth knowledge discovery systems in the TASS 2018 Workshop

This paper presents an analysis of Task 3 eHealth-KD challenge in the TASS 2018Workshop. The challenge consisted of the extraction of concepts, actions, and their corresponding semantic relations from health-related documents written in the Spanish language. The documents were manually annotated with a schema based on triples (Subject, Action, Target) and an additional set of semantic relations. Several research teams presented computational systems, obtaining relevant results in different subtasks. In this paper, the approaches performed by each team are analyzed and the most promising lines for future development are highlighted and discussed. Moreover, an in-depth analysis of the results is presented focusing on the main characteristics of each subtask. The overall eHealth-KD analysis has indicated that the Knowledge Discovery (KD) task, speciffically focused on concrete domains and languages, represents a rich area for further research. In addition, this study considers that the fusion of machine learning {especially deep learning techniques{ and knowledge-based approaches will benefit the KD task. Keywords: Machine learning, natural language processing, knowledge bases, knowledge discovery, eHealth

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

We introduce a new distributionally robust optimization model to address a two-period, multi-item joint pricing and production problem, which can be implemented in a data-driven setting using historical demand and side information pertinent to the prediction of demands. Starting from an additive demand model we introduce a new partitioned-moment-based ambiguity set to characterize its residuals. Unlike the standard moment-based ambiguity set, we can adjust the level of robustness by varying the number of information clusters from being the most robust as the standard moment-based ambiguity set with one cluster to being the least robust as the empirical distribution. The partitioned-moment-based ambiguity set also addresses the key challenges in the stochastic dynamic optimization problem to determine how the second-period demand would evolve from the first-period information in a data-driven setting, without the need to impose additional assumptions on the distribution of demands such as independence. In addition, it also inspires a practicable non-anticipative policy that is adapted to the cluster. In particular, we investigate the joint pricing and production problem by proposing a cluster-adapted markdown policy and an affine recourse approximation, which allow us to reformulate the problem as a mixed-integer linear optimization problem that we can solve to optimality using commercial solvers. Both the numerical experiments and case study demonstrate that, with only a few number of clusters, the cluster-adapted markdown policy and the partitioned-moment-based ambiguity set can improve mean profit over the empirical model---when applied to most out-of-sample tests.

Machine Learning-Based Antenna Selection in Wireless Communications

This letter is the first attempt to conflate a machine learning technique with wireless communications. Through interpreting the antenna selection (AS) in wireless communications (i.e., an optimization-driven decision) to multiclass-classification learning (i.e., data-driven prediction), and through comparing the learning-based AS using k -nearest neighbors and support vector machine algorithms with conventional optimization-driven AS methods in terms of communications performance, computational complexity, and feedback overhead, we provide insight into the potential of fusion of machine learning and wireless communications.

Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach

The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result.

Recent advances on machine learning and Cybernetics

During the past decades, the developments of machine learning and Cybernetics realized in the framework of soft computing techniques such as rough sets, neural networks, evolutionary algorithms, fuzzy logic, support vector machines and Granular Computing have enjoyed a considerable visibility. We have been witnessing a wealth of interesting pursuits when it comes to the fundamentals of machine learning and Cybernetics as well as their numerous applications. This special issue of the Soft Computing Journal reports on some recent advances in the theory and applications of soft-computing-based machine learning and Cybernetics as well as their synergistic undertakings. The papers included in this special issue have been carefully selected from the submissions to the 7th International Conference on Machine Learning and Cybernetics (ICMLC2008) held in Kunming, China, on July 2008. In total, the ICMLC2008 received 1,542 submissions and after a thorough review 750 papers were accepted for presentation. A number of best papers on soft-computingbased machine learning and cybernetics were afterwards selected and the authors were advised to submit to this special issue. All the papers have been thoroughly refereed by experts who evaluated them based on the criteria of originality, significance, quality, and clarity. As a result, there are 16 papers accepted for publication in this special issue. As stressed by the title of this special issue, the original papers are focused on the research issues of the fusion of machine learning and cybernetics being cast in the framework of soft computing. Several contributions elaborate on new machine learning theories, technologies, and algorithms including those coming from supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and kernel-based learning. With reference to soft computing, the papers concentrate on exploring the theoretical foundations and applied approaches to uncertainty processing pertaining to learning and data mining. These studies may help establish important linkages between the field of machine learning and other research domains. Several contributions elaborate on realworld applications of machine learning and cybernetics to medical image processing and business intelligence. The Guest Editors issue would like to take this opportunity and express their sincere thanks to the reviewers for their detailed comments as well as constructive suggestions on how to improve the quality of the manuscripts. We would like to thank the Editor-in-Chief of SOFT COMPUTING for the encouragement, support and guidance during the realization of this special issue.