Existing Machine Learning Techniques Research Articles

Generation of pore-space images using improved pyramid Wasserstein generative adversarial networks

High-resolution three-dimensional X-ray microscopy can be used to image the pore space of materials. Machine learning algorithms can generate a statistical ensemble of representative images of arbitrary sizes for rock characterization, modeling, and analysis. However, current methods struggle to capture features at different spatial scales observed in many complex rocks which have a wide range of pore size. We use the Improved Pyramid Wasserstein Generative Adversarial Network (IPWGAN) to automatically reproduce multi-scale features in segmented three-dimensional images of porous materials, enabling the reliable generation of large-scale representations of complex porous media. A Laplacian pyramid generator is introduced, which creates pore-space features across a hierarchy of spatial scales. Feature statistics mixing regularization enhances the discriminator’s ability to distinguish between real and generated images by mixing their feature statistics, thereby indirectly enhancing the generator’s ability to capture and reproduce multi-scale pore-space features, leading to increased diversity and realism in the generated images. The method has been tested on five sandstone and carbonate samples. The generated images, which can be of any size – including cm-scale ten-billion-cell images – demonstrate the power of the approach. These images have two-point correlation functions, porosity, permeability, Euler characteristic, curvature, and specific surface area closer to those of the training datasets than existing machine learning techniques. The generated images accurately capture geometric and flow properties, demonstrating a considerable improvement over previously published studies using generative adversarial networks. For instance, the mean relative error in the calculated absolute permeability between the Berea sandstone images generated by IPWGAN and the corresponding real rock images can be reduced by 79%. The work allows representative models of a wide range of porous media to be generated, offering potential benefits in carbon dioxide sequestration, underground hydrogen storage, and enhanced oil recovery.

Detection of sarcasm in tweets using hybrid machine learning method

<p>When one wants to express the contrary of what they mean, especially when insulting someone, sarcasm is used. One of the challenges associated with the control and management of content on social networking sites such as Twitter and other social media sites, is the recognition of sarcasm. Because sarcasm is purposefully conveyed through ambiguous word choice, even for humans it can be challenging to identify. Existing methods for automatically detecting sarcasm rely heavily on lexical as well as linguistic clues the majority of the time. On the other hand, these methods have not yielded a large or even a minor enhancement in terms of the correctness of the mood. The purpose of this study is to increase the accuracy of sentiment analysis by proposing a system that is both reliable and effective at identifying sarcasm. In this investigation, three different types of features are focussed: lexical, sarcastic, and context features. These feature sets are utilised in the process of categorising tweets as either sarcastic or not sarcastic. This research presents a sarcastic feature set coupled with an efficient hybrid machine learning approach, which ultimately results in improved accuracy. The results of the experiments reveal that the recommended hybrid machine learning method achieves 97.3% accuracy for sarcastic feature sets which is better when compared to existing machine learning techniques namely k-nearest neighbor, random forest, support vector machine and decision tree for the selection of the appropriate features.<strong></strong></p>

Histopathology image classification: highlighting the gap between manual analysis and AI automation.

The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

Predicting Gross Primary Productivity of the Forest Ecosystems using Machine Learning Techniques: A Review of Existing Approaches

Photosynthesis is a biotic process in which the plants assimilate the atmospheric CO2 into the sugar molecules in the presence of solar energy. The carbon uptake by plants in this process is defined as gross primary productivity (GPP). A part of this assimilated carbon is used by the plants to support their physiological activities which are defined as the respiration. The sequestration of carbon by the terrestrial ecosystems holds significance as a vital element of Earth’s carbon cycle and constitutes a major sink for the climate change mitigation. The crop yield of any agricultural field is directly linked with its GPP which is important in the aspect of food security and economy. Hence, quantifying the GPP of terrestrial ecosystems is an active branch of study and several methods have been used to address this. In recent times, the machine learning (ML) methods connecting the benefits of artificial intelligence (AI) have gained increased interest and different such methods are being used to address different scientific and technological problems. In addition to the traditional methods, several ML techniques have also been explored by several researchers for the GPP estimation. Studies have shown that ML models can produce GPP predictions with more accuracy. A comprehensive review of these methods will be helpful for the researchers due to a rapid development in this field. This paper offers a comprehensive analysis of various existing ML techniques to estimate the GPP, providing a comparative review of their effectiveness.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction.

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks. We design the ML-Joint-Att network with edge and node convolutional operators, an adaptive inception module, and three attention modules, including network-wise, region-wise, and region-wise joint attention modules. The adaptive inception learns brain functional connectivity at multiple spatial scales. The network-wise and region-wise attention modules take the multi-scale functional connectivities as input and learn features at the network and regional levels for individual tasks. Moreover, the joint attention module is designed as region-wise joint attention to learn shared brain features that contribute to and compensate for the prediction of multiple tasks. We employed the Adolescent Brain Cognitive Development (ABCD) dataset (n =9092) to evaluate the ML-Joint-Att network for the prediction of cognitive flexibility and inhibition. Our experiments demonstrated the usefulness of the three attention modules and identified brain functional connectivities and regions specific and common between cognitive flexibility and inhibition. In particular, the joint attention module can significantly improve the prediction of both cognitive functions. Moreover, leave-one-site cross-validation showed that the ML-Joint-Att network is robust to independent samples obtained from different sites of the ABCD study. Our network outperformed existing machine learning techniques, including Brain Bias Set (BBS), spatio-temporal graph convolution network (ST-GCN), and BrainNetCNN. We demonstrated the generalization of our method to other applications, such as the prediction of fluid intelligence and crystallized intelligence, which also outperformed the ST-GCN and BrainNetCNN.

Kibria–Lukman-Type Estimator for Regularization and Variable Selection with Application to Cancer Data

Following the idea presented with regard to the elastic-net and Liu-LASSO estimators, we proposed a new penalized estimator based on the Kibria–Lukman estimator with L1-norms to perform both regularization and variable selection. We defined the coordinate descent algorithm for the new estimator and compared its performance with those of some existing machine learning techniques, such as the least absolute shrinkage and selection operator (LASSO), the elastic-net, Liu-LASSO, the GO estimator and the ridge estimator, through simulation studies and real-life applications in terms of test mean squared error (TMSE), coefficient mean squared error (βMSE), false-positive (FP) coefficients and false-negative (FN) coefficients. Our results revealed that the new penalized estimator performs well for both the simulated low- and high-dimensional data in simulations. Also, the two real-life results show that the new method predicts the target variable better than the existing ones using the test RMSE metric.

Intellectual method for business location selection in smart cities

The relevance of the topic lies in the complexity of selecting a location for starting a business in smart cities, as it requires analyzing a large amount of data and considering vari-ous factors such as population, competition, infrastructure, and other parameters. The use of an intelligent method based on machine learning enables the collection, processing, and analysis of large volumes of data for accurate location assessment and providing recommen-dations to entrepreneurs. This enhances the decision-making process, ensures more informed choices, and increases the chances of business success in a smart city. The problem statement involves the need to expedite the process of selecting an optimal location for business placement in a smart city. This task is challenging and long-term, re-quiring the analysis of extensive data and consideration of various factors that impact busi-ness success, such as geographical position, competition, potential customer base, and other relevant aspects. It is also crucial to provide entrepreneurs with fast access to information and precise recommendations to make informed decisions regarding their business location. Solving this problem will facilitate efficient resource utilization and ensure business success in a smart city. The purpose of the study is to develop an intelligent method for choosing a location for starting a business in a smart city. This method aims to use large amounts of data collected from various sources to determine the most optimal locations for starting a new business. The method is based on existing machine learning techniques such as image recognition, data preprocessing, classification, and clustering of numerical data. Results and key conclusions. A method has been developed, the implementation of which will allow recommending optimal locations for business in smart cities. This will help to increase customer satisfaction, improve the quality of life and increase the profit of entre-preneurs. The intelligent method is a powerful tool for solving the problems of choosing a lo-cation for starting a business in smart cities.

Intrusion detection in big data environment using hybrid deep learning algorithm (VAE-CNN)

In the internet era, billions of devices are connected to the network generates large volume of data and the generation rate increases exponentially every day. As the data increases, the chances for cyber attackers to exploit the data increases which results into numerous security threats to organizations and network. Fast and accurate detection of attacks in big data environment is difficult due to its volume and variety and velocity. Over a decade, numerous attack detection systems are developed using machine learning. However, most of the traditional detection systems cannot recognize the attack types specifically which reduces the detection performances and network performances. Thus, the intrusion detection model presented in this research which incorporates deep variational auto-encoder and convolutional neural network to detect intrusions. Experimentations using benchmark dataset validated the proposed model better performances over existing machine learning techniques like logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, and self-scalable heuristic artificial neural network algorithms using accuracy, recall, precision, and F1-score. The proposed model outperforms with a maximum precision of 97.48%, Recall of 99.52%, F1-score of 98.49% and accuracy of 98.65% over conventional intrusion detection algorithms.

A Secure and Intelligent Software-Defined Networking Framework for Future Smart Cities to Prevent DDoS Attack

Smart cities have experienced significant growth in recent years, transforming people’s lives into a more futuristic version. The smart city initiative includes a diverse collection of specifications, encompassing a large number of users whose requirements vary significantly and heterogeneously. Each device in smart cities generates a significant amount of data, which places a load on the gateways. Smart cities face a major challenge due to the enormous amount of data they generate. Through software-defined networking (SDN), network information paths are optimized, ensuring that traffic flow is evenly distributed across all network nodes. A considerable number of IoT devices with limited resources are susceptible to various security threats, such as device hijacking, ransomware, man-in-the-middle (MiM) attacks, and distributed denial-of-service (DDoS) attacks. These threats can pose a severe challenge to network security. Additionally, DDoS attacks have disrupted web businesses, resulting in the loss of valuable data. To counter DDoS attacks in a smart city, several options exist, yet many challenges remain. This research presents a secure and intelligent system to combat DDoS attacks on smart cities. SDN security controllers and machine learning models with optimization are employed in this study to reduce the impact of common DDoS attacks on smart cities. This work utilizes an SDN based on security controllers and a detection mechanism rooted in a machine learning model with optimization to mitigate various types of prevalent DDoS attacks within smart cities. Employing binary classification, XGBoost achieved an accuracy of 99.99%, precision of 97%, recall of 99%, an F1 score of 98%, and a false-positive rate of 0.05. In multiclass classification, the average accuracy is 99.29%, precision is 97.7%, recall is 96.69%, and the F1 score is 97.51%. These results highlight the superiority of this approach over other existing machine learning techniques.

Machine Learning Techniques for Breast Cancer Prediction: A Concise Review

Breast cancer is the most trending type of cancer globally with close to two and half million cases recorded based on research by the World Health Organization in 2021 and it is also the most common cancer among women in all countries, posing a major cause for public health concern. In Nigeria and the world at large, over a hundred thousand new cases of cancer occur every year with high death among women. It has been researched that early and accurate detection of breast cancer can aid in the diagnosis of the disease for women and it may also reduce the risk of death rate among women. Literature shows that several machine learning techniques have been carried out on breast cancer diagnosis to help provide accurate technology solutions to early detection. The machine learning techniques used have different accuracy rate which varies for dissimilarity conditions. In this study, we compared different methods with many existing machine-learning techniques commonly used for breast cancer detection and diagnosis. Also, the aim of this review method will show an improvement in accuracy performances by implementing different methods and analysis in existing machine learning techniques to proficiently assist doctors in decision-making on an accurate detection and diagnosis of breast cancer and classifying tumors as benign or malignant thereby reducing the risk of death rate among women.

Toward a Reliable Evaluation of Machine Learning Schemes for Network-Based Intrusion Detection

Over the last years, several works introduced network-based intrusion detection schemes based on machine learning techniques for securing IoT devices. Despite the promising results, proposed approaches are rarely adopted in production environments. Networked environments exhibit highly unpredictable behavior, unlike other areas where machine learning has been effectively adopted. Unfortunately, the changing behavior during the time may lead to higher classification errors than those measured in the test phase. In this study, we demonstrate that the existing machine learning techniques applied for network traffic classification fail when facing the characteristics of real-world environments. The experiments analyzed more than 30 TB of data spanning 10 years of real network traffic and 9 intrusion detection datasets. Besides the analysis, we define a set of guidelines to build reliable application of machine learning for network traffic classification, which may guide future research and ensure the reliability of machine learning model deployment in production environments.

Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques

Internet-enabled (IoT) devices are typically small, low-powered devices used for sensing and computing that enable remote monitoring and control of various environments through the Internet. Despite their usefulness in achieving a more connected cyber-physical world, these devices are vulnerable to ransomware attacks due to their limited resources and connectivity. To combat these threats, machine learning (ML) can be leveraged to identify and prevent ransomware attacks on IoT devices before they can cause significant damage. In this research paper, we explore the use of ML techniques to enhance ransomware defense in IoT devices running on the PureOS operating system. We have developed a ransomware detection framework using machine learning, which combines the XGBoost and ElasticNet algorithms in a hybrid approach. The design and implementation of our framework are based on the evaluation of various existing machine learning techniques. Our approach was tested using a dataset of real-world ransomware attacks on IoT devices and achieved high accuracy (90%) and low false-positive rates, demonstrating its effectiveness in detecting and preventing ransomware attacks on IoT devices running PureOS.

An integrated CRN-SVR approach for the quality consistency improvement in a diesel engine assembly process

ABSTRACT As the last production link, the diesel engine assembly process (DEAP) significantly impacts the quality consistency of diesel engine products. Therefore, the quality consistency improvement of DEAP has become an urgent problem for academia and industry. The challenge is how to mine the causal relationship in DEAP and establish a reliable quality prediction model. This paper attempts to describe DEAP using a causal relationship network (CRN) and to provide an effective data-based scheme for improving quality consistency by integrating CRN with support vector regression (SVR). First, a two-step CRN learning method is proposed for describing the DEAP. In the first step, the association relationship network is developed by a hybrid direct association detection method of the maximal information coefficient and network deconvolution, which can accurately measure the data relations. In the second step, the information geometric causal inference is employed to determine the direction of the edges in the association relationship network, thus forming the CRN of DEAP. Then, an integrated CRN-SVR approach is proposed to realize the predictive modeling of the critical quality indicators in DEAP, which integrated SVR into CRN. At the same time, it also provides a feasible idea for the interpretability of existing machine learning techniques. Finally, the proposed approach is tested and verified in a real-world DEAP and the obtained RMSE is only 0.033. The results of this study provide theoretical support and technical guarantee for quality consistency improvement in DEAP.

Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods

Building energy forecasting methodologies utilized by municipal governments tend to be geared heavily towards depicting broader qualitative representations of regional change and are in need of complementary data-driven models that can produce quantitatively reliable depictions of future energy consumption at the neighborhood-level. The current research demonstrates an application of a Machine Learning (ML) model in the form of an Extreme Gradient Boosting (XGBoost) algorithm for forecasting the energy use of commercial and residential buildings. The methodology serves to improve on municipal scenario planning by providing a more spatially granular representation of future energy use. In this way, city government and urban planners can more accurately set carbon emission benchmarks and target specific locales for sustainability initiatives. The second major contribution of the study is to demonstrate how scenario planning approaches can utilize existing Machine Learning techniques to compensate for gaps in the data. This work is developed through a case study of Philadelphia. The study begins with the construction of residential and commercial energy models for the year 2015 and corresponding models for the year 2045. The forecast models integrate regional socioeconomic trends from the Delaware Valley Regional Planning Commission (DVRPC) scenario of Enduring Urbanism. The commercial energy use model is developed from the DVRPC’s open-source Geographic Information System (GIS) datasets, the Commercial Buildings Energy Consumption Survey (CBECS), and CoStar commercial real estate data. The residential model applies the Residential Energy Consumption Survey (RECS), the Public Use Microdata Sample (PUMS), and Census Bureau American Community Survey (ACS) estimates. A corresponding SHAP (SHapley Additive exPlanations) analysis is implemented to pinpoint feature contributions to the model’s energy estimates. By using the PopGen software, the model’s energy estimates could be analyzed at the household level, the smallest possible scale. To provide a useful resource for key stakeholders, the study aggregates model output by Traffic Analysis Zone (TAZ) and Public Use Microdata Area (PUMA) to display a detailed forecast of energy use. The results indicate that the DVRPC Enduring Urbanism trends in income and employment do not significantly affect energy consumption for the study area. However, features related to lower building intensity (e.g., lower square footage, fewer floors per building) were associated with reduced energy use in both models. Additionally, the study found residential buildings under the “single-family attached” zoning designation to correspond with higher energy estimates.

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods.

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

Mobile Application Development for Disease Diagnosis based on Symptoms using Machine Learning Techniques

In recent times most of the people die due to unavailability of proper diagnosis and medical services. In such cases the patient doesn't have quick access to medical services or face difficulties in using them. This could be very helpful in those remote regions where proper medical services are not available in hand or the patient is in need of immediate medical attention. Therefore, we created a self-assessment application which uses the power of machine learning to predict around 40 diseases, based on the symptoms of the disease. In self-assessment it is important that the prediction from the model stays fairly accurate. The existing machine learning techniques suffer from various drawbacks, including decreased accuracy, which is of primary importance in such cases. Our proposed system diagnoses diseases from the symptoms using the following steps. Firstly, data preprocessing was done and then the data was passed through various machine learning classification models, like K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest Classifier, Naïve Bayes and Artificial Neural Network. After performing hyper parameter optimization, the probability vectors of all the models are added and then divided by the number of models. The number of diseases to be shown on application screen depends on the threshold values. The predicted disease(s) is received from the model and they are sent back to the application, where they are shown to the user. Our model proves to be the best when compared to the existing models.

Model for Network Intrusion Detection Based on Machine Learning

Intrusion Detection is an important step to ensure security in computer networks. In this paper, a novel model for intrusion detection is presented. The model has been developed using various existing machine learning techniques viz. decision tree, Naive Bayes, KNN and logistic regression techniques. Preexisting database of network intrusion is used to analyze the performance of the proposed model. As seen from the experimental results, the decision tree gives the best outcome with an accuracy of 96%.

Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques.

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

HF-SCA: Hands-Free Strong Customer Authentication Based on a Memory-Guided Attention Mechanisms

Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the shopping carts abandonment: an Italian bank computed that 22% of online purchases in the first semester of 2021 did not complete because of problems with the SCA. Luckily, the PSD2 allows the use of transaction risk analysis tool to exempt the SCA process. In this paper, we propose an unsupervised novel combination of existing machine learning techniques able to determine if a purchase is typical or not for a specific customer, so that in the case of a typical purchase the SCA could be exempted. We modified a well-known architecture (U-net) by replacing convolutional blocks with squeeze-and-excitation blocks. After that, a memory network was added in a latent space and an attention mechanism was introduced in the decoding side of the network. The proposed solution was able to detect nontypical purchases by creating temporal correlations between transactions. The network achieved 97.7% of AUC score over a well-known dataset retrieved online. By using this approach, we found that 98% of purchases could be executed by securely exempting the SCA, while shortening the customer’s journey and providing an elevated user experience. As an additional validation, we developed an Alexa skill for Amazon smart glasses which allows a user to shop and pay online by merely using vocal interaction, leaving the hands free to perform other activities, for example driving a car.