Miracle-dz/UniFAR-FTUs huggingface dataset:
Propensity modelling for predicting customer behaviour using clustering techniques for effective business analysis

The digital world generates massive volumes of data, and it is critical to analyse them. With machine learning and powerful visualization tools, the competition to innovate and succeed in this industry has made a significant leap towards tailoring data-driven solutions. Analyzing these data has never been easier and the insights gathered can transform many businesses. In this paper, four different clustering techniques were compared namely, K-means, Agglomerative, Birch, and Spectral clustering. Three separate performance metrics were used to find the effectiveness of these algorithms: the Silhouette coefficient, the Calinski-Harabasz score, and the Davies-Bouldin index. Based on the analysis, it was found that the K-means clustering algorithm outscored the other three by a higher performance measure score. The insights gathered from clustering led to the formulation of critical business strategies. This study also provides an in-depth look into expressing corporate strategies with advanced machine learning technologies. There are several applications to it, including classification, clustering, prediction, and big data visualization.
Road Extraction From Point Clouds of Open-Pit Mine Using LPFE-Net

Accurate road network data for open-pit mine is crucial for the effective implementation of ore mining and transportation scheduling systems as well as the operation of unmanned mining trucks. It is still a challenging task to accurately extract roads from open-pit mine because of their blurred boundaries and varying scales. Based on the characteristics of open-pit mine roads, we propose a local point feature enhancement network (LPFE-Net). The core of LPFE-Net is a point feature enhancement unit (PFE Unit) and a multiscale residual module. Among them, the PFE unit is used to fuse the key features of the neighboring points. The multiscale residual module superimposes the PFE unit to expand the perceptual field and uses multilevel residual connections to improve information flow and reduce model complexity. The experimental results show that the proposed LPFE-Net outperforms all the compared methods in the open-pit mine road extraction task while having a relatively fast extraction speed.
A Real- Time Smartphone App for Field Personalization of Hearing Enhancement by Adaptive Dynamic Range Optimization

Adaptive Dynamic Range Optimization (ADRO) is an amplification strategy which is used for hearing aids and other assistive hearing devices. To take into consideration hearing preferences of a specific user in the field, ADRO has been personalized by using maximum likelihood inverse reinforcement learning. A smartphone app is developed in this paper implementing the personalization of ADRO in real-world audio environments so that clinical studies can be carried out in the field. The developed app adjusts the comfort target parameter of ADRO by conducting paired audio comparisons in real-time to reach a personalized setting of gain values in five frequency bands. The audio processing steps taken to enable the app real-time functionality are discussed. The ADRO personalization results of the experiments carried out by using the app in different real-world environments are also presented.
Sport Activity Classification Using Interlaced Multivariate Time Series Signals

This study investigates performance of Time Series Classification (TSC) models in non-laboratory conditions created dataset which contains sport activities in three categories, such as biking, running, and other. The main challenge conducted is to convert multivariate data to univariate. There are several methods to conduct that transformation, but in this case, we develop feature interlacement wherein three features are combined into the same signal from three dimensional multivariate time series data for each sport activity file, producing so called interlaced multivariate signals. Five univariate TSC models from sktime API evaluated in analysis are Time Series Forest, Supervised Time Series Forest, Random Interval Spectral Forest, Random Interval, and Shapelet Transform classifiers. Interlaced multivariate signal data was successfully constructed from the raw dataset and applied to TSC models achieving 91% accuracies on average among the models. Based on the overall scores obtained, applied data transformation method is well applicable in retrospective sport activity classification (SAC) using univariate time series analysis algorithms.
Surface Mesh Segmentation Based on Geometry Features

Today neural networks successfully solve the problem of 2D and 3D image segmentation. Such approaches take a 2D or 3D array as input, and produce a binary pixel or voxel image as an output. The segmentation problem can also be solved on surface meshes of 3D objects. In this case, the segmentation method takes as input the vertices and edges of the surface mesh of an object, and at the output returns an integer value of the class for each vertex or edge of the provided mesh [1]. Surface mesh segmentation tasks are relevant in biomedical applications and in computer graphics. Both these fields suffer from the same problem, which is the lack of relevant data. In our work, we propose a new approach for supervised surface mesh segmentation that can efficiently work with small datasets. We represent 3D geometry using signed distance function, and encode local geometric features to use them as an input for the convolutional neural network. The performance of our method is demonstrated on surface meshes of human heart ventricles from the ACDC Challenge dataset [2]. The most important advantage of our approach is its ability to learn on few examples. It is also able to handle thin and closely spaced surfaces, as it works with the geometry features and does not depend on the size of the surface mesh elements. Furthermore, the required accuracy of our method can be achieved with 15 times less trainable parameters than it is necessary for PointNet on the same dataset.
Improving IPC Video Lag in CCTV Systems by Machine Learning and Software Defined Networks

Many small and medium-sized hotels around the world deploy closed-circuit television (CCTV) networks. In a CCTV network system, IP cameras (IPCs) and other network devices are available, such as Wi-Fi and routers. Usually, when a CCTV network is initially deployed, IPC video surveillance screen jams and distorted images still occur even if network technicians reserve enough uplink bandwidth for the IPCs. In this paper, based on machine learning library by pytorch, we learn and identify the features of IPC traffic data through the deep belief network (DBN) and logistic regression (LR) neural network models to accurately identify IPC traffic. Then, we combine it with hash polarization optimization scheme and use software defined network (SDN) controller to distribute the openflow flow table to handle load balancing of IPC network traffic to solve the problem. At the same time, the quality of service (QoS) policy is applied to the IPC traffic by cooperating with automatic configuration, which can also solve the problem of IPC video surveillance screen lag and screen distortion in the case of network congestion occurring in the surveillance system.
Attack detection in industrial IoT using novel ensemble techniques

The Internet of Things (IoT) is a rapidly growing industry that is expected to connect 22 billion smart devices by 2025 in a market worth $1567 billion. However, Securing IoT devices is challenging due to their integrated and resource-constrained nature. The traditional security measures are relatively ineffective against these continuously changing attacks. Deep learning (DL) and machine learning (ML) offer potential solutions to incorporate intelligence into systems, enabling improved detection of security breaches and attacks. These techniques can also help us suggest suitable mitigation techniques for specific attack types. The paper focuses on suggesting a solution for the detection and classification of attacks on IoT networks using novel ensemble techniques, CatBoost, and XGBoost and training them on realistic Edge-IIoTset Dataset. The performance of the solution is tested by comparing it with traditional ensemble models trained on the same data. The evaluation criteria employed are the accuracy, recall, precision and F1 score.
A Rational Function Approximation Algorithm for the Frequency Sweep of Microwave Tube

Frequency response is an important content in the design of the input/output structures of microwave tubes. Therefore, accurate and efficient computation of the frequency response of the model will undoubtedly improve our work efficiency. In this paper, we introduce an existing rational function approximation algorithm called AAA (adaptive Antoulas-Anderson). It's a speed, flexibility, and robustness algorithm. And this algorithm is extended to the frequency sweep of input/output structures of microwave tubes, by enforcing the realness condition and using the block-AAA algorithm to approximate a matrix-valued function. Through an example of the frequency sweep of a coaxial window, it is proved that the block-AAA algorithm is more accurate and faster than the Vector Fitting.
Scalable Subgraph Counting: Methods Behind the Madness WWW 2019 Tutorial

This is an auxiliary ﬁle containing references to all the papers mentioned in our WWW 2019 tutorial. For each paper, we give a short 1-2 sentence description of the relevance to the ﬁeld of subgraph counting. We have a separate section for each portion of our tutorial, and papers are listed in chronological order within each section.
Machine Learning-Based Ensemble Band Selection for Early Water Stress Identification in Groundnut Canopy Using UAV-Based Hyperspectral Imaging

This letter presents the early identification of water stress in groundnut (GN) canopy using unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) (in 385–1020 nm) and machine learning (ML) techniques. An efficient HSI data analysis pipeline was presented which includes image quality assessment (IQA), denoising, band selection (BS), and classification. A novel ML-based ensemble feature selection (FS) algorithm has been proposed for optimal water stress sensitive waveband selection. The data analysis pipeline and the selected bands were validated on HSI data acquired at two different water stress levels. Wavelengths 515.05, 552.16, 711.92, 724.75, and 931.92 nm were identified as optimal water stress sensitive bands in the GN canopy, using which we could identify early stress with 96.46% accuracy. The proposed data analysis pipeline and ensemble FS algorithm will benefit crop phenotyping applications such as early abiotic stress detection.
On-Body and SAR analysis of a Polyester Textile antenna for Wearable Applications

An polyester substrate textile antenna is proposed for wearable body applications at 4.6 and 5.8 GHz frequencies. The antenna has the required compact design for wearable applications. The X-mass tree-shaped patch, supported by the staircase structure, aids the antenna in operating at treble bands. The antenna's polyester has hydrophobic properties, and the conductive ground patch layers were portrayed by means of the conductive adhesive copper film. A prototype was developed with the polyester material as the substrate, and the total footprints of the antenna are 30x20 mm2. This model was meant for wearable applications, so it was intensively tested in many horizontal and vertical bending positions. Thus the conformability of the antenna was validated. The specific absorption rate (SAR) analysis was also done on a three-level human phantom prototype; comprising muscle, fat and skin. The dual wearable application frequencies of 4.6 and 5.8 GHz applications are validated. The results from the SAR analysis conclude that the antenna is safe to use on the human body with a max SAR of 0.762 and 0.698 w/kg for 1 gram of tissue. The compact design with conformability and safe SAR thresholds aid the antenna for wearable body application.
Jatropha leaf disease classification Using Improved Inception ResNetV2

Agriculture is an essential for feeding the world, providing resources, and supporting livelihoods. However, crop disease identification is a prevalent challenge for farmers. To address this issue, a deep learning model was developed to classify Jatropha leaf disease. As the World moving to Bio Diesel and Electrical as that jatropha leaf is a main yield for generating Bio fuels like the Crop Fertilizer and Bio-Diesel with a High Quality of Bio-Diesel Production. The model used deep learning model is InceptionResNetV2 and augmented binary healthy and unhealthy Jatropha leaf images to increase dataset variability for collection from the various resources. The proposed transfer learning algorithm, InceptionResNetV2, achieved a remarkable accuracy of 97%, demonstrating the potential of deep learning in agriculture for effective disease detection and management. In this transfer learning algorithm can fine tune and classify the Jatropha Leaf by an automated classification System.
Lane detection and traffic sign recognition

In this paper, some image processing techniques are proposed to detect lanes, and Convolutional Neural Networks (CNN) to detect and classify traffic signs as well. As for the lane detection the main steps are getting the region of interest (ROI) from the input image, thresholding this image, segment lane pixels, and draw the created lane on to the main image. For the traffic sign detection a Yolov3 model was used, trained by darknet framework with the GTSDB database, and for the classification, three CNN models were evaluated, which were trained with the GTSRB database. With the GTSRB database, some image augmentation techniques were used to generate more images, so the database would be balanced.
Medicinal Plants Classification by VisualCharacteristics of Leaves Using CNN

Identification of medicinal plants correctly is crucial in Ayurvedic medicine. By their visual characteristics and scent, plants are recognized by human experts. Negative effects could come from incorrectly identifying therapeutic herbs. Using visual morphological traits like the size, color, and texture of the leaves and flowers, it is possible to automate the identification of plants. In this study, we demonstrate the use of machine learning and image processing to identify uncommon medicinal plants with high accuracy. The leaves and petals of rare medicinal plants used in Ayurvedic medicine were scanned and turned into a database for this study. Images were captured of both the anterior and posterior sides of leaves and flowers. According to their distinct feature combination, leaves are categorized. The success rates of identifying plants have been found to be as high as 98% in testing.
A Self-Supervised Method Using Noise2Noise Strategy for Denoising CRP Gathers

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore, as a form of unsupervised learning, self-supervised learning emerged to overcome this difficulty, with its labels coming from the training dataset itself. In this letter, we propose a self-supervised learning method that requires only raw seismic data to train the model by using the Noise2Noise strategy, which takes advantage of the unpredictability of noises to regress from noisy data to clean data. Our method aims at improving the noise suppression effect for common-reflection-point (CRP) gathers. By comparing with conventional methods, both synthetic and field data show that the proposed framework is not only effective in suppressing random noise but also remains effective for coherent noise.
Towards an Integrated Vehicle Health Management for Maintenance of Unmanned Air Systems

With rising numbers of Unmanned Aerial Systems (UASs) national and international authorities are currently negotiating the regulations for the operation of UASs. Even though UASs are smaller and less complex than manned aircraft, Maintenance, Repair and Overhaul (MRO) aspects are not yet sufficiently considered. In a step towards safer and more autonomous operations of UASs, we intend to develop an Integrated Vehicle Health Management (IVHM) for Condition- Based Maintenance. This highly integrated system aims at constantly monitoring the health status of the UAS and at supporting all involved stakeholders. A necessity to IVHM is a vast data gathering process at different points in time as well as at different locations. Multi-stakeholder Digital Twins can achieve this by collecting the necessary data and allowing all stakeholders to base their decisions on the most up-to-date status of their system. We conclude this paper by demonstrating a first prototype operation under realistic conditions.
Dictionary-Based Tensor Decomposition-Aided Time-Varying Channel Estimation for Millimeter Wave MU-MIMO Systems

This research focuses on the multiple-input multiple-output (MIMO) mmWave systems' uplink time-varying channel estimation challenge. We convert the time-varying channel es-timation in the hybrid beamforming structure into a param-eter estimation problem that takes angles of arrival/departure (AoA/AoDs), Doppler shift, and path gains into account. We also suggest a two-stage channel estimation method. We describe the receiving signals as a third-order tensor in the initial stage and provide an algorithm that combines Matching Pursuit (MP) and Alternating Least Squares (ALS) called Dictionary-based Tensor Decomposition to estimate AoA/AoDs. In the second stage, we recover the Doppler shifts and paath gains using the estimated AoA/AoDs. The entire time-varying channel is finally restored. The simulation results demonstrate that the suggested method performs better than the traditional approach in terms of complexity and estimation accuracy.
A Deep Learning based Approach for 3D Mask Face Anti-Spoofing Enhancement

Face recognition system is widely used in recent years, but it is vulnerable to attacks via print paper or 3D masks. By diving into the attack process, we noticed that detecting face occlusion of small stick-like objects benefits the face antispoofing. In this paper, we propose a segmentation-based deep learning method as an enhancement to protect the system being attacked by high-fidelity 3D masks. However, manually annotation is labor intensive and easily involves extra errors, We generated a novel high quality dataset called TinyOcc combining CelebA-HQ and only 50 self-annotated stick-like objects images. Several popular segmentation models are fine-tuned using above novel datasets, achieving the mIoU 0.92(intra),0.87(cross) and precision 0.94, which proved the robustness and effectiveness of our approach.
Machine Learning based Resilient Energy Management System in Residential Building

Modern communities are more reliant on electricity, and they anticipate an uninterrupted supply to meet demand. In this aspect, the sophisticated power system is built to deliver uninterrupted electrical energy and maintain reliability, though it is still vulnerable to severe weather conditions. To maintain a reliable and secure electricity supply to utilities, power system resilience is essential. Outages of the power grid have a negative impact on society and the economy. It is essential to develop an intelligent algorithm to assure the power system runs securely and stably to reduce the socioeconomic effects of power outages. This work aims to develop a Resilience Energy Management System (REMS), which acts as a coherent decision-making authority in each neighborhood. The REMS units, which are distributed across various locations, can communicate with one another to exchange local data, allowing them to disseminate their decision-making. Therefore, we provide various situations that demonstrate the simulation's outcome performance of the system for an own residential house during different outage possibilities.
Evaluation and Analysis Data from Twitter Data By Using Hybrid CNN & LTSM

The central theme for this thesis is the design of an aspect-based sentiment analysis model for the classification of online Italian automotive forums' comments. The work starts with designing a strategy for collecting information about target forums to make it possible to develop a machine learning-based sentiment classification model. The study involved applying the CNN and LTSM model, a state-of-the-art solution based on a parametric model that will improve the performance of a baseline algorithm, especially in case of very noisy data like the ones where this tool is supposed to be to work on. This work has been designed as a two-stage CNN and LTSM classifier in all its parts. It was compared with a one-step classifier to detect the pertinence about some topics, and eventually, the sentiment achieved an accuracy of 96.78% for all comments. The current problem passed from a typical three degrees' polarity sentiment analysis to a four labels text classification, where it will be introduced an additional category for determining whether the text is pertinent to a particular topic or not. Presenting this information, the models must be enhanced, and a cascade classification solution will be proposed. The final model is then utilized for a real-world use case. New data have been classified concerning some selected topics, finally presented exploiting a data visualization but still not satisfactory, thus making sentiment analysis an ongoing and open research subject.
High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network

Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases. Echocardiography-based measurements of LA dimensions and strains are useful biomarkers, but they provide an incomplete picture of atrial deformations. High-resolution dynamic magnetic resonance images (Cine MRI) offer the opportunity to examine LA motion and deformation in 3D, at higher spatial resolution and with full LA coverage. However, there are no dedicated tools to automatically characterise LA motion in 3D. Thus, we propose a tool that automatically segments the LA and extracts the displacement fields across the cardiac cycle. The pipeline is able to accurately track the LA wall across the cardiac cycle with an average Hausdorff distance of $2.51 \pm 1.3~mm$ and Dice score of $0.96 \pm 0.02$.
Differential Flatness and Manipulation of Elasto-flexible Cables Carried by Aerial Robots in a Possibly Viscous Environment

This work considers a system composed of two quadrotors manipulating a deformable and extensible cable. The dynamic model is presented, and it is based on a discrete representation of the cable, which is decomposed into lumped masses interconnected by linear springs through passive spherical joints. A set of flat outputs is found for the system. The flatness is exploited to design a method to manipulate the cable, which is then tested through numerical simulations.