Miracle-dz/UniFAR-FTUs huggingface dataset:
Levels of Financial Inclusion in the WAEMU Countries: A case study using DEA

In this study, we propose a novel methodological approach to evaluate the levels of financial inclusion of countries using an aggregate multidimensional index based on Data Envelopment Analysis (DEA). The approach is applied to countries of the West African Economic and Monetary Union (WAEMU), using the financial inclusion definition and indicators defined by the Central Bank of the West African States (BCEAO). We observed that all the eight countries had increased their levels of inclusion between 2010 and 2017 steadily. The ensuing benchmarking indicates that Senegal is the leader of the Union when it comes to financial inclusion. Further, Senegal, Benin, Ivory Coast, and Togo are the benchmark countries. For the four remaining countries, we identify reference-countries that they should emulate to increase their financial inclusion levels
Prediction Model for Type 2 Diabetes using Stacked Ensemble Classifiers

Diabetes is the number one of major causes of death globally. Undetected and untreated diabetes causes serious issues and the individuals with diabetes are at high risk for complication. Thus, an early diabetes prediction is necessary to help the individuals preventing dangerous conditions at the early stage. This study proposed a prediction model to offer early prognostication of type 2 diabetes. The proposed model incorporates isolation forest and synthetic minority oversampling-tomek link technique to detect as well as remove the outlier data, and balance the data distribution, respectively. The stacked ensemble classifiers are the used learn and predict type 2 diabetes at an early stage. We used three publicly available datasets to evaluate the performance of proposed model as compared to other models such as multi-layer perceptron, support vector machines, decision tree, and logistic regression. We applied 10-fold cross-validation and obtain four performance metrics such precision, recall, f-measure, and accuracy. The experimental results show that the proposed model outperformed other models, achieving accuracy up to 93.18%, 98.87%, and 96.09% for dataset I, II, and III, respectively. It is expected that the early diabetes prediction could help the individuals on taking precautions once type 2 diabetes is detected.
A comparative study of different feature extraction techniques for identifying COVID-19 patients using chest X-rays images

The coronavirus (COVID-19) outbreak has been labeled as a pandemic with no assured vaccine and drug till now. Many medical trials are going on for finding treatment against this disease and some have achieved success but reaching out to all stakeholders is strenuous. A quick and proper identification through testing of a COVID-19 patient is equally important to prevent the spread of the virus to other healthy patients. Thus, a comparative study of different feature extraction techniques for identifying COVID-19 patients using chest X-rays images is done in this work. The combination of local binary patterns features extraction technique and gradient boosting classifier performs the best with 94.453% accuracy as compared to other approaches. So, this work will be of great help in the screening of COVID-19 and also contribute to the healthcare system to fight against it.
An SoC FPAA Based Programmable, Ladder-Filter Based, Linear-Phase Analog Filter

This work demonstrates a Continuous-Time (CT) Ladder filter using transconductance amplifiers as an approximate delay stage implemented on a large-scale Field Programmable Analog Array (FPAA) and characterized on an SoC FPAA. We experimentally demonstrate a reprogrammable CT Analog linear-phase filter by utilizing the ladder filter delay element and Vector-Matrix Multiplication (VMM) both compiled on the SoC FPAA. Using the Ladder Filter as a programmable CT delay operation enables a traditionally difficult analog signal operation. This effort extensively models and characterizes the ladder filter delay stage in terms of its transfer function, delay tunability, power requirements, distortion, and SNR. The theoretical development is compared to experimental measurements on an SoC FPAA with programmable ladder filter delay of <inline-formula> <tex-math notation="LaTeX">$2.9\mu \text{s}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4.2\mu \text{s}$ </tex-math></inline-formula> for multiple input frequencies (e.g. 5kHz, 20kHz). In addition, we show that VMMs can compensate the non-idealities found in the ladder filter delay-line operation.
Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal

In this article, we propose a single-vibration sensor-based method for detecting incipient faults in squirrel cage induction motors (SCIMs). We consider defects in different parts of the bearing (inner raceway, outer raceway, cage train, and rolling element) and in a single bar of the rotor. The vibration signal is dominated by the fundamental rotational frequency and its harmonics. The dominant components result in numerical errors while estimating the relatively indistinct fault-specific spectral components. In this article, we precondition the vibration signal by suppressing multiple dominant components using an extended Kalman filter-based method. The suppression of the dominant components reduces the spectral leakage, exposes minute fault components, and improves the overall amplitude estimation. Subsequently, we estimate the fault frequency and amplitude using an accurate and low-complexity Rayleigh-quotient-based spectral estimator. The thresholds for fault detection are determined from a small number of healthy data, and an adaptive minimum distance-based detector is used for hypothesis testing. The proposed test improves detection and reduces false alarms under noisy conditions. We test the complete algorithm using data from a 22-kW SCIM laboratory setup. The proposed method has achieved 100% accuracy with the publicly available 12-kHz drive-end bearing data from Case Western Reserve University, Cleveland, OH, USA.
Exploring Visualisations for Financial Statements in Virtual Reality

Recent developments in Virtual Reality (VR) provide vast opportunities for data visualization. Individuals must be motivated to understand the data presented concerning their personal financial statements, as with better understanding comes more efficient money management and a healthier future-minded focus on planning for their personal finances. This paper aims to investigate how a 3D in VR visualization scenario influences an immersed users’ capability to understand and engage with personal financial statement data in comparison to a scenario where a user would be interacting with 2D personal financial statement data on paper. This was achieved by creating a 2D paper prototype data and utilizing the benefits of 3D in a VR prototype visualization. Both prototypes consisted of chart representations of personal financial statement data, which were tested in a user study. Also, participants (N=23) were given a set of tasks (such as ”Find the quarter in which the least amount of expenses was spent”) to solve by using 2D or 3D financial statements. Results show that participants reported interaction is statistically significant more natural with 2D data on paper than interactions with 3D data in VR. Also, participants reported that they perceived statistically significant more considerable delay between outcome and their action with 3D data in VR. However, effectiveness, measured by the percentage of correctly solved tasks, in both versions enabled participants to answer almost all given tasks successfully. Those results are showing that VR as a medium could be used for analyzing financial statements. In future developments, several additional directions could be incorporated. Exploring data visualization is essential not only for the financial sector but also for other domains such as taxes or law reports.
Prediction of Real Estate Land Prices in the Kingdom of Bahrain

Data Mining plays an important role in prediction model development and knowledge discovery. This paper follows the Cross-Industry Standard Process for Data Mining in order to develop a prediction model for the real estate land prices in different cities and villages within the Kingdom of Bahrain. The data has been collected from different sources which include social media networks, official newspapers, and advertisement newspapers, and then it has been analyzed using WEKA data mining tool for model development and analysis. The objective of this research paper is to use data mining to help the real estate advisors and consumers in the pricing estimations of the real estate land prices, and to cope with any changes in the land prices according to the market trends. The research study considers a number of factors which affect the land price, such as: land length and width, location, and land classification. Linear Regression is used for analysis. The study shows that the model is affected by the variance of the land price between different areas within the Kingdome, and therefore a number of enhancements have been suggested for future development.
ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis

Multiple sensor data fusion is necessary for effective condition monitoring as the electric machines operate in a wide range of diverse operations. This study investigates sensor acquired vibration and current signals to establish a reliable multi-fault diagnosis framework of a brushless DC (BLDC) motor. Faults in stator and rotor were created deliberately by shorting two adjacent windings and creating a hole on the surface, respectively. The threshold for different health states was obtained by the third harmonic analysis of motor current. Later, the key features from sensor acquired current and vibration signals are selected based on monotonicity and reduced using the principal component analysis (PCA). For future predictions, an artificial neural network (ANN) is used to classify different fault features and its performance is evaluated using several metrics. Analysis of motor current harmonics and impulsive vibration response at the same time provides a thorough health estimation of BLDC motor in the presence of both electrical and mechanical faults. Multiple sensor information is fused to obtain a better understanding of the fault characteristics and mitigate the randomness of fault diagnosis. The proposed model was able to detect and classify multiple fault features with higher accuracy compared to other similar methods.
Development of Microstrip Patch Antenna Integrated on Solar Cell Based Artificial Magnetic Conductor Surface for Green Wireless Applications

In this paper, a novel design for a plurality of wide band microstrip rectangular patch antenna on solar cell integrated Artificial Magnetic Conductor (AMC) surface for Wireless communication is proposed and presented. Here, the general reflection characteristic from an AMC surface with rectangular unit cells as a reflector and effect on integration of the solar cell with the proposed antenna is validated. The designed AMC plane consists of 6 × 6 patches with both copper ground plane and also solar cell ground plane, be placed beneath the rectangular microstrip patch antenna maintaining an air gap between the structures, which achieves unidirectional radiation, and also enhance operating bandwidth simultaneously. The amorphous silicon solar cell in the module is used as both a photovoltaic generator and also from an RF perspective; act as ground plane for the AMC plane. The effectiveness of the proposed antenna structure is confirmed by high-frequency structure simulator (HFSS) tool for 3D full wave electromagnetic field simulation and measurement. The fabricated patch antenna on AMC plane with copper ground plane offers 2.12 GHz - 2.48 GHz band and the same radiating patch on AMC plane with solar cell ground plane offers 2.16 GHz - 3.32 GHz with enhanced impedance bandwidth, effective return loss, good VSWR and radiation pattern characteristics for green WLAN applications.
Impact of Mobility Models on Energy Consumption in Unmanned Aerial Ad-Hoc Network

Unmanned Aerial Ad-hoc Networks (UAANETs) pushed up UAVs’ cooperative tasks. For efficient cooperation, a fitting mobility pattern must be adopted, ensuring simple, flexible, and easy-manageable coordination. Indeed, UAANET faces an inescapable challenge due to the limited energy constraint, which significantly affects the tasks’ productivity and efficiency. Different from the literature, which only studied the impact of two classical factors affecting energy consumption, namely, communication protocols and computation, we highlight the impact of the temporal and spatial correlation involved in mobility models. Indeed, we consider a transitive relationship taking place between temporal and spatial correlation and energy consumption. Those correlation forms do affect the data loss ratio that, in turn, augments energy consumption due to the increased rate of data re-transmission, route re-discovery and maintenance, etc. On this basis, we assume the temporal and spatial correlation impact on energy consumption, which has been demonstrated and analyzed through numerical simulations.
Open-Circuit Fault Diagnosis and Fault-Tolerent Control Strategies for Interleaved Boost Converter

This paper addresses an open circuit fault (OCF) diagnosis method and a fault-tolerant control for a non-isolated interleaved boost converter to optimize reliability. The studied structure is based on interleaved boost converter ensuring maximum power operation and a full bridge converter allowing the regulation of the battery voltage. Unlike several other works in the literature which are generally based on the inductor current or the slope of this current, the proposed method is based on the measurement of the inductor voltage in addition to the diode and switch voltages. To generalize, the detection principle is applied to the others phases of the interleaved dc-dc converter. Once the open switch failure position is detected, a reconfiguration is established allowing the interleaved dc-dc boost converter to continue to operate using the three phases and replacing the faulty switch with arm of the full bridge converter. The major advantage of the proposed method is its simplicity of implementation in addition this method does not require any additional switch to ensure reconfiguration. The proposed method is implemented in MATLAB/Simulink and the obtained results are discussed. The simulation result shows the validity of the theoretical analysis. The proposed fault-tolerant control strategy optimizes the performance of the interleaved boost converter in terms of cost, reliability and service continuity.
Aircraft Type Classification in Remote Sensing Images using Deep Learning

In aeronautic, redundancy of data is strongly desired to make optimal decisions. A large data source is available with the development of the technology used in remote sensing images. Remote Sensing Image Classification (RSIC) is widely exploited in military and civil fields. To improve the performance of multi-label classification, we addressed the problem of RSIC based on the Convolutional Neural Network (CNN) for remote sensing images of aircraft types. Previous studies have used intensive preprocessing which limits the rate of classification. We improved the network structure to make it more accurate and to limit underfitting or overfitting problems. A recent public dataset called Multi-Type Aircraft Remote Sensing Images (MTARSI) is used in this work to validate our method. Extensive experiments prove the effectiveness of the proposed method in terms of accuracy.
A Network Time Security Based Automatic Key Management for PTPv2.1

The PTPv2.1 standard provides new protection mechanisms to ensure the authenticity and integrity of PTP messages. However, the distribution of the necessary security parameters is not part of the specification. This paper proposes a simple and practical approach for the automated distribution of these parameters by using a key management system that enables the Immediate Security Processing in PTP. It is based on the Network Time Security protocol and offers functions for group management, parameter updating and monitoring mechanisms. A Proof-of-Concept implementation provides initial results of the resources required for the key management system and its use.
Smart Urban Traffic for Green Supply Chain Management

Green Supply Chain Management (GrSCM) has appeared as an environmental innovation which involves environmental concerns and issues into the Supply Chain Management (SCM). In this study we aim to demonstrate the added value of the urban traffic control and its incorporation in the concept of Green in the Supply Chain Management (GrSCM) and its impact on the performance improvement in the transportation field. The methodologies used in this study are depth literature review then we will propose a framework for urban green logistics in order to link urban logistics to green supply chain management using technologies related to the internet of things (IoT).
Making Near-future Predictions about the Flow of People, Things, and Information through Big Data Analysis--Gaining an Edge by the Ability to Produce Value

To build a productive relationship between humans and artificial intelligence (AI), we must grasp the current situation as accurately as possible and make investments in the future toward developing such a relationship. This article introduces how we see and interpret the AI, Internet of Things, and big data era from the standpoint of promoting research and development of basic technologies.
Hepatitis Diagnosis By Artificial Neural Network

— Due to the complexities of hepatitis diseases spreading increasingly all over the world, it became necessary to develop a system that helps diagnose the disease using modern technologies. Artificial intelligence is one of the main pillars of the technology industry in the current era. Many studies have been conducted to discover effective methods for the medical diagnosis of many diseases, including hepatitis. This paper reviews the methodologies and classification accuracy in diagnosing hepatitis and also reviews an approach to diagnosing hepatitis through the use of an artificial neural network
Pastprop-RNN: improved predictions of the future by correcting the past

Forecasting accuracy is reliant on the quality of available past data. Data disruptions can adversely affect the quality of the generated model (e.g. unexpected events such as out-of-stock products when forecasting demand). We address this problem by pastcasting: predicting how data should have been in the past to explain the future better. We propose Pastprop-LSTM, a data-centric backpropagation algorithm that assigns part of the responsibility for errors to the training data and changes it accordingly. We test three variants of Pastprop-LSTM on forecasting competition datasets, M4 and M5, plus the Numenta Anomaly Benchmark. Empirical evaluation indicates that the proposed method can improve forecasting accuracy, especially when the prediction errors of standard LSTM are high. It also demonstrates the potential of the algorithm on datasets containing anomalies.
Obstacle Clustering and Path Optimization for Drone Routing

— To enable safe and efficient Unmanned Aircraft Systems (UAS) operations at low altitudes, it is necessary to conduct airspace management and operations for UAS traffic. This study focuses on deterministic clustering-based drone routing, with specific emphasis on the trade-off between horizontal and vertical travel costs. The routing problem is simplified to a 2D problem that we solve at several altitude candidates. Altitude candidates were generated based on clustered static obstacles in low urban airspace. Fast-Marching algorithm is performed to generate the shortest path at each altitude candidate. The optimal altitude is determined by weighing the vertical cost for ascent and descent over the horizontal cruising cost at certain altitude. Experiments are conducted to choose proper number of clusters and weight given to building height in the clustering procedure, and different shortest path algorithms are compared. Larger scale of Unmanned Aerial Vehicles (UAV) missions are simulated, based on which we analyze the relationship between optimal travel altitude and shortest cruise path, and estimate the UAV cost function.
TAP-Net

We introduce the transport-and-pack (TAP) problem, a frequently encountered instance of real-world packing, and develop a neural optimization solution based on reinforcement learning. Given an initial spatial configuration of boxes, we seek an efficient method to iteratively transport and pack the boxes compactly into a target container. Due to obstruction and accessibility constraints, our problem has to add a new search dimension, i.e., finding an optimal transport sequence, to the already immense search space for packing alone. Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search. In our work, we represent the transport constraints using a precedence graph and train a neural network, coined TAP-Net, using reinforcement learning to reward efficient and stable packing. The network is built on an encoder-decoder architecture, where the encoder employs convolution layers to encode the box geometry and precedence graph and the decoder is a recurrent neural network (RNN) which inputs the current encoder output, as well as the current box packing state of the target container, and outputs the next box to pack, as well as its orientation. We train our network on randomly generated initial box configurations, without supervision, via policy gradients to learn optimal TAP policies to maximize packing efficiency and stability. We demonstrate the performance of TAP-Net on a variety of examples, evaluating the network through ablation studies and comparisons to baselines and alternative network designs. We also show that our network generalizes well to larger problem instances, when trained on small-sized inputs.
Investigating EEG Biomarkers for Attention with Deep Learning: Final Report

Electroencephalography (EEG) enables researchers to non-invasively measure semi-localized brain activity and correlate that activity with speciﬁc cognitive functions. We investigate how deep learning models can be applied to the study of attention and the extent to which electrophysiological biomarkers obtained from EEG signals can be measured and used to classify a person’s state of attentiveness. We determined that artiﬁcial neural networks are able to learn an individual’s EEG characteristics well enough to perform binary classiﬁcation tasks but struggle with learning population-wide datasets. Conversely, more complex CNN and RNN architectures are able to better process multi-subject data, but struggle with single-subject data. We also determined that the frontal cortex, parietal lobe, and the occipital lobe regions of the brain are most highly correlated with attention.
Lack of Correlation between Tuber Number and Cognitive Level in Mild TSC Patients: a Clinical and Genetic Study

Methods: To assess a possible relationship between tuber number and cognitive level we exhaustively studied 30 TSC patients from clinical radiological and genetic point of view. We selected a subgroup of 14 patients showing a mild clinical phenotype, with a complete seizures control, IQ in the average range and normal behavior. All patients underwent brain Magnetic Resonance Imaging (MRI) using a specific protocol for TSC disorder. The tuber number in the brain was correlated with cognitive level in each patient.