Miracle-dz/UniFAR-FTUs huggingface dataset:
TabNet to Identify Risks in Chronic Kidney Disease Using GAN's Synthetic Data

The objective of this study was to develop a system for chronic kidney disease (CKD) and to identify relevant prognostic features using a clinical dataset. Accurate classification and major risk factors in chronic kidney disease lead to better prognosis and assist nephrologists. Due to privacy and other factors, the data source is not balanced to trail any models. Therefore, it is difficult to achieve consistent accuracy with an imbalanced dataset, and there will be a variance in results with different machine learning models. In the proposed study, GAN's generated synthesised dataset, which is very close to the original dataset, is used. A hybrid synthesised dataset consists of the original dataset along with the synthesised data generated with the GAN model. The proposed model also includes the most important risk variables for CKD. The metrics used in the study include F1-score, accuracy, and from the plot, it shows that the TabNet with GAN's synthetic data is more consistent and more accurate than the traditional machine learning techniques with imbalanced dataset. The proposed model iterated for 150 times to get the variance, which is much less than in proposed techniques with hybrid preprocessed datasets. The proposed work significantly increased the classification accuracy of chronic kidney disease. These models and parameters show how important health status data is for predicting the risk of and development of kidney disease.
Sleep Analysis for Fatigue of Crewmembers with Deep Multi-Task Learning

This paper targets to investigate the potential performance of crewmembers given the sleep quality surveillance. The fatigue of crewmembers can significantly affect flight safety and service quality. The quantification and prediction of fatigue is highly desired in practice, and lead to better control of the crewmember arrangement and scheduling. Though the working status can be related to many inherent causal factors, sleep quality plays a major role in fatigue. In this work, we propose to develop an end-to-end system to collect the sleep data with wearable devices, and explore its relations with the popular fatigue indexes, e.g., Karolinska Sleepiness Scale (KSS), Psychomotor Vigilance Task (PVT), and NASA Task Load Index (NASA-TLX). Specifically, we collected the sleep time and quality measurement of 256 crewmembers for three days before the flight, and the corresponding KSS, PVT, and NASA-TLX during or just after the flight. Then, a multi-task deep learning framework is developed to learn the mapping from sleep to fatigue indexes. We trained and evaluated on 200 and 56 crewmembers with the quantified results showing that fatigue is highly predictive with the sleep data. The learned deep learning model can potentially be applied to the pre-flight crewmember screen to avoid the fatigue driving and service.
Customer - Churn Prediction Using Machine Learning

The gradual but consistent decrease in the number of customers retained over time is referred to as “customer churn,” and it is a word that is frequently used in the business and financial sectors. If a company can identify the customers who are most likely to leave, they are more likely to take preventative efforts to keep those customers as clients. It is to the bank's advantage to have knowledge about which customers are theoretically and practically most likely to switch banks in the relatively close future. This article explains how to use machine learning algorithms to identify banking customers who may be considering switching financial institutions. This article demonstrates how machine learning models such as Logistic Regression (LR) and Naive Bayes' (NB) can effectively forecast which customers are most likely to leave the bank in the future by using data such as age, location, gender, credit card information, balance, etc. The article also uses data such as age, location, gender, credit card information, balance, etc. In addition, this article demonstrates the probabilistic predictions that may be generated using machine learning models such as Logistic Regression (LR) and Naive Bayes (NB). The findings of this research ultimately point to the conclusion that NB is superior to LR.
Data management and visualization architecture to measure the competitiveness and digitization of SMEs and startups

Currently there is a great interest in innovation, in generating solutions to meet the needs of society through ventures, SMEs or startups, this requires that companies have a very high responsibility to reach their customers through digitization, especially if you have technology such as the Internet, which is a global communication tool accessible to all. Also, having the freedom to create ventures, SMEs or startups generates a higher level of competitiveness.Analyzing whether the company is complying with some points on the issue of digitization and competitiveness, analyzing its strengths and weaknesses is an important topic for analysis.This research article proposes a technological architecture to evaluate the level of digitalization and competitiveness through the use of metrics that are coupled in a company. This architecture proposes a technological process from data collection, analysis and visualization of results, all with the objective of evaluating the company on its levels of competitiveness and digitization.
An Empirical Study of the Effects of Social Related Stress on User’s Unfollow Intentions of Social Network Service

A stream of behavioral research has been performed on the discontinuous usage phase. However, few research has taken look inside into the follow-behavioral process of it. This paper aims to propose a research model to examine how the social related stress affect the un-follow intentions (UFI) on the termination phrase of SNS(Social Network Service), which plays an important role in the life circle of SNS usage. We find that un-follow stress(UFS) reduces UFIs. We observe these effects empirically and provide suggestions on optimization the behavior regulations released by SNS providers for retaining existing users. The results show that multiple factors cause the UFI thus increase discontinuous usage intentions, while UFS reduces these intentions.
Decision Tree based Smart System for Pregnant Women Diagnosis

one of the reasons of women and newborns death in developing countries is the complications during pregnancy and childbirth. An early diagnosis of pregnancy problems would help to alleviate some of these risks. In this work, a suggested solution to help the physicians for fast pregnant diagnosis is proposed. A smart system is designed for easing women diagnosis during pregnancy. The core purpose of this research work is to build up an Android application which gives a deliberate approach in providing the best diagnosis of pregnancy so that women will not be caught in the is the complications during pregnancy. Android application uses Machine Learning algorithms such as decision tree algorithm. In this paper, decision tree algorithm (DT) is used for predicting pregnancy states, which performs better and more robust than the other models. The best diagnosis of pregnancy is predicted using C4.5 and CART algorithms. Proposed system had used decision tree algorithms that employed a database of the most diseases associated with women during pregnancy. Pregnant women is interacted with proposed system via an android application mobile interface, they had provided the algorithm with the case conditions, which processed to guide both physician and pregnant women on the level of risk. Response of the system is based on input data that processed according to predetermined variables and decision tree outcomes. Implemented experiments had proofed the efficiency of the system to facilitate diagnosis for better pregnant mothers' health care. The CART algorithm has gained a better level of evaluation comparing to other C4.5 algorithm. CART algorithm has achieved the highest correctly classified instances and incorrectly classified instances, which were 89.65% in correctly classified instances, 90.77 % in incorrectly classified instances, 9.23% evaluation measure.
Computer simulation of high speed melting of amorphous silicon

The laser melting of amorphous Si is accurately modelled by computer calculations. It is found that the thermal conductivity of the amorphous phase must be set at approximately 10−2 W/cm K, a value much lower than that of crystalline material to obtain close agreement with experimental measurements. This low value is, however, consistent with the thermal conductivities of other amorphous materials. The results of the computations, when compared with experimental observations, confirm that the melting point of ion implanted amorphous Si is below that of crystalline Si, with a best estimate in the range 1185–1385 K.
Detecting Depression in Reddit Posts using Hybrid Deep Learning Model LSTM-CNN

The detection of depression is a critical issue for human well-being. Previous research has shown us that online detection is successful in social media, allowing for proactive intervention for depressed users. It is a serious psychological disorder and it takes hold of more than 300 million people across the globe. A person who is depressed experience anxiety and low self-esteem in their everyday life, which affects their relationships with their family and friends, and can lead to various diseases and, in the most extreme scenario, suicide. With the rise of social media, the majority of individuals now use it to express their emotions, feelings, and thoughts. If a person's depression can be discovered early by analyzing their post, then essential efforts can be taken to save them from depression-related disorders or, in the best scenario, from suicide. The main goal of our work is to inspect Reddit user posts to see whether any factors suggest depression attitudes among relevant internet users. We use sentiment examination and Machine Learning (ML) techniques to train the ML model and assess the efficacy of our suggested strategy for this goal. A lexicon of phrases that are more common in depressed accounts is identified. In this study, we have combined Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) to build a hybrid model that can predict depression by evaluating user textual messages.
Wearable Smart Jacket for Coal Miners Using IoT

Smart wearables are redefining the way people move and behave in real-time. Workers will be alerted to the presence of toxic gases as well as be tracked in the event of an accident if this system is implemented. Additionally, the instrument has sensors for methane and carbon monoxide gases included in its design. The prototype can detect gas in the air, the rate of the miner's breathing, the change in temperature and humidity, and the miner's location at all times. Wi-Fi will be used to transmit all of these parameters to a dynamic internet protocol. Every one of them will be able to make it through the shield. This way, all mineworkers can be monitored, and if something goes wrong, the miner can be rescued as quickly as possible. Using a pulse sensor on the miner's body, the base camp can track the miner's GPS location. It may be necessary to dig a coal mine as soon as possible to save the most people in a disaster. With the help of IoT, we can build a database and, if necessary, communicate with a nearby hospital. Our final consideration will look at market trends and challenges for WHDs to keep in mind.
Metasurface Design and Scattering Characteristics Analysis of Miura-Ori Folded Structure

Origami can transform the metasurface between multiple structures, changing the mechanical properties, space occupation and electromagnetic scattering characteristics of the metasurface according to the needs. In order to realize the electromagnetic scattering reduction of radar targets, this paper analyzed the design and scattering characteristics of the metasurface based on Miura-Ori folded structure. Firstly, based on the equivalent circuit model and particle swarm optimization algorithm a metasurface that can achieve 10dBRCS Reduction in C - band is designed. Based on this structure, the metasurface of Miura - Ori folded structure is designed and its electromagnetic scattering characteristics are analyzed. The simulation results verify the stealth performance of the structure. In addition, the structure can change its RCS by folding and unfolding, which can be used for stealth and camouflage of deformable targets.
A Requirements Model for Cancer Patients’ Management System (CPMS): A Case of National Oncology Center – Hadramout, Yemen

Despite more cancer patients are successfully treated, the cancer registry remains the main challenge in Yemen. The existing cancer registries in Yemen work separately without full coordination and collaboration. Having a comprehensive registry system would assist in planning, controlling, and evaluating cancer in Yemen. However, to date, there has not been any system or model for cancer patients’ management system (CPMS). The standard or generic requirements can help in the system development phase and would improve the reliability of the system deliverables. Therefore, the main aim of this study is to construct a requirements model for CPMS by using Unified Modeling Language (UML). The Agile Kanban method was employed to achieve the study objective. This method has gained significant importance in managing software engineering projects due to its flexibility, responsiveness, and reliability. The results demonstrated the requirements model of CPMS using three core UML diagrams: use case diagram, sequence diagram, and class diagram. Then, a prototype was developed based on the CPMS requirements model. Then, five experts were involved in evaluating the usability of the CPMS prototype. The evaluation results showed positive feedback from the users on the usefulness and ease of use of the CPMS prototype.
Survey of Micro Fluidic Phased Array Antenna for Beam Steering Techniques

A spatially adaptable radio wire cluster (SAA) is an exhibit that can vary the actual field’s actual area. Ability to expand connect limit without employing an expanded boundary or many receive antenna components. This study presents a thorough examination of current microfluidic over 5G technologies and strategies. In this composition, a new RF feed arrangement is shown, along with the first SAA trial check that makes use of microfluidic-based reconfiguration. This study presents a thorough examination of current microfluidic over 5G technologies and strategies. The investigation’s goal was to create a component fix reception antenna using water-based fluidic stage shifters, where the pillar directing could be controlled by filling a few water channels for each antenna, and other approaches were put out by various researchers. These techniques have been broken down into categories that are easy to understand, such as the use of creating a stage Shifter and combining an array with a phase shifter. In each meeting, a limited number of excellent articles were examined and evaluated for idea confirmation. Finally, a comparison of various approaches was made.
Decoupling design of quad polarization patch antenna using neutralization bridge

When antennas are used in a small area, there is a problem that the mutual coupling between the antennas becomes large. In order to reduce the mutual coupling, we consider the neutralization bridge, in which the feeding points are connected by bridges to cancel the currents on each other's antennas. Decoupling structures using suspended bridges are difficult to fabricate then we design and evaluate decoupling structure embedded in dielectric substrate for easy fabrication.
Aspect ratio and end wall effects on the surface pressure coefficient of a circular cylinder

The effects of end wall conditions and aspect ratios on the surface pressure and the local drag coefficients of a circular cylinder were examined experimentally at a blockage ratio of 3.0% and a Reynolds number of 8000. Both end walls supporting the cylinder were flat plates with different tip conditions on which transitional boundary layers were formed. The ratio of the boundary layer thickness to cylinder diameter at the cylinder position was 0.60 and 0.83, and the aspect ratio was varied from 8 to 32. Regarding the thick wall boundary layer, the base pressure coefficient along the cylinder axis was mostly constant for all aspect ratios, with the exclusion of the end wall vicinity, but rapidly rose as the aspect ratio decreased. In the case of the thin wall boundary layer, the base pressure coefficient was very little constant along the cylinder axis, but the difference for various aspect ratios was relatively small. The local pressure drag coefficient at the mid-span increased in the case of the high aspect ratio and the thin boundary layer.
Determining QoE in Real Time from Video Streaming on SDN Networks

Measuring the quality of the experience of users who contract streaming services is fundamental for the provider, since it allows them to measure the level of satisfaction of their service in the customers. However, the way to measure QoE through the MOS technique is quite manual and does not give information in real time. The present project presents an application that allows to approximate the QoS measurements (bandwidth and packet loss) taken in a SDN network in real time, process them and calculate an instantaneous MOS value, which generates a graph and an average MOS during the duration of the Streaming. This result is approximate to the user's actual MOS and allows the provider to anticipate problems that may be occurring on the network.
A Capillary Tube Pulsating Heat Pipe With Asymmetric Adiabatic Channels for Thermal Management

The two-phase flow passive thermal management device, an asymmetric (adiabatic channel) closed-loop pulsating heat pipe (aCLPHP), has been investigated experimentally. The thermo-hydrodynamic behavior of a pulsating heat pipe (PHP) strongly depends on various operating and geometric parameters. In this effort, asymmetry has been adopted to promote the unidirectional two-phase oscillatory motion of the working fluid. In the present study, simultaneous pressure and temperature variations under gradual and sudden heat loads are monitored to estimate thermal performance. The effects of inclination angles (IAs), heat loads, working fluids, and filling ratios (FRs) on thermal performance have been demonstrated using average thermal resistance. In a two-turn (four-channel) aCLPHP in bottom heating mode, acetone and water were used as working fluids within 50%–70% of FRs. The results show that the startup phenomena of two-phase flow and pseudo-steady-state temperature were achieved using acetone at a 60% FR up to 30° angle from a horizontal orientation. In the vertical orientation, the working fluid water at 60% FR performed well under a sudden heat load of 50 W. Adding asymmetric adiabatic channels to a PHP can be a simple and inexpensive way to improve its thermal performance.
Neighbor Graph Based Tensor Recovery For Accurate Internet Anomaly Detection

Detecting anomalous traffic is a crucial task for network management. Although many anomaly detection algorithms have been proposed recently, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper uses the tensor model for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low-rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). Besides only detecting random anomalies, our algorithm can also effectively detect structured anomalies that appear as bursts. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve higher Accuracy and Recall.
Beam-Steering by Using SIW Based 2x2 Butler Matrix for Mm Wave Antenna

A beam steering technique is introduced in this research study, which is developed using SIW method to work at a frequency of 25 Giga Hertz by creating bi - lateral outer walls for millimetre wave applications using aperture coupled antennas. The network for beam generating is created with a SIW directional coupler that acts as a $2\times 2$ Butler Matrix (BM) that is fed with SIW Antenna array with slotted waveguide slots. The signal is distributed to the appropriate input ports. The signal is uniformly dispersed if one of the input ports is supplied with a phase shift is observed between adjacent output ports. An antenna array is created with a gain of 7.8 dBi along with a miniature sized directional coupler which provides a bandwidth at the operating frequency of 20%. The effective results of beam steering make the suggested concept eligible for providing different applications such as tracking in next generation communications systems.
Analysis of Wide Voltage Variation in LLC Converters Using Variable Transformers

One of the most important applications of power converters nowadays are Battery chargers in which the gain of the converter requires a wide range and high efficiency, the LLC resonant converter has become one of the most used topologies in this area. The most used approach for varying the output voltage is by changing its switching frequency. However, when the applications require wide variations of the output voltage it is necessary to perform wide frequency variations. This paper compares two methods to achieve wide voltage variations, the first one the previously mentioned technique of increasing the frequency and the second one the implementation of variable magnetics. The trade-offs of both techniques are presented and simulated.
Recent Advances in Edge Computing for 6G

Edge computing is a new technology that enables 6G development by bringing cloud capabilities closer to end users to overcome traditional cloud flaws such as high latency and a lack of security. Few studies identify recent advances, challenges, and opportunities in 6G edge computing. As a result, this paper presents a review to identify recent advances in edge computing in 6G. It also discusses the challenges and opportunities of such new trends in the computing landscape. The paper identifies challenges (security and privacy, offloading and task division, bandwidth, service management, advanced communication, general-purpose computing on edge nodes, quantum computing and edge computing, and IoT heterogeneous platforms) as well as opportunities (academic-industry collaborations, algorithms and libraries that are lightweight and virtualization and micro-operating systems). Finally, some conclusions have been drawn after identifying current research directions.