Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care

In this paper a unique approach towards decision making process and better quality care inPati healthcare applications is developed by using the Machine Learning (ML) concepts as an alternative mode for identifying the characteristics and patient satisfaction (PS) in the proposed healthcare system. Extracting the information from raw data using some algorithmic approach is known as Data mining. In this a ML approach is used in determining the patient satisfaction in health care sector. Applied regression models to determine the patient satisfaction and also correlation methods is identified as an important attribute to be considered in determining the better quality of health care application models. The data set is taken based on the opinion on three types of data such as a) patient opinion towards hospital care b) nurse opinion towards workplace and c) Administrative aspects of healthcare. Our findings revealed high accuracy in Regression (88%), that helps in concluding by considering the administrative and workplace attributes related to patient satisfaction. The results are validated using traditional statistical methods like binomial correlation and linear regression.
Design of Electrostatic Energy Harvesting Pre charge Circuit

An energy harvesting system uses Pre charge circuits. The need for designing circuits that consume low power is the need of the day. In this paper a pre charge circuit has been designed in 45nm CMOS technology and the output energy and average power consumption of the circuit has been computed. The cascaded double tail comparator and the gate driver circuits are designed. The pre charge circuit is energy efficient and harvests more energy as compared to the invested energy. The capacitance value for designing the pre charge circuit has been obtained from the capacitive pressure sensor in COMSOL Multiphysics 5.3. A plot of capacitance versus pressure gives the equivalent capacitance for the applied pressure. A value of 1pF has been chosen for an applied pressure of 22.5 to 25 KPa. In the pre charge circuit, the energy consumption during charging of the capacitor was computed to be $\mathbf{4.5}\ \mu\mathbf{W}$ and during discharging it was computed to be $\mathbf{10}\ \mu\mathbf{W}$. The invested energy was $\mathbf{0.281\ x\ 10}^{-\mathbf{9}}\mathbf{fJ}$ and the obtained energy was $\mathbf{0.781\ x\ 10}^{-\mathbf{9}}\mathbf{fJ}$. The net energy gain was $\mathbf{0.5\ x\ 10}^{-\mathbf{9}}\ \mathbf{fJ}$ and the percentage gain was 177.94%.
Novel Algorithm for Segmentation of Renal cyst from CT Image Sequences

In this paper a novel hybrid segmentation approach for optimization of the renal cyst diagnosis from CT image sequences is proposed. The approach is based on several segmentation techniques. A locally optimized front propagation algorithm according to the level set paradigm with variable term is in the core of the segmentation of the kidney from CT image sequences. Тhe split & merge and color based k-mean clustering algorithms are used for the cyst segmentation. The accuracy evaluation of the renal segmentation with Dice similarity index represents high results for the whole experimental data set – 90, 97%.
Attention-Based Speech Recognition Using Gaze Information

We assume that there is a correlation between an utterance and a corresponding gaze object, and propose a new paradigm of multi-modal end-to-end speech recognition using multimodal information, namely, utterances and corresponding gaze points. In our method, the system extracts acoustic features and corresponding images around gaze points, and inputs the information into the proposed attention-based multiple encoder-decoder networks. This makes it possible to integrate the two different modalities, and the performance of speech recognition is improved. To evaluate the proposed method, we prepared a simulation task of power-line control operations, and built a corpus that contains utterances and corresponding gaze points in the operations. We conducted an experimental evaluation using this corpus, and the results showed the reduction in the CER, suggesting the effectiveness of the proposed method in which acoustic features and gaze information are integrated.
A Modular Multilevel Dual Buck Inverter With Adjustable Discontinuous Modulation

Conventional single-cell dual buck inverters present high reliability, however they cannot be directly used in high voltage applications, because of the limited voltage rating of commercial switching devices. Therefore, a novel modular multilevel dual buck inverter (MMDBI), capable of processing voltage and power at higher levels, requiring a single dc input voltage, is proposed in this paper. Non-interleaved phase-shifted discontinuous modulation (NIPSDM) and interleaved phase-shifted discontinuous modulation (IPSDM) schemes are applied to the proposed inverter to reduce the current stress in the switching devices. Despite beneficial to the converter efficiency, the use of these discontinuous modulation approaches results in current zero-crossing distortion, which needs to be appropriately managed. In this work, theoretical values for the common-mode and differential-mode current ripples under NIPSDM and IPSDM modulation strategies are derived, and each switching operating mode is fully analyzed. From that, an adjustable current threshold value, which depends on the theoretical current ripple magnitude, is incorporated into the modulation strategy, producing a minimum switching overlap period between the two buck cells that eliminates the current zero-crossing distortion and produces a high-quality output current. Extensive simulation and experimental investigations are presented to demonstrate the feasibility and effectiveness of proposed topology and adjustable discontinuous modulation strategy.
Unsupervised Image Categorization Based on Variational Autoencoder and Student’s-T Mixture Model

In this work, a novel generative robust image catego-rization approach is developed based on variational autoencoder (VAE) and Student’s-T Mixture Model (STMM). The network structure composed of VAE, STMM and Convolutional Neural Network (CNN) generates data. More specifically, first, a cluster is chosen using the STMM. Then, a latent representation is extracted from the selected cluster through a CNN encoder. After that, an observation is generated based on another CNN through a decoding process. The proposed model is learned through variational inference where the Evidence Lower Bound is optimized according to Stochastic Gradient Descent(SGD) and the reparameterization trick. Based on our experimental results, the proposed generative clustering approach is able to outperform classical clustering approaches (e.g. K-means, Gaussian Mixture Models) and other related generative clustering approaches. Furthermore, we show that our generative model is able to generate highly realistic samples without using any supervised information during training.
A Grid-Connected Single Step High Gain Buck-Boost Inverter Using Fuzzy Maximum Power Point Tracking

In this paper, topology of Grid connected single step (GCSS) high gain buck-boost inverter with fuzzy maximum power point tracking (MPPT) controller is discussed. It can step up/down the output from PV & performs DC to AC conversion simultaneously in a single step with the help of fuzzy MPPT controller. This scheme has features such as high gain, less switching loss & with the help of fuzzy MPPT it tracks maximum power from PV very efficiently. Thus this scheme is conformable for PV applications with low voltage & varrying with time conditions. Design of proposed GCSS elements with derived equations & design of fuzzy MPPT is given in this paper. Presented topology is designed using MATLAB/SIMULINK. Detailed simulation of presented GCSS with fuzzy MPPT and it's results are given.
An Improved Grey Prediction Evolution Algorithm Based on Topological Opposition-Based Learning

The grey prediction evolution algorithm based on the even grey model (GPEAe) proposed by Z.B. Hu et al. in 2019 is a competitively stochastic real-parameter optimization algorithm with characters of simple code, less parameters and strong exploration capability. To improve the algorithmic overall performance, a topological opposition-based learning strategy (TOBL) is first developed to enhance its exploitation capability in this paper. The TOBL determines offsprings by calculating the Manhattan distances between the current best individual and all the vertices of the hypercube inspired by the opposition-based learning strategy. An improved grey prediction evolutionary algorithm based on the TOBL (TOGPEAe) is then proposed. The performance of the TOGPEAe is tested on CEC2005, CEC2014 benchmark functions and a test suite composed of six engineering design problems. The experimental results of the TOGPEAe are very competitive compared with those of the original GPEAe and other state-of-the-art algorithms.
I-safe Technology for Smart and Secure Vehicles

Recently, there has been a rise in car theft, lifting, and snatching in various towns of Pakistan. Plus, some harassment cases in Uber/Careem ride booking services are also reported. Therefore, this paper presents an innovative solution, named as I-safe Technology, which introduces black-box in cars. I-safe functionalities include live streaming with audio/video recording, cloud integration, real-time location tracking, personalized user app, and centralized database for the managerial process. The device is water-proof, fire-proof, and compact; it ensures the safety of data for future inspection and helps in the determination of the root cause of incidents.This idea is novel in several ways. Firstly, the problems addressed are uniquely local, and there is no such prior work in technical literature. Secondly, this solution 2x times cost-effective than comparable products available in the market. Thirdly, this solution is highly customizable with a smartphone application that provides ease to users by automating all functionalities of a car.
SDN Resiliency to Controller Failure in Mobile Contexts

Software defined networking (SDN) is a technology for management of computer networks. We are interested in SDNs where the switches are mounted on vehicles, the controller is airborne, and connectivity changes dynamically as a result of vehicular movement. The challenge of interest is that when a controller fails the switches are left with whatever configuration they had at the point of failure. Without a controller, as the switches move and links become unuseable, flows configured to use those links are broken unless the configuration has anticipated such events and provided alternative routes. We describe a high fidelity SDN network simulator which uses snapshots of actual switch configurations and analyzes their resilience to impacts of controller failure. We propose an algorithm for backup paths which is guaranteed to not cause loops, and conduct a simulation study to understand the types of domains where have the greatest impact on network performance.
Efficient Nested Simulation Of Tail Risk Measures

Tail risk estimation for portfolios of complex financial instruments is an important enterprise risk management task. Time consuming nested simulations are usually required for such tasks: The outer loop simulates the evolution of risk factors, or the scenarios. Inner simulations are then conducted in each scenario to estimate the corresponding portfolio losses, whose distribution entails the tail risk of interest. In this paper we propose an iterative procedure, called Importance-Allocated Nested Simulation (IANS), for tail risk estimation. We tested IANS in a multiple-period nested simulation setting for an actuarial application. Our numerical results show that IANS can be an order of magnitude more accurate that a standard nested simulation procedure.
Unbiased Simulation Estimators for Jump-Diffusions

We develop and analyze an unbiased Monte Carlo estimator for a functional of a one-dimensional jump-diffusion process with a state-dependent drift, volatility, jump intensity and jump size. The approach combines a change of measure to sample the jumps with the parametrix method to simulate the diffusions. Under regularity conditions on the coefficient functions as well as the functional, we prove the unbiasedness and the finite variance property of the estimator. Numerical experiments illustrate the performance of the scheme.
FRAGILIDAD EN EL ADULTO MAYOR: DETECCIÓN, INTERVENCIÓN EN LA COMUNIDAD Y TOMA DE DECISIONES EN EL MANEJO DE ENFERMEDADES CRÓNICAS

Physical fragility is defined as “a medical syndrome with multiple causes and factors that contribute to its development and that is characterized by decreased strength and resistance as well as reduced physiological functioning, which increase an individual’s vulnerability to functional dependence or death. ” Different multi-systemic pathophysiological processes are involved in the pathogenesis of frailty syndrome, one of the most important being the loss of muscle mass associated with aging or sarcopenia. Several studies have demonstrated that physical frailty increases with age. Physical exercise is the best form of intervention for preventing and treating frailty. In older adults with chronic illnesses, identifying frailty is very important for decision-making, individualizing management, and considering their values and care preferences.
The Evolution of Reinforcement Learning *

In both the natural and artificial realms, evolution and reinforcement learning are parallel adaptive processes that work on different scales but with similar feedback mechanisms. This makes combined and coordinated study of these phenomena synergistic. In the natural world, evolution is antecedent to all forms of learning but can, nonetheless, be influenced by them. In artificial intelligence, evolutionary computation and reinforcement learning were initially developed independently and in parallel but numerous studies of their interactions exist. As reinforcement learning gains prominence in machine learning, and as it is integrated into more complex learning systems such as deep neural networks, greater understanding of how it has and could be evolved becomes increasingly relevant. This survey covers major trends in the evolution of reinforcement learning and suggests important directions for future research.
IoT Enabled Smart Industrial Pollution Monitoring and Control System Using Raspberry Pi with BLYNK Server

Now a day's environmental pollution becomes important issues in metropolitan cities and industries. The sudden growth of industries, infrastructure and transport vehicles have been producing environmental issues like greenhouse effect and diseases caused by harmful gases present in the environment which directly effect on human's health and thus needs a special monitoring system. To resolve have an effect on the environmental pollution issues, here we have a tendency to implement IoT based pollution monitoring and control system. Where it detects the deleterious gases present in the environment. The proposed IoT technology scrutinizes level of co gases released during industry process, temperature and humidity using sensor technology and Raspberry pi. Each process will have a separate data monitoring and controlling mechanism. The device are going to be put in through a BLYNK native server mobile application which is able to show the live updates of the environmental pollution level of the industries or remote space The thought of IOT helps to access knowledge from remote locations. The most plans behind the implementation work is to form a true time absolutely machine-controlled management mechanisms exploitation IoT.
A Novel Method in Matched Filter Spectrum Sensing to Minimize Interference from Compromised Secondary Users of Cognitive Radio Networks

The void spectrum in spread out geographical location and a congestion in spectrum allotment in different regions of the world has led to the scarcity of precious natural resource - the wireless radio spectrum. This led to cognitive radio wireless networks. In CRN when the secondary users are trying to opportunistically use the licensed band, the probability of misuse is more than tolerable. This unwanted interference to licensed primary users can be muted out or mitigated with intelligent sensing using matched filter spectrum sensing. In this paper we have presented a novel method to minimize the interference using cross-correlation of known sample of primary user and the random sample of secondary user. Using heuristic function and other percepts, the classification of secondary users will be made as compromised or not.
Sentiment Analysis Platform of Customer Product Reviews

Opinions and reviews are very important data coming from the customer for the companies to improve their product or services. The study focuses on creating a platform that would be able to capture the data in the form of comments or reviews. The platform was able to create and generate quantitative and text data based on the reviews and comments. The researchers used the Scrum Agile methodology. This study utilized the descriptive research method with the use of the questionnaire as the instrument testing the acceptability of the system’s application. It was evaluated by 100 respondents. The mean value was determined and compared using analysis of variance to determine if there is a significant difference. ANOVA was also used to test for critical value of the null hypothesis. Based on the results, there is a marked significant difference among the evaluation of the 4 groups of respondents as indicated in the F-value of 6.48972. The respondents gave an acceptable rating and all agreed that the proposed product sentiment analysis application is a useful tool in evaluating company’s products.
Fast Finite-Set Model Predictive Control for Three-Phase Four-Arm Active Front End Modular Multilevel Converters Under Unbalanced and Distorted Network Conditions

This paper focuses on a fast finite-set model predictive control (FFS-MPC) for three-phase four-arm active front end modular multilevel converters (AFE-MMCs) under unbalanced and distorted network conditions. The main aim of this paper is to enhance the steady-state performance of the whole system while remaining computationally feasible. Firstly, a novel topology, which has a good potential to improve the fault tolerance ability of MMCs, is presented in this literature. Secondly, in order to enhance the steady-state control performance, a new FFS-MPC methodology is proposed to serve this purpose. Specifically, the philosophy behind the proposed solution is to formulate a user-predefined cost function formula by embedding a power compensation term and an integral error term at the same time, which improves the power quality under normal and under abnormal conditions. However, it is important to notice that the computational complexity will be increased while applying the proposed solution to the control of three-phase four-arm AFE-MMCs. To solve this issue, a fast MPC is introduced into the proposed methodology to improve the computational efficiency, making it suitable for multilevel converters control. Finally, the effectiveness and feasibility of the proposed FFS-MPC methodology can be validated by the comprehensive results for regulated three-phase four-arm AFE-MMCs.
Big Data Driven Anomaly Detection for Cellular Networks

Anomaly detection for large scale cellular networks can be used by network operators to optimize network performance and enhance mobile user experience. This paper aims at detecting user anomalies from spatio-temporal cell phone activity data. We design an approach combining time series analysis and machine learning to extract the traffic patterns of areal units. This approach can cluster areal units with similar traffic patterns and segment a city into distinct groups. Then, in grouped-areas, we use a clustering technique to detect anomalous behaviors of the cellular network and verify the accuracy of the results using ground truth information collected from online sources. The results indicate that anomalies are associated with abruptly high or unexpected traffic demand at a specific location and time. In addition, we obtain anomaly-free data by removing anomalous data and train a decomposed traffic prediction model. It is observed that the prediction model trained with anomaly-free data can achieve lower normalized mean square error (NMSE), i.e., higher prediction accuracy, than the model trained with anomalous data.
Population-based Search Relying on Spatial and/or Temporal Scale-free Behaviors of Individuals

We develop two types of simple population-based search algorithms that model two types of scale-free behaviors of individuals. The scale-free behavior is a particular behavior of individuals of species that search for food not cooperatively but independently. One type of the scale-free behaviors is that a moving distance of an individual from the present food source follows a power low distribution, which is called the spatial scale-free behavior. The other is that a staying duration of an individual at the preset food source follows a power low distribution, which is called the temporal scale-free behavior. We assume static and dynamic problems in which a position of the best food source (the global optimum) is not changed and changed, respectively. In addition, we assume a special event that individuals near the best food source are probabilistically eliminated. We compare the two search algorithms and show that they are complementary with respect to suitable problems. Therefore, we develop a search algorithm that initially includes both types of individuals in a population and evolutionarily adaptively increases an appropriate type of individuals in it. The algorithm is shown to be not the best but work quite well for any problems used.
Orchini Similarity User Authentication Based Streebog Hash Function for Secured Data Storage in Cloud

Cloud provides a flexible on-demand data outsourcing services for the individuals and organizations to store their data in the server. Security of cloud storage is guaranteed through providing data confidentiality. The cloud computing provides several benefits but the major risk to provide security to the user’s data since the cloud infrastructures vulnerable to various threats. In order to improve the secure cloud storage, an Orchini Similarity Authentication based Streebog Hashing Secured Data Storage (OSA-SHSDS) mechanismis introduced. The main aim of OSA-SHSDS mechanism is to store the cloud user data with higher confidentiality rate and lesser space complexity. In order to store the data in the cloud server, the cloud user personal details are to be registered. After registering, the cloud server generates the ID and password for every registered cloud user. Wheneverthe userwants to store the data, the user has to login with ID and password to the cloud server for authentication. The cloud server verifies whether the user has authenticatedthe user or not through orchini similarity measure. After authentication, the cloud user is allowed to store the data in the cloud server. This helps to increase the data confidentiality rate and to minimize the space complexity. The experimental evaluation of OSA-SHSDS mechanism and existing methods are performed using Amazon dataset with the metrics such as authentication accuracy, computation time, data confidentiality rate and space complexity. The observed results reveal that the proposed OSA-SHSDS mechanism attains higher authentication accuracy and data confidentiality with minimum computation time as well as space complexity than the other state -the- art- methods.