Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Cardiovascular Disease Risk Estimation Prediction System

Cardiovascular diseases have been on the rise from the past few decades. In developing countries, cardiovascular diseases (CVDs) have become one of the most causes of death around the world. Thus, there is a need to predict CVDs at the earlier stages to minimize treatment costs and avoid critical cases. Thus, this work provides a novel method to predict cardiovascular diseases using machine learning. A comparison between various machine learning algorithms is made to analyse the performance on the dataset and the proposed method uses s K Nearest Neighbour which has an accuracy of 92.30%. Additionally, data reduction techniques have been utilised to reduce the number of attributes to 11 which significantly outperforms the related work.
A Low Voltage Ride Enhancement Technique for Wind Energy System

In this paper, the fault current limiter (FCL) technique has been revisited in order to deal with the Low Voltage Ride Through capability (LVRT) of the Wind Energy Conversion System (WECS). An inductive type FCL is suggested for a grid-connected Doubly Fed Induction Generator (DFIG) in combination with the Battery Energy Storage System (BESS). The BESS is located across the DC-link, while FCL is situated on rotor side of DFIG. It is observed that placing BESS in DC-link is effective to eliminate ripple and maintain voltage across it. The dynamic behavior of low voltage ride through is studied under a symmetrical fault condition. The proposed protection system performance is validating by comparing it with the conventional crowbar protection scheme. Using the MATLAB / SIMULINK platform, the proposed system is simulated on a 2MW DFIG-WECS.
Effective Methods for Constraint Handling in Quantum Inspired Evolutionary Algorithm for Multi-Dimensional 0–1 Knapsack Problem

In this paper, we study an evolutionary algorithm inspired by quantum computing, called a Quantum Inspired Evolutionary Algorithm (QIEA) for the solution of Multidimensional 0–1 Knapsack problem (MDKP). During the process of evolution in QIEAs, a repair algorithm is used to modify infeasible solutions produced in each generation in order to make them feasible. The choice of repair strategy impacts the performance of QIEAs to a large extent. Several repair algorithms have been proposed and tested for an effective solution of large problem instances. To demonstrate the effectiveness and applicability, we have tested the algorithms on several popular benchmark instances.
MDP-Based Task Offloading for Vehicular Edge Computing Under Certain and Uncertain Transition Probabilities

Low latency/delay is one of the most critical requirements for the application of vehicular networks. However, frequent real-time information update caused by vehicles high mobility is liable to aggravate the delay. Meanwhile, the task migration between different vehicular edge computing (VEC) servers results in an amount of delay if the computing cannot be completed before the vehicle moves out of the coverage of the current VEC server. In this paper, the problem is concluded as when and to whom to offload the task for VEC, which is formulated as a finite horizon Markov decision process (MDP) to minimize the delay with respect to the communication, computing, handover and migration. Through characterizing the time-space correlation of vehicles mobility, the curse of dimensionality problem in MDP is resolved. Meanwhile, a general expression of the transition probabilities is derived. On this basis, the specific results of highway, 2-D street and real-data scenarios are provided as well. For practical implementation considerations, the transition probabilities are commonly uncertain primarily due to random driver behavior, inaccurate sample data and complex path environment. Under this uncertain environment,a robust time-aware MDP-based task offloading algorithm (RTMDP) is proposed, which has been proved to perform well even under the high uncertain transition probabilities by simulation results.
Dynamic Orchestration of Group Entities for Effective Collaboration Using Number Theory

Collaborative technology based applications such as Emails, chats and messaging services have become ubiquitous in today's world. Generally, applications demand creation of ephemeral and volatile subsets of messaging groups which are currently done in static manner. Static creation of combination of all subgroups for collaboration is known to consume lot of resources and cause huge maintenance overhead. CRUD (creation, read, update and delete) operations on short lived messaging groups pose a big administration task. In this paper a number theory based solution is proposed which realises short-lived groups as logical subgroups thereby mitigating CRUD overhead. The results of the simulations showed for the worst case the data marshalling complexity of group creations reduced from non-polynomial to polynomial
Cellular Segmentation of Bright-field Absorbance Images Using Residual U-Net

Cellular segmentation is one of the essential processes for cytometric analysis. Bright-field microscopy (absorbance) is a widespread technique among all optical microscopy approaches. However, due to the low contrast in absorbance images, membranes of cells are not easily discernible. Convolutional Neural Network (CNN) based deep learning approaches have successfully been applied in many medical image processing applications. In this paper, we propose a residual network-based U-Net for segmentation of cell region in cell absorbance images of human-induced pluripotent Retinal Pigment Epithelial stem cells (iRPE). Additionally, we use a weighted sum of multiple loss functions, namely, Binary Cross-entropy loss, Dice loss, and inverted Dice loss, to optimize the network weights. With this proposed approach, we achieve an average Dice Coefficient of 0.8366 on the validation data split for the intended task.
Analysis of Mutual Inductance Between Rectangular Structured Wireless Coupled Coils with Different Misalignments Using Finite Element Modeling

As the demand for electric and hybrid vehicles is increasing more, the wireless power transfer (WPT) system attracting the researchers. Safety, cost reduction and convenience are just few of the emerging technology's many benefits. One of the current challenges of developing and implementing wireless charging is the tolerance of misalignments between the coupled coils. The efficiency of the WPT system mainly depends on mutual inductance (MI) between the coils and coupling coefficient of the structured coupled coils. Hence this paper proposes a coaxial rectangular structure of the coupled coils to analyze the MI, flux distribution and coupling with various misalignments of the coils using Finite Element Modelling (FEM). The calculated values of the magnetic coupling help to enhance the efficiency and power transfer capacity of the WPT system towards electric vehicle charging application.
Cluster-Based Intrusion Detection Method for Internet of Things

Internet of Things (IoT) is an advanced network which joins intelligent devices with the internet. Most of IoT devices are having sensors, and their work is to monitor the behavior near us. As all the "things" are connected, it also increases the susceptibility of the network. Security attacks may happen at any time in the network and may adversely affect the network. One such attack that we have tried to address in this paper is intrusion detection. We proposed an intrusion detection algorithm in our paper. We worked on the detection of selective forwarding and sinkhole attack in the 6LoWPAN environment using routing protocol RPL. It consists of a hybrid intrusion detection method for detecting two routing attacks. The experimental results of our algorithm showed that it achieved a true positive rate of 96.3% and false positive rate of 6.1% when routing attacks (sinkhole and selective-forwarding) were launched simultaneously.
We Are What We Eat: A Cultural Examination of Immigrant Health and Nutrition in Middle Tennessee

The purpose of this research is to examine recent Hispanic immigrants to Middle Tennessee from an anthropological perspective to discover possible correlations to answer the research questions: how are Latino and Hispanic immigrant populations’ nutritional statuses affected by acculturation to the United States and to Middle Tennessee, and what factors come to play in changing their own food practices? Results include information gleaned from previous ethnographic research as well as from interviews with local Hispanic immigrants relating to food insecurity, the impact of school lunches on immigrant children’s health, and possible remedies to halt the declining health of immigrants to the Middle Tennessee. is research suggests that the immigrant experience is not the same for every group or even for each individual, that health outcomes of other immigrants dier according to their experiences of acculturation.
Secure Wireless Multicasting with Mixture Gamma Distribution for Generalized-K Fading Channels

This paper concentrates on the analysis of security performance for transmitting confidential data from a source to multiple receivers over Generalized-K fading multicasting channel in the presence of multiple eavesdroppers. All the terminals are equipped with single antenna and all the channels are Generalized-K fading based on mixture gamma distribution. In order to investigate the effects of fading parameters imposed on the Generalized-K fading with mixture gamma distribution, we derive the closed-form analytical expressions for the probability of non-zero secrecy multicast capacity, ergodic secrecy multicast capacity and the secure outage probability for multicasting &. The derived analytical expressions are presented in terms of the fading parameters of Generalized-K fading with mixture gamma distribution so that they are helpful to understand the insight, how the security in multicasting can be enhanced adjusting the parameters of Generalized-K fading channels. Monte-Carlo Simulations have also been performed to validate the analytical results.
Power Quality Improvement with Single Phase Boost Rectifier using Fuzzy Logic Control

For regulation of DC output voltage with unity power factor of AC-DC converter, a controlling procedure has been introduced in this paper which accounts for the effects of harmonics in non-linear loads. The scheme incorporates a single phase full bridge rectifier in conjunction with a PFC boost converter controlled by the fuzzy logic controller. The non-linear effects of bridge rectifier is compensated by hysteresis current control technique directed by the boost converter. The results show that the proposed controller can regulate the DC output voltage over a wide range of load and input voltage variation while making the input current sinusoidal with improved power factor.
On the Dissection of Evasive Malware

Complex malware samples feature measures to impede automatic and manual analyses, making their investigation cumbersome. While automatic characterization of malware benefits from recently proposed designs for passive monitoring, the subsequent dissection process still sees human analysts struggling with adversarial behaviors, many of which also closely resemble those studied for automatic systems. This gap affects the day-to-day analysis of complex samples and researchers have not yet attempted to bridge it. We make a first step down this road by proposing a design that can reconcile transparency requirements with manipulation capabilities required for dissection. Our open-source prototype BluePill (i) offers a customizable execution environment that remains stealthy when analysts intervene to alter instructions and data or run third-party tools, (ii) is extensible to counteract newly encountered anti-analysis measures using insights from the dissection, and (iii) can accommodate program analyses that aid analysts, as we explore for taint analysis. On a set of highly evasive samples BluePill resulted as stealthy as commercial sandboxes while offering new intervention and customization capabilities for dissection.
Comparative Review of Feature Selection and Classification modeling

Feature selection is a procedure in machine learning to find a subset of features that help to know the most important features of the data set for model construction. It removes irrelevant and redundant information as much as possible. Fewer attributes is desirable because it reduces the complexity of the model. In many classification problems, it is difficult to learn good classifiers before removing these unwanted features due to the huge size of the data. Reducing the number of irrelevant or redundant features can drastically reduce the running time of the learning algorithms and yields a more general classifier. This helps in getting a better insight into the concept of a classification problem. The objective of feature selection is improving prediction performance and providing a better understanding of the process data. In this paper, we provide an overview of some of the classification methods. Most of these classification methods are based on some threshold values and benchmark algorithms that determine the optimality of the features in the dataset. Selecting the optimal set of features will help us hence reduce the space and time complexity as well as increase the accuracy or purity of classification. We also review some of the feature selection techniques on standard datasets to illustrate the applicability of feature selection techniques.
Multi-contact Stability of Humanoids using ZMP and CWC

We propose a method for checking the multicontact stability of humanoids simultaneously using Zero Moment Point (ZMP) and Contact Wrench Cone (CWC). The main idea of our method is to derive the friction constraints of foot contact using soft-finger contact model. Thanks to the similar definition of ZMP and the soft-finger contact model, it is able to use the friction ellipsoid computed from the soft-finger contact model at ZMP to replace the 6 dimensional wrench that computed from the friction constraints at each contact point of the foot contact. By using our proposed method, the stability of the foot rotation can be judged by using the ZMP while other factors affecting the stability of a robot can be judged by the CWC. By combining two wrench spaces, our method can be used to check the stability of humanoids which walking on the horizontal floor with hand contact with the environment.
Separation of Overlapping Sound using Nonnegative Matrix Factorization

One of the most common problems in sound recognition is the overlapping sound. This phenomena requires sound separation beforehand in order to be recognized. Most studies related to sound separation used artificial data in their research, i.e. using experiment sound data from a controlled environment which is augmented with one or more sound types, and achieve good results. However, when it is implemented in the real condition, it’s performance has dropped dramatically. Thus, in this research we use overlapping data recorded in real environments. The purpose of this research is to separate the speech and non-speech, and noise by using the Non-negative Matrix Factorization (NMF). Our experimental results show that the NMF works well when separating sound and non-sound, and has helped the performance of sound recognition.
COMTEST Project: A Complete Modular Test Stand for Human and Humanoid Posture Control and Balance

This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantitative measures of performance. In order to provide versatility, the design of the system is oriented to modularity: all its components can be replaced or extended according to experimental needs, adding additional perturbation profiles, new evaluation principles, and alternative tracking systems. It will be possible to use the system with different kinds of robots and exoskeletons as well as for human experiments aimed at gaining insights into human balance capabilities.
Analysis of Global Bifurcation and Chaotic Oscillation in Distributed Generation Integrated Novel Renewable Energy System

In this paper, a distributed generation (DG) integrated novel renewable energy system is proposed and analysis of bifurcation, chaotic behavior is observed. The integration of DG is the main property of renewable energy system, so in this model wind turbine generator (WTG) is integrated as a DG. Furthermore, dynamical load and Flexible AC Transmission System (FACTS) devices are also integrated. Renewable energy system is very complex, nonlinear dynamical system and it may exhibit chaos phenomenon in case of small, large disturbances or system parameter variations. The study of bifurcation and chaos behavior is essential in the proposed renewable energy system because it may lead to very serious results like voltage collapse, angle instability sometime blackout. The observation of bifurcation and chaotic oscillations are analyzed for wide range of parameter variations with the help of Lyapunov exponent, Lyapunov spectrum and bifurcation diagram. Simulation are done in MATLAB environment and simulation results reveals that the objective is achieved successfully.
Energy-Efficiency-Oriented Charge Scheduling and Beamforming for a Two-Tier Wireless Powered Network

Long-range radio-frequency wireless charging has received more and more attention in recent years. Unlike the literature that focuses on the mobile charging scenario, which is formulated as a path-planning problem, we consider a static network that consists of a power beacon (PB) and multiple wireless powered user nodes. Each user node is equipped with a rechargeable battery and harvests energy only from the PB. We assume that all user nodes are either in charge or work mode. Under this assumption, we first propose a charge scheduling scheme that achieves the system’s maximal energy efficiency. We then further investigate the system’s improvement with a multiantenna PB using the energy beamforming technique. Next, we extend our scheduling scheme to a two-tier network architecture, where a first-tier PB first transfers energy to the second-tier sub-PBs, and then those sub-PBs deliver energy to the user nodes which belong to their clusters. It is shown that energy beamforming with multiple antennas brings a significant improvement to the system’s performance. Moreover, the two-tier architecture is shown to be superior to the one-tier architecture in terms of the energy transfer efficiency and the system’s realization complexity. Finally, the simulation results demonstrate our theoretical findings.
MacNabbs: Knowledge Extraction from Unstructured Data using Semantic Analysis and User Activity Logging

Decision support tools have emerged as key technologies in data analysis enabling companies to make personalized offers to their customers with the help of data processing and data visualization tools. Embedded agents with conversational interface allow expressing queries in natural language in order to accelerate the report navigation. However the coverage, complexity and scale of such agents are much larger than simple query understanding. Moreover, most of them are often restricted to specific domain and explicit rules. In this paper, we present an approach which uses topic modeling and anomaly detection to point out the information that may be of particular interest to a domain expert. The strength of our approach is that it shows both domain and common sense knowledge and does not require the model to be re-trained for different experts. The proposed model can suggest decision options based on the analyzed data and user's activity. In particular, we propose an unsupervised method for automatic topic labeling, and show how it might be helpful in decision making. We also implement a framework, called MacNabbs, and give some results of our approach.
DC-DC Converter Assisted Fault Current Control in DC Microgrid

Due to absence of natural zero crossing and fast rise in dc current during fault, the fault current clearing in dc microgrid has been a challenge. The fault current is high in pole to pole fault. It is even higher in the presence of multiple dc generators as all the generators feed the maximum possible current in the network during the fault. This results in very high currents in the lines. In order to clear the fault in such a case, the dc breaker needs to break high fault current. However, the dc breaker operation at high fault current level is difficult and unsafe. Therefore, in this paper, a control scheme is proposed for dc-dc converter which reduces the fault current level at which the dc breaker operation is effective and safe. It uses an additional current control loop which is activated during the fault event. This modified control scheme ensures that the current fed by the converter into the network during the fault is low and thus resulting in low line currents in the network. The proposed control scheme is validated using a detailed simulation.
A Novel Approach for Classification of Microblog Tweets

Nowadays, if something happens, then many tweets are posted. Peoples are generally influences what everyone else are saying. A person observes a thing according to their natures, whether they are positive or negative kind of person. When these persons tweet other persons influenced by their thoughts. These tweets contain the situational and non-situational information. Situational tweets may have attributes such as helpline number etc. While non-situational tweets may have the information about the charity etc. Therefore, the classification of tweets into these two categories are much required to extract the meaningful information. In this paper, a new methodology is proposed and implemented to classify situational and non-situational tweets. The present work is based on POS tagger, weighted parameter normalization and normalization of tweets. A comparative study is also presented in this paper in terms of F-measure. The results are satisfactory.