Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the Keras Model for Accurate and Rapid Gender Identification through Detection of Facial Features

As the need for person-aligned, effective, and ethical structures emerges, automatic identification of gender is gaining interest in the era of machine-man interactions. Most of the systems for gender detection were analysed using textual or audiovisual sources. Far too many suggested different approaches for automatic identification of gender using the characteristics acquired from people’s bodies and/or behaviours. But the accuracy has always been a question or a drawback in automated gender detection. In the proposed research work, First, the faces and the facial features which includes eyes, mouth, and nose are detected using Haar Cascade based on Viola-Jones face detection algorithm. Before, the gender detection, the noise is reduced by applying adaptive filters, thereby increasing the accuracy. The obtained facial features are given as the input or test data to the neural network. The neural network is designed to obtain the features and acts as a classifier to detect the genders. Simply, the feature extraction and gender detection is performed using the open source neural network called keras.
Efficacy of Sliding Mode Controller (SMC) over DC-DC Full Bridge Isolated Converter (FBIC)

Non-Linear control is needed for the Variable structure system. Non-linear control requires control design in the state-space domain since time and frequency domain analysis applies to linear circuits. Techniques like Sliding Mode Control, h-infinity control, adaptive neural network are some of the examples of nonlinear control techniques. Systems with non-linear control have got wide acceptance due to sturdiness, resisting from uncertainty and parameter variations due to the disturbances within the system like harmonics, surge-swell, interruptions. This article gives information about the Sliding Mode Control (SMC) Design for DC-DC Full Bridge Isolated Converter (FBIC). The system response with reduction of chattering has been presented. System behaviour with phase portrait has been presented. Simulation model of the converter with sliding mode control has been presented with details of the result for verification of robustness of the SMC method.
Network Performance Evaluation of Distributed SDN using Floodlight Controllers

The last years of this digital age, the world has witnessed a progressive growth in Internet traffic. Many applications such as video streaming, cloud computing, Internet of Things (IoT) accompanied with vast increase in Internet users made a huge burden upon conventional networks. Software Defined Network (SDN) is an effective solution. SDN fostered new innovative opportunities due to its enormous benefits. However, a network with a single centralized controller can handle a limited number of requests, and the whole network performance will be breaks down, if the controller fails. A multi-controller for the control plane becomes a solution that guarantees control plane survivability. This paper tries to shed some light on two of the most common challenges of SDN; which are the ability to scale (scaling configuration mode) and the ability to guarantee a smooth transition in case of controller failure (fault tolerant configuration mode). The proposed distributed SDN networks used floodlight remote controller which is a well-established software controller suited for small to medium sized networks.
A Framework for Decision-Making Within Strength and Conditioning Coaching

ABSTRACT DECISION-MAKING IS A KEY FACTOR IN DEVELOPING COACH EXPERTISE AND EFFECTIVENESS. THIS ARTICLE PRESENTS A FRAMEWORK FOR ENHANCING COACH DECISION-MAKING WITHIN STRENGTH AND CONDITIONING (S&C). BASED ON THEORETICAL UNDERSTANDING OF THE ATHLETE (THE “WHO”), S&C TRAINING PRINCIPLES AND SPORT DEMANDS (THE “WHAT”), AND LEARNING THEORIES AND BEHAVIOR (THE “HOW”), COACHES CAN ENHANCE S&C PRACTICE (“PLANNING, DELIVERING, AND REFLECTING”). IN ADDITION, UNDERSTANDING CONTEXTUAL CHALLENGES (“CONTEXT, CULTURE, AND POLITICS”) AND OWN BELIEFS, VALUES, AND BEHAVIORS (“SELF”) MUST BE CONSIDERED. RECOMMENDATIONS ARE PRESENTED FOR IMPLEMENTING CONSTRUCTIVELY ALIGNED LEARNING PROGRAMS BASED ON THE DECISION-MAKING FRAMEWORK FOR ENHANCING COACH LEARNING AND PROFESSIONAL DEVELOPMENT WITHIN S&C.
Parameter extraction and compact drain current model for IGZO transistor from 210K up to 370K

We target the device parameter analysis and drain current modelling for amorphous indium gallium zinc oxide (IGZO) thin film transistors from 210 to 370K. We study and discuss the temperature behaviour of physical IGZO TFT parameters from low to high temperatures. We demonstrated and explained that in IGZO TFTs, the characteristic temperature of the density of states (DOS) increases, with increasing temperature. Finally, using the extracted parameters, we validate our IGZO TFT drain current model at temperatures from 190K up to 370K.
Optimization of Hetero-Gate-Dielectric Tunnel FET for Label-Free Detection and Identification of Biomolecules

In this article, for the first time, the performance of a double-hetero-gated-dielectric-modulated tunnel FET (DHGDM-TFET) biosensor device, having channel length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text{ch}}$ </tex-math></inline-formula>) of 50 nm, is thoroughly investigated in terms of threshold voltage- (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula>-), subthreshold swing- (SS-), ON-current- (<inline-formula> <tex-math notation="LaTeX">${I}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>-), and OFF-current- (<inline-formula> <tex-math notation="LaTeX">${I}_{ \mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula>-) sensitivity parameters, in order to find its suitability in successful detection and identification of different biomolecules. The investigation is further extended to the optimization of the proposed sensor device, against cavity length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text {Cavity}}$ </tex-math></inline-formula>) and misalignment effect between the cavity and high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> dielectric, underneath the cavity of the sensor device. It is found that our proposed biosensor device works at its optimum with <inline-formula> <tex-math notation="LaTeX">${L}_{\text {Cavity}} =10$ </tex-math></inline-formula> nm, while the cavity is perfectly aligned with the underneath high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> of the device. Furthermore, a comparison of our proposed device is performed against its equivalent high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> only and SiO<sub>2</sub> only TFET-based biosensors. For each aforementioned sensitivity parameter, it is found that our proposed DHGDM-TFET biosensor device outperforms the rest two devices, proving the superiority in sensing action.
A Target Detection Algorithm for 3D Lidar Point Cloud

Aiming at the problem of accurately detecting targets in real time from the 3D Lidar point cloud, a target detection algorithm based on the features of the scanning line segment is proposed. Firstly; the scanning lines are divided according to the Euclidean distance and vertical height difference between adjacent points. Then analyzes the characteristics of the adjacent line segments, such as pitch, slope, and height difference. Finally, line segments are marked as the ground segments, slope segments and obstacle segments by using Markov random field. The comparison experiments show that this algorithm can accurately segment the ground efficiently.
Prediction of Gas Concentration Using Gated Recurrent Neural Networks

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.
Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering

Image segmentation process represents the main stage for most computer vision systems. This paper presents an improved algorithm based on simple linear iterative clustering (SLIC) to reduce the number of used seeds for threshold estimation as well as the entire execution time of image segmentation. These is achieved by using split and merge stages for the location, number of seeds as well as other parameters of the seed points. The obtained results showed the possibility of using various threshold levels instead of a single one which represented a challenge due to the estimation complexity. The independent of the threshold-level estimation can contribute significantly in improving performance of the overall image segmentation process. )
Development of Prognosis Tool for Type-II Diabetics using Tongue Image Analysis

Tongue image analysis is a method to identify the conditions of internal organs of the human body (i.e., pancreas). It has certain limitations due to subjective, qualitative and experimental analysis. A person with diabetics can have a grey colour coating at the center of the tongue. Thus the abnormalities of tongue images exhibit the condition (i.e., whether the person is diabetic or not). Based on that, this paper represent a computerised image analysis to improve the efficiency of the classification. Likewise, the three quantitative features like geometry, colour and texture are measured using MATLAB, then the SVM classifier and CNN are used for the classification images from the dataset and the results of both methods are compared. Based on the accuracy of prediction CNN has better results than SVM.
Non-destructive Electromagnetic Wave Sensor for Hazardous Biological Materials

A novel non-destructive electromagnetic wave (EM) sensor for rapid identification of biological material is presented in this paper. Biological treats could be defined as biological agents such as bacteria spores, viruses and toxins. Spores can disable or kill people, animals and crops. Therefore, it is important to identify the hazard in rapid and nondestructive manner to make a safer environment. In this research, a 2.45 GHz microwave resonator was used to detect the dipliconic acid (DPA), which is the bio-maker of bacillus spores. A promising results were obtained by detecting the DPA from 0.001M – 0.3M concentration at frequency of 2.4 GHz, which are the fundamental mode (TM101) of the designed cavity. In addition, different species of bacillus spores was detected at frequency approximate at 2.36 GHz. The results concluded that electromagnetic wave sensors may have the potential for use as a non-destructive and real time sensor to detect bacillus spores. The EM principle could be extended to detect different hazardous biological materials by identify the “finger print” of specific biological materials on different surfaces.
Alliance Management Capability of Entrepreneurial Nonprofit Organizations and Cross-sector Alliance Performance: An fsQCA Approach

An overarching configuration perspective of alliance management capabilities (AMC) is ignored in the realm of cross-sector alliance research. This study fills this gap by exploring how AMC underpin the successful and greater performance of cross-sector alliances. This study examines four dimensions of AMC that affect the performance of crosssector alliance, viz., social impact realizing, relationship coordination, power balancing and inter-organizational learning, from the perspective of nonprofit organizations (NPOs). Based on a survey to socially entrepreneurial nonprofit organizations and their cross-sector alliances in tourism industry in China, this study uses the method of fuzzyset qualitative comparative analysis (fsQCA) to investigate the connection between configuration of AMC and high alliance performance. The findings identify three solutions of different elements configuration of AMC leading to high cross-sector alliance performance. The findings have some theoretical contributions and practical implications for cross-sector alliance management.
A Neural Learning Approach for Prediction of Research Citations Using Article Semantics

Predicting scholarly output largely depend on the research citations received right from the inception. Classic research articles across multiple research domains as identified by Google Scholar tend to define articles published in 2006 which receive citations even after 10 years and counting. Though author and journal level features tend to attract early stage citations, it is for sure that only the righteousness in the article content would have triggered the continuity of citations all through the rest of years. Thus it is clear that for receiving ‘citation sustainability’, an article has to be well conceived, constructed and delivered. This work presents a neural learning approach that uses article semantic features for predicting the intensity of research citations for classic research articles.
Energy–Aware Task Allocation Algorithm Based on Transitive Cluster-Head Selection for IoT Networks

Internet of Things (IoT) has brought the opportunities to have a smart home, continuous monitoring, healthcare, and business applications, which contribute to shape the future of the world’s life and economy. One of the main challenges of this trending technology is energy consumption. Many researches have been devoted to tackle this issue, although it is still open for new directions. This paper deeply investigates energy consumption in IoT networks and proposes a new task allocation algorithm to reduce communication energy between nodes based on clustering techniques. The designed scheme aims to balance the energy among all sensor devices and consequently prolong network lifetime. The performance of the proposed algorithm is evaluated in terms of energy utilization and nodes lifetime, and the results show good performance of the implemented approach.
Stress- and Trap-Induced Body Fluctuations in 45nm SOI MOSFETs

Phase Arrays antenna systems have enabled Silicon technology to be a competitive solution for many mmWave applications. The mature Silicon infrastructure brings key advantages in cost, manufacturing, design infrastructure and understanding of the fundamental physics of reliability degradation. The superior characteristics of Silicon-on-Insulator (SOI) transistors have proven to be of great importance for RF power amplifier (PA) applications, especially when compared to bulk devices [1]–[2]. In contrast, the absence of a body contact portends body charging effects that modify the internal potential of the transistor (this leads to current fluctuations, e. g. kink effect [3]). These effects are compounded considering that during the dynamic operation of the device, trap generation will additionally affect the channel potential through carrier trapping/de-trapping dynamic events. For the first time, we introduce the Trapping Induce Barrier Raising (TIBR) effect, which is an insight into the dynamic body instabilities correlated to trap generation in advanced 45nm SOI MOSFETs induced through accelerated stress tests.
A Comparative Study of Thermal Impact on Erbium Doped Distributed Feedback Fiber Laser Output Power

The temperature dependency of erbium doped distributed feedback fiber laser output performance is experimentally and theoretically investigated with two major pump schemes. The output power declined linearly as temperature increased from 30 °C to 120 °C for 980 nm and 1480 nm pumped test laser while the later exhibited stronger heat susceptibility. Further experiment and analysis identified the temperature induced absorption and emission cross section variation at pump and signal wavelength together with population inversion rate difference are the originations of the thermal sensitivity discrepancy between the two pump setups. Associated theoretical simulation concludes the erbium doped distributed feedback fiber laser thermal output sensitivity is dependent on its gain dynamics and grating structure simultaneously.
Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques

Classification of documents is measured in terms of accuracy by comparing the actual labels with predicted labels for the classes. There are many machine learning techniques, which can be used to build a classifier, it is almost difficult to manually predict which technique should be used for classification, especially when we are working on Indic languages and there is no reliable method, which can give good results. Such area still need to be explored by processing documents with natural language processing, then applying machine learning techniques to build the classifier. This research article has collected 154 Hindi Poetries from web; processed it using NLP techniques, extracted features to build classifier with 5 Machine Learning methods namely support vector machine, naïve bayes, decision tree algorithm, random forest and k-nearestneighbors. The results were examined in accuracy with holdout accuracy estimator and k-fold cross validation accuracy estimator to check the reliability of the methods. The results of this research will help to select the best document classifier for Hindi Poetries and increase accuracy by working on various feature extraction techniques and NLP techniques. The result of the experiment shows that the results of SVM, NB and random forest methods are better as compared to DTT and K-NN for used data set available in this experiment.
A Mobile Application for Security Assessment Towards the Internet of Thing Devices

The Internet of Things or IoT is one of the disruptive technologies which has been grown its attention rapidly. However, because of the neglect of security awareness of vendors and users, this technology is vulnerable to be leveraged as a cyber weapon by malicious attackers. Therefore, we develop an IoT security solution, called MASai, which encourages general users who are not expert in cybersecurity to perform penetration testing on their IoT devices so that they can be aware of these problems and gain security awareness. MASai is comprised of MASai application on Android phones, MASai box, and MASai server. MASai application allows users to execute penetration testing on targeted IoT devices and targeted mobile applications controlling the devices throughouta mobile interface. MASai application works along with MASai Box, a Raspberry Pi embedded with Kali Linux distribution, to find vulnerabilities of the targeted IoT devices. Several techniques such as information gathering, wireless attacks, and vulnerability scanning are integrated for the assessment. MASai server allows users to perform Android reverse engineering and static code analysis for the mobile application security assessment. All vulnerability assessments and penetration testing are based on OWASP Top 10 IoT Vulnerabilities and OWASP Mobile Top 10. By using MASai, we expect users to gain security awareness and can prevent the unexpected consequences of IoT technology.
A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size. To address this issue, unsupervised learning techniques such as clustering can be used. In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined. A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting. The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes). The performance of clustering and classification is measured in terms of accuracy and time efficiency. The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency. The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging.
Survey Paper on Developing Industrial IoT Broker Capable of Handling Multi Protocol Architecture

The IoT Integration Middleware (IoTIM) assists as an integrating level for distinctive groups of Receptors, Actuators, units, and software. The broking is definitely responsible for getting information right from the linked gateway, digesting the received information, offering the received specifics to connected deals, and managing devices. A gateway can articulate at once by way of the IoT incorporation middleware if it facilitates the suitable spoken switch concept, which contains a related delivery process, like CoAP, HTTP or MQTT. additionally, supplier orientated Architectures (SOA) give favored systems and standards for finding, handling, gain access to control, invocation and structure of service which may easily become presented at the cyberspace, by techniques of leverage as well as, boosting web-affiliated protocols that feature HTTP and so open up model patterns just like XML. Proposed function can be concentrated about middleware broking to offer appropriate multimodal communiqué.
EEG based Visualization and Analysis of Emotional processing in Major Depressive Disorder

Major Depressive Disorder is one of the principal causes of disability globally. It is characterized by low self-regard and thoughts of hopelessness. This study aims at analyzing the brain activity during emotional processing in depressed patients. Fifteen adults with a HDRS-17 score of greater than 20 participated in the study. EEG was recorded while viewing two categories of IAPS pictures namely, neutral and high valence high arousal. SAM scale was utilized to find the perceptual emotions of the participants. The behavioral study showed a neutral response for both the sets of pictures. The mean relative power of alpha was observed to be elevated in left frontal region indicating negative emotion. Significant changes in mean relative beta power was observed between high valence high arousal stimuli ($\mathrm{F}3=0.01,\ \mathrm{F}4=0.016,\ \mathrm{F}7=0.013,\ \mathrm{F}8=0.007$) and neutral stimuli ($\mathrm{F}3=0.004,\ \mathrm{F}4=0.005,\ \mathrm{F}7=0.002,\ \mathrm{F}8=0.002$), indicating arousal though it is not perceived. Frontal alpha asymmetry scores, with the significance of $\mathrm{p} < 0.05$, showed withdrawal behavior while viewing the pictures when compared to rest. It is concluded that negative emotion for both neutral and positive stimuli could be a characteristic feature for Major Depressive Disorder.