bluuebunny/crossref_metadata_2025 huggingface dataset:
Extracted features based multi-class classification of orthodontic images
Authors: Riri Hicham , Ed-Dhahraouy Mohammed , Elmoutaouakkil Abdelmajid , Beni-Hssane Abderrahim , Bourzgui Farid
Published: March 2020

The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.
Bacterial foraging optimization based adaptive neuro fuzzy inference system
Authors: Murugan C. Arul , Sureshkumaar G. , Kannan Nithiyananthan , Thomas Sunil
Published: March 2020

Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
Deep-learning based single object tracker for night surveillance
Authors: Kadim Zulaikha , Zulkifley Mohd Asyraf , Hamzah Nabilah
Published: March 2020

Tracking an object in night surveillance video is a challenging task as the quality of the captured image is normally poor with low brightness and contrast. The task becomes harder for a small object as fewer features are apparent. Traditional approach is based on improving the image quality before tracking is performed. In this paper, a single object tracking algorithm based on deep-learning approach is proposed to exploit its outstanding capability of modelling object’s appearance even during night. The algorithm uses pre-trained convolutional neural networks coupled with fully connected layers, which are trained online during the tracking so that it is able to cater for appearance changes as the object moves around. Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios. Fourteen night surveillance videos are collected for validation purpose, which are captured from three viewing angles. The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data. This algorithm is suitable to be implemented in higher level surveillance applications such as abnormal behavioral recognition.
Water monitoring and analytic based thingspeak
Authors: Miry Abbas Hussien , Aramice Gregor Alexander
Published: March 2020

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.
Conflict-free dynamic route multi-AGV using dijkstra floyd-warshall hybrid algorithm with time windows
Authors: Solichudin Solichudin , Triwiyatno Aris , Riyadi Munawar A.
Published: March 2020

Autonomous Guided Vehicle is a mobile robot that can move autonomously on a route or lane in an indoor or outdoor environment while performing a series of tasks. Determination of the shortest route on an autonomous guided vehicle is one of the optimization problems in handling conflict-free routes that have an influence on the distribution of goods in the manufacturing industry's warehouse. Pickup and delivery processes in the distribution on AGV goods such as scheduling, shipping, and determining the route of vehicle with short mileage characteristics, is very possible to do simulations with three AGV units. There is a windows time limit on workstations that limits shipping. The problem of determining the route in this study is considered necessary as a multi-vehicle route problem with a time window. This study aims to describe the combination of algorithms written based on dynamic programming to overcome the problem of conflict-free AGV routes using time windows. The combined approach of the Dijkstra and Floyd-Warshall algorithm results in the optimization of the closest distance in overcoming conflict-free routes.
Adaptive quantization for spectrum exchange information in mobile cognitive radio networks
Authors: Marwanto Arief , Yusof Sharifah Kamilah Syed , Satria Muhammad Haikal
Published: March 2020

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method.
Data loss prevention (DLP) by using MRSH-v2 algorithm
Authors: Husham Ali Basheer , Jalal Ahmed Adeeb , Al-Obaydy                               Al-Obaydy Wasseem N. Ibrahem
Published: March 2020

Sensitive data may be stored in different forms. Not only legal owners but also malicious people are interesting of getting sensitive data. Exposing valuable data to others leads to severe Consequences. Customers, organizations, and /or companies lose their money and reputation due to data breaches. There are many reasons for data leakages. Internal threats such as human mistakes and external threats such as DDoS attacks are two main reasons for data loss. In general, data may be categorized based into three kinds: data in use, data at rest, and data in motion. Data Loss Prevention (DLP) are good tools to identify important data. DLP can do analysis for data content and send feedback to administrators to make decision such as filtering, deleting, or encryption. Data Loss Prevention (DLP) tools are not a final solution for data breaches, but they consider good security tools to eliminate malicious activities and protect sensitive information. There are many kinds of DLP techniques, and approximation matching is one of them. Mrsh-v2 is one type of approximation matching. It is implemented and evaluated by using TS dataset and confusion matrix. Finally, Mrsh-v2 has high score of true positive and sensitivity, and it has low score of false negative.
A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks
Authors: Al-Azzam Saad , Sharieh Ahmad
Published: March 2020

Continuous data transmission in wireless sensor networks (WSNs) is one of the most important characteristics which makes sensors prone to failure. a backup strategy needs to co-exist with the infrastructure of the network to assure that no data is missing. The proposed system relies on a backup strategy of building a history file that stores all collected data from these nodes. This file is used later on by fuzzy logic to estimate missing data in case of failure. An easily programmable microcontroller unit is equipped with a data storage mechanism used as cost worthy storage media for these data. An error in estimation is calculated constantly and used for updating a reference “optimal table” that is used in the estimation of missing data. The error values also assure that the system doesn’t go into an incremental error state. This paper presents a system integrated of optimal data table, microcontroller, and fuzzy logic to estimate missing data of failing sensors. The adapted approach is guided by the minimum error calculated from previously collected data. Experimental findings show that the system has great potentials of continuing to function with a failing node, with very low processing capabilities and storage requirements.
Performance evaluation of MANET routing protocols based on QoS and energy parameters
Authors: Mohamed Salma S. , Abdel-Fatah Abdel-Fatah I. , Mohamed Mohamed A.
Published: March 2020

Routing selection and supporting Quality of Service (QoS) are fundamental problems in Mobile Ad Hoc Network (MANET). Many different protocols have been proposed in the literature and some performance simulations are made to address this challenging task. This paper discusses the performance evaluation and comparison of two typical routing protocols; Ad Hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance-Vector (DSDV) based on measuring the power consumption in network with varing of the QoS parameters. In this paper, we have studied and analyzed the impact of variations in QoS parameter combined with the choice of routing protocol, on network performance. The network performance is measured in terms of average throughput, packet delivery ratio (PDR), average jitter and energy consumption. The simulations are carried out in NS-3. The simulation results show that DSDV and AODV routing protocols are less energy efficient. The main aim of this paper is to highlight the directions for the future design of routing protocol which would be better than the existing ones in terms of energy utilization and delivery ratio.
A Novel automatic voice recognition system based on text-independent in a noisy environment
Authors: Hamza Motaz , Khodadadi Touraj , Palaniappan Sellappan
Published: March 2020

Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.
Detection of the botnets’ low-rate DDoS attacks based on self-similarity
Authors: Lysenko Sergii , Bobrovnikova Kira , Matiukh Serhii , Hurman Ivan , Savenko Oleg
Published: March 2020

An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.
Acceptance of E-learning among university students in UAE: A practical study
Authors: Mohammad AlHamad Ahmad Qasim
Published: March 2020

The objective of the study is to find out the factors which affect the acceptance of E-learning among students and how the students’ purpose of using E-learning is determined by these factors. The research was based on the Technology Acceptance Model (TAM). The researcher developed a questionnaire to gather data from a sample of 366 university students who actively used the E-learning system. According to the results of the study, subjective norms, perceived usefulness, perceived ease of use, enjoyment, and accessibility” are the vital predictors behind the intention of students for using E-learning systems. This indicates that extended TAM is applicable in the UAE. The results show that policymakers and education developers should take E-learning system seriously since it can be opted not just as a technological solution but also as a learning platform for students belonging to a distant area. The results present practical implications for education developers, policymakers and practitioners in devising useful plans to implement E-learning systems.
Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems
Authors: Alzaqebah Malek , Alrefai Nashat , Ahmed Eman A. E. , Jawarneh Sana , Alsmadi Mutasem K.
Published: March 2020

Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
A comprehensive study of distributed Denial-of-Service attack with the detection techniques
Authors: Ibrahim H. H. , Hamzah A. E. , Saeed H. A. , Qasim H. H. , Hamed O. S. , Alkhalaf Hussein Yahya , Hamza M. I.
Published: March 2020

With the dramatic evolution in networks nowadays, an equivalent growth of challenges has been depicted toward implementing and deployment of such networks. One of the serious challenges is the security where wide range of attacks would threat these networks. Denial-of-Service (DoS) is one of the common attacks that targets several types of networks in which a huge amount of information is being flooded into a specific server for the purpose of turning of such server. Many research studies have examined the simulation of networks in order to observe the behavior of DoS. However, the variety of its types hinders the process of configuring the DoS attacks. In particular, the Distributed DoS (DDoS) is considered to be the most challenging threat to various networks. Hence, this paper aims to accommodate a comprehensive simulation in order to figure out and detect DDoS attacks. Using the well-known simulator technique of NS-2, the experiments showed that different types of DDoS have been characterized, examined and detected. This implies the efficacy of the comprehensive simulation proposed by this study.
Identity-based threshold group signature scheme based on multiple hard number theoretic problems
Authors: Tahat Nedal , Tahat Ashraf A.
Published: March 2020

We introduce in this paper a new identity-based threshold signature (IBTHS) technique, which is based on a pair of intractable problems, residuosity and discrete logarithm. This technique relies on two difficult problems and offers an improved level of security relative to an individual hard problem. The majority of the denoted IBTHS techniques are established on an individual difficult problem. Despite the fact that these methods are secure, however, a prospective solution of this sole problem by an adversary will enable him/her to recover the entire private data together with secret keys and configuration values of the associated scheme. Our technique is immune to the four most familiar attack types in relation to the signature schemes. Enhanced performance of our proposed technique is verified in terms of minimum cost of computations required by both of the signing algorithm and the verifying algorithm in addition to immunity to attacks.
Dynamic routing discovery scheme for high mobility in mobile ad hoc wireless networks
Authors: Alani Haider , Abdelhaq Maha , Alsaqour Raed
Published: March 2020

An innovative technology that is widely used in many applications is the Mobile Ad-hoc Network (MANET). Discovery and maintenance of routes at MANET are important issues. Within MANET, broadcasting is used to discover a path within on-demand routing protocols. Establishing and maintaining a route periodically among the nodes is the challenge that requires the transmitting of control packets across a network. This state leads to the issue of broadcasting storms. Broadcasting control packets increase control packets overhead and decrease network performance. In this paper, we proposed a scheme called AODV-Velocity and Dynamic (AODV-VD) for effective broadcast control packets. The routing protocol for the ad-hoc on-demand distance victor (AODV) is used to implement the proposed AODV-VD scheme. AODV-VD scheme reduces both the excessive route discovery control packets and network overhead. Network simulator version 2.35 (NS2.35) was used to compare the proposed AODV-VD scheme to the AODV routing protocol in terms of end-to-end latency, average throughput, packet transmission ratio and overhead ratio.
Enhancenig OLSR routing protocol using K-means clustering in MANETs
Authors: Hamzaoui Y. , Amnai M. , Choukri A. , Fakhri Y.
Published: March 2020

The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to distribute the nodes into several clusters; it is implemented to standard OLSR protocol giving birth a new protocol named OLSR Kmeans-SDE. The simulations showed that the results obtained by OLSR Kmeans-SDE exceed those obtained by standard OLSR Kmeans and OLSR Kmed+ in terms of, traffic Control, delay and packet delivery ratio.
A hybrid constructive algorithm incorporating teaching-learning based optimization for neural network training
Authors: Khorashadizade Mahdie , Jouyban Morteza , Oskoei Mohammadreza Asghari
Published: March 2020

In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teaching-learning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multiple operations, using statistical tests (MOST), is applied and tested to choose the proper structure. The proposed combinatorial algorithms are applied to ten classification problems and two-time-series prediction problems, as the benchmark. The results are evaluated based on training and testing error, network complexity and mean-square error. The experimental results illustrate that the proposed hybrid method of the modified MOST constructive algorithm and the improved TLBO (MCO-ITLBO) algorithm outperform the others; moreover, they have been proven by Wilcoxon statistical tests as well. The proposed method demonstrates less average error with less complexity in the network structure.
Text classification based on gated recurrent unit combines with support vector machine
Authors: Zulqarnain Muhammad , Ghazali Rozaida , Mohmad Hassim Yana Mazwin , Rehan Muhammad
Published: March 2020

As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
An adaptation of Text2Onto for supporting the French language
Authors: Hajji Morad , Qbadou Mohammed , Mansouri Khalifa
Published: March 2020

The ontologies are progressively imposing themselves in the field of knowledge management. While the manual construction of an ontology is by far the most reliable, this task has proved to be too tedious and expensive. To assist humans in the process of building an ontology, several tools have emerged proposing the automatic or semi-automatic construction of ontologies. In this context, Text2Onto has become one of the most recognized ontology learning tools. The performance of this tool is confirmed by several research works. However, the development of this tool is based on Princeton WordNet (PWN) for English. As a result, it is limited to the processing of textual resources written in English. In this paper, we present our approach based on JWOLF, a Java API to access the free WordNet for French that we have developed to adapt this tool for the construction of ontologies from corpus in French. To evaluate the usefulness of our approach, we assessed the performance of the improved version of Text2Onto on a simplistic corpus of French language documents. The results of this experiment have shown that the improved version of Text2Onto according to our approach is effective for the construction of an ontology from textual documents in the French language.
Predicting depression using deep learning and ensemble algorithms on raw twitter data
Authors: Shetty Nisha P. , Muniyal Balachandra , Anand Arshia , Kumar Sushant , Prabhu Sushant
Published: March 2020

Social network and microblogging sites such as Twitter are widespread amongst all generations nowadays where people connect and share their feelings, emotions, pursuits etc. Depression, one of the most common mental disorder, is an acute state of sadness where person loses interest in all activities. If not treated immediately this can result in dire consequences such as death. In this era of virtual world, people are more comfortable in expressing their emotions in such sites as they have become a part and parcel of everyday lives. The research put forth thus, employs machine learning classifiers on the twitter data set to detect if a person’s tweet indicates any sign of depression or not.
Time and resource constrained offloading with multi-task in a mobile edge computing node
Authors: El Ghmary Mohamed , Hmimz Youssef , Chanyour Tarik , Cherkaoui Malki Mohammed Ouçamah
Published: March 2020

In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines.
Policy resolution of shared data in online social networks
Authors: Shetty Nisha P. , Muniyal Balachandra , Mowla Saleh
Published: March 2020

Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners.
An automated approach to fix buffer overflows
Authors: Shahab Aamir , Nadeem Muhammad , Alenezi Mamdouh , Asif Raja
Published: March 2020

Buffer overflows are one of the most common software vulnerabilities that occur when more data is inserted into a buffer than it can hold. Various manual and automated techniques for detecting and fixing specific types of buffer overflow vulnerability have been proposed, but the solution to fix Unicode buffer overflow has not been proposed yet. Public security vulnerability repository e.g., Common Weakness Enumeration (CWE) holds useful articles about software security vulnerabilities. Mitigation strategies listed in CWE may be useful for fixing the specified software security vulnerabilities. This research contributes by developing a prototype that automatically fixes different types of buffer overflows by using the strategies suggested in CWE articles and existing research. A static analysis tool has been used to evaluate the performance of the developed prototype tools. The results suggest that the proposed approach can automatically fix buffer overflows without inducing errors.
Electrical power generation through concentrated solar technology for the southern cities of Iraq
Authors: Thahab Riyadh Toman , Thahab Ahmed Toman
Published: March 2020

With a continuing mismatch between generating capacity and demand requirements, Iraqi cities are still enduring scheduled power outages. In this work, concentrated solar power (CSP) technology is proposed and designed for Iraqi cities to inject power into distribution networks with the objective of boosting the generating power capacity. Since CSP systems require a preliminary study of the direct normal irradiance (DNI), analyses of monthly data is carried out for each of the candidate cities. This is followed by determination of the amount of solar irradiance that falls on a titled collector per month considering the effects of reflection and diffusion. Finally, a thermal power plant is proposed and simulated using the system advisory model (SAM) per city. Results presented show an encouraging number of metrics and confirm the feasibility of such a plant in southern Iraq. The levelised cost of electricity and capacity factor shows a considerable decrease and increase respectively, when the plant is backed up by a fossil fuel steam cycle under circumstances when a plant loses over 80% of the MW capacity due to drop in solar irradiance. To provide a comparision platform, for each city, a photovolitaic (PV) plant is designed with an indentical electric capacity to that of the CSP plant. Findings from this work confirm that CSP plants can provide a suitstanable and enviroemntl friendly solution to electrical power shortages in the country compared to the current PV trends.
Condition of phase angle for a new VDGA-based multiphase variable phase shift oscillator from 0o to 90o
Authors: Abdalla Kasim K.
Published: March 2020

A novel interesting type of variable phase angle voltage mode oscillator using modern building block has been presented in this paper. The new proposed oscillator configuration which uses four voltage differencing gain amplifier (VDGA) and two grounded capacitors can generate two sinusoidal signals that change out of phase by 0 to 90 degree. It has four floating and explicit voltage mode outputs where every two outputs have the same phase. The circuit is characterized by (i) the condition of phase angle of the oscillation (PO) (this concept is introduced for the first time in this paper) can be tuned electronically (ii) the gain of the floating outputs can be controlled independently (iii) it provides electronic control of condition of oscillation (CO) and independent control of frequency of oscillation (FO). The Total Harmonic Distortion (THD) of the output waveforms was obtained and the results were reasonability values (less than 4.5%). The non-ideal analysis and simulation results are investigated and confirmed the theoretical analysis based upon VDGAs implementable in 0.35μm CMOS technology. Simulation results include time response and frequency response outputs generated by using the PSPICE program.
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data
Authors: Jankatti Santosh , B. K. Raghavendra , S. Raghavendra , Meenakshi Meenakshi
Published: March 2020

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
Spectral filtering experimentation on photovoltaic cells using novel bio-filter made from copper coated hibiscus-ethanol extract
Authors: Emetere Moses E. , Gabe-Oji Testimony , B.M. Durodola
Published: March 2020

The challenges facing solar power grid system in Africa is huge. Most salient of these challenges is the inefficiency of the photovoltaic (PV) panel to sustain its output for more than a year. Certainly, the harsh weather condition in the region can be said to be one of the reasons for the shortcoming that was earlier highlighted. In this research, bio-filters were suggested to filter the harmful radiation hitting the PV panel. The bio-filter is made up of copper coated hibiscus extract. The hibiscus extract was done using ethanol solution. It was observed that the bio-filter was able to filter the some of the harmful radiation as expected. The quantity of the harmful solar radiation was not estimated because of the limitations of the equipment used for the research. It is recommended that this highlighted shortcoming of the research should be taken further to ascertain i.e. in percentage the harmful radiation that has been filtered by the bio-filter during the experiment.
Classification of emotions induced by horror and relaxing movies using single-channel EEG recordings
Authors: Jalilifard Amir , Rastegarnia Amir , Pizzolato Ednaldo Birgante , Islam Md Kafiul
Published: March 2020

It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations' patterns in Theta, Alpha and Beta sub-bands also play important role in brain's emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances\textemdash for each subject \textemdash were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208\% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when \begin{math} k=1 \end{math}) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for \begin{math} K >1 \end{math} in K-NN, SVM has better average classification rate.
∈φ-contraction and some fixed point results via modified ω-distance mappings in the frame of complete quasi metric spaces and applications
Authors: Abodayeh Kamaleldin , Qawasmeh T. , Shatanawi W. , Tallafha A.
Published: March 2020

In this Article, we introduce the notion of an ∈φ-contraction which based on modified ω-distance mappings and employ this new definition to prove some fixed point result. Moreover, we introduced an interesting example and an application to highlight the importance of our work.
Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework
Authors: Alnabelsi Sharhabeel H. , Bany Salameh Haythem A. , Albataineh Zaid M.
Published: March 2020

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.
Adaptive management of technical condition of power transformers
Authors: Levin Vladimir M. , Yahya Ammar A.
Published: March 2020

Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment.
Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search
Authors: Achsas Sanae , Nfaoui El Habib
Published: March 2020

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
Formalization of SOA concepts with mathematical foundation
Authors: H. V. Asha , Nayak Shantharam , Abraham Annamma
Published: March 2020

With the development of the IT industry, agility and dynamicity are the two expected characteristics from any business enterprise. Service oriented architecture (SOA) is emerging as a promising approach aiming to address the current trends/needs of enterprise business applications. The current work aims at giving an overview of the conceptual model of SOA based on set theory approach. Also, it posits mathematical definitions for service, logical service, environment service and application definition with the help of general systems theory and mathematical foundation. With the help of mathematical model, the definition and functionality of services enhances SOA principles which can be used efficiently in integrating heterogeneous components.
Robots for search site monitoring, suspect guarding, and evidence identification
Authors: Wu Yi-Chang , Lee Jih-Wei , Wang Huan-Chun
Published: March 2020

As an initial trial and in response to a lack of technological applications in government agencies, we have developed three multifunctional robots in accordance with the work environment and the nature of our tasks. Search site monitoring robot is fitted with a panoramic camera and large wheels for walk-around search site monitoring. Suspect guarding robot follows and guards a suspect by tracking an augmented reality marker worn by the suspect and identifying the human body through an infrared thermal camera. For the evidence identification robot, You Only Look Once (YOLO) is utilized to identify some specific evidence on search site and is equipped with a carrier and a high-torque motor for evidence transportation; it is set to issue warnings and emails to relevant personnel on specific emergencies. We have performed multiple experiments and tests to confirm the robots’ effectiveness, verifying their applicability of technological task support in government agencies.
Extracorporeal Membrane Oxygenation as a Bridge to Durable Mechanical Circulatory Support
Authors: Loyaga-Rendon Renzo Y. , Boeve Theodore , Tallaj Jose , Lee Sangjin , Leacche Marzia , Lotun Kapildeo , Koehl Devin A. , Cantor Ryan S. , Kirklin James K. , Acharya Deepak
Published: March 2020

Background:

Limited data are available regarding the outcomes of patients supported by extracorporeal membrane oxygenation (ECMO) who undergo durable mechanical circulatory support implantation (dMCS). We analyzed the clinical characteristics, outcomes, and risk factors for mortality in patients who were bridged with ECMO to dMCS.

Methods:

Adult patients who received dMCS between January 2008 and December 2017 (n=19 824), registered in the Society of Thoracic Surgeons-Interagency Registry for Mechanical Assisted Circulatory Support (STS-INTERMACS) database were included. Baseline characteristics, outcomes, risk factors, and adverse events were compared between ECMO-supported patients (n=933) and INTERMACS profile 1 (IP-1) patients not supported by ECMO (n=2362). A propensity match analysis was performed.

Results:

ECMO patients had inferior survival at 12 months (66.1%) than non-ECMO patients (75.4%;
P
<0.0001). The proportion of patients transplanted at 2 years after dMCS was similar between the ECMO (30.8%) and non-ECMO (31.8%) groups (
P
=0.49). A multiphase parametric hazard model identified 2 different periods based on risk of death. ECMO patients had a high hazard for death in the first 6 months after implantation (hazard ratio, 2.18 [1.79–2.66];
P
<0.001). Multivariable analysis showed that ECMO was an independent risk factor associated with poor outcome during the early phase after dMCS (hazard ratio, 1.69 [1.37–2.09];
P
<0.0001) but not during the constant phase. ECMO patients had similar outcomes to non-ECMO patients when a propensity matched cohort was analyzed.

Conclusions:

ECMO-supported patients before dMCS have lower survival compared with other IP-1 patients. A multivariable analysis showed that ECMO is an independent risk factor of poor outcome after dMCS. However, a propensity matched analysis suggested that when important clinical variables are controlled the outcome of both groups is similar. These data support the implantation of dMCS in carefully selected ECMO patients.
Jewish Synagogues and the Topography of Imperial Rome: The Case of the Agrippesioi and Augustesioi
Authors: Flexsenhar Michael
Published: March 2020

This study investigates the Agrippesioi and Augustesioi synagogues of ancient Rome. Known from inscriptions found primarily in the Monteverde Catacombs, the synagogues are conventionally dated to the first century CE. Common opinion is that they were named directly after Marcus Agrippa and the emperor Augustus, both of whom, it is thought, played some part in founding the synagogues. Based on the chronology of the catacombs and the inscriptions, I assign the synagogues to the third and fourth centuries. Taking into account the linguistic and epigraphic comparanda of that period, I argue that the synagogue names were toponyms. They signaled where in Rome the Jewish synagogues were. The analysis has further implications for the history and social setting of Roman Jews. Like other groups at the time, they were identifying themselves based on areas or features in the late antique urban landscape that had been associated with Agrippa and Augustus for centuries.