bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders
Authors: Sharrab Yousef O. , Alsmirat Mohammad , Hawashin Bilal , Sarhan Nabil
Published: February 2021

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.
A flexible method to create wave file features
Authors: J. Qaryouti Ghazi M.
Published: February 2021

Digital audio signal is one of the most important data type at present, it is used in various vital applications, such as human knowledge, security and banking applications, most applications require signal identification and recognition, and to increase the efficiency of these applications we must seek a method to represent the audio file by a small set of values called a features vector. In this paper research we will introduce an enhanced method of features extraction based on k-mean clustering. The method will be tested and implemented to show how the proposed method can reduce the efforts of voice identification, and can minimize the recognition time a set of voice extracted features must be used instead of using the voice wave file.
An index based road feature extraction from LANDSAT-8 OLI images
Authors: Kumar Reddy Sama Lenin , Rao C. V. , Kumar P. Rajesh , G. Anjaneyulu R. V. , Krishna B. Gopala
Published: February 2021

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
Wideband characteristics of density tapered array antennas
Authors: Thanh Binh Nguyen , Dinh Nguyen Quoc , Yamada Yoshihide
Published: February 2021

In this paper, wideband characteristics of density tapered arrays are clarified by comparing directly the array factors and radiation patterns of 3 tapered arrays structures with array factors and radiation patterns of equally spaced arrays. Calculated results for a density tapered distribution array consisting of 30 elements claims that the array can perform within a bandwidth of 2.5:1 with grating lobe levels lower than -7.8 dB. Additionally, this paper shows a method of determining the effectiveness of unequal spacing arrays in the design of actual antennas. The method is based on calculation and analysis of input impedance of array elements caused by mutual coupling effects among array elements.
Power enhancement based link quality for wireless mesh network
Authors: Rahim Al-juaifari Mohammad Khalaf , Alshamy Hussain Mumtaiz , Abed Khammas Noor Hassan
Published: February 2021

Energy consumption of wireless network communication is still a big issue and a lot of research papers have proposed many solutions to increase node life time. The WMN architecture is made up of a fixed and mobile component, whereas the wireless mesh networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. The reduction in the distance by a factor of two can result in at least four times more powerful signals at the receiver. This paper presents suggestions that the links are more reliable without the increase in power of the transmitter in individual nodes. As a result, the present simulations networks are nine mobile nodes for considering coverage issues of the service area. The analytic results show that the link power node for direct communication between two nodes with long distance consuming more power than it is cleared. The improvement in the network performance for maintaining is available and this solution can be used to implement mobility in such case with low power region for the wireless mesh networks.
Butterfly design mesh antenna of optical rectenna for S-band communication systems
Authors: Baccouch Chokri , Bahhar Chayma , Sakli Hèdi , Sakli Nizar
Published: February 2021

A novel optical rectenna design is presented in this paper to operate in S-band communication. We propose a new method of combining antennas and solar cells to collect and transmit optical and radio frequency signals respectively. In this work, we determined the electrical power collected, it can be used for the polarization of a diode or a low-noise amplifier in a receiver block thus simulation results provides a gain of 6.74 dBi at 2.9 GHz with an effective return loss of -33.62 dB and radiated power of 7.08 mW. These good results make it possible to use the antenna particularly in point-to-point communication systems. A three topologies of rectifying circuits are proposed in the present work. The parametric study has been shown that the efficiency RF/DC conversion can reach 66% for an input power of 10 dBm and a load resistance of 3 kΩ.
Performance enhancement of sensor network architecture for monitoring underwater oil pipeline
Authors: Jassim Waseem M. , Abdelkareem Ammar E.
Published: February 2021

In this paper, a deployment mechanism is designed to distribute heterogeneous nodes to optimally cover the pipeline where the mechanism helps locate each node on the wall of the oil pipeline where the number of nodes can be increased depending on this mechanism. The six-layer network hierarchy includes basic sensor nodes (BSN), aggregation relay node (ARN) that added to the network hierarchy, data relay nodes (DRN), data dissemination node (DDN), base station (sinks), and network control center (NCC). This network relies on the improved smart redirect or jump algorithm (SRJ) by sending packets depend on the active relay nodes in both directions that are within the transmission range of the ARNs instead of relying on the number of hops adopted by the SRJ algorithm to reduce the network delay, the energy consumed in relay nodes, and the number of times the DRNs increased transmission range. The OMNeT++ and MATLAB programs were used to implement the simulation scenario. The results showed superiority in terms of the average overhead communication, energy consumption, and end to the end delay with network delay in some cases rely on the number of active relay nodes.
Information system supporting research on rubber in Thailand
Authors: Kajornkasirat Siriwan , Muangprathub Jirapond , Boonnam Nathaphon , Sriklin Teerawad
Published: February 2021

his research aimed to develop an information system supporting research on rubber (ISSR) in Thailand. This system was designed as a web application with responsive web design. The rubber database was developed with MySQL, used Apache Server as a Web Server and programed with PHP-script. High Chart, Google Chart API and Java Technology were used to represent an online information with graphical format. The system was tested with the actual data on rubber research in Southern Thailand. The system has been available online at URL http://www.s-cm.site/issr. There are three type of users: administrator, researcher (member) and generic user. The researcher performed data entry about research with log-in to the system using username and password provided by the automatic system via online registration form. The administrator can manage the research information. The researchers can manage their research information, use searching tool and leave comments on other member’s research. The generic users can access the system without username and password to view the research and general information on rubber. Moreover, the system generates a report on rubber research with online graphical format. In conclusion, this information system enhances investigation on rubber research in Thailand and its strategy planning for rubber plantation in the future.
Global convergence of new conjugate gradient method with inexact line search
Authors: Ahmed Chergui , Tahar Bouali
Published: February 2021

In this paper, We propose a new nonlinear conjugate gradient method (FRA) that satisfies a sufficient descent condition and global convergence under the inexact line search of strong wolf powell. Our numerical experiment shaw the efficiency of the new method in solving a set of problems from the CUTEst package, the proposed new formula gives excellent numerical results at CPU time, number of iterations, number of gradient ratings when compared to WYL, DY, PRP, and FR methods.
New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm
Authors: Benali Brahim Ait , Mihi Soukaina , El Bazi Ismail , Laachfoubi Nabil
Published: February 2021

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.
Big data and remote sensing: A new software of ingestion
Authors: Boudriki Semlali Badr-Eddine , El Amrani Chaker
Published: February 2021

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
Use of production functions in assessing the profitability of shares of insurance companies
Authors: Alkhasawneh Raed Ali , Farhan Mohamed Ahmed Mohamed , Jaradat Samir Abdulwahab , Torky M. Sh. , Alsmadi Mutasem K.
Published: February 2021

In this study the production functions (Cobb-Douglas, Zener-Rivanker, and the transcendental production function) have been used to assess the profitability of insurance companies, by reformulating these nonlinear functions based on the introduction of a set of variables that contribute to increase the explanatory capacity of the model. Then the best production function commensurate with the nature of the variable representing the profitability of insurance companies was chosen, to use it to assess the efficiency of their profitability versus the use of different factors of production and thus the possibility of using it in forecasting. It was found that the proposed model of the production function "Zener-Rivanker" is the best production functions representing the profitability of the Tawuniya and Bupa Insurance Companies. The proposed model of the Cobb-Douglas production function is suitable for the results of both Enaya and Sanad Cooperative Insurance Companies. The explanatory capacity of the production functions was also increased when the proposed variables were added (net subscribed premiums-net claims incurred).
An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification
Authors: Arowolo Micheal Olaolu , Adebiyi Marion O. , Adebiyi Ayodele A. , Aremu Charity
Published: February 2021

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy.
Emotion detection on Myanmar texts
Authors: Swe Thiri Marlar , Wah Naw Lay
Published: February 2021

At this age, World Wide Web is growing faster. Many companies have built and launch social media networks. People so widely use social media to get the latest news, to express their emotions or moods, to communicate with their friends and so on. Emotions of social media users are needed to analyze in order to apply in many areas. Many researchers do research on emotion detection using different techniques with their languages. Currently, there are no emotion detection systems for Myanmar (Burmese) language. So, this paper describes the emotion detection system for Myanmar language. This system uses our pre-constructed M-Lexicon, a Myanmar word-emotion lexicon, in the detection process. This system detects six basic emotions such as happiness, sadness, anger, fear, surprise, and disgust. In order to determine certain emotion from the text, we also apply rule-based decision making on sentence nature. We use Facebook users’ status, which has been written in Myanmar words. Emotions of user groups are also summarized in this system. Our approach achieves 86% accuracy for emotion detection in Myanmar texts.
High speed modified carry save adder using a structure of multiplexers
Authors: Hameed Ahmed Salah , Kathem Marwa Jawad
Published: February 2021

Adders are the heart of data path circuits for any processor in digital computer and signal processing systems. Growth in technology keeps supporting efficient design of binary adders for high speed applications. In this paper, a fast and area-efficient modified carry save adder (CSA) is presented. A multiplexer based design of full adder is proposed to implement the structure of the CSA. The proposed design of full adder is employed in designing all stages of traditional CSA. By modifying the design of full adder in CSA, the complexity and area of the design can be reduced, resulting in reduced delay time. The VHDL implementations of CSA adders including (the proposed version, traditional CSA, and modified CSAs presented in literature) are simulated using Quartus II synthesis software tool with the altera FPGA EP2C5T144C6 device (Cyclone II). Simulation results of 64-bit adder designs demonstrate the average improvement of 17.75%, 1.60%, and 8.81% respectively for the worst case time, thermal power dissipation and number of FPGA logic elements.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis
Authors: Benlahbib Abdessamad , Nfaoui El Habib
Published: February 2021

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
Real-time Arabic scene text detection using fully convolutional neural networks
Authors: Moumen Rajae , Chiheb Raddouane , Faizi Rdouan
Published: February 2021

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.
A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems
Authors: Mfenjou Martin Luther , Abba Ari Ado Adamou , Ndam Njoya Arouna , Kolyang Kolyang , Abdou Wahabou , Gueroui Abdelhak Mourad
Published: February 2021

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.
Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks
Authors: Hadioui Abdelladim , Touimi Yassine Benjelloun , El Faddouli Nour-eddine , Bennani Samir
Published: February 2021

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.
Inactive power detection in AC network
Authors: Schurov Nickolay I. , Myatezh Sergey V. , Myatezh Alexandr V. , Malozyomov Boris V. , Shtang Alexandr A.
Published: February 2021

Using the examples of wave and vector diagrams, we study the conditions for the appearance of components of inactive power in an AC network, which are known as reactive power and distortion power. It is shown that the components of the active, reactive power and distortion power are mutually orthogonal and form a power balance, which can be violated mainly due to methodological errors in calculating these components under conditions of non-stationary mode parameters. It is established that the interaction of reactive power and distortion power occurs at the instantaneous power level, and changing their phase shifts allows you to adjust the shape of the resulting power without involving additional active power in the AC network. The results obtained will allow not only to correctly determine the proportion and nature of the components of inactive capacities, which is valuable for solving the problems of optimizing modes in AC networks, but also to create effective technical means of compensating for the identified inactive capacities in the future.
Implementation of DC voltage controllers on enhancing the stability of multi-terminal DC grids
Authors: Belgacem Moussa , Khatir Mohamed , Djehaf Mohammed Abdeldjalil , Zidi Sid Ahmed , Bouddou Riyadh
Published: February 2021

Because of the increasing penetration of intermittent green energy resources like offshore wind farms, solar photovoltaic, the multi-terminal DC grid using VSC technology is considered a promising solution for interconnecting these future energies. To improve the stability of the multi-terminal direct current (MTDC) network, DC voltage control strategies based on voltage margin and voltage droop technique have been developed and investigated in this article. These two control strategies are implemented in the proposed model, a ±400 kV meshed multi-terminal MTDC network based on VSC technology with four terminals during the outage converter. The simulation results include the comparison and analysis of both techniques under the outage converter equipped with constant DC voltage control, then the outage converter equipped with constant active power control. The simulation results confirm that the DC voltage droop technique has a better dynamic performance of power sharing and DC voltage regulation.
Partial discharges location in power transformers using piezoceramic sensors
Authors: Danouj B. , Tahan S. A. , David E. , Lotfi M.
Published: February 2021

The detection and the spatial localization of partial discharges in high-voltage electrical machines are considered as an effective method in predictive maintenance that can provide valuable information on the health of the insulation system and allow to determine accurately the location of the risky insulation elements, which in turn will avoid any premature equipment’s deterioration by scheduling preventive maintenance action. After confirming in a previous published paper the efficiency of a new generation of piezoceramics sensors (high temperature ultrasonic transducers) to detect and characterize partial discharges, we are going to investigate, in this work, a second potential of this technology to locate the partial discharge sources by relying on its ability to detect acoustic signals emitted by partial discharge sources. We will present experimental results, demonstrating the effectiveness of these sensors to locate partial discharges sources and, we will also present an algorithm for calculating the partial discharge foci, based on the acoustic wave flight time.
New proposed method for traceability dissemination of capacitance measurements
Authors: M. Hamed Heba A. , Gad A. Eliwa , A. Raouf M. Helmy
Published: February 2021

Capacitance measurements at the National Institute of Standards (NIS), Egypt, are traceable to the Bureau International des Poids et Mesures (BIPM). It calibrates the main NIS standard capacitors, AH11A. In this paper, traceability of the BIPM capacitance measurements could be used to evaluate a new accurate measurement method through an Ultra-Precision Capacitance Bridge. The new method is carefully described by introducing some necessary equations and a demonstrating chart. Verification of this new method has been realized by comparing its results for the 10 pF and 100 pF capacitance standards with the results obtained by the conventional substitution method at 1 kHz and 1.592 kHz. The relative differences between the two methods are about 0.3 µF/F, which reflect the accuracy of the new measurement method. For higher capacitance ranges, the new measurement method has been applied for the capacitance measurements up to 1 μF at 1 kHz. The relative differences between the two methods are in the range of 5.5 µF/F on the average which proves the acceptable accuracy and the reliability of the new method to be used.
Outage performance of underlay cognitive radio networks over mix fading environment
Authors: Hong Nhu Nguyen , Van Cuu_Ho , Phan Van-Duc , Nguyen Tan N. , Voznak Miroslav , Zdralek Jaroslav
Published: February 2021

In this paper, the underlay cognitive radio network over mix fading environment is presented and investigated. A cooperative cognitive system with a secondary source node S, a secondary destination node D, secondary relay node Relay, and a primary node P are considered. In this model system, we consider the mix fading environment in two scenarios as Rayleigh/Nakagami-m and Nakagami-m/Rayleigh Fading channels. For system performance analysis, the closed-form expression of the system outage probability (OP) and the integral-formed expression of the ergodic capacity (EC) are derived in connection with the system's primary parameters. Finally, we proposed the Monte Carlo simulation for convincing the correctness of the system performance.
Hybrid solar/wind/diesel water pumping system in Dubai, United Arab Emirates
Authors: Obaid Waleed , Hamid Abdul-Kadir , Ghenai Chaouki
Published: February 2021

This paper proposes a hybrid power system design for water pumping system in Dubai (Latitude 25.25 °N and Longitude 55 °E), United Arab Emirates using solar photovoltaic (PV) panels, wind turbines, and diesel generator. The proposed design considers the changes in weather conditions (humidity percentage, temperature in celsius, and wind speed in m/s) that directly affect solar irradiance values which alter the performance of the hybrid system. The proposed design deals with the problem of rare rainy days in Dubai between December and March and the high temperature throughout the year since that makes providing water to rural and isolated zones essential. The proposed system uses voltage regulator to maintain stable DC voltage from the solar power system, battery bank to store the voltage from solar PV panels, three-phase rectifier to convert the AC voltage from wind power system to DC, three-phase step-down transformers to reduce the AC voltage of the wind turbine and diesel generator, and DC electric motor for water pumping output. The system used neural network for solar irradiance forecasting over an interval of 10 years (from 2009 to 2019). The proposed system will be demonstrated using Simulink to show the stability and performance under different weather conditions.
Visual victim detection and quadrotor-swarm coordination control in search and rescue environment
Authors: Cardona Gustavo A. , Ramirez-Rugeles Juan , Mojica-Nava Eduardo , Calderon Juan M.
Published: February 2021

We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.
All-terrain mobile robot desinfectant sprayer to decrease the spread of COVID-19 in open area
Authors: Megantoro Prisma , Setiadi Herlambang , Pramudita Brahmantya Aji
Published: February 2021

The application of disinfection is becoming popular in recent months due to the COVID-19. Usually, the disinfection is used by spraying the liquid into an object. However, the disinfection process for humans and objects in the human environment is still done manually and takes time and increases exposure to viruses. Robotic technology can be a solution to handle that problem. Following that problem, robot design is proposed with many abilities and features. The robot can operate in remote conditions and full function for approximately 56 minutes and spray the liquid for more than 1 meter. This research can effectively be applied in COVID-19 handlings.
Pain track analysis during gestation using machine learning techniques
Authors: Naik Praveen , Reddy Vandana , Shettigar Ramesha
Published: February 2021

During the gestation period women experience Braxton Hicks which is called the false labor, contractions during the second trimester. These contractions are not in regular intervals and also they are often unnoticed. The real labour or the true labour contractions develop late in the third trimester of the gestation usually beyond 36th week (excluding pre-term birth). Some women often fail to identify these pains in the third trimester of the gestation where an efficient facial recognition algorithm along with the support vector machine (SVM) helps them to identify these pains and take optimum care of themselves. The authors in this paper convey a mechanism to identify the pains effectively by creating a database of images pertaining to the pregnant women, her emotional states through out the pregnancy. Using MATLAB the algorithm of decision tree is implemented and the values obtained from them help us analyze the pain type efficiently.
A heterogeneous short-range communication platform for internet of vehicles
Authors: Zaeri Naser
Published: February 2021

The automotive industry is rapidly accelerating toward the development of innovative industry applications that feature management capabilities for data and applications alike in cars. In this regard, more internet of vehicles solutions are emerging through advancements of various wireless medium access-control technologies and the internet of things. In the present work, we develop a short-range communication–based vehicular system to support vehicle communication and remote car control. We present a combined hardware and software testbed that is capable of controlling a vehicle’s start-up, operation and several related functionalities covering various vehicle metric data. The testbed is built from two microcontrollers, Arduino and Raspberry Pi 3, each of which individually controls certain functions to improve the overall vehicle control. The implementation of the heterogeneous communication module is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 and IEEE 802.15 medium access control technologies. Further, a control module on a smartphone was designed and implemented for efficient management. Moreover, we study the system connectivity performance by measuring various important parameters including the coverage distance, signal strength, download speed and latency. This study covers the use of this technology setup in different geographical areas over various time spans.
Traffic-aware adaptive server load balancing for software defined networks
Authors: Fancy C. , Pushpalatha M.
Published: February 2021

Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.
Files cryptography based on one-time pad algorithm
Authors: Al-Smadi Ahmad Mohamad , Al-Smadi Ahmad , Ali Aloglah Roba Mahmoud , Abu-darwish Nisrein , Abugabah Ahed
Published: February 2021

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it’s successful without losing any information.
K-means clustering-based WSN protocol for energy efficiency improvement
Authors: Lee Jong-Yong , Lee Daesung
Published: February 2021

Since it is very difficult to replace or recharge the batteries of the sensor nodes in the wireless sensor network (WSN), efficient use of the batteries of the sensor nodes is a very important issue. This has a deep relationship with the lifetime of the network. If the node's energy is exhausted, the node is no longer available. If a certain number of nodes (50% or 80%) in a network consumes energy completely, the whole network will not work. Therefore, various protocols have been proposed to maintain the network for a long time by minimizing energy consumption. In recent years, a protocol using a K-means clustering algorithm, one of machine learning techniques, has been proposed. A KCED protocol is proposed in consideration of residual energy of a node, a cluster center, and a distance to a base station in order to improve a problem of a protocol using K-average gung zipper algorithm such as cluster center consideration.
Character attachment in team-based first person shooter game with respect to the role in the combat among Korean young gamers
Authors: Song Doo Heon , Lee Seunghun
Published: February 2021

Character attachment have been studied thoroughly from the view of psychology and media researches. In game playing, the player-avatar relationship is a form of character attachment and affects a good game design as well as management systems such as character customizing and in game purchasing policy. In this paper, we investigate such player-avatar relationship on the theme of team-based FPS where in general the attachment is not expected to be high. However, from the online survey for Tom Clancy’s Rainbow 6 Siege mania groups, we find that there are different character attachment patterns with respect to the role of players in the team–attacker, defender, and supporter. It shows that attackers think avatar as an ‘object, but the defenders show more ‘avatar as others’ than the attackers. The supporters show high responsibility for the avatar and their play style is most like ‘avatar as symbiote’ manner.
Blockchain for automotive: An insight towards the IPFS blockchain-based auto insurance sector
Authors: Nizamuddin Nishara , Abugabah Ahed
Published: February 2021

The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk of error. The insurance sector faces a threat of failure due to losing and misconception of policies and information. We present a decentralized IPFS and blockchain-based framework for the auto insurance sector that regulates the activities in terms of insurance claims for automobiles and automates payments. This article also discusses how blockchain technology’s features can be useful for the decentralized autonomous vehicle’s ecosystem.
Multi-kernel CNN block-based detection for COVID-19 with imbalance dataset
Authors: Mursalim Muhammad Khaerul Naim , Kurniawan Ade
Published: February 2021

COVID-19, which originated from Wuhan, rapidly spread throughout the world and became a public health crisis. Recognizing the positive cases at the earliest stage was crucial in order to restrain the spread of this virus and to perform medical treatment quickly for patients affected. However, the limited supply of RT-PCR as a diagnosis tool caused greatly delay in obtaining examination results of the suspected patients. Previous research stated that using radiologic images could be utilized to detect COVID-19 before the symptoms appeared. With the rapid development of Artificial intelligence in medical imaging in recent years, deep learning as the core of this technology could achieve human-level-performance in diagnostic accuracy. In this paper, deep learning was implemented to detect COVID-19 using a chest X-ray dataset. The proposed model employed a multi-kernel convolution neural network (CNN) block combined with pre-trained ResNet-34 to overcome an imbalanced dataset. The model block adopted different kernel sizes as follows 1x1, 3x3, 5x5, and 7x7. The findings show that the proposed model is capable of performing binary and three class classification tasks with an accuracy of 100% and 93.51% in the validation phase and 95% and 83% in the test phase, respectively.
Counteraction to information influence in social networking services by means of fuzzy logic system
Authors: Molodetska Kateryna , Solonnikov Vladyslav , Voitko Oleksandr , Humeniuk Ihor , Matsko Oleksandr , Samchyshyn Oleksii
Published: February 2021

The article describes a decision support system based on fuzzy inference aimed to automate the procedure of choosing a model of formalizing the interaction between actors in virtual communities of social networking services and synergistic management of such processes. The developed system aims to increase the effectiveness of counteracting threats to information security of the state in social networking services. The mathematical apparatus of the fuzzy set theory and the Mamdani algorithm are the basis for the functioning of the decision support system. The usage of the developed fuzzy inference system will remove the ambiguity of information security expertise in the course of choosing approaches to formalization and the model of synergistic management of actors’ interaction in the conditions of incomplete information and ambiguous assessment of the state information security threat in social networking services.
Comparative analysis of multiple classification models to improve PM10 prediction performance
Authors: Jung Yong-Jin , Cho Kyoung-Woo , Lee Jong-Sung , Oh Chang-Heon
Published: February 2021

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model.
Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm
Authors: Cano Jose Alejandro , Cortés Pablo , Campo Emiro Antonio , Correa-Espinal Alexander Alberto
Published: February 2021

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.
A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning
Authors: Sonker Abhilash , K. Gupta R.
Published: February 2021

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.