bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensored speed control of brushless DC motor based salp swarm algorithm
Authors: Al-Din Abed Wisam Najm , Imran Omar Abbood , Abdullah Ali Najim
Published: August 2022

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.
A predictive sliding mode control for quadrotor’s trackingtrajectory subject to wind gusts and uncertainties
Authors: Meradi Dounia , Benselama Zoubir Abdeslem , Hedjar Ramdane
Published: August 2022

In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time slidingmode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints.
Ambulance detection for smart traffic light applications with fuzzy controller
Authors: Jimenez-Moreno Robinson , Baquero Javier Eduardo Martinez , Rodriguez Umaña Luis Alfredo
Published: August 2022

In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.
Robust control for a tracking electromechanical system
Authors: Dana Karimtaevna Satybaldina , Asylbekkyzy Kalmаganbetova Zhuldyzay
Published: August 2022

A strategy for the design of robust control of tracking electromechanical systems based on 𝐻∞ synthesis is proposed. Proposed methods are based on the operations on frequency characteristics of control systems designed and developed using the MATLAB robust control toolbox. Determination of the singular values for a transfer matrix of the control system reduces the disturbances and guarantees its stability margin. For selecting the weighted transfer functions, the basic recommendations are formulated. The efficiency of the proposed approach is verified by robust control of an elastically coupled two-mass system whose parameter values are adjusted by matching them with the parameters of one of the supplied robots. The simulation results confirm that the proposed strategy of design of robust control of two-mass elastic coupling system using the 𝐻∞ synthesis is very efficient and significantly reduces the perturbation of parameters of the controlled plant.
Fuzzy logic-based energy management strategy on dual-source hybridization for a pure electric vehicle
Authors: Jbari Hatim , Askour Rachid , Idrissi Badr Bououlid
Published: August 2022

This paper presents a fuzzy logic controller (FLC) based energy management strategy (EMS), combined with power filtering for a pure electric vehicle. The electrical power supply is provided by a hybrid energy storage system (HESS), including Li-Ion battery and supercapacitors (SCs), adopting a fully active parallel topology. The vehicle model was organized and constructed using the energetic macroscopic representation (EMR). The main objective of this work is to ensure an efficient power distribution in the proposed dual source, in order to reduce the battery degradation. To evaluate the impact of the developed design and the efficiency of the developed EMS, the proposed FLC strategy is compared to a classical EMS using SCs-filtering strategy and architecture based on battery storage model. To validate the proposed topology, simulation results are provided for the new European driving cycle (NEDC) using MATLAB/Simulink environment.
Duct quiet zones utilization for an enhancement the acoustical air-condition noise control
Authors: Mahmoud Kamel Mohamed , Soliman Ahmed Said EL-Sayed , Mohamed AL-Sagheer Ragab
Published: August 2022

This paper investigates the duct’s noise distribution pattern due to the heating, ventilation, and air conditioning systems. This study is considering the longitudinal sound wave distribution that can permit a higher reduction of these heating, ventilation, and air conditioning systems duct noise. The proposed technique is depending on the lowest sound pressure level points in the duct or duct quiet zones. Moreover, each heating, ventilation, and air conditioning systems duct has several quiet zones, depending on the sound pressure level of the fan noise source and the duct length as well as the duct diameter. Furthermore, the noise standing wave has a wavelength (λ) which is the distance between two successive quiet zones. This work utilizes orthogonal acoustical noise with the standing wave via feed-forward control speakers. This system confirmed that the distance (λ) is linearly proportional to the duct source noise level. This system noise reduction enhancement has been fulfilled by installing further noise feed-forward control speakers at different duct quiet zones. The system simulation results were displaying satisfactory agreement with the field experimental results.
Design and analysis of dual-mode numerically controlled oscillators based controlled oscillator frequency modulation
Authors: Gujjula Ramana Reddy , Perumal Chitra , Kodali Prakash , Rajanna Bodapati Venkata
Published: August 2022

In this paper, the design and analysis of dual-mode numerically controlled oscillators (NCO) based controlled oscillator frequency Modulation is implemented. Initially, input is given to the analog to digital (ADC) converter. This will change the input from analog to digital converter. After that, the pulse skipping mode (PSM) logic and proportional integral (PI) are applied to the converted data. After applying PSM logic, data is directly transferred to the connection block. The proportional and integral block will transfer the data will be decoded using the decoder. After decoding the values, it is saved using a modulo accumulator. After that, it is converted from one hot residue (OHR) to binary converter. The converted data is saved in the register. Now both data will pass through the gate driver circuit and output will be obtained finally. From simulation results, it can observe that the usage of metal oxide semiconductor field effect transistors (MOSFETs) and total nodes are very less in dual-mode NCO-based controlled oscillator frequency modulation.
A new procedure for lung region segmentation from computed tomography images
Authors: Abdullah Mohd Firdaus , Sulaiman Siti Noraini , Osman Muhammad Khusairi , Abdul Karim Noor Khairiah , Setumin Samsul , Isa Iza Sazanita
Published: August 2022

Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
Optimized textural features for mass classification in digital mammography using a weighted average gravitational search algorithm
Authors: Yinka Ogundepo Oludare , Avazi Omeiza Isaac Ozovehe , Oguntoye Jonathan Ponmile
Published: August 2022

Early detection of breast cancer cells can be predicted through a precise feature extraction technique that can produce efficient features. The application of Gabor filters, gray level co-occurrence matrices (GLCM) and other textural feature extraction techniques have proven to achieve promising results but were often characterized by a high false-positive rate (FPR) and false-negative rate (FNR) with high computational complexities. This study optimized textural features for mass classification in digital mammography using the weighted average gravitational search algorithm (WA-GSA). The Gabor and GLCM features were fused and optimized using WA-GSA to overcome the weakness of the textural feature techniques. With support vector machine (SVM) used as the classifier, the proposed algorithm was compared with commonly applied techniques. Experimental results show that the SVM with WA-GSA features achieved FPR, FNR and accuracy of 1.60%, 9.68% and 95.71% at 271.83 s, respectively. Meanwhile, SVM with Gabor features achieved FPR, FNR and accuracy of 3.21%, 12.90% and 93.57% at 2351.29 s, respectively, while SVM with GLCM features achieved FPR, FNR and accuracy of 4.28%, 18.28% and 91.07% at 384.54 s, respectively. The obtained results show the prevalence of the proposed algorithm, WA-GSA, in the classification of breast cancer tumor detection.
New foreground markers for Drosophila cell segmentation using marker-controlled watershed
Authors: Rahali Rim , Salem Yassine Ben , Dridi Noura , Dahman Hassen
Published: August 2022

Image segmentation consists of partitioning the image into different objects of interest. For a biological image, the segmentation step is important to understand the biological process. However, it is a challenging task due to the presence of different dimensions for cells, intensity inhomogeneity, and clustered cells. The marker-controlled watershed (MCW) is proposed for segmentation, outperforming the classical watershed. Besides, the choice of markers for this algorithm is important and impacts the results. For this work, two foreground markers are proposed: kernels, constructed with the software Fiji and Obj.MPP markers, constructed with the framework Obj.MPP. The new proposed algorithms are compared to the basic MCW. Furthermore, we prove that Obj.MPP markers are better than kernels. Indeed, the Obj.MPP framework takes into account cell properties such as shape, radiometry, and local contrast. Segmentation results, using new markers and illustrated on real Drosophila dataset, confirm the good performance quality in terms of quantitative and qualitative evaluation.
Performance analysis of compressive sensing recovery algorithms for image processing using block processing
Authors: Elaveini Mathiyalakendran Aarthi , Thangavel Deepa
Published: August 2022

The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based onmean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.
Remote sensing and geographic information systems technics for spatial-based development planning and policy
Authors: Arimjaya I Wayan Gede Krisna , Dimyati Muhammad
Published: August 2022

Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless
Channel fading attenuation based on rainfall rate for future 5G wireless communication system over 38-GHz
Authors: Shayea Ibraheem A. , Alkhawaldeh Salim A. , Nisirat Mahdi A. , Rahman Tharek A. , Ergen Mustafa
Published: August 2022

In this paper, the effect of heavy rainfall on the propagation of a 38-GHz in a tropical region was studied and analyzed. Real measurement was collected, with a path length of 300 meters, for a (5G) radio linkage in Malaysia, installed at the Universiti Teknologi Malaysia (UTM) Johor Bahru campus. The employed system entails an Ericsson MINI-Link 38 E-0.6 mm, with a horizontal polarization (HP) antenna at the top integrated with a rain gauge and a data logger. Daily registered samples with a single minute span, for a full study period of 1 month, were collected and evaluated. The obtained rain rate was found as 56 mm/hr with a specific rain attenuation of 18.4 dB/km for 0.01% of the time. In addition to that, a calculated average rain attenuation of 5.5 dB for the transmission path of 300 meters length, was calculated. Based on these findings, a recommendation to update the International Telecommunication Union (ITU) specification of the rain attenuation for Malaysia is proposed. Based on the results, we suggest shifting the zone classification of Malaysia from zone P to zone N-P. Therefore, accurate design for future 5G systems would rely on more precise estimated attenuation levels leading to enhanced performance.
Design and optimization of a rectangular microstrip patch antenna for dual-band 2.45 GHz/ 5.8 GHz RFID application
Authors: El Mattar Sara , Baghdad Abdennaceur
Published: August 2022

This paper introduces a new rectangular slot antenna structure based on a simple rectangular shape with two symmetrical rectangular slots on the radiated element. The aim of this work is to design an antenna and enhance it to function in the band (2.45 GHz and 5.8 GHz). We formulated the dimensions of the antenna using the transmission line model of the analytical methods and then we optimized these parameters using the CST Microwave Studio simulator. We made changes to two important parameters in our design: the position and width of the slots when the other parameters are kept constant. The resulting antenna provides good adaptation, high gain that achieves 5.96 dBi at 2.45 GHz and 6.491 dBi at 5.8 GHz, good return loss values of -49.859 dB and -34.303 dB for the lower and upper operating frequencies respectively. For radio frequency identification (RFID) implementations, the proposed antenna is ideal, and its main advantage is that it has high gain and is simple to design and fabricate.
Field programmable gate array implementation of multiwavelet transform based orthogonal frequency division multiplexing system
Authors: Hadi Suha Qasim , Jafaar Awatif Ali , Alameri Ban Mohammed , Malallah Sarah Ameer
Published: August 2022

This article offers an efficient design and implementation of a discrete multiwavelet critical-sampling transform based orthogonal frequency division multiplexing (DMWCST-OFDM) transceiver using field programmable gate array (FPGA) platform. The design uses 16-point discrete multiwavelet critical-sampling transform (DMWCST) and its inverse as main processing modules. All modules were designed using a part of Vivado® Design Suite version (2015.2), which is Xilinx system generator (XSG), and is compatible with MATLAB Simulink version R2013b. The FPGA implementation is carried out on a Zynq (XC7Z020-1CLG484) evaluation board with joint test action group (JTAG) hardware co-simulation. According to the results obtained from the implementation tools, the implemented system is efficient in terms of resource utilization and could support the real-time operations.
Design of miniaturized patch crossover based on superformula slot shapes
Authors: Banat Mutaz Akram , Dib Nihad Ibrahim
Published: August 2022

In this paper, miniaturized microstrip crossover circuits are proposed using slots shapes obtained using the superformula. The design starts by using a conventional half-wavelength square patch crossover. For miniaturization purposes, different superformula slot shapes are introduced on the square patch. The proposed crossovers are designed to operate at 2.4 GHz using a 0.8 mm thick FR-4 substrate with a relative permittivity of 4.4. The designs are simulated using the high frequency structure simulator (HFSS). One of the miniaturized designs is fabricated and its scattering parameters are measured using a vector network analyzer. Simulated and measured results agree very well. At the design frequency, the measured input port matching is better than ˗19 dB, while 𝑆12, 𝑆13 and 𝑆14 have the values of ˗12 dB, ˗2.2 dB and ˗10 dB, respectively. Furthermore, a 71% size reduction is achieved as compared to the conventional crossover area.
Design and implementation of portable electrocardiogram recorder with field programmable gate arrays and IoT interface
Authors: Rassal Raj Nisha Rexline , Priya Ebenezer , Savari George Fernandez
Published: August 2022

The electrical activities of the heart are used to monitor cardiovascular diseases. It can be measured using electrocardiogram (ECG), a simple, painless test that can be recorded graphically. The physician, to predict the patient’s heart conditions and recommend suitable treatments, uses electrodes placed on the patient’s skin surface, to record these signals. The P, Q, R, S, T waves in the ECG signal can be used to determine the normality and abnormality of the heart's condition. The time interval differs for each cardiovascular condition of the heart. In this work, the ECG signal is acquired real-time using an intelligent sensor module, and the recorded value is processed to find the peak values. The data is sent to the web serverusing internet of things technology at a minimal time, where the physician can view it and proper decision can be taken. The real-time ECG data acquisition is also made using the field programmable gate array kit as it is a low cost, high-speed device and the output is viewed in the computer. The developed model is validated through MATLAB software and implemented for real-time applications.
Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical moment’s features
Authors: Alsakaa Akeel Abdulkareem , Hussein Mohsin Hasan , Nasralla Zaid Hasan , Alsaqaa Hazim , Nermend Kesra , Borawska Anna
Published: August 2022

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.
Application of machine learning in cement price prediction through a web-based system
Authors: Afolabi Adedeji Olushola , Abimbola Oluwamayowa
Published: August 2022

Cement is one of the most common building materials in the construction industry. Simultaneously, its price fluctuation can affect the success or failure of the construction project's performance. The study aimed to develop a web-based platform that uses machine learning algorithms on historical data of cement prices, petrol prices, diesel prices, interest rate, and exchange rate to predict future prices of cement products. The web-based learning platform was developed using hypertext markup language (HTML), cascading style sheet (CSS), MySQL, and hypertext preprocessor (PHP). For building a reliable machine learning model, python language was used to train the system. The front end, the back end, and the machine learning model were integrated with a flask python framework. A system block diagram was designed to show the web-based learning platform's interfaces. The web-based learning platform's system implementation led to the login page, the home page, database page, and cement price analytics interface. In training the machine learning model to make reliable cement price predictions, the study obtained an 80% fitted model in the linear regression. The web-based machine learning platform was able to predict the prices of cement. The rationale behind the machine learning prediction shown by the scatter plot diagram revealed that the cement increases by 250 naira biannually.
An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods
Authors: Evangelista Edmund De Leon , Sy Benedict Descargar
Published: August 2022

Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods.
A new three-term conjugate gradient method with application to regression analysis
Authors: Idalisa Nur , Rivaie Mohd , Mohd Noh Nur Hidayah , Salim Nasir Mohd Agos , Fadhilah Nurul Hafawati , Alias Norma
Published: August 2022

Conjugate gradient (CG) method is well-known for its ability to solve unconstrained optimization (UO.) problems. This article presenting a new CG method with sufficient descent conditions which improves the former method developed by Rvaie, Mustafa, Ismail and Leong (RMIL). The efficacy of the proposed method has been demonstrated through simulations on the Kijang Emas pricing regression problem. The daily data between January 2021 to May 2021 were obtained from Malaysian Ministry of Health and Bank Negara Malaysia. The dependent variable for this study was the Kijang Emas price, and the independent variables were the coronavirus disease (COVID-19) measures (i.e., new cases, R-naught, death cases, new recovered). Data collected were analyzed on its correlation and coefficient determinant, and the influences of COVID-19 on Kijang Emas price was examined through multiple linear regression model. Findings revealed that the suggested technique outperformed the existing CG algorithms in terms of computing efficiency.
Performance evaluation of route optimization management of producer mobility in information-centric networking
Authors: Low Xian Wee , Leau Yu-Beng , Yan Zhiwei , Park Yong-Jin , Anbar Mohammed
Published: August 2022

Named data networking (NDN) is a network service evolving the Internet's host-based packet delivery model. The idea of NDN is to use named data for routing, which specifies what they are looking for, instead of using location addresses that determine where they expect it to be provided. This architecture is expected to solve many issues that are currently faced by transmission control protocol/internet protocol (TCP/IP) architecture, such as scalability, robustness, mobility, security, and etcetera. One of the problems is about handling producer mobility. Considering the explosion growth rate of Internet connection in public transport vehicles, this is a challenge that needs to be overcome. Therefore, we have proposed a new scheme called route optimization management of producer mobility (ROM-P) with new features such as distributing anchor points and caching by using the same data name and com-paring our previous scheme, efficient producer mobility support (EPMS). This paper shows the analysis result between the ROM-P and EPMS by using simulation. All simulations were conducted using ndnSIM 2.4 NS-3 based. Throughout the simulation ROM-P shows a promising development in better performing compares to EPMS.
A digital game for preserving food cultural heritage: design and evaluation of ThaiFoodAdventure game
Authors: Chaisriya Kannattha , Gilbert Lester , Suwangerd Ratchada , Rattanarungrot Sasithorn
Published: August 2022

Digital games are a powerful tool for the presentation of food cultural heritage. A digital game was designed and developed to raise and enhance young people’s interest in and knowledge of Thai food cultural heritage, currently an under-researched field. The platform game was played on a mobile device and required the collection of food ingredients appropriate to popular cuisine in four Thai regions while overcoming obstacles. A sample (N=61) of young people (mean age=19 years) played the game, and the differences in their pre and post-test knowledge of and interest in Thai food and its cultural heritage were analyzed. The findings showed a highly significant increase in interest in and knowledge of Thai food cultural heritage, and did so despite the opinion of some participants that learning games were less interesting than conventional games, or that games were not a good way of raising interest in cultural heritage.
The impact of channel model on the performance of distance-based schemes in vehicular named data networks
Authors: Ahed Kaoutar , Benamar Maria , El Ouazzani Rajae
Published: August 2022

Distance-based schemes present one of the methods to avoid the broadcast problem in vehicular named data networks. However, such schemes overlook the most factor in performance evaluation which is the variation in received signal strength caused by the propagation model choice. Thus, they are evaluated under one propagation model while neglecting the effect of the others. This paper evaluates the impact of the propagation variation model over three distance-based schemes, namely rapid named data networking (RNDN), enhanced vehicle on named data networking (EVNDN) and opportunistic interest forwarding protocol (OIFP). Simulation experiments are performed over three propagation models. Simulation results show that Nakagami significantly degrades network performance. However, it has a noticeable positive effect over higher distance resulting in a higher interest satisfaction ratio as compared to the other models. The RNDN exhibits a higher number of retransmissions across the Nakagami. In contrast, a higher number of retransmissions is exhibited by EVNDN when compared to the other schemes over the Friis and random. The OIFP show a higher interest satisfaction ratio when compared to EVNDN and RNDN under all models. OIFP shows robustness towards the adverse fading effects resulting from the Nakagami and exhibits lower end to end delays.
Korean-popular Facebook fan page analytics in Thailand
Authors: Chansanam Wirapong , Tuamsuk Kulthida , Kwiecien Kanyarat , Oh Sam Gyun
Published: August 2022

The purpose of this study was to gather, analyze, and elaborate big data on Facebook’s essential information, with a specific focus on the information obtained from Korean-popular (K-Pop) fan pages on the social networking site. For this analysis, a total of 3,531,736 comments by Korean-pop fans were gathered from various K-pop Facebook pages. In order to interpret how 11 extremely popular Facebook pages shape Thai fans’ enthusiasm for the South Korean music industry, descriptive statistics and visualization analysis were employed. Finally, data analytics and correlation analysis were used to evaluate the essential understanding of the Facebook pages. The research revealed three key findings: i) K-pop fan pages provide more opportunities for Thai fans to express their support for K-pop artists and advocate for causes, ii) K-pop fan pages provide more opportunities for Thai fans to communicate with K-pop artists, and iii) K-pop fan pages build opportunities for Thai fans to establish a more glamorous online presence despite limitations concerning financial resources, foreign language skills, and opportunities. In the future, the research outcomes may be valuable for academic studies and practice.
Efficient feature descriptor selection for improved Arabic handwritten words recognition
Authors: Hamida Soufiane , El Gannour Oussama , Cherradi Bouchaib , Ouajji Hassan , Raihani Abdelhadi
Published: August 2022

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.
Statistical features learning to predict the crop yield in regional areas
Authors: Pani Ramanahalli Pinaka , Kollegal Siddamallu Hemanth , Kumar Yelwala Basavaraju Ravi
Published: August 2022

The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region.
Efficient failure detection and consensus at extreme-scale systems
Authors: Kolisetty Soma Sekhar , Srinivasa Rao Battula
Published: August 2022

Distributed systems and extreme-scale systems are ubiquitous in recent years and have seen throughout academia organizations, business, home, and government sectors. Peer-to-peer (P2P) technology is a typical distributed system model that is gaining popularity for delivering computing resources and services. Distributed systems try to increase its availability in the event of frequent component failures and functioning the system in such scenario is notoriously difficult. In order to identify component failures in the system and achieve global agreement (consensus) among failed components, this paper implemented an efficient failure detection and consensus algorithm based on fail-stop type process failures. The proposed algorithm is fault-tolerant to process failures occurring before and during the execution of the algorithm. The proposed algorithm works with the epidemic gossip protocol, which is a randomly generated paradigm of computation and communication that is both fault-tolerant and scalable. A simulation of an extreme-scale information dissemination process shows that global agreement can be achieved. A P2P simulator, PeerSim, is used in the paper to implement and test the proposed algorithm. The proposed algorithm results exhibited high scalability and at the same time detected all the process failures. The status of all the processes is maintained in a Boolean matrix.
Two-stage parametric identification procedure to predict satellite orbital motion
Authors: Chernikova Oksana Sergeevna , Chetvertakova Yuliya Sergeevna
Published: August 2022

The paper presents a new step-by-step procedure for constructing a navigation satellite motion model. At the first stage of the procedure, the parameters of the radiation pressure model are estimated using the maximum likelihood method. The statistic estimator based on the continuous-discrete adaptive unscented Kalman filter is proposed for the solar radiation model parameters estimation. Step-by-step scheme of filtering algorithm used for the software development are given. At the second stage, the parameters of the unaccounted perturbations model are estimated based on the results of residual differences measurements. The obtained results lead to signiﬁcant improvement of prediction quality of the satellite trajectory.
Content validity study: a gamification model to drive behavior change in defeating coronavirus disease 2019 pandemic
Authors: Mat Zain Nurul Hidayah , Abdul Aziz Siti Rahayu , Moketar Nor Aiza , Ishak Norshahidatul Hasana , Hendrayati Heny
Published: August 2022

Gamification refers to transforming the environment to become more game-like to produce a positive experience. In this study, the researchers developed a gamification model, namely the GAMEBC model, to drive behavior change through a health awareness campaign in defeating the coronavirus disease 2019 (COVID-19) pandemic. The GAMEBC model was developed based on the self-determination theory (SDT) and gamification design literature. The GAMEBC model in this study involves four elements: competence, relatedness, autonomy, and engagement. Each element includes criteria that drive behavior change in health awareness campaigns. However, studies that validated the gamification model elements are limited, specifically to drive behavior change. Therefore, the content validity of the GAMEBC model instrument was carried out, and the analysis was based on thirteen expert reviews. The mean value and inter-rater agreement approach were implemented to examine the content validity ratio (CVR), item content validity index (I-CVI), and scale content validity index (S-CVI). The expert evaluation approach was implemented to review the GAMEBC model in terms of relevance and clarity. The data were analyzed using descriptive analysis. As a result of this work, we formulated an instrument that can be used to model and measure behavior change through the gamification approach in health awareness campaigns.
A hybrid framework for detecting structured query language injection attacks in web-based applications
Authors: Furhad Md. Hasan , Chakrabortty Ripon K. , Ryan Michael J. , Uddin Jia , Sarker Iqbal H.
Published: August 2022

Almost every web-based application is managed and operated through a number of websites, each of which is vulnerable to cyber-attacks that are mounted across the same networks used by the applications, with much less risk to the attacker than physical attacks. Such web-based attacks make use of a range of modern techniques-such as structured query language injection (SQLi), cross-site scripting, and data tampering-to achieve their aims. Among them, SQLi is the most popular and vulnerable attack, which can be performed in one of two ways; either by an outsider of an organization (known as the outside attacker) or by an insider with a good knowledge of the system with proper administrative rights (known as the inside attacker). An inside attacker, in contrast to an outsider, can take down the system easily and pose a significant challenge to any organization, and therefore needs to be identified in advance to mitigate the possible consequences. Blockchain-based technique is an efficient approach to detect and mitigate SQLi attacks and is widely used these days. Thus, in this study, a hybrid method is proposed that combines a SQL query matching technique (SQLMT) and a standard blockchain framework to detect SQLi attacks created by insiders. The results obtained by the proposed hybrid method through computational experiments are further validated using standard web validation tools.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary equilibrium generative adversarial networks
Authors: Iklima Zendi , Kadarina Trie Maya , Ihsanto Eko
Published: August 2022

Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
Modified SEIR and machine learning prediction of the trend of the epidemic of COVID-19 in Jordan under lockdowns impact
Authors: Alsmadi Mutasem Khalil
Published: August 2022

Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.
A novel convolutional neural network based dysphonic voice detection algorithm using chromagram
Authors: Islam Rumana , Tarique Mohammed
Published: August 2022

This paper presents a convolutional neural network (CNN) based non-invasive pathological voice detection algorithm using signal processing approach. The proposed algorithm extracts an acoustic feature, called chromagram, from voice samples and applies this feature to the input of a CNN for classification. The main advantage of chromagram is that it can mimic the way humans perceive pitch in sounds and hence can be considered useful to detect dysphonic voices, as the pitch in the generated sounds varies depending on the pathological conditions. The simulation results show that classification accuracy of 85% can be achieved with the chromagram. A comparison of the performances for the proposed algorithm with those of other related works is also presented.
Automatic COVID-19 lung images classification system based on convolution neural network
Authors: Mahdy Lamia Nabil , Bahgat El Seddawy Ahmed Ibrahim , Ezzat Kadry Ali
Published: August 2022

Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
An efficient lightweight key exchange algorithm for internet of things applications
Authors: Kasfa Ali Fasila , Mathew Sheena
Published: August 2022

Internet of things (IoT) gained wide popularity in recent years, and this is proved by tremendous increase in use of IoT applications worldwide. Distributed IoT applications can be implemented securely with the support of blockchain. By default, blockchain will ensure authentication of involved entities as well as integrity of data. Due to storage restrictions, use of hybrid system is preferred, and this involves cloud server for storage and blockchain for other functionalities. Data kept in cloud has to be encrypted by a strong encryption algorithm. Even though core security objectives are achieved, it is necessary to provide a secure method to exchange the key. Since, the key is the backbone of a security algorithm, protection of the key has to be ensured. In this work, an algorithm is proposed to provide a no-share key exchange between two communicating parties in a resource constrained environment. The same was implemented and compared with conventional key sharing algorithms. Security analysis was formally conducted by using widely accepted automated validation of internet security protocols and applications (AVISPA) tool and the proposed method proved to be secure.