bluuebunny/crossref_metadata_2025 huggingface dataset:
Guaranteed Deterministic Approach to Superhedging: Case of Binary European Option
Authors: Smirnov Sergey N. , Zanochkin Andrey Yu.
Published: June 2021

For the superreplication problem with discrete time, a guaranteed deterministic formulation is considered: the problem is to guarantee coverage of the contingent liability on sold option under all admissible scenarios. These scenarios are defined by means of a priori defined compacts dependent on price prehistory: the price increments at each point in time must lie in the corresponding compacts. In a general case, we consider a market with trading constraints and assume the absence of transaction costs. The formulation of the problem is game theoretic and leads to the Bellman–Isaacs equations. This paper analyses the solution to these equations for a specific pricing problem, i.e., for a binary option of the European type, within a multiplicative market model, with no trading constraints. A number of solution properties and an algorithm for the numerical solution of the Bellman equations are derived. The interest in this problem, from a mathematical prospective, is related to the discontinuity of the option payoff function.
Antiamylase, Antiglucosidase, and Antiglycation Properties of Millets and Sorghum from Sri Lanka
Authors: Senevirathne Ilangasingha Gamlathge Nethmini Hansika , Abeysekera Walimuni Kanchana Subhashini Mendis , Abeysekera Walimuni Prabhashini Kaushalya Mendis , Jayanath Nileththi Yasendra , Galbada Arachchige Sirimal Premakumara , Wijewardana Danthasingha Chithra Mulacharige Susantha Indika
Published: June 2021

The present study evaluated a range of biological activities of selected millet types and sorghum varieties in Sri Lanka in relation to diabetes and its complications management. Five millet types, namely, proso millet, white finger millet, kodo millet, foxtail millet, and finger millet (Oshadha and Rawana), and two sorghum varieties, namely, sweet sorghum and sorghum ICSV 112, were used in this study. Methanolic extracts of whole grains were studied for antiamylase, antiglucosidase, and early- and middle-stage antiglycation and glycation reversing activities in vitro. Tested millets and sorghum showed significant (

p

 < 0.05) and dose-dependent antiamylase (IC50: 33.34 ± 1.11–1446.70 ± 54.10 μg/ml), early-stage antiglycation (IC50: 15.42 ± 0.50–270.03 ± 16.29 μg/ml), middle-stage antiglycation (135.08 ± 12.95–614.54 ± 6.99 μg/ml), early-stage glycation reversing (EC50: 91.82 ± 6.56–783.20 ± 61.70 μg/ml), and middle-stage glycation reversing (393.24 ± 8.68–1374.60 ± 129.30 μg/ml) activities. However, none of the studied millet and sorghum showed antiglucosidase activity. Out of the samples studied, pigmented samples, namely, sweet sorghum, Oshadha, and Rawana, exhibited significantly high (

p

 < 0.05) antiamylase and early- and middle-stage antiglycation and glycation reversing activities compared to other millet and sorghum samples. Interestingly, sweet sorghum exhibited nearly four times potent antiamylase activity compared to the standard drug acarbose (IC50 111.98 ± 2.68 μg/ml) and sweet sorghum, kodo millet, Oshadha, and Rawana showed comparable early-stage antiglycation activities in comparison to the reference standard Rutin (IC50 21.88 ± 0.16 μg/ml). Therefore, consumption of whole grains of pigmented millet and sorghum in Sri Lanka may play an important role in the prevention and management of diabetes and its complications. Interestingly, this is the 1st study to report all the tested biological activities for millet and sorghum in Sri Lanka and the 1st study to report both early- and middle-stage glycation reversing activities of millet and sorghum worldwide.
Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate
Authors: Nguyen Van-Hai , Le Tien-Thinh , Truong Hoanh-Son , Le Minh Vuong , Ngo Van-Luc , Nguyen Anh Tuan , Nguyen Huu Quang
Published: June 2021

This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.
[Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm
Authors: Chang Jie , Zuo Xiaojun , Hou Botao , Shi Lipeng , Zhang Guanghua
Published: June 2021

This paper analyzes the current real‐time monitoring system based on grey‐related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey‐related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three‐dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real‐time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision‐level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.
An Improved Prediction Method of Soil-Water Characteristic Curve by Geometrical Derivation and Empirical Equation
Authors: Zhou Jie , Ren Junjie , Li Zeyao
Published: June 2021

Much attention has been paid on the soil-water characteristic curve (SWCC) during decades because it plays great roles in unsaturated soil mechanics. However, it is time-consuming and costly to obtain a series of entire saturation-suction data by experiments. The curves acquired by directly fitting empirical equations to limited experimental data are greatly different from the actual SWCC, and the relevant soil parameters obtained by inaccurate curve are also incorrect. Thus, an improved prediction method for more accurate entire SWCC was established. This novel method was based on the analysis of shape characteristics of SWCC with three critical points

S

,

C

1

, and

C

2

under the hypothesis of geometrical symmetric relation. The theoretical computation was specifically deduced under conventional Gardner, VG, and FX models, respectively, and then inferred on different soil types of 45 collected SWCC datasets. This geometrical symmetric relation exhibited well in all these three conventional empirical equations, especially in Gardner equation. Finally, a series of filer paper tests on sand, silt, and clay were also carried out to acquire entire SWCC curve for the verification and evaluation of the proposed geometrical method. Results show that this improved prediction method effectively decreases deviation resulting from directly fitting empirical equations to limited data of wide types of soils. The averaged improvement was larger under VG equation than under Gardner and FX equation. It proved that the accuracy of predicting greatly depends on the shape characteristic point of maximum curve curvature (point

C

2

), other than the number of points. This research provides a novel computation method to improve prediction accuracy even under relative less experimental data.
Development of a Gas Chromatography-Time-of-Flight Method for Detecting Glucosinolate Metabolites and Volatile Organic Compounds in Kimchi
Authors: Kim Ho Jin , Lee Mi Jin , Hur Suel Hye , Jeong Min Hee
Published: June 2021

This study examined the volatile organic compounds (VOCs) and metabolites of glucosinolate (GLS) contained in kimchi and analyzed GLS using myrosinase. The analysis was conducted using gas chromatography-time of flight (GC-TOF), and VOC and the metabolite quantities were detected and analyzed. Based on 22 samples, tests were conducted, and 12 metabolites and 52 VOCs were found. When the detected metabolites were compared in general, the rate of isothiocyanate, which is well known for its anticancer effects and various other activities, was the highest. A total of 52 VOCs, including 15 aliphatic hydrocarbons, 7 acids, and 6 alcohols, were detected by GC-TOF. Therefore, the analytical methods provide a good basis to examine VOC and GLS metabolites; furthermore, the methods are of great help to secure excellent kimchi and evaluate its quality.
Peripheral Inflammatory Yellow Exudative Retinal Coats-Like Vitreoretinopathy Misdiagnosed as Acute Retinal Necrosis in a Retinitis Pigmentosa Patient after Cataract Surgery
Authors: Tran Lara , Papasavvas Ioannis , Fleischhauer Johannes , Herbort Jr. Carl P.
Published: June 2021

We would like to describe a case with Coats-like exudative vitreoretinopathy after cataract surgery in a patient with retinitis pigmentosa (RP) misdiagnosed as acute retinal necrosis (ARN). A patient with RP underwent cataract surgery that was complicated by macular oedema. Following sub-Tenon’s injection of triamcinolone acetonide, evolution was initially favourable. However, 2 months later, after 2 sub-Tenon’s injections, the patient complained again of floaters and a drop of visual acuity. Aqueous flare measured by laser flare photometry was increased and posterior segment examination showed vitreitis, posterior haemorrhages and a temporal-inferior peripheral white-yellowish area in left eye. Serology (IgGs) for varicella-zoster virus (VZV) was slightly elevated and more so for toxoplasmosis. The whole clinical context strongly evoked ARN not excluding completely ocular toxoplasmosis. Valacyclovir and clindamycin were introduced without benefit. When examining the extreme periphery of the right fellow eye, discreet yellow lesions were also detected rendering the infectious hypothesis less probable. A vitrectomy finally excluded infectious causes and the diagnosis of Coats-like exudative vitreoretinopathy in a RP patient was retained. Increased flare despite 2 sub-Tenon’s injections, the presence of micro-haemorrhages, and peripheral yellow retinal necrotic areas drew our attention away from a well-known albeit rare condition of Coats-like response in RP patients, a diagnosis which has to be considered in such circumstances.
Meta-Analysis of the Relationship between Abdominal Obesity and Diabetic Kidney Disease in Type 2 Diabetic Patients
Authors: Zhao Qinying , Yi Xiaoyan , Wang Zhihong
Published: June 2021

Background and Objectives: The meta-analysis aimed to investigate the association of visceral fat area (VFA), waist circumference (WC), waist-hip ratio (WHR) and waist-height ratio (WHtR) with diabetic kidney disease (DKD) in type 2 diabetic patients. Methods: Included studies were searched from Pubmed, Embase, and the Cochrane Library before July 2020. We synthesized the pooled results of the above relationships by meta-analysis. Results: Fourteen cross-sectional studies were enrolled. The pooled results indicated there was a significant difference in continuous VFA, WC and WHR/WHtR between patients with DKD and those without DKD (standard mean difference, SMD, 0.24, 95% confidence interval, CI, 0.13–0.36, p = 0.000). For VFA, patients with DKD had higher VFA levels than those without DKD (SMD 0.27, 95% CI 0.03–0.50). In the WC subgroup, patients with DKD had higher WC levels than those without DKD (SMD 0.17, 95% CI 0.10–0.24); similarly, abdominal obesity (dichotomized WC) was significantly associated with an increase in the odds of DKD (expected shortfall, ES, 1.57, 95% CI 1.32–1.86). However, the association of continuous WHR/WHtR with DKD was not statistically significant (SMD 0.43, 95% CI –0.12 to 0.97), while we found this relationship was statistically significant when analyzed categorically (ES 1.58, 95% CI 1.22–2.06). Conclusion: In this meta-analysis, we found abdominal obesity parameters (continuous VFA, WC) were associated with increased odds of DKD, and type 2 diabetic patients with DKD were more likely to have abdominal obesity (categorized using WC or WHR/WHtR).
iPark: automated smart parking system
Authors: Atiqur Rahman
Published: June 2021

Parking problems became ubiquitous and are growing at an alarming rate in every  major  city.  It  causes  parking  problems  which  cause  a  trafficjam, driver’s frustration, and pollution. One time we visit differentpublic  places for example shopping center, multiplex movies hall & lodging house during carnivals  or  holidays,  it  makes  the  added  parking  difficult.  Checking  out  a parking lot results in 40% to 50% of traffic jams. Due to this, car parking, in turn,  causes  the  traffic  issue.  There  are  some  problems  associated  with  car parking like the way to control the amount of the car within the parking zone, monitoring  the  movement  of  the  car  in/outside  of  the  parking  zone,  check whether there's an area inside for more cars or not and therefore the safety to park. This paper proposes an answer for these problems using the concept of the web of Things. iParkisa fully automated system that senses the entry and exit  of  a  car,  displays  the  entire  number  of  cars  parked  within  the  parking zone  on  screen,  and  the  way  many  free  spaces  are  available  within  the parking lot.
Design and implementation of an African native language-based programming language
Authors: Olatunji Ezekiel Kolawole , Oladosu John. B. , Odejobi Odetunji A. , Olabiyisi Stephen O.
Published: June 2021

Most of the existing high level programming languages havehitherto borrowed their lexical items from human languages including European and Asian languages. However, there is paucity of research information on programming languages developed with the lexicons of an African indigenous language. This research explored the design and implementation of an African indigenous language-based programming language using Yoruba as case study. Yoruba is the first language of over 30 million people in the south-west of Nigeria, Africa; and is spoken by over one hundred million people world-wide. It is hoped, as established by research studies, that making computer programming possible in one’s mother tongue will enhance computer-based problem-solving processes by indigenous learners and teachers. The alphabets and reserved words of the programming language were respectively formed from the basic Yoruba alphabets and standard Yoruba words. The lexical items and syntactic structures of the programming language were designed with appropriate regular expressions and context-free grammars, using Backus-Naur Form (BNF) notations. A prototype implementation of the programming language was carried out as a source-to-source, 5-pass compiler. QBasic within QB64 IDE was the implementation language. The results from implementation showed functional correctness and effectiveness of the developed programming language. Thus lexical items of a programming language need not be borrowed exclusively from European and Asian languages, they can and should be borrowed from most African native languages. Furthermore, the developed native language programming language can be used to introduce computer programming to indigenous pupils of primary and junior secondary schools.
A systematic overview of secure image steganography
Authors: Ahmed Bakhan Tofiq
Published: June 2021

Information is a vitalthing that needs to be secured and well protected during transmission between two or more parties over the internet. This can be achieved by steganography technology. Steganography is the concealing science in which the information is concealed inside other information in a way that the concealed information cannot be detectable by the human eye. Many ways are available to hide data inside a cover media for example text, image, and audio steganography, but image steganography is the most utilized technique among the others. Secure image steganography has a high-security level than traditional technique by combining steganography with cryptography due to encrypting secret information by cryptography algorithm before embedding it into the cover media by steganography algorithm. In this paper,a systematic review has been presented about secure image steganography and its renowned types. Many researchers proposed secure image steganography by using various cryptography and steganography algorithms which have been reviewed. The least significant bit ‘LSB’ was the renowned steganography algorithm which has been used by researchers due to its simplicity, while various cryptography algorithms like advanced encryption standard (AES) and blowfishhave been used to propose secure image steganography in the reviewed papers. The comparison among the reviewed papers indicated that the LSB with hash-RSA gave a greater peak signal-noise ratio ‘PSNR’ value than the others which was 74.0189 dB.
Methods and instruments for stray current verification in DC rapid transit and railway systems
Authors: Mariscotti Andrea
Published: June 2021

Modern electrified transportation systems feature increasing installed power and performance and correspondingly stray current phenomena and corrosion are receiving more attention in terms of contractual specifications and request of final validation of proposed solutions, as well as a maintenance program that can actively tackle stray current issues. The problem is complex for the system physical extension, difficult measurement conditions and variability of electrical parameters. This work considers validation of stray current protection performance, in terms of track voltage, track leakage and collected current, and impressed potential on structures along the right-ofway. The exemplification of the specifications of a hypothetical instrument and setup support the discussion.
Switched capacitor based multi-level boost inverter for smart grid applications
Authors: Al gabalawy Mostafa , Hossam Ramy M. , Hussien Shimaa A. , Hosny Nesreen S.
Published: June 2021

To link DC power sources to an AC grid, converters are needed. Inverters are the power electronic devices, which are used for this purpose. Conventional inverters employ harmonic filters and transformers that are lossy and expensive. Multilevel inverters (MLIs) are an alternative to conventional ones, proposing reduced total harmonic distortion (THD), increased range of control, and inductor-less design. They generate a stepped waveform, with close similarity to a sine wave. Many distributed sources may be employed in a smart grid. If those sources have minimal THD, the filtering process could be reduced at the point of common coupling. This paper presents two switched capacitor based MLIs, proposing boost capability and low THD. Inverters have inherent charge balancing capability, which eliminates the need for auxiliary circuits and voltage sensors. Inverters switches are modulated using phase opposition disposition pulse-width modulation (PODPWM) method that ease the balancing of the voltage and decrease the losses of switching. Designs were verified by simulation and the output waveforms were introduced.
IoT for wheel alignment monitoring system
Authors: Sulaiman Mohamad Hadi , Sulaiman Suhana , Saparon Azilah
Published: June 2021

A great deal of previous research into wheel alignment has focused on techniques of the alignment, which involve big, bulky and high cost to maintain. Even though several approaches are required, the works are tedious and only performed in spacious area and trained mechanics. IoT is the alternatives due to the evolution of smartphone with numerous sensors to support and assist the research and development for IoT applications in vehicles. In this work, smaller and portable wheel alignment monitoring system is introduced by using communication protocol between sensors, microcontroller and mobile phone application. Thus, graphical user interface (GUI) is utilized to the system via wireless communication technology using TCP/IP Communication Protocol. The system has been tested to suit the functioning architecture system for the wheel alignment to provide the user awareness on early detection of wheel misalignment. In addition, the application has been successfully integrated with Android mobile application via TCP/IP communication protocol and view the results in smart phone in real-time.
Analysis of on-off current ratio in asymmetrical junctionless double gate MOSFET using high-k dielectric materials
Authors: Jung Hakkee , Kim Byungon
Published: June 2021

The variation of the on-off current ratio is investigated when the asymmetrical junctionless double gate MOSFET is fabricated as a SiO2/high-k dielectric stacked gate oxide. The high dielectric materials have the advantage of reducing the short channel effect, but the rise of gate parasitic current due to the reduction of the band offset and the poor interface property with silicon has become a problem. To overcome this disadvantage, a stacked oxide film is used. The potential distributions are obtained from the Poission equation, and the threshold voltage is calculated from the second derivative method to obtain the on-current. As a result, this model agrees with the results from other papers. The on-off current ratio is in proportion to the arithmetic average of the upper and lower high dielectric material thicknesses. The on-off current ratio of 104 or less is shown for SiO2, but the on-off current ratio for TiO2 (k=80) increases to 107 or more.
The study of convex-dual-layer remote phosphor geometry in upgrading WLEDs color rendering index
Authors: Dang Huu Phuc , Phuong Loan Nguyen Thi , Kim Chung Nguyen Thi , Quoc Anh Nguyen Doan
Published: June 2021

The white-light light-emitting diode (LED) is a semiconductor light source that usually has one chip and one phosphor layer. Because of that simple structure, the color rendering index (CRI) is really poor. Therefore, structure with double layer of phosphor and multiple chips has been studied with the phosphorus proportions and densities in the silicone are constantly changed to find the best option to improve optical properties. In research, we use red phosphor Ca5B2SiO10:Eu3+ layer to place above the yellow phosphor one, and both of them have a convex design. Then, the experiments and measurements are carried out to figure out the effects of this red phosphor as well as the convex-double-layer remote phosphor design on the LED’s performances. The measured results reveal that the light output is enhanced significantly when using convex-dual-layer structure instead of the single-layer design. Additionally, the Ca5B2SiO10:Eu3+ concentration benefits CRI and CQS at around 6600 K and 7700 K correlated color temperature (CCT). Yet, the lumen output shows a slight decline as this red phosphor concentration surpass 26% wt. Through the experiments, it is found that a double layer of chip and double phosphorus is the best structure which could support the quality of CRI and luminous flux.
Online multiclass EEG feature extraction and recognition using modified convolutional neural network method
Authors: Abdulkarim Haider , Al-Faiz Mohammed Z.
Published: June 2021

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy
Maximum convergence algorithm for WiFi based indoor positioning system
Authors: Truong-Quang Vinh , Ho-Sy Thong
Published: June 2021

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.
Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform
Authors: Qasmieh Isam Abu , Alquran Hiam , Alqudah Ali Mohammad
Published: June 2021

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability.
Outage performance analysis of non-orthogonal multiple access systems with RF energy harvesting
Authors: Van Hoang Thien , Anh Vo Tien , Le Danh Hong , Phu Ma Quoc , Nguyen Hoang-Sy
Published: June 2021

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison.
Efficiency of LSB steganography on medical information
Authors: Abikoye Oluwakemi Christiana , Ogundokun Roseline Oluwaseun
Published: June 2021

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.
Elastic hybrid MAC protocol for wireless sensor networks
Authors: Bhar Jamila , Bouazzi Imen
Published: June 2021

The future is moving towards offering multiples services based on the same technology. Then, billions of sensors will be needed to satisfy the diversity of these services. Such considerable amount of connected devices must insure efficient data transmission for diverse applications. Wireless sensor network (WSN) represents the most preferred technology for the majority of applications. Researches in medium access control (MAC) mechanism have been of significant impact to the application growth because the MAC layer plays a major role in resource allocation in WSNs. We propose to enhance a MAC protocol of WSN to overcome traffic changes constraints. To achieve focused goal, we use elastic hybrid MAC scheme. The main interest of the developed MAC protocol is to design a medium access scheme that respect different quality of services (QoS) parameters needed by various established traffic. Simulation results show good improvement in measured parameters compared to typical protocol.
Outage probability analysis for hybrid TSR-PSR based SWIPT systems over log-normal fading channels
Authors: Van Hoang Thien , Van Hoang-Phuong , Le Danh Hong , Phu Ma Quoc , Nguyen Hoang-Sy
Published: June 2021

Employing simultaneous information and power transfer (SWIPT) technology in cooperative relaying networks has drawn considerable attention from the research community. We can find several studies that focus on Rayleigh and Nakagami-m fading channels, which are used to model outdoor scenarios. Differing itself from several existing studies, this study is conducted in the context of indoor scenario modelled by log-normal fading channels. Specifically, we investigate a so-called hybrid time switching relaying (TSR)-power splitting relaying (PSR) protocol in an energy-constrained cooperative amplify-and-forward (AF) relaying network. We evaluate the system performance with outage probability (OP) by analytically expressing and simulating it with Monte Carlo method. The impact of power-splitting (PS), time-switching (TS) and signal-to-noise ratio (SNR) on the OP was as well investigated. Subsequently, the system performance of TSR, PSR and hybrid TSR-PSR schemes were compared. The simulation results are relatively accurate because they align well with the theory.
DiaMe: IoMT deep predictive model based on threshold aware region growing technique
Authors: Abbas Safia , Mahmoud Abeer M.
Published: June 2021

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Selecting the best stochastic systems for large scale engineering problems
Authors: Alrefaei Mahmoud H. , Almomani Mohammad H. , Alabed Alhadi Sarah N.
Published: June 2021

Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
Research trends on CAPTCHA: A systematic literature
Authors: O. Igbekele Emmanuel , A. Adebiyi Ayodele , A. Ibikunle Francis , O. Adebiyi Marion , Oludayo Olugbara O.
Published: June 2021

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.
Smart transit payment for university campus transportation using RFID card system
Authors: Adebiyi Marion Olubunmi , Ogundokun Roseline Oluwaseun , Nathus Alabo Inatimi , Adeniyi Emmanuel Abidemi
Published: June 2021

In the transportation business, we aim to be cost-efficient and effective in our customer service but with the traditional transit payment system, it is not so. Lately, transit companies all over the world are moving towards superior client service, nimbleness, receptiveness to necessities that diverge at a time scale that was absurd even two decades ago. The aim of this study was to create an electronic transit payment system that will allow for full pliability and solutions functionality that Covenant Universities and Nigerian transit companies should adopt to become more effective and efficient. We achieved this with the use of radio frequency identification (RFID) smart cards and card readers aiding a computer program that was programmed using C#. In addition, the program was simple and not expensive to implement in order to eliminate the mismanagement of ticket funds, loiter paper in bus stations, and so on. Together all this became our payment system.
Comparative analysis of routing techniques in chord overlay network
Authors: Osemwegie Omoruyi , John Samuel , Adeyinka Adewale , Noma-Osaghae Etinosa , Okokpujie Kennedy
Published: June 2021

Overlay networks are not a new field or area of study. This domain of computing will someday drive P2P systems in various application areas such as block-chain, energy trading, video multicasting, and distributed file storage. This study highlights the two widely known methods of routing information employed in one of such overlay networks called chord. In this study, simulations of both routing modes (iterative and recursive) and their variations under no-churn (leaving and joining of nodes) and churn conditions was carried out. The routing parameter (successor list size) was varied for each of the routing techniques in a simulation study. The results obtained show that semi recursive routing gives a better routing performance under churn scenarios.
Software engineering based secured E-payment system
Authors: Croock Muayad Sadik , Taaban Rawan Ali
Published: June 2021

Nowadays, the E-payment systems have been considered to be the safe way of money transfer in most of modern institutes and companies. Moreover, the security is important side of these systems to ensure that the money transfer is done safely. Software engineering techniques are used for guaranteeing the applying of security and privacy of such systems. In this paper, a secure E-payment system is proposed based on software engineering model and neural network technology. This system uses different proposed algorithms for applying authentication to the devices of users as mobile application. They are used to control the key management in the system. It uses the neural network back-propagation method for ensuring the security of generated keys that have sufficient random levels. The proposed system is tested over numerous cases and the obtained results show an efficient performance in terms of security and money transfer. Moreover, the generated keys are tested according to NIST standards.
Multi-objective NSGA-II based community detection using dynamical evolution social network
Authors: Alkhalec Tharwat Muhammed E. Abd , Md Fudzee Mohd Farhan , Kasim Shahreen , Ramli Azizul Azhar , K. Ali Mohammed
Published: June 2021

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Deep learning for COVID-19 diagnosis based on chest X-ray images
Authors: Alrefai Nashat , Ibrahim Othman
Published: June 2021

Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.
Challenges for using organizational climate tools for measuring teacher job satisfaction
Authors: Don Yahya , Yaakob Mohd Faiz Mohd , WanHanafi Wan Rozimi , Yusof Mat Rahimi , Kasa Muhamad Dzahir , Omar-Fauzee Mohd Sofian , In-Keeree Hareesol Khun
Published: June 2021

The organizational environment includes not only the landscape of a school, but also the components of school learning resources, student relations, communication-collaboration, decision-making and instructional innovation. According to the ministry of education, many schools do not have the new facilities needed for successful teaching and learning to meet the potential needs of the workforce. Further investigation is still needed as to how these elements make teachers enjoy a working environment, particularly in Kedah, Malaysia. It would also help the State department of education to address unresolved school issues. The aimed of this research is to examine primary school teachers' understanding of their teacher satisfaction and organizational environment. The design of this study was mix-method consisting of a survey (quantitative approach) and a semi-structured interview (qualitative approach). A sample of 220 teachers in 23 primary schools in Alor Janggus, Kedah participated in this study. On the other hand, a total of 20 teachers were interviewed to identify factors that would lead to teacher satisfaction at work and an organizational environment. The content analysis was used to identify the themes that emerged from the semi-structure interview. The themes emerged from the semi-structure interviews are student relationships, decision-making, school infrastructure, teamwork and educational creativity. Suggestions for future research are discussed in more detail.
School space selection preferences: Architectural perspective toward formal school
Authors: Widiastuti Kurnia , Susilo Mohamad Joko , Nurfinaputri Hanifah Sausan
Published: June 2021

School space plays an essential role in creating a pleasurable learning atmosphere. The tendency of everyone to choose a school space also varies. By knowing this trend pattern, schools can be designed to improve student learning effectiveness. The purpose of this study was to find out which school spaces students choose to study, what kind of room criteria are needed, and distribution patterns of students' preference choices. This research used both the qualitative exploratory and quantitative methods using an open-ended question questionnaire for data collection. Data analysis techniques used qualitative analysis methods consisting of open coding, axial coding, and selective coding. The results showed that the library, mosque, and multimedia laboratory were the most preferred space for students to study at school. Some factors that influence the selection include thermal comfort, completeness of supporting facilities, and acoustic comfort.