Bulletin of Electrical Engineering and Informatics
The Bulletin of Electrical Engineering and Informatics (BEEI), ISSN: 2089-3191, e-ISSN: 2302-9285, is open to submissions from scholars and experts in the wide areas of electrical engineering and informatics from around the world. The BEEI is an open-access peer-reviewed journal published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Universitas Ahmad Dahlan (UAD) and Intelektual Pustaka Media Utama (IPMU) that continuously presents the results of original research, reviews, surveys, or new data/concepts across all electrical engineering, electronics, instrumentation, control, robotics, telecommunication, computer engineering, information systems, information technology, and informatics (computer science) fields of interest. Articles must be understandable and written in standard English. This journal is indexed by Scopus (Elsevier)/ScimagoJR and has SNIP: 0.730, CiteScore: 2.4, and SJR: 0.357. The Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia has given this journal its SINTA 1 (S1) seal of approval. This journal is published bimonthly (February, April, June, August, October & December) in both print and online versions. All publications are available in full text and are free to download.
Institute of Advanced Engineering and Science
A new grid search algorithm based on XGBoost model for load forecasting
XGBoost is a highly effective and widely used machine learning model and its hyperparameters take an important role on the performance of the model. This paper presents a new grid search (GS) algorithm for obtaining optimal hyperparameters of the XGBoost model based on the median values of their error loss. A benchmark method used to evaluate the proposed and original GS algorithms is introduced. Datasets with measured daily electricity demand load values of Ho Chi Minh City, Vietnam and Tasmania state, Australia are analyzed for the performance of both algorithms. The error metrics, mean squared errors (MSEs), of the proposed algorithm are found to be 2,282 MW and 501 MW that are smaller than those of original algorithms, which are 2,424 MW and 537 MW in case of Ho Chi Minh City and Tasmania state, respectively. These results then verify the accuracy of the proposed algorithm.
Copyright (c) 2023 Institute of Advanced Engineering and Science https://creativecommons.org/licenses/by-sa/4.0
Influence of natural clouds on the performance of solar cell systems in Iraq
Solar energy generated by photovoltaic (PV) technology can be supplied to standalone systems, as it combines efficiency and cost-effectiveness. However, this combination is achieved only after considering the effects of shading, which can significantly influence electrical output. The primary factor that influences the use of solar energy in electricity generation is irradiation. PV cells are significantly impacted by shading, where the output of the PV cell reduces in the presence of a shadow. In this study, the researchers have presented an experimental analysis of how shading affects two PV cells, using the series and parallel configurations. The experimental work is installed at the University of Mosul, Department of Electrical Engineering, Renewable Lab (Iraq). MATLAB was used to simulate, evaluate, and compared the results to understand the effects of shading on PV cell output. This research offers an analytical technique to determine the probable effects of Partial shadowing conditions on PV power generation. The results provide the effects of partial shadowing in an annual performance loss of ≥10–30%. The orientation of the PV panels' tilt angle has an impact on their output power. When the tilt angle deviates from its ideal value, the PV panel's output drops off substantially.
Control technique for power quality improvement of isolated wind power generation system
Power quality (PQ) problems for renewable energy system (RES) is major challenge for using these systems. Amplitude, frequency, and waveform is important PQ parameters especially for standalone or isolated systems. In this paper we propose RES with wind turbine (WT) and permanent magnet synchronous generator (PMSG) to feed different loads (liner, non-liner) through AC-DC-AC converter. The PMSG operate with fuzzy logic inertia controller, to convert turbine torque form PU to Nm. The inverter stage in the converter operate according to proportional integral derivative (PID) controller with 3rd harmonic injection pulse width modulation (PWM) to regulate the AC voltage in load side. For the same case study and operating condition, we compared the result for system parameters when using ordinary PWM and using PID with 3rd harmonic injection. Voltages total harmonics distortion (THD) was less than 2% with liner loads, and 3.5% with non-liner load and the same was for current load, the voltage amplitude was between (1-1.03) PU under different wind speed by using the proposed controller.
Design and manufacture control system for water quality based on IoT technology for aquaculture in the Vietnam
In this paper, presents solutions to apply internet of things (IoT) technology in the field of high-tech agriculture and aquaculture in coastal areas of Vietnam, which is currently a new problem. The system includes an application on a smartphone; access parameters via the web, a computer and an IoT control circuit capable of automatically drawing electricity from the solar panel to the energy storage to serve as a source of power for devices (monitoring cabinets). The product has the function of monitoring: water environmental indicators in shrimp ponds (temperature, pH, dissolved oxygen (DO), salinity, redox index at the bottom of oxygen reduction potential (ORP)), on the website 24/24h, and controllable. Automatic control of pump system and DO generator with Inverter technology. The research results in this paper have brought high economic benefits with an automatic water quality control system to improve the productivity and quality of shrimp farming in practice for people in the aquaculture area in Bach Long commune, Giao Thuy district, Nam Dinh province, Vietnam.
Performance evaluation of microgrid with extreme learning machine based PID controller
The enhanced penetration of the renewable energy sources (RES) is dependent on microgrid (MG) to a power system is impact stability of the system due to a variation in dynamic properties of the MG from a traditional generator. As a result, analyzing the new issues with dynamic stability and controlling the operation of the power system in the connection of rising MG penetration becomes critical. This paper contains a MG system with renewable energy assisted, superconducting magnetic energy storage (SMES) storage and an extreme learning machine (ELM) based proportional integral derivative (PID) controller. The effect of renewable-based MG penetration on a dynamic stability and control of the multi machine multi area system under varied operating situations is comprehensively investigated in this study. Non-linear time-domain simulations and several performance indicators are used to evaluate the controller's ability with the different MG penetration percentages under various disturbances and operational conditions.
Reactive power planning with the help of multi-objective genetic algorithm and flexible AC transmission systems devices
In this paper power quality of 3-bus solar-based hybrid system has been presented (where one or more than one distribution generator unit is connected to the grid). The injection of solar power into grid-connected systems creates power quality problems such as current consistency, electrical fluctuations, and inefficient power demand. A power quality control strategy based on a real-time self-regulation method for autonomous microgrid operation has been presented. In this paper solar farm design and satisfactory performance tests such as PV-static synchronous compensator (STATCOM) to improve the power quality of grid-based systems have been presented using the MATLAB/Simulink environment. Pulse width modulator (PWM) with proportional-integral derivative (PID) controller used for frequency control, reactive var compensation is used to control voltage profile. Multi-objective genetic algorithm (MOGA) for reactive power planning (RPP) with the objective of reactive power minimization is introduced. The optimization variables are generator voltage, transformer tap changer, and various operational constraints.
An efficient method for estimating energy losses in distribution's feeder
This paper explains a simple and efficient methodology for calculating monthly energy losses (EL) using the load loss factor technique and a representative composite load profile. One of the important benefits of the proposed work is a simpler, more efficient and less rigorous method to estimate the system-wide energy loss of an extensive distribution network with reasonable accuracy. The sum of all EL provided by each feeder section is used to calculate the total feeder EL. A base case feeder with a typical cable type and power factor is used to generate regression equations, a peak power loss function to estimate the EL. A case study is then used to show the models and techniques that have been established. The result indicates a high level of agreement with the time-series load flow simulations (smaller than 10% deviations). With this model, an approach to estimate the EL of all radial feeders of various configurations and characteristics could be extended and implemented. The spreadsheet approach is ideal for completing a quick energy audit of existing distribution feeder EL and determining the sensitivity of distribution network efficiency to changes in feeder sections and load characteristics.
Improve PV-diesel operating management system considering sun availability time
This paper presents a hybrid photovoltaic (PV)-diesel management system performance analysis considering weather conditions. The system is designed to operate based on charging current, battery capacity, and loading current. During charging current is decreased but the battery capacity is high, and the PV system remains supplied for low and medium loading current. When the charging current is lowered and the battery capacity is in the middle, the PV system still supplies for low loading current. However, when the charging current decreases and the battery voltage decreased below 22.3 volts, the supply instantly switches to the diesel generator. The charge current is calculated by subtracting the PV current from the load current. A real-time clock is implemented as a timer to guarantee that there is no repetitive switching throughout the night when charging from the panel is missing. The system works automatically through microcontroller control during the day for 9 hours. The improvement that has been obtained is to overcome the problem of repeated switching processes by limiting the transfer process back to PV when sunlight is not available.
Solar power plant on the rooftop of the Diponegoro University Rectorate: a technical and economic study
Diponegoro University's Rectorate building uses electricity from the National Electricity Company with a S2 social subscription type of 105 kVA. The designed solar power plant has a capacity of 25 kW, or 25% of the installed electrical capacity. This research aims to compare the solar panels and inverter configurations that will be used in solar power plants. Moreover, this study aims to find out which configuration will provide the best results and the biggest savings. Technical analysis is carried out with the photovoltaic system (PVSyst) software to calculate the energy produced by solar panels, inverter losses, and other results. On the other hand, economic analysis is carried out with RetScreen software to calculate net present value (NPV), benefit cost ratio (BCR), and payback period (PP). Based on the PVSyst simulation results, the estimated energy production for each variant is 39,684 kWh; 39,633 kWh; 39,507 kWh; and 39,446 kWh. The first variant has the biggest performance ratio value of 84.1%. Based on the Retscreen calculation result, the third variant has an NPV value of $22,698, a BCR value of 2, and a PP of 8.7 years, which has the best result and the highest advantages.
Copyright (c) 2022 Institute of Advanced Engineering and Science https://creativecommons.org/licenses/by-sa/4.0
Pulse charging based intelligent battery management system for electric vehicle
Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.
Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions
Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.
A Chameleon algorithm for solving economic dispatch problem in microgrid system
The economic power dispatch (ED) is considered a critical optimization issue in the power system and microgrid. There are several objectives through the economic load dispatch issues such as reducing the operating cost, total emission, or start-up operations. This paper presents a Chameleon swarm algorithm (CSA) for determining the optimal ED of distributed energy resources (DER) in the microgrid. The proposed technique numerically mimics and executes chameleons' behavioral processes to their quest for food, such as their action for rotating their eyes to identify and grab prey using the speed of their sticky tongues. In this study, the CSA has been applied to obtain the optimal power dispatch from DER using the photovoltaic (PV) plants, combined heat power (CHP) systems, and traditional diesel generators. The obtained results from the simulation are compared with the conventional metaheuristic algorithms which have been used in previous studies, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC). Experimentally, the proposed algorithm for solving the ED problem determined the best value of the power dispatch and high performance compared to other metaheuristics methods.
Comparative analysis of grid-connected bifacial and standard mono-facial photovoltaic solar systems
This paper describes the design of a solar photovoltaic (PV) system using simulation of PVsyst software. This work involves the simulation of bifacial and mono-facial PV solar in a large-scale solar system. For a bifacial system, the yearly total energy to the grid is 1699.6 MWh, with an average of 4.57 kWh/kWp/day. For a mono-facial system, the total energy to the grid over the year is 1645.3 MWh, with an average of 4.05 kWh/kWp/day. The average collection losses obtained for bifacial and mono-facial modules were 0.33 kWh/kWp/day and 0.82 kWh/kWp/day, respectively, with system losses of 0.15 kWh/kWp/day (bifacial) and 0.18 kWh/kWp/day (mono-facial). The average performance ratio for bifacial and mono-facial was 0.904 and 0.801, respectively. The bifacial PV system was able to generate profit in terms of Return on investment (ROI), around 357%, and reach a breakeven around 7 years. The payback period for a mono-facial PV system was around 8.1 years, with an ROI of 290.4%. This work mainly focuses on a comparative analysis of bifacial and mono-facial photovoltaics, emphasizing the effectiveness and the implementation suitability of bifacial photovoltaics over mono-facial PV solar systems.
Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid
The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.
Design high voltage gain DC converter based on maximum power point resistance for photovoltaic applications
Due to the nonlinear properties of photovoltaic (PV) modules, the design of the high-gain direct current (DC) converter for maximum power point tracking (MPPT) is complicated. In this paper, the design of a new step-up DC converter for MPPT applications is proposed. The proposed converter is structured of two symmetrical reverse-parallel DC-DC boost converters. This structure is supported by voltage multiplier cells equipped to increase output voltage and decrease voltage stress on semiconductor switches. To simplify the high-gain DC converter design, the PV module's maximum power point is treated as resistance by using the incremental conductance (INC) method. The MPPT boost converter's inductance, input capacitance, and output capacitance are calculated using the derived equations using nine parameters. The results showed that the proposed DC converter simulation meets the necessary requirements. The size of the input capacitor, inductor, and output capacitor have been decreased. When the proposed converter is compared to a traditional converter, there is less voltage stress, low current ripple, and an increase in voltage gain. This has led to an improvement in the overall converter efficiency.
Performance analysis of wireless power transfer using series-to-series topology
Wireless power transfer (WPT) is a technology used to transmit power using air as it medium of transfer. The function of the WPT is similar to a transformer. The power provided on the primary side will be transferred through air to the secondary side. The performance of the WPT is crucially depends on the air gap length, 𝛿 that separates primary and secondary side. In this paper discusses the performance of WPT using series-to-series topology. The performance of the WPT was estimated using finite element analysis (FEA). During the study, focus was given to effect of turn ratio, a, air gap length, 𝛿 and capacitor value at primary Cp, and secondary, Cs winding to voltage ratio of the WPT. The air gap length, 𝛿 was set to 1 and 2 mm while the capacitors were set depend on the ratio of primary to secondary winding capacitor, Cp/Cs. The WPT performance also being tested under no-load and loaded operation to observed it effect. As a result, the WPT with turn ratio, a at 0.37, air gap length, 𝛿 at 1 mm and ratio of primary to secondary winding capacitor, Cp/Cs at 0.5 produce the best voltage ratio compared to other settings.
The one-phase SrMg2La2W2O12:Tb3+, Sm3+, Tm3+ phosphor and its optical features in multicolor and white-illumination LEDs
Researchers propose the phosphors emit many colors SrMg2La2W2O12:Tb3+, Sm3+, Tm3+ (SMLLW:RE3+) (RE3+=Tb3+, Sm3+, Tm3+) synthesized using the solid-status reacting technique as promising downward-transformation luminous substances for diodes emit white illumination and screens in the current study. The structural and binding data given by X-ray diffraction (XRD) data and fourier transform infrared (FTIR) spectroscopy suggest the corresponding orthorhombic configuration and vibrational powers, respectively. The stimulation and radiation bands of color of SMLLW:RE3+ phosphor show that such phosphors may be successfully stimulated via ultraviolet (UV) illumination and generate green, orange-red, and blue (stands for G, O-R, B) illumination, in turn. For different doses of the triggers Tb3+, Sm3+, and Tm3+ within the SMLW phosphor base, luminescence, decomposition periods, Commission Internationale De L'eclairage (CIE) color coordination, along with correlated hue heats (Tcct) are specified. When a triple-doped SMLW phosphor is activated using a ligand-to-metal charge transition (LMCT), it produces G, O-R, B hues at the same time and can be adjustable to white light, according to the results. An effective power transfer among rare-earth ions was found and investigated using decay curve analysis. According to the findings, SMLW:RE3+ (RE=Tb, Sm, Tm) are suitable options to use for light-emitting diodes (LEDs) and screens creation.
Statistical analysis of the predictors of annual electric vehicle mileage
This study evaluates the impact of technical and economic factors related to electric vehicles and the impact of socio-demographic factors related to electric vehicle owners on annual electric vehicle mileage from a statistical perspective. The data set was analyzed using regression and correlation analyses using Ms Excel and several Python libraries. The influence of the socio-demographic characteristics of the respondents was estimated as minimal and requiring reassessment. It was shown that among the socio-demographic factors considered, only the age of vehicle owners correlates with the annual mileage of electric vehicles. It is shown that technical and economic parameters are much more closely related to the annual mileage of electric cars than socio-demographic parameters. Significant factors among the technical and economic ones were battery capacity, power consumption of the electric car, and the size of the respondent's locality.
Experimental and simulation investigation for rotor bar fault diagnosis in closed-loop induction motors drives
This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.
An improvement of direct torque controlled PMSM drive using PWM technique and kalman filter
The paper describes pulse-width-modulation (PWM) technique and Kalman filter (KF) process to improve performance of direct torque controlled permanent magnet synchronous motor (DTC-PMSM) drive. Performance of DTC methods are strongly affected by high stator current ripple. For lowering the ripple, high switching frequency space vector PWM and KF are utilized in the paper. Mathematical model of PMSM and calculations of important quantities of DTC applied to PMSM drive are presented in the ﬁrst part. The second part shows computation process of space vector PWM and KF. Performance indices are utilized to evaluate the drive structures. Theorectical assumptions are validated via simulations with Gaussian noised stator current measurement.
Advanced optimal GA-PID controller for BLDC motor
The brushless direct current (BLDC) motor is characterized by high torque, which made it widely used in many industrial applications. To improve the working performance of the BLDC motor, the addition of controllers such as proportional integral derivative (PID) is adopted. To obtain high-performance controllers, the design process is adopted to develop a suitable algorithm. The genetic algorithm (GA) was chosen to tune the PID controller and get suitable parameters for each of kp, ki and kd with self-tuning. The design process is based on BLDC motor control using the GA to tune the traditional PID controller. Simulations were carried out for three cases including the absence of controllers secondly, by using the traditional control unit and finally with the GA. The integral time absolute error (ITAE) type error control standard for BLDC motor control system was selected. After conducting the simulation, the results demonstrated the superiority of the GA over the traditional ones in terms of response speed, (stability, rise, and settling) time and percentage overshootas details will be mentioned the model in the subsequent paragraphs of the research, finally the simulation results indicate the development and improvement of BLDC motor operation and performance during real time.
Features selection for estimating hand gestures based on electromyography signals
Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.
Distributed formation control for groups of mobile robots using consensus algorithm
The increasing implementation of autonomous robots in industries and daily life demands the development of a robust control algorithm that enables robots to perform their tasks successfully. Critical tasks such as rescue missions and area exploration require robots to work cooperatively in a formation to accomplish the tasks quickly. Moreover, the existence of obstacles in the environment requires all the robots' to rapidly process environmental changes to maintain the formation pattern. Thus, this paper introduces a distributed robot formation control system using the consensus algorithm that enables a group of robots to establish and maintain formations using only the local information of the robots. Furthermore, an obstacle avoidance algorithm based on the distance and angle between robots and obstacles is introduced to ensure safe navigation for the group of robots. The algorithm's effectiveness is demonstrated by a multi-robot system with randomly generated starting positions and velocities, where it is shown that the robots can agree on the control variables and establish the required formation while also avoiding obstacles in the environment.
Modeling and simulation of a pipeline leak detection using smart inspection ball
Recently, pipelines have replaced more carbon-intensive transportation methods making them more environmentally friendly for transporting energy and water supplies. However, pipelines can pollute the air, water, soil, and climate when they leak, causing economic, and environmental damage. Pipeline online monitoring provides data analysis and suitable controlling strategies to contain the risk. This paper proposes a three-dimensional numerical model simulation taking advantage of the fluids moving through pipelines at specific speeds. The transport speeds depend on many conditions, such as pipe diameter, the pressure through which the fluid is being transported, and other factors, such as terrain's topography and viscosity of the fluid. Under these conditions, the inspection approach uses a self-charging movable ball. The sensors inside the ball capture data as it travels through the pipe. The simulation focuses on spherical flow and pipe noise with and without leakage based on the COMSOL software platform. The paper shows the effect of several parameters, including leak location, sensor placement, ball diameter, sound pressure level propagation along a pipe and around the sphere, velocity, and temperature distribution that give the background for future smart ball design in a promising practical pipeline test project.
SrAlOCl:Bi3+@SiO2 phosphor with broad emission band and its effects on LED optical power and correlated color temperature
Sr3Al2O5Cl2:Bi3+ (SAlOCl:Bi3+)phosphor for broadband emission was made using a solid-state method. From the extensive spectroscopic analysis and theoretical computation, significant conclusions about the origin of the Bi3+ emission were drawn. For the Sr 3 and Sr 1 sites, respectively, the dipole-quadrupole and quadrupole-quadrupole interactions were responsible for the concentration quenching in SAlOCl:Bi3+. The resulting luminescence mechanism demonstrated that the crystallization of Bi3+ at the two sites is what causes the emission from each site. The warm white light emitting diodes (LED) models were built with a 380-nm ultraviolet (UV) chip, SAlOCl:Bi3+, and two other phosphors. Then, the color rendering indeces (CRI) and the correlated color temperature (CCT) were calculated. Particularly, the CRI values ranged from 84.3 to 86.2 under operating currents of 20–50 mA, respectively. The increasing SAlOCl:Bi3+ dosage also heightened particle density, resulting in higher scattering coefficients. High scattering results in improved color coordination (lower color variance). The CRI and luminous flux are reduced as the phosphor SAlOCl:Bi3+ concentration increases more than owing to color loss and energy loss by backscattering and re-absorption. Thus, it is advisable to consider SAlOCl:Bi3+ carefully before applying in production.
Design of CRC circuit for 5G system using VHDL
In this document, we focus on how to design cyclic redundancy check (CRC) circuits with different 5G polynomial divisor using very high-speed integrated circuit (VHSIC) hardware description language (VHDL) to integrate in field-programmable gate array (FPGA) suitable kit using a suitable design code. The different between designed circuits came from the different of data size according to polynomials requirements conditions since there are huge data size in 5G system that required divide it with suitable method and then implemented the required circuit. CRC code as a polar code and short low density parity check (LDPC) is proposed in 5G new radio (NR) systems, CRC properties to divided data and CRC cod make it particularly very useful for codes with higher data rate and longer lengths, and for codes with low data rates and small length as an error detection method. The CRC encoder circuit (transmitter side) and CRC decoder circuit (receiver side) with different polynomial and data size have been designed using VHDL. Xilinx ISE 14.3 simulator, where the test bench simulation results give the expected simulator results of proposed decoding circuit scheme so to integrated using ZYNQ FPGA kit.
Detection of lung disease using relative reconstruction method in electrical impedance tomography system
Lung disease can be diagnosed with the image-based medical devices, including radiography, computed tomography, and magnetic resonance imaging. The devices are very expensive and have negative effects. An alternative device is electrical impedance tomography (EIT). The advantages of EIT arelow cost, fast, real-time, and free radiation, so it is very appropriate to be used as a monitoring device. The relative reconstruction method has succeeded in producing functional images of lung anomalies by simulation. In this study, the relative reconstruction method was used to obtain functional images of four lungs conditions, namely a healthy person, patient with left lung tumor with organized left pleural effusion, one with pulmonary tuberculosis with right pneumothorax and one with pulmonary tuberculosis with left pleural effusion. The relative reconstruction method can be used to obtain functional images of an individual’s lung conditions by using expiratory-respiratory potential data with results that can distinguish between the lungs of a healthy person and a diseased patient, but the position of the lung disease may have less details. The potential data from comparison between the data of a patient and a healthy person can be used as a reference to obtain more accurate functional image information of lung disease.
Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication
In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.
A novel octagonal slot circular compact ultrawideband antenna with detailed frequency and time-domain analysis
With time, the device's reliance on wireless communication and its users grows tremendously. The ultrawideband (UWB) technology offers a 7.5 GHz uncontrolled bandwidth. Because it allows for larger data rates over a short distance, UWB technology is the current and future requirement of new generation mobile networks. As a result, a novel octagonal slot circular compact UWB antenna with detailed frequency and time-domain analysis is proposed in this research. Throughout the UWB range, the hexagonal-shaped circular radiator aids in reaching better S11 parameters (S11<-10 dB). The modified trapezoidal like partial ground plane allows for a wide bandwidth. The proposed UWB antenna operates at frequencies ranging from 3.2 to 13.1 GHz, allowing for a bandwidth of 9.9 GHz and a fractional bandwidth of 121.4%. Voltage standing wave ratio (VSWR) is seen to be in the range of 1 to 2 throughout this UWB range. Throughout the operational bandwidth, the antenna's gain ranges from 2.8 to 4.1 dB. The suggested UWB antenna has the advantages of being small, having superior impedance matching, a simple design, and having a high gain, all of which can be used to deploy the antenna for the aforementioned purposes.
Ultra-wide band antipodal Vivaldi antenna design using target detection algorithm for detection application
This work presents a technique for detecting targets between two walls using ultra-wide band (UWB) modified antipodal Vivaldi antenna (MAVA). The detection system works on principle of time domain reflectrometry (TDR) using through wall imaging (TWI). This technique utilized a vector network analyzer (VNA) to produced short and small pluses to irradiate through an antenna array system onto the wall under study. The purposed detection system operated in UWB frequency spectrum (3.1 GHz to 10.6 GHz). Furthermore, an algorithm for hidden target detection has been developed. The results of the simulation of the designed antenna revealed a significant level of penetration, demonstrating a smart advancement in detecting and imaging system, to locate hidden metallic targets with good accuracy. A signal processing technique have been employed to improve the resolution of the target image. Using computer simulation technology (CST) software, the development and optimization process of an antenna is carried out, and the parametric performance of return loss, directivity and radiation pattern is evaluated.
A 2.45 GHz microstrip patch antenna design, simulation, and anlaysis for wireless applications
This paper designs, simulates, and analyzes the S-band microstrip patch antenna (MPA) for wireless applications. FR-4 (lossy) and Rogger RT/duroid, whose dielectric permittivity is 4.3 and 2.2, respectively, have been used as substrate materials. Simulation is done by computer simulation technology (CST) suite studio 2019 software. Simulations with FR-4 material showed that the return loss was -20.405 dB, the gain was 2.592 dB, the directivity was 7.47 dBi, the voltage standing wave ratio (VSWR) was 1.221, the bandwidth (BW) was 0.0746 GHz, and the efficiency was 34.69%. Also, Rogers RT/duroid material gives results of a return loss of -12.542 dB, a bandwidth (BW) of 0.0349 GHz, a gain of 8.092 dB, a directivity of 8.587 dBi, and an efficiency of 94.24%. The main goal of this antenna is to have a low return loss while getting as close as possible to a VSWR of 1. This will improve the antenna's gain, directivity, and efficiency compared to other antennas. Copper was used to make the patch and the ground, which were 0.35 mm and 0.0077 mm thick, respectively. The results obtained from the proposed antenna were better than those previously published in various in modern scientific journal and conference papers.
Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review
The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.
Reconfigurable graphene-based multi-input multi-output antenna design for THz applications
This paper presents a compact graphene-based multi-input multi-output (MIMO) antenna for wireless communications operating in frequency band (0.1-10) THz. This work has been performed with four ports microstrip antennas based on 37×88 μm² a silicon dioxide (Sio₂) substrate and copper on the ground layer, with high isolation by a series of unit cells of graphene selected between adjacent patches to reduce the transmission coefficient and antenna size. Graphene's chemical potential will change by changing the connected DC voltage, leading to bandwidth and resonant frequency variation. The simulation has a reflection coefficient is less than -10 dB at (4.5-10) THz of the frequency scale, mutual coupling (<-15 dB), and the gain from (4.7-9) THz is (1.6-6.7254) dB. This paper aims to provide wideband, efficient and reconfigurable with simple graphene-based MIMO antenna for THz applications.
Bandwidth enhancement of millimeter-wave microstrip patch antenna array for 5G mobile communication networks
This paper proposed enhancing the bandwidth of a millimeter wave microstrip patch antenna (MPA) and its array for a 5G mobile communication network. The proposed antenna is designed and fabricated on a Rogers RT Duroid 5,880 substrate with a standard thickness of 0.5 mm, a relative dielectric constant of 2.2, and a tangent loss of 0.0009. With a center frequency of 28 GHz, a measured return loss of -21.37 dB, a bandwidth of 1.14 GHz, and a gain of 6.27 dBi, the proposed single element operates in the local multipoint distribution service band. The proposed antenna is designed and manufactured as an array of 1×2 and 1×4 elements. The 2-element MPA array has a measured bandwidth of 1.207 GHz and a gain of 7.76 dBi, higher than that of a single element. The 4-element MPA array achieved a measured bandwidth of 2.685 GHz and a gain of 9.87 dBi, which is higher than the 2-element and single-element arrays at 28 GHz. This demonstrates that the array of antennas improves gain and bandwidth significantly. Hence, the proposed antenna and array are suitable for 5G mobile communication networks due to their small size.
Incorrect facemask-wearing detection using image processing and deep learning
Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.
Classification of 27 heart abnormalities using 12-lead ECG signals with combined deep learning techniques
An electrocardiogram (ECG) machine with a standard 12-lead configuration is the primary clinical technique for diagnosing abnormalities in heart function. Automated 12-lead ECG machines have the capacity to screen the general population and provide second opinions for physicians. However, expertise and time are required for manual ECG interpretation. Therefore, computer-aided diagnoses are of interest to the medical community. Hence, this study aims to build a deep learning (DL) model with an end-to-end structure that can categorize 12-lead ECG results into 27 different disorders. We use multivariate time-series data to construct a novel end-to-end DL model (based on combined convolutional neural networks (CNNs), long short-term memory, gated recurrent units, and a deep residual network structure) for feature representations and determining spatial relations among deep features. In addition, a dataset of 43,101 classified standard ECG recordings was collected from six different sources to guarantee the model’s ability to generalize and alleviate data divergence. As a result, the residual network-based model obtained promising outcomes and an accuracy of 0.97. According to the experimental data, it outperforms other methods.
License plate recognition in slow motion vehicles
The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.
Glaucoma classification using a polynomial-driven deep learning approach
In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image pu[1]rification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGC[1]Net has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net perfor[1]mance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).
An algorithm for enhancement of audio content classification
Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.
SiulMalaya: an annotated bird audio dataset of Malaysia lowland forest birds for passive acoustic monitoring
The laborious point count method of conducting bird surveys is still a common practice in Malaysia. An alternative method known as passive acoustic monitoring (PAM) is deployed in many countries by placing sound recorders at surveying sites to collect bird sounds. Studies revealed that the number of bird densities counted by human observers was agreeable with those obtained using PAM. However, one of the most essential gaps in conducting PAM is the lack of expert-verified bird-call databases. Therefore, the aim of this study is to construct the first annotated Malaysia lowland forest bird sounds called SiulMalaya to be used as ground-truth datasets for PAM-related activities. The raw bird sounds dataset was downloaded from Macaulay Library using the eBird platform. Data pre-processing was done to produce annotated audio tracks that can be used as training datasets for bird classification. SiulMalaya dataset was further validated by two bird experts from the Department of Wildlife and National Parks, Malaysia. A bird identification experiment was carried out to assess and validate SiulMalaya dataset using a convolutional neural network (CNN) learning model. Even though the accuracy of bird identification is slightly above 50%, the annotated dataset is shown to be viable for PAM-related operations.
Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning
Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment
In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.
Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark
Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.
New efficient fractal models for MapReduce in OpenMP parallel environment
Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.
Use of mobile applications in citizen security
An abstract is often presented separately from the article, so it must be able to in the following article the use of the mobile application is recommended as a tool that serves public safety because currently crimes of extortion and fraud are committed by making false calls to people's mobile devices. Therefore, the objective of this research is to help families with their welfare and safety and motivate the use of mobile applications. In the present work the situation, task, action, dan result (STAR) methodology was applied to elaborate the present work, this technique will allow improving the difficulties of the people. This methodology is very important because it is used to evaluate the circumstances in which people find themselves, a sequence must be followed to achieve the proposed objectives. The result obtained is an average of 84.5% on the design of the mobile prototype; being approved by the experts. The beneficiaries of the research are citizens through the mobile application.
Machine learning techniques for accurate classification and detection of intrusions in computer network
An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent the defences that are in place. Training and examination are the two basic components that make up the intrusion detection system (IDS) and each one may be analysed separately. During training, a number of distinct models are built, each of which is able to distinguish between normal and abnormal behaviours that are included within the dataset. This article proposes a combination of ant colony optimization (ACO) and the firefly approach for feature selection. The final outcome of giving careful thought to the selection of features will eventually result in greater accuracy of categorisation. When classifying various sorts of features, we make use of a wide variety of machine learning (ML) algorithms, including AdaBoost, gradient boost, and Bayesian network (BN), amongst others. The tests and assessments made use of data obtained from three distinct datasets, namely NSL-KDD, UNSW-NB15, and CICIDS 2017. The degree of performance of an individual may be broken down into its component parts, which include the F1 score, accuracy, precision, and recall. Gradient boost performs far better when it comes to recognising and classifying incursions.
Medication correlation analysis for outbreak prediction
Outbreak prediction is a way to predict the epidemic potentials of diseases using the pattern of medication sales values. Successful prediction might result in being cautious of the outbreak of diseases and taking necessary measures to prevent the predicted outcome. As medication sales values are too random, the analysis of medication correlation is one of the most interesting and challenging parts for the researchers. The major objective of this proposed research method is to analyze medication drug sales values for a certain period of a pharmaceutical company using statistical methods. It is also the intent of this research to make a comparative analysis of the output generated by the deep learning model with the real sales values of a month. Our method successfully predicts the outbreak potential of diseases with competent accuracy, so that we will have enough time to take precautions and prevent future pandemics through precautionary measures.
Enhanced and authenticated cipher block chaining mode
Due to the increased attacks on different applications, data security has become crucial. Many modes can be used to operate the advanced encryption standard (AES), some of which provide integrity, and some outperform other modes in security and simplicity. In this paper, the chain block cipher (CBC) mode has been modified to provide more security to the encrypted data by making it robust against the bit-flipping attack and adding an integrity approach using the keyedhash function. In addition, using the keyd-hash function increases the number of keys needed in CBC-AES to two keys, and this can make the proposed model more secure against bruteforce attacks and Grover’s quantum search algorithm.
Segmenting the eating behaviour of university students using the K-means algorithm
Universities that teach arts education do not only teach how to play an instrument or conduct musical ensembles; they are agents of change in eating behaviour for the praxis of teaching and dissemination of healthy education. The objective of the research was to segment the eating behaviour of students of the artistic education-music speciality of the National University of Education "Enrique Guzmán y Valle" by applying the K-means algorithm. To do this, the methodology consisted of understanding the problem, understanding the data collected, preparing the data, modelling and evaluating the model. For modelling, the free software Weka was used through the K-means clustering technique on a data matrix of 148 instances with forty-three nominal variables collected online based on an instrument designed and validated to assess eating behaviour in university students. Two was determined to be the optimal clustering for eating behaviour in university students, using the elbow method, with a distribution of 49% for the first cluster and 51% for the second cluster. The results of the study population showed that the eating behaviour of university students is adequate.
Wind power forecasting model based on linguistic fuzzy rules
The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.
Bengali Slang detection using state-of-the-art supervised models from a given text
Almost all Bengalis who own smartphones also have social media accounts. People from different regions occasionally employ regional Slang that is unfamiliar to outsiders and confuses the meaning of the sentence. Nearly all languages can now be translated thanks to modern technology, but only in very basic ways, which is a concern. Bengali Slang terms are difficult to translate due to a dearth of rich corpora and frequently occurring new Slang terms developed by people, making it impossible for speakers of other languages to understand the context of a sentence in which Slang is used. We developed a solution to this issue. To create models that can detect Bengali Slang terms from social media, we gather various Slang phrases from various regions and develop a modest corpus. Our suggested method nearly always succeeds in extracting Bengali Slang terms from fresh material. We create a total of 7 supervised models and assess which is the most effective for our study. One of them has a 70% accuracy and 86% recall rate for successful identification. Our models may be linked to the social media platform's backend to restrict the use of Bengali Slang in posts, blogs, comments, and other areas.
K-nearest neighbor algorithm analysis for path determination in network simulation using software defined network
Software defined network (SDN) is a new approach concept for planning, developing, and operating computer networks. Routing is the process of selecting paths on a network system to send or forward packets to the destination network. This study aims to obtain the results of the calculation analysis using the k-nearest neighbor (k-NN) method as the implementation of the recommendations. The results of the analysis use a mesh topology design approach with predicted values of throughput, jitter, delay, and packet loss. This value is used as a recommendation to the network manager to determine the best path. The best path selection from the analysis results is i) path-1 (very good) which includes switch 1, switch 2, and switch 4; ii) path-2 (good) to switch 1, switch 3 and switch 4 switch 4; iii) channel-3 (moderate) through switch 1, switch 2, switch 3 and switch 4; and iv) channel-4 (bad) through switch 1, switch 3, switch 2, and switch 4. While the calculation using the confusion matrix is accuracy=72.31%, precision=96.08% and recall=87.84%.
Handwritten signature identification based on MobileNets model and support vector machine classifier
Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
Development of electrooculogram based human computer interface system using deep learning
The patients with diseases that cause severe movement disabilities was noticeably increasing. These disabilities made patients unable to carry out their daily activities or interact with their external environment. However, the existence of human-computer interfaces (HCI) gave those patients a new hope to be able to interact once again. HCI enabled these patients to communicate with their environment by recognizing the movement of their eyes. Eye movements are recorded by an electro-oculogram (EOG) through some electrodes that are put vertically and horizontally on the eyes. In this paper, EOG vertical and horizontal signals were analyzed to detect six eye movements (up, down, right, left, double blinking, and center). Three deep learning models namely convolution neural network (CNN), visual geometry group (VGG), and inception had been examined on filtered EOG signals. The experimental results reveal the superiority of the inception model in providing the best average accuracy 96.4%. Accordingly, a writing system is presented based on the detected movements.
Federated learning security mechanisms for protecting sensitive data
One of the new trends in the field of artificial intelligence is federated learning (FL), which will have promising roles in many real-world applications due to the work characteristics of its architecture. The learning mechanism for this technique is based on making training in a distributed manner on the local data for each client using decentralized data, then collecting parameters for each local training and uploading it to the server, which in turn will send model updates to all clients to give the final learning result. To provide a broad study on FL from security and privacy aspects, this research paper introduces a general view of FL and its categories, most attacks that can befall it, the safety mechanisms used by existing works in attacks defense, enhancing the safety and privacy of FL whether in the transmission or collecting of data. Then, the usage of FL in network security by many research papers has been presented, and how good results were achieved, and finally a comparison has been made between these papers.
Lane change decision aid and warning system using LoRa-based vehicle-to-vehicle communication technology
Among the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for both host and approaching vehicle drivers. Experiments shows that V2V using LoRa can provide contextual information which are useful to assist drivers in deciding whether to change lane or not on highway use case settings.
Using machine learning approach towards successful crowdfunding prediction
Crowdfunding is a concept that emerged due to difficulties in raising funds for community business projects, social activities, micro-enterprises, and start-ups conventionally. Crowdfunding uses internet technology as a bridge between the donor and the recipient of funds so that it can reach a wider range of donors. This study aims to compare the performance of machine learning approaches in predicting crowdfunding campaign success. Three machine learning algorithms were employed to predict crowdfunding campaign success, namely logistic regression, random forest, and extreme gradient boosting (XGBoost). The dataset used in this study contains data about all projects posted on Kickstarter from January 2020 to September 2022. To improve the prediction model's performance, experiments using principal component analysis (PCA) feature reduction and log transformation were conducted. The results show that the implementation of log transformation on the dataset can increase the prediction model's performance. Meanwhile, XGBoost algorithm performs better than linear regression and random forest.
Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset
According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is being utilized to validate the findings of this study. The comparison was made using the following performance metrics: accuracy, sensitivity, false omission rate, specificity, false discovery rate and area under curve. The LR method achieved a maximum accuracy of 99.12% among all eight algorithms and was compared to other comparable studies in the literature. The five features chosen are used to calculate the model's fidelity-to-interpretability ratio (FIR), which indicates how much interpretability was sacrificed for performance. The uniqueness of this work is the explainability approach taken in the model's performance, which aims to make the model's outputs more understandable and interpretable to healthcare experts.
Hash algorithm comparison through a PIC32 microcontroller
This paper presents a comparative study involving SHA-3 final round candidates along with recent versions of hash algorithms. The proposed comparison between hash functions is performed with respect to cycles per byte and memory space. Tests are also carried out on a PIC32-based application taking into account several input cases, thus resulting in a set of ranked algorithms in terms of their specified metrics. The outcome of this work represents a considerable contribution in data protection and information security in relation to various communication and transmission systems, serving as a handy reference for developers to select an appropriate hash algorithm for their particular use condition.
A proposed approach to discover nearest users on social media networks based on users' profiles and preferences
Social media sites (SMSs) become essential platforms used by people, customers, and companies for communication and marketing. Social media networks (SMNs) allow access to individuals who share their information and this, in turn, can lead to build users' profiles. Profiles can consist of a basic description of users' characteristics such as name, age, gender, education, marital status, email, phone number, and location. Preferences, on the other side, describe users' behavior on SMNs. Earlier literature defined a user's identity in different networks based on matching his/her name only. This research, however, proposes an integrated approach for discovering the nearest users by considering profiles and preferences. The proposed approach includes three key steps. First, users are grouped based on their preferences such as personal interests. Second, properties’ values of a user profile and preferences are integrated to identify the nearest users. Finally, the nearest users to a certain user are identified by measuring the boundary distance. The findings show that the proposed approach can effectively identify the nearest users. Comparing the performance of the proposed approach with the two highly adopted approaches that rely on either users' profiles or preferences suggests that the proposed approach in this research is less prone to error.
Secure two-factor mutual authentication scheme using shared image in medical healthcare environment
The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.
Group decision support system model to determine supervisor lecturers for student creativity programs
A group decision support system (GDSS) model was created in this study by implementing the weighted product (WP) method and the borda method to determine prospective assistant or supervisor lecturers for student creativity programs (PKMs) that are routinely carried out every year at the Sriwijaya State Polytechnic. This study applies 5 criteria, including i) education level, ii) academic position, iii) group tenure, iv) lecturer certification, and v) achievement in the field of three pillars of higher education. The decision-makers in this study consisted of the head of the department (DM–1), the secretary of the department (DM–2), and the head of the study program (DM–3) where they carried out the decision-making process in groups. The WP method is used to make preferences independently of the decision-makers to determine the best alternative based on predetermined criteria. The borda method is currently used to aggregate the decision-makers to obtain the final result in the form of an alternative ranking. The results of this study are sufficient to be used as a reference in determining the supervisor lecturer for PKM activities at the Department of Computer Engineering, State Polytechnic of Sriwijaya.
Abnormal heart rate detection through real-time heart monitoring application
Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.
Malaysia coin identification app using deep learning model
Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning (ML) have designed many algorithms to solve diverse human problems. However, due to a lack of exposure to image processing, identification technology is still not widely employed in Malaysia. This paper outlines the steps involved in creating a mobile application for coin identification using ML. In the literature review, the history of the coins is studied in more depth and the features of already existing coin identification mobile applications are compared by their advantages and disadvantages. In addition, using the neural network model, the classification accuracy of successfully identified coins is recorded and disclosed. This study includes the limitations of the prototype mobile application and future improvements that could be added.
Implementation of blended learning approach in teaching introductory computer science course
Information communication and technology has great impact on modern education architecture of 21st century. This research intends to analyze the effectiveness of blended learning considering its basic principles and factors. Also, this study is focused on determining core problems of blended learning thus highlighting its design approach. A descriptive and inferential statistical analysis has been conducted on the data that reveal that the majority of students prefer blended learning system regardless their major, gender and any other factors if they have good infrastructure and effective electronic platform. It is recommended that for effective implementation of blended system there should be opportunities of face to face as well as online synchronous, and non-synchronous communication between students and teachers.