bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Smart Medical System Based on NB-IoT Technology
Authors: Cheng Yanming , Zhao Xieqiu , Wu Jing , Liu Hemiao , Zhao Yulian , Al Shurafa Mahmoud , Lee Ilkyoo
Published: June 2021

In recent years, with the constant development and integration of Internet technology, Internet of Things technology, and intelligent terminal technology, to make people’s work and life more comfortable and convenient, these new technologies have been more and more widely used in daily home life such as social education, agricultural production, industrial production, medical, and other fields. However, at present, there are still a lot of room for development in the application of new technologies in the medical and health industry. Especially in the context of hospital information construction and medical difficulties, the functions of fixed information points such as traditional nurse stations and doctor stations no longer meet the growing medical needs of people. Therefore, how to introduce these new technologies and design to a practical and low-cost intelligent medical information management system that realizes the convenient management and efficient management of medical personnel has become a top priority. In view of the problems faced by the hospital, this paper proposes an intelligent medical plan based on NB-IoT technology and develops a smart hospital information management system. Test results indicate that patients can easily and conveniently register their appointments through the Android mobile client. Through checking medical records and hospital news updates, medical staff can more easily complete their work. Comparison results of performance variance between the common server and adaptive algorithm web server demonstrate that an adaptive load balancing algorithm can achieve a more accurate allocation of the load. Therefore, the web smart medical information management platform can manage hospitals more comprehensively.
Mitigating power quality disturbances in smart grid using FACTS
Authors: Esmail Yahia M. , Alaboudy Ali H. Kasem , Hassan M. S. , Dousoky Gamal M.
Published: June 2021

Power quality (PQ) assurance is a vital part of electrical distribution networks. There are many advantages and benefits of improving PQ, especially in the modern/smart grid. Smart grid (SG) has a lot of complicated and sensitive electrical components (non-linear loads) in addition to renewable energy systems (wind-solar) that may also be a source of PQ disturbances. PQ problems harm personal life and national production. Static synchronous compensator (STATCOM) and unified power quality conditioner (UPQC) are among the fastest response flexible alternating current transmission systems (FACTS) installed in smart grids to mitigate power quality disturbances such as voltage fluctuations, sag, swell, and harmonics. In this research, STATCOM and UPQC are designed and simulated in MATLAB/Simulink to overcome PQ-related disruptions in smart grids. Accordingly, the differences between the proposed two solutions are highlighted across this research and renewable energy sources' reliability during faults. Therefore, the reader will be able to choose the appropriate FACTS devices. This study emphasizes the extent of the smart grid need for the FACTS. As per the given results of this study, STATCOM and UPQC have shown exemplary performance in the PQ improvement investigations conducted in the context of smart/modern grids.
System identification applied to a single area electric power system under frequency response
Authors: Barrios José Angel , Sanchez F. , Gonzalez-Longatt Francisco , Claudio Gianfranco
Published: June 2021

This research paper proposes a methodology to apply identification methods to find a simplified model of three different governors in a single area electric power system (SAEPS). A SAEPS with different governors-turbine is presented: a hydraulic turbine, a steam turbine and a steam reheat turbine. In this same investigation, an analytic reduction has been performed, a fifth order system was found analytically, thus a transfer function equivalent to the three different governor-turbine elements was obtained, this equivalent transfer function models the complete behavior of the three devices. Two systems identification (SI) algorithms have been proposed to apply them to this generic subspace state-space (N4SID) and generalized poisson moment functionals (GPMF) electrical system, these presented similar results. The results of the performance and simulation analysis exhibit that using the SI technique, fifth, fourth and third-order systems were obtained that graphically show a very small estimation error compared to the original signal, this fact could be check simulating the simplified models using the same input-output data. The results are presented in a table that shows a comparison of the model respond the fifth, fourth, third and second-order systems.
An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya
Authors: Kurniawan Adi , Harumwidiah Anisa
Published: June 2021

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.
Model of an intelligent energy harvesting system from microbial fuel cells in wastewater treatment process
Authors: Quach Ngoc-Thinh , Quoc Viet Thieu Quang , Toan Pham Van , Dao Minh-Trung
Published: June 2021

This paper presents a model of an intelligent energy harvesting system from microbial fuel cells (MFCs) in the wastewater treatment process. The model consists of two direct current (DC/DC) converters connected in a cascade. One DC/DC converter is used to capture energy from MFC and store it in a supercapacitor. The other DC/DC converter is responsible for increasing the low output voltage to a higher voltage level. In the paper, the MFC is modeled by a DC voltage source instead of a real MFC that contains wastewater inside it. The experimental results demonstrate that the model of an intelligent energy harvesting system can increase the low output voltage of MFC up to 3.3 V and achieve intermittent output power at a high level that can use in practice.
Home security monitoring system with IoT-based Raspberry Pi
Authors: Desnanjaya I Gusti Made Ngurah , Arsana I Nyoman Alit
Published: June 2021

Home security monitoring system is a system that is able to monitor the house from unwanted events such as theft. Home monitoring systems can monitor and send users notices about the condition of their homes at the same time. Notifications sent in the form of pictures of the state of the room in the house, temperature conditions and gas density conditions. The home security monitoring system was created using Raspberry Pi as the control center of the system. It was connected with several sensors namely PIR sensor is used to detect objects that enter the room, raspicam is used to take pictures when the PIR sensor detects objects, temperature sensors and gas sensors are used to detect the state of temperature and gas concentration, and telegram is used as a liaison application to send notifications from Raspberry Pi to tool users. The final result of this research is to build a home security monitoring system with Raspberry Pi based on telegraph messenger. This system is able to monitor the security of the house from burglars or intruders, notify the temperature of the house and detect smoke or gas.
Local stereo matching algorithm using modified dynamic cost computation
Authors: Kadmin A. F. , Hamzah R. A. , Manap M. N. Abd , Hamid M. S. , Wook T. F. Tg.
Published: June 2021

Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.
Improving the speed of ball detection process and obstacle detection process in ERSOW robot using omnidirectional vision based on ROS
Authors: Haq Muhammad Abdul , Wibowo Iwan Kurnianto , Dewantara Bima Sena Bayu
Published: June 2021

This paper presents a novel approach for improving the computation speed of the ball detection and obstacle detection processes in our robot. The conditions of obstacle detection and ball detection in our robot still have a slow processing speed, this condition makes the robot not real-time and the robot's movement is hampered. To build a good world model, things to note are obstacle information and real-time ball detection. The focus of this research is to increase the speed of the process of the ball and obstacle detection around the robot. To increase the speed of the process, it is necessary to optimize the use of the OpenCV library on the robot operating system (ROS) system to divide the process into several small processes so that the work can be optimally divided into threads that have been created. Then, minimize the use of too many frames. This information will be sent to the base station and also for obstacle avoidance purposes.
Half-face based recognition using principal component analysis
Authors: Alkababji Ahmed M. , Abd Sara Raed
Published: June 2021

Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.
Customer’s spontaneous facial expression recognition
Authors: Morshed Golam , Ujir Hamimah , Hipiny Irwandi
Published: June 2021

In the field of consumer science, customer facial expression is often categorized either as negative or positive. Customer who portrays negative emotion to a specific product mostly means they reject the product while a customer with positive emotion is more likely to purchase the product. To observe customer emotion, many researchers have studied different perspectives and methodologies to obtain high accuracy results. Conventional neural network (CNN) is used to recognize customer spontaneous facial expressions. This paper aims to recognize customer spontaneous expressions while the customer observed certain products. We have developed a customer service system using a CNN that is trained to detect three types of facial expression, i.e. happy, sad, and neutral. Facial features are extracted together with its histogram of gradient and sliding window. The results are then compared with the existing works and it shows an achievement of 82.9% success rate on average.
Twelfth mode on-demand band notch UWB antenna for underlay cognitive radio
Authors: Abdullah Laith Wajeeh , Wali Musa H. , Saloomi Adheed H.
Published: June 2021

A wide-slot ultra-wideband (UWB) antenna with on-demand band rejection characteristics that can serve underlay cognitive radio is presented in this paper. This antenna is designed to work in twelve operation modes; one to cover the whole UWB while each of the rest modes excludes one or more of the ranges that are allocated for Worldwide Interoperability for Microwave Access (WiMax), C-band, wireless local area network (WLAN), X-band and International Telecommunication Union (ITU) in a single, dual, triple, quad or penta band rejection state. A spiral shape slot in the patch and three slots mainly based on half-circular structures in the ground plane are the means to create the desired frequency notches. A positive-intrinsic-negative (PIN) diode across each of these slots is used to enable/disable band(s) rejection process. Configuration of the proposed antenna to the desired mode of operation is decided by the state of its four PIN diodes. This work is simulated by computer simulation technology (CST) v.10. It’s S11, voltage-standing-wave-ratio (VSWR) and realized gain results when combined with antenna's 25x25x0.8 mm3 compact size and the large number of modes and states, all ensure its capability to eliminate or reduce the interference within the targeted bands and hence being suitable for the applications of underlay cognitive radio.
Public key cryptosystem based on multiple chaotic maps for image encryption
Authors: Najaf Yousif S. , K. Mahmood Al-Azawi Maher
Published: June 2021

Image is one of the most important forms of information. In this paper, two public key encryption systems are proposed to protect images from various attacks. Both systems depend on generating a chaotic matrix (I) using multiple chaotic maps. The parameters for these maps are taken from the shared secret keys generated from Chebyshev map using public keys for Alice and secret key for Bob or vice versa. The second system has the feature of deceiving the third party for searching for fake keys. Analysis and tests showed that the two proposed systems resist various attacks and have very large key space. The results are compared with other chaos based systems to show the superiority of these two proposed systems.
Internal pilot insertion for polar codes
Authors: Abdulwahab Walled K. , Kadhim Abdulkareem A.
Published: June 2021

Two internal pilot insertion methods are proposed for polar codes to improve their error correction performance. The presented methods are based on a study of the weight distribution of the given polar code. The insertion of pilot bits provided a new way to control the coding rate of the modified polar code on the basis of the Hamming weight properties without sacrificing the code construction and the related channel condition. Rate control is highly demanded by 5G channel coding schemes. Two short-length polar codes were considered in the work with successive cancellation list decoding. The results showed that advantages in the range of 0.1 to 0.75 dB were obtained in the relative tolerance of the modified coded signal to the additive white Gaussian noise and fading channels at a bit error rate of 10−4. The simulation results also revealed that the performance improvements were possible with a careful insertion of the pilots. The modified polar code with pilot insertion provided performance improvement and offered the control of the coding rate without any added complexity at both the encoder and the decoder.
Energy consumption study of channel access modes and modulation schemes of the 2.4 GHz narrowband IEEE 802.15.6
Authors: Boumaiz Marwa , El Ghazi Mohammed , Fattah Mohammed , Bouayad Anas , El Bekkali Moulhime
Published: June 2021

Energy efficiency is an important challenge for wireless body area networks. Therefore, choosing the channel access modes and modulation schemes that guarantee lower energy consumption is necessary to increase the network lifetime, especially in wireless body area network (WBAN) medical applications. The purpose of this paper is to analyze the network energy consumption in on-body medical applications (which are classified as low data rate, medium data rate, and high data rate applications) for two channel access mechanisms: random and scheduled access modes, and two modulation schemes: differential quadrature phase shift keying (DQPSK) and differential binary phase shift keying (DBPSK), which are supported by the 2.4 GHz band of the Institute of Electrical and Electronics Engineers (IEEE) 802.15.6 standard. The considered on-body area network (BAN) of the study supports two communication scenarios: the line-of-sight transmission and the non-line-of-sight communications, referenced as CM3A and CM3B path loss models respectively. Simulation results have demonstrated that the scheduled access mode based on time-division multiple access (TDMA), and DQPSK are the optimal choices to be made at the media access control (MAC) and physical layer levels respectively, in terms of energy efficiency, in low, medium, and high data rate on-body WBAN applications.
Pre-trained deep learning models in automatic COVID-19 diagnosis
Authors: Reza Ahmed Wasif , Hasan Md Mahamudul , Nowrin Nazla , Ahmed Shibly Mir Moynuddin
Published: June 2021

Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. This study presented an alternative way to identify COVID-19 patients by doing an automatic examination of chest X-rays of the patients. To develop such an efficient system, six pre-trained deep learning models were used. Those models were: VGG16, InceptionV3, Xception, DenseNet201, InceptionResNetV2, and EfficientNetB4. Those models were developed on two open-source datasets that have chest X-rays of patients diagnosed with COVID-19. Among the models, EfficientNetB4 achieved better performances on both datasets with 96% and 97% of accuracies. The empirical results were also exemplary. This type of automated system can help us fight this dangerous virus outbreak.
New ideas and framework for combating COVID-19 pandemic using IoT technologies
Authors: Haglan Hussein M. , Mahmoud Akeel Sh. , Al-Jumaili Mustafa Hamid , Aljaaf Ahmed J.
Published: June 2021

The internet of things (IoT) is one of the most advanced technologies that have emerged in the last decade. In recent years, IoT has been used in many medical fields. With the emergence of the Coronavirus pandemic, some IoT technologies were employed to serve the health sector to make quick and accurate decisions to save people's lives. However, there are still many ideas and works not yet implemented that could be applied in many aspects to preserve people's lives. Therefore, it is necessary to collect works and ideas that depend on IoT to produce modern systems quickly to serve the health sector. In this paper, a review of the most recent technologies of IoT against Coronavirus disease (COVID-19) has been done. A comparative and analysis among the previous works have been done to reach the most efficient depending on comparing the services that each work has provided. Besides that, suggest several new ideas that can be adopted as systems use IoT technologies and the expected advantages that can be gain from applying these ideas. A framework for a proposed idea to build a comprehensive monitoring system based on IoT technologies on the patient and hospital sides and expected advantages of implementing the system has been done.
Information system for monitoring of urban air pollution by heavy metals
Authors: Oralbekova Zhanar , Khassenova Zarına , Mynbayeva Bakhyt , Zhartybayeva Makpal , Iskakov Kazizat
Published: June 2021

The authors of the article formulated the information monitoring system of Almaty city (The Republic of Kazakhstan). One of the urgent problems of the modern world is the ecological safety of the urban environment. The well-known unfavorable natural and anthropogenic factors of the Almaty city include the following: geographical and climatic features of the area, the lack of technologies for monitoring atmospheric air and traffic jams. All of the above factors increase the emissions of pollutants into the atmosphere, in particular, toxic heavy metals. A promising direction for solving this problem is the development of real-time monitoring using a mathematical model of data assimilation and the creation of software that allows you to assess the state of the system in real time. In this regard, this article describes the developed information system for the algorithm of data assimilation that optimizes the process of monitoring atmospheric air pollution with heavy metals.
Unsupervised feature selection with least-squares quadratic mutual information
Authors: Sainui Janya , Srivisal Chouvanee
Published: June 2021

We propose the feature selection method based on the dependency between features in an unsupervised manner. The underlying assumption is that the most important feature should provide high dependency between itself and the rest of the features. Therefore, the top m features with maximum dependency scores should be selected, but the redundant features should be ignored. To deal with this problem, the objective function that is applied to evaluate the dependency between features plays a crucial role. However, previous methods mainly used the mutual information (MI), where the MI estimator based on the k-nearest neighbor graph, resulting in its estimation dependent on the selection of parameter, k, without a systematic way to select it. This implies that the MI estimator tends to be less reliable. Here, we introduce the leastsquares quadratic mutual information (LSQMI) that is more sensible because its tuning parameters can be selected by cross-validation. We show through the experiments that the use of LSQMI performed better than that of MI. In addition, we compared the proposed method to the three counterpart methods using six UCI benchmark datasets. The results demonstrated that the proposed method is useful for selecting the informative features as well as discarding the redundant ones.
Implementation multiple linear regresion in neural network predict gold price
Authors: Yanto Musli , Sanjaya Sigit , Yulasmi Yulasmi , Guswandi Dodi , Arlis Syafri
Published: June 2021

The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.
Two-versions of descent conjugate gradient methods for large-scale unconstrained optimization
Authors: Jabbar Hawraz N. , Hassan Basim A.
Published: June 2021

The conjugate gradient methods are noted to be exceedingly valuable for solving large-scale unconstrained optimization problems since it needn't the storage of matrices. Mostly the parameter conjugate is the focus for conjugate gradient methods. The current paper proposes new methods of parameter of conjugate gradient type to solve problems of large-scale unconstrained optimization. A Hessian approximation in a diagonal matrix form on the basis of second and third-order Taylor series expansion was employed in this study. The sufficient descent property for the proposed algorithm are proved. The new method was converged globally. This new algorithm is found to be competitive to the algorithm of fletcher-reeves (FR) in a number of numerical experiments.
Child tracking and hidden activities observation system through mobile app
Authors: Alam Mohammad Jahangir , Chowdhury Tanjia , Hossain Sohrab , Chowdhury Shusmoy , Das Tanmoy
Published: June 2021

The world is changing rapidly due to information technology, and all the people around the world are busy with their jobs, and business, adjusting to this race. Now, parents are giving more time at their business, office, and jobs, instead of passing the time at home, but they always get worried and scared about their children due to abuse of Information Technology and the country's law and order situation. So, parents are wanted to track and monitor their child's activities and location from anywhere to resolve their pressure. But is not possible for every parent to monitor their child physically due to many reasons. This paper presents a system that will help parents monitor their child activities from anywhere using a mobile phone to solve the problem. This android app uses global positioning system (GPS) and mobile services to find the child location and secretly stored all the call logs, hort message service (SMS) logs, contact lists, and accurate locations without knowing the children. Children assume that they are using Facebook, browsing the net or watching videos from youtube. It will not hamper any activities of the child. Parents can check all the activities of children using this app.
Tuned bidirectional encoder representations from transformers for fake news detection
Authors: Pardamean Amsal , Pardede Hilman F.
Published: June 2021

Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate news from real news can be done with natural language processing (NLP) technologies. In this paper, we proposed bidirectional encoder representations from transformers (BERT) for inaccurate news identification. BERT is a language model based on deep learning technologies and it has found effective for many NLP tasks. In this study, we use transfer learning and fine-tuning to adapt BERT for inaccurate news identification. The experiments show that our method could achieve accuracy of 99.23%, recall 99.46%, precision 98.86%, and F-Score of 99.15%. It is largely better than traditional method for the same tasks.
Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem
Authors: Sabri Sharizal Fadlie , Ahmad Noor Azurati , Sahibuddin Shamsul , Dziyauddin Rudzidatul
Published: June 2021

CubeSat is a small-sized satellite that provides a cheaper option for the manufacturer to have a fully operational satellite. Due to its size, CubeSat can only generate limited power, and this will restrict its functionality. This research aims to improve CubeSat’s power consumption by implementing the dynamic voltage and frequency scaling (DVFS) technique to on-board and data handling subsystem (OBDH). DVFS will find the best operating frequency to execute all of OBDH’s task. This paper explains how we determined the task set, representing all routine tasks performed by OBDH during normal operation mode. We have simulated the task set using two DVFS algorithms, static earliest deadline first (EDF) and cycle conserving edf (CC EDF). The result shows that both scheduling algorithms give a similar result to our task set. However, when the scheduler is configured as non-preemptive, the simulator failed to schedule the critical task. It means that the system fails to work as intended. Therefore, we conclude that we need to implement mixed-criticality scheduling to prevent critical tasks from being aborted by the system.
Multi-agent class timetabling for higher educational institutions using prometheus platform
Authors: Guia Angelita D. , Ballera Melvin A.
Published: June 2021

A in a university setting, class scheduling is vital for teaching and learning process. Academic institutions rely on time tables in their day to day activities. University Course Timeframe problem can be resolved by using multi-agent systems-based method which may increase the independence of each department's class scheduling, adaptability in a distributed environment and prevents conflicts between events or resources, and unforeseen allocation through intervention between agents in a dispersed environment. Class timing is performed manually in most of the higher educational institutions, which is a very challenging and time-consuming process. The main objective of the study is to build a multi-agent class timing system that automates the process of class scheduling of higher education institutions (HEIs) using the Prometheus methodology. The implementation of the Prometheus approach in the development of a multi-agent framework has resulted in a complete and comprehensive system covering all phases of software development as applied to the agent systems.
Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm
Authors: Joshi Aparna Shashikant , Munisamy Shayamala Devi
Published: June 2021

In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms
Prediction of student’s performance through educational data mining techniques
Authors: Z. Salih Nibras , Khalaf Walaa
Published: June 2021

Many educators have worried about the failures of students through academic education. Thus, a variety of predictions have been applied to general information including culture, social, and economic information which wasn’t related to student performance. We have gathered an actual dataset from three years of academic stages of Mustansiriyah University in Iraq. The dataset consists of academic information without any socioeconomic data, it includes forty-four undergraduate students with thirteen attributes. We have proposed a model that explains the correlation between two main subjects which are, mathematics, and control systems. This study aimed to identify student failure of the control systems subject in the third year depending on the academic features of the mathematics subjects in the first and second years. Three algorithms were applied to the dataset including Naïve Bayes, support vector machine, and multilayer perceptron. Since the dataset was imbalanced, this leads to appear overfitting problem in the results so the synthetic minority oversampling technique was utilized to solve this problem. Our results show that the support vector machine algorithm proves an efficient classification after applied synthetic minority oversampling technique. The accuracy of the classifiers was measured from the confusion matrix using the Waikato environment for knowledge analysis (WEKA) tool and its related metrics.
Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system
Authors: Shri Shaima Hamdan , Mijbas Ayad Fadhil
Published: June 2021

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.
An improved technique for power transformer protection using fuzzy logic protective relaying
Authors: Marei Marwa M. , Nawir Manal H. , Altahir Ali Abdul Razzaq
Published: June 2021

The three-phase power transformer in the transmission or distribution substations represents one of the essential devices on electric power networks. Losing this devise cause a disconnection of the power utility to a large number of electrical loads. The robust protection system must be designed to protect the device during abnormal operations. A complete protection system for a poly-phase power transformer for one of the Karbala transmission networks (East Karbala substation) is modeled and simulated, adopting a fuzzy logic protective relaying using MATLAB/SIMULINK environment. This study discusses fuzzy logic-based relaying for a power transformer safety, as well as internal faults that are clearly identified. Two principles of operation are used to protect the transformer; differential relay and overcurrent relay. The differential relay is proposed as the unit protection, while the overcurrent is backup protection. The proposed fuzzy logic controller (FLC) is used to detect abnormal operation; it is also modeled to organize the operation between unit and backup protection. The numerical results clarify that the proposed model can perform fast, rigorous, and authoritative protection for the transformer. Also, modeling of the protection mode decreases the complexity of designing various subsystem and combining them in one controller.
LTE network performance evaluation based on effects of various parameters on the cell range and MAPL
Authors: S. Daghal Asaad. , Al-Hilfi Haider Mohammed Turki
Published: June 2021

These days long term evaluation (LTE) is considered the common mobile technology around the world and there is a need to maximize the network performance to satisfy the increased demand in terms of the cell capacity and coverage. These are many parameters in the network configuration and in the surrounded environment, which have great effects on the network performance. Examples of parameters are the system overhead rations, the required capacity of the network, neighbor cell load, and link budget parameters. The determination of the optimum configuration parameters, which achieve the best network performance, is a main step in the planning process in addition to it is continuous step in network optimization phase. In this study, the effects of some parameters will be investigating to get the best parameters that achieve the best network performance in terms of capacity of the cells and coverage area. The study will start by discussing introduction about LTE network components and protocols, and then the main parameters of the protocols will be revising. The study will display the results of changing many parameters related to LTE protocols and surrounding environment parameters on the LTE network performance.
Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method
Authors: Utomo Satryo B. , Irawan Januar Fery , Alinra Rizqi Renafasih
Published: June 2021

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.
First report on the occurrence of Sargassum Weed Fish Histrio histrio (Lophiliformes: Antennariidae) in Nigeria deep water, Gulf of Guinea
Authors: Dirisu Abdul-Rahman , Uyi Hanson S. , Uyi Meshack
Published: June 2021

We report the first occurrence of Sargassum Weed Fish Histrio histrio at Bonga Field in Nigeria Outer Continental Shelf approximately 120 km south-east of Delta State in February 2014. At high tides and under difficulty, we sampled some seaweeds in the epi-pelagic realm using a secured standard plankton net to the environmental research vessel that was cruising at a dead speed (≤4 knots). Upon examination of the weeds we collected, one adult Sargassum Weed Fish along with two gelatinous fingerlings were realised. This finding suggests the probable occurrence of more population of Sargassum Weed Fish in their macro-habitat (the Sargassum seaweeds) which are abundantly bound in the Gulf of Guinea deep waters. The specimen was photographed on-board and preserved immediately in 10 % formalin solution for a 24-h period and was thereafter reprocessed, and permanently preserved with 70 % ethanol. We recommend a major study of the marine seaweeds and their colonisers amongst Gulf of Guinea member states.
A new distribution record of the Horn Coral Caryophyllia grandis Gardiner & Waugh, 1938 (Anthozoa: Scleractinia) from the Karnataka Coast, India
Authors: Yogesh Kumar J. S. , Raghunathan C.
Published: June 2021

Surveys were conducted under the project entitled “Resource exploration and Inventorisation of Deep Sea ecosystem & Marine ecosystem Dynamics: Sagar Sampada Cruise” by on-board FORV Sagar Sampada Cruise No. 374 from 2 April to 15 April 2018 from Okha to Cochin along the west coast of India.  The species Caryophyllia (Caryophyllia) grandis is recorded from a depth of 580m off Karwar, Karnataka and is reported herein as a new distribution record to the Karnataka coast, India.
Population assessment and habitat distribution modelling of the threatened medicinal plant Picrorhiza kurroa Royle ex Benth. in the Kumaun Himalaya, India
Authors: Chandra Naveen , Singh Gajendra , Lingwal Shashank , Bisht M.P.S. , Tiwari Lalit Mohan
Published: June 2021

Kumaun Himalaya is a home to various threatened medicinal and aromatic plants.  Picrorhiza kurroa is a threatened medicinal plant useful in curing many diseases in Indian Himalayan region.  Due to overharvesting from the wild its population is decreasing at an alarming rate.  The present study attempted to assess its availability and predict highly suitable areas for in situ conservation in the alpine region of Kumaun.  Availability of P. kurroa across various meadows was evaluated through rapid mapping exercise.  MaxEnt model was used to predict the geographical distribution of the species using various environmental and physiographic parameters, and 29 primary distribution points.  The results reveal that potential habitat of P. kurroa is located near forest fringes.  Of the 3,828km2 area (vegetated) of the alpine region of Kumaun, about 202km2 is recorded highly suitable, 489km2 less suitable and the rest not suitable for the species.  It is also revealed that Napalchu nala, Panchachuli base, Chhipla Kedar, Rongkong, Ralam, Milam, Dwali, and Pindari areas are highly suitable areas for distribution of  P. kurroa.
The tribe Cnodalonini (Coleoptera: Tenebrionidae: Stenochiinae) from Maharashtra with two new records
Authors: Hegde V.D. , Vasanthakumar D.
Published: June 2021

The tenebrionidae collections of Western Regional Centre, Zoological Survey of India Pune were identified.  The species identified as Promethis brevicornis (Westwood, 1842) and Gebienocamaria girardi Masumoto, 1993 and collected from Sangli District and Koyna Wildlife Sanctuary, Satara, respectively constitute a new record for Maharashtra State.
A new distribution record of stomatopods Odontodactylus japonicus (De Haan, 1844) and Lysiosquilla tredecimdentata (Holthuis, 1941) from the Puducherry coastal waters, east coast of India
Authors: Mary S. Nithya , Ravitchandirane V. , Gunalan B.
Published: June 2021

Stomatopods in India are well known with 79 species recorded to date. Here I report the Odontodactylus japonicus (De Haan, 1844) and Golden Mantis Shrimp Lysiosquilla tredecimdentata Holothuis, 1941 for the first time in Puducherry coastal waters. A single specimen of Lysiosquilla tredecimdentata was collected from by-catch in the Nallavadu landing centre, Puducherry coast on 19 November 2019 and two specimens of L. tredecimdentata were recorded again in Pillaichavadi landing centre of Puducherry coast on 22 November 2019. One specimen of Odontodactylus japonicus was collected at Nallavadu landing centre, Puducherry coast on 20 December 2019. The present study was undertaken to identify the status of distribution, habitat, and ecological aspects along with the information of spread, confinement, endemism as well as rare, threatened and endangered species. The significance of these new observations is to discern the taxonomic position and characteristics for better understanding of the mantis shrimp group. The specimens were identified, described, illustrated, and measured morphometrically.
Birds of Surat-Dangs: a consolidated checklist of 75 years (1944–2020) with special emphasis on noteworthy bird records and bird hotspots from northern Western Ghats of Gujarat, India
Authors: Jambu Nikunj , Patel Kaushal G.
Published: June 2021

Surat-Dangs, historically known as a Bhil State, is the northernmost tip of Western Ghats in the state of Gujarat.  Despite being a part of an endemic bird area and global biodiversity hotspot, avifaunal diversity has been less documented in the landscape.  Two protected areas, Purna Wildlife Sanctuary and Vansda National Park, are designated in the landscape.  A handful of studies on birds have been conducted after Dr. Salim Ali’s collection in 1944–48.  We surveyed the landscape (both protected and non-protected areas) in 2012–13 and 2015–2018 for documentation of the avifaunal diversity.  We present a consolidated checklist of birds from our surveys as primary data and all published literature and eBird checklists as secondary data.  We have reported a total of 297 bird species belonging to 70 families and 21 orders including the first record of Nilgiri Wood-Pigeon Columba elphinstonii for the State as well as Purna Wildlife Sanctuary.  Here, we have re-reported various species, which was suspected to be locally extinct from the protected area or landscape among other noteworthy bird records.  We have identified bird-rich localities outside the protected areas based on the survey done by Salim Ali (1944–48) that can be used for future surveys.  We also propose the landscape to be declared as an Important Bird Area (IBA) as per Global IBA criteria (A1, A2, & A3), which will pave the milestone for future conservation endeavors in the landscape.
Identification of a unique barb from the dorsal body contour feathers of the Indian Pitta Pitta brachyura (Aves: Passeriformes: Pittidae)
Authors: Dey Prateek , Ray Swapna Devi , Sharma Sanjeev Kumaar , Pramod Padmanabhan , Singh Ram Pratap
Published: June 2021

Earlier research on feather morphology emphasized comprehensively on the body contour feather than various other types of feathers. Therefore, we conducted a systematic study on all feather types of the Indian Pitta Pitta brachyura, a passerine bird native to the Indian subcontinent. Feather barbs from wing contour, tail contour, body contour, semiplume, down, powder down, and bristle feathers were retrieved from the bird and observed under a light microscope. Primary flight feathers from the right and left wing were longest (85.17 mm and 87.32 mm, respectively), whereas bristle feathers were the shortest (5.31 mm). The mean barb length was observed to be the highest (11.37±0.47 mm) in the wing feather followed by body contour (8.31±0.39 mm), semiplume (8.27±0.22 mm), tail feather (7.85±0.50 mm), down (6.45±0.21 mm), powder down (6.04±0.23 mm), and bristle (2.70±0.07 mm).  Pearson correlation was found positive for barb length and feather length of down feathers (r= 0.996, p ≤0.05). We observed a novel type of barb the first time from dorsal body contour feather having plumulaceous barbules at the base followed by pennaceous barbules. This unique barbule arrangement is termed ‘sub-plumulaceous’ as it is distinct and analogous to known ‘sub-pennaceous’ type arrangement found absent in passerines.
New distribution and range extension records of geometrid moths (Lepidoptera: Geometridae) from two western Himalayan protected areas
Authors: Dey Pritha , Hausmann Axel
Published: June 2021

This article presents new distribution and range extension records (including new records from the state of Uttarakhand) of 12 species of the Geometridae family along with their taxonomic records. The records are based on field collections, where sampling was done along elevation and vegetation gradients in the buffer zones of Nanda Devi Biosphere Reserve and Kedarnath Wildlife Sanctuary, two prominent protected areas in the western Himalayan Indian state of Uttarakhand. DNA barcoding was performed for some of the species for confirmation of identification in addition to the morphological identifications. Voucher specimens are deposited in a public repository for future reference.