bluuebunny/crossref_metadata_2025 huggingface dataset:
Soil moisture level prediction using optical technique and artificial neural network
Authors: Xuan Wai Mars Hong , Huong Audrey , Ngu Xavier
Published: February 2021

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management.
DEDA: An algorithm for early detection of topology attacks in the internet of things
Authors: Karande Jalindar , Joshi Sarang
Published: February 2021

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time.
COVID-19 fever symptom detection based on IoT cloud
Authors: Hasan Mustafa Wassef
Published: February 2021

This paper presents a new method of detection COVID-19 fever symptoms depending on IoT cloud services to solve the higher time delay of checking the crowded clients that enter public or private agencies which can lead to a dangerous field to spread the disease. An automatically checking process is suggested using a practical experiment is developed using (ESP8266 Node MCU, Ultrasonic (SR-04), RFID (RC522), human body temperature (MAX30205) sensors, and ThingSpeak platform). Where Node MCU is open-source hardware used to transmit the received data (human temperature sensor) from the (MAX30205) to the cloud platform (ThingSpeak) then alert the monitoring manager user when the collected data reached a critical value that specified previously and automatically take action to solve this situation. At the same time, the cloud platform will provide a graphical representation of the received data to display it using different monitoring devices such as (computers, mobiles, and others).
Three zone detection and distance relay co-ordination of power system protection
Authors: Sriram Cholleti , Kusumalatha Y.
Published: February 2021

To secure the transmission lines against power system faults, the distance relays are mostly used. Distance relay has its own Resistance (R)–Reactance (X) characteristics. Co-ordination of different distance relays is necessary for the fast operation of circuit breaker. Various distance relays which are being tripped with respect to circuit breakers which are attached at individual buses faraway from each other. These relays will be operated with respect to the distance between the occurred fault and relay location. In this paper, detection of three zones using relay characteristics, co-ordination of distance relays and circuit breakers are shown with the faults placed at different locations of an IEEE Nine bus system using MATLAB/Simulink GUI environment. A comparison also made between the relays performance and circuit breaker tripping operation with respect to severe faults at different locations on IEEE Nine bus system.
FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints
Authors: H. Elroby M. M. , Mekhamer S. F. , A. Talaat H. E. , Moustafa Hassan M. A.
Published: February 2021

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system.
Power quality considerations for embedded generation integration in Nigeria: A case study of ogba 33 kV injection substation
Authors: F. Agbetuyi Ayoade , Ademola Abdulkareem , H. E. Orovwode , K. Oladipupo Oluwafemi , Simeon Matthew , A. Agbetuyi Oluranti
Published: February 2021

The deregulation of the Nigerian power sector has resulted in the quest to explore power generation options for power quality improvement. One of such options is the pattern shift from central power generation to embedded power generation. Network integration of embedded generators (EGs) causes several regulatory, technical and economic issues. This research focuses on power quality challenges that may arise as a result of network integration of embedded generation in a weak electricity networks using Ogba 33 kV injection substation as case study. The embedded generators considered comprised of gas turbine and diesel generators. NEPLAN software was used to perform the load flow analysis with and without EGs connection on the network. This was necessary so as to ascertain the healthiness of the existing distribution network for EGs integration. The power quality issues considered in the study were bus voltage profiles and the total line losses. Simulation results showed that EGs connection improved the voltage profile, for example, bus voltage at PTC 11 kV, improved from 0.881 pu to 0.958 pu while the total active power loss was reduced by 78.16%. The results obtained suggest that the grid is healthy enough to accommodate the EGs with no quality issues.
Improvement the voltage stability margin of Iraqi power system using the optimal values of FACTS devices
Authors: Salman Ghassan Abdullah , Abood Hatim G. , Ibrahim Mayyadah Sahib
Published: February 2021

The detection of potential voltage collapse in power systems is essential to maintain the voltage stability in heavy load demand. This paper proposes a method to detect weak buses in power systems using two stability indices: the voltage stability margin factor (dS/dY) and the voltage collapse prediction index (VCPI). Hence, the paper aims to improve the voltage stability of Iraqi transmission grid by allocating FACTS devices in the optimal locations and optimal sizes. Two types of FACTS are used in this paper which are Thyristor controlled series compensator (TCSC) and static var compensator (SVC). The objective function of the problem is fitted using particle swarm optimization (PSO). The proposed method is verified using simulation test on Diyala-132 kV network which is a part of the Iraqi power system. The results observed that improvement the voltage stability margin, the voltage profile of Diyala-132 kV is increased and the power losses is decreased.
Innovative earthing systems for electric power substations using conductive nanoparticles
Authors: Thabet Ahmed , Mobarak Youssef
Published: February 2021

The earthing system is very important to safe human’s lives and protect power system from normal and abnormal faults. High soil resistivity regions is the main problem of installation the earthing systems in electric power substations to pass the current through the earth's surface. This paper has been overcome on high soil resistivity regions by penetrating conductive nanoparticles to have extremely low grounding resistance. Moreover, it has been succeeded to examine the methodology of the proposed Nano-Tech earthing systems in case of single rods, multiple rods and grids. Also, it has been defined optimal types and concentrations of nanoparticles for Nano-Tech grounding system to provide excellence protection for electrical substations with respect to built beneath of soil where substation is located. A comparative study has been discussed and analyzed the results of traditional and nanotechnology grounding systems.
Dual objective multiconstraint swarm optimization based advanced economic load dispatch
Authors: Maharana Himanshu Shekhar , Dash Saroj Kumar
Published: February 2021

In electric power system, the vital topic to be mooted is economic load dispatch (ELD). It is a non-linear problem with some unavoidable constraints such as valve point loading and ramp rate constraint. For solving ELD problem distint methods were devised and tried for different electric supply systems yielding slow convergence rates. To achieve fast convergence, dual objective multi constraint swarm optimization based advanced economic load dispatch (DOMSOBAELD) algorithm is proposed making use of simulated values of real power outages of a thermal power plant as initial estimates for PSO technique embedded in it and used for optimizing economic dispatch problem in this article. DOMSOBAELD method was developed in the form of amalgamating fluids. Presence of power line losses, multiple valves in steam turbines, droop constraints and inhibited zones were utilized to optimize the ELD problem as genuinely approximate as possible. The results obtained from DOSOBAELD are compared with particle swarm optimization (PSO), PSOIW and differential particle swarm optimization (DPSO) techniques. It is quite conspicuous that DOMSOBAELD yielded minimum cost values with most favourable values of real unit outputs. Thus the proposed method proves to be advantageous over other heuristic methods and yields best solution for ELD by selecting incremental fuel cost as the decision variable and cost function as fitness function.
Indirect power control of DFIG based on wind turbine operating in MPPT using backstepping approach
Authors: Dbaghi Yahya , Farhat Sadik , Mediouni Mohamed , Essakhi Hassan , Elmoudden Aicha
Published: February 2021

This paper describes a MPPT control of the stator powers of a DFIG operating within a wind energy system using the backstepping control technique. The objective of this work consists of providing a robust control to the rotor-side converter allowing the stator active power to be regulated at the maximum power extracted from the wind turbine, as well as maintaining the stator reactive power at zero to maintain the power factor at unity, under various conditions. We have used the Matlab/Simulink platform to model the wind system based on a 7.5 kW DFIG and to implement the MPPT control algorithm in a first step, then we have implemented the field-oriented control and the backstepping controller in a second step. The simulation results obtained were very satisfactory with a fast transient response and neglected power ripples. They furthermore confirmed the high robustness of the approach used in dealing with the variation of the internal parameters of the machine.
Feasibility of using photovoltaic solar energy for water treatment plants
Authors: Saavedra Alexander , Galvis Nataly Alejandra , Castaneda Mónica , Zapata Sebastian , Mesa Fredy , Aristizábal Andrés Julián
Published: February 2021

The purpose of this research is to determine the feasibility of supplying photovoltaic solar energy for the electrical requirements of drinking water and wastewater treatment plants, in six regions of Colombia, with different geographic and climatological conditions: Andean Region, Amazon Region, Orinoquía Region, Caribbean Region, Pacific Region, and Insular Region. The study revealed that the costs of the photovoltaic systems oscillate between USD 32,486.86 and USD 40,716.86, without using a storage system with batteries, since the price of these makes the investment recovery unfeasible. Instead of using batteries to store energy, a water storage system is used. The total daily load of the sanitation systems is 57 kWh/day, which would avoid 7120.44 kgCO2 per year. Besides, during the entire useful life of the 20-year project, 142.4 tCO2 would not be released into the environment.
Flexible milimeter-wave microstrip patch antenna array for wearable RF energy harvesting applications
Authors: Bashri Mohd Saiful Riza , Ramli Noor Amalina
Published: February 2021

In this paper, a series-fed milimeter-wave microstrip patch antenna array operating at 28 GHz is presented for wearable radio-frequency (RF) energy harvesting applications. The antenna array is made of 4×4 rectangular microstrip elements on a polyethylene terephthalate (PET) substrate to provide conformability when directly attached on human body parts. A 4-way Wilkinson power divider is connected to the array for RF power combining. The overall size of the antenna is 47×28×0.25 mm. The half-power beamwidth (HPBW) of the antenna array can be increased up to 151.9⁰ via structural deformation making it suitable for energy harvesting applications. The performance of the antenna array is investigated in terms of impedance matching, gain and radiation pattern. The average simulated specific absorption rate (SAR) of the antenna is 0.52 W/kg which is well below the safety limit of 1.6 W/kg averaged over 1 g of tissue for 100 mW of input power.
Sliding-mode controller for a photovoltaic system based on a Cuk converter
Authors: Ramos-Paja Carlos Andrés , Gonzalez-Motoya Daniel , Villegas-Seballos Juan Pablo , Serna-Garces Sergio Ignacio , Giral Roberto
Published: February 2021

The wide range of step-up and step-down input-output voltage characteristic of the Cuk converter makes it a good candidate to interface photovoltaic arrays in both classical and distributed maximum power point tracking systems. Because its two inductor structure, Cuk converters have continuous input and output currents, which reduce the additional filtering elements usually required for interfacing dc/dc converter topologies. However, PV systems based on Cuk converters usually do not provide formal proofs of global stability under realistic conditions, which makes impossible to ensure a safe operation of the PV installation. Therefore, this paper proposes a high performance sliding-mode controller for PV systems based on Cuk converters, which regulates the PV voltage in agreement with the commands imposed by a MPPT algorithm, rejecting both load and environmental perturbations, and ensuring global stability for real operation conditions. Finally, the performance of the regulated PV system is tested using both simulations and experiments.
Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia
Authors: Tajudin Noraisyah , Ya'acob Norsuzila , Mohd Ali Darmawaty , Adnan Nor Aizam
Published: February 2021

Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.
Clustering using kernel entropy principal component analysis and variable kernel estimator
Authors: Fattahi Loubna El , Sbai El Hassan
Published: February 2021

Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the kernel function into the EPCA, the input space can be mapped implicitly into a high-dimensional of feature space. Then, the Shannon’s entropy estimated via the inertia provided by the contribution of every mapped object in data is the key measure to determine the optimal extracted features space. Our proposed method performs very well the clustering algorithm of the fast search of clusters’ centers based on the local densities’ computing. Experimental results disclose that the approach is feasible and efficient on the performance query.
New scheme for PAPR reduction in FBMC-OQAM systems based on combining TR and deep clipping techniques
Authors: Senhadji Salima , Bendimerad Yassine Mohammed , Bendimerad Fathi Tarik
Published: February 2021

Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) system is a very efficient multicarrier modulation technique for 5G, but it suffers as all multicarriers designs from large peak-to-average power ratio (PAPR). Tone reservation (TR) is a method designed to solve this problem by reserving several subcarriers called tones in the frequency domain to generate a cancellation signal in the time domain to eliminate high peaks. In this paper, we suggest a serial combination of tone reservation (TR) method with an enhanced version of clipping called deep clipping (DC) method (TR&DC) to enhance the peaks (PAPR) mitigation in FBMC-OQAM signal model without signiﬁcantly impacting the quality of transmission. Numerical results and analysis show that the new TR&DC approach allows better overall performance and offers remarkable gain in term of PAPR mitigation than the TR method, with similar BER performance to TR over additive white gaussian noise channel and Rapp HPA model.
High frequency of low noise amplifier architecture for WiMAX application: A review
Authors: Ibrahim Abu Bakar , Zulkifli Che Zalina , Ariffin Shamsul Arrieya , Kahar Nurul Husna
Published: February 2021

The low noise amplifier (LNA) circuit is exceptionally imperative as it promotes and initializes general execution performance and quality of the mobile communication system. LNA's design in radio frequency (R.F.) circuit requires the trade-off numerous imperative features' including gain, noise figure (N.F.), bandwidth, stability, sensitivity, power consumption, and complexity. Improvements to the LNA's overall performance should be made to fulfil the worldwide interoperability for microwave access (WiMAX) specifications' prerequisites. The development of front-end receiver, particularly the LNA, is genuinely pivotal for long-distance communications up to 50 km for a particular system with particular requirements. The LNA architecture has recently been designed to concentrate on a single transistor, cascode, or cascade constrained in gain, bandwidth, and noise figure.
An enhanced OFDM light weight physical layer encryption scheme
Authors: Alabdel Abass Ahmed A. , Prarthana Divvala Navya
Published: February 2021

The broadcast nature of wireless networks makes them susceptible to attacks by eavesdroppers than wired networks. Any untrusted node can eavesdrop on the medium, listen to transmissions and obtain sensitive information within the wireless network. In this paper, we propose a new mechanism which combines the advantages of two techniques namely iJam and OFDM phase encryption. Our modified mechanism makes iJam more bandwidth efficient by using Alamouti scheme to take advantage of the repetition inherent in its implementation. The adversary model is extended to the active adversary case, which has not been done in the original work of iJam and OFDM phase encryption. We propose, through a max min optimization model, a framework that maximizes the secrecy rate by means of a friendly jammer. We formulate a Zero-Sum game that captures the strategic decision making between the transmitter receiver pair and the adversary. We apply the fictitious play (FP) algorithm to reach the Nash equilibria (NE) of the game. Our simulation results show a significant improvement in terms of the ability of the eavesdropper to benefit from the received information over the traditional schemes, i.e. iJam or OFDM phase encryption.
Investigating the PageRank and sequence prediction based approaches for next page prediction
Authors: Da Nguyen Thon , Hanh Tan
Published: February 2021

Discovering unseen patterns from web clickstream is an upcoming research area. One of the meaningful approaches for making predictions is using sequence prediction that is typically the improved compact prediction tree (CPT+). However, to increase this method's effectiveness, combining it with at least other methods is necessary. This work investigates such PageRank-based methods related to sequence prediction as All-K-Markov, DG, Markov 1st, CPT, CPT+. The experimental results proved that the integration of CPT+ and PageRank is the right solution for next page prediction in terms of accuracy, which is more than a standard method of approximately 0.0621%. Still, the size of the newly created sequence database is reduced up to 35%. Furthermore, our proposed solution has an accuracy that is much higher than other ones. It is intriguing for the next phase (testing one) to make the next page prediction in terms of time performance.
5G uplink interference simulations, analysis and solutions: The case of pico cells dense deployment
Authors: Younes Balboul , Mohammed Fattah , Saïd Mazer , El Bekkali Moulhime
Published: February 2021

The launch of the new mobile network technology has paved the way for advanced and more productive industrial applications based on high-speed and low latency services offered by 5G. One of the key success points of the 5G network is the available diversity of cell deployment modes and the flexibility in radio resources allocation based on user’s needs. The concept of Pico cells will become the future of 5G as they increase the capacity and improve the network coverage at a low deployment cost. In addition, the short-range wireless transmission of this type of cells uses little energy and will allow dense applications for the internet of things. In this contribution, we present the advantages of using Pico cells and the characteristics of this type of cells in 5G networks. Then, we will do a simulation study of the interferences impact in uplink transmission in the case of PICO cells densified deployment. Finally, we will propose a solution for interference avoidance between pico cells that also allows flexible management of bands allocated to the users in uplink according to user’s density and bandwidth demand.
Reducing power consumption in LEO satellite network
Authors: Hussein Mohammed , Abu-Issa Abdellatif , Tumar Iyad , Awad Abdalkarim
Published: February 2021

Current low earth orbit (LEO) satellite network display poor power efficiency, running network devices at full capacity all the time regardless of the traffic matrix and the distribution of the population over the Globe. Most of the research on energy efficiency of LEO satellites has focused on component level or link level. Therefore, this kind of research is not holistic to try to look at the satellite system as a single node. To enhance the energy efficiency. The solution should exploits multipath routing and load balancing. LEO network is overprovisioned, and hence selectively shutting down some satellite nodes and links during off-peaks hours seems like a good way to reduce energy consumption. In this paper, we exploit the fact that due to geographical and climatic conditions, some satellite links are expected to be loaded with data while others remain unused. Our approach is to power down satellite nodes and links during period of low traffic, while guaranteeing the connectivity and QoS. Finding the optimal solution is NP-problem and therefore, we explore in this work two heuristic algorithms. We evaluate our heuristics on a realistic LEO topology and real traffic matrices. Simulation results show that the power saving can be significant.
Tigrigna language spellchecker and correction system for mobile phone devices
Authors: Kiros Atakilti Brhanu , Aray Petros Ukbagergis
Published: February 2021

This paper presents on the implementation of spellchecker and corrector system in mobile phone devices, such as a smartphone for the low-resourced Tigrigna language. Designing and developing a spell checking for Tigrigna language is a challenging task. Tigrigna script has more than 32 base letters with seven vowels each. Every first letter has six suffixes. Word formation in Tigrigna depends mainly on root-and-pattern morphology and exhibits prefixes, suffixes, and infixes. A few project have been done on Tigrigna spellchecker on desktop application and the nature of Ethiopic characters. However, in this work we have proposed a systems modeling for Tigrigna language spellchecker, detecting and correction: a corpus of 430,379 Tigrigna words has been used. To indication the validity of the spellchecker and corrector model and algorithm designed, a prototype is developed. The experiment is tested and accuracy of the prototype for Tigrigna spellchecker and correction system for mobile phone devices achieved 92%. This experiment result shows clearly that the system model is efficient in spellchecking and correcting relevant suggested correct words and reduces the misspelled input words for writing Tigrigna words on mobile phone devices.
Optimization of network traffic anomaly detection using machine learning
Authors: Do ChoXuan , Dam Nguyen Quang , Lam Nguyen Tung
Published: February 2021

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.
Interaction of IT investment mandates and mobile savvy affecting mobile office performance in corporates: Focusing on the moderating effects of IT savvy
Authors: Junghoon Moon , Sunggoo Yoo , Jongtae Lee
Published: February 2021

This study investigates the effect of IT investment portfolios on the performance of mobile business services, as well as the moderating role of IT savvy. This study pulls the concept of IT investment mandates into the conceptual research framework of mobile investment. A survey for the IT specialists working at 123 enterprise-level companies was conducted and hierarchical regression analysis was adopted. Our results show that IT investment and organizational IT capabilities influence the performance of the mobile office and that IT savvy plays as a moderator in the relationship between investment mandates and mobile office performance. This research also may indicate that transactional assets are most helpful factors for a change by the adoption of mobile technology. This study is a rare research paper to explain the impact of IT investment portfolios on the mobile office performance in an academic methodology.
Web document classification using topic modeling based document ranking
Authors: Lee Youngseok , Cho Jungwon
Published: February 2021

In this paper, we propose a web document ranking method using topic modeling for effective information collection and classification. The proposed method is applied to the document ranking technique to avoid duplicated crawling when crawling at high speed. Through the proposed document ranking technique, it is feasible to remove redundant documents, classify the documents efficiently, and confirm that the crawler service is running. The proposed method enables rapid collection of many web documents; the user can search the web pages with constant data update efficiently. In addition, the efficiency of data retrieval can be improved because new information can be automatically classified and transmitted. By expanding the scope of the method to big data based web pages and improving it for application to various websites, it is expected that more effective information retrieval will be possible.
Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis
Authors: Guanchen Wu , Kim Minkyu , Jung Hoekyung
Published: February 2021

As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user's subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user.
Prediction model of algal blooms using logistic regression and confusion matrix
Authors: Yun Hongwon
Published: February 2021

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
A smart method for spark using neural network for big data
Authors: Rahman Md. Armanur , Hossen J. , Sultana Aziza , Al Mamun Abdullah , Ab. Aziz Nor Azlina
Published: February 2021

Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.
Data analysis of financial burden index through KBO league FA pitcher's performance and contract amount size
Authors: Kim Semin , You Kangsoo
Published: February 2021

Due to the recent pandemic of COVID-19, sports events around the world are being hit hard in 2020. However, with the KBO league having been opened even though it is late, it has attracted the attention of baseball fans around the world as it becomes an overseas broadcast. However, since the Republic of Korea is also affected by the global economic crisis, the financial burden of professional baseball teams that rely on their parent company has become a hot topic. Therefore, in this study, we collected records such as IP, ERA, and WAR of KBO league FA pitchers and news related to FA contracts. Then, we preprocessed the data and analyzed it. To this end, we analyzed the performance and financial burden indicators according to the pitcher's age and contract years. In addition, it was found that the performance of athletes was maintained until their mid 30’s, and through rational FA contracts, the team and athletes were able to suggest win-win strategies. And with this basic data analysis in the sports industry, the possibility of utilizing these strategies was confirmed.
History-based consistency algorithm for the trickle-timer with low-power and lossy networks
Authors: Albalas Firas A. , Taamneh Haneen , Mardini Wail E.
Published: February 2021

Recently, the internet of things (IoT) has become an important concept which has changed the vision of the Internet with the appearance of IPv6 over low power and lossy networks (6LoWPAN). However, these 6LoWPANs have many drawbacks because of the use of many devices with limited resources; therefore, suitable protocols such as the Routing Protocol for low power and lossy networks (RPL) were developed, and one of RPL's main components is the trickle timer algorithm, used to control and maintain the routing traffic frequency caused by a set of control messages. However, the trickle timer suffered from the short-listen problem which was handled by adding the listen-only period mechanism. This addition increased the delay in propagating transmissions and resolving the inconsistency in the network. However, to solve this problem we proposed the history based consistency algorithm (HBC), which eliminates the listen-only period based on the consistency period of the network. The proposed algorithm showed very good results. We measured the performance of HBC trickle in terms of convergence time; which was mainly affected, the power consumption and the packet delivery ratio (PDR). We made a comparison between the original trickle timer, the E-Trickle, the optimized trickle and our HBC trickle algorithm. The PDR and the power consumption showed in some cases better results under the HBC trickle compared to other trickle timers and in other cases the results were very close to the original trickle indicating the efficiency of the proposed trickle in choosing optimal routes when sending messages.
Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies
Authors: El Midaoui Omar , El Ghali Btihal , El Qadi Abderrahim
Published: February 2021

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.
Comparison of mutual information and its point similarity implementation for image registration
Authors: Chehade Wassim El Hajj , Rogelj Peter
Published: February 2021

Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to the best match. This paper shows that this presumption is not always valid and this leads to registration error. To overcome this problem, we propose to use point similarity measures (PSM) which in contrast to MI allows constant intensity dependence estimates called point similarity functions (PSF). We compare MI and PSM similarity measures in terms of registration misalignment errors. The result of the comparison confirms that the best alignment is not at the highest value of MI but near to it and it shows that PSM performs better than MI if PSF matches the correct intensity dependence between images. This opens a new direction of research towards the improvement of image registration.
Fully automatic segmentation of intima/adventitia of the vessel using Bezier curve from intravascular ultrasound
Authors: Kim Kwang Baek , Song Doo Heon
Published: February 2021

Although medical image segmentation field is regarded as one of most established fields, still fully automatic segmentation to extract target object with high accuracy from intravascular ultrasound (IVUS) is very active area of research. In this paper, we propose a fully automatic morphological approach using Bezier curve in interpolating the boundaries of intima/adventitia of the vessel from IVUS with careful binarization algorithms. In experiment with 800 IVUS images, the proposed method is as good as fuzzy C-means based approach in comparison with human expert’s result with 84.4% satisfaction and better than other morphological method in all performance indices of curve fitting with 97.02% in accuracy and 58.19% in precision.
Gender inequality among champions and players’ reception of gender disproportion of utility support champions in league of legends
Authors: Song Doo Heon , Rhee Hae Kyung , Kim Jeong Hoon
Published: February 2021

Designing female character in video game has been criticized as being sexually objectified and underrepresented in quantity (number of characters and their appearance rate in the game) and in quality (take only secondary role and inferior ability statistics given). In this paper, we analyze world leading multiple-user online battle arena game league of legends to see if previously criticized gender inequality of champions still stands and conduct a survey of 1,403 players of that game and asks how they feel about serious gender disproportion of utility support champions (all females). The result shows that league of legends still has serious gender disparity in performance parameters and there has been only a small change in 5-year span (2014-2019). The survey result tells us that game players also feel political incorrectness of such gender disproportion, but they accept such gender prototype because they have been taught as such as social role theory explains gender inequality issues.
Assessing Knowledge and Predicting Grief Counseling Skills Among University Counseling Center Therapists
Authors: Jankauskaite Greta , O’Brien Karen M. , Yang NaYeon
Published: February 2021

This mixed-method study advances knowledge regarding the practice of grief counseling in a sample of 171 university counseling center therapists. First, several components of therapists’ self-reported work with grieving clients was assessed (e.g., training level, initial responses to a grieving client, principles applied in counseling, therapist comfort, and potential therapist issues related to counseling grieving clients). Second, grounded in the death competence model (Gamino & Ritter, 2012), predictors of perceived grief counseling skills were examined. Cognitive competence and emotional competence predicted perceived grief counseling skills, with training/experience being the most robust predictor. Notably, the participants in this study rarely received education regarding death, dying, and grieving in their graduate programs, and they indicated that their knowledge about grief counseling was insufficient. Moreover, the therapists’ qualitative responses to a case vignette were lacking in many grief-specific domains. Future directions for training, research, and clinical work are discussed.
Digital Stories Representing Agency Enhancement at Work
Authors: Hakanurmi Satu , Palonen Tuire , Murtonen Mari
Published: February 2021

This case study about agency enhancement at work in a business organization is based on narrative inquiry. After a staff development project lasting 2½ years, the employees produced digital stories concerning their meaningful moments at work. Through social interactional narrative analysis, multimodal transcription, and text analysis, we examined how agency was enhanced according the narratives. Agency enhancement involved the incoherency between present cognitive models, attitudes, and practices of work compared with inner or outer expectations. Employees used lifelong experiences in their digital stories, which provided a rich source of data, including the visuals and transcripts, offering a unique vantage point for narrative analysis. These digital stories revealed the sociocultural, transformative, and situational modalities of agency enhancement as well as the relationship between epistemic selves and sociocultural bindings in the reforming of agency.
Gender Differences in the Relationship between Anger and Aggressive Behavior
Authors: Fahlgren Martha K. , Cheung Joey C. , Ciesinski Nicole K. , McCloskey Michael S. , Coccaro Emil F.
Published: February 2021

Evidence is mixed regarding differences in prevalence of aggressive behavior, with many (though not all) studies suggesting that men are more aggressive than women. Furthermore, while aggression often occurs in response to provocation-induced anger, this relationship may be stronger for men; women may be more likely to engage in non-aggressive (e.g., affiliative) behaviors in response to provocation, particularly at low-level provocation. This study examined gender differences in aggression as well as differences in the relationship between anger and aggression for men and women. Adults ( N = 424) participated in a behavioral aggression task, and a subset of participants ( n = 304) completed a questionnaire assessing trait levels of anger as part of a larger study at a large midwestern university. Results indicated that while men and women aggressed at similar levels, aggression was significantly associated with trait anger for men only, at low levels of provocation, with only a trending relationship for high provocation. This suggests that while men and women may be equally aggressive in certain situations, this behavior may be differentially associated with anger.
Effects of Group Formation on Student Satisfaction and Performance: A Field Experiment
Authors: Müller Adrienne , Bellhäuser Henrik , Konert Johannes , Röpke René
Published: February 2021

This study analyzes the relation of group formation on outcomes of a 4-week online course for prospective students. Group formation was experimentally manipulated based on predefined criteria, personality traits conscientiousness and extraversion. As research questions, groups were considered advantageous if they were formed (a) heterogeneously in extraversion, and (b) homogeneously in conscientiousness. As a result, no uniform outcome was identified. Most variance could be explained on group level, but no significant main effect for experimental grouping was found. Significant interaction between both main effects hint that the results do not provide final answers, but guidance for further research.
Therapeutic Controversies in the Medical Management of Valvular Heart Disease
Authors: Barry Arden R. , Wang Erica H. Z.
Published: February 2021

Objective:

To evaluate the evidence for common therapeutic controversies in the medical management of valvular heart disease (VHD).

Data Sources:

A literature search of PubMed (inception to December 2020) was performed using the terms angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) and aortic stenosis (AS); and adrenergic β-antagonists and aortic valve regurgitation (AR) or mitral stenosis (MS).

Study Selection and Data Extraction:

Randomized controlled trials (RCTs) and meta-analyses conducted in humans and published in English that reported ≥1 clinical outcome were included.

Data Synthesis:

Nine articles were included: 3 RCTs and 1 meta-analysis for ACE inhibitors/ARBs in AS, 1 RCT for β-blockers in AR, and 4 RCTs for β-blockers in MS. Evidence suggests that ACE inhibitors/ARBs do not increase the risk of adverse outcomes in patients with AS but may delay valve replacement. β-Blockers do not appear to worsen outcomes in patients with chronic AR and may improve left-ventricular function in patients with a reduced ejection fraction. β-Blockers do not improve and may actually worsen exercise tolerance in patients with MS in sinus rhythm.

Relevance to Patient Care and Clinical Practice:

ACE inhibitors/ARBs and β-blockers can likely be safely used in patients with AS or AR, respectively, who have a compelling indication. There is insufficient evidence to recommend routine use of β-blockers in patients with MS without atrial fibrillation.

Conclusions:

Common beliefs about the medical treatment of VHD are not supported by high-quality data. There remains a need for larger-scale RCTs in the medical management of VHD.