bluuebunny/crossref_metadata_2025 huggingface dataset:
Long Noncoding RNA TFAP2A-AS1 Suppressed Hepatitis B Virus Replication by Modulating miR-933/HDAC11
Authors: Cheng Yu , Shi Weiwu , Cui Xudong , Sun Lei , Nan Yi , Yao Hong , Fan Jian , Zhu LiYing , Yu Lei
Published: April 2022

Objective. Studies have shown that long noncoding RNAs (lncRNAs) play crucial roles in multiple tumor types and regulate various biological processes. The present study tried to study lncRNA TFAP2A-AS1 in HBV infection hepatocellular carcinoma. Methods. The level of TFAP2A-AS1 and miR-933 in HCC cell and samples were detected by qRT-PCR assay. Luciferase reporter gene assay was carried out to study the mechanism of TFAP2A-AS1 and miR-933. Cell proliferation was measured by CCK-8 assay. HBV DNA replication was detected by RT-qPCR. Results. We firstly demonstrated that TFAP2A-AS1 was downregulated in HCC cell lines and HBV-infected HCC samples compared with nontumor tissues. However, miR-933 was upregulated in HCC cell lines and HBV-infected HCC samples compared with nontumor tissues, and miR-933 was negatively associated with the expression of TFAP2A-AS1 in HBV-correlated HCC samples. TFAP2A-AS1 and HDAC11 expression was decreased and miR-933 was upregulated in the HBV-infected cell HepG2.2.15. TFAP2A-AS1 acted as a sponge for miR-933 and HDAC11 was one direct target gene for miR-933. Overexpression of TFAP2A-AS1 suppressed cell growth, HBV DNA replication, HbeAg, and HbsAg expression, while knockdown of TFAP2A-AS1 enhanced cell proliferation, HBV DNA replication, HbeAg, and HbsAg expression in HepG2.2.15 cell. In addition, ectopic expression of miR-933 promoted cell growth, HBV DNA replication, HbeAg, and HbsAg expression in HepG2.2.15 cell. TFAP2A-AS1 suppressed HBV replication and infection through regulating HDAC11. Conclusion. These data demonstrated that TFAP2A-AS1 acted crucial roles in the modulation of HbeAg and HbsAg expression and HBV replication and may be one potential target for HBV infection treatment.
Anticolon Cancer Targets and Molecular Mechanisms of Tao-He-Cheng-Qi Formula
Authors: Zhang Zexin , Lin Siqi , Liu Zifeng , Han Jun , Li Jing , Yu Yi
Published: April 2022

Background. Tao-He-Cheng-Qi Formula (THCQF) is a traditional Chinese medicine that has been proven to have antitumor effects. The aim of this study was to elucidate the molecular targets and mechanisms of THCQF against colon cancer and construct a prognostic model based on network pharmacology, bioinformatics analysis, and in vitro experiments. Methods. Potential THCQF compounds and targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine databases. Differentially expressed genes for colon cancer were screened in The Cancer Genome Atlas and Gene Expression Omnibus databases. The anticolon cancer mechanisms of THCQF were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking simulations and molecular dynamics analysis were used to evaluate the binding between target proteins and active compounds. Finally, the identified compounds were used to treat colon cancer cells from the HCT116 cell line, and expression of mRNA and protein after relevant posttreatment were tested using real-time polymerase chain reaction and western blotting. Results. A total of 27 anticolon cancer targets of THCQF were selected, among which four genes (CCNB1, CCNA2, IL1A, and MMP3) were shown to effectively predict patient outcomes in a prognostic colon cancer model. GO and KEGG enrichment analyses indicated that the activity against colon cancer of THCQF was associated with the interleukin (IL)-4 and IL-3 signaling pathways. Two compounds in THCQF, aloe emodin (AE) and quercetin (QR), were shown to efficiently bind to cyclin B1, the protein encoded by CCNB1. Finally, incubation of HCT116 cells with AE and QR significantly decreased CCNB1 mRNA expression and cyclin B1 levels. Conclusions. Taken together, the results indicate that AE and QR are the pivotal active compounds of THCQF, and CCNB1 is the main molecular target through which THCQF exerts its anticolon cancer effects. The study findings provide insight for studies investigating the anticancer effects of other traditional Chinese medicines.
Research and Design of High-Fidelity Experimental Bed System for Wireless Sensor Network
Authors: Huang Heqing , Liu Wenjing
Published: April 2022

Through in-depth analysis of the typical international wireless sensor network experimental system, the current design generally lacks the real-time diagnosis of the health state of the fault node and the experimental platform, the management and control mechanism to minimize the interference of the experimental task, and the highly integrated and friendly experimental entrance, which will have an obvious distortion impact on the experimental results. In view of the above shortcomings, on the basis of fully absorbing the design advantages in this research field, this paper studies and designs a high fidelity WSN experimental system, which covers the elements of open source, heterogeneity, analysis visualization, and experimental repeatability and innovates the mechanisms of real-time diagnosis, hierarchical parallel control, and TDMA to collect a large amount of experimental data. In order to support a variety of applications based on IEEE802.15.4 band, a high-fidelity and user-friendly experimental platform software and hardware system with 15 clusters and 130 heterogeneous nodes is studied and designed, which can simulate outdoor mesh networks with more than 6 hops. This paper is designed and implemented on Linux open-source platform. The whole system has low cost and can be replicated. The design results have good reference and application value for the research and design of wireless sensor network experimental system.
Fatigue Life Scatter Modeling and Failure Probability Prediction of Multisite Damage Structural Part under Variable Amplitude Load History
Authors: Qin Bo , Xie Liyang
Published: April 2022

An “index stress” (a constant amplitude cyclic stress) is introduced to characterize the scatter of the fatigue life under a variable amplitude load history. It is defined as a fatigue damage weighted average of the amplitudes of the individual stress cycles in a variable amplitude load history. The fatigue test results show that the scatter of fatigue life under variable amplitude load histories can be well characterized by such defined index stress. For probabilistic fatigue life prediction and failure probability estimation, a multiple-site damage structural part is taken as a series system, and the total probability theorem is applied to reflect the effect of load history uncertainty on failure probability.
Analysis of Cylindrical Damper Effects on Turbine Meters Accuracy in a Pulsating CNG Suction Line: An Optimal Design through CFD Simulations
Authors: Faramarzi Mostafa , Dinarvand Saeed , Farzaneh-Gord Mahmood , Tamim Hossein
Published: April 2022

Turbine meters are used in compressed natural gas (CNG) stations in abundance to measure the volumetric flow rate of the inlet gas. These types of meters are very sensitive to oscillating and pulsating flows. When the station’s reciprocating compressor starts to work, due to the piston and valve performance, pulsating flow will create in the suction line and downstream of turbine meter. This pulsation makes false pulses in the meter and finally makes difference between the measured gas flow from the metering of the gas company and the amount of gas that has been sold at the station. In this study, numerical simulation of dampeners has been investigated after turbine meter and before entering gas to compressor. The goal is eliminating or reducing the effects of operating compressor in CNG station on turbine meter and reducing measurement errors. Numerical simulation of dampener was investigated by Ansys-Fluent CFD software for a CNG station with the gas inlet pressure 250 psi, inlet and outlet pipe diameter of 2 inches. In this study, several parameters such as (1) height-to-diameter ratio in the cylinder, (2) distance between dampener and compressor, and (3) volume-to-minimum volume ratio according to API 618, have been checked. The numerical results show that by increasing height-to-diameter ratio up to 3, the pulsating amplitude on Plane 1 will be reduced, and this is the best ratio according to the present results. Also, by increasing the outlet pipe length, the pressure pulsation amplitude decreases from 3.4% of pressure line in 2 m length to 0.7% in 5 m length. Moreover, the results showed 2.4% increase and 1.9% decrease in the maximum and minimum pressure for CV = 1 and CV = 16.7, respectively. Comparing fluent numerical method with previous studies shows less than 2% difference that demonstrates the validity and reliability of present investigation.
AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease
Authors: Mahesh T. R. , Dhilip Kumar V. , Vinoth Kumar V. , Asghar Junaid , Geman Oana , Arulkumaran G. , Arun N.
Published: April 2022

As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.
Comparison of Male and Female Sexual Dysfunction between Hemodialysis and Peritoneal Dialysis in Patients with End-Stage Renal Disease: An Analytical Cross-Sectional Study
Authors: Zeighami Shahryar , Dehghankhalili Saeid , Heiran Khadije , Azarchehry Seyede Pegah , Heiran Alireza , Sayadi Mehrab , Azadian Fatemeh
Published: April 2022

Background. Maintenance dialysis is the most common treatment for end-stage renal disease (ESRD) patients. One of the most ignored but important health issue among dialysis patients is sexual dysfunction, which interferes with quality of life (QoL). Studies showed that the side effects of the two conventional methods of dialysis (hemodialysis (HD) and peritoneal dialysis (PD)) are different on a patient’s health. Therefore, we sought to compare the sexual dysfunction score, both male and female, between patients undergoing HD and PD. Methods. One hundred seventy adults (85 HD and 85 PD) subject with end-stage renal disease (ESRD) on dialysis for at least 2 months were included. For male subjects, the erectile function (EF) domain of the International Index of Erectile Function (IIEF) questionnaire was calculated. Moreover, the Female Sexual Function Index (FSFI) questionnaire was calculated for females. Data were analyzed via SPSS software. Two independent sample t-test with two-sided significance level of 5% was used for comparing the sexual dysfunction score between HD and PD patients. Results. Out of 170 patients with mean age of 49.34 ± 11.7 years, 52.9% were female. Better sexual function scores were obtained in the HD group’s females for desire, orgasm, and satisfaction domains, as well as the total score (

P

 = 0.03, 0.016, 0.02, and 0.039, respectively). The erectile function was significantly better in the PD group’s males (

P

). Conclusion. We found better sexual life in the HD group’s females and PD group’s males. Considering dialysis as a life-long treatment of CKD patients, this part of a patient’s life must be taken seriously by the healthcare providers to choose the most suitable method for patients based on their personalized conditions.
Research on Mental Health Education Model of College Students under the Background of Internet
Authors: Xia Xiaoxin
Published: April 2022

With the development of information technology, the Internet has been integrated into our life. The psychological education of college students, as the most active group in cyberspace, tends to be diversified, and they will inevitably face various psychological puzzles and conflicts. In order to improve the effectiveness of mental health education in colleges and universities in the network age, colleges and universities use the network platform to build online education mode, which has achieved initial results. This paper analyzes the development status of college students' mental health education in the Internet age, studies its existing problems and challenges, explores its future development trend, expounds the advantages of developing it, and puts forward the main strategies for strengthening it. In order to meet these challenges, college teachers should improve their cognition, constantly explore and develop in practice, actively introduce modern information technology, and give full play to the role of campus culture, so that mental health education can be smoothly carried out in schools.
An IL-2 Protein Therapeutic for Cancer that Gains Function on the Spot
Authors: Silva Daniel-Adriano
Published: April 2022

Recombinant human IL-2 (rhIL-2) is now rarely used to treat patients with cancer because it too often causes severe toxicities. In this issue, Nirschl and colleagues report the development and preclinical characterization of an engineered IL-2 prodrug called WTX-124 that activates in the tumor microenvironment and has minimal systemic toxicity. It will be intriguing to watch the translation of this approach to the clinic.

See related article by Nirschl et al., p. 581 (5).
Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading
Authors: Ajao Adewale Adeyinka , Abraham Ben Obaje , Osaghae Etinosa Noma , Olatunji Okesola , Ekong Edikan , Ademola Abdulkareem
Published: April 2022

Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
Exploiting user grouping and energy harvesting in downlink cellular system
Authors: Van Nguyen Minh-Sang , Dang Phuc Huu , Nguyen Nhan Duc
Published: April 2022

A mobile communication system combining energy harvesting with a cooperative nonorthogonal multiple access (NOMA) system is presented in this research. In the proposed scheme, the relay is assumed to have a limited power source, and it will harvest radio energy from the base station (BS) to serve the near and far users. In this scenario, we consider two possible situations during information transmission in the NOMA application system: perfect successive interference cancellation (SIC) and imperfect successive interference cancellation. The system performance is assessed primarily based on closed-form outage probability expressions. Numerical simulations are conducted to examine the outage probability of the proposed scheme and to verify the derived formulas. The study results have proved that the system performance is still good under the imperfect SIC condition, and several optimal parameters to improve the system performance have been found. Moreover, our research results have shown the superior performance of the proposed model compared with current orthogonal multiple access (OMA) networks.
A spectrum sensing approaches in cognitive radio network by using cloud computing environment
Authors: Jasim Sabbar Insaif , Akawee Mustafa Mahmood , Hasan Raed Abdulkareem
Published: April 2022

A spectrum agreement has failed to meet the demands of new applications due to the fixed spectrum allocation (FSA) concept. But current efforts are targeted towards the utilization of cognitive radio as a way of addressing the issue of resources deficiency. The number of radio spectrum users keeps increasing daily owing to the advancement in technology in all aspects of life; even the licensed band users are currently demanding for extension of their radio spectrum and to balance the congestion in radio spectrum, some users may have to be placed on other bands. This article focused on voids detection (via spectrum sensing) in radio spectrum and secondary user assignment in cloud computing. Spectrum sensing was approached in two was in this study-underlay and interweave spectrum allocation. Both approaches are evaluated using certain performance metrics, such as throughput enhancement and queuing time minimization.
Learning algorithm of artificial neural network factor forecasting power consumption of users
Authors: Shiralievich Tavarov Saijon , Ivanovic Sidorov Alexander , Mamanazarovna Shonazarova Shakhnoza , Olimovich Sultonov Olamafruz , Yunusov Parviz
Published: April 2022

Seasonal fluctuations in electricity consumption, an uneven load of supply lines reduce not only the indicator of energy efficiency of networks but also contribute to a decrease in the service life of elements of power supply systems. Revealing the patterns of such fluctuations makes it possible to build models of power consumption, predict its dynamics, which in general will contribute to ensuring the energy efficiency of urban electrical networks and increasing the reliability of power supply systems. A computational, computer and neural network model is proposed that allows to increase the accuracy of the forecast of electricity consumption by household consumers. Based on the developed mathematical model, taking into account the obtained factor coefficients - ti, h, c, s, k for 2020 for 9 cities of the Republic of Tajikistan, monthly coefficients characterizing the terrain conditions (αi)  were calculated. The results obtained using the proposed method was compared with the results of a computer and neural network model.
Wireless body-area network monitoring with ZigBee, 5G and 5G with MIMO for outdoor environments
Authors: Qasim Kamil Al-Asadi Ahmed Mohammed , Muttair Karrar Shakir , Wadday Ahmed Ghanim , Mosleh Mahmood Farhan
Published: April 2022

Wireless body area network (WBAN) works near or inside the human body and is characterized by its accuracy in sending and receiving data. It works for long hours and must be low in energy consumption. One of its problems is that the transmission and reception distance is few and does not exceed a few meters. We worked on simulations based on a network consisting of ZigBee or fifth-generation (5G) and 5G with multiple-input and multiple-output (MIMO) nodes to deliver information to the center (hospital) at a 2.4 to 2.8 or 5 GHz frequency to solve this problem. Suppose the sensors are connected to the Arduino, which in turn is connected to the transmitter connection. The proposed method transmits data obtained from the sensor that touched the patient by multi-node to the hospital. The suggested method shows the best scenario to reduce energy consumption based on the number of active nodes. Based on the results obtained, we have noticed that ZigBee devices reduce energy use, perform better, and significantly extend the life of the nodes. While 5G devices increased the response speed in transferring data. In addition, MIMO antennas have the advantage of adding more stability in the connection between nodes.
Cloud resources modelling using smart cloud management
Authors: Chyad Haitham Salman , Mustafa Raniah Ali , George Dena Nadir
Published: April 2022

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).
Design of FFNN architecture for power quality analysis and its complexity challenges on FPGA
Authors: Ekanthaiah Prathibha , Megra Mesfin , Manjunatha Aswathnarayana , Ramalingappa Likhitha
Published: April 2022

As we all know, power quality (PQ) issues are a major concern these days. Field programmable gate array (FPGA) are essential in PQ analysis, particularly in smart meters for data processing, storage, and transmission. One of the most significant advantages of FPGA is its reconfigurability, with vast hardware resources that can be used to implement complex as well as time-critical data processing units. Because the FPGA architecture supports fixed point arithmetic, data loss occurs in the data path unit, necessitating the realization of the PQ event detection module, and classification model to be more accurate than software implementation algorithms. The majority of the work reported, with feed forward neural network (FFNN) structure occupying large number of multipliers and adders for classification, most of the work reported has not addressed to minimize the data path resources for FFNN instead have addressed in improving classification accuracy. Based on these issues, this paper addresses the implementation challenges in FFNN architecture design by proposing improved and fast architectures. The proposed FFNN architecture design using optimum resources. The FFNN based classifier are designed to perform PQ event detection and classification with 99.5% accuracy. The FFNN processor operates at maximum frequency of 238 MHz.
Comparison study of channel coding on non-orthogonal multiple access techniques
Authors: Ibrahim Sarmad Khaleel , Ismaeel Nooruldeen Q. , Abdulhussien Saif A.
Published: April 2022

Some of the benefits of fifth-generation (5G) mobile communications include low latency, fast data rates, and increased amount of perceived service quality of users and base station capacity. The purpose of this paper is to solve some of the problems in the traditional mobile system by increasing the channel capacity, non-orthogonal multiple access (NOMA), has a chance of winning the race, power-domain NOMA (PD-NOMA) is widely used in but it requires a large power imbalance between the signals allocated to various users to work. This paper also proposes an improved mobile system model and compares it with a traditional mobile system, then evaluates the effect of channel coding types on the spectrum efficiency performance. A proposed mobile system relied on increasing the number of users as well as increasing the frequency spectrum and is also proposed to improve the error rate, which is incorporated into NOMA and orthogonal frequency division multiplexing (OFDM) schemes at the same time to provide great ﬂexibility and compatibility with other services, such as the 5G and sixth-generation (6G) systems. The mobile gully system (MGS) system is compared to a traditional system, the result is demonstrated that the proposed outperforms the orthogonal multiple access (OMA) system in terms of sum-rate capacity, and bit error rate (BER) performance.
AutoKeras and particle swarm optimization to predict the price trend of stock exchange
Authors: Fattah Doaa A. , Naim Amany A. , Desuky Abeer S. , Zaki Mervat S.
Published: April 2022

The stock price varies depending on time, so stock market data is time-series data. The prediction of the trend of a stock price is a more interesting topic for investors to take an investment decision in a specific stock. Prediction of stock price always depends on machine learning algorithms. In this work, optimizing deep neural network (DNN) is used for predicting if the close price is reached to the profit which is determined by the investor or not and improve the prediction accuracy. Particle swarm optimization (PSO) and auto machine learning (AutoML) are used as optimizers with DNNs. The methods are applied to data of nine companies in Indonesia and National Stock Exchange (NSE) of India. The data is got from yahoo finance. Based on the experimental results, AutoML of deep learning proved to have the best accuracy rate, which is varying from 81 percent to 92 percent across all companies, and the accuracy after optimizing DNNs using PSO is varying from 73 percent to 82 percent across all companies.
Scalable epidemic message passing interface fault tolerance
Authors: Kolisetty Soma Sekhar , Rao Battula Srinivasa
Published: April 2022

Resilience and fault tolerance are challenging tasks in the field of high performance computing (HPC) and extreme scale systems. Components fail more often in such systems, results in application abort. Adopting fault–tolerance techniques can be consistently detect failures and continue application’s execution even if the failures exist. A prominent parallel programming specification, message passing interface (MPI), as it would be used to implement failure detection and consensus algorithm in this paper. Although the MPI does not facilitate fault tolerant behavior, this work presents a fault tolerant, matrix based failure detection and consensus algorithm. The proposed algorithm uses Gossiping. To detect failures, randomised pinging will be applied during the execution of the algorithm by using piggybacked gossip messages. In order to achieve consensus on the failures in the system, failed processes’ information will be sent using the same piggybacked gossip messages to all the alive processes. The algorithm was implemented in MPI framework and is completely fault tolerant. The results exhibit all the MPI process failures were detected using randomised pinging and global consensus has achieved on failed MPI process in the system.
Corner detection in aerospace image by using an expandable mask
Authors: Hammed Alzaki Haider Makki , Hutaihit Maha Abbas , Shauchuk Оksana
Published: April 2022

An algorithm for searching the breaks in contours based on an expandable mask in the image is proposed. The essence of the algorithm is to eliminate straight contour lines using a form factor. Sequential application of a  pixel binary mask for each pixel of the contour, except the endpoints, and joining the unit elements to the mask (growing) in the vicinity of the pixel, for which it is impossible to determine whether the fracture using a mask  pixel. The analysis of the proposed algorithm is compared with Harris method when changing registration conditions of images, specially, brightness, contrast, and rotation. Shown, that the proposed algorithm is more stable with increasing contrast and has a shorter running time compared with Harris method on account of loss the stability with decreasing contrast.
Forecasting epidemic diseases with Arabic Twitter data and WHO reports using machine learning techniques
Authors: Bani Baker Qanita , Shatnawi Farah , Rawashdeh Saif
Published: April 2022

Twitter is one of the essential social media tools used by many people because they express their views, daily problems, and what they suffer from the health aspects. On Twitter, we can detect and track the spread of the most serious diseases like flu; by analyzing people's tweets and collecting reports from health organizations. In this paper, the data from Twitter was collected in the Arabic language related to the spread of influenza using many Arabic keywords. Then, we applied several machine learning algorithms, which are random forest, multinomial naïve bayes, decision tree, and voting classifier. We also found the correlation between the collected tweets and the reports collected from the World Health Organization (WHO) website according to three experiments. These experiments are: i) between the tweets and reports based on the 13 countries regardless of the time, ii) between the tweets and reports based on the Arab regions that depend on these countries' dialects irrespective of the time, iii) between all tweets and all reports based on the week number. The results from these experiments show that there is a strong correlation between the tweets and the reports, which means that the tweets and the WHO reports can together detect the flu outbreaks in the Arab world.
Processing time increasement of non-rice object detection based on YOLOv3-tiny using Movidius NCS 2 on Raspberry Pi
Authors: Budiyanta Nova Eka , Sereati Catherine Olivia , Manalu Ferry Rippun Gideon
Published: April 2022

This study aims to increase the processing time of detecting non-rice objects based on the you only look once v3-tiny (YOLOv3-tiny) model. The system was developed on the Raspberry Pi 4 embedded system with the Movidius neural compute stick 2 (NCS 2) implementation approach. Data object in the form of gravel on a bunch of rice in the video. The video data was obtained using a webcam with a resolution of 1920 x 1080 pixels with a total of 2759 frames. From the test results, frames per second (FPS) have increased by 1.27x in the Movidius NCS 2 implementation compared to processing using the central processing unit (CPU) from the Raspberry Pi 4. The object detection processing on video data is complete at 1871.408 seconds with 1.474 FPS using the CPU from the Raspberry Pi 4 and finished at 1477.141 seconds with 1.868 FPS using Movidius NCS 2. From these differences, it can be seen that the application of Movidius NCS 2 succeeded in increasing the object detection processing in this study by 26.69% with the YOLOv3-tiny model approach on the Raspberry Pi 4 embedded system.
Combined EEMD and ANN improved by GA for tourist visit forecasting
Authors: Latif Muhammad , Herawati Sri
Published: April 2022

This study has proposed forecasting tourist visits use an ensemble empirical mode decomposition (EEMD) and optimized artificial neural networks (ANN) using genetic algorithms (GA). The data used is monthly data on tourist visits in Sumenep Regency. The data was obtained from the Sumenep district government from January 2015 to December 2019. EEMD algorithm breaks down tourist visit data into several intrinsic mode function (IMF) and residues. Then, EEMD results was normalized and then learned using ANN. GA is used to optimize weight and bias of the ANN. Experiments carried out to analyze performance in forecast results of proposed method compared with the EEMD-ANN without optimization of the GA. The experimental results show that the proposed method has better performance, namely the error value is reduced by 37%, 21% for MSE, RMSE, respectively.
Improvement of energy consumption in MIMO with cognitive radio networks
Authors: Dawood Zinah Osamah , Fadhil Heba M.
Published: April 2022

The employment of cognitive radio (CR) is critical to the successful development of wireless communications. In this field, especially when using the multiple input multiple output (MIMO) antenna technology, energy consumption is critical. If the principal user (PU) is present, developers can utilize the energy detecting approach to tell. The researchers employed two distinct phases to conduct their research: the intense and accurate sensing stages. After the furious sensing step was completed, the PU user was identified as having a maximum or minimal energy channel. There are two situations in which the proposed algorithm's performance is tested: channels for fading AWGN and Rayleigh. When the proposed methods' simulation results are compared with conventional approaches, the complexity of MIMO was reduced by roughly 42% at low SNR levels. With 70 samples and an acceptable drop in detection performance, the results were suitable for further testing.
Reconfigurable software-defined radar testbed with built-in validation
Authors: Carlos Martínez Quintero Juan , Paola Estupiñán Cuesta Edith , Stiven García Ramírez Johan
Published: April 2022

This article presents a radar testbed for speed detection through micro-doppler effect in a controlled environment using software defined radio (SDR) technology. The target moves along a conveyor belt with software-controlled speed. The speed is detected by an SDR radar, and it is possible to compare it to an encoder-based sensor implemented on the testbed. The testbed as well as the SDR radar are reconfigurable and a (continuous wave) CW radar was implemented for the validation of the testbed; however, the testbed is not limited to this implementation. The testbed can be remotely operated because it includes the mechanism to move the target and control its velocity. The article shows the way in which the testbed was designed and implemented, the generation and processing of the radar signal using a limeSDR, and the validation of the radar measurements compared to the encoder-based speed sensor. The maximum speed obtained by the target in the testbed is 15.69cm/s. Results show a difference in the speed measured with the SDRadar of no more than 5% compared to the sensor measurememt. Results obtained allow characterizing the behavior of the SDR platform in the detection of low speeds.
Decision support for predicting revenue target determination with comparison of double moving average and double exponential smoothing
Authors: Khairina Dyna Marisa , Daniel Yulius , Widagdo Putut Pamilih , Maharani Septya , Shabrina Shabrina
Published: April 2022

The success of the company requires careful planning. Perusahaan Daerah Air Minum (PDAM) is a drinking water facility management company that plays an important role in supporting the smooth development of the region with the influence of revenue targets. Prediction of revenue targets is deemed necessary for accurate and effective decision making. Predictions are made by comparing the double moving average (DMA) and double exponential smoothing (DES) methods which refer to the actual data from the previous five (5) years. Measuring forecasting accuracy using mean absolute percentage error (MAPE) and assessing accuracy analysis results using tracking signal. Prediction test uses five (5) order values on DMA and five (5) alpha values on DES. Based on the test, it shows that the DMA has the advantage of a smaller MAPE value <10 with very good criteria and the results of the analysis of the pattern graph on the tracking signal that do not exceed the upper control limit (UCL) and the lower control limit (LCL). It is concluded that the DMA method is more recommended as a reference approach to support decisions to determine PDAM revenue targets and as a basis for planning and policy making to predict future revenue targets.
Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system
Authors: Anshori Mohamad Yusak , Shawyun Teay , Madrigal Dennis V. , Rahmalia Dinita , Susanto Fajar Annas , Herlambang Teguh , Adzkiya Dieky
Published: April 2022

Corona virus disease (COVID-19) have become a world health problem because they have attacked many people worldwide. Because this virus has spread massively in almost all countries, including Indonesia, the Indonesian government made some policies and rules to close down the hotels and restaurants to avoid the spread of COVID-19. Because of that, estimation of the number of closed down restaurants and hotels in Jakarta is vital for avoiding COVID-19 spreads further to other people, either domestic or foreign. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is chosen as the estimation method. In estimating the number of closed restaurants and hotels using ANFIS, supporting variables such as the amount of casualties in Jakarta, the amount of casualties in Indonesia, and the amount of casualties in the world is required. As a result, ANFIS can estimate the amount of closed down restaurants and hotels approaching the target. The simulations are organized by partitioning the dataset into two parts: data of (80%) and data of testing (20%). According to ANFIS simulations, ANFIS can estimate the number of closed down restaurants and hotels in training data with optimal RMSE equals 0.5324 and testing data with optimal RMSE equals 5.3198.
Toward a multitask aspect-based sentiment analysis model using deep learning
Authors: Tran Trang Uyen , Thi Hoan Ha Thanh , Dang Phuong Hoai , Riveill Michel
Published: April 2022

Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches.
Convolution neural networks for hand gesture recognation
Authors: Somanatti Umesha , Patil Basavaraj A. , Hadimani Lingaraj
Published: April 2022

Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using CNN with a trained model. The model was constructed using 1,100 gestures images. The recognition rate was obtained with 91% of accuracy. The proposed method was found to be highly efficient in distinguishing and classifying gestures.
Efficient histogram for region based image retrieval in the discrete cosine transform domain
Authors: Belalia Amina , Belloulata Kamel , Zhu Shiping
Published: April 2022

Recently, several approaches of content based-image retrieval (CBIR), based on the characteristics of discrete cosine transform (DCT), such as decorrelation and concentration of energy in only a few coefficients, have been proposed. To reduce the semantic gap between high level search and low level patterns, a new concept based on region based search region-based image retrieval (RBIR) has been proposed. Recently, we proposed to use shape-adaptive (SA) DCT in a new RBIR system. In this paper, we propose an efficient histogram optimization suited to our region-based concept. This histogram takes into account the pattern’s from the SA-DCT of the border blocks as well as the DCT coefficients of the internal blocks. Our proposed scheme has greatly improved the results compared to region-based reference methods. Regionbased search is limited to the object of interest only, i.e. a region of the query image can only match a region of another image in the database.
Information technology based smart farming model development in agriculture land
Authors: Al-Khowarizmi Al-Khowarizmi , Ridho Lubis Arif , Lubis Muharman , Fadillah Rahmat Romi
Published: April 2022

Smart farming in various worlds is not just about applying technology in terms of storing data on agricultural land. However, having a concept of measurable data based on available computational techniques trained and then generating knowledge. As an application, the agri drone sprayer can be used for the process of applying pesticides and liquid fertilizers on each side. In addition, drone surveillance is also useful in implementing smart farming such as mapping land so that farmers will know the condition of their agricultural land. However, the soil and weather sensor will also help the farmers to monitor the farmland as well. Devices with sensors can only obtain data in the form of air and soil humidity, temperature, soil pH, water content and forecasting the harvest period. So that the smart farming model can help farmers to get recommendations, in preventing the predicted damage to their land and crops. However, according to its geographical location, the application of smart farming can be a smart solution to agricultural problems in Indonesia and make the future of Indonesian Agriculture a technology-based smart agriculture.
Face detection and recognition with 180 degree rotation based on principal component analysis algorithm
Authors: Thary Al-Ghrairi Assad H. , Abdulwahhab Mohammed Ali , Sameen Esraa Zuhair
Published: April 2022

This paper presents a simple and fast recognition system with various facial expressions, poses, and rotation. The proposed system performed in two phases. Face detection is the first phase. The front and profile face detected cropped face area from the image by Viola-Jones algorithm and the right side face is detected from the image by taking the flip of the profile image. Principal component analysis (eigenfaces) algorithm is used in the recognition phase and depends on created database models used to be compared with test face image input to the recognition procedure. For training and testing the system, two sets of the image of the file exchange interface (FEI) database have been used to identify the person. The experimental result shows the effectiveness and robustness of the method used for the detection of the face and achieves high accuracy of 96%, which improves the recognition performance with low execution time. Furthermore, the accuracy of 35 trained images for recognition is 97.143% with average time execution which is (0.323657s). Also, the accuracy of 15 tested images for recognition is 93.315% with average time execution which is (0.3348s) which indicates a good and strong success and accuracy method for facial recognition.
Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming
Authors: Megat Amerudin Megat Nabil Irwan , Abdul Rahim Siti Khatijah Nor , Omar Nasiroh , Sulaiman Mohd Suffian , Jaafar Amir Hamzah , Hamzah Raseeda
Published: April 2022

Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.
A hunger game search algorithm for economic load dispatch
Authors: Aribowo Widi , Muslim Supari , Suprianto Bambang
Published: April 2022

This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.