bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Effect of Asperity Order on Mechanical Character of Joint Specimen from the Perspective of Damage
Authors: Yuan Zhouhao , Ye Yicheng , Luo Binyu
Published: August 2021

The joint morphology is multiscale. The effect of each asperity order on the mechanical properties of joints is different. The shear mechanical properties of joint specimens are related to its surface damage characteristics. At present, there are still few studies on the effect of roughness on the shearing mechanical properties of joint from the perspective of damage of each asperity order. In this paper, the standard roughness profile was chosen as initial morphology. The standard roughness profile was decomposed into waviness and unevenness by the method combine the ensemble empirical mode decomposition (EEMD) and the cut-off criterion. Then, the joint specimen which contains waviness and unevenness and the specimen which only contains waviness were prepared by the 3D engraving technology. The 40 sets of joint specimens with different asperity order were subjected to direct shear tests under different normal stresses. Based on the 3D scanning technology and ICP iterative method, the damaged area and the damage volume were calculated. Based on the damage volume data and the acoustic emission (AE) data, the effect of asperity order to the joint mechanical behaviour was studied. The results indicate that (1) under low normal stress, the unevenness plays a control role in the failure mode of the joint specimen. Under low normal stress, the joint surface containing only waviness exhibits slip failure, and the joint surface with unevenness exhibits shear failure. With the increase of the normal stress, the failure mode of the specimen containing only waviness changes from slip failure to shear failure; (2) the unevenness controls the damage degree of the joint specimen. The damaged area, damage volume, AE energy rate, and accumulative AE energy of the joint specimen with unevenness are larger than those of the specimen with only waviness, and this difference increases with the normal stress increase; (3) the difference between the joint specimen with unevenness and specimen with only waviness mainly exists in the prepeak nonlinear stage and the postpeak softening stage. The characteristic parameters of acoustic emission generated in the postpeak softening stage of the joint specimen with unevenness are greater than those of the specimen with only waviness. This phenomenon can be used to explain the stress drop difference at the postpeak softening stage; (4) the AE

b

value can be used to evaluate the damage of joint specimens. Analysing the damage difference of each asperity order under different normal stresses is of great significance to the analysis of the influence of the morphology of the joint surface on the mechanical properties of the joint.
An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities
Authors: Jin Yong , Yang Yiwen , Yang Baican , Zhang Yunfu
Published: August 2021

There is currently no fair, rational, or scientific approach for evaluating college teachers’ teaching abilities. Mathematical methods are frequently used to measure the teaching capacity of college instructors in order to make it more scientific. Traditional statistical analysis evaluation models, fuzzy evaluation methods, grey decision methods, and the analytic hierarchy process (AHP) are only a few examples. Because teacher assessment is a nonlinear problem, even though the preceding methods have produced some positive results, they are vulnerable to some subjectivity. In this paper, the neural network model is incorporated into the adaptive vector and momentum of the modified BP neural network of a gradient descent method to boost the model’s convergence speed, and the model is thoroughly researched to evaluate university teaching quality, and the network structure is omitted to address the complex nonlinear problem of college and university teaching quality assessment. The model’s comprehensive evaluation of teaching activities is then bolstered by the addition of new evaluation indexes to the existing ones.
An Implicit Preference‐Aware Sequential Recommendation Method Based on Knowledge Graph
Authors: Wang Haiyan , Yao Kaiming , Luo Jian , Lin Yi
Published: August 2021

Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference‐aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.
Artificial Intelligence‐ (AI‐) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming Networks
Authors: Rathee Geetanjali , Khelifi Adel , Iqbal Razi
Published: August 2021

The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud‐based services affect the real‐time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI‐based systems in multihoming big data with IoT‐ and AI‐integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT‐based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg‐Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI‐assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F‐measure.
Consumer Decision‐Making Power Based on BP Neural Network and Fuzzy Mathematical Model
Authors: Li Weijie
Published: August 2021

In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision‐making power of consumers, so it is of great practical significance to study the decision‐making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision‐making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision‐making reasoning and prediction of individual consumers in the network and the decision‐making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, behavioral intention = 5.72. Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.
Gene Correlation Guided Gene Selection for Microarray Data Classification
Authors: Yang Dong , Zhu Xuchang
Published: August 2021

The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.
Impact of IQ Imbalance on RIS‐Assisted SISO Communication Systems
Authors: Bouhlel Asma , Sakly Anis
Published: August 2021

Reconfigurable intelligent surface (RIS) for wireless networks has emerged as a promising future transmission technique to create smart radio environments that improve the system performance by turning the wireless channel into an adjustable system block. However, transceivers come with various hardware impairments, such as phase noise and in‐phase/quadrature‐phase imbalance (IQI). Hence, for robust configuration of RIS‐based communication under practical conditions, assuming the identical performance analysis when subject to IQI, will lead to inaccurate analysis. In this paper, the implementation of this novel transmission technique is thoroughly investigated under intensive realistic circumstances. For this purpose, based on the maximum likelihood (ML) detector, a novel analytical expression of average pairwise error probability under IQI is proposed and compared to the standard ML detector. Further, the proposed analytical approaches are confirmed by numerical simulations.
Successful Use of Kidneys from a Deceased Donor with Active Herpes Zoster Infection
Authors: Vinson Amanda J. , Chauhan Prakash , Daley Christopher , De Silva Himanthi , Tennankore Karthik K. , Bonnar Paul , West Kenneth A.
Published: August 2021

Background. The limited donor pool and increasing recipient wait list require a reevaluation of kidney organ suitability for transplantation. Use of higher infectious risk organs that were previously discarded may help improve access to transplantation and reduce patient mortality without placing patients at a higher risk of poor posttransplant outcomes. There is very little data available regarding the safe use of kidney organs from deceased donors with varicella zoster virus infection at the time of organ retrieval. Case Presentation. Here, we report a case of successful transplantation of both kidneys from a deceased donor with active herpes zoster infection at the time of organ retrieval. Recipients were treated preemptively with acyclovir. At 4 months posttransplant, both kidney recipients experienced no infectious complications and were off dialysis with functioning transplant grafts. Conclusions. The use of kidney organs from donors with active herpes zoster infection appears to be a safe option to expand the kidney donor pool.
Cooperative Offloading in D2D‐Enabled Three‐Tier MEC Networks for IoT
Authors: Wu Jingyan , Zhang Jiawei , Xiao Yuming , Ji Yuefeng
Published: August 2021

Mobile/multi‐access edge computing (MEC) takes advantage of its proximity to end‐users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device‐to‐device (D2D) cooperation can help to process small‐scale delay‐sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D‐enabled three‐tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three‐tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed‐integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D‐enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.
Parallel Genomic Alterations of Antigen and Payload Targets Mediate Polyclonal Acquired Clinical Resistance to Sacituzumab Govitecan in Triple-Negative Breast Cancer
Authors: Coates James T. , Sun Sheng , Leshchiner Ignaty , Thimmiah Nayana , Martin Elizabeth E. , McLoughlin Daniel , Danysh Brian P. , Slowik Kara , Jacobs Raquel A. , Rhrissorrakrai Kahn , Utro Filippo , Levovitz Chaya , Denault Elyssa , Walmsley Charlotte S. , Kambadakone Avinash , Stone James R. , Isakoff Steven J. , Parida Laxmi , Juric Dejan , Getz Gad , Bardia Aditya , Ellisen Leif W.
Published: August 2021

Sacituzumab govitecan (SG), the first antibody–drug conjugate (ADC) approved for triple-negative breast cancer, incorporates the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 (TOP1) inhibitor payload. We sought to identify mechanisms of SG resistance through RNA and whole-exome sequencing of pretreatment and postprogression specimens. One patient exhibiting de novo progression lacked TROP2 expression, in contrast to robust TROP2 expression and focal genomic amplification of TACSTD2/TROP2 observed in a patient with a deep, prolonged response to SG. Analysis of acquired genomic resistance in this case revealed one phylogenetic branch harboring a canonical TOP1E418K resistance mutation and subsequent frameshift TOP1 mutation, whereas a distinct branch exhibited a novel TACSTD2/TROP2T256R missense mutation. Reconstitution experiments demonstrated that TROP2T256R confers SG resistance via defective plasma membrane localization and reduced cell-surface binding by hRS7. These findings highlight parallel genomic alterations in both antibody and payload targets associated with resistance to SG.

Significance:

These findings underscore TROP2 as a response determinant and reveal acquired SG resistance mechanisms involving the direct antibody and drug payload targets in distinct metastatic subclones of an individual patient. This study highlights the specificity of SG and illustrates how such mechanisms will inform therapeutic strategies to overcome ADC resistance.

This article is highlighted in the In This Issue feature, p. 2355
Developing a parenting program for young couples
Authors: Hidayah Nurul , Fakhruddiana Fuadah , Maharani Ega Asnatasia
Published: August 2021

In support of developing a parenting program for young couples, this study conducted to determine whether and how significantly the module was affects parenting practice among young couples in a small groups. Data were collected through experimental research. The subjects of this research were young couples with requirements as follows: have children whose age is maximum five years old, graduated from high schools, able to communicate fluently, and are willing to attend all interventions and are not joining any similar training. The subjects were 26 parents who are domiciled in the Province of the Special Region of Yogyakarta, Indonesia and recruited through social media. The data analysis technique was a paired sample t-test. The result indicated -6.299 value t and value p=0.000 or (p0.01). It showed a significant development of parenting comprehension before and after training (mean pretest is about 62.13 and mean post-test is about 78.26). This parenting program module needs to be tested in the wider population to form into final results.
Career of horizontal education mismatch workers: Career competency, job crafting, and work engagement
Authors: Wardani Laila Meiliyandrie Indah , Sekarini Dyah Ayu , Syaputra Rahmat Dwi , Kartikawati Mayang Safira , Dawanti Rizki , Mulia Dian Din Astuti , Malek Mohd Dahlan A.
Published: August 2021

Education is one of many factors that has the biggest impact toward unemployment rate due to the fact that there are mismatches between educational background and the intended job, and it is named horizontal education mismatch. The employee who is run into educational mismatch condition is seen less-competent, less-qualified, and less accomplished associated with company and work engagement which should be owned by every employee, both supervisors and subordinates. The purpose of this study was to test out that Job Crafting can play a role as a relation mediator between career competencies and work engagement toward employees which run into horizontal education mismatch. This was quantitative research; with purposive sampling method to recruit the respondent. The respondent of this research was people with age range 17-65 years old and using Process v3.5 by Hayes, The Simple Mediation Model No.4. Considering the phenomenon of Horizontal Education Mismatch which has an impact on competency and work engagement. The uniqueness of this research was to pay attention to the suitability of educational background with the current occupation, which indirectly affects the competence of workers. The results of this study were in accordance with the aims and expectations of the researchers. The results of this study indicated that job crafting plays a role as a mediator in the correlation between career competencies and work engagement. Hopefully, it will be able to meet the competency needs of employees to increase employee engagement with the company.
Perspectives of Thai educators toward 21st century instruction
Authors: Prachagool Veena , Nuangchalerm Prasart
Published: August 2021

The rapidly changed in our society and education may influence to nature of learning and learning behaviors in young new generation. The 21st century learning is requiring educators to prepare our children by quality of learning and teaching. Teachers are key change agent to help learners live and learn with new learning environments. The study aimed to explore perspective of Thai educators toward 21st century learning and teaching by surveying method. The study investigated 41,991 educators who have responsibility or concerns in teaching and learning. Data were collected through online questionnaire and then analyzed by statistical package in terms of frequency and percentage. Finding revealed that 21st century learning and teaching in classroom should be conducted in appropriate ways, but a few misunderstand in support system and learning environment were reported. Educators should be discussed and prepared both teachers and learners to modern classroom.
Effect of guided-discovery technique on Colleges of Education Social Studies students’ learning outcome
Authors: Ayodele Abdulsalam Alliyu , Nasiru Olokooba Issa
Published: August 2021

This study examined the effect of Guided-Discovery (GD) technique on Colleges of Education Social Studies students’ learning outcome in Nigeria. The study employed 2x2x2 randomized research design. A total of 100 students participated in the study, consisted of 50 GD and 50 Lecture Technique. Social Studies Performance Tests (SSPT) was used to collect data. It was designed using National Commission for Colleges of Education curriculum and validated by four Social Studies experts. The reliability value of 0.82 was obtained for the instrument using Cronbach Alpha. ANOVA and t-test were used for data analyses. This study found that GD technique of teaching Social Studies was next in producing higher significant effect in students’ learning outcome than LT. Gender has no significant interactive effect on students’ learning outcome in Social Studies. The mode of entry had no significant interactive effect in the learning outcome of Colleges of Education Social Studies Students. The study concluded that GD was found to be more effective in enhancing Social Studies Students’ learning outcome in Colleges of Education. It was recommended that GD technique should be used in teaching Social Studies in Colleges of Education.
Opposition based red wolf algorithm for solving optimal reactive power problem
Authors: Kanagasabai Lenin
Published: August 2021

This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.
True power loss reduction by mountain zebra, augmented bat and improved kidney search algorithms
Authors: Kanagasabai Lenin
Published: August 2021

In this paper optimal reactive power problem is solved by mountain zebra algorithm (MZA), augmented bat algorithm (AB) and improved kidney search (IKS) algorithm. In the proposed algorithm, an intermediate state has been established at first, and then explores the intermediate state in order to obtain the global optima. Iterative local search implemented in this proposed algorithm. This technique enhances the search procedure in rapid mode. Then in this work, IKS algorithm has been proposed for solving optimal reactive power problem. In initial phase, a random population of probable solutions is created and re-absorption, secretion, excretion are imitated in the search process to check various conditions entrenched to the algorithm. The algorithm has been built to advance the search even a potential solution moved to waste (W) and it will be brought back to the filtered blood (FB). Glomerular filtration rate (GFR) test is utilized to verify the fitness of kidneys. Better efficiency of the proposed MZA, AB and IKS algorithm confirmed by successful evaluation in standard IEEE 14-bus, 118-bus, and 300-bus test systems. The results show that active power loss has been reduced.

 
Coordinated planning in improving power quality considering the use of nonlinear load in radial distribution system
Authors: Faraby Muhira Dzar , Penangsang Ontoseno , Wibowo Rony Seto , Fitriati Andi
Published: August 2021

Power quality has an important role in the distribution of electrical energy. The use of non-linear load can generate harmonic spread which can reduce the power quality in the radial distribution system. This research is in form of coordinated planning by combining distributed generation placement, capacitor placement and network reconfiguration to simultaneously minimize active power losses, total harmonic distortion (THD), and voltage deviation as an objective function using the particle swarm optimization method. This optimization technique will be tested on two types of networks in the form 33-bus and 69-bus IEEE Standard Test System to show effectiveness of the proposed method. The use of MATLAB programming shows the result of simulation of increasing power quality achieved for all scenario of proposed method.
Development of cost-effective phasor measurement unit for wide area monitoring system applications
Authors: Raju V. Vijaya Rama , Shree K. H. Phani , Kumar S. V. Jayarama
Published: August 2021

Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards.
Solving hybrid-vehicle routing problem using modified simulated annealing
Authors: Alsumairat Nour , Alrefaei Mahmoud
Published: August 2021

In this paper, we consider the hybrid vehicle routing problem (HVRP) at which the vehicle consumes two types of power: fuel and electricity. The aim of this problem is to minimize the total cost of travelling between customers, provided that each customer is visited only once. The vehicle departs from the depot and returns after completing the whole route. This optimization problem is solved using a modified simulated annealing (SA) heuristic procedure with constant temperature. This approach is implemented on a numerical example and the results are compared with the SA algorithm with decreasing temperature. The obtained results show that using the SA with constant temperature overrides the SA with decreasing temperature. The results indicate that SA with decreasing temperature needs twice the number of iterations needed by the SA with constant temperature to reach a near optimum solution.
Implementation and evaluation of a 2.4 GHz multi-hop WSN: LoS, NLoS, different floors, and outdoor-to-indoor communications
Authors: Chaoboworn Vasin , Sasiwat Yoschanin , Buranapanichkit Dujdow , Saito Hiroshi , Booranawong Apidet
Published: August 2021

In this paper, the communication reliability of a 2.4 GHz multi-hop wireless sensor network (WSN) in various test scenarios is evaluated through experiments. First, we implement an autonomous communication procedure for a multi-hop WSN on Tmote sky sensor nodes; 2.4 GHz, an IEEE 802.15.4 standard. Here, all nodes including a transmitter node (Tx), forwarder nodes (Fw), and a base station node (BS) can automatically work for transmitting and receiving data. The experiments have been tested in different scenarios including: i) in a room, ii) line-of-sight (LoS) communications on the 2nd floor of a building, iii) LoS and non-line-of-sight (NLoS) communications on the 1st floor to the 2nd floor, iv) LoS and NLoS communications from outdoor to the 1st and the 2nd floors of the building. The experimental results demonstrate that the communication reliability indicated by the packet delivery ratio (PDR) can vary from 99.89% in the case of i) to 14.40% in the case of iv), respectively. Here, the experiments reveal that multi-hop wireless commutations for outdoor to indoor with different floors and NLoS largely affect the PDR results, where the PDR more decreases from the best case (i.e., the case of a)) by 85.49%. Our research methodology and findings can be useful for users and researchers to carefully consider and deploy an efficient 2.4 GHz multi-hop WSN in their works, since different WSN applications require different communication reliability level.
Hybrid approach for disease comorbidity and disease gene prediction using heterogeneous dataset
Authors: K. S. Lakshmi , G. Vadivu
Published: August 2021

High throughput analysis and large scale integration of biological data led to leading researches in the field of bioinformatics. Recent years witnessed the development of various methods for disease associated gene prediction and disease comorbidity predictions. Most of the existing techniques use network-based approaches and similarity-based approaches for these predictions. Even though network-based approaches have better performance, these methods rely on text data from OMIM records and PubMed abstracts. In this method, a novel algorithm (HDCDGP) is proposed for disease comorbidity prediction and disease associated gene prediction. Disease comorbidity network and disease gene network were constructed using data from gene ontology (GO), human phenotype ontology (HPO), protein-protein interaction (PPI) and pathway dataset. Modified random walk restart algorithm was applied on these networks for extracting novel disease-gene associations. Experimental results showed that the hybrid approach has better performance compared to existing systems with an overall accuracy around 85%.
A new method for watermarking color images using virtual hiding and El-Gamal ciphering
Authors: Ayoob Noor Kadhim , Hussein Asraa Abdullah , Neamah Rusul Mohammed
Published: August 2021

One of the important issues in the era of computer networks and multimedia technology development is to find ways to maintain the reliability, credibility, copyright and non-duplication of digital content transmitted over the internet. For the purpose of protecting images from illegal usage, a watermark is used. A hidden digital watermark is the process of concealing information on a host to prove that this image is owned by a specific person or organization. In this paper, a new method has been proposed to use an RGB logo to protect color images from unlicensed trading. The method depends on retrieving logo data from specific locations in the host to form a logo when the owner claims the rights to those images. These positions are chosen because their pixels match the logo data. The locations of matching pixels are stored in a table that goes through two stages of treatment to ensure confidentiality: First, table compression, second, encoding positions in the compressed table through El-Gamal algorithm. Because the method depends on the idea of keeping host pixels without change, PSNR will always be infinity. After subjecting the host to five types of attack, the results demonstrate that the method can effectively protect the image and hidden logo is retrieved clearly even after the attacks.
Multiclassification of license plate based on deep convolution neural networks
Authors: Uthaib Masar Abed , Croock Muayad Sadik
Published: August 2021

In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data augmentation techniques (rotation, horizontal flip, zoom range) to increase the number of datasets. After that, we feed the augmented images into the convolution layer model, which consists of four blocks of convolution layer. For calculating and optimizing the efficiency of the classification model, a categorical cross-entropy and Adam optimizer used with a learning rate was 0.0001. The model's performance showed 99.17% and 97.50% of the training and validation sets accuracies sequentially, with total accuracy of classification is 96.66%. The time of training is lasting for 12 minutes. An anaconda python 3.7 and Keras Tensor flow backend are used.
Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training
Authors: Jouyban Morteza , Khorashadizade Mahdie
Published: August 2021

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.
An approximation of balanced score in neutrosophic graphs with weak edge weights
Authors: Srisarkun V. , Jittawiriyanukoon C.
Published: August 2021

Neutrosophic concept is known undirected graph theory to involve with complex logistic networks, not clearly given and unpredictable real life situations, where fuzzy logic malfunctions to model. The transportation objective is to ship all logistic nodes in the network. The logistic network mostly experiences in stable condition, but for some edges found to be volatile. The weight of these erratic edges may vary at random (bridge-lifting/bascule, ad hoc accident on road, traffic condition) In this article, we propose an approximation algorithm for solving minimum spanning tree (MST) of an undirected neutrosophic graphs (UNG), in which the edge weights represent neutrosophic values. The approximation upon the balanced score calculation is introduced for all known configurations in alternative MST. As the result, we further compute decisive threshold value for the weak weights amid minimum cost pre-computation. If the threshold triggers then the proper MST can direct the decision and avoid post-computation. The proposed algorithm is also related to other existing approaches and a numerical analysis is presented.
The adoption of bitcoins technology: The difference between perceived future expectation and intention to use bitcoins: Does social influence matter?
Authors: Almarashdeh Ibrahim , Eldaw Kamal Eldin , Alsmadi Mutasem , Alghamdi Fahad , Jaradat Ghaith , Althunibat Ahmad , Alzaqebah Malek , A. Mohammad Rami Mustafa
Published: August 2021

Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context.
Numerical approach of riemann-liouville fractional derivative operator
Authors: Albadarneh Ramzi B. , Batiha Iqbal M. , Adwai Ahmad , Tahat Nedal , Alomari A. K.
Published: August 2021

This article introduces some new straightforward and yet powerful formulas in the form of series solutions together with their residual errors for approximating the Riemann-Liouville fractional derivative operator. These formulas are derived by utilizing some of forthright computations, and by utilizing the so-called weighted mean value theorem (WMVT). Undoubtedly, such formulas will be extremely useful in establishing new approaches for several solutions of both linear and nonlinear fractionalorder differential equations. This assertion is confirmed by addressing several linear and nonlinear problems that illustrate the effectiveness and the practicability of the gained findings.
Image retrieval based on swarm intelligence
Authors: Khaleel Shahbaa I. , Khaled Ragad W.
Published: August 2021

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.
Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
Authors: Krishnan Surenthiran , Magalingam Pritheega , Ibrahim Roslina
Published: August 2021

This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification
Authors: Zawani Mahabob Noratikah , Yusoff Zakiah Mohd , Mohd Amidon Aqib Fawwaz , Ismail Nurlaila , Nasir Taib Mohd
Published: August 2021

Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.
Family planning instructor’s performance in the toddler family development program
Authors: Hutagalung Simon Sumanjoyo , Hermawan Dedy , Husin Amir
Published: August 2021

Family planning instructors are vital in implementing the toddler family development program considering their function. This article aims: i) Analyze the performance of family planning instructors in the implementation of the toddler family development program/bina keluarga balita (BKB); ii) Analyse the factors supporting and inhibiting the effectiveness of the performance of family planning instructors in the community. This research used quantitative methods through survey instruments with a population of all family planning instructors which implementing toddler family development program. The pandemic situation has made the data collection process carried out by online questionnaires with Google Forms application. In order to obtain the required data, data collection was carried out through a questionnaire with a sample of 84 respondents. Data analysis was performed through descriptive statistical analysis using MS.Excel and SPSS. The data analysis concluded that the performance of family planning instructors in managing the program is in the good category. In the process approach, effectiveness is shown from the fulfillment of these performance variables in the implementation of the program, so that by looking at the categories in the cumulative analysis that are in the good category.
Game addiction among adolescents and its’ health impacts
Authors: Djannah Sitti Nur , Tentama Fatwa , Sinanto Rendi Ariyanto
Published: August 2021

Online gaming is a self-entertaining activity and can be addictive. Game addiction has a negative impact. This study aimed to determine the health belief model of adolescent game disorders on health impacts. This study used a qualitative phenomenology method. Data obtained through semi-structured interviews. The sample consisted of six participants. The results showed thatall participants felt vulnerable to disease, then overall realized the dangers of playing games such as staying up late, delaying eating, and delaying urinating. Then all participants learn about the benefits of reducing playtime and understand the importance of rest. Overall, all participants had the same obstacles, namely the invitation to make friends, boredom, free time, fun playing games, and not control themselves and the trend of electronic sports. Then an action stimulus was obtained, namely that all participants experienced changes in sleep patterns. All participants were sure and wanted to change their habits and continue to try, but it wasn't easy. Suggestions for participants to maintain health by limiting their playing hours to at least one hour a day to meet their physical needs.