bluuebunny/crossref_metadata_2025 huggingface dataset:
Matching geometric and expansion characteristics of wild chaotic attractors
Authors: Jelleyman Hamish , Osinga Hinke M.
Published: January 2022

Wild chaotic attractors exhibit chaotic dynamics with a robustness property that cannot be destroyed with small perturbations. We consider a discrete-time system with the smallest possible dimension, namely, defined by a non-invertible map on the complex plane. For this map, wild chaos has been proven to exist in a small parameter region. Recently, it was conjectured to exist in a much larger region of parameter space, past a so-called backward critical tangency, at which a sequence of pre-images of a critical point converges to a saddle fixed point. Geometrically, a backward critical tangency leads to an abundance of homoclinic and heteroclinic tangencies between invariant manifolds of different dimensions, generating precisely what are believed to be the necessary ingredients for wild chaos. In this paper, we present corroborating evidence for this conjecture by computing Lyapunov exponents associated with the attractor. When the sum of the two (largest) Lyapunov exponents is positive, the dynamics is wild chaotic for this non-invertible map. We find that the zero-sum locus matches the locus of backward critical tangency, confirming its role as a boundary of existence of wild chaos.
Pennington Corner: Membership in the New Millennium
Authors: Calvo Roque J.
Published: January 2022

The author Alphonse Karr once wrote, “plus ça change, plus c’est la même chose” (the more things change, the more they remain the same), and that’s a reasonably accurate description of the Society’s membership benefits package for the coming millennium.* The desire to expand and improve Society programs has led to numerous changes in our meetings, publications, and online services. But as we plan for the next millennium, the Society continues to maintain certain fundamental principles that guide our decisions about ECS’s future.
CAD Fractal Art Pattern Design and Evaluation Based on RBF Network Algorithm
Authors: Liu Lina , Zhu Danjun
Published: January 2022

In order to effectively solve the problems of unreasonable design and low evaluation level in the process of fractal art pattern design and evaluation, a CAD fractal art pattern design and evaluation is proposed based on RBF network algorithm. Through the corresponding sequence sorting of RBF network algorithm, the image surface with the same CAD fractal art pattern design and evaluation attributes is divided into two parts. The main shape in the algorithm is mainly the node of feature tree. According to the later stage of image features, using high-level design basic knowledge and evaluation methods, RBF network algorithm is introduced into the process of original image design modification. Finally, the experimental results show that the algorithm used in this paper has certain advantages in CAD fractal art pattern design and evaluation.
Research on a Strategy of Improving Students’ Satisfaction with Teaching: An Empirical Study Based on Undergraduate Universities in Sichuan
Authors: Yao Anshou , Wu Junhua , Yang Yongpan
Published: January 2022

Objective. To understand the impact of platform selection on each influencing factor of students’ teaching evaluation. Method. The factor analysis method was used to analyze the questionnaire survey of students’ satisfaction with teaching evaluation in 16 undergraduate colleges and universities in Sichuan Province. The impact of five key factors on students’ satisfaction with teaching, i.e., teachers’ teaching ethics, teaching ability, teaching content, teaching method, and teaching effect, was explored. Results. It shows that platform selection has a significant impact on various factors in the whole online teaching process. Regarding the degree of impact of platform selection on students’ teaching evaluation satisfaction, senior students are slightly higher than junior students, boys are slightly higher than girls, and students majoring in science and technology are slightly higher than those in other disciplines. Conclusion. Due to the great influence of teaching platform selection on online teaching satisfaction, teachers must choose an appropriate teaching platform according to teaching needs.
Industry 4.0 Engineering Product Life Cycle Management Based on Multigranularity Access Control Model
Authors: Yu Longfei , Zhu Shifan
Published: January 2022

In order to improve the management efficiency of the safety status of Industry 4.0 engineering products, the multigranularity access control model (MGACM) Industry 4.0 engineering product life cycle management (PLM) is adopted to optimize the safety management mode of Industry 4.0 engineering products in this paper. The multigranularity access control model is constructed in this paper, which has strong nonlinearity and better fault tolerance. In addition, the parameters of PLM are optimized through the multiparticle access control model, and PLM search is enabled. Taking into account the slow and easy convergence of the multigranular access control model, a niche technology with full life cycle heterogeneity and elimination mechanism is proposed to solve the premature convergence problem of the multigranular access control model. The final simulation results of this paper show that, compared with traditional algorithms, the proposed multigranularity access control model is more reliable and effective and has faster convergence speed and higher management efficiency.
Observation Selection, Total Variation, and L-Curve Methods for LiDAR Data Denoising
Authors: Zhang Jie , Chan Pak Wai , Ng Michael K.
Published: January 2022

In this article, we propose a light detection and ranging (LiDAR) data denoising scheme for wind profile observation as a part of quality control procedure for wind velocity monitoring and windshear detection. The proposed denoising scheme consists of several components. (i) It selects LiDAR observations according to their SNR values so that serious noisy data can be removed. (ii) A polar-based total variation smoothing term is employed to regularize LiDAR observations. (iii) The regularization parameters are automatically determined to balance the data-fitting term and the total variation smoothing term. Numerical results for LiDAR data collected at the Hong Kong International Airport are reported to demonstrate that the denoising performance of the proposed method is better than that of the testing LiDAR data denoising schemes in the literature.
Research on Evolution Characteristics of Unloading Energy in Excavation Face of High-Stress Pillar
Authors: Liu Chenyang , Sun Guanghua , Liang Peng , Feng Yufan , Wang Yue , Zhang Jinxia
Published: January 2022

In the mining process of the stage empty field subsequent filling mining method, aiming at the problem of unloading failure and instability of the excavation face of the high-stress ore pillar, the theory of unloading stress component is introduced and combined with the energy release theory to explore the unloading failure mechanism of the excavation face of the ore pillar. The results show the following. (1) The degree of accumulation of strain energy in the pillar is related to the tangential stress. After the peak of the tangential stress, the energy changes from accumulation to release, indicating that energy release is the main cause of pillar damage. (2) In pillar unloading, there are 4 typical characteristic areas of stress during the loading process: crushing area, shaping area, elastic area, and original rock stress area. The energy accumulation area and the stress component area show good correspondence, and they all appear from the top to the bottom of the pillar. At 1/4, the starting point of unloading failure can be determined. (3) The mutation mechanism in the process of energy release is related to the time effect of tangential stress. On the contrary, the greater the tangential stress is, the longer the elastic zone lasts, and the more significant the energy accumulation is. Also, the increase in tangential stress will lead the energy release time becomes shorter, and a large amount of accumulated energy is released in a short time, causing local instability and destruction of the pillar, and eventually spreads to the whole.
LncRNA GAS5 Interacts with MicroRNA-10b to Inhibit Cell Proliferation and Migration and Induces Apoptosis in Colorectal Cancer
Authors: Yang Rui-qi , Jin Zhe-zhu , Jiang San-ya , Jin Yong-jun
Published: January 2022

Objective. The purpose of this study was to study the effects of the GAS5/microRNA-10b (miR-10b) axis on proliferation, migration, and apoptosis of colorectal cancer (CRC). Methods. The expression levels of GAS5 and miR-10b in CRC tissues and cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Wound healing experiment was used to detect the effects of GAS5 and miR-10b on the migration of CRC cells. The luciferase reporter gene experiment was used to verify miRNA targets. Immunohistochemical assay was used to detect the expression of proteins related to metastasis and apoptosis in tumor tissues. Results. The expression of GAS5 was downregulated in CRC tissues and cell lines. The overexpression of GAS5 can inhibit cell proliferation and progression, induce apoptosis in vitro, and inhibit the growth of CRC tumor in vivo. In contrast, the expression of miR-10b, a downstream target of GAS5, was increased in CRC tissues and cells. Suppression of the miR-10b gene can inhibit proliferation and metastasis and cause apoptosis of CRC cells. In addition, luciferase reports show that GAS5 inhibits the progression of CRC cells by binding to miR-10b. Rescue experiments showed that overexpressed miR-10b could reverse GAS5-mediated antitumor effect on CRC cells in vivo and in vitro. Conclusions. LncRNA GAS5 interacts with miR-10b to inhibit cell proliferation and migration and induces apoptosis in colorectal cancer. GAS5 and miR-10b could become potential therapeutic targets for CRC.
Research on Management Efficiency and Dynamic Relationship in Intelligent Management of Tourism Engineering Based on Industry 4.0
Authors: Hou Tianchen
Published: January 2022

The digital age of artificial intelligence marks the rapid development of tourism engineering and the gradual improvement of intelligent management theory. This study aims to solve the problems of low efficiency of dynamic relationship analysis and low data utilization in traditional intelligent management methods of tourism engineering. This work studies the dynamic optimization model of tourism engineering management theory based on the artificial intelligence data analysis model and designs the dynamic analysis model of tourism engineering management data based on the convolution neural network. The model can collect dynamic data information of tourism management from many aspects and can also be used to study and analyze human behavior patterns based on the convolutional neural network algorithm. According to the human behavior data analysis model and convolution neural network algorithm, this study formulates the real-time management data scheme of tourism engineering and better extracts the characteristic information of the dynamic data of tourism engineering management. The results show that the topology optimization model of tourism intelligent management based on the convolutional neural network achieves high feasibility, high data accuracy, and high response speed. It can improve the collaborative coupling relationship between management efficiency and dynamic data in tourism engineering management based on big data analysis technology. It realizes the effective combination of tourism management, digital management, and artificial intelligence algorithm.
Oxidative Stress Signaling Mediated Pathogenesis of Diabetic Cardiomyopathy
Authors: Tang Zhaobing , Wang Peng , Dong Chao , Zhang Juan , Wang Xiong , Pei Haifeng
Published: January 2022

As a serious cardiovascular complication, diabetic cardiomyopathy (DCM) refers to diabetes-related changes in myocardial structure and function, which is obviously different from those cardiomyopathy secondary to hypertension, coronary heart disease, and valvular disease. The clinical features of DCM are left ventricular hypertrophy, myocardial fibrosis, and impaired diastolic function. DCM will lead to cardiac dysfunction, eventually progress to cardiac arrhythmia, heart failure, and sudden cardiac death. At present, the pathogenesis of DCM is complex and not fully elucidated, and oxidative stress (OS), inflammatory response, glucolipid metabolism disorder, etc., are considered as the potential pathophysiological mechanisms. As a consequence, there is no specific and effective treatment for DCM. OS refers to the imbalance between reactive oxygen species (ROS) accumulation and scavenging, oxidation, and antioxidants in vivo, which is widely studied in DCM. Numerous studies have pointed out that regulating the OS signaling pathways and reducing the generation and accumulation of ROS are potential directions for the treatment of DCM. This review summarizes the major OS signaling pathways that are related to the pathogenesis of DCM, providing ideas about further research and therapy.
Multimedia Pop Music Teaching Model Integrating Semifinished Teaching Strategies
Authors: Dong Kangtan
Published: January 2022

In order to improve the effect of popular music teaching, this paper combines the intelligent music frame feature recognition technology and the semifinished product teaching strategy to construct a multimedia popular music teaching system. Moreover, this paper reexpresses energy detection and zero-crossing detection mathematically from the perspective of aggregation and proposes a new two-level detection method for music frames. In addition, this paper uses the fusion and expansion of the zero-crossing rate detection result to judge whether a piece of speech is a valid musical speech. Finally, this paper uses a four-tier architecture to design the system framework, constructs the system functional modules, builds a multimedia pop music teaching system that incorporates semifinished teaching strategies, and then validates the performance of the system. From the experimental research, it can be seen that the multimedia pop music teaching system integrated with the semifinished product teaching strategy proposed in this paper can play an important role in music teaching and effectively improve the effect of music teaching.
A Randomized Trial Comparing Suprachoroidal and Intravitreal Injection of Triamcinolone Acetonide in Refractory Diabetic Macular Edema due to Epiretinal Membrane
Authors: Abdelshafy Tabl Ahmed , Tawfik Soliman Tarek , Anany Elsayed Mohamed , Abdelshafy Tabl Marwa
Published: January 2022

Purpose. To compare the efficacy and safety of suprachoroidal and intravitreal injection of triamcinolone acetonide in pseudophakic patients with refractory diabetic macular edema (DME) due to epiretinal membrane (ERM). Study Design. This study is a randomized clinical trial (RCT). Participants. Twenty-three nonvitrectomized pseudophakic eyes of 23 subjects (9 M and 14 F with mean age: 54.8 years) with refractory DME due to ERM. Methods. The eyes were randomized to suprachoroidal triamcinolone acetonide injection 4 mg/0.1 ml (SCTA) (n = 13 eyes) or intravitreal triamcinolone acetonide 4 mg/0.1 ml (IVTA) (n = 10 eyes) and were evaluated at baseline and 1 and 3 months after injection to assess outcome measures. Main Outcome Measures. Changes in best-corrected visual acuity (BCVA) (primary outcome), central foveal thickness (CFT) by optical coherence tomography (OCT), and intraocular pressure (IOP) measurement (secondary). Results. Baseline median BCVA (logMAR) was 1.0 (range 0.8–1.0) in both groups, improved within the SCTA group to 0.8 on the 1st and 3rd months, while in the IVTA group, median BCVA changed to 0.8 and 0.9 on the 1st and 3rd months, respectively. No significant differences were noted between groups regarding BCVA at baseline (

P
=
0.927

), and 1st (

P
=
0.605

) and 3rd months (

P
=
0.313

). Regarding mean CFT, no significant differences were observed at baseline (

P
=
0.353

) and at the first month (

P
=
0.214

) between both groups, while at the third month, CFT was significantly higher in the IVTA group (385 um) than in the SCTA group (323 um) (

P
=
0.028

). Mean IOP was significantly higher in the IVTA group (15 mmHg) on 1st month than in the SCTA group (12 mmHg) (

P
=
0.011

); after 3rd month, IOP was significantly higher within the IVTA group (18 mmHg) than SCTA (14 mmHg) (

P
=
0.028

). No significant difference was noted between both groups at baseline IOP (

P
=
0.435

). Conclusions. Both SCTA and IVTA are effective in reduction of CFT and improvement of patients’ visual acuity, but with a higher recurrence rate and rise in IOP after IVTA when compared to SCTA. Both treatments have temporary effects with the possibility of recurrence of DME and the need for retreatment.
Parameter Control of Stochastic Neural Network System with Markov Switching
Authors: Hong Bo , Wang Hui
Published: January 2022

The stochastic control of Markov switching systems with time-delay feedback neural networks under the interference of external environment is studied in this paper. By designing a memoryless state feedback controller, a set of sufficient conditions for the stochastic stability of the switching system and the disturbance attenuation are obtained by using the stochastic assessment method and the linear matrix inequality, and the key

H

∞

control of this problem is realized.
The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining
Authors: Li Shengnan , Zheng Cong , Li Lixiang
Published: January 2022

Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.
Research on Supply Chain Risk Transmission Mechanism Based on Improved SIRS Model
Authors: Wang Hongchun , Zhang Xinyan
Published: January 2022

An improved SIRS model is constructed as a supply chain risk propagation model, based on the actual situation that node enterprises are eliminated in the supply chain network. From the perspective of the small-world network, simulation is carried out to analyze the risk transmission mechanism in the supply chain network and the influence of various parameters on the supply chain risk transmission and the node enterprises in the supply chain network. Research shows that the process of eliminating node enterprises is also a process of supply chain network’s adjustment and gradual stability. In order to reduce the negative impact of this process on the supply chain network, node enterprises should take effective measures based on their own conditions to improve risk prevention capabilities, coordinate risk transmission, and enhance risk immunity, so as to improve the resistance of the entire supply chain to risks.
The Effect of Skin-to-Skin Contact on Postoperative Depression and Physical Recovery of Parturients after Cesarean Section in Obstetrics and Gynecology Department
Authors: Zheng Ying , Xia Yanping , Ye Weijuan , Zheng Congxia
Published: January 2022

The effect of skin-to-skin contact (SSC) on postpartum depression and physical rehabilitation of cesarean section women was explored. 280 parturients undergoing cesarean section were selected and were rolled into two groups randomly. The parturients in the control group (Con group, 140 cases) received routine care, and those in the experimental group (Exp group, 140 cases) received SSC on the basis of routine care. The postpartum depression and physical recovery of parturients in two groups were compared. It was found that, in the Exp group, the number of pregnant women with no or with very mild depression was much more 85% vs. 55%), the number of women with adequate lactation was more (53 cases vs. 27 cases), the first lactation time (FLT) was dramatically shorter (

41.25
±
4.81
 
h

vs.

58.43
±
5.43
 
h

), the breastfeeding success rate (BFSR) and breastfeeding rate (BFR) were obvious higher, the days for uterine involution was much shorter (

6.96
±
1.13

days vs.

9.47
±
1.75

days), the descent of uterine fundus 24 hours after delivery (24 h-DUF) was obviously larger (

3.17
±
0.26

 mm vs.

1.56
±
0.43

 mm), the duration of lochia (DOL) was remarkably shorter (

33.21
±
5.32

days vs.

25.32
±
3.54

days), and the Visual Analogue Scale (VAS) score was lower (

4.88
±
0.32

points vs.

3.41
±
0.53

points), showing statistical differences for all above indicators (

P
<
0.05

). It suggested that SSC could effectively relieve the postpartum depression of uterine parturients, promote the lactation, increase the BFR, facilitate uterine involution, and alleviate chronic uterine inflammation and postpartum pain, showing high clinical application and promotion value.
Image compression using singular value decomposition by extracting red, green, and blue channel colors
Authors: Abd Gani Shamsul Fakhar , Hamzah Rostam Affendi , Latip Ramlan , Salam Saifullah , Noraqillah Fatin , Herman Adi Irwan
Published: January 2022

This paper presents an image compression using singular value decomposition (SVD) by extracting the red, green, and blue (RGB) channel colors. Image compression is needed in the development of various multimedia computer services and applications for example in the telecommunications and storage technologies. Now a days, video technology, digital broadcast codec and teleconferencing become popular and always requires high image compression process for display. Hence, efficient image compression is compulsory to reduce the number of storage sizes and maintain the image quality. Therefore, this article proposes image compression using SVD, which this method is efficiently reducing the image storage size and at the same time maintaining the image quality. The SVD removes redundant pixel values based on RGB colors to make the storage image size decreased. Based on the experimental analysis on two different type of image extensions (i.e., jpg and png), the SVD is capable to reduce the image size and at the same time preserving the image quality.
Enhancing optical properties of WLEDs with LaOF:Eu3+&SiO2 application
Authors: Huu Phuc Dang , Nguyen Thi My Hanh
Published: January 2022

After many efforts, the core-shell nanostructure of LaOF:Eu3+SiO2 that emits bright red radiation can be fabricated by simple solvothermal application succeeded by heat treatment. The resulted particles from the fabrication process are small in size, able to demonstrate circular form more efficient and prevent stacking. Photoluminescence (PL) emission spectra exhibits intense peaks at 593 nm, 611 nm, 650 nm corresponds to 5D0 -- 7FJ (J = 0, 1 and 2) Eu3+ transitions respectively. The spectral intensity parameters and Eu-O ligand behaviors are estimated by means of Judd-Ofelt (J-O) theory. CIE co-ordinates are found to be (x = 0.63, y = 0.36) which is very close to standard NTSC values (x = 0.67, y = 0.33). CCT value is 3475 K which is less than 5000 K, as a result this phosphor is suitable for warm light emitting diodes. The optimized core-shell SiO2 (coat III)@LaOF:Eu3+ (5 mol%) was used as a fluorescent labeling marker to identity latent fingerprints on both porous and non-porous surfaces. The fingerprints results are highly sensitive, selective and also has no obstruction caused by the back-ground which supports level-I to level-III fingerprint ridge recognition. The experiments outcomes suggest that the enhancements brought by the core-shell NS structure can be further examined to apply in forensic and solid state lightning applications.
Intelligent mobile detection of cracks in concrete utilising an unmanned aerial vehicle
Authors: Ali Alheeti Khattab M. , Al-Ani Muzhir Shaban , Najem Al-Aloosy Abdulkareem Kareem , Alzahrani Abdulkareem , Sattar Rukan Duaa Abdul
Published: January 2022

Cracks in constructions may result in negative consequences in terms of expenditure and safety. This in turn highlights the importance of finding ways to detect these cracks easily and effectively. Hence, technological advances play significant role in enabling effective and innovative ideas, such as the use of autonomous drones and artificial intelligence solutions. In this research, we utilise a type of drone called an unmanned aerial vehicle, equipped with a high-speed camera that can capture images of cracks in buildings, and pass the information to the system. We utilised a dataset that has images collected from different Middle East Technical University (METU) campus buildings with various concrete surfaces (with and without cracks). The crack detection approach uses statistical measures and a support vector machine that prevents overfitting, attains a good rate of accuracy, tackles problems in real-time, and can train a model when a small dataset exists. The combination of an unmanned aerial vehicle, artificial intelligence, and digital image processing gives excellent results. Performance metrics reported for seven rounds of experiments showed rates of accuracy in detection ranging from 83.3–100% (with 100% achieved in two rounds). This demonstrates the effectiveness of our proposed detection system in detecting cracks in constructions.
Mobile learning by English literature students: the role of user satisfaction
Authors: Mussa Ibtihal Hassan , Sazalli Nurhasmiza Abu Hasan , Hassan Zainudin
Published: January 2022

The purpose of this study is to determine the predictors of using mobile learning (ML) among English literature students in Iraq. This study assumes that variables of UTAUT such as effort expectacny (EE) and performance expectancy (PE) as well as the subjective norms (SN) from theory of planned behavior (TPB) will affect the behavioral intention (BI) to use ML. BI and availability (FC) will affect the actual use (AU). In addition, user satisfaction is proposed as a mediating variable between PE, EE, and SN with BI. Data of this study was collected using a questionnaire from 240 undergraduate students. The data was analyzed using PLS. The findings showed that PE, EE and SN are important factors for the BI of ML. BI and FC significantly affected the AU. User satisfaction mediated only the effect of PE and EE on BI. Easier processes and instruction must be provided by univeristies to enhance the adoption of ML among English literature students.
Smart irrigation system with phovolataic supply
Authors: Hassan Elia Erwani , Chung Leong Lek , Sulaima Mohamad Fani , Bahaman Nazrulazhar , Abdul Kadir Aida Fazliana
Published: January 2022

Maximizing crop yielding is an extensive problem faced by the population in a country. The main issue comes from the farmer who still implemented the conventional method of irrigation that required human actions, especially for water pump operation. As an alternative, the automatic solution becomes a demand with the internet of things (IoT) support system to overcome the agriculture scenario. Meanwhile, multiple sensors controlled by the ESP32 microcontroller are also used to measure the crucial parameters that influenced the living conditions of crops and are called input parameters. Meanwhile, the implementation of a fuzzy logic controller is to control the timing of water volume based on the inputs data obtained through the sensors' responses. Solar energy is the main supply because of the zero-cost expense and environmentally friendly energy generation. In large, this research developed the smart irrigation system (SIS) with photovoltaic (PV) panels as a supply to sustain the energy required for empowering the entire process. As a result, the SIS is found as a successful system in controlling the best suitable time of water irrigation. The soil evaporation contents obtained from the experiment were also close to actual accurate data reference for Melaka state to verify the solution.
An automated navigation system for blind people
Authors: Rahman Md. Atiqur , Siddika Sadia , Al-Baky Md. Abdullah , Mia Md. Jueal
Published: January 2022

Proper navigation and detailed perception in familiar or unfamiliar environments are the main roles for human life. Eyesight sense helps humans to abstain from all kinds of dangers and navigate to indoor and outdoor environments. These are challenging activities for blind people in all environments. Many assistive tools have been developed by the blessing of technology like braille compasses and white canes that help them to navigate around in the environment. A vision and cloud-based navigation system for the visually impaired or blind person was developed. Our aim was not only to navigate them but also to perceive the environment in as much detail as a normal person. The proposed system includes ultrasonic sensors detecting obstacles, stereo camera to capture videos to perceive the environment using deep learning algorithms. Face recognition approach identified known faces in front of him. Blind people interacted with the whole system through a speech recognition module and all the information was stored in the cloud. Web and android applications were developed to track blinds so that guardians were monitoring them while visiting and reached them in an emergency. The experimental results showed the proposed system could provide more plenty information and user-friendly interaction.
Breast cancer segmentation using K-means clustering and optimized region-growing technique
Authors: Abdulla Srwa Hasan , Sagheer Ali Makki , Veisi Hadi
Published: January 2022

Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.
Development of regional load management system based on rural, semi urban and urban loads-a critical analysis
Authors: Ganguly Ayandeep , Sil Arindam Kumar
Published: January 2022

The sharp rise in population during the last half century has created immense pressure on the resources required for generation of energy essential to lead a comfortable and healthy lifestyle. The drive towards 100% electrification in developing countries like India has also contributed to this increase in demand. Till recently, fossil fuel was use to supply the bulk of this power. Now, the world is moving more and more towards renewable energy. This paper presents a model where several regions are combined together based on the demand profile of the regions segregated as urban, semi urban and rural along with the flexibility to schedule loads on the basis of availability of renewable energy sources within the area of the regions. The main focus is on detailed neural-networking based load forecasting and developing a load management system to manage load based on availability of distributed generation capacity and available tariff system. A solution is proposed in this paper based on a new approach to answer load management on the basis of region, population demographics and per capita energy consumption. A considerable amount of improvement to manage demand is intended to be attained and has been demonstrated in this research work.
Identification of forensic artifacts from the registry of windows 10 device in relation to idrive cloud storage usage
Authors: Adesina Adesoji A. , Adebiyi Ayodele Ariyo , Ayo Charles K.
Published: January 2022

The accessibility of cloud storage over the internet as a result of cloud computing technology provides the opportunity to store, share and upload data online with the use of digital devices which can be accessed anytime and anywhere. These benefits can also be exploited by the cybercriminals to perform various criminal activities including storing and exchanging of illegal materials on cloud storage platforms. The logs of malicious usages can be obtained from the cloud service providers for forensic investigations but the privacy issue among other factors make it difficult for such logs to be shared. Therefore, there is a need to perform client-side forensics to be able to carry out forensic investigation on digital devices as related to the activities on cloud storage. This study identifies relevant artifacts that can be forensically extracted from the registry of a window 10 device that accessed iDrive cloud storage. The study explores different experimental setups for the forensic analysis and adopted an integrated conceptual digital forensic framework in the investigation process to detect relevant forensic artifacts from the registry of a windows 10 device. This study increases the knowledge of cloud storage forensics and the significance of registry analysis during digital investigations.
Explicit kissing scene detection in cartoon using convolutional long short-term memory
Authors: Muhammad Fadzli Muhammad Arif Haikal , Abu Hassan Mohd Fadzil , Ibrahim Norazlin
Published: January 2022

The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learning-based classifier to detect the kissing scene in the cartoon by using Darknet-19 for frame-level feature extraction, while the feature aggregation in the temporal domain is using convolutional long short-term memory (conv-LSTM). This paper also has discussed a few steps related to evaluation and analysis regarding the performance of the models. Extensive experiments prove that the proposed system provides excellent results of 96.43% accuracy to detect the kissing scene in the cartoon. Due to high accuracy performance, the model is suitable to be a kissing scene filter feature in a digital video player that may able to decrease the excessive exposure to explicit content for kids.
Model to enhance knowledge sharing process in academia during COVID-19
Authors: Alsulami Zainab Amin , Hashim Hayder Salah , Abduljabbar Zaid Ameen
Published: January 2022

Knowledge sharing (KS) is considered one of the essential processes of knowledge management (KM). Recently, the emerging Web 2.0 technologies were used by academicians and students for KS during the COVID-19 pandemic. The purpose of this study is to identify the factors that affect the knowledge-sharing process through Web 2.0 (KSWT 2.0) technologies and their impact on academic performance within higher educational institutions (HEIs) in Iraq. The technology-organization-environment (TOE) theory was used in this study to develop the conceptual framework. The population includes students and academicians at Basrah University in Iraq. Using stratified random sampling, a total of 415 academicians and students participated in this study. The results revealed that the most significant factors that positively affect KSWT 2.0 technologies were the technological factor such as relative advantage, compatibility. From the organisational factor academicians’ skills and knowledge, and from environmental factor, the peer pressure. In addition, the results proved that the KSWT 2.0 technologies positively maximised academic performance. The results presented in this study are valuable for Iraqi universities that implement or aim to use Web 2.0 technologies in the knowledge-sharing process.
Designing and building earthquake warning system with Arduino and web-based SES60 SLP sensor
Authors: Edidas Edidas , Putra Rusnardi Rahmat
Published: January 2022

Indonesia is a country that is crossed by a world ring of fire that has the potential for earthquakes and tsunamis. An earthquake is an occurrence of shaking the ground due to the sudden release of energy into the earth. Since earthquakes occur within an undetermined period, we need a device to detect earthquake vibrations as early as possible within few seconds and announce real-time warning information on the website. The monitor screen displays a flat, a slightly increasing, the alarm displays a sound "weak earthquake”, "moderate earthquake", "strong earthquake", "very strong earthquake”. When there are no earthquake waves, very small shaking, PGA200 gals, PGA400 gals, PGA600 gals and PGA800 gals respectively. The alarm announces watch out (hati-hati) for moderate earthquake PGA200 gals, caution (waspada) PGA400 gals and ask to evacuate for PGA600 gals. The earthquakes detecting devices are useful in reducing their negative impacts, for instance, fatalities and material losses. The development of this system is to provide faster and more accurate information, and we can access the information on the internet network with broad coverage. The system can send the information within 5 second to user.
Oil palm unstripped bunch detector using modified faster regional convolutional neural network
Authors: Aji Wahyu Sapto , Bin Ghazali Kamarul Hawari , Akbar Son Ali
Published: January 2022

The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we compared it to the faster region based convolutional neural networks (R-CNN) USB detector with the VGG16 standard backbone. Based on the validation test, the USB faster R-CNN detector with modified VGG16 can improve the performance of the USB faster R-CNN detection system based on the original VGG 16 backbone. The proposed system can work faster (100% faster) with an mAP value of 0.782 (7.42% more precise) than the USB Detector with the original VGG16. In the training process, the proposed system on the speed parameter has better training parameters, which is 58.9% faster, the total loss is smaller (43.4% smaller), and the proposed system has better best accuracy (98%) than the previous system (93%). Still, it has a smaller overlap bounding box (23.91% less).
An enhanced support vector regression model for agile projects cost estimation
Authors: Najm Assia , Zakrani Abdelali , Marzak Abdelaziz
Published: January 2022

The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.
Machine learning algorithms for electrical appliances monitoring system using open-source systems
Authors: Duong Viet Hoang , Nguyen Nam Hoang
Published: January 2022

Two main methods to minimize the impact of electricity generation on the environment are to exploit clean fuel resources and use electricity more effectively. In this paper, we aim to change the user's electricity usage by providing feedback about the electrical energy consumed by each device. The authors introduced two devices, load monitoring device (LMD) and activity monitoring device (AMD). The function of the LMD is to provide feedback on the operating status and energy consumption of electrical appliances in a home, which will help people consume electrical energy more efficiently. The parameters of LMD are used to predict the on/off state of each electrical appliance thanks to machine learning algorithms. AMD with audio sensors can assist LMD to distinguish electrical devices with the same or varying power over time. The system was tested for three weeks and achieved a state prediction accuracy of 93.60%.
Solving a traveling salesman problem using meta-heuristics
Authors: Nejad Anahita Sabagh , Fazekas Gabor
Published: January 2022

In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.
Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification
Authors: Mohamad Aseri Nur Azieta , Ismail Mohd Arfian , Fakharudin Abdul Sahli , Ibrahim Ashraf Osman , Kasim Shahreen , Zakaria Noor Hidayah , Sutikno Tole
Published: January 2022

The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.
A deep learning-based multimodal biometric system using score fusion
Authors: Medjahed Chahreddine , Rahmoun Abdellatif , Charrier Christophe , Mezzoudj Freha
Published: January 2022

Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work proposes efficent multimodal biometric systems based on matching score concatenation fusion of face, left and right palm prints. Multimodal biometric identification systems using convolutional neural networks (CNN) and k-nearest neighbors (KNN) are proposed and trained to recognize and identify individuals using multi-modal biometrics scores. Some popular biometrics benchmarks such as FEI face dataset and IITD palm print database are used as raw data to train the biometric systems to design a strong and secure verification/identification system. Experiments are performed on noisy datasets to evaluate the performance of the proposed model in extreme scenarios. Computer simulation results show that the CNN and KNN multi-modal biometric system outperforms most of the most popular up to date biometric verification techniques.