bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification
Authors: Prabhakar Sunil Kumar , Rajaguru Harikumar , Won Dong-Ok
Published: October 2021

Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.
Research on X‐Ray Inspection of Basin Insulators and Wireless Image Sensing Technology
Authors: Yan Chunjiang , Zhao Liuxue
Published: October 2021

This paper presents an in‐depth study and analysis of X‐ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X‐ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo‐Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three‐dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X‐ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.
On the Reformulated Multiplicative First Zagreb Index of Trees and Unicyclic Graphs
Authors: Ali Akbar , Nadeem Atif , Raza Zahid , Mohammed Wael W. , Elsayed Elsayed M.
Published: October 2021

The multiplicative first Zagreb index of a graph

H

is defined as the product of the squares of the degrees of vertices of

H

. The line graph of a graph

H

is denoted by

L

H

and is defined as the graph whose vertex set is the edge set of

H

where two vertices of

L

H

are adjacent if and only if they are adjacent in

H

. The multiplicative first Zagreb index of the line graph of a graph

H

is referred to as the reformulated multiplicative first Zagreb index of

H

. This paper gives characterization of the unique graph attaining the minimum or maximum value of the reformulated multiplicative first Zagreb index in the class of all (i) trees of a fixed order (ii) connected unicyclic graphs of a fixed order.
Sine Half-Logistic Inverse Rayleigh Distribution: Properties, Estimation, and Applications in Biomedical Data
Authors: Shrahili M. , Elbatal I. , Elgarhy Mohammed
Published: October 2021

A new lifetime distribution with two parameters, known as the sine half-logistic inverse Rayleigh distribution, is proposed and studied as an extension of the half-logistic inverse Rayleigh model. The sine half-logistic inverse Rayleigh model is a new inverse Rayleigh distribution extension. In the application section, we show that the sine half-logistic inverse Rayleigh distribution is more flexible than the half-logistic inverse Rayleigh and inverse Rayleigh distributions. The statistical properties of the half-logistic inverse Rayleigh model are calculated, including the quantile function, moments, moment generating function, incomplete moment, and Lorenz and Bonferroni curves. Entropy measures such as Rényi entropy, Havrda and Charvat entropy, Arimoto entropy, and Tsallis entropy are proposed for the sine half-logistic inverse Rayleigh distribution. To estimate the sine half-logistic inverse Rayleigh distribution parameters, statistical inference using the maximum likelihood method is used. Applications of the sine half-logistic inverse Rayleigh model to real datasets demonstrate the flexibility of the sine half-logistic inverse Rayleigh distribution by comparing it to well-known models such as half-logistic inverse Rayleigh, type II Topp–Leone inverse Rayleigh, transmuted inverse Rayleigh, and inverse Rayleigh distributions.
Wireless Sensor Acquisition of Human Motion Parameters Based on Blockchain
Authors: Peng Daibin , Liu Yonghong
Published: October 2021

With the rapid development of intelligent technology, people’s lives have gradually entered the era of information and intelligence. Traditional vision‐based and sensor‐based human motion detection methods have shortcomings such as high illumination requirements, difficulty in preserving privacy, and poor portability. The wireless sensor detection method based on a wireless network can overcome the shortcomings of these methods, so it has become a hot research direction. Human motion parameters are very important data. If leaked, it may cause unimaginable harm to individuals. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and data is not easy. The modification provides a feasible solution for us to realize the data upload. Combining the advantages of wireless sensing and blockchain technology, this paper proposes a method of wireless sensing collection of human motion parameters based on blockchain, and designs a human motion recognition system. The experiment found that under the experiments given in this article, the traditional accuracy rate of sensor data collection is 71.82%, and the accuracy rate of the wireless sensing collection method of human motion parameters based on the blockchain in this article has reached 95.31%, which verifies the effectiveness and superiority of the system designed in this article.
Measurements and Characterization for Millimeter-Wave Massive MIMO Channel in High-Speed Railway Station Environment at 28 GHz
Authors: Li Shuangde , Liu Yuanjian , Lin Leke , Sun Qi
Published: October 2021

The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. Therefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28 GHz. The multipath components (MPCs) parameters are estimated for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). The correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. The large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. The Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using the multipath component distance (MCD). The analysis of these results from mmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28 GHz.
A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network
Authors: Yu Naigong , Yu Hejie , Liao Yishen , Wang Zongxia , Sie Ouattara
Published: October 2021

Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.
Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19
Authors: Chen Yanhui , Zhang Minglei , Lo Kai Lisa , Mi Jackson Jinhong
Published: October 2021

This study proposes to use the fractional-order accumulation grey model (FGM) combined with the fractional-order buffer operator to predict the cumulative confirmed cases in different stages of COVID-19. In the early stages of COVID-19 outbreak, when the cumulative confirmed cases increased rapidly, we used the strengthening buffer operator in the prediction process. After the government’s prevention measures started to take effect, the growth rate of cumulative confirmed cases slows down. Therefore, the weakening buffer operator is applied in the prediction process. The fractional order of the buffer operator is derived from the historical data, which are more relevant. The empirical analysis of seven countries’ data shows that the FGM with the fractional-order buffer operator achieves the best results for most cases. The fractional-order buffer operator improves the prediction accuracy of the FGM in this study. Our study also suggests a practical way for predicting the trend of epidemic diseases.
Improving the Security and Confidentiality in the Internet of Medical Things Based on Edge Computing Using Clustering
Authors: Hatamian Anita , Tavakoli Mohammad Bagher , Moradkhani Masoud
Published: October 2021

Families, physicians, and hospital environments use remote patient monitoring (RPM) technologies to remotely monitor a patient’s vital signs, reduce visit time, reduce hospital costs, and improve the quality of care. The Internet of Medical Things (IoMT) is provided by applications that provide remote access to patient’s physiological data. The Internet of Medical Things (IoMT) tools basically have a user interface, biosensor, and Internet connectivity. Accordingly, it is possible to record, transfer, store, and process medical data in a short time by integrating IoMT with the data communication infrastructure in edge computing. (Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. A common misconception is that edge and IoT are synonymous.) But, this approach faces problems with security and intrusion into users’ medical data that are confidential. Accordingly, this study presents a secure solution in order to be used in the IoT infrastructure in edge computing. In the proposed method, first the clustering process is performed effectively using information about the characteristics and interests of users. Then, the people in each cluster evaluated by using edge computing and people with higher scores are considered as influential people in their cluster, and since users with high user interaction can publish information on a large scale, it can be concluded that, by increasing user interaction, information can be disseminated on a larger scale without any intrusion and thus in a safe way in the network. In the proposed method, the average of user interactions and user scores are used as a criterion for identifying influential people in each cluster. If there is a desired number of people who are considered to start disseminating information, it is possible to select people in each cluster with a higher degree of influence to start disseminating information. According to the research results, the accuracy has increased by 0.2 and more information is published in the proposed method than the previous methods.
Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology
Authors: Zhao Qiang
Published: October 2021

The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.
“Yiqi Huayu, Wenyang Lishui” Prescription (YHWLP) Improves the Symptoms of Chronic Obstructive Pulmonary Disease-Induced Chronic Pulmonary Heart Disease by Inhibiting the RhoA/ROCK Signaling Pathway
Authors: Huang Hui , Shan Kuizhong , Cai Min , Chen Hong , Wu Fengmei , Zhao Xiaoyan , Zhuang Huawei , Li Hong , Shi Suofang
Published: October 2021

Background. Chronic pulmonary heart disease (CPHD) is a common type of heart disease. In China, chronic obstructive pulmonary disease (COPD) is one of the main causes of CPHD. At present, there is no specific therapy for COPD-induced CPHD, so it is of great importance to identify a new therapy for CPHD. Objective. The purpose of this study was to explore the effects of “Yiqi Huayu, Wenyang Lishui” prescription (YHWLP) on CPHD symptoms. Methods. Eighty patients with COPD-induced CPHD were randomly divided into the control group and the YHWLP group, both involving treatment for 3 months. Both groups were treated with Western medicine, and the YHWLP group was also treated with YHWLP. The changes (relative to baseline) in the symptoms, pulmonary arterial pressure, prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen (Fbg), D-dimer (D-D), and ratio of phosphorylated (p)-myosin-binding subunit (MBS)/total (t)-MBS in peripheral blood (which indirectly indicates the activation/inhibition of RhoA/ROCK signaling) were compared between the two groups. Results. YHWLP plus Western medicine was superior to Western medicine alone at reducing symptoms, pulmonary arterial pressure, PT, aPTT, Fbg, D-D, and p-MBS/t-MBS. Conclusion. YHWLP can relieve CPHD by inhibiting the RhoA/ROCK signaling pathway, which means YHWLP is a potential treatment for CPHD.
HES5 Activates Long Noncoding RNA UCA1 to Induce Colorectal Cancer Progression by Modulating miR-185/NOTCH3 Signaling
Authors: Wang Mingming , Zheng Qinlin , Zhao Zhengfei , Deng Hong , Zhang Qiang , Yao Hui
Published: October 2021

Colorectal cancer (CRC) is one of the most common diagnosed cancers around the world. The poor prognosis and high fatality caused by metastasis are still the challenges for clinical treatment. Therefore, it is promising to clarify the detailed molecular mechanism of CRC metastasis. Accumulating evidences indicate that long noncoding RNAs (lncRNAs) play important roles in cancer progression including CRC. In this study, the function of lncRNA UCA1 was investigated. UCA1 was confirmed to be highly expressed in colorectal cancer. Moreover, the UCA1 expression level was positively related to tumor stages. Silencing UCA1 showed inhibitory effect on cell proliferation and metastasis. Both UCA1 and NOTCH3 were validated as direct targets of miR-185. Silencing UCA1 repressed NOTCH3 expression through the miR-185 sponge. NOTCH3 was found to be highly expressed in CRC patients and positively related to UCA1 expression. Furthermore, HES5 was verified as a transcription factor of UCA1, which induced UCA1 expression. In conclusion, UCA1 is a direct target of HES5. UCA1 promotes CRC metastasis through regulating the miR-185/NOTCH3 axis.
Activated Human Umbilical Cord Blood Platelet-Rich Plasma Enhances the Beneficial Effects of Human Umbilical Cord Mesenchymal Stem Cells in Chemotherapy-Induced POF Rats
Authors: Wang Jiawei , Zhao Yunxiao , Zheng Fengque , Ma Nana , Qin Rongyan , Qin Weili , Liu Bo , Qin Aiping
Published: October 2021

Saving the ovarian function of premature ovarian failure (POF) patients undergoing chemotherapy is an important problem in the field of reproductive medicine. At present, human umbilical cord mesenchymal stem cells (HucMSCs) have been used in the treatment of POF, but the effect is still not optimal. The purpose of this study was to determine whether human umbilical cord blood platelet-rich plasma (ucPRP) enhances the beneficial effects of HucMSCs in the treatment of POF. First, we observed the effects of changes in the biological activity of ucPRP on HucMSCs in vitro. Subsequently, we tracked the distribution and function of the HucMSCs in POF rats, and the rats’ estrus cycle and serum sex hormones, follicular development, ovarian angiogenesis, ovarian granulosa cell proliferation, and apoptosis were assessed. The results of the study showed that the addition of ucPRP in vitro accelerates proliferation and reduces apoptosis of the HucMSCs while upregulating the stemness gene of the HucMSCs. The combined transplantation of HucMSCs and ucPRP resulted in more stem cells being retained in the ovaries of POF rats, the estrus cycle of the POF rats being restored, the levels of serum E2, AMH, and FSH improving, and damaged follicles beginning to grow. Finally, we confirmed that the potential mechanism of the combination of HucMSCs and ucPRP to rescue the ovarian function of POF rats is to promote ovarian angiogenesis and to promote the proliferation and reduce the apoptosis of ovarian granulosa cells. The upregulation of AMH and FHSR expression and the downregulation of caspase-3 expression in granulosa cells are potential mechanisms for the recovery of ovarian function. Our research results suggest that the combined application of HucMSCs and ucPRP is a safe and efficient transplantation program for the treatment of POF, thus providing a reliable experimental basis for the clinical application of stem cell therapy in POF.
Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model
Authors: Zhang Bingjie , Liu Yiming
Published: October 2021

With the rapid development of information society, a large amount of vague or uncertain English interpretation information appears in daily life. Uncertain information processing is an important research content in the field of artificial intelligence. In this paper, we combine the three-branch concept lattice and linguistic values with the digital elevation model and propose the three-branch fuzzy linguistic concept lattice as well as the attribute approximation method. In this paper, an improved serial algorithm for sink accumulation is proposed. The improved algorithm changes the order of cell calculation; after the cumulative amount of a “sub-basin” is calculated, all cells of the next “sub-basin” are calculated until all cells are calculated. The improved algorithm reduces the overhead space in the calculation process, reduces the pressure of cells entering and leaving the queue, and improves the calculation efficiency. The improved cumulant algorithm is compared with the commonly used recursive cumulant algorithm and the nonrecursive cumulant algorithm, and the improved algorithm improves by about 17% compared with the nonrecursive algorithm at 106 cell level, and the computation time of the recursive algorithm is about 3 times of the improved algorithm. Because the sink accumulation serial algorithm is an important part of the parallel calculation of sink accumulation, and the execution time is shorter by using the improved algorithm, this paper applies the proposed improved accumulation serial algorithm to the process of parallel calculation of accumulation.
Human Umbilical Cord-Mesenchymal Stem Cells Survive and Migrate within the Vitreous Cavity and Ameliorate Retinal Damage in a Novel Rat Model of Chronic Glaucoma
Authors: Wang Yao , Lv Jiexuan , Huang Changquan , Li Xiaohong , Chen Yongxiong , Wu Wutian , Wu Renyi
Published: October 2021

Glaucoma is the leading cause of irreversible blindness worldwide, and pathologically elevated intraocular pressure (IOP) is the major risk factor. Neuroprotection is one of the potential therapies for glaucomatous retinal damage. Intravitreal mesenchymal stem cell (MSC) transplantation provides a viable therapeutic option, and human umbilical cord- (hUC-) MSCs are attractive candidates for cell-based neuroprotection. Here, we investigated the ability of transplanted hUC-MSCs to survive and migrate within the vitreous cavity and their neuroprotective effects on chronic glaucomatous retina. For this, we developed a chronic ocular hypertension (COH) rat model through the intracameral injection of allogeneic Tenon’s fibroblasts. Green fluorescent protein-transduced hUC-MSCs were then injected into the vitreous cavity one week after COH induction. Results showed that a moderate IOP elevation lasted for two months. Transplanted hUC-MSCs migrated toward the area of damaged retina, but did not penetrate into the retina. The hUC-MSCs survived for at least eight weeks in the vitreous cavity. Moreover, the hUC-MSCs were efficient at decreasing the loss of retinal ganglion cells; retinal damage was attenuated through the inhibition of apoptosis. In this study, we have developed a novel COH rat model and demonstrated the prolonged neuroprotective potential of intravitreal hUC-MSCs in chronic glaucoma.
An Optimal Allocation Method of Power Multimodal Network Resources Based on NSGA‐II
Authors: Xiong Ao , Tong Yuanzheng , Guo Shaoyong , Wang Yanru , Shao Sujie , Mei Lin
Published: October 2021

Basic services for power business were provided by the power multimodel network providers. However, because the power multimodal network is usually complex and changeable, the service of power business is often unstable. This problem can be solved by a suitable network resource optimization method. Therefore, how to design a network resource optimization method that seeks a compromise between multiple performance indicators that achieve the normal operation of power multimode networks is still extremely challenging. An optimal allocation method of power multimodal network resources based on NSGA‐II was proposed by this paper. Firstly, the power multimodal network‐resource model is established, and the problems existing in the resource optimization process are analyzed. Secondly, preprocessing technology and indirect coding technology are applied to NSGA‐II, which solves the coding problem and convergence problem of the application of genetic algorithm to the optimization of network resource allocation. Finally, the simulation results show that, compared with the control algorithm, this method has further optimized the various indicators of the resource allocation of the power multimodal network, and the performance has been improved by more than 6%.
Influence of the Multicavity Shape on the Solid Scramjet
Authors: Yang Pengnian , Xia Zhixun , Ma Likun , Chen BinBin , Feng Yunchao , Li Chaolong , Zhao Libei
Published: October 2021

In this paper, a modular solid scramjet combustor with multicavity was proposed. The influence of multicavity shape on the performance of solid scramjet was investigated by the direct-connected tests. The experiments simulated a flight Mach 5.5 at 25 km. The boron-based fuel-rich propellant was used. The microstructure of combustion products was analyzed by SEM. The experimental results show that the fuel-rich mixture produced by the gas generator would ignite rapidly in the solid scramjet combustor. The combustion process showed a typical characteristic of establishment-development-maintenance-attenuation. Compared to the flame-holding cavity, the other shapes of cavities, e.g., narrow and lobe, can improve mixing and combustion. In our experiment, the combustion efficiency increased from 0.41 to 0.48, and the total pressure recovery was 0.36. In summary, the proposed solid scramjet combustor can effectively solve the ignition delay problem of the fuel-rich mixture, and the narrow/lobe cavity shows the ability to improve the mixing and combustion of the fuel-rich mixture.
Perioperative Circulating Tumor Cells (CTCs), MCTCs, and CTC-White Blood Cells Detected by a Size-Based Platform Predict Prognosis in Renal Cell Carcinoma
Authors: Wang Zhenlong , Zhang Peng , Chong Yue , Xue Yuquan , Yang Xiaojie , Li Hecheng , Wang Li , Zhang Yaping , Chen Qi , Li Zhaolun , Xue Li , Li HongLiang , Chong Tie
Published: October 2021

To explore the clinical significance of the perioperative counts of circulating tumor cells (CTCs), mesenchymal CTCs (MCTCs), and CTC- white blood cells (WBCs) in renal cell carcinoma patients. A total of 131 patients with renal cancer who underwent operation excision from our hospital were enrolled. In addition, 20 patients with benign renal diseases were recruited as a control. Blood samples were collected from the 131 patients, before operation and 3 months after surgery. Samples were also obtained simultaneously from the control group. CanPatrol CTC detection technique was used to enrich and identify CTCs, MCTCs, and CTC-WBCs. All enrolled patients were T1-3N0M0. From these, 52 patients with renal cancer underwent radical resection, while other 79 patients underwent nephron-sparing surgery. The positive rate of CTC, MCTC, and CTC-WBC before surgery were 95.4% (125/131), 61.1% (80/131), and 11.5% (15/131), respectively. Preoperative total CTCs, MCTCs, or CTC-WBCs were poorly correlated with patients’ parameters. Preoperative CTC, MCTC, or CTC-WBC showed no association with progression-free survival (PFS). In contrast, postoperative total CTCs (≥6), positive MCTCs, and positive CTC-WBCs significantly correlated with recurrence and metastasis. These results remained independent indicators for worse PFS. In addition, the increased CTC and MCTC count after surgery also correlated with unfavorable PFS. The detection of six or more total CTCs, MCTC, or CTC-WBCs in peripheral blood after surgery might help to identify a subset of patients that have higher recurrent risk than the overall population of patients with at different stages of renal cancer.
Meta-Analysis of Joint Test of SNP and SNP-Environment Interaction with Heterogeneity
Authors: Jin Qinqin , Shi Gang
Published: October 2021

Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.
Caregiver Burden in a Culturally Diverse Memory Clinic Population: The Caregiver Strain Index-Expanded
Authors: Franzen Sanne , Eikelboom Willem S. , van den Berg Esther , Jiskoot Lize C. , van Hemmen Judy , Papma Janne M.
Published: October 2021

Introduction: Although qualitative studies have highlighted substantial barriers to dementia diagnosis and care in culturally diverse populations in Europe, quantitative studies examining the level of caregiver burden in these populations have been lacking thus far and are urgently needed. Methods: We compared the caregiver burden levels on the Caregiver Strain Index (CSI)-Expanded of 63 culturally diverse patient-caregiver dyads from a multicultural memory clinic with 30 native Dutch patient-caregiver dyads and examined the association between caregiver burden and determinants of burden. Results: Informal caregivers in the multicultural memory clinic cohort experienced a high level of caregiver burden (mean CSI-score multicultural cohort: 6.1 [SD: 3.3]; mean CSI-score native Dutch cohort: 4.8 [SD: 3.2]). Burden was significantly associated with impairment on proxy-rated and objective measures of cognitive functioning, such as the Informant Questionnaire on Cognitive Decline and the Rowland Universal Dementia Assessment Scale, and with instrumental activities of daily living. Burden was the highest in spousal caregivers. The positive subscale of the CSI-Expanded provided limited additional information. Conclusion: Caregivers of culturally diverse patients experience a high level of caregiver burden, in particular at more advanced disease stages. This study highlights the need to screen culturally diverse caregivers in European memory clinics on caregiver burden to identify those in need of caregiver support.
Brain stroke computed tomography images analysis using image processing: A Review
Authors: Hasanah Ali Nur , Rahim Abdullah Abdul , Mohd Saad Norhashimah , Sobri Muda Ahmad , Sutikno Tole , Hatta Jopri Mohd
Published: October 2021

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.
Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors
Authors: S Yallamandaiah , Purnachand N.
Published: October 2021

Face recognition has been using in a variety of applications like preventing retail crime, unlocking phones, smart advertising, finding missing persons, and protecting law enforcement. However, the ability of face recognition techniques reduces substantially because of changes in pose, illumination, and expressions of the individual. In this paper, a novel face recognition approach based on a non-subsampled shearlet transform (NSST), histogram-based local feature descriptors, and a convolutional neural network (CNN) is proposed. Initially, the Viola-Jones algorithm is used for face detection and then the extracted face region is preprocessed by image resizing operation. Then, NSST decomposes the input image into a low and high-frequency component image. The local feature descriptors such as local phase quantization (LPQ), pyramid of histogram of oriented gradients (PHOG), and the proposed CNN are used for extracting features from the low-frequency component of the NSST decomposition. The extracted features are fused to generate the feature vector and classified using support vector machine (SVM). The efficiency of the suggested method is tested on face databases like Olivetti Research Laboratory (ORL), Yale, and Japanese female facial expression(JAFFE). The experimental outcomes reveal that the suggested face recognition method outperforms some of the state-of-the-art recognition approaches.
A systematic literature review of machine learning methods in predicting court decisions
Authors: Rosili Nur Aqilah Khadijah , Hassan Rohayanti , Zakaria Noor Hidayah , Kasim Shahreen , Rose Farid Zamani Che , Sutikno Tole
Published: October 2021

Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets
Authors: Khanday Owais Mujtaba , Dadvandipour Samad , Lone Mohd Aaqib
Published: October 2021

Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage
Authors: Mishra Pragnyaban , Srinivas P. V. V. S.
Published: October 2021

The facial emotion recognition by the machine is a challenging task. From decades, researchers applied different methods to classify facial emotion into the different classes. The expansion of artificial intelligence in a form of deep convolutional neural network (CNN) changed the direction of the research. The facial emotion recognition using deep CNN is powerful in terms of taking bulk input images for processing and classify with high accuracy. It has been noticed in a few cases the classification model does not judge the facial images into appropriate classes due to the influence of noises. So, it is highly recommended to apply a noiseless image to the facial emotion recognition model for classification. We adopted a mechanism and proposed a model for classifying facial image into one of the seven classes with high accuracy. The images are smoothed before applying to the model by different smoothing process as part of image preprocessing. We claim facial emotion recognition with image smoothing by different filters or a mixture of filter are more robust than without preprocessing. The detail is explained in the subsequent sections.
A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization
Authors: Farisi Olief Ilmandira Ratu , Setiyono Budi , Danandjojo R. Imbang
Published: October 2021

This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.
MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time
Authors: Pudaruth Sameerchand , Mahomoodally Mohamad Fawzi , Kissoon Noushreen , Chady Fadil
Published: October 2021

To facilitate the recognition and classification of medicinal plants that are commonly used by Mauritians, a mobile application which can recognise seventy different medicinal plants has been developed. A convolutional neural network (CNN) based on the TensorFlow framework has been used to create the classification model. The system has a recognition accuracy of more than 90%. Once the plant is recognised, a number of useful information is displayed to the user. Such information includes the common name of the plant, its English name and also its scientific name. The plant is also classified as either exotic or endemic followed by its medicinal applications and a short description. Contrary to similar systems, the application does not require an internet connection to work. Also, there are no pre-processing steps, and the images can be taken in broad daylight. Furthermore, any part of the plant can be photographed. It is a fast and non-intrusive method to identify medicinal plants. This mobile application will help the Mauritian population to increase their familiarity of medicinal plants, help taxonomists to experiment with new ways of identifying plant species, and will also contribute to the protection of endangered plant species.
Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier
Authors: Toulni Youssef , Benayad Nsiri , Taoufiq Belhoussine Drissi
Published: October 2021

The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%.
Image-based gramian angular field processing for pedestrian stride-length estimation using convolutional neural network
Authors: Doan Tinh Pham , Hoang Bui Huy , Cuong Nguyen Duc
Published: October 2021

In an age when people spend most of their time indoors and smartphones become a necessity, there is an increasing demand to navigate user absolute position in indoor environments. While global positioning system (GPSs) perform well outdoors, their inaccuracy can not be tolerated in places where GPS signal is weak or barely detected. This leads to a number of solutions which utilize smartphone inertial measurement unit (IMU) to track user location. Most IMU-based methods track the trajectory of a person by using stride-length and heading estimation. Thus, the accuracy of stride-length estimation plays a very important role in these methods. Inspired by recent success in the field of computer vision and machine learning, we proposed an image-based stride-length estimation method that employs gramian angular field (GAF) in converting accelerometer data into images, and then feed them into a convolutional neural network (CNN) to predict the stride-length. We evaluate the performance of our proposed method by using a public dataset from Qu Wang in his GitHub repository (available at https://github.com/Archeries/StrideLengthEstimation). The result shows that our proposed method is superior in terms of accuracy in one stride and in large walking distance than others using only data collected from the accelerometer.
Abundance and diversity of threatened birds in Nangal Wetland, Punjab, India
Authors: Kaur Rajwinder , Brraich Onkar Singh
Published: October 2021

Anthropogenic threats to wetland ecosystems, including discharge of industrial effluents, municipal sewerage, land reclamation, erosion and deforestation, have contributed to the rapid declines in populations of many bird species. The present study aimed to document avian diversity, including birds on the IUCN Red List, at Nangal Wetland, Punjab from February 2013 to January 2015. A total of 155 species belonging to 48 families (resident and migratory) under 17 orders were recorded, of which 13 come under various IUCN Red List categories: one ‘Endangered’—Egyptian Vulture Neophron percnopterus; five ‘Vulnerable’—Common Pochard Aythya ferina, Greater-Spotted Eagle Aquila clanga, Sarus Crane Grus antigone, Lesser White-Fronted Goose Anser erythropus, and Woolly-necked Stork Ciconia episcopus; and seven ‘Near Threatened’—Ferruginous Duck Aythya nyroca, River Lapwing Vanellus duvaucelii, Indian River Tern Sterna aurantia, Painted Stork Mycteria leucocephala, Oriental Darter Anhinga melanogaster, Blossom-headed Parakeet Psittacula roseata, and Alexandrine Parakeet Psittacula eupatria. The Shannon-Weaver index of diversity was highest during winter (H’= 1.9) followed by autumn (H’= 1.9) then spring (H’= 1.5), and was lowest during summer (H’= 1.4). Though this wetland is highly productive and provides homes to many threatened species, untreated industrial effluents from adjoining areas sometimes create problems. The discharge of pollutants should be stopped through strict enforcement of environmental laws and policies.
Grass species composition in tropical forest of southern India
Authors: M. Ashokkumar  , S. Swaminathan  , R. Nagarajan
Published: October 2021

Grass composition was assessed by plot method (1 m2; n= 1,749) in three habitats (dry deciduous-DDF, moist deciduous-MDF, and thorn forest-TF) at Mudumalai Tiger Reserve, southern India across different seasons from Jan 2004 to Dec 2007. The grass species richness and availability (per cent composition) varied significantly with habitats. Seventy-four species of grasses and sedges were recorded in all three habitats, with a few species common in all habitats. Grass availability varied significantly in different habitats across seasons and was positively influenced by precipitation. Among biotic factors, regeneration and shrub density had a primary influence on grass availability, followed by herb, sedge and weed density. The principal coordinate analysis revealed seven major associations in the tropical forest. There were considerable changes in the composition and association of grasses when compared to the past. Fire resistant species such as Themeda triandra, Heteropogan contortus and T. cymbaria dominated in the DDF. Grass species Aristida/Eragorstis were recorded in the TF, which were considered as indicators of heavy grazing pressure. Grass species that were reported rare and sporadic in the earlier study were not recorded, which emphasizes better pasture management in the tropical forest. Grass species composition and availability was threatened by invasion of weeds.