bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Frame Work for Detection of Chemically Ripened Mango Fruits Using Dominant Colour Descriptors
Authors: Laxmi V. , Roopalakshmi R.
Published: November 2020

Nowadays computer vision systems are widely used for identification, classification and grading of different kind of fruits. Existing research concentrates on features like size of the fruit, colour, shape and texture for classification and maturity detection of mango fruits. Colour of the fruit is one of the prominent feature, though a lot of effort is focused towards identification, maturity and defect detection of mango using colour features,less attempts are made in the direction of the identification of artificially ripening of mango fruits.From another perspective very few attempts are concentrated towards deeper analysis of colour features of the fruits. In order to solve these issues this research paper proposes a new framework for detection of artificial ripening of mango fruit based on MPEG-7 colour descriptors. The proposed scheme includes three stages: first, a pre-processing stage consisting of masking, filtering, segmenting and cropping of an image followed by dominant colour extraction using dominant colour descriptors which are finally mapped with the help of clustering to identify the artificial ripening of mango fruit. The results of experimentsis carried out on two different datasets involving four types of mangoes which demonstrates robustness and efficiency of the proposed method against various other methods.
Influence of Noise Channel in Quantum One Time Password Authentication
Authors: Kr sharma Mohit , Nena Manisha J.
Published: November 2020

This paper presents an overview of quantum errors and noise channels, their mathematical modeling and its implementation in quantum one time password (QOTP) based user authentication. Quantum noise plays a pivotal role in understanding quantum information theory which is important to build up quantum communication theory. The Kraus operators provide a powerful mathematical tool in understanding and modeling various quantum channels. Use of QOTP provides an impressive method of carrying out user authentication involving quantum operations based on user biometrics. However, the efficiency of this method can be better envisaged by incorporating noise models during qubit transmission.
Review of Performance Improvement of VSS FLC and NN MPPT Photo Voltaic Power System
Authors: A Chandramouli , R Arulmurugan , Sivachidambaranathan
Published: November 2020

In this paper guides in the fuzzy logic and Neural network variable step size Incremental conductance maximum power point tracking controller were analyzed as well suggested on further work. Firstly, the Fuzzy Logic Control (FLC) controller based hardware system derived paper were discussed followed by Neural Network (NN) based VSS Inc-Cond were discussed in the second half. In the first paper derived simple FLC I.e short, medium and long distance were considered to derive the 25 rules. The input of FLC are current and voltage, the output of FLC is duty cycle or D. The duty cycle apply into variable step Inc-conductance method to find the optimal point of the PV system. Further discussed experimental block diagram of FLC-Inc-Cond system. At the end of the paper authors compared various hardware to show tabulations. In the second half, initially discussed conventional Inc-Cond, followed by general Fuzzy logic controller were discussed. Further author derived VSS Inc-Cond based NN MPPT were developed on Matlab simulink. The output of FLC-Inc Cond as well NN based MPPT is great performance when compare to conventional system.
Data Mining Technique Based Critical Disease Prediction in Medical Field
Authors: J Preetha , S Raju , Kumar Abhishek , Samee Sayyad , R Vengatesan
Published: November 2020

In the present days’ deaths because of some critical disease has become a significant issue in the medical field. Data mining is one of the significant territories of research that is famous in wellbeing associations. Data mining has a functioning job for finding new patterns and examples in the healthcare association which is valuable for every one of the gatherings related to this field. The medical dataset has heterogeneous data as numbers, content, and pictures that can be mined to convey an assortment of helpful data for the physicians. The examples picked up from the medical data can be helpful for the physicians to find diseases, foresee the survivability of the patients after disease, the seriousness of diseases and so forth. The focal point of this paper is to break down the utilization of data mining in medical space and a portion of the systems utilized in critical disease prediction. We have completely reviewed many research papers of data mining identified with some critical disease prediction.
Detection and Transmission of EEG Signal Using VLC
Authors: A Dhanalakshmi , S Priyadarshni , P Kayalvizhi , G Devi Priya , S Oviya
Published: November 2020

The healthcare and the management of quality is very essential part to the recent period of medical environment. To enhance these, to make sufferer easy with timely and best remedial care.EEG systems are closely relevant on the performance using wireless system because of its more capabilities. But it has various disadvantages like line frequency interference and signal loss,. Currently there is usage of RF in healthcare environment and medical test centre which have dangerous for the health of the patients. So we are in need of alternative eco-friendly source of communication in health care. So the alternative way is proposed here is the VLC (Visible Light Communication) transmission technology that uses white LED for data transmission which has efficiency, reliability and high precision. The ADC produces data stream of EEG signal which is modulated using OOK .Here LED works as a transmitter and photodiode with sensor works as receiver and uses computer simulation for further evaluation.
Monitoring and Tracking of Cattle Using IoT
Authors: S Lakshmi , A Manonmani , L U Gayathri , Sharon Sujitha J , R V Pooja
Published: November 2020

The scale of maintaining the dairy worldwide had changed with a move towards larger, more intensive, profit-driven enterprises, primarily due to market pressures. Productive cattle health monitoring can help the farmers as well as the landlords who can regularly monitor the poor health condition of their cattle and intimate the unavailability of veterinary. The main focus of our project is to avoid the condition of emergency, reduce cost, labour inputs while increasing farm productivity and also help in diagnosis of cattle’s diseases in the early stage.
Device Authentication and Secure Routing in MANET for Internet of Things
Authors: Thiyagarajan Kirubadevi , S Ramamoorthy , S Neelavathy Pari , P S Rajakumar
Published: November 2020

In the next generation of communication mobile adhoc network (MANET) will play a major role in Internet of Things (IoT).MANET can be defined as a network among a group of nodes with no infrastructure. Internet of Things is the composition of a variety of networks like Wi-Fi, ZigBee, Wireless Sensor Networks (WSNs). MANETs, and Radio Frequency Identifier (RFID). There is a need for the compatibility of MANET with IoT for the deployment of smart infrastructure. Authenticity and security in communication between devices are essential for the realization of IoT with MANET. Authentication and providing security in MANET are always challenging due to its mobility in nature. Devices in IoT are usually resource constrained in energy, memory, computation and bandwidth. Therefore, it is difficult for each node to authenticate all the devices in the network. In this paper, an authentication scheme is proposed without any third party provider to distinguish the authorized and unauthorized devices. This authentication consists of two level process; in the 1st level mutual devices are authenticated, and in the 2nd level secure data routing between the devices is carried out. Mutual authentication is performed by Clustering based on keys and unique identities and by the cluster heads interpreting for secure routing. This scheme ensures the early measure of authenticity for the message requested enters into the IoT networks and disagreement against attacks.
Uniform Rectangular Array Synthesis and Optimization Using Modified Genetic Algorithm
Authors: J Mercy Sheeba , Deepa S.
Published: November 2020

This paper aims to synthesize a uniform rectangular array (URA) which spans beamwidth of -30∘ to 30∘ in the azimuthal direction with the interference direction as 40∘ in the azimuth plane. In this paper, a Modified Genetic Algorithm is proposed which works to produce a beam pattern with a narrow beamwidth, high directivity and maximized side lobe level (SLL) suppression. The simulation results for the proposed algorithm demonstrates that the synthesized beam pattern for a 16x16 URA at a frequency of 1 GHz converges to the desired optimum solution producing a maximum SLL suppression of -30dB.
Effective Usage of Available Spectrum Using Dispersion Detection Technique
Authors: R Durga , D Selvaraj
Published: November 2020

Spectrum sensing techniques are used for aquising the frequency spectrum in cognitive radio. From research, the efficiency of the spectrum sensing technique increases only if its complexity is increased and if its complexity is decreased then its efficiency decreases. so, a new technique is proposed in this paper based on Dispersion Detection (DD) to balance both complexity and efficiency. Using this detection technique, the false alarm probability is derived for multiple antenna using test statistic distribution. The decision threshold is derived to provide the accurate results. The derived values are verified with Monto Carlo simulation.
A Distributed Item Based Similarity Approach for Collaborative Filtering on Hadoop Framework
Authors: Gunjal S.N. , S K Yadav , D B Kshirsagar
Published: November 2020

Now a day’s multiple website provides millions of product to their on-line users. Due to the interaction of millions customer with the e-commerce websites creates massive volume of data. Recommendation system is a dynamic data capturing system filters massive volume of information generated through the interaction of users to web-portals & generate suggestion that fits the user expectations. Recommendation framework is data filtering tools that make use of algorithms and user rating data to recommend the most relevant items to a particular user. Collaborative filtering is one of the successful techniques in the recommendation system to recommend the top N-item. In the majority of the Recommendation framework information sparsity, high dimensionality, adaptability which are normal issue in the RS domain have adversely influence the exhibition of CF. The proposed system is developed to resolve the mentioned problems in most of the recommendation system using item based similarities approach in CF with the help of user sub space clustering approach on hadoop framework. In the proposed system user subspaces formed by considering the interest of the users in the items like Interested, Neither Interested nor Uninterested (NIU), and Uninterested. After the user subspace clustering the neighbor item tree is constructed. To find out the similarities between the items the similarities measures is developed from the neighbor item tree. It observed that in traditional item-based collaborative filtering method requires more computational cost but the computation of item similarities is performed off line & computational cost required for on line prediction is less. In proposed work to improve the computation speed of off line item-item similarity the computation is performed on various nodes in cluster in the hadoop distributed system. The similarity between the two items is used to predict the rating provided by user on the target items. The proposed method tested on the Movie lens 100K, Movie lens 1M in order to make comparisons with the existing techniques. The proposed method improves the performance of the recommendation systems by resolving the issues like scalability, high dimensionality, data sparsity etc.
A Wearable Patient Healthcare Monitoring System Using IoT and Cloud Computing Based Security
Authors: S Chitra Selvi
Published: November 2020

Now-a-days, a developing number of individuals at some point of a developing international locations like India forces to seem for brand spanking new answers for the persistent tracking of fitness check-up for stable data. It’s emerge as a need to go to hospitals frequently for doctor’s consultation, which has Growth to be financially associated and a time ingesting process. To beat this situation, we endorse a design to observe the patient’s fitness situations like heartbeat, temperature, ECG and BP and ship the message to guardian the use of GSM. Within the recent improvement of internet of factors (IoT) makes all objects interconnected and cloud been diagnosed due to the fact the subsequent technical revolution and now not secure for patient data. Patient monitoring is one a few of the IoT application to watch the affected person fitness status to collect facts to security for both medical doctor and patients Internet of things makes clinical equipment greater efficient with the aid of allowing actual time tracking of health in security privateness of the patient. Using IoT doctor can continuously monitor the patient’s cloud the usage of protection on his smartphone and also the affected person history could be stored on the web server and health practitioner can get entry to the statistics on every occasion wished from anywhere.
Circularly Polarized MIMO Antenna Using F-Shaped Mirrored Structure
Authors: S Pavithra , A AmeeliaRoseline
Published: November 2020

In MIMO(multiple input multiple output) system, antenna performance are degraded by mutual coupling hence to overcome this we go for circular polarization. In this paper we use planar, circularly polarized MIMO patch with three grounded stubs, F-shaped mirrored structure to achieve same time isolation &matching with offset feeding between two patches for circularly polarization.The elements of antenna are closely packed with 0.06λ0 of edge to edge distance at 2.5 GHZ frequency. The proposed antenna will results the impedance matching S11 < -10 dB and high isolation of S12 < -20 dB.
Dimensionality Reduction of Production Data Using PCA and DBSCAN Techniques
Authors: S Umadevi , NirmalaSugirthaRajini
Published: November 2020

Now a day’s data mining concepts are applied in various fields like medical, agriculture, production, etc. Creation of cluster is one of the major problems in data analysis process. Various clustering algorithms are used for data analysis purpose which depends upon the applications. DBSCAN is the famous method to create cluster. This article describes DBSCAN clustering concept applied on production database. The main objective of this research article is to collect and group the related data from large amount of data and remove the unwanted data. This clustering algorithm removes the unwanted attributes and groups the related data based upon density value.
A Study of Compact Branch Line Coupler
Authors: Jutru Vijay , S Maheswari
Published: November 2020

In this paper we analyze various structures of branch line coupler with different frequencies of 1GHz to 2.5GHz. Couplers are used for splitting or combining power. It is basically a 3dB power divider or combiner with symmetrical four port network for obtaining 900 phase difference between its output.It has been used in many applications such as in mixers, power amplifiers, modulators, anteena beam forming networks.The major advantages of branch line coupler are low profile, easy to fabricate, light weight.
Enhancement of Efficiency in E-Vehicle Using Hybrid Energy
Authors: S Chitra Selvi
Published: November 2020

In the current world due to scarcity of fossil fuel, an alternative energy is required. And now thanks to shortage of gasoline in future and its dangerous impact on the environment, and its necessary to develop an alternate power to give solution for insufficient energy source. Electric motors are creating noise but we want very smooth noise-less operation and have more efficiency as compared to the traditional Engines. Wind generation is smooth and sustainable that has to be absolutely utilized by the car industry. Solar electricity is harvested by PVarrays, the terminal voltage of Photovoltaic arrays switched through a DC/DC converter to buck boost converter. Then, we are going to hybrid those energy and use it to run the vehicle. we increased the efficiency by way of using 80% on direct usage and 20% for battery charging.
Prediction of Cancer and Suggestion of Therapies
Authors: M Bhavani , V Pavithra , R Monesh
Published: November 2020

Cancer is becoming one among the common diseases in day to today life, determining cancer in an earlier stage is still problematic. Identification of genetic and environmental factors is necessary to predict the type of cancer. The idea is to develop a cancer prediction system that predict lung and oral cancer depending on the symptoms. The gathered data is pre-processed and the data mining algorithm such as decision tree, logistic regression, Random Forest and Support Vector machines are used to measure the performance. The attribute selection algorithms are used to obtain the mandatory attributes. The main aim of this study is to do a comparative analysis using different algorithms for cancer prediction and suggestion of therapy.
Design Secure WSN with Advancement in Finding Rouge Access Point with Soft Computing Tools
Authors: Bodhe Abhijit S. , Dhanrao Pravin , Sangle Abhimanyu , N. Jagdisha
Published: November 2020

Now a days wireless communication is become very vast, important and easy to access with multiple wireless sensor network in existence as it having very less cost associated and easily available via multiple mobile devices with sensors to create a hotspots, It creates a potential threat for community using wireless media for communicating some secure information like banking passwords, military information, biometric data etc. on unsecured network. This proposed paper will expose one of such potential threat Rouge Access Point (RAP) detection by making the use of soft computing prediction tool in our case fusion of neural network with fuzzy logic known as neuron-fuzzy method and design a fuzzy controller (FLC) to find such RAP & secure the existing wireless network where multiple sensors are actively working in real time to provide the real time data.
A Review Paper on Big Data Analytics in Process Industry
Authors: S Lakshmi , A Manonmani
Published: November 2020

Today, in modern large-scale industrial processes, each step-in manufacturing produces a bulk of variables, which are highly precise in nature. However, great challenges are faced under different real-time operating conditions when using just the basic data-driven methods. One of the sultriest research points for convoluted process control is the usage of big data analytics. The aim of big data analytics is to take full advantages of the large amounts of obtained process data and mine helpful details present within. Compared to the well-developed model-based approaches, usage of big data analytics provides productive elective answers for various modern issues under different working conditions. Majority of the modelling in process control in a closed loop system is based on varying the command input to obtain desired controlled output. However, modelling of the process control in a closed loop system based on the disturbance using conventional methods is time consuming since disturbance data is too big and too complex. Utilization of advanced big data analytical methods to mine the disturbance data can lead towards more informed decisions to model the process control in the system. Thus, relevant solutions can be obtained to some of the challenges in the modeling of process control using big data analytics.
Automated Smart Irrigation System Based on IoT
Authors: G Bhavani , S Malathi
Published: November 2020

India is an agricultural country with variety of crops. Water flow to the crop field is found important with accurate level. Multiple crops can be grown in single field with accurate water level. Water level need to be controlled due to water scarcity. Exact amount of water can be made to flow in the field through IoT techniques. Inorder to bring efficient crop growth and to avoid excess water flow, automation in agriculture can be done. Automation in agriculture can reduce excess water usage in water scarce and dry area. Automation is done with sensors and various IoT techniques to improve water level in agricultural field.
Using the Generative Adversarial Network to Generate Recommendations
Authors: Prosvetov A.V.
Published: November 2020

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.
Assessment of Socio-Economic Efficiency of the Scientific and Educational Complex: Approach and System of Indicators
Authors: Slavikovsky Alexey , Vladimirova Olga , Khusainov Manon
Published: November 2020

The article is devoted to the current topic of choosing the optimal organizational option of multilateral integration of the scientific and educational sphere, business and the state in the process of globalization of the world economy. The authors justify the format of formation of scientific and educational complex on the basis of network interaction, which allows to obtain the greatest synergistic effect. In order to justify the effective network interaction of the scientific and educational complex, an analysis of existing methods of assessing the efficiency of its functioning was carried out and an author’s system of performance indicators and its assessment was proposed in accordance with the general purpose of the integration mechanism and the specific purpose of each interaction subject. The model contains a system of heterogeneous indicators reflecting the principles of formation of a scientific and educational complex on the basis of network interaction, which allows, along with an evaluation task, to determine, using factor models, further directions of inter-network relations of subjects in order to better understand the current processes and identify problem areas of coordination of their innovative activity.
The Diagnosis of Alzheimer’s Disease: An Ensemble Approach
Authors: Qiu Jingyan , Li Linjian , Liu Yida , Ou Yingjun , Lin Yubei
Published: November 2020

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.
An Improved Decision Tree Algorithm for Electricity Theft Prediction and Analysis
Authors: Chen Xiaofeng , Zhang Lipeng , Xu Zhongping , Zhu Feng , Qi Xiaoming
Published: November 2020

At present, there are many kinds of electricity theft and the corresponding approaches to combat this are insufficient. Manual approaches result in a heavy staff workload and are inefficient. In this paper, the data from an electricity information acquisition system is collected and mined using Python. Based on an understanding of the business and an analysis of the information value (IV) measure, important characteristic indexes are selected and an improved decision tree algorithm is used to construct a model of power theft by users. This method effectively narrows the range of users suspected of power theft, improving the pertinence of audit, and providing strong support for reducing the financial losses of power supply enterprises and ensuring the safety of power grid operation.
Distance Metrics of D Numbers
Authors: Fei Liguo , Feng Yuqiang
Published: November 2020

Belief function has always played an indispensable role in modeling cognitive uncertainty. As an inherited version, the theory of D numbers has been proposed and developed in a more efficient and robust way. Within the framework of D number theory, two more generalized properties are extended: (1) the elements in the frame of discernment (FOD) of D numbers do not required to be mutually exclusive strictly; (2) the completeness constraint is released. The investigation shows that the distance function is very significant in measuring the difference between two D numbers, especially in information fusion and decision. Modeling methods of uncertainty that incorporate D numbers have become increasingly popular, however, very few approaches have tackled the challenges of distance metrics. In this study, the distance measure of two D numbers is presented in cases, including complete information, incomplete information, and non-exclusive elements
Fuzzy Expert Pricing Systems and Optimization Techniques in Marketing Science
Authors: Dairo Adeolu , Szűcs Krisztián
Published: November 2020

Despite the advancement in the mathematical models for solving and modeling marketing problems, there exists gap between model predictions and market reality. Fuzzy logic bridges this gap by providing marketers with the opportunity of combining robust human experts in the form of linguistic rules to formulate a knowledge-base. These rules are systematically developed into a marketing tool which provides intelligent pricing decision support for marketers in a dynamic and competitive environment. In this paper, these expert pricing systems which are designed and developed to help businesses in pricing decision-making are examined. Price-Strat, FuzzyPrice, and exPrice expert pricing systems are compared and discussed. These three expert pricing systems are explored along with their fuzzy development approach, effectiveness, and attributes in pricing products and services.
Realistic Face Image Generation System Based on GANs
Authors: Han Zhike , Yang Bin , Du Yiren , Du Xingyu , Xing Hao , Cheng Qinnan
Published: November 2020

The purpose of this paper is to study the help of generative adversarial networks (GAN) for face generation, and to explore whether the network can have an effect on complex face generation. Training an image translation neural network model based on a generative adversarial network with the help of a large number of real human face data sets. Using the CV2-based face tagging algorithm and the HED-based face edge extraction algorithm to obtain input information, and then based on the translation neural network model Developing a face generation system through Tensorflow, Torch and other frameworks to realize the function of generating real faces through sketches or “changing faces” through existing faces. Finally, this model provides training configuration and training information.
Development of IoT Based Fresh Food Delivery Tracking System Using GPS
Authors: Yuen Man-Ching , Choi Yip-Hin , Ng Tsun-Hin , Poon Chun-Tung , Fung Kin-Chung
Published: November 2020

Home delivery service is an essential service for online shopping. The need for reliable delivery system to ensure freshness of foods is challenging with consumers’ busy schedule and heavy traffic. An efficient fresh food delivery tracking system is designed and implemented for tracking the fresh food delivery ordered on an online shopping system. The proposed system made use of Internet of Things (IoT) technology, Global Positioning System (GPS) and a smart online shopping system. Using this GPS system, consumers are able to track their delivery and arrival of their grocery to ensure the freshness of products. To create the user-friendly access website, we have included four functions in this website: Online shopping cart, system to support various payment methods, GPS food tracking system, and members’ easily access account data system. Since the cost of this fresh food delivery tracking system is low, it is suitable for online shop of start-ups.
A Classification for Electronic Nose Based on Broad Learning System
Authors: Wang Yu , Peng Xiaoyan , Cui Hao , Jia Pengfei
Published: November 2020

The odor of citrus juice changes during the storing process. We use an electronic nose (E-nose) to detect the volatile odors released by citrus juice and use the detect results to classify citrus juices from different storage periods. In this article, a novel classifier of E-nose, namely broad learning system (BLS) is introduced. BLS is different from traditional classifier. It has a simple network model, which can greatly reduce the training time of the model. BLS can effectively combine feature extraction and classification recognition to make the model more efficient. We apply BLS to the analysis of valencia citrus juice data. The experimental results show that BLS can effectively identify the current stage of the stored valencia citrus juice. Compared with traditional classifier such as radical basis function neural network (RBFNN) and linear discriminant analysis (LDA), the results show that BLS has better performance for the storage period classification of valencia citrus juice.
3D Single Person Pose Estimation Method Based on Deep Learning
Authors: Yuan Xinrui , Wang Hairong , Wang Jun
Published: November 2020

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.
Research Progress and Advantage Analysis of Big Data Application in Chinese Academic Journals
Authors: Wu Qin , Wu Hui
Published: November 2020

The mining and application of big data in academic journals can accelerate the construction of data journals, enhance journal’s influence, and promote the sharing and dissemination of scientific data in academic journals worldwide. This paper uses bibliometric method to retrieve published articles with the theme of big data and journal in CNKI database, analyzes the academic achievements of the development of academic journals with the application of big data in the recent five years using quantitative visualization analysis, expounds the research progress of academic journals in big data field, and discusses the advantages of big data application in periodical industry. The results show that: study on the application of big data in academic journals are gradually deepening and scientific, and the relevant research still needs more financial fund from the state and social units, big data has prominent advantages such as accuracy, scientificity and value maximization in the workflows of academic journals. The mining and application of massive data is very important for promoting the development of high-quality academic journals and optimizing the supply-demand relationship of knowledge services of academic journals.
Focused Crawler Strategy Based on Improved Energy Landscape Paving Algorithm
Authors: Liu Jingfa , Zhang Wei , Yang Zhihe , Liu Ziang
Published: November 2020

The traditional crawlers have difficulty in implementing semantic analysis. Therefore, the focused crawler technologies with topic preference characteristics have received many attentions in the recent years. To increase the precision of focused crawlers and prevent “topic drifting”, this paper adopts the comprehensive relevancy evaluation (CRE) of hyperlinks based on the combination of web content and link structure. In addition, the improved version of the energy landscape paving (ELP) algorithm that is a class of metropolis-sampling-based global optimization method is proposed to avoid the focused crawler falling into local optima. By incorporating the CRE strategy into the improved ELP, a novel focused crawler strategy denoted by IELP is proposed. The experimental results on rainstorm disasters domain show that the precision of the proposed focused crawler is obviously promoted compared to other focused crawlers in literature, illustrating the ability of the IELP to retrieve topic-related web pages.
Personalized Mobile Information Recommendation Based on Fine-Grained User Behaviors
Authors: Wang Yilei , Chen Xueqin
Published: November 2020

At present, the enthusiasm of users to score actively in mobile information recommendation system is generally poor. Moreover, the existing research works rarely start with the analysis of fine-grained reading behaviors of mobile terminal users, but mostly based on the analysis of reading content and the improvement of model. It is difficult to find out the objective, short-term and local behavioral preferences of users. To solve the above problems, we propose six kinds of explicit fine-grained reading behaviors and integrate them into the user reading interest model to form the SVR-ALL model. The effectiveness of these six fine-grained behaviors is verified by ablative experiments. On the basis of SVR-ALL model, four implicit fine-grained reading behaviors are further mined by considering the difference of user reading habits, and then propose the user reading preference model called F-AFC. The updating mechanism for user preference designed in F-AFC can fully reflect the changes of users’ reading habits in different periods. Experiments show that the accuracy of the user interest model considering user’s reading preference and its update can be improved to some extent.
A Brief Review of Relation Extraction Based on Pre-Trained Language Models
Authors: Xu Tiange , Zhang Fu
Published: November 2020

Relation extraction is to extract the semantic relation between entity pairs in text, and it is a key point in building Knowledge Graphs and information extraction. The rapid development of deep learning in recent years has resulted in rich research results in relation extraction tasks. At present, the accuracy of relation extraction tasks based on pre-trained language models such as BERT exceeds the methods based on Convolutional or Recurrent Neural Networks. This review mainly summarizes the research progress of pre-trained language models such as BERT in supervised learning and distant supervision relation extraction. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. The survey may help readers understand and catch some key techniques about the issue, and identify some future research directions.
Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis
Authors: Lejko Nena , Larabi Daouia I. , Herrmann Christoph S. , Aleman André , Ćurčić-Blake Branislava
Published: November 2020

Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.
Iron Serum Markers Profile in Frontotemporal Lobar Degeneration
Authors: De Luca Anastasia , Fostinelli Silvia , Ferrari Clarissa , Binetti Giuliano , Benussi Luisa , Borroni Barbara , Rossi Luisa , Rongioletti Mauro , Ghidoni Roberta , Squitti Rosanna
Published: November 2020

Frontotemporal lobar degeneration (FTLD) is a progressive neurodegenerative syndrome. Defects of copper (Cu) and iron (Fe) homeostasis are involved in the development of several neurodegenerative diseases and their homeostasis is interconnected by the Cu-protein ceruloplasmin (Cp), responsible for Fe oxidative state. In this study we assessed Fe, transferrin (Trf), ferritin, Cp specific activity (eCp/iCp), Cp/Trf ratio, and Trf saturation in 60 FTLD patients and 43 healthy controls, and discussed the results in relation to Cu homeostasis. The significant decrease of the eCp/iCp in the FTLD patients supports the involvement of Fe imbalance in the onset and progression of FTLD.
E-Learning perception and satisfaction among health sciences students amid the COVID-19 pandemic
Authors: Abbasi Maria S. , Ahmed Naseer , Sajjad Batool , Alshahrani Abdullah , Saeed Sumera , Sarfaraz Shaur , Alhamdan Rana S. , Vohra Fahim , Abduljabbar Tariq
Published: November 2020

BACKGROUND: E-learning is increasingly used during the COVID-19 pandemic, however the impact of this change on students is not known. This study aimed to evaluate perception and satisfaction of health sciences students towards E-learning during the COVID-19 lockdown. METHODS: A structured questionnaire was distributed to 2000 health care students either through email or social media platforms. The questionnaire was divided into two sections: the first section addressed demographic information such as age, education level, course of study, number of lectures attended and country. The second section gathered information on perception and satisfaction of students using 13 close-ended questions. Frequencies and percentages were assessed for demographic data, perception and satisfaction level of students. The paired sample t-test, independent t test and Spearman correlation were applied to evaluate statistical significance between different variables of the study. A p-value of <0.05 was considered significant. RESULTS: Data from 1255 participants were included at a response rate of 66.4%. More than one third (37%) of the students took 25 or more online E-learning sessions and 47% preferred Zoom as an online platform. Participants belonged to 11 countries from developed and developing nations. 41% reported interference of E-learning due to network problems. 60% considered that clinical and practical skills are best learned in clinics and laboratories. More than one third of the students preferred classroom teaching and 34% of the students did not feel confident enough to take exit exams after E-learning sessions. CONCLUSION: E-learning satisfaction levels were better among developed countries (7.34) compared to developing countries (5.82). The majority of participants agreed that E-learning was satisfactory in acquiring knowledge, however not effective in acquiring clinical and technical skills. As the COVID-19 lockdown eases, there is a need for improvement in the methods employed in E-learning and more blended learning among healthcare students is recommended.
Professional training in mental health self-care for nurses starting work in hospital departments
Authors: Bernburg Monika , Groneberg David , Mache Stefanie
Published: November 2020

BACKGROUND: Nurses working in hospitals can suffer from occupational stress due to high workloads and low job and/or personal resources. This can lead to work-related stress, exhaustion, health problems, and low quality of care. OBJECTIVE: The aim of the study was to evaluate the effectiveness of work-related self-care skill training for nurses. METHODS: A pilot study was conducted with 94 nurses in hospital departments in Germany. Nurses were either assigned to the intervention group that received competence training or to a waitlist control group. The intervention took place in groups over a period of 12 weeks. Training content included i.e. work-related stress management training, problem-solving techniques, and solution-focused counselling. The outcomes studied were changes in work-related stress, emotional exhaustion, emotion regulation, and job satisfaction. Three follow-up assessments were arranged. RESULTS: Nurses in the IG achieved a decrease in perceived job stress and emotional exhaustion as well as improvements with regard to enhanced emotion regulation skills. The intervention was evaluated with high satisfaction scores. CONCLUSIONS: This study showed first indications that training of mental health self-care skills for junior nurses could be a supportive approach for nurses starting work in hospital departments. However, replication studies are needed to verify the results.
Awareness of obstructive sleep apnea and its effects on daily life among health professionals from Karachi, Pakistan
Authors: Sohail Rida , Hayat Sarah , Tariq Jawaria , Ashraf Danial , Karim Meenaz , Rizvi Beenish Elahee , Saad Saadia , Alam Mohammad Khursheed , Qamruddin Irfan
Published: November 2020

BACKGROUND: Sleep disturbances are be poorly documented by primary care physicians. OBJECTIVE: Our objective for this survey was to assess the awareness level amongst medical and dental house officers, medical and dental practitioners, and ear, nose and throat specialists (ENTs) regarding OSA and its effects on daily life. METHODS: This was a questionnaire-based survey with yes and no and true and false type questions. In order to assess knowledge, questions related to the most common signs and symptoms of OSA were selected. Questionnaires were distributed in seven medical and dental colleges in Karachi, Pakistan under graduates and above. Stratified random sampling technique was used to assess specific professionals which made a total sample size of 614. The data of this descriptive study was compiled and analyzed using SPSS version 20. RESULTS: 84% of the participants stated their awareness about OSA. After analyzing a detailed response in the form of questionnaires, an average of only 15% showed adequate knowledge of the problem in question, the rest was unaware of the pathophysiology, signs, symptoms and management of a patient suffering from OSA. CONCLUSION: knowledge about pathophysiology of obstructive sleep apnea and its effect on daily routine work is insufficient among health professionals. It is essential to create awareness among medical and dental fraternity regarding OSA as it is often not diagnosed and treated.
Periodontal status of the second molar adjacent to the third mandibular molar
Authors: Luzuriaga Chávez Katherine Daniela , López Jurado Santiago Andrés
Published: November 2020

Third molars have an unusual eruption pattern. Studies suggest that, due to the position and angulation of these, various pathologies could occur in the adjacent molars, such as periodontal problems and the appearance of distal caries. The objective of this work is to describe the periodontal status of mandibular second molars adjacent to mandibular third molars. For this, a descriptive transversal study was carried out. We worked with a sample of 277 patients that went to the Dental UCSG Clinic to have their third molars extracted in the period A-2018. An intraoral clinical examination was performed, and the following variables were observed: probing depth, gingival state, and presence of caries in mandibular second molars. In addition, panoramic x-rays of the patients were reviewed to determine the position of the third mandibular molar and the level of the distal bone crest of the second molars. The results showed that the depth of distal probing of the mandibular second molars were normal (from 1 to 3mm) in 31% of the cases, while in 69% depths greater than 3mm were found. Regarding the presence of bone loss, in 43% of the cases there was no loss, in 35% of the cases there was a slight loss, 22% presented moderate loss and only 1% had severe loss. The presence of mandibular third molars significantly affects the periodontal status of the adjacent second molar, which can lead to periodontal pockets, bone los sand caries in their distal area.