bluuebunny/crossref_metadata_2025 huggingface dataset:
Framework for rare event detection using Artificial Neural Network based context free grammar
Authors: Srinivasan Palanivel , Doraipandian Manivannan
Published: September 2020

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.
Information security in WSN applied to smart metering networks based on cryptographic techniques
Authors: Varela Noel , Pineda Lezama Omar Bonerge , Neira Harold
Published: September 2020

The principle assaults on a Wireless Sensor Network (WSN) essentially influence the uprightness and accessibility of the data gathered, for example, Deni-al of Service, Blackhole, Wormhole, and assault on the data being transmitted. Privacy is not an important security objective because the data caught by the sensors are typically not delicate or mystery from individuals. A remote sensor organizes applied to shrewd metering frameworks might be adequately powerful as far as robotization and adjustment of the information that is gathered, however, if the system doesn’t have satisfactory security, both the client and the organization offering the support might be influenced by assaults on the respectability and accessibility of the data transmitted. This research proposes the use of MESH encryption techniques and Star topology to find the best combination that meets the requirements of a Smart Metering System.
An inspired haxby brain perceptual model for facial images quality assessment
Authors: Nourbakhsh Azamossadat , Moin Mohammad-Shahram , Sharifi Arash
Published: September 2020

Face is the most important and most popular biometric used in many identification and verification systems. In these systems, for reducing recognition error rate, the quality of input images need to be as high as possible. Face Image Compliancy verification (FICV) is one of the most essential methods for this purpose. In this research, a brain functionality inspired model is presented for FICV using Haxby model, which is a face visual perception consistent model containing three bilateral areas for three different functionalities. As a result, contribution of this work is presenting a new model, based on human brain functionality, improving the compliancy verification of face images in FICV context. Perceptual understanding of an image is the motivation of most of the quality assessment methods, i.e., the human quality perception is considered as a gold standard and a perfect reference for recognition and quality assessment. The model presented in this work aims to make the operational process of face image quality assessment system closer to the performance of a human expert. Three basic modules have been introduced. Face structural information, for initial information encoding, is simulated by an extended Viola-Jones model. Face image quality assessment is presented by International Civil Aviation Organization (ICAO), in ICAO (ISO / IEC19794 -11) requirements’ compliancy assessment document. Like Haxby model, perception is performed through two distinct functional and neurological pathways, using Hierarchical Maximum pooling (HMAX) and Convolutional Deep Belief Networks (CDBN). Information storing and fetching for training are similar to their corresponding modules in brain. For simulating the brain decision making, the final results of two separate paths are integrated by weighting sum operator. Nine ISO / ICAO requirements were used for testing the model. The simulation results, using AR and PUT databases, shows improvements in six requirements using the proposed method, in comparison with the FICV benchmark.
An enhanced approach for three factor remote user authentication in multi - server environment
Authors: Patel Chintan , Joshi Dhara , Doshi Nishant , Veeramuthu A. , Jhaveri Rutvij
Published: September 2020

With the agile development of the Internet era, starting from the message transmission to money transactions, everything is online now. Remote user authentication (RUA) is a mechanism in which a remote server verifies the user’s correctness over the shared or public channel. In this paper, we analyze an RUA scheme proposed by Chen for the multi-server environment and prove that their scheme is not secured. We also find numerous vulnerabilities such as password guessing attack, replay attack, Registration Center (RC) spoofing attack, session key verification attack, and perfect forward secrecy attack for Chen’s scheme. After performing the cryptanalysis of Chen’s scheme, we propose a biometric-based RUA scheme for the same multi-server environment. We prove that the proposed authentication scheme achieves higher security than Chen’s scheme with the use of informal security analysis as well as formal security analysis. The formal security analysis of the proposed scheme is done using a widely adopted random oracle method.
Emotion recognition from physiological signals and video games to detect personality traits
Authors: Callejas-Cuervo Mauro , Martínez-Tejada Laura Alejandra , Alarcón-Aldana Andrea Catherine
Published: September 2020

This paper presents a system that allows for the identification of two values: arousal and valence, which represent the degree of stimulation in a subject, using Russell’s model of affect as a reference. To identify emotions, a step-by-step structure is used, which, based on statistical data from physiological signal metrics, generates the representative arousal value (direct correlation); from the PANAS questionnaire, the system generates the valence value (inverse correlation), as a first approximation to the techniques of emotion recognition without the use of artificial intelligence. The system gathers information concerning arousal activity from a subject using the following metrics: beats per minute (BPM), heart rate variability (HRV), the number of galvanic skin response (GSR) peaks in the skin conductance response (SCR) and forearm contraction time, using three physiological signals (Electrocardiogram - ECG, Galvanic Skin Response - GSR, Electromyography - EMG).
Leading countries in computer science: A bibliometric overview
Authors: Zurita Gustavo , Merigó José M. , Lobos-Ossandón Valeria , Mulet-Forteza Carles
Published: September 2020

This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995–2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.
Application of wavelet transform image processing technology in financial stock analysis
Authors: Wang Juan
Published: September 2020

Traditional mathematical models have problems in the analysis of financial stocks that are not intuitive enough. In order to improve the intuitiveness of the stock forecasting model, based on the image recognition technology, this study normalizes the image and performs feature recognition with grayscale images. At the same time, this paper ignores the small fluctuations and combines the characteristics of stock images to remove the drying process and proposes an algorithm model based on feature recognition. In addition, in order to improve the image accuracy, the model combines the edge extraction technology to extract features, which reflects the actual rise and fall of the stock. Finally, this paper designs experiments to conduct research and analysis. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Research on basketball players’ action recognition based on interactive system and machine learning
Authors: Li Jin , Gu Deping
Published: September 2020

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes’ gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes’ gesture recognition, the Kalman filter algorithm is used to solve the athlete’s posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory.
Intelligent landscape design and land planning based on neural network and wireless sensor network
Authors: Peng Li
Published: September 2020

From the point of view of urban landscape design objectives, techniques and evaluation, the continuous development of digital information and digital technology has a positive impact on urban landscape design. The core of landscape planning model is to explore various possibilities and determinants of landscape spatial system by changing experimental conditions or parameters.In this paper, the author analyzes the intelligent landscape design and land planning based on neural network and wireless sensor network. The digital generation and construction is through the use of relevant digital technology groups for landscape design and construction activities. Parametric design makes great changes in modern urban landscape planning and design, and has a significant impact on the concept of landscape design, the auxiliary means of design and the construction of landscape entities. It is an indispensable and important link in the process of digital landscape design. Reasonable planning and design of urban landscape can make better use of urban land resources, alleviate the waste of land resources, and optimize the use of resources.
Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics
Authors: Mao Li , Zhang Wanhong
Published: September 2020

In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.
Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval
Authors: Song Gang
Published: September 2020

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.
Performance analysis of distance teaching classroom based on machine learning and virtual reality
Authors: Cai Yuanyuan , Zhao Tingting
Published: September 2020

In remote intelligent teaching, the facial expression features can be recorded in time through facial recognition, which is convenient for teachers to judge the learning status of students in time and helps teachers to change teaching strategies in a timely manner. Based on this, this study applies machine learning and virtual reality technology to distance classroom teaching. Moreover, this study uses different channels to automatically learn global and local features related to facial expression recognition tasks. In addition, this study integrates the soft attention mechanism into the proposed model so that the model automatically learns the feature maps that are more important for facial expression recognition and the salient regions within the feature maps. At the same time, this study performs weighted fusion on the features extracted from different branches, and uses the fused features to re-recognize student features. Finally, this study analyzes the results of this paper through control experiments. The research results show that the algorithm proposed in this paper has good performance and can be applied to the distance teaching system.
Diagnostic evaluation model of English learning based on machine learning
Authors: Chen Yuanyuan , Wang Xuan , Du Xiaohui
Published: September 2020

The diagnostic evaluation model of English learning is difficult to judge the subjective factors in student learning, so some diagnostic evaluation models of English learning are difficult to apply to English learning practice. In order to improve the effect of English learning, based on machine learning technology, this study combines the needs of English evaluation to build a diagnostic evaluation model of English learning based on machine learning. Moreover, this study compares the methods of random forest, Bayesian network, decision tree, perceptron, K-nearest neighbor and multi-model fusion, and selects the best algorithm for diagnostic analysis. The diagnostic evaluation model of English studies constructed in this paper mainly evaluates and judges the errors in students’ English learning. In addition, this study validates the methods proposed in this study through controlled experiments. The research results show that the method proposed in this study has a certain effect.
Feature recognition of spoken Japanese input based on support vector machine
Authors: Han Yanping
Published: September 2020

The feature recognition of spoken Japanese is an effective carrier for Sino-Japanese communication. At present, most of the existing intelligent translation equipment only have equipment that converts English into other languages, and some Japanese translation systems have problems with accuracy and real-time translation. Based on this, based on support vector machines, this research studies and recognizes the input features of spoken Japanese, and improves traditional algorithms to adapt to the needs of spoken language recognition. Moreover, this study uses improved spectral subtraction based on spectral entropy for enhancement processing, modifies Mel filter bank, and introduces several improved MFCC feature parameters. In addition, this study selects an improved feature recognition algorithm suitable for this research system and conducts experimental analysis of input feature recognition of spoken Japanese on the basis of this research model. The research results show that this research model has improved the recognition speed and recognition accuracy, and this research model meets the system requirements, which can provide a reference for subsequent related research.
Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet
Authors: Yu Yanan
Published: September 2020

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.
Monitoring and analysis of athletes’ local body movement status based on BP neural network
Authors: Zhang Ling , Liang Faze
Published: September 2020

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.
Design of English text-to-speech conversion algorithm based on machine learning
Authors: Dongmei Li
Published: September 2020

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.
Learning perception prediction and English hierarchical model based on neural network algorithm
Authors: Wenjuan Zhang
Published: September 2020

The traditional English examination and the current examination system have been unable to meet the needs of the education industry for English examinations. In view of this, based on the neural network algorithm, this study proposes a hierarchical network management model from the user’s perspective. Based on the in-depth study of the neural network, this study combined with the network performance characteristics of large data volume, complex data to propose a new BP neural network algorithm. By dynamically changing the momentum factor and learning rate, the algorithm has greatly improved the accuracy and stability of the error. In addition, this study proposes a user perception prediction model, and the model is continuously trained on the model based on the improved BP neural network algorithm and the monitored network performance. In order to study the performance of the research model, a control experiment is designed to analyze the performance of the model. The research results show that the intelligent model and algorithm proposed in this paper are completely feasible and effective.
Design of intelligent home pension service platform based on machine learning and wireless sensor network
Authors: Cui Yan , Zhang Lijun , Hou Yumei , Tian Ge
Published: September 2020

At present, there is a certain lag in the construction of the service platform of the smart home pension system in my country, which does not reflect the use characteristics of the elderly. In order to improve the reliability of the smart service system for the elderly, this research builds a smart home care service platform based on machine learning and wireless sensor networks around the state of the elderly’s home life, disease stage, physical state, and intellectual state. Moreover, after comparing the advantages and disadvantages of several wireless sensor communication network technologies and in-depth understanding of communication principles and network topology, the overall design of the system is proposed. In addition, this study combines the design requirements of the system to optimize and improve the wearable physiological parameter collection system and focuses on the design and implementation of the hardware and software of the physiological parameter collection module in the construction of the new system platform. Finally, this study analyzes the performance of the model in this study through controlled trials. The results of the study show that the platform constructed in this paper is effective.
Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system
Authors: Ma Wenjuan , Zhao Xuesi , Guo Yuxiu
Published: September 2020

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.
Traditional village protection based on big data under the impact of COVID-19
Authors: Zhaoguo Liu , Tingting Liang , Wenzhan Wang
Published: September 2020

Under the influence of novel corona virus pneumonia epidemic, the protection of traditional villages is put forward higher request. The spread of the epidemic among villages will make the situation of epidemic prevention and control more difficult. As an important part of culture, traditional villages have high historical value. In this paper, the traditional village protection method, a new geographical data algorithm IData storage method. Compared with the traditional ArcGIS method, it improves the efficiency and accuracy of topographic map entry. IData’s data factory can use the symbolic technology of skeleton lines to represent all the figures in the national standard mode, and any complex figure can only be represented by one element. Idate can quickly load data and render symbols in a drawing. With the powerful data processing engine of IData data factory, we can check out the errors that other software can’t find and process the data automatically. Records of the loss of traditional villages can be recorded quickly. The establishment and protection of traditional villages have had a beneficial impact.
The impact of COVID-19 on the protection of rural traditional village
Authors: Gang Li , Fang Wang , Sishi Quan
Published: September 2020

COVID-19’s significant impact on economic and social life has rightfully garnered the attention of citizens and policymakers alike. In response to the pandemic, governments have issued strict guidelines and restrictions to shut down some cities and many rural villages in China. With no cure or vaccine on the horizon, governments are working to mitigate the damage of the lockdowns on rural cultural village. Over the past two decades, rural village has been negatively impacted by terrorism, lack of funding and loss of population. COVID-19 has had similar effect, but in an incredibly short period of time. During the control period of COVID-19, traditional data are widely used in village protection and renewal. Collect and sort out the original data of Huizhou culture to prepare for the subsequent calculation. After the data is ready, the data is processed as the basis of mining its potential application value. In this paper, the key words of Huizhou cultural resources are summarized. The data analysis platform is established. This paper analyzes people’s preference for Huizhou cultural resources. To better realize the more effective and far-reaching development and exploitation of Huizhou cultural resources.
Reversible data hiding method based on pixel expansion and homomorphic encryption
Authors: Anushiadevi R. , Praveenkumar Padmapriya , Rayappan John Bosco Balaguru , Amirtharajan Rengarajan
Published: September 2020

Digital image steganography algorithms usually suffer from a lossy restoration of the cover content after extraction of a secret message. When a cover object and confidential information are both utilised, the reversible property of the cover is inevitable. With this objective, several reversible data hiding (RDH) algorithms are available in the literature. Conversely, because both are diametrically related parameters, existing RDH algorithms focus on either a good embedding capacity (EC) or better stego-image quality. In this paper, a pixel expansion reversible data hiding (PE-RDH) method with a high EC and good stego-image quality are proposed. The proposed PE-RDH method was based on three typical RDH schemes, namely difference expansion, histogram shifting, and pixel value ordering. The PE-RDH method has an average EC of 0.75 bpp, with an average peak signal-to-noise ratio (PSNR) of 30.89 dB. It offers 100% recovery of the original image and confidential hidden messages. To protect secret as well as cover the proposed PE-RDH is also implemented on the encrypted image by using homomorphic encryption. The strength of the proposed method on the encrypted image was verified based on a comparison with several existing methods, and the approach achieved better results than these methods in terms of its EC, location map size and imperceptibility of directly decrypted images.
Parameter estimation in uncertain differential equations with exponential solutions
Authors: Li Zhiming , Ai Mingyao , Sun Shuman
Published: September 2020

This paper proposes three methods to estimate the parameters in uncertain differential equations (UDEs) based on discrete observation data. The first method is designed for a class of UDEs in which their solutions have the explicit expressions of uncertainty distribution. The second method is given to solve the estimation problem through the inverse uncertainty distribution. In the third method, the unknown parameters of UDEs are estimated by the solution of the corresponding α-path. These methods are interpreted to be efficient and practical by using a popular UDE with exponential solutions and obtaining the detailed estimators of the parameters.
An intelligent approach for the evaluation of transformers in a power distribution project
Authors: Samanlioglu Funda , Ayağ Zeki
Published: September 2020

In this study, a hybrid approach is presented for the evaluation and selection of transformers in a power distribution project. Ranking transformers and selecting the best among alternatives is a complex multiple criteria decision making (MCDM) problem with various possibly conflicting quantitative and qualitative criteria. In this research, two hesitant fuzzy MCDM methods; hesitant fuzzy Analytic Hierarchy Process (hesitant F-AHP) and hesitant fuzzy Preference Ranking Organization Method for Enriching Evaluations II (hesitant F-PROMETHEE II) are combined to evaluate and rank transformers. In the hesitant fuzzy AHP-PROMETHEE II, hesitant F-AHP is implemented to determine criteria weights and hesitant F-PROMETHEE II is applied to rank transformer alternatives, utilizing obtained criteria weights. An illustrative example is presented to demonstrate the effectiveness and applicability of the proposed approach. In the example, five transformers are evaluated based on twelve criteria by three decision makers (DMs) and best alternative is selected. For comparison analysis, integration of hesitant F-AHP and hesitant fuzzy Technique for Order Preference by Similarity to Ideal Solution (hesitant F-TOPSIS) is used and results are compared.
N-soft rough sets and its applications
Authors: Zhang Di , Li Pi-Yu , An Shuang
Published: September 2020

In this paper, we propose a new hybrid model called N-soft rough sets, which can be seen as a combination of rough sets and N-soft sets. Moreover, approximation operators and some useful properties with respect to N-soft rough approximation space are introduced. Furthermore, we propose decision making procedures for N-soft rough sets, the approximation sets are utilized to handle problems involving multi-criteria decision-making(MCDM), aiming at electing the optional objects and the possible optional objects based on their attribute set. The algorithm addresses some limitations of the extended rough sets models in dealing with inconsistent decision problems. Finally, an application of N-soft rough sets in multi-criteria decision making is illustrated with a real life example.
Optimization of a fuzzy model used for the prevention of floods in homes surrounding zones of risk in the river Magdalena
Authors: Moreno Jenny , Sánchez Juan , Espitia Helbert
Published: September 2020

Floods are a climatic phenomena that affect different regions worldwide and that produces both human and material losses; for example in 2017, six of the worst floods were the cause of 3.273 deaths worldwide. In Colombia, the strong winter wave presented between 2010 and 2011, caused 1,374 deaths and 1,016 missing persons. The main river in Colombia is the Magdalena, which provides great benefits to the country but is also susceptible to flooding. This article presents a proposal to optimize a fuzzy system to prevent flooding in homes adjacent to areas of risk to the Magdalena River. The method used is based on evolutionary algorithms to perform a global search, including a gradient-based algorithm to improve the solution obtained. The best result achieved was the Mean Square Error (MSE) of 7, 83E - 05. As a conclusion, it is needed to employ optimization methods for the adjustment of parameters of the fuzzy system when considering that the sets and the rules are systematically obtained.
Feature reduction fuzzy C-Means algorithm leveraging the marginal kurtosis measure
Authors: Pan Xingguang , Wang Shitong
Published: September 2020

The feature reduction fuzzy c-means (FRFCM) algorithm has been proven to be effective for clustering data with redundant/unimportant feature(s). However, the FRFCM algorithm still has the following disadvantages. 1) The FRFCM uses the mean-to-variance-ratio (MVR) index to measure the feature importance of a dataset, but this index is affected by data normalization, i.e., a large MVR value of original feature(s) may become small if the data are normalized, and vice versa. Moreover, the MVR value(s) of the important feature(s) of a dataset may not necessarily be large. 2) The feature weights obtained by the FRFCM are sensitive to the initial cluster centers and initial feature weights. 3) The FRFCM algorithm may be unable to assign the proper weights to the features of a dataset. Thus, in the feature reduction learning process, important features may be discarded, but unimportant features may be retained. These disadvantages can cause the FRFCM algorithm to discard important feature components. In addition, the threshold for the selection of the important feature(s) of the FRFCM may not be easy to determine. To mitigate the disadvantages of the FRFCM algorithm, we first devise a new index, named the marginal kurtosis measure (MKM), to measure the importance of each feature in a dataset. Then, a novel and robust feature reduction fuzzy c-means clustering algorithm called the FRFCM-MKM, which incorporates the marginal kurtosis measure into the FRFCM, is proposed. Furthermore, an accurate threshold is introduced to select important feature(s) and discard unimportant feature(s). Experiments on synthetic and real-world datasets demonstrate that the FRFCM-MKM is effective and efficient.
Imbalanced sentiment classification based on sequence generative adversarial nets
Authors: Wang Chuantao , Yang Xuexin , Ding Linkai
Published: September 2020

The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.
Reduction of fuzzy-crisp concept lattice based on order-class matrix
Authors: Lin Yidong , Li Jinjin , Liao Shujiao , Zhang Jia , Liu Jinghua
Published: September 2020

Knowledge reduction is one of critical problems in data mining and information processing. It can simplify the structure of the lattice during the construction of fuzzy-crisp concept lattice. In terms of fuzzy-crisp concept, we develop an order-class matrix to represent extents and intents of concepts, respectively. In order to improve the computing efficiency, it is necessary to reduce the size of lattices as much as possible. Therefore the judgement theorem of meet-irreducible elements is proposed. To deal with attribute reductions, we develop a discernibility Boolean matrix in formal fuzzy contexts by preserving extents of meet-irreducible elements via order-class matrix. A heuristic attribute-reduction algorithm is proposed. Then we extend the proposed model to consistent formal fuzzy decision contexts. Our methods present a new framework for knowledge reduction in formal fuzzy contexts.
Natural History of Type 1 Spinal Muscular Atrophy in a Series of Argentinian Children
Authors: Aguerre V. , De Castro F. , Mozzoni J. , Gravina LP. , Araoz HV. , Monges S.
Published: September 2020

Background: SMA1 natural history is characterized by early development of chronic respiratory failure. Respiratory interventions in type 1 SMA infants are subject to great practice variability. Nusinersen, has been recently approved in Argentina. The advent of novel treatments has highlighted the need for natural history studies reporting disease progression in type 1 SMA. Objective: To analyze the progression, respiratory interventions and survival based on the type of respiratory support in type 1SMA patients, in a third level pediatric hospital in Argentina. Methods: Cohort of SMA1 patients followed at the Interdisciplinary Program for the Study and Care of Neuromuscular Patients (IPNM). Patient survival was analyzed by using the Kaplan-Meier method. Log-rank test was performed to compare the survival curve for three respiratory intervention groups. Results: 59 patients. Mean age of symptom onset was 2.19 (±1.4) months, age at diagnosis was 3.9 (±2.1) months. Patients developed respiratory failure at 5.82 months (±2.32) and 13.8 months (±5.6) in Type 1B and Type 1C, respectively (p < 0.001) 53 p were SMA1B. Three copies were found in 1/6 SMA1C. Respiratory interventions: SRC 23 p (56.1%); SRC + NIV 8 p (19.5%); SRC + IV 10 p (24.4%). 8 patients were already on invasive ventilation when included in the IPNM. Patients with invasive ventilation showed longer survival. Conclusions: This series provides valuable information on respiratory intervention requirements and life expectancy in children with SMA1 before the implementation of novel treatments that increase the expression of the SMA protein.
Squalamine Restores the Function of the Enteric Nervous System in Mouse Models of Parkinson’s Disease
Authors: West Christine L. , Mao Yu-Kang , Delungahawatta Thilini , Amin Jessica Y. , Farhin Sohana , McQuade Rachel M. , Diwakarla Shanti , Pustovit Ruslan , Stanisz Andrew M. , Bienenstock John , Barbut Denise , Zasloff Michael , Furness John B. , Kunze Wolfgang A.
Published: September 2020

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder thought to be caused by accumulation of α-synuclein (α-syn) within the brain, autonomic nerves, and the enteric nervous system (ENS). Involvement of the ENS in PD often precedes the onset of the classic motor signs of PD by many years at a time when severe constipation represents a major morbidity. Studies conducted in vitro and in vivo, have shown that squalamine, a zwitterionic amphipathic aminosterol, originally isolated from the liver of the dogfish shark, effectively displaces membrane-bound α-syn. Objective: Here we explore the electrophysiological effect of squalamine on the gastrointestinal (GI) tract of mouse models of PD engineered to express the highly aggregating A53T human α-syn mutant. Methods: GI motility and in vivo response to oral squalamine in PD model mice and controls were assessed using an in vitro tissue motility protocol and via fecal pellet output. Vagal afferent response to squalamine was measured using extracellular mesenteric nerve recordings from the jejunum. Whole cell patch clamp was performed to measure response to squalamine in the myenteric plexus. Results: Squalamine effectively restores disordered colonic motility in vivo and within minutes of local application to the bowel. We show that topical squalamine exposure to intrinsic primary afferent neurons (IPANs) of the ENS rapidly restores excitability. Conclusion: These observations may help to explain how squalamine may promote gut propulsive activity through local effects on IPANs in the ENS, and further support its possible utility in the treatment of constipation in patients with PD.
Personalized Care Management for Persons with Parkinson’s Disease
Authors: van Halteren Angelika D. , Munneke Marten , Smit Eva , Thomas Sue , Bloem Bastiaan R. , Darweesh Sirwan K. L.
Published: September 2020

There is a growing awareness that delivery of integrated and personalized care is necessary to meet the needs of persons living with Parkinson’s disease. In other chronic diseases than Parkinson’s disease, care management models have been deployed to deliver integrated and personalized care, yielding positive effects on patients’ health outcomes, quality of life and health care utilization. However, care management models have been highly heterogeneous, as there is currently no clear operationalization of its core elements. In addition, most care management models are disease-specific and not tailored to the individual needs and preferences of a patient. In this viewpoint we present an integrated and personalized care management model for persons with Parkinson’s disease costing of five core elements: (1) care coordination, (2) patient navigation, (3) information provision, (4) early detection of signs and symptoms through proactive monitoring and (5) process monitoring. Following the description of each core element, implications for implementing the model into practice are discussed. Finally, we provide clinical and methodological considerations on the evaluation of care management models.
Perceptions of Compensation Strategies for Gait Impairments in Parkinson’s Disease: A Survey Among 320 Healthcare Professionals
Authors: Tosserams Anouk , Nijkrake Maarten J. , Sturkenboom Ingrid H.W.M. , Bloem Bastiaan R. , Nonnekes Jorik
Published: September 2020

Compensation strategies are an essential part of managing gait impairments in people with Parkinson’s disease (PD). We conducted an online survey among 320 healthcare professionals with specific expertise in PD management, to evaluate their knowledge of compensation strategies for gait impairments in people with PD, and whether they applied these in daily practice. Only 35% of professionals was aware of all categories of compensation strategies. Importantly, just 23% actually applied all seven available categories of strategies when treating people with PD in clinical practice. We discuss the clinical implications, and provide recommendations to overcome this knowledge gap.
Complementary feeding practice and associated factors among children aged 6 to 12 months attending governmental health facilities in Addis Ababa: Ethiopia
Authors: Hilemariam Fantu , Ketema Kassahun , Endalifer Melese Linger
Published: September 2020

BACKGROUND: Inappropriate feeding practices, coupled with high rates of infectious diseases, are the proximate causes of malnutrition during the first two years of life. Around 10 million under-five children died annually of which over one-third were due to inappropriate complementary feeding. OBJECTIVE: To assess factors associated with timely initiation of complementary feeding among children aged 6 to 12 months in public health care facilities of Addis Ababa Ethiopia. METHODS: An institutional-based cross-sectional study design was conducted. Data was collected using a pretested structured questionnaire. Binary and multivariable logistic regressions analyses were performed. Variables with p-value ≤0.05 were considered as statistically significant. RESULTS: The proportion of timely initiation of complementary feeding was 65.2% (95% CI = 60.8, 69.5). Being married [AOR = 2.622, 95% CI: 1.182–5.817], belongs to 18–24 years age at first marriage [AOR = 1.671, 95% CI: 1.019–2.742], being grade 9 and above [AOR = 1.910, 95% CI: 1.401–2.258], average income > 1500 Ethiopian birr [AOR = 2.138, 95% CI: 1.762–2.347], and postnatal care follow up [AOR = 1.641, 95% CI:1.435–3.224] were significantly associated with complementary feeding. CONCLUSIONS: In this study, the magnitude of timely initiation complementary feeding was higher as compared to the country level figure. Implementing nutrition strategy which scales-up mother’s living standards improve complementary feedings practices.
Organoid Maturation by Circadian Entrainment
Authors: Alvarez-Dominguez Juan R. , Donaghey Julie , Rasouli Niloofar , Kenty Jennifer H. R. , Helman Aharon , Charlton Jocelyn , Straubhaar Juerg R. , Meissner Alexander , Melton Douglas A.
Published: September 2020

Stem cell-derived tissues that recap endogenous physiology are key for regenerative medicine. Yet, most methods yield products that function like fetal, not adult tissues. Organoids are typically grown in constant environments, while our tissues mature along with behavioral cycles. Here, we show that inducing circadian rhythms in pancreatic islet organoids, by entraining them to daily feeding-fasting cycles, elicits their metabolic maturation. Our results show that rhythms can be harnessed to further functional maturation of organoids destined for human therapeutics.