دانلود و نمایش مقالات مرتبط با Neural network::صفحه 1
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نتیجه جستجو - Neural network

تعداد مقالات یافته شده: 173
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1 تحلیل احساسات مبتنی بر یادگیری عمیق در متن رومی اردو
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 9
آنالیز احساسات با توجه به رویکرد همه جانبه در آنالیز احساسات کاربران شبکه های اجتماعی مختلف، انجمن ها، سایت های بازاریابی الکترونیکی و وبلاگ ها، اهمیت زیادی دارد. داده های مربوط به احساسات در وب اهمیت زیادی دارد و بر مشتریان، خوانندگان و شرکت های تجاری تأثیر می گذارد. شبکه عصبی مکرر به طور گسترده ای در انجام وظایف پردازش زبان طبیعی مورد استفاده قرار گرفته است، زیرا برای مدل سازی داده های متوالی به صورت موثر طراحی شده است.
در این مقاله از مدل عصبی عمیق حافظه کوتاه-طولانی مدت (LSTM) استفاده شده است. توانایی فوق العاده ای در ضبط اطلاعات دور برد و حل مشکل کاهش گرادیان و همچنین ارائه اطلاعات متنی آتی، معناشناسی توالی لغات با شکوه دارد. این مقاله پایه و اساس تطبیق روش های یادگیری عمیق در آنالیز رومن اردو است. نتایج تجربی نشان داد که مدل ما دقت قابل توجهی دارد و دقت بیشتری از روش های یادگیری ماشین دارد.
کليدواژه: شبکه عصبی مکرر (RNN)| حافظه کوتاه-بلند مدت (LSTM) | آنالیز معنایی رومن اردو | تعبیه لغت
مقاله ترجمه شده
2 A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
مقاله انگلیسی
3 Processing big-data with Memristive Technologies: Splitting the Hyperplane Efficiently
پردازش داده های بزرگ با تکنولوژی Memristive: تقسیم Hyperplane به طور موثر-2018
An important cornerstone of data processing is the ability to efficiently capture structure in data. This entails treating the input space as a hyperplane that needs partitioning. We argue that several modern electronic systems can be understood as carrying out such partitionings: from standard logic gates to Artificial Neural Networks (ANNs). More recently, memristive technologies equipped such systems with the benefit of continuous tuneability directly in hardware, thus rendering these reconfigurable in a power and space efficient manner. Here, we demonstrate several proof-of-concept examples where memristors enable circuits optimised to carry out different flavours of the fundamental task of splitting the hyperplane. These include threshold logic and receptive field based classifiers that are presented within the context of a unified perspective.
Keywords: memristor, Metal Oxide RRAM, Artificial Neural Networks, Threshold Logic Gates, Template Pixel, Texel, Clusterer ,Fuzzy Gate
مقاله انگلیسی
4 Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
پیش بینی حجم گردشگر موثر پشتیبانی شده توسط PCA و BPNN بهبود یافته با استفاده از شاخص بایدو-2018
The precise forecasting of tourist volume is a very challenging task. This paper aims to propose an effective model named PCA-ADE-BPNN for forecasting tourist volume based on Baidu index. The principal component analysis (PCA), a dimensional reduction, is employed to decorrelate the input data before training a back propagation neural network (BPNN) architecture, and the adaptive differential evolution algorithm (ADE) is for getting global optimization of BP networks weight values and threshold values to enhance the forecasting performance of BPNN. The PCA-ADE-BPNN model is a new combination of a dimensional reduction algorithm, an optimization algorithm, and a neural network. The validity of this model is demonstrated by conducting case studies of Beijing City and Hainan Province, China. The results indicate the proposed PCA-ADE-BPNN always outperforms other models in terms of forecasting accuracies. Therefore, the proposed PCA-ADE-BPNN is a potential candidate for the effective forecasting of tourist volume.
keywords: Tourist volume forecasting |Principal component analysis |Baidu index |Back-propagation neural network |Adaptive differential evolution
مقاله انگلیسی
5 Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018
Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization
مقاله انگلیسی
6 Artificial neural network based prediction of malaria abundances using big data: A knowledge capturing approach
پیشگیری از فراوانی مالاریا بر اساس شبکه عصبی مصنوعی با استفاده از داده های بزرگ: رویکرد جذب دانش-2018
Background and objective: Malaria is one of the most prevalent diseases in urban areas. Malaria flourishes in subtropical countries and affect the public health. The impact is very high, where health monitoring facilities are very limited. To minimize the impact of malaria population in sub-tropical domains, a suitable disease prediction model is required. The objective of this study is to determine the malaria abundances using clinical and environmental variables with Big Data on the geographical location of Khammam district, Telanagana, India. Methods: Prediction model is based on the data collected from primary health centres of department of vector borne diseases (DVBD) of Khammam district and satellite data such as rain fall, relative humidity, temperature and vegetation taken for the time period of 1995–2014. In this study, we test the efficacy of the artificial neural network (ANN) for mosquito abundance prediction. Prediction model was developed for the period of 2015 using a feed forward neural network and compared with the observed values. Results and conclusions: The results vary from area to area based on clinical variables and rainfall in the prediction model corresponding to areas. The average error of the prediction model ranges from 18% to 117%. Clinical data such as number of patients treated with symptoms and without symptoms can improve the prediction level when combined with environmental variables. We perform preliminary findings of malaria abundances by collecting clinical big data across different seasons. Further, more exploration is required in prediction of malaria using big data to improve the accuracy in real practice. In this manuscript, we perform some preliminary findings of malaria abundances by collecting larger data across different seasons. Till today, many models have been developed to examine the malaria prediction with different approaches, but malaria prediction with environmental and clinical data is a new approach with big data analysis.
Keywords: Malaria prediction ، Primary health centers (PHCs) ، Big data ، Artificial neural networks (ANNs)
مقاله انگلیسی
7 A Novel Adaptive Feature Extraction for Detection of Cardiac Arrhythmias Using Hybrid Technique MRDWT & MPNN Classifier from ECG Big Data
رویکرد استخراج ویژگی تطبیقی برای تشخیص آریتمی های قلب با استفاده از تکنیک ترکیبی MRDWT و MPNN طبقه بندی از داده بزرگ ECG-2018
The efficient automatic detection of cardiac arrhythmia using a hybrid technique from ECG big data has been proposed with novel feature extraction technique using Multiresolution Discrete Wavelet Transform (MRDWT) and Multilayer Probabilistic Neural Network (MPNN) classifier. Big Data of ECG signals have been selected from MIT–BIH arrhythmia database for detection of two types of arrhythmias LBBB (Left Bundle Branch Block) and RBBB (Right Bundle Branch Block). The proposed technique can accurately detect and classify LBBB and RBBB along with normal heartbeat. A novel and hybrid method of detection of cardiac arrhythmia have four main stages: denoising of raw ECG, baseline wander removal, proposed feature extraction, and detection of abnormal heartbeats using MPNN neural classifier. 8600 ECG beats were selected, including 4200 normal and 4400 abnormal beats (2200 LBBB and 2200 RBBB) were utilized for testing the proposed technique. The detection outcome using MPNN was compared with other two neural classifiers: Feed Forward Neural Network (FFNN) and Back Propagation Neural Network (BPNN) classifiers. The accuracy and efficiency of classifiers performance were attained in terms of CER (Classification Error Rate), SP (Specificity), Se (Sensitivity), Pr (Precision), PPr (Positive Predictivity) and F Score. The system performance is achieved with 96.22%, 97.15% and 99.07% overall accuracy using FFNN, BPNN and MPNN. The average percentage of classification error rate (CER) using MPNN classifier is lowest 0.62% whereas FFNN and BPNN show 2.2% and 1. 90% average CER.
Keywords: Big data ، Cardiac arrhythmias ،Biomedical signal processing ، Artificial intelligence ، Machine learning
مقاله انگلیسی
8 A dynamic neural network architecture with immunology inspired optimization for weather data forecasting
یک معماری شبکه عصبی پویا با ایمنولوژی بهینه سازی برای پیش بینی داده های آب و هوایی-2018
Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.
Keywords: Recurrent Neural Networks ،Immune Systems Optimisation، Time Series Data analytics ، weather forecasting
مقاله انگلیسی
9 Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System
پیش بینی مصارف صرع با استفاده از داده های بزرگ و یادگیری عمیق: به سوی سیستم سیار-2018
Background: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individuals needs. Methods: Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results: The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion: This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.
Keywords: Epilepsy ، Seizure prediction ، Artificial intelligence، Deep neural networks ، Mobile medical devices ، Precision medicine
مقاله انگلیسی
10 ارزیابی کارایی تکنیک های طبقه بندی داده کاوی برای پیش بینی بیماری قلبی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 12
بیماری قلبی ممکن است یکی از دلایل اصلی مرگ باشد. به علت فقدان دانش و تجربیات متخصصان درمورد علائم نارسایی قلب برای پیش بینی اولیه این بیماری، کار آسان برای تشخیص بیماری نیست. در نتیجه، پیش بینی مبتنی بر رایانه؛ مبتلایان به بیماری قلبی می تواند نقش مهمی را در تشخیص پیش از مرحله برای انجام اقدامات مناسب با توجه به بهبودی بیماران بازی کند. با این حال، انتخاب روش طبقه بندی مناسب داده کاوی می تواند به طور موثر پیش بینی مرحله اولیه بیماری را برای بازگشت از آن به همراه داشته باشد. در این مقاله، سه تکنیک طبقه بندی استفاده شده غالب از قبیل ماشین بردار پشتیبانی (SVM)، نزدیکترین همسایۀ k (KNN) و شبکه عصبی مصنوعی (ANN) را مورد بررسی قرار می دهیم، با توجه به ارزیابی آنها برای پیش بینی بیماری های قلبی با استفاده از مجموعه داده های بیماری کلیوی استاندارد مورد مطالعه قرار گرفته است.. نتایج تجربی نشان می دهد که دقت طبقه بندی با استفاده از SVM (85.1852٪) بهتر از استفاده از KNN (82663٪) و ANN (73.3333٪) است.
لغات کلیدی: داده کاوی | ماشین بردار پشتیبانی | نزدیکترین همسایۀ k | شبکه عصبی مصنوعی | پیش بینی بیماری قلبی | تکنیک های طبقه بندی
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