66 research outputs found
Time series prediction and forecasting using Deep learning Architectures
No full textNature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures
Performance Evaluation of Interactive Video Streaming over WiMAX Network
No full textNowadays, the desire of internet access and the need of digital encodings have influenced quite a large number of users to access high quality video application. Offering multimedia services not only to the wired but to wireless mobile client is becoming more viable. In wireless medium, video-streaming still has high resource requirements, for example, bandwidth, traffic priority, smooth play-backs. Therefore, bandwidth demands of these applications are far exceeding the capacity of 3G and Wireless Local Area Networks (LANs). The current research demonstrates the introductory understanding of the Worldwide Interoperability for Microwave Access (WiMax) network, applications, the mechanisms, its potential features, and techniques used to provide QoS in WiMAX, and lastly the network is simulated to report the diverse requirements of streamed video conferencing traffic and its specifications. For this purpose two input parameters of video traffic are selected, i.e, refresh rate, which is monitored in terms of frames per second and pixel resolutions which basically counts the number of pixels in digital imaging. The network model is developed in OPNET. Different outcomes from simulation based models are analyzed and appropriate reasons are also discussed. Apart from this, the second aim of the current research is to address whether WiMAX access technology for streaming video applications could provide comparable network performance to Asymmetric Digital Subscriber Line (ADSL). For this purpose network metrices such as End to End delay and throughput is taken into consideration for optimization.</jats:p
Time Series Forecasting for Outdoor Temperature using Nonlinear Autoregressive Neural Network Models
No full textWeather forecasting is a challenging time series forecasting problem because of its dynamic, continuous, data-intensive, chaotic and irregular behavior. At present, enormous time series forecasting techniques exist and are widely adapted. However, competitive research is still going on to improve the methods and techniques for accurate forecasting. This research article presents the time series forecasting of the metrological parameter, i.e., temperature with NARX (Nonlinear Autoregressive with eXogenous input) based ANN (Artificial Neural Network). In this research work, several time series dependent Recurrent NARX-ANN models are developed and trained with dynamic parameter settings to find the optimum network model according to its desired forecasting task. Network performance is analyzed on the basis of its Mean Square Error (MSE) value over training, validation and test data sets. In order to perform the forecasting for next 4,8 and 12 steps horizon, the model with less MSE is chosen to be the most accurate temperature forecaster. Unlike one step ahead prediction, multi-step ahead forecasting is more difficult and challenging problem to solve due to its underlying additional complexity. Thus, the empirical findings in this work provide valuable suggestions for the parameter settings of NARX model specifically the selection of hidden layer size and autoregressive lag terms in accordance with an appropriate multi-step ahead time series forecasting
A Hybrid Approach for Time Series Forecasting Using Deep Learning and Nonlinear Autoregressive Neural Networks
No full textDuring recent decades, several studies have been conducted in the field of weather forecasting providing various promising forecasting models. Nevertheless, the accuracy of the predictions still remains a challenge. In this paper a new forecasting approach is proposed: it implements a deep neural network based on a powerful feature extraction. The model is capable of deducing the irregular structure, non-linear trends and significant representations as features learnt from the data. It is a 6-layered deep architecture with 4 hidden units of Restricted Boltzmann Machine (RBM). The extracts from the last hidden layer are pre-processed, to support the accuracy achieved by the forecaster. The forecaster is a 2-layer ANN model with 35 hidden units for predicting the future intervals. It captures the correlations and regression patterns of the current sample related to the previous terms by using the learnt deep-hierarchal representations of data as an input to the forecaster
Powered by caring: daily struggles to keep the WPS Agenda alive. Interview with Sanam Naraghi-Anderlini
No full textThis interview originated from the encounter between the guest editors of the Special Issue and Sanam Naraghi-Anderlini during the activities of the project “Enhancing Women’s Participation in Peace and Security (WEPPS)”. The interview was held via Zoom on the afternoon of October 1, 2021. At the time, the international community was dealing with the consequences of the sudden US withdrawal from Afghanistan that occurred on August 31. Sanam Naraghi Anderlini is a British-Iranian activist and researcher who has acquired about twenty-five years of experience in the field of women, peace and security. Having participated as a civil society leader to the drafting of UNSC Resolution 1325, she has worked in several projects and initiatives concerning women’s participation to peacebuilding processes. Founder and Executive Director of the International Civil Society Action Network (ICAN), she spearheads the Women’s Alliance for Security Leadership (WASL). She is the author of Women Building Peace, What they do, Why it Matters (Lynne Reinner Publishers, 2007). In 2011, she was appointed as the first Senior Expert on Gender and Inclusion on the UN Mediation Standby Team. She has been working in a number of conflict situations in different regions of the world (e.g. Somalia, Libya, Syria, Nepal). In 2019, she joined the London School of Economics and Political Science (LSE) as Director of the Centre for Women, Peace and Security. In 2020, she was awarded an MBE for her services to International peacebuilding and Women’s Rights
EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder
No full textA Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information. During EEG acquisition, artifacts are induced due to involuntary eye movements or eye blink, casting adverse effects on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy. One of the designed SAE models outperforms the performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study, may provide a contribution towards the state of the art performance on the problem of EEG eye state classification
Internet of Plants Application for Smart Agriculture
No full textNowadays, Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex system. The IoT provides the acquired data from the environment to the Internet through the
service providers. This further helps users to view the numerical or plotted data. In addition, it also allows objects which are located in long distances to be sensed and controlled remotely through embedded devices which are important in agriculture domain. Developing such a system for the IoT is a very complex task due to the diverse variety of devices, link layer technologies, and services. This paper proposes a practical approach to acquiring data of temperature, humidity and soil moisture of plants. In order to accomplish this, we developed a prototype device and an android application which acquires physical data and sends it to cloud. Moreover, in the subsequent part of current research work, we have focused towards a temperature forecasting application. Forecasting metrological parameters have a profound influence on crop growth, development and yields of agriculture. In response to this fact, an application is developed for 10 days ahead maximum and minimum temperatures forecasting using a type of recurrent neural network
The Depiction of Class Conflict in Qurratulain Haider's Novel” Mere Bhi Sanam khane”: قرۃالعین حیدر کے ناول’’ میرے بھی صنم خانے‘‘ میں طبقاتی کشمکش کی عکاسی
No full textThe writer is often referred to as the "eyes of society." Whatever the writer observes in society, they blend with imagination and bring it to the page, presenting various subjects through their creations. In fictional literature, diverse societal themes are found, reflecting different aspects of life. Novels, in addition to other topics, often explore the conflict between different social classes. The shining star of Urdu literature, Qurratulain Haider, has reflected this class conflict in her various works. Her first novel, Mere Bhi Sanam khane, deals with the decline of the upper class due to the Partition of India. In this novel, she has portrayed various social classes. The subject of this research paper is an analysis of the depiction of class conflict in Qurratulain Haider's novel Mere Bhi Sanam khane. The purpose of this research study is to explore how class conflict is expressed in the novel, which classes are represented, and what perspective the author has on these different classes. By using Qualitative research methods, it has been concluded that the author has skillfully portrayed the various social classes of her time in this novel. She highlights the nature of the relationships between these classes, presenting the behaviors and characteristics of characters from different classes, thus bringing forth both the virtues and flaws of that era. The study also examines to what extent the author successfully highlights the existing conflict between these social classes.
Keywords: Qurratulain Haider, Class conflict, Mere Bhi Sanamkhane, Partition of India, Depiction of social classes
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