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Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data 15th

Long-Short Term Memory for an Effective Short-Term Weather F...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Hewage, Pradeep Behera, Ardhendu Trovati, Marcello Pereira, Ella Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom

ISBN: (纸本)9783030198220

Numerical Weather Prediction (NWP) requires considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a lightweight data-driven weather forecasting model by exploring state-of-the-art deep learning techniques based on Artificial Neural Network (ANN). Weather information is captured by time-series data and thus, we explore the latest Long Short-Term Memory (LSTM) layered model, which is a specialised form of Recurrent Neural Network (RNN) for weather prediction. the aim of this research is to develop and evaluate a short-term weather forecasting model using the LSTM and evaluate the accuracy compared to the well-established Weather Research and Forecasting (WRF) NWP model. the proposed deep model consists of stacked LSTM layers that uses surface weather parameters over a given period of time for weather forecasting. the model is experimented with different number of LSTM layers, optimisers, and learning rates and optimised for effective short-term weather predictions. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate short-term weather forecasting. © 2019, IFIP international Federation for Information Processing.

关键词： Long short-term memory

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Performance Analysis of Convolutional Neural Network Models

Performance Analysis of Convolutional Neural Network Models

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international conference on Advanced computer Control, ICACC

作者： S Kala Debdeep Paul Babita R Jose Jimson Mathew S Nalesh Cochin University of Science And Technology Kerala India - 682 022 Department of Electrical Engineering IIT Patna India Department of Computer Science and Engineering IIT Patna India Department of Electronics Cochin University of Science And Technology Kerala India - 682 022

Deep Neural Networks (DNNs) have become a promising solution for numerous machine learning tasks, which include object detection, classification, weather forecasting, video surveillance and safety management. Convolutional Neural Network (CNN) is a class of DNN, which can achieve state-of-art accuracy and high performance for most of the computer vision tasks. Training of CNNs is computationally intensive and may take multiple days for completion. In this work we have performed the feed forward path implementation and training of AlexNet, VGG-16 and ResNet models on Nvidia GeForce GTX 1080 Ti GPU with 3584 CUDA Cores, 1582 MHz, 11GB GDDR5X, using Caffe framework. Inference and training of these models are also performed on Nvidia Jetson TX2 hardware platform. We also present a detailed analysis of graphics processing unit (GPU) and general purpose processor (GPP) implementations of AlexNet, VGG-16 and ResNet-50 network models. A hardware architecture for performing convolution operations in feed forward path of CNN has also been implemented in Virtex-7 XC7VX690T FPGA, with throughput of 184 GFLOPS for AlexNet, 172 GFLOPS for VGG-16 and 197 GFLOPS for ResNet-50 model.

关键词： convolutional neural nets graphics processing units learning (artificial intelligence)

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Learning Automata-Based Solutions to the Single Elevator Problem 15th

Learning Automata-Based Solutions to the Single Elevator Pro...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Ghaleb, O. John Oommen, B. School of Computer Science Carleton University OttawaK1S 5B6 Canada

ISBN: (纸本)9783030198220

the field of AI has been a topic of interest for the better part of a century, where the goal is to have computers mimic human behaviour. Researchers have incorporated AI in different problem domains, such as autonomous driving, game playing, diagnosis and security. this paper concentrates on a subfield of AI, i.e., the field of Learning Automata (LA), and to use its tools to tackle a problem that has not been tackled before using AI, namely the problem of the optimally scheduling and parking of elevators. In particular, we are concerned with determining the Elevators’ optimal "parking" location. In this paper, we specifically work with the Single (We consider the more complicated multi-elevator problem in a forthcoming paper.) Elevator Problem (SEP), and show how it can be extended to the solution to Elevator-like Problems (ELPs), which are a family of problems with similar characteristics. Here, the objective is to find the optimal parking floors for the single elevator scenario so as to minimize the passengers’ Average Waiting Time (AWT). Apart from proposing benchmark solutions, we have provided two different novel LA-based solutions for the single-elevator scenario. the first solution is based on the well-known LRI scheme, and the second solution incorporates the Pursuit concept to improve the performance and the convergence speed of the former, leading to the LRI scheme. the simulation results presented demonstrate that our solutions performed much better than those used in modern-day elevators, and provided results that are near-optimal, yielding a performance increase of upÂ to 80%. © 2019, IFIP international Federation for Information Processing.

关键词： Learning systems

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Interpretability of a Deep Learning Model for Rodents Brain Semantic Segmentation 15th

Interpretability of a Deep Learning Model for Rodents Brain ...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Matos, Leonardo Nogueira Rodrigues, Mariana Fontainhas Magalhães, Ricardo Alves, Victor Novais, Paulo Computer Science Department Federal University of Sergipe São Cristóvão Brazil School of Medicine University of Minho Braga Portugal Algoritmi Center University of Minho Braga Portugal

ISBN: (纸本)9783030198220

In recent years, as machine learning research has become real products and applications, some of which are critical, it is recognized that it is necessary to look for other model evaluation mechanisms. the commonly used main metrics such as accuracy or F-statistics are no longer sufficient in the deployment phase. this fostered the emergence of methods for interpretability of models. In this work, we discuss an approach to improving the prediction of a model by interpreting what has been learned and using that knowledge in a second phase. As a case study we have used the semantic segmentation of rodent brain tissue in Magnetic Resonance Imaging. By analogy with what happens to the human visual system, the experiment performed provides a way to make more in-depth conclusions about a scene by carefully observing what attracts more attention after a first glance in en passant. © 2019, IFIP international Federation for Information Processing.

关键词： Magnetic resonance imaging

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Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms 15th

Hybrid Data Set Optimization in Recommender Systems Using Fu...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Papaleonidas, Antonios Pimenidis, Elias Iliadis, Lazaros School of Engineering Department of Civil Engineering Faculty of Mathematics Programing and General Courses Democritus University of Thrace Kimmeria Xanthi Greece Department of Computer Science and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom

ISBN: (纸本)9783030198220

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. the evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations. © 2019, IFIP international Federation for Information Processing.

关键词： Fuzzy logic

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Design of Reversible Shift Registers Minimizing Number of Gates, Constant Inputs and Garbage Outputs 7

Design of Reversible Shift Registers Minimizing Number of Ga...

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7th international conference on advances in Computing, Communications and Informatics, ICACCI 2018

作者： Ghose, Partho Rahman, Md Naimur Alam Polash, Md Masbaul Acharjee, Uzzal Kumar Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh

ISBN: (纸本)9781538653142

Reversible logic is an emerging technology that plays an important role in the fields of low power computation and can be applied in cryptography, communications, quantum computing etc. Reversible shift registers are one of the most important elements in fabricating reversible memory circuits. In this paper, we present efficient design of different reversible shift registers such as Serial In Serial Out, Serial In Parallel Out, Parallel In Serial Out and Parallel In Parallel Out registers. We have also outlined appropriate lemmas to illustrate different properties of the proposed designs. Suitable algorithms for designing the reversible shift registers are also mentioned. Comparative analysis reveals that our proposed design requires minimal number of reversible gates and constant inputs, and also produces less number of garbage outputs than the state-of-the-art design. Furthermore, to clarify the validity of our design, all the proposed circuits have been simulated using DSCH3. © 2018 IEEE.

关键词： Shift registers

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Shot Boundary Detection for Automatic Video Analysis of Historical Films 2nd

Shot Boundary Detection for Automatic Video Analysis of Hist...

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2nd international Workshop on Recent advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and Processing, ICIAP 2019

作者： Helm, Daniel Kampel, Martin Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

ISBN: (纸本)9783030307530

In automatic video content analysis and film preservation, Shot Boundary Detection (SBD) is a fundamental pre-processing step. While previous research focuses on detecting Abrupt Transitions (AT) as well as Gradual Transitions (GT) in different video genres such as sports movies or news clips only few studies investigate in the detection of shot transitions in historical footage. the main aim of this paper is to create an SBD mechanism inspired by state-of-the-art algorithms which is applied and evaluated on a self-generated historical dataset as well as a publicly available dataset called Clipshots. therefore, a three-stage pipeline is introduced consisting of a Candidate Frame Range Selection based on the network DeepSBD, Extraction of Convolutional Neural Network (CNN) Features and Similarity Calculation. A combination of pre-trained backbone CNNs such as ResNet, VGG19 and SqueezeNet with different similarity metrics like Cosine Similarity and Euclidean Distance are used and evaluated. the outcome of this paper displays that the proposed algorithm reaches promising results on detecting ATs in historical videos without the need of complex optimization and re-training processes. Furthermore, it points out the main challenges concerning historical footage such as damaged film reels, scratches or splices. the results of this paper contribute a significant base for future research on automatic video analysis of historical videos. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Predictive Analysis to Improve Crop Yield using a Neural Network Model 7

Predictive Analysis to Improve Crop Yield using a Neural Net...

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7th international conference on advances in Computing, Communications and Informatics, ICACCI 2018

作者： Kulkarni, Shruti Mandal, Shah Nawaz Sharma, G. Srivatsa Mundada, Monica R. Meeradevi Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9781538653142

Agriculture has been the sector of paramount importance as it feeds the country population along with contributing to the GDP. Crop yield varies with a combination of factors including soil properties, climate, elevation and irrigation technique. Technological developments have fallen short in estimating the yield based on this joint dependence of the said factors. Hence, in this project a data-driven model that learns by historic soil as well as rainfall data to analyse and predict crop yield over seasons in several districts, has been developed. For this study, a particular crop, Rice is considered. the designed hybrid neural network model identifies optimal combinations of soil parameters and blends it with the rainfall pattern in a selected region to evolve the expectable crop yield. the backbone for the predictive analysis model with respect to the rainfall is based on the Time-Series approach in Supervised Learning. the technology used for the final prediction of the crop yield is again a branch of Machine Learning, known as Recurrent Neural Networks. With two inter-communicating data-driven models working at the backend, the final predictions obtained were successful in depicting the interdependence between soil parameters for yield and weather attributes. © 2018 IEEE.

关键词： Time series analysis

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A Machine Learning Tool for Interpreting Differences in Cognition Using Brain Features 15th

A Machine Learning Tool for Interpreting Differences in Cogn...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Azevedo, Tiago Passamonti, Luca Lió, Pietro Toschi, Nicola Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge Cambridge United Kingdom Department of Biomedicine and Prevention University of Rome "Tor Vergata" Rome Italy A.A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Boston United States

ISBN: (纸本)9783030198220

Predicting variability in cognition traits is an attractive and challenging area of research, where different approaches and datasets have been implemented with mixed results. Some powerful Machine Learning algorithms employed before are difficult to interpret, while other algorithms are easy to interpret but might not be as powerful. To improve understanding of individual cognitive differences in humans, we make use of the most recent developments in Machine Learning in which powerful prediction models can be interpreted with confidence. We used neuroimaging data and a variety of behavioural, cognitive, affective and health measures from 905 people obtained from the Human Connectome Project (HCP). As a main contribution of this paper, we show how one could interpret the neuroanatomical basis of cognition, with recent methods which we believe are not yet fully explored in the field. By reducing neuroimages to a well characterised set of features generated from surface-based morphometry and cortical myelin estimates, we make the interpretation of such models easier as each feature is self-explanatory. the code used in this tool is available in a public repository: https://***/tjiagoM/interpreting-cognition-paper-2019. © 2019, IFIP international Federation for Information Processing.

关键词： Machine learning

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Data visualization of supplier selection using business intelligence dashboard 6th

Data visualization of supplier selection using business inte...

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6th international conference on advances in Visual Informatics, IVIC 2019

作者： Nik Alwi, Nik Nur Ayuni Hassan, Noor Hafizah Baharuden, Ahmad Fazreen Bakar, Nur Azaliah Abu Maarop, Nurazean Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Data Micron Sdn. Bhd. Jalan Pinang Kuala Lumpur Malaysia

ISBN: (纸本)9783030340315

the emergent of Business Intelligence (BI) tools and techniques could help the transportation company decision maker to make informed decisions regarding their supplier selection. this research aims to help transportation company to identify factors influencing supplier selection in the transportation company also to visualize their data graphically. Other than that, the aim is to highlight the importance of business intelligence dashboard to their business and how it could help them in getting the information from their data thus to aid them to focus straight on the main problem of their company. Data visualization is one of the methods used in this study. BI dashboard of supplier selection is developed throughout this research. Based on the findings, there are three factors that strongly influencing supplier selection which are Price, Quality, and Delivery and data visualization offers a pictorial story platform that makes the decision makers understand the information in a timely manner compared to previous approach which is traditional way that needs the usage of complex queries and involve a time-consuming manipulation. Multiple graphs, charts also other visualizations could be created promptly by using data visualization software or tools. Results from evaluation shows that BI dashboard is very useful to aid transportation company to visualize the information related on supplier selection for decision making process. © Springer Nature Switzerland AG 2019.

关键词： Data visualization

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