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Accurate Trajectory Prediction in a Smart Building using Recurrent Neural Networks 20

Accurate Trajectory Prediction in a Smart Building using Rec...

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ACM international Joint conference on Pervasive and Ubiquitous computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Das, Anooshmita Kolvig-Raun, Emil Stubbe Kjaergaard, Mikkel Baun Univ Southern Denmark Odense Denmark

ISBN: (纸本)9781450380768

Occupant behavioral patterns, once extracted, could reveal cues about activities and space usage that could effectively get used for building systems to achieve energy savings. The ability to accurately predict the trajectories of occupants inside a room branched into different zones has many notable and compelling applications. For example - efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, security, and evacuation or managing personnel. This paper proposes future occupant trajectory prediction using state-of-the-art time series prediction methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. These models are being implemented and compared to forecast occupant trajectories at a given time and location in a non-intrusive and reliable manner. The considered test-space for the collection of the dataset is a multi-utility area in an instrumented public building. The deployed 3D Stereo Vision Cameras capture the spatial location coordinates (x- and y- coordinates) from a bird's view angle without eliciting any other information that could reveal confidential data or uniquely identify a person. Our results showed that the GRU model forecasts were considerably more accurate than the LSTM model for the trajectory prediction. GRU prediction model achieved a Mean Squared Error (MSE) of 30.72 cm between actual and predicted location coordinates, and LSTM achieved an MSE of 47.13 cm, respectively, for multiple occupant trajectories within the monitored area. Another evaluation metric Mean Absolute Error (MAE) is used, and the GRU prediction model achieved an MAE of 3.14 cm, and the LSTM model achieved an MAE of 4.07 cm. The GRU model guarantees a high-fidelity occupant trajectory prediction for any given case with higher accuracy when compared to the baseline LSTM model.

关键词： Occupant Behavior pattern recognition Knowledge Discovery Trajectory Prediction Prediction Models LSTM GRU Deep Learning

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Person Localization in an Indoor Environment with Artificial Intelligence 19

Person Localization in an Indoor Environment with Artificial...

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5th international conference on computing and Artificial Intelligence (ICCAI)

作者： Acevedo, Elena Orozco, Ricardo Acevedo, Antonio Felipe, Federico Inst Politecn Nacl Av IPN S-N Lindavista 07738 Mexico

ISBN: (纸本)9781450361064

Associative models are Artificial Intelligence tools and have been used in many applications such as pattern recognition, classification, encryption, among others. In this paper we applied these models to trace a person in an indoor environment by the means of the power of a Wi-Fi signal. We deal with this problem as a classification task. We used a preprocessing for the data to improve the results. Our performance was 95.75%.

关键词： Artificial Intelligence Associative Models pattern recognition Classification User Localization Indoor Environment

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The Elastic k-Nearest Neighbours Classifier for Touch Screen Gestures 18th

The Elastic k-Nearest Neighbours Classifier for Touch Screen...

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18th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Rzecki, Krzysztof Siwik, Leszek Baran, Mateusz Cracow Univ Technol Fac Phys Math & Comp Sci Ul Warszawska 24 PL-31155 Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9783030209124;9783030209117

Touch screen gestures are a well-known method of person authentication in mobile devices. In most applications it is, however, reduced to checking if the user entered the correct pattern. Using additional information based on the speed and shape of finger movements can provide higher security without significantly impacting the convenience of this authorization method. In this work a new distance function for the k-nearest neighbour (kNN) classifier is considered in the problem of person recognition based on touch screen gestures. The function is based on the well-known L-p distance and the elastic distance considered in elastic shape analysis. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of 12 people. Only four gesture performances per gesture for each person are used to train a model. The effects of sampling rate on the classifier performance is also measured. The kNN classifier with the proposed distance function has higher accuracy than both the L-p distance and the elastic distance.

关键词： Touch screen gestures Biometrics Classification Elastic shape analysis pattern recognition

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Coniferous Trees Needles-Based Taxonomy Classification

Coniferous Trees Needles-Based Taxonomy Classification

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international conference on Image and Vision computing New Zealand (IVCNZ)

作者： Haindl, Michal Zid, Pavel Czech Acad Sci Inst Informat Theory & Automat Pattern Recognit Dept Prague Czech Republic

ISBN: (纸本)9781728141879

This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species.

关键词： Coniferous needles categorization Tree taxonomy recognition Spiral Markov random field model

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Extended Goal recognition: A Planning-Based Model for Strategic Deception 21

Extended Goal Recognition: A Planning-Based Model for Strate...

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international conference on Autonomous Agents and Multiagent Systems

作者： Peta Masters Michael Kirley Wally Smith The University of Melbourne

ISBN: (纸本)9781713832621

Goal recognition is the problem of determining an agent's intent by observing its actions. In the context of AI research, the problem is tackled for two quite different purposes: to determine an agent's most probable goal or, for human-aware planning including planned - or strategic - deception, to determine an observer's most likely belief about that goal. Making no distinction, contemporary models tend to assume an infallible observer, deceived only while it has limited access to information or if the environment itself is only partially observable. Focusing on the second purpose, we propose an extended framework that incorporates formal definitions of confirmation bias, selective attention and memory decay. In contrast to pre-existing models, our approach combines explicit consideration of prior probabilities with a principled representation of observer confidence and distinguishes between potential observations - i.e., every observable event within the observer's frame of reference - and recalled observations which we model as a function of attention and memory. We show that when these factors are taken into consideration, false beliefs may arise and can be made to persist, even in a fully observable environment - thus providing a perceptual model readily incorporated into the "thinking" of an adversarial agent for the purpose of strategic deception.

关键词： Goal recognition Deception Planning Probabilistic reasoning Deception soft computing Access to Information Memory Decay Planning Selective Attention limited access

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Optimization of Modular Neural Networks for pattern recognition with Parallel Genetic Algorithms 18th

Optimization of Modular Neural Networks for Pattern Recognit...

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18th Mexican international conference on Artificial Intelligence (MICAI)

作者： Valdez, Fevrier Melin, Patricia Castillo, Oscar Tijuana Inst Technol Div Grad Studies Calzada Tecnol S-N Tijuana 22450 Mexico

ISBN: (纸本)9783030337490;9783030337483

We describe in this paper the use of Modular Neural Networks (MNN) for pattern recognition with parallel processing using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN for face recognition with large databases to validate the efficiency of the proposed approach. Also, a parallel genetic algorithm for architecture optimization was used to achieve an optimal design of the MNN. The main idea of this paper is the use of parallel genetic algorithms to find the best architecture with large databases of faces, because when the database to be considered is large, the main problem is the processing time to train the MNN. Network parameters are adjusted by a combination of the training pattern set and the corresponding errors between the desired output and the actual network response. To control a learning process, a criterion is needed to decide the time for terminating the process.

关键词： Neural networks Genetic algorithms Optimization methods Evolutionary computation

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17th international conference on Advanced Nano Materials, ANM 2020

17th International Conference on Advanced Nano Materials, AN...

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17th international conference on Advanced Nano Materials, ANM 2020

The proceedings contain 10 papers. The topics discussed include: QSAR and molecular docking study of quinazoline derivatives as anticancer agents using molecular descriptors;QSAR and docking molecular models to predict anti-cancer activity on a series of azacalix [2] arene [2] pyrimidine derivatives as anticancer agents;QSAR and molecular docking study of quinolin derivatives with topoisomerase i inhibitory properties as potential anticancer agents using statistical methods;QSAR modeling, molecular docking studies and ADMET prediction on a series of phenylaminopyrimidine-(thio) urea derivatives as CK2 inhibitors;method of obtaining multicomponent fine-grained powders for boron carbide matrix ceramics production;electronic and optoelectronic properties of intrinsic and cooper-doped germanium nanowire network devices;speech recognition using hmm and soft computing;a new low-cost carbon–silica composite adsorbent from a by-product of the sugar industry;validation of helioclim-3 irradiance with ground observations in Senegal using four typical climatic zones;and economic analysis of graphene aerogel polyurethane sponge production as adsorbent for oil-water separation.

关键词： Molecular modeling

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1st international conference on Applied computing Research to Support Industry: Innovation and Technology, ACRIT 2019

1st International Conference on Applied Computing Research t...

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1st international conference on Applied computing Research to Support Industry: Innovation and Technology, ACRIT 2019

ISBN: (纸本)9783030387518

The proceedings contain 39 papers. The special focus in this conference is on Applied computing Research to Support Industry: Innovation and Technology. The topics include: Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication;deep Learning Approach Based Dominant Age Group Based Classification for Social Network;a Novel Detection System for Human Retina Based on Fuzzification Neural Network;a Novel Car-Pooling Optimization Method Using Ant Colony Optimization Based on Network Analysis (Case Study: Tehran);A Trusted MANET Routing Algorithm Based on Fuzzy Logic;Minimum Array Elements and Snapshots for Performance Analysis Based on Direction Of Arrival (DOA) Estimation Methods;experimenting Two Machine Learning Methods in Classifying River Water Quality;multi View Face Detection in Cattle Using Infrared Thermography;deep Learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory;Detection of Copy-Move Forgery in Digital Image Based on SIFT Features and Automatic Matching Thresholds;the Application of Artificial Intelligence Technology in Healthcare: A Systematic Review;Gain Scheduling Fuzzy PID Controller for Distributed Control Systems;comparative Analysis for Arabic Sentiment Classification;political Articles Categorization Based on Different Naïve Bayes Models;single Runway Aircraft Landing Scheduling Using Simulated Annealing and Exact Timing Method;towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions;prediction of Compressive Strength of High-Performance Concrete: Hybrid Artificial Intelligence Technique;cancellable Face Biometrics Template Using AlexNet;pattern of Diffusion recognition in a Molecular Communication Model;querying Encrypted Data in Graph Databases;an Effective Protein Multiple Structure Alignment Using Parallel computing.

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PSI (ψ) Invariant Features for Face recognition

PSI (ψ) Invariant Features for Face Recognition

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international conference on Mathematical Modelling and Scientific computing

作者： Cholin, Ajaykumar S. Vinay, A. Bhat, Aditya D. Deshpande, Arnav Ajay Murthy, K. N. B. Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence 100 Feet Ring RdBanashankari Stage 3 Bengaluru 560085 Karnataka India

ISBN: (纸本)9789811513381;9789811513374

Over last few decades, mathematics has played a crucial role in developing efficient algorithms for Face recognition (FR) used in biometric systems. FR using Machine Learning (ML) techniques has impacted FR systems tremendously, towards efficient and accurate models for FR. Existing FR systems used in biometrics use ML techniques to learn patterns in the images by extracting various features from them and often require pre-processed face image data for the learning process. In this paper, we have used various pre-processing techniques and compared them in the deployed FR framework. It was observed that the Steerable Pyramid (SP) filter was the most efficient pre-processing technique among all techniques used for pre-processing in this work. Though existing feature extraction methods such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) have been used in the past, they have not been accurate enough in various vision based biometric systems. Hence, a novel PSI (Pose Scale and Illumination) invariant SURF-RootSIFT technique is proposed by extending the well known SIFT-RootSIFT feature extraction technique which is achieved by calculating the Bhattacharya Coefficient between the feature vectors. A framework which uses the proposed novel feature extraction technique is deployed in this work. This paper demonstrates that the novel SURF-RootSIFT based framework is proven to perform more accurately and efficiently than the other techniques, with 99.65, 99.74 and 97.93% accuracy on the Grimace, Faces95 and Faces96 databases respectively.

关键词： Face recognition Image pre-processing SIFT SURF RootSIFT Bhattacharya coefficient VLAD

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2nd international conference on Security with Intelligent computing and Big-data Services, SICBS 2018

2nd International Conference on Security with Intelligent Co...

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2nd international conference on Security with Intelligent computing and Big-data Services, SICBS 2018

ISBN: (纸本)9783030169459

The proceedings contain 75 papers. The special focus in this conference is on Security with Intelligent computing and Big-data Services. The topics include: A Lossless Polynomial-Based Secret Image Sharing Scheme Utilizing the Filtering Operation;CT Image Secret Sharing Based on Chaotic Map and Singular Value Decomposition;Robust Speaker recognition Using Improved GFCC and Adaptive Feature Selection;outlier Detection Based on Local Density of Vector Dot Product in Data Stream;multi-level Competitive Swarm Optimizer for Large Scale Optimization;Experimental Comparison of Free IP Geolocation Services;integrating Multiple Feature Descriptors for Computed Tomography Image Retrieval;Automatic Forgery Localization via Artifacts Analysis of JPEG and Resampling;Arbitrary Style Transfer of Facial Image Based on Feed-Forward Network and Its Application in Aesthetic QR Code;toward a Blockchain Based Image Network Copyright Transaction Protection Approach;Aesthetic QR Code Authentication Based on Directed Periodic Texture pattern;A Heterogeneous Multiprocessor Independent Task Scheduling Algorithm Based on Improved PSO;cross-Domain Text Sentiment Classification Based on Wasserstein Distance;WebRTC-Based On-Site Operation and Maintenance Adaptive Video Streaming Rate Control Strategy;evaluation of Enzymatic Extract with Lipase Activity of Yarrowia Lipolytica. An Application of Data Mining for the Food Industry Wastewater Treatment;load-Balancing-Based Reliable Mapping Algorithm for Virtual Resources in Power Communication Network;Enhancing Network Intrusion Detection System Method (NIDS) Using Mutual Information (RF-CIFE);you Are What You Search: Attribute Inference Attacks Through Web Search Queries;new Publicly Verifiable Cloud Data Deletion Scheme with Efficient Tracking;A Transient Grid Security Control Algorithm Based on EMS System;a Fog-Based Collusion Detection System.

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