scholarly journals Bipartite Weighted Graph Access for Optimal Label Prediction

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1848-1852
Keyword(s):  
Weighted Graph ◽  
Optimization Techniques ◽  
Computational Overhead ◽  
Proposed Model ◽  
Diversity Assessment ◽  
Label Prediction ◽  
Benchmark Datasets ◽  
Feature Optimization ◽  
Graph Access ◽  
Assessment Strategies

This article portrayed a novel bipartite weighted graph strategy for Feature optimization for machine learning models. Unlike many of the existing optimization techniques of diverse categories such as evolutionary computation techniques, diversity assessment strategies, the proposal is deterministic approach with minimal computational overhead, which has referred further as Bipartite Weighted Graph Approach for Optimal label prediction (BWG-OLP). The proposed model is about to derive a given feature is optimal or not by the respective feature’s correlation with the records and the correlation with the fellow features. The experimental study has carried on benchmark datasets to estimate the significance of the proposed method.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

2020 ◽  
Vol 34 (05) ◽  
pp. 7797-7804
Author(s):  
Goran Glavašš ◽  
Swapna Somasundaran
Keyword(s):  
State Of The Art ◽  
Language Transfer ◽  
Text Segmentation ◽  
Word Embeddings ◽  
Neural Architecture ◽  
Text Coherence ◽  
Sentence Level ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Cross Lingual

Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model – a neural architecture consisting of two hierarchically connected Transformer networks – is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

scholarly journals A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

2020 ◽  
Vol 34 (05) ◽  
pp. 9098-9105
Author(s):  
Amir Veyseh ◽  
Franck Dernoncourt ◽  
Dejing Dou ◽  
Thien Nguyen
Keyword(s):  
Prior Work ◽  
Semantic Consistency ◽  
Learning Framework ◽  
Local Structures ◽  
Joint Inference ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Sequential Labeling ◽  
Sentence Classification ◽  
Model Features

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms and definitions). The previous works for DE have only focused on one of the two approaches, failing to model the inter-dependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies. Our model features deep learning architectures to exploit the global structures of the input sentences as well as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representation vectors for DE. Besides the joint inference between sentence classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that the prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet considered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs graph convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of the terms and definitions both globally (i.e., increasing semantic consistency between the representations of the entire sentences and the terms/definitions) and locally (i.e., promoting the similarity between the representations of the terms and the definitions). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.1

scholarly journals The Effects of Feature Optimization on High-Dimensional Essay Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Bong-Jun Yi ◽  
Do-Gil Lee ◽  
Hae-Chang Rim
Keyword(s):  
Poor Performance ◽  
Feature Space ◽  
Optimization Techniques ◽  
Training Data ◽  
High Dimensional ◽  
Automated Essay Scoring ◽  
Training Time ◽  
Training And Performance Improvement ◽  
Feature Optimization ◽  
And Performance

Current machine learning (ML) based automated essay scoring (AES) systems have employed various and vast numbers of features, which have been proven to be useful, in improving the performance of the AES. However, the high-dimensional feature space is not properly represented, due to the large volume of features extracted from the limited training data. As a result, this problem gives rise to poor performance and increased training time for the system. In this paper, we experiment and analyze the effects of feature optimization, including normalization, discretization, and feature selection techniques for different ML algorithms, while taking into consideration the size of the feature space and the performance of the AES. Accordingly, we show that the appropriate feature optimization techniques can reduce the dimensions of features, thus, contributing to the efficient training and performance improvement of AES.

scholarly journals Multimodal Feature Learning for Video Captioning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sujin Lee ◽  
Incheol Kim
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Feature Learning ◽  
Visual Features ◽  
Semantic Features ◽  
Video Captioning ◽  
Video Feature ◽  
Proposed Model ◽  
Generation Network ◽  
Benchmark Datasets

Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD) and Microsoft Research Video-to-Text (MSR-VTT), demonstrate the performance of the proposed model.

Efficient Spatial Dynamics for Continuum Arms

2015 ◽  
Author(s):  
Isuru S. Godage ◽  
Raul Wirz ◽  
Ian D. Walker ◽  
Robert J. Webster
Keyword(s):  
Dynamic Models ◽  
Spatial Dynamics ◽  
High Accuracy ◽  
Computationally Efficient ◽  
Pneumatic Muscle ◽  
Computational Overhead ◽  
Proposed Model ◽  
Continuum Robot ◽  
Transient And Steady State ◽  
The Continuum

Continuum robot dynamic models have previously involved a choice between high accuracy, numerically intensive models, and low accuracy, computationally efficient models. The objective of this paper is to provide an accurate dynamic model with low computational overhead. Our approach is to place point masses at the center of gravity of the continuum section, rather than along the robot’s backbone or centerline. This enables the model to match the robot’s energetic characteristics with many fewer point masses. We experimentally validate the model using a pneumatic muscle actuated continuum arm. We find that the proposed model successfully captures both the transient and steady state dynamics of the arm.

scholarly journals InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction

2019 ◽  
Author(s):  
Xiaowang Zhang ◽  
Qiang Gao ◽  
Zhiyong Feng
Keyword(s):  
Neural Network ◽  
Nonlinear Interaction ◽  
Feature Interaction ◽  
Feed Forward Neural Network ◽  
Low Level ◽  
Feature Interactions ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
High Level ◽  
Dense Features

In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction. To characterize multilevel interaction of features, InteractionNN consists of three modules, namely, nonlinear interaction pooling, layer-lossing, and embedding. Nonlinear interaction pooling (NI pooling) is a hierarchical structure and, by shortcut connection, constructs low-level feature interactions from basic dense features to elementary features. Layer-lossing is a feed-forward neural network where high-level feature interactions can be learned from low-level feature interactions via correlation of all layers with target. Moreover, embedding is to extract basic dense features from sparse features of data which can help in reducing our proposed model computational complex. Finally, our experiment evaluates on the two benchmark datasets and the experimental results show that InteractionNN performs better than most of state-of-the-art models in sparse regression.

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