Adaptive Window Algorithm Research Articles

Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data

ABSTRACT The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset. The process begins with pre-processing steps including Tokenization, Stop words Elimination, Filtering, Hashtag Removal, and Multiword Grouping. The Gray Level Co-occurrence Matrix Window Adaptive Algorithm is employed to extract features, such as emoticon counts, punctuation counts, gazetteer word existence, n-grams, and part of speech tags. These features are selected using Entropy–Kurtosis-based Feature Selection approach. Finally, the Tree Hierarchical Deep Convolutional Neural Network enhanced by the Sheep Flock Optimization Algorithm is used to categorize the Twitter data as positive, negative, and neutral sentiments. The proposed SCTD-THDCNN-SFOA method demonstrates superior performance, achieving higher accuracy and lesser computation time than the existing models, respectively. The SCTD-THDCNN-SFOA framework significantly improves the accuracy and efficiency of sentiment classification for Twitter data.

Self-attention generative adversarial capsule network optimized with atomic orbital search algorithm based sentiment analysis for online product recommendation

Self-Attention based Generative Adversarial Capsule Network optimized with Atomic orbital search algorithm based Sentiment Analysis is proposed in this manuscript for Online Product Recommendation (SFA-AGCN-AOSA-SA-OPR). Here, Collaborative filtering (CF) and product-product (P-P) similarity method is utilized for designing the new recommendation system. CF is employed for predicting the best shops and P-P similarity method is employed to predict the better product. Initially, the datas are gathered via Amazon Product recommendation dataset. After that, the datas are given to pre-processing. During pre-processing, Markov chain random field (MCRF) co-simulation is used to remove the unwanted content and filtering relevant text. The preprocessing output is fed to feature extraction. The features, like manufacturing date, Manufacturing price, discounts, offers, quality ratings, and suggestions or reviews are extracted using Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. Finally, Self-Attention based Generative Adversarial Capsule Network (SFA-AGCN) categorizes the product recommendation as excellent, good, very good, bad, very bad. Atomic orbital search algorithm optimizes the SFA-AGCN weight parameters. The performance metrics, like accuracy, precision, sensitivity, recall, F-measure, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) is examined. The efficiency of the proposed method provides higher mean absolute percentage error 98.23%, 88.34%, 90.35% and 78.96% and lower Mean squared error 92.15%, 90.25%, 89.64% and 92.48% compared to the existing methods, such as sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS (DLMNN-IANFIS-SA-OPR), intelligent sentiment analysis approach using edge computing based deep learning technique (DCNN-SA-OPR), sentiment analysis for online product reviews in Chinese depending on sentiment lexicon and deep learning (CNN-BiGRU-SA-OPR) and sentiment analysis on product reviews depending on weighted word embedding and deep neural networks (CNN-LSTM-SA-OPR) respectively.

Auto-Metric Graph Neural Network optimized with Capuchin search optimization algorithm for coinciding diabetic retinopathy and diabetic Macular edema grading

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major eternal blindness in aged people. In this manuscript, Auto-Metric Graph Neural Network (AGNN) optimized with Capuchin search optimization algorithm is proposed for coinciding DR and DME grading (AGNN-CSO-DR-DME). The novelty of this work is to identify the Diabetic retinopathy and diabetic macular edema grading at initial stage with higher accuracy by decreasing the error rate and computation time. Initially, input image is taken from two public benchmark datasets that is ISBI 2018 imbalanced diabetic retinopathy grading dataset and Messidor dataset. Then, the input fundus image is pre-processed by APPDRC filtering method removes noise in input images. Also, the pre-processed images are given to the Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. The extracted features of the DR and DME are fed to AGNN for classifying the grading of both DR and DME diseases. Generally, AGNN not reveal any adoption of optimization methods compute optimum parameters for assuring correct grading of both DR and DME diseases. Thus, CSOA is used for optimizing the AGNN weight parameters. The proposed method is carried out in python, its efficiency is assessed under performances metrics, such as f-measure, execution time and accuracy. The proposed method attains higher accuracy in ISBI 2018 IDRiD dataset 99.57 %, 97.28 %, and 96.34 %, compared with existing methods, like CANet-DR-DME, HDLCNN-MGMO-DR-DME, ANN-DR-DME and 91.17 %, 96.52 % and 97.36 %higher accuracy in Messidor dataset compared with existing methods, like CANet-DR-DME, TCNN-DR-DME, and 2-d-FBSE-FAWT-DR-DME.

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

Research on flame location based on adaptive window and weight stereo matching algorithm

Flame location based on binocular vision has some problems such as low matching accuracy, slow matching speed and so on. To solve the above problems, we propose stereo edge matching algorithm based on adaptive window and weight. Since image edge regions has rich information, an edge based adaptive window algorithm is proposed. For non-edge regions, adaptive weight algorithm based on row and column is accumulated separately, which is used to reduce computational complexity. Finally it gets the position of the flame according to disparity map. The experimental results show that the matching algorithm based on adaptive window and weight can locate the fire source. Its error is within 10 cm and the matching speed is higher and the effect is better.

A fast and adaptive bi-dimensional empirical mode decomposition approach for filtering of workpiece surfaces using high definition metrology

The surface topography of workpieces has an important influence on the final performances of the product. The digital filtering is a critical step to analyze the surface topography of workpieces. Bi-dimensional empirical mode decomposition (BEMD) approach is superior to conventional filtering approaches in the analysis of non-stationary and non-linear data. High definition metrology (HDM) can generate massive point cloud data to represent the three-dimensional (3D) surface topography of workpieces, which provides a new opportunity for surface topography analysis. This paper develops a fast and adaptive bi-dimensional empirical mode decomposition (FABEMD) approach for filtering of workpiece surfaces using HDM. Firstly, the neighboring window algorithm is presented to extract local extrema and draw the extrema spectrum. Secondly, the adaptive window algorithm is developed to automatically select the optimal window size of the order statistics filter, and plot the envelope spectrum. Finally, the average smoothing filter is presented for smooth filtering and generating of the mean envelope. The performance of the proposed FABEMD-based filter is validated by a simulated surface data and three real-world surface data. Compared with Gaussian filter (ISO 11562:1996, ASME B46.1-2002), the BEMD-based filter and the recent shearlet-based filter in the qualitative and quantitative analysis, the proposed FABEMD-based filter is superior for the separation and extraction of different surface components.

FPGA implementation of an efficient similarity-based adaptive window algorithm for real-time stereo matching

Stereo matching is one of the most widely used algorithms in real-time image processing applications such as positioning systems for mobile robots, three-dimensional building mapping and both recognition, detection and three-dimensional reconstruction of objects. In area-based algorithms, the similarity between one pixel of the left image and one pixel of the right image is measured using a correlation index computed on vicinities of these pixels called correlation windows. To preserve edges, small windows need to be used. On the other hand, for homogeneous areas, large windows are required. Due to only local information is used, matching between primitives is difficult. In this article, FPGA implementing of an efficient similarity-based adaptive window algorithm for dense disparity maps estimation in real-time is described. To evaluate the proposed algorithm's performance, the developed FPGA architecture was simulated via ModelSim-Altera 6.6c using different synthetic stereo pairs and different sizes for correlation window. In addition, the FPGA architecture was implemented in an FPGA Cyclone IIEP2C35F672C6 embedded in an Altera development board DE2. The disparity maps are computed at a rate of 76 frames per second for stereo pairs of 1280 $$\times$$ 1024 pixel resolution and a maximum expected disparity equal to 15. The developed FPGA architecture offers better results with respect to most of the real-time area-based stereo matching algorithms reported in the literature, allows increasing the processing speed up to 93,061,120 pixels per second and enables it to be implemented in the majority of the medium gamma FPGA devices.

Colour interpolation using adaptive window for Bayer colour filter array

Most demosaicking solutions focus on improving the accuracy of direction estimation for interpolation, and they achieve a highly precise gradient estimation performance. Direction estimation techniques achieve great improvement in objective image quality; however, in terms of subjective image quality there are remaining demosaicking problems to solve. An adaptive window algorithm to reduce demosaicking artefacts by exploiting the colour similarity of neighbouring pixels is introduced. Experimental results confirm that the proposed method produces the best colour peak signal-to-noise ratio and FSIM performance, and achieves great improvement in subjective image quality.

An explicit window adaptation algorithm over TCP networks using supervisory control

This paper describes a feedback-based congestion control algorithm to guarantee the fair sharing among the TCP connections based on the supervisory control framework. Specifically, a supervisory control scheme is proposed to implement an explicit window adaptation algorithm. In particular, the feedback value is determined by the supervisor and conveyed to the source by modifying the receiver's advertised window in TCP acknowledgments returning to the source. The performance of the proposed algorithm is compared with the conventional one through a simulation study.

Angular MAC: a framework for directional antennas in wireless mesh networks

Capacity of wireless mesh networks can be enhanced through the use of smart directional antennas, which not only enable nodes to have high quality links but also increase network throughput by allowing spatial reuse. This paper proposes a new MAC protocol and framework, called Angular MAC (ANMAC) that enables directional antennas in wireless mesh networks. The protocols and algorithms of the ANMAC framework fit well with the requirements of mesh networks such as neighbor discovery and self-configuration, while providing significant throughput enhancements. The throughput enhancements are proven by comprehensive simulations with realistic antenna patterns, including performance comparisons of ANMAC with directional schemes using a similar node architecture and omni 802.11. Also, the effect of contention window size is analyzed and a dynamic contention window adaptation algorithm is proposed to maximize the throughput of the self-configuring mesh network, by taking instantaneous traffic conditions into account.

Explicit window adaptation algorithm over TCP wireless networks

A feedback-based congestion control algorithm is described which improves transmis- sion control protocol (TCP) performance over wireless networks. An explicit feedback scheme is proposed for the fair sharing of the bandwidth by modifying the receiver's advertised window in TCP acknowledgments returning to the source. Using the feedback information, the proposed algorithm adapts the window size to avoid the congestion and sees the packet loss only due to the wireless link error. Based on the asymptotic analysis, it is shown that the proposed algorithm guarantees the fair sharing and improves wireless TCP performance. The effectiveness of the proposed algorithm is demonstrated by simulations.