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Gaussian Mixture Model based Deep Learning-based Simple Online Real-Time Tracking for Video Object Detection and Tracking

Gaussian Mixture Model based Deep Learning-based Simple Onli...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Vijaya Bhaskar Reddy Muvva P Nagarathna Kasim Jumhaa Sasikumar Gurumoorthy Divyashree N Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tiruchirappalli India School of Computing and Information Technology REVA University

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, the increasing demand for intelligent video analysis has driven the need for accurate and efficient video object detection and tracking systems. However, the existing You Only Look Once (YOLO) model have limitations in handling large-scale video data and modeling complex object interactions. Hence, this research proposes Deep Neural Network with Gaussian Mixture Model based Deep Learning-based Simple Online Real-Time Tracking (GMM-DeepSORT) to handle the large-scale video data. The proposed GMM-DeepSORT detect and track objects across frames for accurate video object detection and tracking. Initially, the input video data is collected from Large-scale Single Object Tracking (LaSOT) dataset which consists of video sequences by providing both visual and lingual annotation for each sequence. Then, GMM is employed to eliminate the background from videos and detect all the moving objects in input video frames. Finally, the DeepSORT is introduced to track the objects by predicting object motion and associating object across video frames. From the results, the proposed GMM-DeepSORT established better results in terms of accuracy (97.92%) and processing time for tracking (0.31s) when compared to Deeply Supervised Object Detector (DSOD) respectively.

关键词： YOLO Visualization Accuracy Tracking Video sequences Streaming media Real-time systems Data models Integrated circuit modeling Gaussian mixture model

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Intelligent Mixture Design of Steel Fibre Reinforced Concrete Using Support Vector Machine with Improved Firefly Algorithm

Intelligent Mixture Design of Steel Fibre Reinforced Concret...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： S. Vijayan M. Tholkapiyan Prahalad N Tengli Mohammed Al-Farouni Kothuri Parashu Ramulu Department of Civil Engineering Saveetha School of Engineering Kuthambakkam India Department of Aeronautical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Computer Science and Engineering Indur Institute of Engineering and Technology Nizamabad India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The Steel Fibre Reinforced Concrete (SFRC) is majorly utilized in construction of concrete industry as that considers a significant part to evaluate the concrete technique. That has steel fibers of different sizes, shapes and geometric which influenced a concrete mixture composition and properties of mechanic. Through, compared with classical concrete, that is challenging for designing mixture ratios due to much influencing variables to optimize multi properties has compressive strength, flexural strength and cost. Hence, current research has Artificial Intelligence (AI) based Multi Objective Optimization (MMO) method for enabling effective model for identifying optimal mixture design of SFRC. In this research, Support Vector Regression (SVR) method is proposed for predicting UCS and FS in SFRC. The hyperparameters of SVR methods are tuned by Improved Firefly Algorithm (IFA) and examined the significance of inputs and output variables in algorithm. The huge correlation coefficient of 0.9142 on UCS and 0.88 on FS datasets that attained on testing datasets.

关键词： Support vector machines Shape Prediction algorithms Concrete Steel Object recognition Artificial intelligence Optimization Tuning Testing

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Improved Political Optimizer and Deep Neural Network-based Resource Management Strategy for fog Enabled Cloud Computing 2

Improved Political Optimizer and Deep Neural Network-based R...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Prakash, M. Vijayaganth, V. Shadrach, Finney Daniel Menaha, R. Daniya, T. Guha, Tapas Vit University School of Computer Science and Engineering Vellore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Gmr Institute of Technology Department of Information Technology Andhra Pradesh Rajam India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Cloud-based processing of Internet of Things (IoT) applications isn't always the best option, especially when it comes to time-sensitive applications. For managing the massive amounts of data bandwidth required by end devices, fog and edge computing offer a viable alternative. Large volumes of newly generated data must be processed locally, rather than in the cloud, according to these paradigms. Allocating resources, balancing workloads, providing resources, scheduling tasks, and ensuring quality of service are all aspects of resource management that play an important role in cloud-based IoT systems with the goal of improving system performance. In this paper, we discuss different approaches to resource management in the context of cloud, fog, and edge computing. We introduce a methodology Improved Political optimizer (IPO) with Deep Neural Networks (DNN) for ranking resource management algorithms in the context of cloud, fog, and edge systems. Therefore, we start with the challenges of resource management in that industry. Current research contributions can be better categorised, which will aid in the creation of an evaluation scheme. There is a substantial addition made by reviewing and analysing research papers on methods for resource management. Existing Fog resource managers only account for a subset of Fog resource management criteria, such as system network bandwidth and Response Time. The administration of computing assets in the cloud, fog, and at the network's periphery are the primary topics of our studies. The purpose of this analysis is to develop a set of criteria for judging algorithms used for managing resources in cloud, fog, and edge environments. We employed Deep Neural Networks and the Improved Political Optimizer in our search for better methods of managing available resources (DNN). © 2022 IEEE.

关键词： Deep neural networks

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Anwar Basha H Deepak R Babu M Shaik Thasleem Bhanu B Amutha R. Madhavan Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Bengaluru Karnataka India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science and Business Systems Jeppiaar Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO; therefore, it is able to provide an effective solution for the optimization of features within medical image analysis.

关键词： Accuracy Artificial neural networks Retina Feature extraction Vectors Classification algorithms Particle swarm optimization Optimization Tuning Genetic algorithms

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Robust Fingerprint Minutiae Extraction and Matching Using Fully Connected Deep Convolutional Neural Network and Improved SIFT

Robust Fingerprint Minutiae Extraction and Matching Using Fu...

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Data science and Network Security (ICDSNS), International Conference on

作者： Saif O. Husain R.Archana Reddy Piyush Kumar Pareek Srinivasan Jagannathan M. Jyothi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science and Engineering SR University Warangal India Department of AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Applications Madanapalle institute of technology & Science Madanapalle India Department of Information Technology G.Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Fingerprint is a most well-known biometric based authentication system that gives a unique identity for each person. In this paper, an authentication framework to enhance the Fingerprint Minutiae Extraction and Matching (FMEM) technique is proposed. A Fully Connected Deep Convolutional Neural Network with Improved Scale-Invariant Feature Transform (FCDCNN-ISIFT) is proposed for feature extraction and feature matching. Initially, the fingerprint images are collected from Fingerprint Verification Competition 2004 (FVC2004) which are first pre-processed using Histogram Equalization (HE) and Normalization. The images are segmented and enhanced by estimating ridge orientation and ridge frequency, and performing Gabor filtering, binarization and thinning. The fingerprint feature extraction and matching test is done using FCDCNN-ISIFT. The FCDCNN is built for detecting minutiae and the ISIFT is applied to the high contrast images to enhance the quality of latent fingerprints. Finally, the loss function is measured to determine the minutiae detection loss. The experimental results show better accuracy of 99.41%, f1-score of 97.73%, and Equal Error Rate (EER) of 1.75% when compared to results of existing approaches like Contrast Limited Adaptive Histogram Equalization - Random Sample Consensus (CLAHE - RANSAC) and Deep Convolutional Neural Network (DCNN).

关键词： Image segmentation Histograms Image matching Authentication Transforms Fingerprint recognition Feature extraction Loss measurement Frequency estimation Convolutional neural networks

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Dynamic Adaptive Weight Sparrow Search Algorithm for High-Strength Self-Consolidating Concrete Mix Design

Dynamic Adaptive Weight Sparrow Search Algorithm for High-St...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Megha Kulkarni Mohammed H. Fallah R. Gobinath Nijaguna G S Manohar M Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq Department of Civil Engineering SR University Warangal India Department of Information Science and Engineering S.E.A. College of Engineering & Technology Bangalore India Department of Computer Science and Engineering CHRIST (Deemed to be University) Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Concrete is the most used human-made material that has been utilized in the construction industry. Mix design optimization is difficult due to properties of concrete based on chemical and physical properties of the components as well as their relative proportions. Since number of parameters are comprised in a concrete mix design, identifying an optimal mixing design has always been a challenge for concrete engineers. Hence, this research proposes the Dynamic Adaptive Weighting Sparrow Search Algorithm (DAWSSA) approach for the High Strength Self-Consolidating Concrete mixt design (HSCC). This proposed methodology utilizes the data normalization and data partitioning techniques to normalize the data for enhancing the performance. Then, Best-Worst Method (BWM) approach is accomplished for pairwise comparisons of the quality criteria. The effectiveness of the proposed DAWSSA is estimated by utilizing various error metrices like R-Square (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of values about 0.86, 1.57 and 2.29 respectively when compared to the previous works such as Multi-objective Salp Swarm Algorithm (MOSSA) and SSA-Extreme Gradient Boosting (SSA-XGB).

关键词： Manufacturing processes Communication systems Heuristic algorithms Concrete Partitioning algorithms Root mean square Optimization Chemicals Design optimization Construction industry

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Scoring Impressions and Associations for Improved Concept Map Excavating from Dominion Text Demonstration

Scoring Impressions and Associations for Improved Concept Ma...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Mohan Gowda V Sanjeev Kumar A N Dhanesh Kumar Hemalatha K.L Chaithra K N Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

A concept map is a graphical tool used to organize and represent knowledge or information visually. It consists of a collection of concepts or ideas that are linked together by relationships or connections to form a structured and hierarchical representation of a subject or topic. A concept map can be used to show the relationships between ideas, identify patterns, and clarify complex information. It is commonly used in education, research, and problem-solving to help individuals better understand and visualize information. The graph's entropy serves as a useful indicator of how accurately it represents the document. This study explains how to construct a concept map from the document's sentences by using a tree-based convolution neural network to extract key concepts. The use of latent relations based on a shared word embedding representation of concepts enables the removal of duplication and the efficient organization of concepts. This leads to a more precise, concise, and unambiguous concept map. The paper also describes the modifications made to the concept map and the additional data incorporated into it. By utilizing the latent semantic structure and hierarchical linkage of ideas and by adding significant information to the concepts themselves, the information content of the concept map is improved.

关键词： Couplings Measurement Integrated circuits Semantics Neural networks Education Organizations

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Deep Learning based Brain Tumor Detection with Internet of Things

Deep Learning based Brain Tumor Detection with Internet of T...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Sandya, Vooradi Baligeri, Veeresh Lal, Bechoo Petli, Vishwanath Pradeep Kumar, S. Cmr Technical Campus Department of Cse Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India H. K. E. Society's S. L. N. College of Engineering Department of Electronics and Communication Engineering Raichur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Classification, preprocessing, feature extraction, and segmentation are all parts of the planned study that will be utilized to categories and detect brain tumor pictures. Magnetic resonance imaging (MRI) gives direct information about anatomical structures as well as possibly abnormal tissues where patients are being watched by physicians, making brain tumor identification not as much simpler for clinical diagnosis. This suggested system utilizes a machine learning strategy to identify, and categories brain tumors known as gliomas. Kirsch's edge detected pixels are used to identify the edges of the boundaries. Using this improved brain scan, the ridge let transform is used to extract the ridge let multi-resolution coefficients. As an added step, the ridge let converted coefficients are used to create features, which are then improved with the help of the CANFES classifier. Evaluation factors like as sensitivity, specificity, and accuracy are applied to the results in the context of tumor detection. Both the old approach and the suggested methodology are implemented in simulation using a programming environment like MATLAB, and the results of these simulations are compared to demonstrate the efficacy of the proposed algorithm. The suggested tumor detection approaches employing Co-Active Adaptive Neuro-Fuzzy Expert System Classifier have an accuracy of 98.73%, which offers iv accurate detection of the tumor, and so should be regarded as superior to the current traditional procedures. © 2023 IEEE.

关键词： Tumors

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Improved Artificial Bee Colony based Compressive Strength Prediction and Optimization in Concrete Samples

Improved Artificial Bee Colony based Compressive Strength Pr...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Mohammed H. Fallah Muralidhar H G. Swamy Yadav Nijaguna G S Manohar M The Islamic University Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Civil Engineering SR University Hasanparthy India Department of Information Science and Engineering S.E.A. College of Engineering & Technology Bangalore India Department of Computer Science and Engineering CHRIST (Deemed to be University) Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The usage of Silica Fume (SF) and Fly Ash (FA) materials are stated in concrete sample design and it used numerous industrial applications because of the benefit of these materials. Through, the proper usage of correct mixes is a significant factor according to concrete weight and strength. Hence, this paper proposed an Improved Artificial Bee Colony (IABC) algorithm to estimate the concrete CS through laboratory experiments. The database contains SF, FA, Total Cementitious Material (TCM), Age of Samples (AS) and High-Rate Water Reducing Agent (HRWRA) is utilized for estimate and forecast concrete Compressive Strength (CS). The proposed IABC formed a connection among above mentioned parameters. The IABC based on whale dynamic selection probability and search are reached an optimal solution and stable operate reasonable and state ability configuration. In the innovative technique, every parameter influence on CS is defined. Lastly, the IABC algorithm is used for optimal conditions of concrete samples compressive strength is presented and it is able to progress connection which is serve as better replacement for empirical models.

关键词： Fly ash Databases Communication systems Heuristic algorithms Cement industry Metaheuristics Whales Prediction algorithms Concrete Silicon dioxide

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep Learning

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： H Aditya Pai Piyush Kumar Pareek M S Guruprasad S Chandrappa Krishna Bhargava MIT School of Computing MIT Art Design and Technology University Pune India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Graphic Era (Deemed to-be University) Dehradun India School of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings.

关键词： Deep learning Pneumonia Accuracy Pollution Transfer learning Lung cancer Lung X-ray imaging Neoplasms Biomedical imaging

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