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Support Vector Regression Based Traffic Prediction Machine Learning Model*

Support Vector Regression Based Traffic Prediction Machine L...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Ansh Srivastava Mrigaannkaa Singh Somesh Nandi Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India Dept. of Electronics and Communication RV College of Engineering Bengaluru India Dept. of AI/ML RV College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues.

关键词： Support vector machines Training Computational modeling Urban areas Machine learning Predictive models Vectors Vehicle dynamics Optimization Traffic congestion

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies

An Approach of Fog Computing and Edge Computing for Computin...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems.

关键词： System performance Focusing Minimization Load management Reliability Resource management Dynamical systems Distributed computing Optimization Edge computing

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Hybrid Energy Aware Bio Inspired Technique to Improve Network Lifetime for MANETs

Hybrid Energy Aware Bio Inspired Technique to Improve Networ...

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IoT, communication and Automation Technology (ICICAT), International Conference on

作者： Parveen Kumar Sharma Rinkesh Mittal Vikas Gupta Pooja Sahni Parveen Singla Computer Science and Engineering Department Chandigarh Engineering College Landran Mohali Punjab India Electronics and Communication Engineering Department Chandigarh Engineering College Landran Mohali Punjab India Electronics and Communication Engineering Department Department of RISE Chandigarh Engineering College Landran Mohali Punjab India

The main characteristic of nodes used in mobile ad hoc networks is smaller batteries. This paper represents the bio inspired hybrid approach of two techniques namely Ant colony optimization (ACO) and Firefly Algorithm (FA) to optimize the network performance. The firefly algorithm requires the timestamps of the node pairs to find the attractiveness factor. However, this technique considers only those nodes to opt for the Hello Packet forwarding procedure that are having the sufficient residual energy. Analysis of the network's performance took into account throughput, packet delivery ratio, and remaining energy. This hybridization has certainly improved the performance of the network.

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Anomaly Detection using Generative Adversarial Networks on Firewall Log Message Data 13

Anomaly Detection using Generative Adversarial Networks on F...

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13th International Conference on electronics, computers and Artificial Intelligence, ECAI 2021

作者： Kulyadi, Sandeep Pai Mohandas, Pavitra Kumar, Sudesh Kumar Santhosh Raman, M J Shankar Vasan, V S Department of Electronics and Communication Engineering Chennai India Department of Computer Science and Engineering Chennai India RISE Lab Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665425346

Firewalls produce a lot of log messages while logging internet traffic through the system that they are protecting. This is a huge amount of data that can be used to find various insights. One of these insights knows if a malware attack has taken place. We treat this sequence of log messages as time series data. In this paper, we propose a Generative Adversarial Network (GAN) model based on Recurrent Neural Networks (RNN) that learns the normal behavior of the firewall, the complex spatio-temporal correlations in the data. We then apply anomaly detection techniques to detect an anomaly which can potentially be a malware. © 2021 IEEE.

关键词： Generative adversarial networks

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Performance Evaluation of IoT Architecture for Heterogeneous Networks

Performance Evaluation of IoT Architecture for Heterogeneous...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Monika Kashyap Zaineb Naaz Neeti Bansal Vidushi Sharma Department of Computer Science and Engineering Gautam Buddha University Greater Noida India Department of Electronics and Communication Engineering Meerut Institute of Engineering and Technology Meerut India

With the increased deployment of IoT networks in different applications, the need to develop resource-efficient IoT networks arises. The traditional networks based on a single communication and connectivity protocol (either wired or wireless) fail to serve the dynamic requirements of the IoT. Heterogeneous networks combining wired (Ethernet, Power Line communication, etc.) and wireless (ZigBee, Wi-Fi, etc.) protocols are the future networks that will serve the purpose of IoT in the best possible manner. A real-world test bed analysis is essential to analyze and enhance the efficiency of hybrid wired and wireless-based heterogeneous networks. The paper presents an experimental investigation of traditional wired and hybrid (partially wired and wireless) networks based on IoT architectures. The results depict a reduction in the PDR (packet delivery ratio) and an increase in the packet loss rate for hybrid topology compared to a completely wired architecture.

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Fusion of Images to Detect Brain Tumors Using DWT, IDWT and VGG19 Algorithms

Fusion of Images to Detect Brain Tumors Using DWT, IDWT and ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Noorul Julaiha A G B. Vasudevan Ruhit Krishnan M Suriya M S Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Dept. of Electronics & Communication Engineering St. Joseph's College of Engineering Chennai

Detecting brain tumors from medical images is a complex task in medical image analysis and is crucial for accurate diagnosis and treatment planning. Over the past few years, there has been significant development in the field of medical image processing. One such technique that has been employed is the multi-scale decomposition of brain images, which is achieved through the discrete wavelet transform (DWT). DWT decomposes an image into two different components, structural and textural information, without compressing the image. As a result, it provides high accuracy details and it preserves edge and texture details when reconstructing the image from its original frequency, and reduces problems like blocking and ringing artifacts. The low frequency sub-band coefficients are fused by selecting the coefficient with the maximum spatial frequency, indicating the overall active level of an image, while the high frequency subband coefficients are fused by selecting the coefficient with the maximum code value. Brain tumor detection using wavelet transform involves the use of both CT scan and MRI scan images. CT scan uses X-ray images taken from different angles of a specific part of the human body to provide detailed information about its internal structure, while MRI scan employs strong magnetic fields, radio waves, and field gradients to generate images of the inner human body. Finally, the fused image is reconstructed by taking the inverse Inverse Discrete Wavelet Transformation (IDWT) of two varied frequency sub-bands. The methods such as Visual Geometry Group (VGG19) which is a sub class of Convolutional Neural Network (CNN), watershed algorithm, Procrustes Analysis Algorithm, data argumentation are used in registration, fusion and segmentation process to detect brain tumor. Overall, the application of DWT and the fusion of multi-scale components of brain images has significant potential in improving the accuracy and quality of medical image processing, especially in t

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Customer Churn Analysis in Financial Domain using Deep Intelligence Network

Customer Churn Analysis in Financial Domain using Deep Intel...

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2023 International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2023

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Nanda, Sudhansu Sekhar Phatak, Gururaj Hongal, Pushpa Gowda, V. Dankan Department of Computer Science and Engineering Udupi District Karkala Taluk India Kirloskar Institute of Management Karnataka India Kirloskar Institute of Management Harihar India Karnatak University Kousali Institute of Management Studies Dharwad India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9781665474511

Customer churn analysis is regarded as a crucial indicator that determines the revenues and profitability of the organisation in the modern day due to the advancement of technology and business models. Regardless of the company's size including startups, it is relevant to all business sectors, mainly because 65% of the company's revenue comes from its current clients. The aim of the analysis of customer churn is to identify the clients most likely to leave the existing organisation. In order to accurately predict customer churn in the banking industry, this research aims to develop an optimized deep neural network model. Five predictive analytical models from various categories of machine learning algorithms are also compared to the suggested model in the comparison study such as Gaussian Naive Bayes algorithms, decision trees, logistic regression and Artificial neural networks. The outcome demonstrates that, when compared to all other predictive models, the proposed deep intelligence network model exceeds them all in terms of accuracy. © 2023 IEEE.

关键词： Deep neural networks

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation

An Ensemble Deep Learning Framework for Binary Classificatio...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Somesh Mishra Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making.

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An Intelligent Hybrid Algorithm for Smooth AUV Path Generation 3

An Intelligent Hybrid Algorithm for Smooth AUV Path Generati...

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3rd IEEE International Conference on Industrial electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paramjeet Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

The development of effective multi-objective optimization strategies for Autonomous Underwater Vehicles (AUVs) operating in uncharted territories constitutes a considerable challenge. This endeavor necessitates the simultaneous minimization of travel time, reduction of path length, enhancement of navigational safety, and smoothing of trajectories. In light of these challenges, this research presents an innovative real-time path planning framework that seamlessly integrates Particle Swarm Optimization (PSO) with Simulated Annealing (SA), thereby enhancing AUV navigation within static environments. The PSO algorithm is utilized to generate optimal temporary waypoints across the scenarios examined, by balancing the crucial optimization parameters. Subsequently, an optimal path is formulated, informed by both the initial and terminal points. The efficacy of the proposed methodology is assessed through simulations conducted across three distinct maps. Each obstacle map demonstrates varying densities of obstacles. The outcomes of these simulations emphasize the robustness and adaptability of the proposed strategy, highlighting its ability to formulate optimal paths within complex underwater environments, irrespective of the arrangement of obstacles. The AUVs display remarkable agility in maneuvering through intricate underwater landscapes, adeptly avoiding debris and obstacles while preserving operational efficiency. This enhanced path planning technique not only elevates the responsiveness of the AUVs during critical missions - such as search and rescue operations and structural assessments - but also significantly bolsters their overall navigational safety. Through this research, we introduce a formidable tool for enhancing AUV capabilities in practical applications, ultimately facilitating more efficient underwater exploration and intervention. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Target Driven Autoencoder: A Supervised Learning Approach for Tumor Segmentation

Target Driven Autoencoder: A Supervised Learning Approach fo...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Abhishek Das Saumendra Kumar Mohapatra Rakesh Kumar Pattanaik Balaram Tripathy Gyana Ranjan Patra Mihir Narayan Mohanty Department of Computer Science Engineering Centurion University of Technology and Management Paralakhemundi Odisha India Department of Computer Science Engineering Shri Shri University Cuttack Odisha India Department of Electronics and Communication Engineering DNR College of Engineering and Technology Bhimavaram Andhra Pradesh India Department of Electronics and Telecommunication Engineering S.K.D.A.V. Government Polytechnic Rourkela Odisha India Department of Electronics and Communication Engineering ITER Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

A particular kind of neural network called an autoencoder (AE) is trained to discover a reduced and effective representation (encoding) of input data. In most of the applications of AE, it is found that this model has been used for generating the same images in compressed form. AE has also been used for encoding the images and signals. In this work, a supervised autoencoder is designed that is trained with both input images and corresponding targets that are also the images. This allows the model to learn a mapping from the input image to the target image specifically for segmentation. The proposed method is verified and trained with brain MRI images and segmented tumors. The dice score obtained in this method is 98.43% in producing segmented tumors.

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