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Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. Applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

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Smart Interaction: Merging Augmented Reality and Internet of Things for Seamless Device Control

Smart Interaction: Merging Augmented Reality and Internet of...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny B. Sai Mahitha M. Shashank N. Vamsi Krishna Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The combination of Augmented Reality (AR) and Internet of Things (IoT), often referred to as AR + IoT, offers exciting possibilities for future research and development. IoT serves as a platform for communication and remote control of connected devices, while AR provides an intuitive interface for visualizing and managing these devices through enhanced, information-rich displays. This research aims to develop an interactive and accessible home automation system that integrates IoT and AR technologies, enabling seamless remote control of electrical devices. Utilizing an ESP32/NodeMCU board interfaced with the Blynk IoT platform, the system supports device management through a mobile application and an AR interface designed in Unity. The AR interface facilitates intuitive interactions via URL-based signals, which are transmitted to the IoT hardware for execution. This dual-control mechanism enhances user flexibility and convenience, making smart home automation more accessible to users with varying technical expertise. The proposed system not only improves user interaction and control efficiency but also demonstrates the potential of AR in augmenting IoT applications, bridging the gap between physical and virtual control environments. Future enhancements could include advanced AR features, voice control, and cloud-based analytics to further optimize system performance and scalability.

关键词： Visualization System performance Scalability Smart homes Systems support Internet of Things Augmented reality Remote control Wireless fidelity Research and development

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Cloud-Native Hybrid Deep Learning Pipeline for Network Traffic Classification using Convolutional Recurrent Neural Network

Cloud-Native Hybrid Deep Learning Pipeline for Network Traff...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Satish, E.G. Sudheer Kumar, Komuravelly Adnan, Myasar M. Krithika, M. Sundari, MandapatiVenkata Rama Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vels Institute of Science Technology and Advanced Studies Department of BCA/IT Pallavaram India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate strategies to learn about the encrypted network flow. The research focuses on developing a cloud native hybrid Deep Learning (DL) pipeline for Network Traffic Classification (NTC). Initially, the ISCXVPN2016 dataset is considered that consists encrypted and non-encrypted traffic where the raw data is pre-processed with packet length normalization. The features are extracted applying Convolutional Neural Networks (CNNs) to define statistical flow features including inter-arrival time, packet size and flow duration. The data from network traffic is processed and classified using Convolutional Recurrent Neural Network (CRNN) with microservices executed in serverless containers. The hyper parameters in the DL models are then tuned using grid search and the random search algorithms. Experimental results showed that the proposed cloud native CRNN pipeline achieved accuracy of 98.67%, precision of 95.46%, and 94.32% when compared to the existing NTC models Stacked Autoencoders (SAE) based CNN (SAE-CNN) and Autoencoders based CNN (AE-CNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing Machine Learning for Enhanced Distributed Computing in Wireless Network Systems

Harnessing Machine Learning for Enhanced Distributed Computi...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rashmi, K. Anandhi, R.J. Singh, Navdeep Kumar, V. Surya Pavan Gupta, Krashnkant Albawi, Ali Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India Amity University Greater Noida Department Of Electronics & Communication Engineering India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

A complete wireless network system for flexible resource sharing, spectrum management, security, and cross-layer optimization is described in this paper. Five algorithms comprise the system. These are ADS, QoS-ml, CLO, and DSM. Wireless networks adapt and grow, and these applications solve issues. Real-time resource allocation using predictive models enhances network performance, latency, and efficiency with the PRA algorithm. DSM uses adaptive spectrum utilization to reduce interference. ADS uses machine learning to detect unusual activity and implement flexible safety measures to make networks safer. QoS-ml improves routing options using real-time QoS data to ensure network-wide QoS. CLO alters layer connections, utilizes metrics across layers, and is continually searching for network improvements. A detailed analysis of the research examined each program's distinct qualities and how they function together. The discussion focused on how well the framework performed in various settings and was adaptable. In the conclusion, the algorithms' strengths and potential in distributed computing environments were highlighted. The recommended technique outperformed others in speed, latency, security, energy efficiency, scalability, and customer satisfaction. Performance trends, parameter distributions, and trade-offs were shown, making the structure more dependable. By offering a full, adaptive, and successful method, this study advances wireless network management. Optimizations, scalability, and real-world applications might be studied to ensure the system works in various conditions. In challenging and changing situations, the proposed architecture supports wireless network expansion. © 2024 IEEE.

关键词： Machine learning

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BERTing Emotions: Exploring Textual Emotion Recognition using NLP

BERTing Emotions: Exploring Textual Emotion Recognition usin...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Praveen Kumar Pannala Ramya G.Sai Teja V.Rama Krishna Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Emotion recognition in text is a crucial area within Natural Language Processing (NLP), serving diverse applications such as sentiment analysis, mental health assessment, and human-computer interaction. This study explores the use of Bidirectional Encoder Representations from Transformers (BERT) for this purpose, capitalizing on its bidirectional transformer architecture to grasp intricate contextual relationships in text. By fine tuning a pretrained BERT model on a dataset annotated with emotional categories like happiness, sadness, anger, surprise, and fear, the model achieves precise emotion classification. BERT's pre-trained linguistic knowledge and ability to interpret complex semantic and syntactic patterns provide a strong foundation for this task. Experimental results demonstrate that BERT surpasses traditional machine learning approaches, including Support Vector Machines (SVM) and logistic regression, in terms of accuracy, owing to its advanced contextual embeddings. Additionally, the model exhibits excellent generalization capabilities across diverse emotional expressions, highlighting its effectiveness for emotion-aware NLP applications. This project also leverages Streamlit to develop interactive web applications that integrate NLP-based emotion detection, showcasing its practicality and accessibility for real-world use cases.

关键词： Support vector machines Emotion recognition Sentiment analysis Semantics Bidirectional control Mental health Syntactics Transformers Encoding Tuning

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Deep Reinforcement Learning for Dynamic Compliance Management in Blockchain Supply Chains: An LSTM Perspective 2

Deep Reinforcement Learning for Dynamic Compliance Managemen...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Ranjith, Jinugu Al-Farouni, Mohammed Anitha, R. Poornima, Ediga Sharma, Kamal Kumar, Neelam Sanjeev CMR College of Engineering & Technology Department of CSE India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Mechanical Engineering Mathura India SRM Institute of Science & Technology Vadapalani Department of Computer Science & Engineering Chennai26 India

ISBN: (纸本)9798350368109

Supply chain management is one of the industries that benefit greatly from implementing blockchain technology since it supplies chains with a high level of transparency, security, and traceability. Nonetheless, keeping up to date with different regulatory requirements or specifications that change frequently and still meeting contract requirements still pose a major problem. In this paper, we introduce a new method in the dynamic compliance management of blockchain implemented supply chain system by the use of deep reinforcement learning (DRL) and Long Short-Term Memory (LSTM) networks. Through DRL, supply chains can obtain real-time insights in learning from the environment and apply the knowledge effectively to satisfy regulatory standards as well as contractual terms. The inclusion of the LSTM component allows the system to capture sequential dependency and long-horizon effects within the supply-chain data. To prove the efficiency of our strategy, we provide simulation results as well as the analysis of real-world applications that discuss the positive shift in percentages and potential increase in productivity. © 2024 IEEE.

关键词： Deep reinforcement learning

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Waste Product Identification to assist EPR using Object Detection 15

Waste Product Identification to assist EPR using Object Dete...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Anil Prasad, R. John, Ayain Gandiboina, Harika Jayadev, Gopika Frugal Scientific Pvt Ltd Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Ai & Ml Bengaluru India

ISBN: (纸本)9798350370249

With the increase in population, there has been a large increase in waste production. This has affected the environment and in turn have led to land pollution. To increase the recycling of products and increase the lifespan of raw materials, several countries of the world have implemented EPR policy. EPR requires collection of data related to the amount of non biodegradable waste generated by each company. With the recent technological advancements in machine vision, it has become possible to develop an infrastructure to easily collect such data in realtime using various object detection models like Yolo, SSD, RCNN and so on. This paper provides the details of a complete prototype that can help assist in the identification of non biodegradable waste products into their respective producers which can in turn help to implement the EPR policy and also elaborates on the model selection process for this application. The models that were used and compared in this paper includes CNN, Faster RCNN and Yolo v5 © 2024 IEEE.

关键词： CNN Extended Producer Responsibility (EPR) Faster RCNN Object Detection Techniques Waste Management YOLO

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks 4

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Srivastava, Shubhi Bhargav, H.K. Gurani, Vijayakumar Chaturvedi, Arpana Gandhi, Ankita Dharangutti, Yogesh New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Karnatak University Department of Computer Science Dharwad India New Delhi Institute of Management Department of IT/AI/ML New Delhi India Parul Institute of Engineering and Technology Department of Computer science and Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

This paper presents a novel ai-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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SSDP DDoS Attacks Detection using Naïve Bayes Classifiers with Wrapper Feature Selection Methods

SSDP DDoS Attacks Detection using Naïve Bayes Classifiers w...

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Delhi Section Conference (DELCON), IEEE

作者： Manu Sharanya Bhadriraju Kishorebabu Dasari Computer Science and Engineering Department Keshav Memorial Institute of Technology Hyderabad Telangana India Computer Science and Engineering (AI&ML) Department Keshav Memorial Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

One of the most destructive cyber-attacks nowadays is Distributed Denial of Service (DDoS). A Simple Service Discovery Protocol (SSDP) DDoS attack is established by using the advantage of holes in the Simple Service Discovery Protocol to overwhelm a victim network. Early SSDP DDoS attack detection is essential for minimizing consequences. The SSDP DDoS attack detection is analyzed in this work using various Naïve Bayes classifier types, including Gaussian, Bernoulli, Multinomial, and Complement. For feature selection, this study used various wrapper methods such as Forward Feature Selection, Backward Feature Elimination, and Recursive Feature Elimination. The CIC-DDoS2019 dataset is used in this study. The assessment measures used in this study were accuracy, K-Fold Cross-Validation accuracy, ROC-AUC, and log-loss values. Bernoulli Naïve Bayes classifier gives best accuracy value 99.96 with all feature subsets.

关键词： Accuracy Protocols Denial-of-service attack Feature extraction Reflection Naive Bayes methods Classification algorithms computer crime Cyberattack

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Customer Churn Prediction Using Hierarchical Clustering and Ensemble Learning

Customer Churn Prediction Using Hierarchical Clustering and ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Konda Avinash Varala Aditya Ram Seguru Rithvik Reddy Bharath Kumar Nangunuri M.A. Jabbar P. Swetha Computer Science Engineering(AI&ML) Vardhaman College of Engineering Shamshabad Hyderabad Information technology Vardhaman College of Engineering Shamshabad Hyderabad

The capacity to forecast which consumers are most likely to discontinue their use of a service or leave is known as customer churn prediction. This prediction holds significant importance for many companies, as acquiring new customers often proves to be more costly than retaining existing ones. Once customers at risk of churn are identified, it becomes crucial to determine the most effective marketing efforts for each individual customer in order to maximize their likelihood of staying. This entails recognizing that customers exhibit different behaviors, preferences, and reasons for canceling their subscriptions. Therefore, actively engaging with each customer is essential in order to maintain them on the customer list. Understanding which marketing activities are most effective for individual customers, as well as the optimal timing for these activities, is typically achieved through training a supervised machine learning model. This model was trained on historical data, which may have included customer service interactions, account activity, and purchase history, and it reached an extremely impressive accuracy of 87 percent. The model is then applied to new data to forecast things like whether a new customer would churn within a given period of time.

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