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International Conference on Electrical Energy Systems (ICEES)

作者： T.M.S. Mekala Rani A. Suresh S. Bhargavi M. Harsha Vardhan Reddy K. Sai Nikhil G. Chandu Priya Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) Siddharth Institute of Engineering & Technology Andhra Pradesh India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a strong and flexible system that can recognize and stop fraudulent activity in real-time by utilizing machine learning techniques. By utilizing dataset containing the features related to average time between sent and recived transactions, amount involved. This project focuses on creating a proactive defense mechanism that can analyze transaction patterns, detect anomalies, and promptly alert users or intervene to mitigate potential threats. By combining cutting-edge machine learning techniques with the ever-evolving landscape of cryptocurrency security, this project aims to safeguard digital assets and promote trust in the rapidly growing crypto ecosystem.

关键词： Electric potential Machine learning algorithms Ecosystems Machine learning Real-time systems Cryptocurrency Security

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Design and Evaluation of IoT Prototypes: Leveraging Test-Beds for Performance Assessment and Innovation

Design and Evaluation of IoT Prototypes: Leveraging Test-Bed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kotiyal, Arnav Shrivastava, Anurag Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

When you mention the Internet of things (IoT), connected devices and systems can be made seamless. Whilst creating and prototyping IoTs are oftentimes necessary, design, testing and evaluating IoT prototypes require robust frameworks and methodologies to achieve reliable performance and innovation. In this paper, test beds as controlled environments for performance assessment of IoT prototypes are discussed and these tested in terms of design and evaluation. Of importance are test-beds as they aid in simulating real world conditions to do iterative improvements, and foster innovation in IoT solutions. It presents some key methodologies for developing IoT prototypes, underlines the role of test beds in the validation process, and reports about evaluation of some case studies demonstrating the impact of such frameworks on alleviating inefficiency, unreliability, and non-scalability. The paper combines leading edge technologies with strong data driven decision making to present actionable feedback on exploiting test beds to catalyse IoT innovation against pivotal challenges including interoperability, security, and scalability. © 2025 IEEE.

关键词： Testbeds

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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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Real-Time air Quality Monitoring with Edge ai and Machine Learning Algorithm

Real-Time Air Quality Monitoring with Edge AI and Machine Le...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： V. Ramu Usikela Naresh P.Arun Prakash G Shyamala P Saritha Athiraja Atheeswaran Department of CSE (AI&ML) Kakatiya Institute of Technology and Science Hanamkonda Telangana India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Mangalpalli Rangareddy Telangana India Vadasithur (via) Coimbatore Tamil Nadu India Department of Civil Engineering School of Engineering SR University Warangal Telangana Department of Civil Engineering Malla Reddy Engineering College (Autonomous) Maisammaguda Secunderabad India Department of CSE (AI&ML) Malla Reddy College of Engineering Hyderabad India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Real-time air quality monitoring is essential for public health and environmental protection, but typical centralized systems have latency, bandwidth, and privacy concerns. This work describes a novel approach to decentralized air quality monitoring that employs Edge ai and powerful machine learning techniques. IoT sensors outfitted with NVIDIA Jetson devices collect and evaluate air quality data locally. Machine learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), analyze pollutants including PM2.5, CO2, and NOx in real time. Implemented across 50 urban sites with varied pollution levels, the technology reduced latency by up to $75 \%$ and bandwidth use by $60 \%$ when compared to typical cloud-based solutions. The pollutant detection accuracy was evaluated against conventional reference monitors and yielded an average correlation coefficient of 0.92. This system also used federated learning to update models based on local data, improving model accuracy by up to $15 \%$ over six months. This new strategy not only increases data processing speed and accuracy, but also protects data privacy and lowers operational costs. Significant improvements in latency, bandwidth efficiency, and model accuracy demonstrate the utility of decentralized air quality monitoring systems in urban settings.

关键词： Data privacy Accuracy Federated learning Atmospheric modeling Edge ai Bandwidth air quality Real-time systems Data models Monitoring

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Dynamic Framerate SlowFast Network for Improving Autonomous Driving Performance

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 261-268页

作者： Jeon, Byeong-Uk Chung, Kyungyong Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, deep learning algorithms that detect even the action of things require high computing performance. In addition, the computing performance of autonomous driving vehicles for processing such tasks is inferior. These classification processes are not used for recognizing and determining autonomous driving vehicles because it is impossible to process the classification of the action of autonomous driving vehicles in an autonomous driving vehicle on a real-time basis. This paper proposes a Dynamic Framerate SlowFast network for improving autonomous driving performance. Unlike pre-existing studies, the proposed model includes a cropping process through the YOLO model. In addition, it measures the similarity between unit frames through the SSIM and skips the input when the similarity exceeds a certain level. This process made it possible to reduce the number of frames entered into the model. Compared to the existing SlowFast Network, the performance evaluation compared the time required to analyze one image and the AUC of classification results when the number of input frames was reduced through similarity analysis techniques. The similarity analysis technique achieved the highest AUC when the SSIM was applied. The Dynamic Framerate SlowFast network proposed in this study achieved an AUC of 0.7126 and took an FPS of 0.7912 to analyze the entire verification video data. Compared to the pre-existing SlowFast network, which took an FPS of 0.5285 to achieve an AUC of 0.7531, the Dynamic Framerate SlowFast network achieved faster and more accurate results. Therefore, using the proposed technique, it is possible to achieve faster detection results while maintaining the object action detection AUC of the SlowFast network. © 2023 Institute of Electronics and Informatio

关键词： Autonomous vehicles

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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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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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