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Human Activity Recognition using ShuffleNetV2 Model

Human Activity Recognition using ShuffleNetV2 Model

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shanufa Nazrin, P.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Dept. of Computer Science Kerala Thrissur India Indian Institute of Information Technology Dept.of Computer Science and Engineering Kerala kottayam India

ISBN: (纸本)9798350394580

HAR is specified as the crucial task within the field of computer vision and artificial intelligence. Main aim of HAR is to observing and labelling different human activity images of distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage. © 2023 IEEE.

关键词： HAR Human activity ShuffleNetV2

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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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Advancements in Quantum Machine Learning and Quantum Deep Learning: A Comprehensive Review of Algorithms, Challenges, and Future Directions

Advancements in Quantum Machine Learning and Quantum Deep Le...

引用

2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Priyanka, G.S. Venkatesan, M. Prabhavathy, P. Nit Puducherry Dept. of Computer Science & Engineering Karaikal India Vit University Dept. of Information Technology Vellore India

ISBN: (纸本)9798350314946

At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation. © 2023 IEEE.

关键词： Deep learning

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Prioritization of Medical Image Security Features: Fuzzy AHP Approaches 6

Prioritization of Medical Image Security Features: Fuzzy AHP...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Satish Devi, Mausmi Singh, Sunil Chaurasia, Pawan Kumar Khan, Raees Ahmad BBA University Dept. of Information Technology Lucknow India PSIT Dept. of Computer Science & Engineering AKTU Lucknow India

ISBN: (纸本)9798350304480

Medical images are sensitive data about patients, hospitals, healthcare sector and, therefore, require secure transmission to protect their privacy. To ensure the security of medical image transmission, three essential factors are vitally employed: integrity, confidentiality, and authentication. In this study, we propose an F-AHP technique to determine the relative importance of these security factors. The technique was implemented using 'Octave MATLAB tools'to obtain the weights for each factor, and the results showed that integrity is the most critical factor for the transmission of medical image security than confidentiality and authentication. This technique can help healthcare organizations to choose the appropriate security measures for transmitting medical images while ensuring that patient data is protected during transmission. Overall, the researcher highlights the importance of security in medical image transmission and provides a framework for healthcare organizations to implement effective security measures. © 2023 IEEE.

关键词： Authentication Confidentiality Fuzzy AHP information Security Integrity Medical Image Security

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Blockchain-based Access Control Model for Student Academic Record with Authentication 7

Blockchain-based Access Control Model for Student Academic R...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Maheswari, M. Prasath, S. Rajesh, R. Ramalakshmi, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Dept. of Information Technology Chennai India

ISBN: (纸本)9798350397253

Schools and Institutions maintain student's records and certificates for future needs like verification and credit transfer. These procedures are carried out manually to ensure that documents and the data are valid and non-tampered, which usually needs more energy and time. Converting these procedures into digital form minimizes the time and energy consumption but with the cost of high-security risks. Storing student's data on a centralized system has problems like data breaches due to human error, injection attacks, and buffer overflow attacks, and even if the server gets damaged physically, it'll take more time to repair and recover the damaged data and start running. But in blockchain technology, the data stored in blocks with a cryptographical code of the previous block, gives enhanced security compared to the traditional database. If anyone tries to tamper or hack the data, the decentralized technology can easily recognize the modification and prevent it from happening. Three layers of blockchain are being employed to give the Academic Record System the highest level of security possible. Data Storage Blockchain is the third layer of the model which stores the student's records. The Access Control Model Blockchain is the second layer, which includes additional layer security to the Data Storage Blockchain as it records all the activities around it. And the final layer is the Authentication Blockchain, which is based on Ethereum Blockchain. This model validates transaction using Smart Contracts given to them, creating a new user and validating the login process securely for the Authority, Institution, and the Student for accessing the Data Storage. © 2023 IEEE.

关键词： Smart contract

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XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU) 8

XNLI 2.0: Improving XNLI dataset and performance on Cross Li...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Kumar Upadhyay, Ankit Kumar Upadhya, Harsit JSS Academy of Technical Education Dept. of Computer Science and Engineering Bengaluru India JSS Academy of Technical Education Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350334012

Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose applications extend beyond the training language. We can leverage the power of machine translation to skip the tiresome part of translating datasets from one language to another. In this work, we focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI, including the test and dev sets of XNLI using Google Translate. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference. We then expand our boundary to investigate if we could improve performance in low-resource languages such as Swahili and Urdu by training models in languages other than English. © 2023 IEEE.

关键词： Natural language processing systems

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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2024 International Conference on information Technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study in Pedestrian Detection for Autonomous Driving Systems

A Comparative Study in Pedestrian Detection for Autonomous D...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Ramchandani, Monica Sahu, Satya Prakash Dewangan, Deepak Kumar National Institute of Technology Dept. of Information Technology Raipur India Tiet Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665492942

Technology development has altered how people live. Intelligent vehicles are being developed as a result of technological advancements in the automobile industry. Pedestrians are the most vulnerable road users, accounting for more than half of all traffic fatalities. Thus, the key thing to take into account is pedestrian detection. In order to safeguard both drivers and pedestrians, an intelligent vehicle system can detect pedestrians in front of moving vehicles and either loudly warn the driver of the risk or automatically slow down the vehicle. The purpose of this study is to examine different sensor-based pedestrian detection techniques, weigh their benefits and drawbacks, and determine how they may be enhanced to make them more useful in real-world situations. Radar, vision sensors, and LiDAR are the mechanisms employed. This study analyses the datasets utilized in the research and identifies numerous state-of-the-art methodologies. © 2023 IEEE.

关键词： Autonomous vehicles

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Enhancing wireless sensor network longevity and security: A quad-leach approach

Enhancing wireless sensor network longevity and security: A ...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E. Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Researchers have increasingly focused on Wireless Sensor Networks (WSNs) in recent years, recognizing their versatile applications. The primary focus within WSNs is enhancing network longevity for efficient resource utilization, coupled with a strong emphasis on security and authentication. This paper specifically delves into the implementation of Quad LEACH for homogeneous networks, aiming to substantially improve network lifetime, stability, and throughput. The proposed protocol achieves this by employing a cluster head selection process and integrating security and authentication measures between cluster heads in the Quad-LEACH routing protocol. The research addresses the integration of a security agent within WSNs using the NS-2 tool, contributing to enhanced authentication. The implementation is carried out through the simulation software NS 2, where the security agent is seamlessly combined with the WSN infrastructure. The objective of this research is to incorporate a reliable authentication and security agent into NS-2 in conjunction with the LEACH protocol, which is the standard for wireless sensors networks (WSNs). The efficacy of the suggested methodology will be validated through simulated outcomes. © 2024 IEEE.

关键词： Wireless sensor networks

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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