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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...

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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...

引用

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

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

引用

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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Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism 14

Simplifying Structure and Pattern in Complex Social Networks...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Kshama, S.B. Swasti, S. Sameeksha, B.H. Puri, Sonia Rubini, P.A. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks. © 2023 IEEE.

关键词： Pattern matching

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Sarcasm Detection in Libyan Arabic Dialects Using Natural Language Processing Techniques 4

Sarcasm Detection in Libyan Arabic Dialects Using Natural La...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Alakrot, Azalden Dogman, Aboagela Ammer, Fathi Yefren College of Medical Technology Dept. of Information Technology Yefren Libya Libyan Academy for Postgraduate Studies Dept. of Electrical and Computer Engineering Al Zentan Libya University of Zawia Dept. of Computer Science Azawia Libya

ISBN: (纸本)9798350372632

Sarcasm is often used to convey the opposite of what is actually said (positive words, negative meaning). It is a type of humor that relies on the listener or reader to understand the intended meaning of the words. Sarcasm can be difficult to detect in written text, especially on social media platforms where people often use abbreviated language and emojis to convey their messages. The sarcasm detection can be a crucial role in refining Automated Sentiment (SA). In order to enhance the effectiveness of data-driven approaches for customer service and product development. Furthermore, the existing research on sarcasm detection in Arabic is limited. Also, there is a noticeable absence of studies in particular, the sarcasm detection in Libyan Arabic dialects. This study utilizes a data-set of 5082 collected from Facebook platform, labeled by team annotators experienced in the Libyan dialects as (sarcastic or non-sarcastic), and extracting some lexical and syntactic features which used to training Support Vector Machine (SVM) model. The results show commendable performance with an accuracy of 79.15%, precision of 79.3%, recall of 79.7%, and an F1 score of 79.5%. © 2024 IEEE.

关键词： Support vector machines

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