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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Tran, Tan D. Pham, Canh V. Trung, Dung P. Nguyen, Uyen T. Faculty of Information Technology VNU University of Engineering and Technology Hanoi Viet Nam ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Institute of Engineering and Technology Vinh University Nghean Viet Nam

ISBN: (纸本)9789819706686

This paper introduces an efficient streaming algorithm for a well-known Minimum cost Submodular Cover (MSC) problem. Our algorithm makes O(logn) passes over the ground set, takes O(nlogn) query complexity and returns a (1/ϵ,1-ϵ)-bicriteria approximation solution with the ground set of size n and the precise parameter ϵ>0. Therefore, it is better than the existing streaming algorithms in terms of both solution guarantees and query complexity. Besides the theoretical performance, the experiment results on two applications, Revenue Threshold and Coverage Threshold, further show the superiority of our algorithm with the state-of-the-art ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Approximation algorithms

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Deep Reinforcement Learning in Automated Network Vulnerability Detection

Deep Reinforcement Learning in Automated Network Vulnerabili...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Sreenivasappa, B.V. Anoop, A. Naval, Preeti Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

In today's rapidly changing digital environment, identifying security holes in a network is an essential part of maintaining online safety. This research suggests a novel strategy for improving automated network vulnerability identification by making use of Deep Reinforcement Learning (DRL). Our approach combines cutting-edge algorithms to provide a versatile and responsive system;these algorithms include Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Dueling Deep Q-Network (DDQN). Using six different methods - including signature-based detection, heuristic-based detection, machine learning-based detection, intrusion detection systems (IDS), packet sniffers, and vulnerability scanners - we evaluate how well the suggested method performs in comparison. The detection rate, the number of false positives and negatives, the precision, the recall, the speed of execution, and the capacity to scale are only few of the major performance parameters we use in our analysis. Across a variety of metrics, the results show that the suggested approach performs better than the state of the art. It has an incredibly low false positive (3%) and false negative (2%) rates on top of its 95% detection accuracy. Both its accuracy and efficiency in finding security flaws are demonstrated by its high precision (94%) and recall (96%). The suggested technique also has a low execution time of 25 ms and a good scalability rating of 5, showing it can scale to deal with increasing network complexity. In conclusion, our study demonstrates the use of DRL in automating the process of detecting security holes in networks. The suggested method is an excellent tool for improving network security since it is more accurate and efficient than conventional methods and can be adapted to deal with new threats. By incorporating sophisticated DRL algorithms, a resilient and flexible defensive system may be created, one that can keep up with the ever-changing threats in the cyber world. © 2023 IEEE

关键词： Recurrent neural networks

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Exploring Data as Quantum Bits: Analysis of Quantum Image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum Algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image Processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies 3

Precision Cardiac Risk Evaluation through Flexible Classific...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Das, Neeraj Ganorkar, Akshaya M Jayaprakash, B. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350343274

Accurate methods of measuring cardiac risk are desperately needed, as cardiovascular diseases remain a major global health concern. The aim of a newly released study titled 'Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies' was to establish new standards in the field of cardiac risk assessment. This work suggests a novel method to increase the accuracy and precision of cardiac risk assessment by utilizing flexible category mining algorithms. The study included a proposed strategy. There are clear limitations to traditional risk assessment methods, especially with regard to how accurately they can identify those who are at risk. As a result of this work, innovative new methods for improving cardiac risk assessment without imposing these limitations have been created. Each of these enhancements is supported by the research's conclusions. Combining state-of-the-art data analysis and machine learning technology allows the recommended strategy to enhance forecast precision and adjust to changing risk indicators. The most significant and remarkable outcome of this study is the development of a prediction model with enhanced AUC-ROC values over previous methods and exceptional sensitivity, specificity, and accuracy. The suggested approach shows a higher ability to recognize individuals who may have cardiovascular diseases. By reducing the likelihood of missed diagnoses and the frequency of false negatives, this is achieved. The research also emphasizes the model's interpretability, which gives medical practitioners a better understanding of the variables that affect the predictions. This interpretability increases the technique's potential impact in clinical settings by ensuring that medical practitioners can provide individualized treatment and make correct diagnoses. To put it another way, interpretability ensures that health care providers can provide each patient with the individualized attention they require. © 2023 IEEE.

关键词： Diagnosis

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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A 140 GHz LTCC-based High-efficiency Heterogeneous Reflectarray Antenna

A 140 GHz LTCC-based High-efficiency Heterogeneous Reflectar...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： He, Xianhui Yang, Wanchen Li, Yongzheng Xue, Quan Che, Wenquan South China University of Technology Department of Electronic and Information Engineering Guangzhou China Hohai University Department of Computer and Information Nanjing China

ISBN: (纸本)9798350336535

One design strategy of heterogeneous reflectarray antenna (RA) is presented. A 140 GHz high-efficiency low-profile RA based on heterogeneous reflective elements is designed, fabricated and measured. Compared with homogeneous RA, the proposed heterogeneous RA shows better directionality, higher aperture efficiency, wider gain-bandwidth and lower cross-polarization. The measured results of the proposed RA indicate the realized gain of 31.39 dBi at 140 GHz and aperture efficiency of 46.22%. With above features, the proposed RA is expected to use for short distance communication applications in THz band. © 2023 IEEE.

关键词： Efficiency

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Combining CNN and SVM for High-Precision Wheat Disease Classification using Leaf Image Analysis 2

Combining CNN and SVM for High-Precision Wheat Disease Class...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Jain, Anuj Kumar Jain, Sonam Kaur, Urvinder Dahiya, Atisha Bhandari, Rahul Goel, Sandeep Chandigarh University Department of Cse Punjab Mohali India Panipat Institute of Engineering and Technology Department of Computer Applications Haryana Samalkha India Panipat Institute of Engineering and Technology Department of Information Technology Haryana Samalkha India

ISBN: (纸本)9798350388916

This study presents a novel classification algorithm designed to accurately identify plant diseases using leaf image analysis. The model employs a hybrid architecture containing a CNN (Convolutional Neural Network) and an SVM (Support Vector Machine) classifier, featuring 2 convolutional layers and 2 max-pooling layers. A variety of datasets encompassing five distinct plant diseases have been utilized for assessment: Stripe Rust, Powdery Mildew, Leaf Rust, Septoria Leaf Blotch, and Fusarium Head Blight. For each disease category, the model can correctly classify a positive case with precision values ranging from 92.45% to 93.75%. Similarly, the recall values are consistent with a range from 91.84% to 94.12%, which were very good too and would be shown that no matter what was taken into account either ring or original brain scans researchers could obtain positive instances well enough using this model resources The model showcases a significant performance, with F1-Score (a balanced metric concerning precision and recall) having high values constant between 92.78% and 93.75%. Support Proportions indicate how much of the overall dataset can be attributed to a given disease class and Support Values show where there are occurrences in each proportion. Its great Weighted Average Accuracy of 93.25% shows that the model is trained on a wide range of plant diseases so it does not only work for one or two datasets but across all datasets. This endto-end macro-average and micro-average evaluation, again only shows all around effectiveness of the model. Results & conclusions: This study provides a highly accurate automated plant disease diagnostic model, which is of great significance for crop management and precision agriculture. If validated through follow-up studies, the findings could improve crop yields and sustainability as well as help develop remote sensing technology that rapidly identifies plant disease. © 2024 IEEE.

关键词： Precision agriculture

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Revolutionizing ECG Signals with Enhanced Deep Learning Methods 3

Revolutionizing ECG Signals with Enhanced Deep Learning Meth...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jayabalan, Bhuvana Singh, Akhilendra Pratap Manaswini, R. Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

The tremendous advances in computing technology are directly responsible for the substantial developments that are presently taking place in the medical sector. A new age of health data analytics and predictive modeling has begun with this change, allowing medical personnel to better utilize data to improve patient outcomes, treat diseases more preventatively, and spend resources more effectively. This article investigates the role of computers in redefining healthcare analytics and the implications for the healthcare system. The advent of contemporary computers has ushered in a new era in healthcare data analytics, one in which huge amounts of data can be reviewed quickly without losing accuracy. In the healthcare business, machine learning approaches such as Random Forest, Elastic Net Regression, and Long Short-Term Memory (LSTM) Networks have become key tools for predictive modeling. These algorithms, in conjunction with the integration of several healthcare data sources, have given medical practitioners and researchers the ability to make thorough and evidence-based judgments. Contributions can take numerous forms, but some examples include the creation of novel methodologies and improvements to forecast reliability. In terms of accuracy, precision, and recall, the developed Integrative Health Data Analytics Framework beats more traditional methodologies, as proven in the preceding section. © 2023 IEEE.

关键词： Long short-term memory

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Multimodal attention-driven visual question answering for Malayalam

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Neural Computing and Applications 2024年第24期36卷 14691-14708页

作者： Kovath, Abhishek Gopinath Nayyar, Anand Sikha, O.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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A Deep Learning Approach for Detecting Malware Using Autoencoder

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu Madhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, R. Veeraiah, Duggineni Ramesh, Eluri Lakshmi, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

Research in the field of malware detection is currently leaning toward methods based on artificial intelligence algorithms due to the increasing limitations of traditional detection methods and the increasing accuracy of these methods. This study therefore introduces a novel approach to malware detection. This method integrates a deep learning model's autoencoder network with a grayscale graphic depiction of malware. By looking at the autoencoder's reconstruction error, we may assess if the grey-scale picture approach is feasible to use for malware detection. Additionally, it differentiates between malicious and safe software by utilizing the autoencoder's dimensionality reduction capabilities. Using the Android dataset, our newly introduced detection model attained a 95% accuracy and maintained a consistently high F-score of approximately 95%. This outperformed more conventional methods of machine learning detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Malware

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