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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

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Frontiers of computer science 2023年第4期17卷 83-96页

作者： Muazzam MAQSOOD Sadaf YASMIN Saira GILLANI Maryam BUKHARI Seungmin RHO Sang-Soo YEO Department of Computer Science COMSATS University IslamabadAttock CampusAttock 43600Pakistan Department of Computer Science Bahria UniversityLahore 54600Pakistan Department of Industrial Security Chung-Ang UniversitySeoul 06974Republic of Korea Department of Computer Engineering Mokwon UniversityDaejeon 35349Republic of Korea

Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city ***,intelligent video-surveillance systems are critical to increasing the security of these smart *** precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by *** researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision *** this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person *** the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel *** addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature ***,the model’s effectiveness is validated on the CUHK01 and CUHK02 *** compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively.

关键词： Internet of Everything(IoE) visual surveillance systems big data security systems person re-identification(Re-ID) deep learning

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A Deepfake Image Classifier System for real and Doctored Image Differentiation 7

A Deepfake Image Classifier System for real and Doctored Ima...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thomas, Juanith Mathew Ebenezer, V. Richard, Rohan Paul Karunya Institute of Technology And Sciences Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Sciences Division of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350359299

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet"architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we'll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%. © 2024 IEEE.

关键词： Digital storage

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Design and Implementation of Side Channel Attack Based on Deep Learning LSTM

Design and Implementation of Side Channel Attack Based on De...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665482585

Encryption algorithms and encryption devices both play a key role in ensuring the safety of data that has been encrypted. Various types of attacks, such as energy analysis, can be used to assess the reliability of the encryption devices. Since it was originally introduced, side channel attacks' deep learning-based methodology has drawn plenty of attention. This is one of several different attack strategies. In this paper, a side channel attack method based on the LSTM deep learning network is suggested. The method use Correlation Power Analysis (CPA) to find the relevant information in the side channel power consumption data. The choice of a suitable interest interval to utilize as the feature vector in the creation of the neural network model is then guided by the position of the interest points. The trials' findings show that the LSTM model outperforms both MLP and CNN in terms of how well it executes side channel attacks. © 2023 IEEE.

关键词： Side channel attack

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A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning 18

A Novel Approach for Anomaly Detection using Snort Integrate...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Preethi, T. Reddy, Ponnuru Rakshitha Likhitha, Lekkala Kumar, Pendyala Pavan Kamani, Abhinav Vnr Vignana Jyothi Institute of Engineering and Technology Data Science and Artificial Intelligence & Data Science Department of Cse - Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

In today's digital world, it is crucial to keep a company's information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort's capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Tree Intrusion Detection System K-Nearest Neighbours (KNN) Machine Learning Naive Bayes NSL-KDD Dataset Random Forest Snort Support Vector Machine (SVM)

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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Diagnosis of Plant Leaf Disease using Vision Transformer 10

Diagnosis of Plant Leaf Disease using Vision Transformer

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sundaraj, Ashik Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of Handwritten Image-To-Speech Using Deep Learning

Investigation of Handwritten Image-To-Speech Using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Manju, S. Anitha, J. Sujitha Juliet, D. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

In the realm of accessibility technology, this paper introduces a pioneering method for converting handwritten images to speech. The work primarily focuses on recognizing handwritten text and subsequently converting it into audible speech. The work comprises two fundamental phases: initially, a deep learning algorithm is employed to recognize and extract text from handwritten images precisely. Following this, the recognized text is converted into speech using the pyttsx3 library which offers flexibility in voice selection, speech rate, and other parameters, making it highly customizable for different applications. To ascertain the most effective algorithm for the text recognition phase, a comparative analysis is conducted among the top three algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models - based on their accuracy in recognizing handwritten text. Experimental results indicate that CNNs surpass the other two algorithms, achieving an accuracy of 92%. This work offers a pragmatic approach to converting handwritten content into speech, with potential applications in aiding the visually impaired and enhancing accessibility to written information. © 2024 IEEE.

关键词： Long short-term memory

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Sequential Masking Imitation Learning for Handling Causal Confusion in Autonomous Driving 8th

Sequential Masking Imitation Learning for Handling Causal C...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Zhang, Huanghui Zheng, Zhi College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789819975891

Training agents for autonomous driving using imitation learning seems like a promising way since its only requirement is the demonstration from expert drivers. However, causal confusion is a problem existing in imitation learning, which is that with more features offered, an agent may perform even worse. Here, we aim to augment agents’ imitation ability in driving scenarios under sequential setting, by a novel method we proposed: Sequential Masking Imitation Learning(SEMI). First, we train a Vector Quantised-Variational AutoEncoder(VQ-VAE) to encode a sequence of images into a latent representation with discrete codes. After that we deploy several masks on the encoded images, the masks here will randomly hide some semantic objects in the encoded images. Finally, we design the behavior clone network as a predictor of expert action, using an encoded and masked image sequence as input, encouraging the network to make expert-like predictions when some partition of information about the environment is missing. The masking procedure in SEMI helps the imitator identify the contribution of each encoded feature to the expert’s prediction. We demonstrate that this method could alleviate causal confusion in driving simulation by deploying it to the CARLA simulator, and compared it with other methods. Experimental results show that SEMI can effectively reduce confusion in autonomous driving. The agent trained with SEMI method reduces the collision rate by 45 % compared to methods without masking procedure, and obtain the highest average survival timesteps among competing methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Maturity Model for Corporate Sector Based on Zero Trust Adoption 10

Maturity Model for Corporate Sector Based on Zero Trust Adop...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ilyas, Muhammad Akal, Mustafa Althebyan, Qutaibah Al Ain University Department of Cyber Security Abu Dhabi United Arab Emirates Altinbas University Department of Electrical and Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331532895

The rapid evolution of cybersecurity threats necessitates the adoption of robust security frameworks. One such approach gaining significant attention is the concept of zero trust, which emphasizes continuous and strong verification, strict access controls and identity management, and well-planned strategy and assessment to mitigate risks. However, organizations often face challenges in effectively implementing and assessing their progress in zero trust adoption. We propose a maturity model for zero trust adoption, designed to assist organizations in evaluating their current security posture, identifying gaps, and developing a roadmap for achieving higher levels of zero trust maturity. The model encompasses various dimensions, including network segmentation, access controls, data protection, and incident response. Additionally, a comprehensive set of self-assessment queries is provided to enable organizations to gauge their progress and identify areas for improvement. Through the utilization of this maturity model and self-assessment framework, organizations can enhance their understanding of zero trust principles, align their security strategies, and prioritize necessary investments to strengthen their overall security posture. © 2024 IEEE.

关键词： cybersecurity self-assessment framework security posture zero trust adoption zero trust approach zero trust maturity model

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