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检索条件"机构=Inst. of Computer Science Information Engineering"

共 1195 条记录，以下是1-10 订阅

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

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6th International Conference on Electrical engineering and information and Communication Technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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Cloud Insider Threat Detection using Deep Learning Models 18

Cloud Insider Threat Detection using Deep Learning Models

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

作者： Shanmugapriya, D. Dhanya, C.J. Asha, S. Padmavathi, G. Suthisini, D. Nethra Pingala Avinashilingam Inst. for Home Science and Higher Education for Women Department of Information Technology Coimbatore India Avinashilingam Inst. for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9789380544519

Insider attacks are a major threat to cloud security since they can harm organizational assets and have overlapping security mechanisms. Therefore, insider threat detection in the cloud environment is necessary to compromise such attacks. Past research applied machine learning and Deep Learning (DL) techniques for recognizing insider threats in the cloud. The self-learning capabilities in network layers of deep learning could enhance and handle class imbalance problems in detecting and recognizing cloud insider threats. In this paper, the pre-processed insider threat data is obtained by applying various data preprocessing techniques, including data integrity, data transformation, and data sampling using Synthetic-Minority Over-sampling Technique (SMOTE) to deal with the issue of the imbalanced dataset. The balanced data obtained from preprocessing algorithms are classified using DL algorithms, including Conventional Neural networks (CNN) and Long Short-Term Memory (LSTM) for cloud Insider Threat Detection. The experimental result shows that the performance of CNN with SMOTE-based balanced data outperforms LSTM with SMOTE regarding the accuracy, f-score, precision, and recall for detecting cloud insider threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Class Imbalance Cloud CNN Deep Learning Insider threat LSTM SMOTE

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Miyazaki, Takeru Araki, Shunsuke Uehara, Satoshi Kyushu Inst. of Information Sciences Faculity of Management and Information Sciences Fukuoka Japan Kyushu Inst. of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan The Univ. of Kitakyushu Faculty of Environmental Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes. © 2023 IEEE.

关键词： Digging Out the Walls Maze Generator Pseudorandom Bit Sequence

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Design and In-training Optimization of Binary Search ADC for Flexible Classifiers 25

Design and In-training Optimization of Binary Search ADC for...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lozano Duarte, Paula Carolina Afentaki, Florentia Zervakis, Georgios Tahoori, Mehdi Department of Computer Science Karlsruhe Inst. of Tech. Karlsruhe Germany Computer Engineering and Informatics Department Univ. of Patras Patras Greece

ISBN: (纸本)9798400706356

Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2× compared with the state-of-the-art Binary design and up to 5.4× compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5× for less than 1% accuracy loss. © 2025 inst.tute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Budget control

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Detecting Cross-Site Scripting Attack using Machine Learning Algorithms 18

Detecting Cross-Site Scripting Attack using Machine Learning...

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

作者： Karthika, S. Padmavathi, G. Roshni, A. Varshini, S. Avinashilingam Inst. for Home Science and Higher Education for Women Department of Information Technology Coimbatore India Avinashilingam Inst. for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Avinashilingam Inst. for Home Science and Higher Education for Women Centre for Cyber Intelligence Coimbatore India

ISBN: (纸本)9789380544519

Cross-Site scripting attacks can have a significant impact on a user's computer due to their ability to infect it with malware. This is because the code is embedded in a vulnerable program, allowing attackers to gain access through session cookies and carry out any action that is allowed by the user's account. This can range from credential theft to session hijacking, as well as a variety of other security vulnerabilities. Therefore the detecting and preventing the XSS attacks are critical to assure the safeguard of web-based applications. This research work concentrates on the automation of Cross-Site scripting attack detection by employing machine learning algorithms like Naive Bayes, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. XSS attacks frequently exhibit patterns and characteristics that can be learnt from historical data. These patterns are well recognized by supervised machine learning algorithms, which can also generalize from training data to detect similar harmful patterns in real-time web traffic. XSSDataSet1, a large dataset of both genuine and malicious On-the-Web (OTW) requests and attack methods is utilized for training and testing the Machine learning models. While extracting the data, the request's payload, request parameters, and contextual information are taken into account. The evaluation of each algorithm includes an analysis of various performance metrics such as accuracy, precision, recall, and the F1 Score. An accuracy level of 99.91% has been attained with decision tree algorithm. The results of the extensive analysis showed that decision tree classifier, random forest, support vector machine, and K-Nearest Neighbors are more efficient than Naive Bayes in detecting the XSS attack. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Classification Cross-Site Scripting Machine Learning Supervised Learning XSS

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Decoding the Skin with AI: A Review of Cutting-Edge Technologies and Applications 2

Decoding the Skin with AI: A Review of Cutting-Edge Technolo...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumari, Sejal Umrao, Sandhya Kushwaha, Dileep Noida Inst. of Engg. and Technology Computer Science & Engineering Greater Noida India Greater Noida India

ISBN: (纸本)9798350371055

The skin of humans can tell stories through its textures and colors, providing insights into both health and disease. Dermatologists have been interpreting these stories for years, using their expertise to diagnose and treat conditions. They have an ally in this pursuit: artificial intelligence and machine learning (AI/ML). In this review, we will delve into the meeting point of AI/ML and dermatology, exploring the applications, challenges and the promising future. Looking ahead, we can imagine a future where emerging technologies drive advancements. In this future, AI will work hand in hand with dermatologists in procedures guided by images, speed up the process of discovering drugs, and empower individuals to keep track of their skin health using AI-powered sensors. This goes beyond upgrading technology. It signifies a revolution in how we perceive and approach skin health. It promises a future where early detection, personalized treatment and preventive measures become the standard. Join us on our journey at the intersection of skin and technology as we witness the beginning of an era in dermatology. With AI shedding light on the secrets of our skin we pave the way for a healthier tomorrow. © 2024 IEEE.

关键词： Artificial intelligence Derma-tology Diagnosis Machine learning Personalized medicine Skin cancer Treatment Wearable sensors

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Chinese Medical Record Entity Recognition Based on Lexicon and Self-attention

Chinese Medical Record Entity Recognition Based on Lexicon a...

引用

2023 IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2023

作者： Wang, Le Zhao, Yahui Cui, Rongyi Jin, Guozhe Wang, Jinxiang Yanbian University Inst. of Intelligent Information Processing Yanji133002 China Yanbian University Computer Science and Technology College of Engineering Yanji China

ISBN: (纸本)9798350313734

Named Entity Recognition (NER) is a pivotal challenge in the field of natural language processing. In the medical field, NER plays a crucial role in identifying medical entities, such as diagnoses and drugs, from electronic medical records. To address the issue of incomplete and inaccurate word recognition in Chinese electronic medical records, we propose a BERT-BiLSTM-CRF model that incorporates both a lexicon and self-attention *** our approach, we first utilize the BERT model for preprocessing to generate word vectors. These vectors capture important semantic information about the words. Next, we employ a BiLSTM network to further capture contextual information from the input sequence. This helps to enhance the understanding of the text's meaning. To reinforce the significance of the sequence information, we introduce a self-attention layer that learns to prioritize relevant contextual details. Subsequently,the extracted features are fed into a CRF model for final training. The CRF layer takes into account the dependencies among the labels and improves the overall sequence labeling accuracy. Given the specialized nature of the medical domain, we integrate a medical lexicon, incorporating external knowledge features into the model. This inclusion enables better recognition of medical terms and enhances performance on medical-related *** extensive experiments on the CCKS2019 dataset, our proposed model demonstrates favorable performance. It get relatively excellent results in identifying medical entities accurately and effectively from Chinese electronic medical records. © 2023 IEEE.

关键词： Diagnosis

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Detection of Iris Template Attacks using Machine Learning and Deep Learning Methods 10

Detection of Iris Template Attacks using Machine Learning an...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Shanmugapriya, D. Padmavathi, G. Aysha, A. Avinashilingam Inst. for Home Science and Higher Education for Women Dept. of Information Technology Coimbatore India Dept. of Computer Science Avinashilingam Inst. for Home Science and Higher Education for Women Coimbatore India

ISBN: (纸本)9789380544472

Iris recognition is a biometric identification method that uses patterns in the iris, the coloured ring around the eye's pupil, to identify individuals. One of the most vulnerable attacks in iris detection is the template attack. An iris template attack is a type of biometric spoofing where an attacker attempts to gain access to a secure system by presenting a fabricated iris image or video as the authentic iris of the legitimate user. It is essential to detect these attacks to ensure the security and reliability of iris recognition systems and to prevent potential misuse and abuse. Machine learning and deep learning methods can potentially be used to detect template attacks on iris recognition systems, such as attempts to deceive the system using forged or altered iris templates. These methods involve training a model on a large dataset of iris images with both genuine and altered and using the trained model to classify new iris images as genuine or attacked. Hence, we propose a methodology that uses Logistic Regression (LR) and Convolutional Neural Network (CNN) to detect the Iris template attack. Through extensive experiments with the CASIA-IrisV1 dataset, CNN achieves an average accuracy of 98.75% and surpasses LR in recognizing iris template attack. Meanwhile, the performance of CNN is enhanced by applying the max pooling property to achieve 100% accuracy. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Convolutional neural networks

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Performance Analysis of Cyberbullying Threats in Social Media using Machine Learning Techniques 18

Performance Analysis of Cyberbullying Threats in Social Medi...

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

作者： Suthisini, D. Nethra Pingala Merlien, J. Juliet Shanmugapriya, D. Padmavathi, G. Roshni, A. Avinashilingam Inst. for Home Science and Higher Education for Women Department of Information Technology Coimbatore India Avinashilingam Inst. for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Centre for Cyber Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India

ISBN: (纸本)9789380544519

Cyberbullying is the use of internet media to harass, threaten, or hurt others, has become a widespread problem on social media platforms. As social media becomes more prevalent, so does cyberbullying, which hurts victims' mental health and well-being. This study examines the performance of multiple Machine Learning (ML) algorithms in detecting cyberbullying tweets on social media. Racism and sexism-based Twitter dataset is taken for study obtained from Kaggle repository consisting of 28,357 samples with 5 attributes. The dataset which consists of raw tweets applied for data preprocessing using a Natural Language Toolkit (NLTK). To detect and classify cyberbullying (sexism/racism) tweets, five Supervised Machine Learning (SML) models are developed: a Naive Bayes, a Random Forest, a K-Nearest Neighbor, a Support Vector Machine, and a Decision Tree model for classification. Precision, Accuracy, F1-Score, Recall, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (RS) are used to evaluate the designed models. Based on comparison of the models, it is discovered that the Random Forest and Support Vector Machine algorithms outperforms with maximum accuracy of 97% in identifying cyberbullying tweets. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cyberbullying NLTK Racism Sexism Supervised Machine Learning

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MoFLeuR: Motion-based Federated Learning Gesture Recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

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