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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kurmi, Raviraj Singh Gupta, Ashish Krishnamoorthy, Ramkumar Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India

ISBN: (纸本)9798350357769

Industrial communication protocols are used by SCADA (supervising control and data acquisition) networks and Industrial Control Systems (ICS) to enhance their functionality. Modbus is an example of an application protocol that facilitates communication between millions of automation units. Sadly, Modbus lacks essential security features, which makes it vulnerable to errors in both design and implementation. These flaws leave systems open to eavesdropping, replay, and man-in-the-middle assaults, among other types of attacks. Taking advantage of these vulnerabilities may have serious repercussions for businesses as well as the general public, especially if important infrastructure assets like power stations, water distribution systems, and train transportation networks are attacked. The Modbus association has published security requirements in an effort to fix the security flaws in the protocol. Through the integration of TLS (Transport Layer Security) with the conventional Modbus protocol, these standards seek to improve security. Modbus packets are encapsulated by TLS in order to safeguard message integrity and offer authentication. Digital certificates based on X.509v3 are used by the security features to authenticate the client and server. This paper proposes a new secure version of the role-based access control architecture (RBAC) in order to solve security vulnerabilities with the Modbus protocol. This approach uses a message authorized via unit ID, a unique identifier for approving the Modbus frame, and an authorization procedure via roles, introduced as an arbitrary extensions within the X.509v3 certificate, to approve only the client & the Modbus frame. To summarise, the incorporation of TLS into Modbus, in conjunction with the suggested RBAC framework, effectively tackles security issues and fortifies the protocol's general resilience against any intrusions, guaranteeing the safe and effective functioning of industrial systems. © 2023 IEEE.

关键词： Industrial Control Systems Modbus protocol architecture role-based access control (RBAC) SCADA security requirements and centralized architecture security vulnerabilities TLS X.509v3 certificate

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

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Procedia computer science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet Encoder-decoder framework

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Semi-automated meningioma segmentation with bounding boxes

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Procedia computer science 2024年 245卷 583-590页

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores.

关键词： Bounding boxes Meningioma Semi-automated segmentation

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Stabilizing Adversarial Training for Generative Networks

Stabilizing Adversarial Training for Generative Networks

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Gerych, Walter Hickey, Kevin Hartvigsen, Thomas Buquicchio, Luke Alajaji, Abdulaziz Chandrasekaran, Kavin Mansoor, Hamid Agu, Emmanuel Rundensteiner, Elke University of Virginia School of Data Science CharlottesvilleVA United States King Saud University College of Computer and Informational Sciences Riyadh Saudi Arabia University of Victoria Department of Computer Science VictoriaBC Canada Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States

ISBN: (纸本)9798350324457

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results. © 2023 IEEE.

关键词： distribution GAN support alignment

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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Non-adaptive Quantitative Group Testing via Plotkin-Type Constructions

Non-adaptive Quantitative Group Testing via Plotkin-Type Con...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Soleymani, Mahdi Mahdavifar, Hessam Javidi, Tara University of California San Diego Halicioglu Data Science Institute United States University of Michigan The Department of Electrical Engineering and Computer Science Ann Arbor United States University of California San Diego Halicioglu Data Science Institute The Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665475549

In this paper, we study the quantitative group testing problem, also known as the heavy hitter detection problem and the coin weighing problem, in a non-adaptive setting. In this problem, the aim is to recover k defective items from a group of n items with the smallest possible number of quantitative/additive tests, where each such test returns the number of defective items participating in the test. In the non-adaptive setting, that we study in this paper, all tests are designed at once and can be, in principle, run in parallel. We establish a novel construction method for designing non-adaptive test matrices with a nested structure inspired by the Plotkin concatenation in the coding theory literature. Our proposed algorithm identifies k defective items among the collection of n items with high probability using k(1 + o(1)) log (n/k) non-adaptive tests in the sub-linear regime of k = o(n). Furthermore, our analysis demonstrates that the probability of decoding failure approaches zero exponentially in k as k, n ? 8. Our approach outperforms existing state-of-the-art methods for designing non-adaptive test schemes with efficient decoders for the quantitative group testing problem in terms of the required number of measurements. © 2023 IEEE.

关键词： Decoding

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Analysis of Bitcoin Prediction using Transparent and Opaque Machine Learning Algorithms 15

Analysis of Bitcoin Prediction using Transparent and Opaque ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Singh, Kavita R. Jamar, Anushri V. Khadgi, Pracheta P. Kapse, Smita Kumar, Ajay Department of Computer Technology Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India School of Big Data and Computer Science Guizhou Province Guiyang City China

ISBN: (纸本)9798331300579

The unpredictable nature of cryptocurrency markets, particularly Bitcoin, has attracted significant attention from investors, researchers, and financial institutions seeking to understand and predict price movements. This study aims to analyse the effectiveness of both transparent and opaque machine learning algorithms in predicting Bitcoin prices. Transparent algorithms, such as linear regression and decision trees, provide clear insights into the factors influencing predictions. On the other hand, opaque algorithms, like ensemble methods, offer complex, non-linear models that may capture intricate patterns in the data. The main analysis is to lower the mean absolute error and enhance the r2 square, both key indicators of forecast accuracy. The study presents that linear regression and random forest outperformed others, achieving the smallest error and highest variance explanation, suggesting their effectiveness in Bitcoin price prediction with and without outliers respectively. In contrast, the support vector machine underperforms compared to all other machine learning algorithms under consideration. This paper sheds light on the most capable machine-learning techniques for Bitcoin forecasting, aiding trader investment choices and guiding academic study in the field of prediction. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Sign Language to Sentence Formation:A Real Time Solution for Deaf People

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computers, Materials & Continua 2022年第8期72卷 2501-2519页

作者： Muhammad Sanaullah Muhammad Kashif Babar Ahmad Tauqeer Safdar Mehdi Hassan Mohd Hilmi Hasan Amir Haider Bahauddin Zakariya University Department of Computer ScienceMultan60000Pakistan Air University Department of Computer ScienceMultan60000Pakistan Air University Department of Computer ScienceIslamabad44000Pakistan Centre for Research in Data Science Department of Computer and Information SciencesUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Communication is a basic need of every human being to exchange thoughts and interact with the *** peoples usually confab through different spoken languages,whereas deaf people cannot do ***,the Sign Language(SL)is the communication medium of such people for their conversation and interaction with the *** SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed *** acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words *** signs for singular words such as I,eat,drink,home are unalike the plural words as school,cars,players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign *** researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand *** complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world ***,there is still a demand for specific environment to automate such a communication solution to interact with such type of special *** research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in ***,these are converted into the respective words/sentences within a reasonable *** provide a real time solution for the deaf people to communicate and interact with the society.

关键词： Sign language machine learning conventional neural network image processing deaf community

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Toward Identifying The Best Base Classifier in Multi Label Classification-an Investigative Study 24

Toward Identifying The Best Base Classifier in Multi Label C...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Alzyoud, Mazen Alazaidah, Raed Alzoubi, Haneen Al-Shanableh, Najah Aljaidi, Mohammad Almatarneh, Sattam Al Al-Bayu University Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Artificial Intelligence Zarqa Jordan

ISBN: (纸本)9798350384307

Classification is a significant task in data mining, machine learning and data science. It aims to predict the class label for a new case accurately. Classification is of two types: Single Label Classification (SLC) and Multi-label Classification (MLC). In SLC, cases and instances are linked to one class label only, while in MLC, cases and instances could be linked to more than once class labels. Several SLC algorithms have been adapted and upgraded to handle MLC and showed different predictive performance. Hence, this paper attempts to identify and investigate the best SLC that could handle the problem of MLC with respect to three datasets and using three evaluation metrics. Moreover, the paper also aims to identify the best Problem Transformation Method (PTM) among five well-known methods. The results revealed that RandomForest and DecisionTable showed the best performance among the fifteen classifier. Also, Most Frequent Label (MFL) is the best transformation method among the five considered PTMs in term of Accuracy, Precision, and Recall. © 2023 IEEE.

关键词： Algorithms classification data mining data science machine learning multi-label classification

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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