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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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Deep Learning-Based Classification Methods for Prediction of Lung Cancer Images

Deep Learning-Based Classification Methods for Prediction of...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Santosh Kumar Jagriti Singh Gunjan Chhabra Manish Gupta Vikram Rajpoot Computer Science ERA University Lucknow Uttar Pradesh Manipal University Jaipur Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand GLA University Mathura Madhav Institute of Technology & Science Gwalior

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Cancer, a disease with a devastating impact on lives, is often brought about by a confluence of hereditary disorders and other aberrant changes. Cancer is a sickness were body cells multiply too quickly. The brain and lymph nodes are among the organs where lung cancer may spread after starting in the lungs. Lung cancer changes depending on a number of medical factors and geographical places. The study presents a comparative study for lung cancer detection by CT (Computed Tomography) images, encompassing data collection from LIDC-IDRI (Lung Image Database Consortium and Image Database Resource Initiative). Through data collection, preprocessing, and splitting. Deep learning (DL) models including GoogleNet, AlexNet, CNN, and FocalNet are employed for classification. In comparison to the other models that were evaluated, FocalNet exhibited exceptional performance according to F1-score, accuracy, precision, and recall. It achieved outstanding scores of 99.81 %, 99.78%, 99.36%, and 99.56%, individually. The results of this study demonstrate that FocalNet is an effective method for computer-aided identification of lung cancers. Furthermore, this study highlights the potential value of FocalNet in clinical practice for the purpose of improving lung cancer detection and management. This study offers valuable insights into a development of efficient computational frameworks for medical image analysis, particularly in oncology diagnostics.

关键词： Image analysis Image databases Computed tomography Computational modeling Lung cancer Lung Data collection Oncology Medical diagnostic imaging Lymph nodes

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GAN-CNN Ensemble: A Robust Deepfake Detection Model of Social Media Images Using Minimized Catastrophic Forgetting and Generative Replay Technique

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Procedia computer science 2024年 235卷 948-960页

作者： Preeti Sharma Manoj Kumar Hitesh Kumar Sharma Research Scholar School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun 248007 India Associate Professor Faculty of Engineering and Information Sciences University of Wollongong in Dubai UAE MEU Research Unit Faculty of Information Technology Middle East University Amman 11831 Jordan

Deep-fake photographs are difficult to discern from real ones, especially when utilized in social media platforms. Anyone can willfully create disinformation about public personalities, politicians, and celebrities using these deep fake photographs. So, it is an important need of society to work for an effective model for its detection. The models for deep fake detection commonly use CNN-based detectors. These detectors experience a drop in performance when used for transfer learning or continual learning techniques. A significant limitation in this process is CNN's catastrophe forgetting defect. For the solution of this problem, a Generative replay technique in the form of a GAN-CNN model is implemented that works to minimize this catastrophe forgetting issue that further helps for better detection. It involves generating and storing samples from previous tasks and then replaying them during the training of new tasks which makes CNN more robust to identify deep fakes. The GAN model used in this work is traditional DCGAN improved with necessary adjustments to achieve training stability. It is observed that the model attained a good accuracy of 98.67%(training),70.08% (testing) and minimum loss with a value of 0. 0337 for 100 epochs. Also, it acquired good precision values of 68% and 72%, Recall values are 74% and 66%, and F1 scores of 71% and 69% for classes 0 and 1 respectively. The model outcome is found stable and reliable in deep fake detection under dynamic training conditions. Optimum values of evaluation parameters ensure the model's capacity to learn new tasks preserving the existing task-learning knowledge.

关键词： Deep Learning CNN GAN Catastrophe forgetting CNN continual learning Lifelong learning GAN-CNN deep fake detector

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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A novel enhanced lemurs optimization-based hybrid attention network for classifying arrthymia from ECG signal using optimal weighted feature

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Australian Journal of Electrical and Electronics Engineering 2024年第3期21卷 311-335页

作者： Wajgi, Rakhi Brammya, G. Ramesh, Gopisetty Deotare, Vilas Vasudeo Govindasamy, Chinnaraj Preetha, N.S. Ninu Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Electronics and Communication Engineering Resbee Info Technologies Private Limited Chennai India Department of EIE CVR College of Engineering Mangalpalli India Professor in E&TC Savitribai Phule Pune University Pune India Department of Computer Science and Engineering Saveetha School of Engineering Kuthambakkam India

An arrhythmia is just a disease associated with irregular heart rhythm. Any kind of arrhythmia can be determined with an ECG test. However, this requires long and excellent-quality of ECG samples. This makes an affordable, reliable, and automated system for recognizing and diagnosing arrhythmias. To meet the aforementioned criteria, a new technique with the aid of an attention-aided deep learning approach is proposed to classify arrhythmia in this paper. "Higher-order statistical (HOS)" features are extracted from the original EEG signal and the deep features are obtained from the analyzed QRS complex signals and RR interval by means of a "One-Dimensional Convolution Neural Network (1DCNN)". The Risk Rate Enhanced Lemurs Optimization Algorithm (RRE-LOA) is utilized to modify the weight, and the extracted features are selected optimally before carrying out the feature selection followed by feature concatenation with weight fusion in order to generate the Weighted Fused Features (WFF). A range of indicators is used to compare the performance of the executed classification model with the traditional models. The results demonstrate that the proposed methodology for classifying arrhythmias offers an enhanced performance while classifying the arrhythmia thus aiding in faster treatment of the arrhythmia disease. ©, Engineers Australia.

关键词： Electrocardiograms

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One algorithm for testing annulling of mixed trigonometric polynomial functions on boundary points 30

One algorithm for testing annulling of mixed trigonometric p...

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30th Telecommunications Forum, TELFOR 2022

作者： Malesevic, Branko Banjac, Bojan Sesum-Cavic, Vesna Korolija, Nenad University of Belgrade School of Electrical Engineering Department of Applied mathematics Belgrade Serbia University of Novi Sad Computer graphics chair Faculty of Technical sciences Novi Sad Serbia Digital innovation team Intens d.o.o. Novi Sad Serbia University of Belgrade Faculty of Civil Engineering Department for Geoinformatics Belgrade Serbia University of Belgrade School of Electrical Engineering Department for Computer science and information technology Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

This paper considers one problem of automated proving of MTP inequalities f(x) > 0 over [0, π/2] on boundary points. It suggests one algorithm for symbolic checking for f(0) = 0 or f(π/2) = 0 without use of numerical estimations. With stated approach it gives one contribution to theory and practice in this part of the field of automated proving. © 2022 IEEE.

关键词： Rational functions

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On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

arXiv

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arXiv 2022年

作者： Terhörst, Philipp Bierbaum, Florian Huber, Marco Damer, Naser Kirchbuchner, Florian Raja, Kiran Kuijper, Arjan Norwegian University of Science and Technology Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art FR models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. Future works might analyse the utility of MasterFaces for understanding and enhancing the robustness of face recognition models. © 2022, CC BY.

关键词： Face recognition

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Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints

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Procedia computer science 2021年 192卷 1245-1254页

作者： Grzegorz Baron Urszula Stańczyk Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.

关键词： Cross-validation Fold Evaluation of Performance Classification Distribution Stylometry

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ReGenMorph: Visibly realistic GAN generated face morphing attacks by attack re-generation

arXiv

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arXiv 2021年

作者： Damer, Naser Raja, Kiran Süßmilch, Marius Venkatesh, Sushma Boutros, Fadi Fang, Meiling Kirchbuchner, Florian Ramachandra, Raghavendra Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Germany Department of Computer Science TU Darmstadt Germany Norwegian University of Science and Technology Norway

Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms

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Procedia computer science 2023年 230卷 90-99页

作者： Dharini N Jeevaa Katiravan Sruthi Priya D M Sakthi Sneghaa V A Associate Professor Department of Computer Science Engineering(Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur 601206 Professor Department of Information Technology Velammal Engineering College Surapet Chennai 600066 Software Engineer embedUR Chennai 600013 Jr. Customer Service Executive Tata Communications Transformation Services Chennai 600037

Security poses a substantial challenge in the context of wireless sensor networks (WSNs). WSNs, which are frequently employed for real-time monitoring in various Internet of Things (IoT)-based applications, are particularly vulnerable to a range of Denial of Service (DoS) attacks. These attacks represent a significant security concern and are among the most prevalent issues confronting WSNs *** our research, we have introduced a WSN DoS dataset using the Matrix Laboratory (MATLAB) tool. This dataset was developed through the utilization of the Leach Protocol and incorporates various types of DoS attacks, including Wormhole, Blackhole, Grayhole, Flooding, and Time Division Multiple Access (TDMA) attacks. In our study, we assessed the performance of several machine learning techniques using our dataset. We employed various performance evaluation metrics that gauge both the classification accuracy and the complexity of these machine learning models. These metrics included parameters such as the number of accurately classified and misclassified instances, along with the time required for constructing the model. This comprehensive evaluation aimed to demonstrate the effectiveness of our proposed *** results of our analysis revealed that the Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Bagging algorithms exhibited strong performance on our dataset. They achieved impressive accuracy levels of up to 99.16%, 99.16%, and 99.8%, respectively, when using 14 features. Additionally, these algorithms achieved accuracy rates of 99.16%, 98.75%, and 99.8%, respectively, with 5 features, and 95.41%, 95%, and 93.75%, respectively, when considering only 3 features such as node ID and Energy-Consumed, Attack_type. Notably, LightGBM outperformed the others in terms of speed, as it employs a leaf-wise growth strategy, consumes less memory, and requires less time for model construction. Consequently, LightGBM emerged as the most efficien

关键词： Denial of Service (DoS) attacks Intrusion Detection System (IDS) Leach Protocol WSN DoS dataset Ensemble Learning Feature Selection Accurately Classified and Misclassified instance Light Gradient Boosting Machine (LightGBM)

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