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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Rohan Joel, J. Benjamin, Ruth Moly Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector. © 2024 IEEE.

关键词： Food industry

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A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

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computers, Materials & Continua 2023年第5期75卷 2741-2757页

作者： Muhammad Usman Sana Zhanli Li Tayybah Kiren Hannan Bin Liaqat Shahid Naseem Atif Saeed College of Computer Science and Technology Xi’an University of Science and TechnologyXi’anShaanxi710054China Department of Computer Science(RCET Campus) University of Engineering and TechnologyLahore39161Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54700Pakistan Department of Computer Science COMSATS UniversityIslamabadLahore54700Pakistan

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being *** cloud computation,data processing,storage,and transmission can be done through laptops andmobile *** Storing in cloud facilities is expanding each day and data is the most significant asset of *** important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s *** have to be dependent on cloud service providers for assurance of the platform’s *** security and privacy issues reduce the progression of cloud computing and add ***;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured *** this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of *** implementation of a modified RSA algorithm results guaranteed the security of data in the cloud *** enhance the user data security level,a neural network is used for user authentication and ***;the proposed technique develops the performance of detection as a loss function of the bounding *** Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.

关键词： Cloud computing data security RSA algorithm Faster R-CNN

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Mitigating Light Detection and Ranging Spoofing Attacks in Connected Vehicle Systems Using Machine Learning-Based Anomaly Detection 2

Mitigating Light Detection and Ranging Spoofing Attacks in C...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Karthik, R. Marshal, S. Asmitha Shree, R. Vidhya, R. Saranya, N. Gomathy, A. Karunya Institute of Technology and Sciences Deemed to be University Division of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Light Detection and Ranging (LiDAR) technology is one of the integral parts of systems involving connected vehicles. It provides the necessary two-dimensional accuracy to enable proper navigation, object identification, and collision avoidance through LIDAR technology. However, it is highly used and highly susceptibility to spoofing attacks: interference on signals radiated by LIDAR technology to confuse the Vehicle Perception System. This paper proposes a machine learning-based anomaly detection framework which is capable of discernment and countering of spoofing attacks. The proposed framework collects data from a set of sensors, including cameras, radar, and LiDAR, and is used to formulate a holistic, multidimensional perception model. In that sense, the system utilizes advance machine learning methods, like ensemble classifiers, neural networks, and, more importantly, autoencoders, to find anomalies in real-time that might point to spoofing activity. Simulations and empirical experiments, both benign and adversarial, evaluate the systematic approach to spoofing sophistication levels where LiDAR systems indicate noteworthy improvements in detection accuracy and robustness compared with the traditional techniques, thereby reducing false positives and negatives. In addition, through sensor fusion, the system demonstrates resilience by sustaining consistent performance even through the most complex and dynamic environments. This type of study really highlights that how the power of machine learning would be important for improvement in the cybersecurity framework of connected vehicles scalable and adaptable toward the protection of LiDAR-based systems from growing threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Wrist joint synovial hypertrophy and effusion detection in musculoskeletal ultrasound images using self-attention U-Net

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Multimedia Tools and Applications 2024年第41期83卷 89317-89334页

作者： Chang, Chuan-Wang Chang, Chuan-Yu Zhu, Yu-Xian Wang, Sz-Tsan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Douliu Taiwan Division of Allergy Immunology and Rheumatology Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Dalin Chiayi Taiwan

Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its reliance on operator expertise often leads to inconsistent interpretations and diagnostic variability. In this study, we present a novel diagnostic system aimed at detecting rheumatoid arthritis (RA) in the wrist joint, with a focus on identifying synovial hypertrophy and effusion using musculoskeletal ultrasound images. Leveraging deep learning techniques, specifically semantic segmentation models, we introduce SEAT-UNet, which combines the U-Net architecture with a self-attention mechanism to enhance the accuracy of lesion classification and localization. SEAT-UNet addresses the challenge of discontinuous dispersion encountered in conventional segmentation models, particularly when delineating lesion areas. Our experimental results demonstrate exceptional performance, achieving a sensitivity and Dice coefficient of 100% and 84%, respectively, in synovial hypertrophy detection, and 86% sensitivity with an 84% Dice coefficient in effusion detection. These findings underscore the potential of SEAT-UNet as a valuable tool for early RA diagnosis, offering improved patient outcomes and facilitating more effective disease management strategies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Switching Control for Identification Deception in Cyber-Physical Systems 63

Switching Control for Identification Deception in Cyber-Phys...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Mavridis, Christos Kanellopoulos, Aris Sandberg, Henrik Johansson, Karl Henrik Kth Royal Institute of Technology Division of Decision and Control School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching. © 2024 IEEE.

关键词： Markov processes

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PWM-Based Double Integral Sliding Mode Control for Current Control Loop of DC-DC Boost Converter 14

PWM-Based Double Integral Sliding Mode Control for Current C...

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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Vongkoon, Pokkrong Benjanarasut, Jirawut Liutanakul, Pisit Division of Instrumentation and Automation Engineering Technology Faculty of Engineering and Technology King Mongkut's University of Technology North Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9781643685199

The use of dc-dc boost converters is common for increasing output voltage and supplying power. These converters, being time-varying nonlinear circuits, encounter uncertainties in load and variations in input voltage and current over a wide range. Nonlinear sliding mode control (SMC) has emerged as a promising strategy, offering advantages in terms of stability and robustness against parameter and load fluctuations. To address switching frequency variations in conventional hysteresis-based SMC, the indirect SMC or pulse-width modulation (PWM)-based SMC has been adopted. However, the adoption of PWM-based SMC raised concerns regarding steady-state error in power converters. Despite incorporating the integral of the state variable into the sliding surface of PWM-based SMC, the steady-state error still exists. Focusing on the current control loop of a dc-dc boost converter with the goal of reducing steady-state error, this study proposes double integral sliding mode control (DISMC), which employs a sliding surface that includes the double integral term of the current error. Simulation and experimental results demonstrate that the proposed approach effectively mitigates the steady-state error of the inductor current. © 2024 The Authors.

关键词： Pulse width modulation

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Machine Learning Diagnosis of Lumpy Skin Disease in Cattle Herds 3

Machine Learning Diagnosis of Lumpy Skin Disease in Cattle H...

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

作者： Krishna, Nimmagadda Kumar Benjamin, Ruth Moly Tejaswi, Nimmagadda Institute of Technology and Sciences Division of Computer Science and Engineering Karunya Coimbatore India Shri Vishnu Engineering College for Women Division of Artificial Intelligence and Data Science Bhimavaram India

ISBN: (纸本)9798350375190

In order to diagnose lumpy skin disease in cattle herds, machine learning techniques such as Support Vector Machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective of the study is to create a diagnostic tool that would be both accurate and effective to identify and treat this infectious disease early on. The algorithms were trained to classify cases with high accuracy using a dataset that included different clinical and demographic characteristics related with the presence or absence of Lumpy Skin Disease. SVM is used because of its capacity to manage intricate and non-linear correlations in the data, and Random Forest and Gradient Boosting algorithms were chosen due to their predictive performance. © 2024 IEEE.

关键词： Support vector machines

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Editorial—A Time for Reflection

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Proceedings of the IEEE 2024年第12期112卷 1758-1760页

作者： Setti, Gianluca King Abdullah University of Science and Technology (KAUST) Computer Electrical and Mathematical Sciences and Engineering Division Thuwal 23955 Saudi Arabia

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in engineering & Sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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