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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Signature Based Identification using Locality-Sensitive Hashing for IoT Devices 2

Signature Based Identification using Locality-Sensitive Hash...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singh, Amit Sindhu Madhuri, G. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project. © 2023 IEEE.

关键词： Internet of things

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Big Data Analytics Using Graph Signal Processing

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Computers, Materials & Continua 2023年第1期74卷 489-502页

作者： Farhan Amin Omar M.Barukab Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Computing and Information Technology-Rabigh King Abdulaziz UniversityJeddahSaudi Arabia

The networks are fundamental to our modern world and they appear throughout science and *** to a massive amount of data presents a unique opportunity to the researcher’s *** networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep ***,this paper initiates a discussion on graph signal processing for large-scale data *** first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,*** filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of *** then consider a graph filter as a model that helps to extend the application of GSP methods to large *** show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the *** several rounds of simulation *** see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based *** using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

关键词： Big data data science big data processing graph signal processing social networks

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A deep learning approach for recognizing and solving handwritten mathematical equations

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Neural computing and Applications 2025年第14期37卷 8759-8772页

作者： Yadav, Pavinder Shantilal, Sankhala Bhavik Kumar, Vipin Sihag, Parveen Sharma, Pawan Kumar Rana, Pankaj Department of Mathematics and Scientific Computing National Institute of Technology HP Hamirpur India Department of Civil Engineering Chandigarh University Punjab Mohali India Department of Physical Sciences Baba Mastnath University Asthal Bohar Haryana Rohtak India

Recognizing handwritten equations is considerably more challenging than identifying printed ones, primarily due to variations in writing styles. Even though there have been recent improvements in handwriting recognition for things like equation solvers and automation, it is still hard for machines to understand a handwritten mathematical expression (HME). This paper presents a deep learning model for recognizing and solving handwritten equations, as well as its graphical user interface (GUI). A manually constructed large dataset comprising approximately 66k images of digits, mathematical symbols, and characters is utilized, with a fine-tuned InceptionV3 model employed for image classification and segmentation. The system is designed to extract handwritten equations from an image, perform character recognition, and generate the roots of the polynomial equation up to degree four. According to the findings, the trained model achieves remarkable accuracy in recognizing and solving handwritten equations, with over 94% accuracy on the training set. By eliminating the possibility of human error, automatic handwritten equation solvers provide more accurate results than manual methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform 18

Maximizing Profit of the Service Providers through Efficient...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Gandhi, Preet Mahanta, Abhishriya Ghose, Manojit Sahu, Aryabartta Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach. © 2024 IEEE.

关键词： Cloud platforms

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An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

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Computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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A Theoretical Study on the Efficiencies of Black Silicon Photovoltaic Cells in Thermophotovoltaic Applications

A Theoretical Study on the Efficiencies of Black Silicon Pho...

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International Conference on Renewable Energy and E-mobility, ICREEM 2022

作者： Chai, Jasman Y. H. Wong, Basil T. Juodkazis, Saulius Department of Mechanical Engineering Faculty of Engineering Computing and Science School of Engineering Swinburne University of Technology Sarawak Campus Kuching Malaysia Center Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9789819959457

Silicon photovoltaic cells have been widely used in harvesting solar energy, and research efforts have driven significant improvements in the efficiencies of the photovoltaic cells. However, research on the application of silicon photovoltaic cells in thermophotovoltaic systems is significantly underdeveloped. In this study, a simulation study was conducted to study the performance of a black silicon photovoltaic cell in thermophotovoltaic applications. The photovoltaic cell was paired with two emitters made of different materials: a 1735 K Yb2O3 emitter, and a 1500 K Ta PhC emitter. The results of the simulation showed that when paired with the Yb2O3 emitter and the Ta PhC emitter, the cell efficiency was 1.7% and 0.76% respectively. The low efficiency in both was attributed to the limited amount of energy that is available above the band gap of silicon. The simulation results indicate that the Yb2O3 yielded better efficiency, as the selectivity of the emitter was better compared to the Ta PhC emitter. © Institute of technology PETRONAS Sdn Bhd (Universiti Teknologi PETRONAS) 2024.

关键词： Waste heat

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Blockchain-Enabled Decentralized Trust Management and Secure Voting system 7

Blockchain-Enabled Decentralized Trust Management and Secure...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Minu, R.I. Nagarajan, G. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

In the era of digital transformation, trust and security are paramount in ensuring the integrity of various online transactions and processes, including voting systems. Traditional centralized trust management and voting systems have faced challenges related to security, transparency, and accountability. To address these issues, this research introduces a novel Blockchain-Enabled Decentralized Trust Management and Secure Voting System. It leverages blockchain technology, a decentralized and immutable ledger, to enhance trust, transparency, and security in trust management and voting processes. This system combines several key components, including smart contracts, cryptographic techniques, and distributed ledger technology, to create a robust and tamper-resistant infrastructure. In the trust management aspect, the proposed system employs a decentralized reputation system where individuals and entities can build trust through transparent and verifiable interactions. Smart contracts automate trust-building processes and provide a reliable mechanism for dispute resolution. This system allows for trust to be quantified and established in a trustless environment. The secure voting component introduces a tamper-proof and transparent voting system. Through the use of cryptographic keys and digital signatures, voters can securely cast their votes while ensuring anonymity and integrity. All voting transactions are recorded on the blockchain, providing a permanent and auditable record of the election process. Verification of election results becomes accessible to all stakeholders, enhancing transparency and trust in the electoral process. The Blockchain-Enabled Decentralized Trust Management and Secure Voting System represents a significant step towards enhancing trust and security in critical online processes, such as voting. By combining blockchain technology, cryptography, and decentralized trust mechanisms, it offers a promising solution to address the challenges associat

关键词： Decentralized finance

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Design and Material Optimization of BLDC Motor Windings using FEM for Motor-Pump Applications using Existing Stator Stamping

Design and Material Optimization of BLDC Motor Windings usin...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Richard Pravin, A. Pongiannan, R.K. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

This paper covers the design of a 1 Hp, 48 V brushless DC (BLDC) motor-pump utilizing an existing V4 stator stamping. The conceptual BLDC motor design is simulated by choosing different winding configuration and material options, where the number of turns also changed. An analytical approach specifically suited to pumping applications is proposed. Detailed scrutiny of the BLDC motor is carried out based on Finite Element Method (FEM) incorporating sustainable design practices. Simulations of magnetic flux distribution in Infolytica MagNet 7.1 are executed to determine the output characteristics. BLDC motors at 48 V corresponds to an efficiency of 94.06% produced power is 745 W. Torque maximum and speeds corresponded at 7.16 Nm and 3000 rpm whereas cogging torque is 0.74 Nm. The motor's efficiency and stable speed improve overall conversion efficiency by 6% in operational limits, confirming the proposed BLDC motor-pump is energy efficiency. © 2024 IEEE.

关键词： Stators

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