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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images

International Journal of Intelligent Networks 2024年第1期5卷 10-18页

作者： George, Neetha Shine, Linu N, Ambily Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering Rajiv Gandhi Institute of Technology Kottayam Kerala India Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Kerala India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Government Engineering College Wayanad Kerala India

The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases. © 2024

关键词： Optical tomography

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A Survey on Logic-Locking Characteristics and Attacks

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1073-1087页

作者： Subbiah, Karthik Chinnathevar, Sujatha Electronics and Communication Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India Computer Science and Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India

Integrated circuits (ICs) are ubiquitous and a crucial component of electronic systems, from satellites and military hardware to consumer devices and cell phones. The computing system’s foundation of trust is the IC. Most semiconductor businesses are shifting to fabless manufacturing and outsourcing to foundries worldwide as integrated circuit feature sizes continue to decrease. That puts the design business at risk for several things, such as unauthorized overproduction, resale on the black market, and illegal copying brought on by intellectual property theft. Logic locks offer one solution in which the chip’s actual functioning is "locked" with a key only known to the inventor. The design will only function as intended if specific keys are pressed. Unlocking overproduced chips should be impossible for supply chain attackers since designers open them after manufacturing them. Logical locks against the risk of overproduction are the main subject of this research. We examine current locking systems, define features based on crucial processing, and identify commonalities and discrepancies between the employed attacker models. This research paper is intended to assist scientists, IP distributors, and SoC developers in rapidly investigating and comprehending the most recent technologies that should be considered and analyzed for additional research on logic-locking techniques. © The Institution of Engineers (India) 2024.

关键词： Locks (fasteners)

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Enhancing epigraphy: a deep learning approach to recognize and analyze Tamil ancient inscriptions

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Neural Computing and Applications 2024年第31期36卷 19839-19861页

作者： Bhuvaneswari, S. Kathiravan, K. Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India

This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model’s ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and

关键词： Image segmentation

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From Past to Present: A Comprehensive Review of Antenna Technology in Modern Wireless communication

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Journal of engineering science and Technology Review 2024年第3期17卷 179-200页

作者： Mishra, Brijesh Singh, Aditya Kumar Satheesha, T.Y. Verma, Ramesh Kumar Singh, Vivek Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology U.P. Greater Noida India School of Computer Science Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

This research looks at microwave devices, specifically, patch antenna along with electromagnetic spectrum, shape, mechanism, analytical methods, simulation tools, and feeding procedures. Patch antennas are distinguished by their rectangular form, with a patch on one side and a ground plane on the other. Patch antennas work by exciting electromagnetic waves inside the patch, which are subsequently transmitted into the surrounding environment. The report also outlines numerous ways for evaluating the performance of microstrip patch antennas. The electromagnetic characteristics of the antenna are analyzed using the transmission line model, cavity model, and multiport network model (MNM). The integral equations that regulate the behavior of the antenna are solved using the method of moments (MoM) and the finite element method (FEM). The spectral domain technique (SDT) is used to analyze the antenna’s frequency response, while the finite difference time domain (FDTD) approach is used to analyze the antenna’s time-domain behavior. Overall, these methodologies give a thorough understanding of microstrip patch antenna performance and may be utilized to optimize their design. Furthermore, several patch antenna feeding methods, such as probe feed, microstrip line feeding, aperture coupling, proximity coupling, and CPW feed, are investigated. Attaching a microstrip line to the patch, which is subsequently linked to the RF source, is what microstrip line feeding entails. Aperture coupling entails making a hole in the ground plane that allows the RF source to feed the patch directly. Proximity coupling is accomplished by placing a probe near the patch, which creates an electromagnetic field on the patch. Patch antenna simulation software includes programmers such as HFSS, CST, and FEKO. These tools simulate the patch antenna’s performance, including its radiation pattern, gain, and input impedance. These simulations may be used to optimize the patch antenna design for specific a

关键词： Microstrip antennas

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Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

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Soft Computing 2024年第13-14期28卷 7659-7670页

作者： Singh, Mahesh K. Kumar, Sanjeev Ranjan, Rajeev Nandan, Durgesh Department of Electronics and Communication Engineering Aditya Engineering College Surampalem India Department of Electronics and Communication Engineering Chandigarh University Punjab Gharuan India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

This paper discussed an efficient technique to detect near-duplicate images with higher accuracy. The proposed method improved the existing accuracy of near-duplicate image detection and classification using Haar wavelet feature extraction and classification algorithm. Due to advancements in digital image acquisition and image editing software, near-duplicate image counterfeiting has become predictable. This expanding problem needs the creation of sophisticated detection algorithms capable of effectively preventing the spread of near-duplicate image deception. Support vector machine (SVM) and K-near neighbor (KNN) classifiers are used in the classification stage of a modified deep learning model with a convolutional neural network (CNN). The classification model finds the better result on the Imperial College London dataset, with 99.12% using CNN with KNN classifier and 98.63% for CNN with SVM classifier. The classification model also gives better results on the Nanyang Technological University dataset, with 98.36% using CNN with the KNN classifier and 97.85% for CNN with the SVM classifier. The model is tested using publicly available images to find the results. According to the comparison results, this model performed significantly better result than the previous model. As a result, digital image forensics has become more critical, particularly in identifying image duplication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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Accurate segmentation of COVID-19 infected regions in lung CT scans with deep learning

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Neural Computing and Applications 2024年第35期36卷 22511-22531页

作者： Lenin Marksia, U. Yesubai Rubavathi, C. Department of Electronics and Communication Engineering Francis Xavier Engineering College Tamil Nadu Tirunelveli India Department of Computer Science and Engineering Saveetha College of Engineering Tamil Nadu Chennai India

The widespread impact of coronavirus disease 2019 (COVID-19) has led to a severe health crisis and loss of life affecting billions of people. Detecting COVID-19 early on and distinguishing it from other illnesses is a major challenge in the pandemic. Computed tomography scans are vital for diagnosis, but they are difficult and slow for radiologists to interpret. So, deep learning technology is being used to speed up disease diagnosis, especially for COVID-19. This research employs a multi-scale feature extraction module that captures features at different scales to detect small and large structures in infection areas. The channel attention module enhances feature representation by focusing on informative channels. A bidirectional pyramid module is utilized to acquire fine-grained details and reduce spatial dimensions. The global dependencies and long-range contextual relationships among the images are captured through global and edge paths, resulting in clear and accurate images. Finally, the outputs from both modules are merged in the feature fusion Network to provide an accurate segmentation mask for COVID-19-related areas. The testing is conducted using the COVID-19 lung scan image dataset. Extensive trials are performed using standard metrics, and the output is compared with recently proposed approaches. The results demonstrate that this method achieves 98.6% accuracy, 96.54% precision, 97.52% sensitivity, 98.55% specificity, 98.59% kappa score, and an execution time of 3.2 s. These results suggest that this method performs better than other comparative methods and accurately identifies regions in the lungs affected by COVID-19. © The Author(s) 2024.

关键词： COVID-19

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A transformer based real-time photo captioning framework for visually impaired people with visual attention

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Multimedia Tools and Applications 2024年第41期83卷 88859-88878页

作者： Muhammed Kunju, Abubeker Kiliyanal Baskar, S. Zafar, Sherin A R, Bushara S, Rinesh A, Shafeena Karim Kerala Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Kerala Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and voiced explanations. This research investigates a two-layer transformer architecture to capture extensive relationships in visual data effectively. Using a two-layer transformer design, visual attention mechanism enables the model to autonomously identify relevant regions within a picture while generating a particular word within the anticipated caption. To get the features from the images, a pre-trained Inception V3 model is used, and the feature maps are extracted from the final convolutional block. The dataset used in this research was Flick8k, Flick30k, among bespoke datasets. The model was trained on a TPU platform and implemented on a Raspberry Pi 4B graphics processing unit. To assess the efficacy of the two-layer transformer model for photo captioning, a series of experiments were undertaken using benchmark datasets and with state-of-the-art models. The quality of the generated captions was evaluated using quantitative criteria such as BLEU, BLEUR, ROUGE and F1 score. Furthermore, a series of qualitative assessments were conducted, employing human evaluators, to measure the overall coherence, and fluency of the captions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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