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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

computer Systems science & engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar P.Saranya Vijayakumar Ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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Vehicle Identity Anonymity Framework with Accountability for VANET Environment

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Journal of Internet technology 2024年第2期25卷 267-280页

作者： Lo, Nai-Wei Chuang, Chi-Ying Luo, Jia-Ning Yang, Chong-Long Department of Information Management National Taiwan University of Science and Technology Taiwan Department of Computer Science and Information Engineering National Defense University Taiwan

In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collision warning systems. Simultaneously, car manufacturers and technology companies are actively exploring technologies in the realm of autonomous driving. To facilitate such applications, vehicles are required to communicate with each other, exchanging vital information such as position, speed, and acceleration. However, this exchange of information poses a potential risk of drivers’ personal data being compromised. For safety purposes, vehicles must undergo appropriate authentication before engaging in communication with other vehicles. Ensuring this authentication process maintains the anonymity of the vehicles is crucial. Yet, striking a balance between protecting vehicle anonymity and enabling vehicle identification when necessary remains a challenging issue. This paper introduces a multi-tier Vehicular Ad-Hoc Network (VANET) framework designed to uphold the conditional anonymity and traceability of vehicles. The implementation of a group signature mechanism facilitates anonymous authentication, thereby enabling the realization of conditional anonymity and traceability. Moreover, comprehensive simulations and security analyses were conducted to validate the effectiveness of the proposed framework, demonstrating its efficiency while incorporating robust safety considerations. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Vehicular ad hoc networks

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

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IEEE Transactions on Automation science and engineering 2025年 22卷 15941-15952页

作者： Wang, Pengyu Lin, Hin Wang Li, Jialu Wang, Jiankun Shi, Ling Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southern University of Science and Technology Jiaxing Research Institute Jiaxing China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong University of Alberta Canada Department of Electrical and Computer Engineering Canada

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1). © 2004-2012 IEEE.

关键词： Motion planning

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Exploring Unbiased Activation Maps for Weakly Supervised Tissue Segmentation of Histopathological Images

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2631-2642页

作者： Kang, Yuxin Li, Hansheng Shi, Xiaoshuang Zhang, Xiao Xing, Yaqiong Wen, Yuting Wang, Yi Cui, Lei Feng, Jun Yang, Lin Yulin University Department of Information Engineering Shaanxi Yulin China The School of Information Science and Technology Northwest University Shaanxi Xi'an China Northwest University School of Information Science and Technology Shaanxi Xi'an China University of Electronic Science and Technology of China Department of Computer Science and Engineering Sichuan Chengdu China Xi'An Jiaotong University School of Computer Science and Technology Shaanxi Xi'an China

Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised Semantic Segmentation (WSSS) methods strive to utilize image-level labels to achieve pixel-level segmentation, aiming to minimize the need for detailed annotations. Most of these methods rely on Class Activation Maps (CAM) extracted from classification models, frequently leading to poor coverage of objects. The major cause is attributed to the strong inductive bias of the classification model, focusing primarily on discriminative feature of objects, rather than non-discriminative features. Inspired by this, we propose a simple yet effective method that introduces a self-supervised task by exploiting both the discriminative and non-discriminative features, and generate Unbiased Activation Maps (UAM) to encompass the whole object. Specifically, our method entails clustering all spatial features of an object class to derive semantic centers. Each center then works as a spatial filter that amplifies similar feature and suppresses dissimilar feature, and extract high-quality pseudo-labels (some noise at object boundaries). Moreover, we further propose a Noise-Reduced (NR) Learning method to train the segmentation network towards credible signals and lessen the impact of false predictions. Comprehensive experimental results on two public histopathology image datasets demonstrate the superior performance of our method over the state-of-the-art weakly supervised segmentation methods. © 2025 IEEE. All rights reserved.

关键词： Semantic Segmentation

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Fault-tolerant capability analysis of six-phase induction motor with distributed, concentrated, and pseudo-concentrated windings

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Scientia Iranica 2024年第10期31卷 775-789页

作者： Alemi-Rostami, Mehdi Rezazadeh, Ghasem Tahami, Farzad Akbari-Resketi, Hamid Reza Ministry of Science Research and Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to the three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under open-circuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting the motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters such as the change of output power, power loss, power factor, efficiency, and expected load loss are considered as the fault-tolerant capability parameters to evaluate the windings reliability. The aforementioned parameters are reported using the finite element analysis for different fault scenarios and different load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated shows a promising performance with high fault-tolerant capability. © 2024 Sharif University of technology. All rights reserved.

关键词： Reliability analysis

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