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RecGuard: An efficient privacy preservation blockchain-based system for online social network users

Blockchain(Research and Applications) 2023年第1期4卷 36-47页

作者： Samuel Akwasi Frimpong Mu Han Edward Kwadwo Boahen Rexford Nii Ayitey Sosu Isaac Hanson Otu Larbi-Siaw Isaac Baffour Senkyire Department of Computer Science and Technology Jiangsu University212013ZhenjiangChina Department of Computer Engineering Ghana Communication Technology UniversityGhana Department of Computer Science Ghana Communication Technology UniversityGhana Department of Telecom Engineering Ghana Communication Technology UniversityGhana School of Finance and Economics Jiangsu University 212013ZhenjiangChina

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and ***,it raises several questions about privacy,especially for users who prefer to remain anonymous,especially on online social networks(OSNs).Moreover,due to the commercialization of online users'data,some service providers sell users'data to third parties at the blind side of the users,which leads to trust issues between users and service *** matters call for a system that gives online users much-needed control and autonomy of their *** the advancement of blockchain technology,many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and *** resolve these limitations,we propose RecGuard,a privacy preservation blockchain-based network *** developed two smart contracts,RG-SH and RG-ST,to ensure the security and privacy of user *** RG-SH manages user data,whereas the RGST stores data.A graph convolutional network(GCN)was integrated with the blockchain-based system to detect malicious ***,we implemented our framework prototype on a locally simulated *** analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.

关键词： Blockchain Privacy Online social network Recommender system

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Exploring Transfer Learning in Brain Tumor Classification with Deep Neural Networks 11

Exploring Transfer Learning in Brain Tumor Classification wi...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rawat, Vikas Singh Johar, Sakshi Kumar, Amit Singh, Ninni University Department of Computer Science and Engineering Dehradun India Cmr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350357

In today's era, medical advancements have reached their peak but still, some bugs led to the emergence and involvement of advanced techniques like Machine learning, Artificial Intelligence, and Deep Learning. These three are interconnected and have strong relationships that work as such, to increase the growth of work in any domain with rapid accuracy and without any error. This paper deals with the identification and categorization of tumors. Brain tumors are a result of abnormal behavior of cell division which leads to uncontrolled growth of cells and results in the formation of masses and lumps in the brain. The detection of tumors is done using radiology with the help of MRI techniques or MRI scans. Traditional scans sometimes lead to errors causing misdiagnoses. This process is also timeconsuming and as such requires high expertise. To elevate this process, in this paper, we used a multi-layered CNN model which takes the MRI images as input and classifies it into one of the four major categories i.e., 'Glioma', 'Meningioma', 'Pituitary' or 'No Tumor'. The publicly available dataset used to train this model contains over 7000 images which have been segregated into testing and training where both classes contain 4 different segmentations depicting the different brain tumors. The training folder contains nearly 5200 images, and the testing folder has over 1500 images. The accuracy of the model has been tested to over 96 percent. Then we converted the above model to the VGG16 model by using the study of Transfer Learning and the model gave up to 98.04% accuracy and validation accuracy was approximately 94.73%. © 2024 IEEE.

关键词： Tumors

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Skin Cancer Detection Using Deep Learning 10

Skin Cancer Detection Using Deep Learning

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mahalakshmi, A. Padmavathy, C. Priya, N. Priyanka, G. Babu, Lidiya Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353778

Skin cancer is a significant global health concern that requires early detection and accurate diagnosis for effective treatment. Traditionally, dermatologists with specialized training have been responsible for diagnosing skin cancer. However, the emergence of deep learning models, particularly Convolutional Neural Networks (CNNs), offers a promising approach for utilizing dermatoscopic images in the early identification and categorization of skin cancer. The HAM10000 dataset, comprising a vast library of high-quality dermatoscopic images displaying a variety of skin lesions, significantly contributes to advancing skin cancer diagnosis. This research leverages the HAM10000 dataset to develop and evaluate a CNN model tailored for accurate skin cancer classification. The suggested CNN model is an advanced deep learning architecture adept at image classification tasks, particularly in recognizing various forms of skin cancer. It consists of multiple layers of dense neural networks, pooling, and convolution designed to extract detailed characteristics from skin lesion images. To ensure comprehensive representation of various skin lesions and maximize performance, the training dataset is extensively oversampled. This oversampling technique enhances the model's ability to generalize across different lesion types, thereby improving classification accuracy. Furthermore, the Adam optimizer refines the model's learning process by effectively adjusting its parameters during training, leading to increased accuracy. By training the model for more than one hundred epochs with a batch size of 323, it learns intricate patterns and distinguishing features within skin lesion photos, which enhances its ability to classify skin cancer accurately. These advancements in deep learning-based skin cancer categorization represent a significant step towards leveraging artificial intelligence to improve early diagnosis and detection. Such innovations have the potential to support medical profe

关键词： Convolutional neural networks

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Dehazing using Generative Adversarial Network - A Review

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SN computer science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave and Future Networks 17

TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave a...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Prakash, Ujjayant Baswade, Anand M. Indian Institute of Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331531195

The rise in mobile internet usage especially using cellular networks demands efficient performance for web traffic, primarily made up of short TCP flows. For TCP, Cubic is the most widely deployed congestion control algorithm. mmWave and THz offer very high bandwidth and low latency, enabling faster speeds and reduced delay for companies deploying innovative applications, positioning them as the future of wireless technology. As such, we are compelled to thoroughly evaluate the performance of Cubic in various mmWave specific NLoS/LoS scenarios. In the process, we find Cubic really struggling when it comes to static NLoS scenarios and long connections particularly because Cubic keeps on attempting recovery from slow-start but fails due to the NLoS condition. This is where TCP Cubiwood comes to the rescue with an improvement of 189%in a simulated static NLoS scenario with results comparable to Cubic in wired conditions as well, thus becoming a suitable contender for replacing the historical algorithm. Delayed Congestion Response (DCR) tries to make TCP more robust towards channel errors in wireless networks so we also propose an additional flavor of Cubiwood, called Cubiwood-DCR which increases performance slightly in both wired and wireless high frequency scenarios. We extensively evaluate Cubiwood against Cubic, both with and without DCR enabled and make our code available for reproducibility and further research in the same direction. Moreover, the simplicity of this method makes our proposal a much more attractive solution. We believe the proposed solution is easy to implement, can replace Cubic and has the potential to improve performance in THz communication as well which is one of the competing spectrum bands for 6G networks. © 2025 IEEE.

关键词： Congestion control (communication)

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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Improving Gemstone Identification Accuracy Using Convolutional Neural Networks

Improving Gemstone Identification Accuracy Using Convolution...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Raheem, Raza Ahmad, Wakeel Adnan, Syed M. Department of Computer Science University of Engineering and Technology Taxila Pakistan

ISBN: (纸本)9798331504465

Accurate identification of gemstones is crucial in various fields, including gemology research, jewelry appraisal, and consumer protection, where authenticity verification is paramount to prevent fraud and ensure trust in the gem trade. Traditional methods of gemstone identification heavily depend on the subjective expertise of gemologists, which can lead to inconsistencies and is often time-consuming, particularly when dealing with rare or complex gemstones. This study presents the development of a Convolutional Neural Network (CNN) model designed for the automated classification of gemstones. The model leverages advanced image preprocessing techniques, including resizing, data augmentation, and normalization, to enhance the robustness of the classification process. Trained on a meticulously curated dataset comprising images of 15 distinct gemstone types, the CNN model achieved a notable validation accuracy of 88%. These results highlight the potential of CNN-based approaches as a reliable and efficient alternative to traditional gemstone identification methods, offering significant improvements in speed, consistency, and scalability. © 2024 IEEE.

关键词： Convolutional neural networks

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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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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Enhancing Diabetic Retinopathy Diagnosis with ResNet-50-Based Transfer Learning: A Promising Approach

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Annals of Data science 2024年第1期11卷 1-24页

作者： Karthika, S. Durgadevi, M. Rani, T. Yamuna Department of Computer Science and Engineering SRM Institute of Science and Technology College of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India ACMS Railway Hospital ICF Chennai Tamil Nadu India

Diabetic retinopathy is considered the leading cause of blindness in the population. High blood sugar levels can damage the tiny blood vessels in the retina at any time, leading to retinal detachment and sometimes glaucoma blindness. Treatment involves maintaining the current visual quality of the patient, as the disease is irreversible. Early diagnosis and timely treatment are crucial to minimizing the risk of vision loss. However, existing DR recognition strategies face numerous challenges, such as limited training datasets, high training loss, high-dimensional features, and high misclassification rates, which can significantly affect classification accuracies. In this paper, we propose a ResNet-50-based transfer learning method for classifying DR, which leverages the knowledge and expertise gained from training on a large dataset such as ImageNet. Our method involves preprocessing and segmenting the input images, which are then fed into ResNet-50 for extracting optimal features. We freeze a few layers of the pre-trained ResNet-50 and add Global Average Pooling to generate feature maps. The reduced feature maps are then classified to categorize the type of diabetic retinopathy. We evaluated the proposed method on 40 Real-time fundus images gathered from ICF Hospital together with the APTOS-2019 dataset and used various metrics to evaluate its performance. The experimentation results revealed that the proposed method achieved an accuracy of 99.82%, a sensitivity of 99%, a specificity of 96%, and an AUC score of 0.99 compared to existing DR recognition techniques. Overall, our ResNet-50-based transfer learning method presents a promising approach for DR classification and addresses the existing challenges of DR recognition strategies. It has the potential to aid in early DR diagnosis, leading to timely treatment and improved visual outcomes for patients. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer

关键词： Blood vessels

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