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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Person re-identification using vision transformer and centroid triplet loss

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Multimedia Tools and Applications 2024年第29期83卷 73777-73788页

作者： Ijjina, Earnest Paul Medipelly, Rampavan Beerukuri, Santosh Kumar Vinnakota, Sowmya Nelakurthi, Vijay Chowdary Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Warangal506004 India

In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage from a different camera or the same camera at various points in time. The goal is to accurately identify a person despite variations in their appearance due to changes in pose, illumination, and occlusions. Person re-ID is a key component in video surveillance with practical applications in public safety, retail, and transportation, among others. However, it remains a difficult problem due to the inherent variability in appearance and the lack of robust features to capture the subtle differences between individuals. Even the existing Convolution Neural Networks (CNNs) based approaches for person re-ID task struggle to address the issues due to variations in pose, occlusions, and background clutter. To tackle these issues in person re-ID task, we propose an approach using Vision Transformers (ViT) with Centroid Triplet Loss (CTL). Experimental studies conducted on Market1501 and DukeMTMC datasets, yielded better results than the existing approaches, indicating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cameras

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PCA-IFNNR: Principal component analysis based image fusion directed nearest neighbor representation for dark face image super resolution

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Multimedia Tools and Applications 2024年 1-19页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science & amp Engineering National Institute of Technology Patna Patna800005 India

Facial image captured under uncontrolled environment often degraded by blur, noise and low-light effect which significantly affect the performance of different real life applications. To solve this problem numerous face image super resolution (FISR) methods developed in last two decades. However, getting state-of-the-art accuracy for low-light face images is still a challenging task. Hence, to solve this problem a new FISR method for dark and low-resolution face images is proposed in this paper to enhance the robustness of the existing FISR techniques against darkness (low-light) problems. The proposed method tends to remove the darkness effects present in the input low-resolution (LR) face image. This method first uses the brightness enhancement approach to generate two most suitable brighter images with different brightness levels. Further, the Principal Component Analysis (PCA) based image fusion technique is developed to fuse those two images in order to generate darkness free image. After this, min-max based contrast enhancement technique is modified and applied on the fused image to make it compatible with dataset (dictionary) images for the reconstruction purpose. Further, this enhanced image is used in nearest neighbor representation to generate its equivalent brighter and high-resolution (HR) version. The simulation results using publicly accessible data sets and some real-world images imply that the proposed method outperforms existing state-of-the-art methods in darker scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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Sentiment analysis of multi social media using machine and deep learning models: a review

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Multimedia Tools and Applications 2024年第42期83卷 90033-90051页

作者： Vasanthi, P. Madhu Viswanatham, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

The Internet has become an important origin of text information, which is then used inside a wide range of research domains. This has been regarded as a necessary foundation for institutions to obtain valuable information in addition to assessing consumers' feelings and views on a particular subject. Message clustering is the method of assigning specific tags to known categories based on one ‘s content material. It is difficult to classify the text using aspect-based sentiment. So each research on sentiment has addressed this issue, as existing research focuses on file and predominant component assessment instead of the specifics of the emotions. This survey reviewed the sentiment analysis based on both X and Facebook. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Mining user's navigation structure by filtering impurity nodes for generating relevant predictions

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 248-258页

作者： Jindal, Honey Sardana, Neetu Vidyarthi, Ankit Gupta, Deepak Mahmud, Mufti Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India United Kingdom

Web Navigation Prediction (WNP) has been popularly used for finding future probable web pages. Obtaining relevant information from a large web is challenging, as its size is growing with every second. Web data may contain irrelevant noise, and thus cleaning such data is essential. In this paper, we emphasize the identification and elimination of noisy information from user-navigated web pages. The pruning of user browsed sessions by removing noisy webpages and their relations can help in the development of high-performing prediction models with fewer prediction errors. To minimize the prediction errors, we have proposed four pruning models namely PM3ER, PM3EN, PM3GI, and PM3EP which remove noisy web pages and their relations from the model consisting of varied simple and complex navigations. The study reveals that PM3ER is effective in prediction for websites with complicated structures resulting in complex navigations, and PM3EN is effective in predicting websites with a simple tree-like structure resulting in simple navigations. PM3ER has shown an improvement in predictions by up to 3% whereas PM3EN has attained improvement up to 5.45%. © 2023

关键词： Forecasting

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Correlation between hydration properties and electrochemical performances on Ln cation size effect in layered perovskite for protonic ceramic fuel cells

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Journal of Energy Chemistry 2024年第1期88卷 1-9,I0001页

作者： Inhyeok Cho Jiwon Yun Boseok Seong Junseok Kim Sun Hee Choi Ho-Il Ji Sihyuk Choi Department of Mechanical Engineering Kumoh National Institute of TechnologyGumi 39177GyeongbukRepublic of Korea Department of Aeronautics Mechanical and Electronic Convergence EngineeringKumoh National Institute of TechnologyGumi 39177GyeongbukRepublic of Korea Center for Energy Materials Research Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Center for Hydrogen and Fuel Cell Research Clean Technology Research DivisionKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Energy and Environmental Engineering University of Science and TechnologySeoul 02792Republic of Korea Nanomaterials Science and Engineering Korea University of Science and Technology(UST)KIST CampusSeoul 02792Republic of Korea

PrBa_(0.5)Sr_(0.5)Co_(1.5)Fe_(0.5)O_(5+δ)(PrBSCF) has attracted much research interest as a potential triple ionic and electronic conductor(TIEC) electrode for protonic ceramic fuel cells(PCFCs). The chemical formula for Pr BSCF is AA'B_(2)O_(5+δ), with Pr(A-site) and Ba/Sr(A'-site) alternately stacked along the c-axis. Due to these structural features, the bulk oxygen ion diffusivity is significantly enhanced through the disorder-free channels in the PrO layer;thus, the A site cations(lanthanide ions) play a pivotal role in determining the overall electrochemical properties of layered perovskites. Consequently, previous research has predominantly focused on the electrical properties and oxygen bulk/surface kinetics of Ln cation effects,whereas the hydration properties for PCFC systems remain unidentified. Here, we thoroughly examined the proton uptake behavior and thermodynamic parameters for the hydration reaction to conclusively determine the changes in the electrochemical performances depending on LnBa_(0.5)Sr_(0.5)Co_(1.5)Fe_(0.5)O_(5+δ)(LnBSCF,Ln=Pr, Nd, and Gd) cathodes. At 500 ℃, the quantitative proton concentration of PrBSCF was 2.04 mol% and progressively decreased as the Ln cation size decreased. Similarly, the Gibbs free energy indicated that less energy was required for the formation of protonic defects in the order of Pr BSCF < Nd BSCF < Gd BSCF. To elucidate the close relationship between hydration properties and electrochemical performances in LnBSCF cathodes, PCFC single cell measurements and analysis of the distribution of relaxation time were further investigated.

关键词： Protonic ceramic fuel cell Cathode Triple ionic and electronic conductor Hydration property Proton uptake Gibbs free energy

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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