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20th IEEE India Council International Conference, INDICON 2023

作者： Ghosh, Ahona Ghosh, Lidia Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India Rcc Institute of Information Technology Department of Computer Application Kolkata India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application India

ISBN: (纸本)9798350305593

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial Pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms. © 2023 IEEE.

关键词： Electroencephalography Linear Discriminant Analysis OpenManipulatorX Phase-sensitive Common Spatial Pattern Rehabilitation

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Efficient net-based deep learning: A deep-fake image detection

Efficient net-based deep learning: A deep-fake image detecti...

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Communication, Control and Intelligent Systems (CCIS)

作者： Rohan Singh Dilip Kumar Sharma Praphula Kumar Jain Department of Computer Science and Engineering GLA University Mathura Department of Computer Engineering and Application GLA University Mathura Department of Computer Science and Engineering MITS Gwalior MP

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10.

关键词： Training Deep learning Accuracy Social networking (online) Image edge detection Streaming media Market research Feature extraction Spatial databases Intelligent systems

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An end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation

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Multimedia Tools and applications 2025年第16期84卷 16281-16299页

作者： Zhou, Hui Tao, Huanjie Duan, Qianyue Hu, Zhenwu Deng, Yishi School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Cyberspace Security Northwestern Polytechnical University Xi’an710129 China Engineering Research Center of Embedded System Integration Ministry of Education Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China

Weakly supervised pavement crack segmentation aims to assign each pixel of pavement surface images a category label (crack or non-crack) using limited annotation information. Most existing methods adopt the multi-stage approach based on the class activation map (CAM), which outputs from a pre-trained crack classification model, to generate the pixel-level pseudo labels, and then train a segmentation model using these pseudo labels. However, these methods have two notable drawbacks: (i) CAM focuses primarily on the most discriminative regions of the image, potentially resulting in an incomplete estimation of crack regions, and (ii) the process of obtaining pixel-level pseudo labels from CAM is complex and may introduce noise. To avoid these issues, we propose an end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation. Specifically, to better guide the model to pay more attention to the repair process of crack regions, we introduce the segmentation loss, which assigns different weights for crack regions and background regions, through the Residual Project Network. Additionally, to alleviate the impact of the domain gap between synthetic and real datasets, we introduce the consistency loss through the joint training paradigm of unpaired data that incorporates both crack and non-crack images into the training process simultaneously. Furthermore, to enable the model to effectively extract local features and reduce the model's computational complexity, we introduce the Lightweight Multi-head Self-attention module. The experiment results on the CRACK500 Dataset, the CFD Dataset, and the AEL Dataset demonstrate that our model achieves better performance than some existing weakly supervised pavement segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Repair

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection.

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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Exploring the Capabilities and Limitations of Generative AI applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 IEEE.

关键词： Generative adversarial networks

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

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computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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SketchConf: A Framework for Automatic Sketch Configuration 39

SketchConf: A Framework for Automatic Sketch Configuration

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Miao, Ruijie Dong, Fenghao Zhao, Yikai Zhao, Yiming Wu, Yuhan Yang, Kaicheng Yang, Tong Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github. © 2023 IEEE.

关键词： Frequency estimation

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Rough Set Based Recipe Clustering Approach

Rough Set Based Recipe Clustering Approach

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Priyanka Das Chandrani Mukherjee Samiksha Gupta Sayan Roy Khushi Shah Shreya Singh Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique.

关键词： Uncertainty Communication systems Decision making Rough sets Clustering algorithms Food products Information systems

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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