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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sethi, Shuchi Patil, Ratna Rani, Seema Anjum, Misbah Chaudhary, Alka Amity University Noida Amity Institute of Information Technology Uttar Pradesh India Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Deaprtment Maharashtra India

ISBN: (纸本)9789380544519

The prevailing of encrypted traffic in all network flows, including social media, has significantly enhanced privacy and security for personal data. However, this widespread adoption of encryption poses challenges for the detection of malicious traffic, as adversaries are exploiting this technology to evade detection. Traditional content analysis-based detection techniques are rendered ineffective in the face of encrypted traffic. A survey of current detection methods and technologies underscores the challenges posed by encrypted traffic for existing approaches. This underscores the imperative for sophisticated detection techniques to effectively handle malicious activities within encrypted traffic. In response to this challenge, this paper introduces a framework based on deep learning for the identification of malicious data traffic within the encrypted deep web. The framework's performance is evaluated using a combination of datasets from five different public sources, and the results are compared with existing detection technologies. The pre-processing allows extracting important features and an application of deep learning techniques give high accuracy. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Classification Deep learning Deep web Encrypted traffic Malicious traffic

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CryptoCertify: Certificate Validation and Authentication Using Blockchain technology 1

CryptoCertify: Certificate Validation and Authentication Usi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Jadhav, Balasaheb Maharnawar, Niranjan Lakhotiya, Rushikesh Malpani, Rushikesh Ligde, Vaidehi Savale, Phalguni Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune411037 India Sagitech Solutions Pvt. Ltd. India

ISBN: (纸本)9798350361780

CryptoCertify is a new method for certificate validation and verification in the dynamic field of digital certification. With the implementation of Solidity on the Polygon blockchain and simple integration with Metamask and IPFS, CryptoCertify aims to improve security and usefulness. A decentralized and resistant solution is required since traditional certificate validation methods have problems with centralization and fraud. By establishing an irreversible and trustless infrastructure, smart contracts implemented with Metamask and written in Solidity significantly reduce the risks related to fake certificates. The certificate information is safely kept on IPFS and the Polygon blockchain, forming a distributed ledger that is cryptographically secure and time-stamped. By using Metamask, stakeholders might independently validate certificates, thereby improving validation process transparency. By integrating blockchain, IPFS, Metamask, and decentralization, this research contributes to the growing body of knowledge on safe digital certification methods. It additionally offers a solution that is versatile, scalable, and resistant to fraud for certificate validation and authentication in the future. © 2024 IEEE.

关键词： Smart contract

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Extracting Valuable Insights from The Handwritten Feedback 7

Extracting Valuable Insights from The Handwritten Feedback

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Chawale, Yugandhar Patwardhan, Prasanna Dewani, Rahul More, Ankit Jadhav, Varsha Pandey, Mrityunjoy Sharma, Neha Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Maharashtra Pune India Army Institute of Technology Department of Computer Engineering Maharashtra Pune India Society for Data Science Pune India

ISBN: (纸本)9798331527822

This paper describes the collection and analysis of feedback data which is crucial for evaluating the success of any event and identifying areas of improvement. However, when feedback forms are distributed in paper format, extracting the text responses into a machine-readable format for analysis can be a tedious and error-prone manual process. Optical Character Recognition (OCR) technology offers an automated solution for digitizing text from scanned documents, but its accuracy can be impacted by various factors like image quality, font styles, and language complexities. This study investigates the use of Gemini 1.5 Pro, a multimodal AI, to extract text data from PDF scans of collected handwritten feedback forms. After evaluation of Gemini's performance across a range of PDF qualities and comparing its output to manually transcribed data, the results demonstrate Gemini's high accuracy rates, even on lower quality scans, enabling efficient extraction of feedback responses while minimizing manual effort. This solution offers a scalable and reliable approach for educational institutions, professional organizations and other bodies to digitize survey responses for deeper qualitative and quantitative computational techniques. © 2024 IEEE.

关键词： data accuracy

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

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Move Match: Live Dance Motion Monitoring and Feedback System

Move Match: Live Dance Motion Monitoring and Feedback System

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Saral, G. Baby Subasri, S. Ramya Shree, R. Bose, K. Regin Rajalakshmi Institute of Technology Dept. Artificial Intelligence & Data Science Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Rajalakshmi Institute of Technology Dept. AI&DS & Cloud Computing Chennai India

ISBN: (纸本)9798331512965

Dance movement accuracy prediction is a critical component of modern dance training, yet traditional methods often lack objectivity due to the subjective nature of evaluations and the complexity of human movement. To address these challenges this paper introduces MoveMatch, which is a web-based platform built for real-time dance movement prediction. By leveraging deep learning techniques, the platform performs posture estimation in real-time and detects key points. The system compares the detected poses using Euclidean distance to calculate the alignment between corresponding key points which provides a quantitative measure of the user's accuracy. Based on this comparison, MoveMatch delivers real-time feedback, offering an accuracy score and personalized suggestions for improvement. This immediate feedback fosters a more efficient and engaging learning experience for dancers. Extensive testing of MoveMatch in various dance styles demonstrates its effectiveness in objectively assessing performance, enhancing both training quality and accessibility. The integration of pose estimation, deep learning and real-time comparison offers a novel, user-friendly solution for dancers seeking to improve their skills, transforming traditional dance education with AI-driven insights. © 2025 IEEE.

关键词： Deep learning

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Automated Image Captioning and Speech Synthesis 2

Automated Image Captioning and Speech Synthesis

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Mankawade, Amruta Pavitha, N. Vpatil, Amruta Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science India Vishwakarma University Department of Computer Engineering India Sjjtu Rajasthan India

ISBN: (纸本)9798350308846

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated Image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology. © 2023 IEEE.

关键词： Deep learning Image captioning LSTM RNN

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of Computer science & technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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Designing Web Crawler Based on Multi-threaded Approach For Authentication of Web Links on Internet 6

Designing Web Crawler Based on Multi-threaded Approach For A...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Vayadande, Kuldeep Shaikh, Rahebar Narnaware, Tejas Rothe, Suraj Bhavar, Neha Deshmukh, Saurav Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9781665482714

In the generation with the growth of communication technology and improving social requirements, this web crawler has been used extensively in the majority of search engines and research fields. In single-threaded crawlers, concurrency cannot be achieved because it is the single-threaded approach. Due to the use of Single threaded crawlers parsing takes higher time consumption. To overcome this a multi-threaded web crawler is proposed which can reduce the parsing time. An online crawler is a web bot that consistently browses the World Wide Web, generally for the aim of Web indexing. Any computer program uses these indexes, web graphs, and associated acceptable algorithmic rules to rank the pages or keep archival records of websites. Mostly the focus of the project would be to implement a multi-threaded transferer website crawler that may download multiple websites and their paths through the initial hostnames at a certain number of threads with their ranking and at a reduced time. © 2022 IEEE.

关键词： Web crawler

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Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Analysis of GAN for Melanoma Skin CancerClassification with ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational intelligence, RAEEUCCI 2024

作者： Saranya, N. Jowin Alfred, C. Rr, Rishikesh Gilbert Idayan, I. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location. © 2024 IEEE.

关键词： Dermatology

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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