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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Mandawkar, Umakant Shahade, Makarand Wadekar, Samruddhi Kachhava, Chetan Patil, Yash Mandwekar, Sakshi Department of Computer Engineering SVKM Institute of Technology Dhule India

ISBN: (纸本)9781032642987

Even today the Textile Industries are using manpower to detect and classify the fabric defect, the process of depending on human inspectors for fabric defect detection can be considered as not an optimized way since it is time-consuming as well as an error prone process. To tackle this issue, here we propose a groundbreaking development- A real-time, automated fabric defect detection system. Making the most use of all the available cutting-edge advanced image processing techniques and machine learning algorithms, the proposed system will identify, locate and categorize a wide range of fabric defect where precision and accuracy will be the hallmarks of the proposed methodology The system will operate in real time, capturing highresolution fabric surface images, leading to precise and swift defect identification. Its automated and real-time capabilities render it to be an essential instrument in textile manufacturing as it will enhance its quality by diminishing the requirement of human inspectors, and evolving overall inspection process efficiency. Comprehensive and thorough testing across various fabric materials will prove its high accuracy rates in defect detection. It will possess the power to revolutionize the textile industry by enhancing product quality, cutting costs and boosting productivity. © 2024 The Author(s).

关键词： Textile industry

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STPM: Spatial-Temporal Token Pruning and Merging for Complex Activity Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期35卷 5407-5418页

作者： Su, Yumeng Zhang, Jiachao Yan, Rui Li, Pengpeng Xie, Guosen Shu, Xiangbo Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing Institute of Technology Artificial Intelligence Industrial Technology Research Institute Nanjing211167 China Nanjing University Department of Computer Science and Technology Nanjing210023 China

Lightweight video representation techniques have advanced significantly for simple activity recognition, but they still encounter several issues when applied to complex activity recognition: (i) The presence of numerous individuals and varying spatial positions makes it difficult for traditional token pruning methods to maintain accuracy. (ii) Simply discarding entire frames may result in the loss of crucial clues. (iii) To maintain parallel computing, applying the same pruning rate to every frame leads to significant redundancy in frames with low information content. To this end, we propose a lightweight and novel Spatial-Temporal Token Pruning and Merging (STPM) framework, specifically designed for complex action videos where human actors occupy a small spatial resolution within video frames. Our framework considers two critical factors: semantic importance and spatial-temporal redundancy, to further reduce overhead. For semantic importance, STPM captures class-specific attention scores by learning multiple class tokens within the transformer to guide token pruning. For spatial-temporal redundancy, STPM employs an anchor graph and temporal attention to perform spatial and temporal token merging, preserving appearance and temporal cues while eliminating semantic duplication and redundancy. We conduct extensive experiments on JRDB-PAR primarily using recently introduced video transformer backbones, e.g., MViT and ViT. Our framework achieves similar results while requiring 40% less computation. © 1991-2012 IEEE.

关键词： Economic and social effects

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Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

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computer Systems Science & engineering 2023年第7期46卷 181-193页

作者： V.S.Vaisakhi D.Surendran Department of Electronics and Communication Engineering Nehru Institute of Engineering and TechnologyCoimbatore641105India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641105India

In signal processing and communication systems,digital filters are widely *** some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter *** strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the *** technology advances,more complicated systems with several filters become *** of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input ***,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was *** fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the *** primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty ***,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image *** approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR *** the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of *** were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

关键词： Fault tolerance filters efficiency redundancy image denoising error correction codes double modular redundancy

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ITTS model: speech generation for image captioning using feature extraction for end-to-end synthesis

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International Journal of Intelligent Systems Technologies and Applications 2023年第2期21卷 176-198页

作者： Ghorpade, Tushar H. Shinde, Subhash K. Department of Computer Engineering Ramrao Adik Institute of Technology Nerul Navi Mumbai India Department of Computer Engineering Lokmanya Tilak College of Engineering Kopar Khairane Navi Mumbai India

The current growth in e-content is attributed to, information exchanged through social media, e-news, etc. Several researchers have proposed an encoder-decoder model with impressive accuracy. This paper exploits feature extraction from images and text for the encoder model using a word embedding method with proposed convolutional layers. State-of-the-art image-to-text and text-to-speech (ITTS) systems learn models separately, one describes the content of an image and the other follows with speech generation. We adopted the Tacotron model for the naturalness of a text with most popular datasets. It can also consistently analyse using evaluation metrics like bilingual evaluation understudy (BLEU), METOr, and mean opinion scale (MOS). The proposed method can significantly enhance the performance and competitive results of a standard image caption and speech generation model. The results show that we obtained an improvement by almost 4% to 5% BLEU score in image captioning model and approximately MOS is 3.73 in speech model. © 2023 Inderscience Enterprises Ltd.

关键词： Speech synthesis

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Generative Adversarial Networks (GANs) for Education: State-Of-Art and Applications

Generative Adversarial Networks (GANs) for Education: State-...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Kavyashree, K.N. Shidaganti, Ganeshayya Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819767090

In the ever-changing educational landscape, technology integration has become essential to improving students’ understanding of basic topics. The COVID-19 pandemic hastened the transition to online education while posing new difficulties for teachers and students alike. The disruption of traditional classroom dynamics was exacerbated by the lack of digital innovation and interactive instructional tools, which made student participation less effective. Traditional teaching paradigms must make way for ones that encourage self-directed learning and fortify technical infrastructure in order to handle these difficulties. Generative Adversarial Networks (GANs), a cutting-edge use of deep learning, are starting to revolutionize educational technology. With the use of GANs, a range of features that are tailored to the individual learning preferences of each student may be created, thereby bridging knowledge gaps and providing a more dynamic and interesting learning environment for students. This research investigates several state-of-the-art features that leverage technology linked to GANs to enhance students’ virtual learning environments. The novel applications of generative adversarial networks are explored, with a focus on applications for student observation, academic performance, questioning and answering, and personalized learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Comparative Study of Machine Learning Models for Stock Price Prediction 2

A Comparative Study of Machine Learning Models for Stock Pri...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ashtagi, Rashmi Deshmukh, Amogh Dhomse, Siddhesh Dhumal, Gayatri Dhurve, Siddhartha Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350368413

The stock market has always been evolutionary. It has been an area of interest for researchers to forecasting the stock prices too guide investment decisions and avoiding financial risk since many years. This paper highlights the significance of precise stock price prediction and investigates the use of several machine learning techniques to strengthen the predictive accuracy. The proposed system makes uses two different approaches: Regression, which predicts the company's stock closing price, and Classification predicts whether the closing price of the stocks will increase or decrease the next day. Historical stock data is examined carefully and preprocessed to ensure high quality of data. Using feature engineering appropriate features are extracted to identify the intrinsic patterns and trends within the stock market. Algorithms like Linear Regression, ARIMA, LSTM and Random Forest are implemented and thoroughly evaluated for their efficiency in stock price prediction. After conducting a comparative analysis of the two algorithms, it becomes evident that while LSTM performs exceptionally in regression tasks, identifying the temporal associations and patterns that guide the stock market data in its development, Random Forest provides more firm predictions of the stock price movement. This study provides the extensive analysis of the real-life application of machine learning for discussions on the stock market, thus allowing investors to derive a more reliable resource to refer to when dealing with the challenges of the stock market. © 2024 IEEE.

关键词： Prediction models

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A Smart Contract-Based Decentralised Crowdfunding Platform for Increased Security and Transparency 1

A Smart Contract-Based Decentralised Crowdfunding Platform f...

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

作者： Antad, Sonali Vidhale, Arpit Vikhar, Shivani Thite, Sanket Warke, Aditya Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350361780

In the contemporary landscape of financial systems and digital innovations, the convergence of blockchain technology and crowdfunding models has unveiled a promising avenue for revolutionizing traditional fundraising platforms. This research paper delves into the intricate fabric of designing and implementing a robust crowdfunding platform underpinned by the decentralized principles of blockchain technology integrated with Artificial Intelligence. The research embarks on an in-depth exploration of the underlying mechanics of blockchain, elucidating its distributed consensus, immutability, and transparency features. A comprehensive survey of existing literature pertaining to crowdfunding and blockchain integration is presented, establishing a solid foundation for the proposed platform's development. The conceptual framework delineates the architectural blueprint of the decentralized crowdfunding platform, intricately woven with smart contracts, artificial intelligence, cryptographic protocols, and a personalized recommendation system. The amalgamation of these components engenders an environment that ensures trust, security, and automation throughout the crowdfunding lifecycle. Moreover, this paper delves into the pivotal aspect of tokenization within the blockchain-based crowdfunding ecosystem. By implementing these cases against theoretical frameworks, the paper elucidates a comprehensive set of best practices and potential pitfalls that can guide the development and deployment of a successful blockchain-based crowdfunding platform. © 2024 IEEE.

关键词： Smart contract

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Harnessing Natural Entity Recognition for Intuitive Spreadsheet Control: A Voice-Driven Google Sheets Extension 5

Harnessing Natural Entity Recognition for Intuitive Spreadsh...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Joglekar, Pushkar Jha, Divyanshu Dhorage, Prathamesh Thakkar, Dhruv Dhumal, Om Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350361155

This paper introduces an innovative Voice-Controlled Google Sheets Extension employing Natural Language Processing (NLP) capabilities. The system seamlessly integrates a Chrome extension and a meticulously trained Named Entity Recognition (NER) model. Trained on custom datasets, the NER model proficiently extracts crucial entities, such as command types, cell references, and values from user speech, enabling accurate interpretation of complex commands. The extension enhances user interaction with spreadsheets, offering a voice-driven interface that simplifies spreadsheet management. The backend, utilizing Flask-based REST APIs, facilitates seamless communication with the Google Sheets API, ensuring precise execution of actions dictated by the interpreted commands. Experimental results underscore the system's accuracy and efficiency, establishing its potential as an intelligent productivity tool. This research contributes to the evolution of smart computing interfaces, providing users with an intuitive and accessible means of interacting with spreadsheet applications. © 2024 IEEE.

关键词： Natural language processing systems

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Digital Agriculture Platforms: Empowering Farmers and Enhancing Market Accessibility

Digital Agriculture Platforms: Empowering Farmers and Enhanc...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patil, Kavita T Patil, Shruti Patil, Harshada Salunke, Madhavika Chandwadkar, Shreyash Shahade, Makarand SVKM's Institute of Technology Department of Computer Engineering Dhule India

ISBN: (纸本)9798331508685

In the present day and age of technology and digitalization our proposed platform revolutionizes the agricultural market concept by providing a direct connection between the farmers and the consumers thus reducing the middlemen and providing a clear and viable market solution. The system also benefits farmers in that it makes it easier for them to display their produce, set price and sell. By having extensive product catalogue, using multiple languages, and interacting through the website in real-time, farmers are able to directly deal with the buyers from their respective regions and they are fairly paid for their work. In turn, consumers get to enjoy new, fresh, local and affordable foods from the markets they get supplied to. The secure mode of payment and simplified interface makes it easily accessible for all clients to create a sustainable and positive impact on the economy of the agricultural sector. © 2025 IEEE.

关键词： Agricultural economics

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid Systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 IEEE.

关键词： Cloud security

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