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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Adithya, G. Kiran, M Lalith Sofi, Mohammad Azhar Kalpana, A.V. Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India

ISBN: (纸本)9798350352689

A variety of leaf diseases that can negatively impact crop output and quality present major problems to the development of potatoes. In this paper, a multi-model deep learning approach utilizing single convolutional neural networks (SCNN), long short-term memory networks (LSTM), and convolutional neural networks (CNN) is presented for the detection of potato leaf disease. The CNN classifies diseases in potato leaves based on RGB images, the LSTM analyzes temporal patterns, and the SCNN focuses on spatial features. Trained on a diverse dataset, the models are evaluated using performance metrics and compared with traditional methods, showcasing the effectiveness of deep learning. The CNN model is deployed as a user-friendly application for real-time disease prediction, enhancing practical usability in agriculture. This comprehensive approach offers a versatile solution for automated potato leaf disease detection, benefiting farmers in crop health management. © 2024 IEEE.

关键词： Convolutional neural networks

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TwitAnalyze: A Deep Dive into Twitter Profile and Comment Sentiment Analysis 7

TwitAnalyze: A Deep Dive into Twitter Profile and Comment Se...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Thorat, Bhagwan Kshirsagar, Aayusha Kawthekar, Omkar Muley, Kaustubh Kausadikar, Anushka Kawade, Devendra Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331527822

Twitter is a platform where millions of users voice their opinions and sentiments on various topics on a daily basis. In this digital landscape, businesses recognize the importance of validating their online reputation, using customer opinions to maximize profits. This project specifically focuses on gathering data, evaluating it, and extracting public and private opinions. The key aspect of the analysis is the implementation of vectorization through a pipeline which enables the sentiment classification of new tweets. It explores the significance of opinion mining in shaping online reputation and business strategies. Through rigorous machine learning model evaluation, the study seeks to provide insights into sentiment analysis methodologies and their real-world applications. © 2024 IEEE.

关键词： Tweets

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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Enhanced deepfake detection through CNN and Deepfake Architecture 4

Enhanced deepfake detection through CNN and Deepfake Archite...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Dongre, Shital Jadhav, Nilesh Hanamant Jadhav, Ravindra Konkane, Sumedh Jaiswal, Krishna Nilesh Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350364729

Artificial intelligence has gone crazy in such a way that any ordinary man with technical knowledge could, in a sense, be poised to create an artificial deepfake simply by observing facial movements. The more the videos appear, the more realistic they become with these technologies. This presently presents a significant challenge with the rise of fake news, which, although seemingly bad for social networks, is currently one big technological threat in the spread of deepfakes online. Deepfakes pose allsided danger such as manipulation, misinformation, humiliation, and defamation. In this research paper, an overview of mechanisms that go into this approach would be provided by specializing in its ability to detect subtle inconsistencies within the manipulated videos. This project would be taking up the hybrid approach that includes Hybrid CNN and RNN to nullify the threats of deepfakes. This method, therefore, captures spatial features within video frames by CNN and frame-to-frame inspects for temporal inconsistencies by RNN. Audio-video misalignment is also considered in it. This hybrid approach has yielded good accuracy at any one point in the time of 96% with respect to our model, which thus has the capability to very precisely detect manipulated content. © 2024 IEEE.

关键词： Social networking (online)

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Smart Toll System with Theft Detection Using Tesseract 1

Smart Toll System with Theft Detection Using Tesseract

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1st International Conference on Pioneering Developments in Computer science and Digital Technologies, IC2SDT 2024

作者： Devesh Stuti, Nagar ABES Institute of Technology CSE-Data Science Department Ghaziabad India

ISBN: (纸本)9798350365016

All toll booths on highways are manually operated, with an attendant taking the driver's payment. We frequently experience gridlock at the toll booths on congested highways because this procedure can be slow. Time, effort, and human resources will be saved by automating the toll collection process. In this work, we propose a low-cost and effective method for collecting tolls called Smart Toll Collection using Automatic Number Plate Recognition (ANPR) that automatically recognises the vehicle's number plate after applying various pre-processing techniques to the input image of the car. The number plate could then be masked and segmented out either by detecting contours or by using the CNN, after which it could be recognised using Tesseract (OCR). When they pass through the toll plaza, the toll will be deducted from the account of the owner. Additionally, the owner will be informed that the vehicle entered the toll plaza at the specified time. We also assume that the owner keeps an electronic wallet, so that tolls are automatically deducted from the account at the toll booth upon identification of that specific licence plate. The shortcomings of the current manual and semi-automatic RFID system can be resolved by using this method of toll collection. This toll collection procedure will save time, effort, manpower and enhanced security features. © 2024 IEEE.

关键词： Toll collection

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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A Comprehensive Study and Implementation of the Mock Interview Simulator with AI and Pose-Based Interaction 1

A Comprehensive Study and Implementation of the Mock Intervi...

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

作者： Jadhav, Balasaheb Sawant, Avadhut Shah, Arnav Vemula, Pranamya Waikar, Abhijeet Yadav, Srushti Artificial Intelligence and Data Science Vishwakarma Institute Of Technology Pune India

ISBN: (纸本)9798350361780

The contemporary job market is increasingly competitive, placing a premium on a combination of technical prowess and essential soft skills. The ability to navigate job interviews effectively has emerged as a decisive factor in securing coveted positions. Recognizing this imperative, our contribution to the field is a pioneering solution - the Mock Interview Simulator. This simulator stands as a transformative tool in the landscape of job preparation, offering users an unprecedented advantage. In the current job market milieu, the Mock Interview Simulator redefines conventional preparation methods, providing a revolutionary approach for job seekers. This innovative platform leverages state-of-the-art technologies to empower users to tailor their interview experiences. The simulator introduces AI-driven interviewers, delivering a personalized and dynamic environment. This not only prepares candidates for a diverse range of interview scenarios but also imparts valuable insights into their performance. This paper delves into the intricate design and functionality of our Mock Interview Simulator, elucidating how cutting-edge technologies converge to create a realistic and highly beneficial interview preparation experience. By amalgamating speech recognition, text-to-speech synthesis, and the OpenAI GPT-3.5 Turbo API, the simulator crafts a lifelike interview setting. Additionally, posture detection using the Mediapipe framework adds a physical dimension, ensuring candidates not only articulate their responses effectively but also present themselves professionally. © 2024 IEEE.

关键词： Commerce

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data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction 3

Data Analytics on Blitz E-sports: Enhancing Gaming Experienc...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Kusuma, T. Shruthi, G. Avanish Rao, B.S. Anand, Nikhil Abhishek, M. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (纸本)9798331515911

This paper delves into the comprehensive data analytics of Blitz E-sports, a dynamic gaming cafe, to enhance operational efficiency and customer satisfaction. The study focuses on three pivotal objectives: revenue optimization, game recommendations, and churn prediction. Through meticulous analysis of customer data, gaming trends, and operational metrics, this proposal equips Blitz E-sports with action able insights to enhance profitability, customer engagement, and long-term sustainability. The potential of data-driven decision-making in the competitive e-sports industry is demonstrated by leveraging advanced machine learning techniques and big data technologies. This paper aims to reveal significant improvements in personalized user experiences, optimized revenue strategies, and effective customer retention measures. © 2025 IEEE.

关键词： Sales

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A Study of data Leakage Prevention Techniques in Remote Collaborative Work Application 24

A Study of Data Leakage Prevention Techniques in Remote Coll...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Xu, Wenfang Zhao, Shusheng School of Data Science and Computer Engineering Xiamen Institute of Technology China

ISBN: (纸本)9798400718137

Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protection measures have increasingly become the focus of attention, posing a severe challenge to enterprise data security. To address this challenge, this paper proposes an innovative comprehensive data leakage prevention technical solution. This solution integrates cloud services and kernel technology and creates a set of data security protection systems for the remote collaboration working environment. This technical solution adopts a series of effective technical means, including asymmetric encryption, one-way data flow control, dynamic digital watermarking technology, injection attack defense, and program anti-hanging mechanism, significantly enhancing the security of data in the collaborative office system. The constructed framework includes multiple modules such as user management, service support, kernel driver, and user terminal. This technical solution can be integrated with enterprise remote collaboration office software, providing a higher level of security guarantee for the remote office environment, effectively curbing the risk of sensitive data leakage, and also providing solid technical support for the healthy and stable development of remote collaboration work. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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Sensemaking of Socially-Mediated Crisis Information 3

Sensemaking of Socially-Mediated Crisis Information

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3rd Workshop on Bridging Human-Computer Interaction and Natural Language Processing, HCI+NLP 2024

作者： Koli, Vrushali Yuan, Jun Dasgupta, Aritra Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)9798891761117

In times of crisis, the human mind is often a voracious information forager. It might not be immediately apparent what one wants or needs, and people frequently look for answers to their most pressing questions and worst fears. In that context, the pandemic has demonstrated that social media sources, like erstwhile Twitter, are a rich medium for data-driven communication between experts and the public. However, as lay users, we must find needles in a haystack to distinguish credible and actionable information signals from the noise. In this work, we leverage the literature on crisis communication to propose an AI-driven sensemaking model that bridges the gap between what people seek and what they need during a crisis. Our model learns to contrast social media messages concerning expert guidance with subjective opinion and enables semantic interpretation of message characteristics based on the communicative intent of the message author. We provide examples from our tweet collection and present a hypothetical social media usage scenario to demonstrate the efficacy of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Tweets

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