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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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Emotion Trajectories in Cinematic Narratives: A Transformer-Based Analysis

Emotion Trajectories in Cinematic Narratives: A Transformer-...

引用

Software, Systems and Information Technology (SSITCON), International Conference on

作者： Vijay Sai Kumar Sudabathula Banoth Krishna Mohan Naik Shifa Ismail Sri Harsh Mattaparty Gagan Deep Arora Guda Sravan Yadav Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

This paper introduces a novel method for analyzing movie dialogues to predict characters’ primary emotions and the overall emotional tone of a film. Using natural language processing, we assess dialogues across emotional dimensions such as happiness, sadness, anger, fear, and more. Our approach involves collecting and preprocessing movie scripts, followed by building emotion classification models. By examining the context, sentiment, and linguistic features of dialogues, we predict character emotions and aggregate them to map the emotional trajectory of the movie. Diverging from traditional rating prediction models, our method aligns emotional experiences from movie scripts with reviewer feedback for improved accuracy. We also propose a sentimentbased movie recommender system that matches users’ emotions with film sentiments, offering a unique alternative to existing recommendation systems. Through extensive evaluation, our approach contributes to affective computing and provides deeper insights into the emotional structure of cinematic narratives.

关键词： Analytical models Affective computing Visualization Accuracy Computational modeling Motion pictures Transformers Vectors Trajectory Recommender systems

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NexusFlow: A Unified Platform for Streamlined Intercollegiate Event Coordinator

NexusFlow: A Unified Platform for Streamlined Intercollegiat...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Ramya S Sree Aswin Rajha R S Shyam B Mohanavarma M G Boobeshan A C Department of Artificial Intelligence and Machine Learning M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper introduces a centralized web platform for college event management, which uses dual-panel architecture (administrative and user) to streamline event registration, categorization, and personalized engagement. Administrators will manage logistics, real-time monitoring, and faculty assignments, while students access an AI-driven recommendation system that leverages machine learning classifiers, including collaborative filtering and content-based algorithms, to prioritize events aligned with their interests, such as hackathons for coding enthusiasts. The platform uses NLP for the automated tagging of events and history-based participation analysis to fine-tune suggestions along with scalable back-end infrastructure supporting real-time updates and efficiency. Testing showed 40% in participation rates through tailored event summaries sent via email and in-app notifications to the students. AI-driven personalization, integrated into robust administrative tools, enhances the transparency of this system, minimizes overheads, and makes student engagement modernized on the campus.

关键词： Analytical models Adaptation models Automation Collaborative filtering machine learning Tagging Aerodynamics Real-time systems Classification algorithms Testing

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Developing an AI-Based Library Assistant: Enhancing Book Retrieval with Natural Language Processing and machine learning

Developing an AI-Based Library Assistant: Enhancing Book Ret...

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Computational intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Nadimpalli Madana Kailash Varma Adla Aryan Pagilla Dhanush Rangisetti Manikanta Nalla Chandhu Gagan Deep Arora Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The current college library system is hindered by inefficiencies in book searching, availability updates, identity verification, and checkout processes, resulting in delays and errors that negatively impact student experiences. This research paper presents an AI-based librarian system designed to address these challenges through automation and digital transformation. Leveraging a Large Language Model (LLM), the proposed system facilitates personalized interactions between students and library resources. Upon student verification, the AI librarian provides real-time information on book availability, location, and tailored recommendations, significantly reducing the time spent on manual searches. The streamlined checkout process allows for automated book issuance and instant confirmation notifications, minimizing human error and enhancing record- keeping. This innovative solution not only improves operational efficiency but also enriches the user experience by offering a user-friendly interface and timely assistance. Future enhancements, such as voice integration and mobile application support, are suggested to further modernize library services. This research underscores the potential of AI technologies to revolutionize library management and improve service delivery in academic settings.

关键词： Knowledge engineering Automation Large language models Manuals Libraries Natural language processing User experience Real-time systems Mobile applications Usability

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Sign Language Detection in Real-Time Applications

Sign Language Detection in Real-Time Applications

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Milinda, Renata Rachael Sara Ramsiej, Barrister Yasmin, Reshma Ambeth Kumar, V.D. Sekar, K. Anna University India Dept. of Artificial Intelligence and Machine Learning Anna University India Dept. of Computer Engineering Mizoram University Aizawl India Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331510299

Individuals with hearing and speech impairments often rely on gesture language for communication, but a lack of widespread understanding of this language among others creates significant barriers. A machine learning model that recognizes hand signals and converts them into English can help bridge this gap, facilitating communication between hearing and deaf individuals. Existing non-verbal communication identification systems, while leveraging computational learning and AI models for both single- and double-handed gestures, generally lack real-time capabilities. This research proposes a live symbolic language identification platform that uses a webcam to build a regional gesture communication dataset and TensorFlow for cross-domain learning. Despite a smaller dataset, the system targets high accuracy in recognition. Advanced technologies, including computer vision and deep learning, are applied to improve communication for deaf individuals by developing accessible technical applications and platforms. The proposed model, utilizing TensorFlow and OpenCV, aims to identify commonly used American Sign Language gestures in real time with accuracy and efficiency, including signs such as "hello, " "thanks, " "bye, " "yes, " "no, " "dad, " and "mom". © 2024 IEEE.

关键词： Adversarial machine learning

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

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IoT Empowered AI: Transforming Object Recognition and NLP Summarization with Generative AI 2

IoT Empowered AI: Transforming Object Recognition and NLP Su...

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2nd IEEE International Conference on Computer Vision and machine intelligence, CVMI 2023

作者： Joseph, Samuel Priya, Bhagavathi S. Poorvaja, R. Santhosh Kumaran, M. Shivaraj, S. Jeyanth, V. Shivesh, R.P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305142

In anticipation of the widespread adoption of augmented reality in the future, this paper introduces an advanced mobile application that seamlessly integrates AR and IoT technologies. The application aims to make these cutting-edge technologies more affordable and accessible to users while highlighting their immense benefits in assisting with household appliance control, as well as providing interactive and educational experiences. The app employs advanced algorithms such as object detection, Natural Language Processing (NLP), and Optical Character Recognition (OCR) to scan the smartphone's camera feed. Upon identification, AR controls for appliances, their power consumption, and electric bill tracking are displayed. Additionally, the application makes use of APIs to access the internet, retrieving relevant 3D generative models, 360-degree videos, 2D images, and textual information based on user interactions with detected objects. Users can effortlessly explore and interact with the 3D generative models using intuitive hand gestures, providing an immersive experience without the need for additional hardware or dedicated VR headsets. Beyond home automation, the app offers valuable educational benefits, serving as a unique learning tool for students to gain hands-on experience. Medical practitioners can quickly reference organ anatomy and utilize its feature-rich functionalities. Its cost-effectiveness, requiring only installation, ensures accessibility to a wide audience. The app's functionality is both intuitive and efficient, detecting objects in the camera feed and prompting user interactions. Users can select objects through simple hand gestures, choosing desired content like 3D generative models, 2D images, textual information, 360-degree videos, or shopping-related details. The app then retrieves and overlays the requested information onto the real-world view in AR. In conclusion, this groundbreaking AR and IoT -powered app revolutionizes home automation and le

关键词： Optical character recognition

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Drug Review: Sentiment Analysis, Using Advanced machine learning

Drug Review: Sentiment Analysis, Using Advanced Machine Lear...

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： V Akshaya B Raja Ruben K Santhosh M Shafiya Banu Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

“Sentiment Analysis of Drug Reviews Using machine learning Techniques,” focuses on employing advanced machine learning methods, including Bidirectional LSTM and TF-IDF, to analyze sentiments expressed in clinical drug reviews. The aim is to develop sentiment analysis model capable of classifying and describing the patient experiences with medications. the usage of Bidirectional LSTM architecture to capture contextual dependencies within sequential data, enhancing the accuracy of sentiment predictions is used. Additionally, the TF-IDF technique is employed for feature extraction, emphasizing the importance of sentiments of patients and addressing some challenges due to language. The performance results obtained during evaluation of the model contains an accuracy of 94.21%. Thus, the usage of these techniques aims to provide a comprehensive understanding of sentiment patterns in clinical drug reviews, offering valuable insights for healthcare professionals.

关键词： Drugs Deep learning Sentiment analysis Analytical models Logistic regression Reviews Computational modeling

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Insight Retina: A Deep learning Approach to Diagnose Diabetic Retinopathy

Insight Retina: A Deep Learning Approach to Diagnose Diabeti...

引用

Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： V Akshaya S Sashank G M Subhash Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The greatest cause of blindness in the world and a frequent cause of diabetes melitus is Diabetic-Retinopathy (DR). To stop vision loss in afflicted people, early identification and decision making are essential. Diabetic - Retinopathy is a eye disorder that is currently being diagnosed manually by ophthalmologists. The diagnosis and recovery takes a great deal of time, effort and money, sometimes even a risk of misdiagnosis. Deep-learning has been the most highly emerging popular method that performs better in many fields, especially in the process analysing data and as well as classifying images from medical imaging. The IdX-DR and EyeArt AI are some existing systems that are legally approved and widely used in many places. Convolution neural networks and transfer learning are widely used as they are more effective. The dataset called Asia Pacific Tele-Ophthalmology Society (APTOS) is a large collection, which contains images acquired using Fundus Imaging and is available in open-source which is used extensively. The Insight Retina uses Contrast Limited Adaptive Histogram Equalization for enhancement of images and pre-trained model Res-Net for detection, thus providing improved performance and a reliable 0.893 Kappa accuracy score from the DR model. Insight Retina then generates a report for the input image providing accurate information about the stage of the Diabetic-Retinopathy.

关键词： Adaptation models Accuracy Transfer learning Neural networks Blindness Retina Feature extraction

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The EcoSync: A Comprehensive Study on Water Quality Prediction and Waste Image Recognition

The EcoSync: A Comprehensive Study on Water Quality Predicti...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Ahana Mukherjee Bipasha Chatterjee Indrakshi Das Koushiki Chakraborty Madhushree Pramanik Anjan Kumar Payra Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Kolkata India Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Institute of Engineering and Management Newtown Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research was devised to analyze how machine learning tools could assess water quality and classify waste aiming to build a better system for sustaining our environment. We unified decision trees, random forests, and convolutional neural networks to create a multi-model system that identifies and sorts waste via image recognition. To strengthen our model's performance, we expanded our dataset to include sundry labelled images. Using a multilayer perceptron with Adam optimizer, our system achieved 89% accuracy in classifying waste types and 94% accuracy in identifying waste materials. Water potability analysis, however, relied on machine learning models that were trained on a comprehensive dataset of chemical and physical water parameters. The system was able to show an accuracy of 87% in predicting water safety with a low false positive rate of 3.2% when real time monitoring was integrated. These systems when integrated gave a solution that was better than traditional methods by 47% and the processing time was reduced by 62%. Our results showed that machine learning can effectively enhance environmental management systems. This scalable, technology-driven approach proved successful for both waste and water management, making it particularly valuable for regions struggling with these challenges, regardless of their development status.

关键词： Waste management Waste materials Accuracy Image recognition Biological system modeling Water quality Real-time systems Decision trees Convolutional neural networks Random forests

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Hybrid machine learning for Personalized Ayurvedic Drug Recommendation via Prakriti Assessment

Hybrid Machine Learning for Personalized Ayurvedic Drug Reco...

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Women in Computing (InCoWoCo), International Conference for

作者： Jimsha K Mathew J Maruthu Pandi V Harish Kumar Vadagam V Sri Vishnu Charan Bindu S M Bhoomika H Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Ayurveda, an ancient system of medicine, emphasizes personalized treatment based on individual body constitutions, known as Prakriti. In this paper, we present a hybrid machine learning approach for recommending Ayurvedic drugs, integrating traditional Prakriti assessment with modern symptom-based analysis. Using clustering and classification algorithms, the system analyzes patient symptoms and body constitution to generate personalized drug recommendations. This method avoids the computational complexity of deep learning, providing an interpretable, efficient, and scalable solution. Experimental results demonstrate the system's accuracy in identifying Dosha imbalances and its effectiveness in recommending appropriate treatments.

关键词： Drugs Deep learning Accuracy Medical services Real-time systems Data models IEEE Constitution System analysis and design Engines Guidelines

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