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Persona-Driven Dialog Generation: Enhancing User Engagement Through Linguistic Proficiency and Personalization 3rd

Persona-Driven Dialog Generation: Enhancing User Engagement ...

引用

3rd International Conference on computing, Communication and Learning, CoCoLe 2024

作者： Abhishek, A. Akhilesh, R. Swetha, B. Vindhya Vasini, V. Deepika, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031790409

In this paper we focus upon building on the existing methods of using persona and auxiliary information on improving and humanizing a conversational model as there are still limitations when it comes to personification and dialogue generation by incorporating the users’ linguistic proficiency with the CEFR (Common European Framework of Reference for Languages) categories. OneStopEnglish corpus dataset and Readability dataset are used in the neural network models to predict the CEFR levels. The user-provided persona information is used in the Large Language Models (LLMs) to provide auxiliary information to personify and generate dialogues and scripts. In addition to advancing dialog models, this multidisciplinary study establishes a professional benchmark for enhancing user engagement in the ever-changing world of over-the-top, movies, series, novels and anime-inspired fan content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linguistics

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Heart Disease Prediction Analysis with Machine Learning

Heart Disease Prediction Analysis with Machine Learning

引用

Disruptive Technologies (ICDT), International Conference on

作者： Mannat Manocha A Amit Kumar Vivek Kumar Singh Yuddhvir Singh School of Computing Science and Engineering Galgotias University Gautam Buddha Nagar Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

When early diagnosis and treatment of heart diseases becomes increasingly challenging. However, various studies and researches discovered that recent development in technologies affects the healthcare industry with the advanced machine learning techniques. A machine learning predictive model (LumenCardia) for heart-diseases based on relative parameters is the aim of this work. In this project, we had employed the dataset called Cleveland heart disease dataset, which includes 14 distinct heart disease-related factors. In this study we had applied ML techniques like Random Forest, Logistic Regression, KNN, and Decision Tree to our model. We also looked for relationships among different features in our dataset using conventional ML techniques. The outcome demonstrates that Random Forest produces predictions with more accuracy (93.18%) in less time than other machine learning algorithms. The medical professionals may find LumenCardia beneficial as a decision support system.

关键词： Heart Radio frequency Logistic regression Accuracy Machine learning algorithms Medical services Predictive models Nearest neighbor methods Random forests Diseases

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An Investigation of GCN-based Human Action Recognition Using Skeletal Features 27

An Investigation of GCN-based Human Action Recognition Using...

引用

27th International Conference on Automation and computing, ICAC 2022

作者： Dai, Chuan Wei, Yajuan Xu, Zhijie Chen, Minsi Liu, Ying Fan, Jiulun University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Xi'an University of Posts and Telecommunications School of Computer Science Xi'an China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Human action recognition is one of the most challenging and attractive areas in the field of computer vision. Conventional research on human action recognition has mainly focused on data modality of video or optical flow. However, the human skeletal feature has much stronger expressive power of motion dynamics, which is not sensitive to illumination and scene variation. Owing to the advantages of deep learning approaches on skeleton data in recent years, many pilot approaches have been proposed, which are merited by their significant performance enhancements on both baseline and large-scale datasets. This research investigates these models and their breakthroughs, especially focusing on the graph convolution network (GCN) and skeleton-based data techniques. The report work mainly covers the following aspects: comparing RNN, CNN and GCN-based approaches from the perspective of their operational logics;a detailed review of the best referred models in recent years;a development framework of skeletal feature-based human action recognition framework is proposed with preliminary assessments using benchmarking datasets;and finally, the envisaged future directions for skeletal feature-based human action recognition study are discussed. © 2022 IEEE.

关键词： Deep learning

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A4RSIC: Attention-Augmented Adversarial Autoencoder for Remote Sensing Image Compression

A4RSIC: Attention-Augmented Adversarial Autoencoder for Remo...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Saran Sivarajan Akshara Preethy Byju Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

Reconstruction of images from the recent deep learning based models for image compression tasks does not necessarily corroborate with human perception and lose relevant inherent structural details. To address this, in this research work, a novel deep attention-augmented adversarial autoencoder based lossy remote sensing image compression $(\mathbf{A}^{4}\mathbf{RSIC})$ approach is proposed that achieves superior compression ratio compared to the state-of-the-art methods. The $\mathbf{A}^{4}\mathbf{RSIC}$ model embeds an encoder-generator duo to generate a compact latent vector representation of the input image, which is then used to reconstruct the original image with minimal loss. The proposed model utilizes a discriminator, trained to differentiate between real and $\mathbf{A}^{4}\mathbf{RSIC}$ generated images using an adversarial approach, ensuring the fidelity of the reconstructed images. Furthermore, the model integrates a convolutional block attention module (CBAM) at relevant strategic points within the encoder and generator to enhance the salient features by leveraging spatial and channel-wise attention mechanism. This improves the quality of feature representation and compression performance. Experimental results demonstrate the efficacy of the proposed method compared to JPEG as well as other state-of-the-art methods.

关键词： Visualization Image coding Image edge detection Autoencoders Transform coding Vectors Generators Intelligent systems Image reconstruction Hyperspectral imaging

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Enhancing Security for Online Transactions through Supervised Machine Learning and Block Chain Technology in Credit Card Fraud Detection 7

Enhancing Security for Online Transactions through Supervise...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Raj, A. Thilakesh Shobana, J. Nassa, Vinay Kumar Painuly, Sakshi Savaram, Mythreya Sridevi, M. Sri Venkateswara College of Engineering Department of Information Technology Tamilnadu India Srm Institute of Science and Technology School of Computing Tamilnadu India Rajarambapu Institute of Technology Department of Computer Science and Engineering Maharashtra Uran India Graphic Era Hill University Dehradun R/S Graphic Era Deemed to Be University School of Computing Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Cvr College of Engineering Department of Cse Telangana India

ISBN: (纸本)9798350341485

This research probed the capabilities of supervised machine learning algorithms in credit card fraud detection, an essential facet of contemporary digital finance, and integrated the transformative idea of blockchain for added security and transparency. Findings highlighted the predominant efficiency of the XGBoost algorithm, which recorded an accuracy of 97%, a precision of 94%, and an AUC of 0.97. Other notable performers included the Gradient Boosting Machine and Random Forest. The study underscores the potential of integrating advanced machine learning techniques and blockchain technology to significantly enhance fraud detection systems, ultimately enhancing user trust and the security of online transactions. Recommendations for the adoption of algorithms like XGBoost and the utilization of blockchain ensures immutable transaction records and continuous monitoring for adaptive defense against evolving cyber threats. Future research directions might encompass the exploration of hybrid models, blockchain integration, and deep learning techniques to further enhance fraud detection mechanisms. This research provides research reference for financial institutions that strive to optimize the digital transaction standards. © 2023 IEEE.

关键词： Blockchain

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Enhancing Air Quality Index Forecasting through Machine Learning Models

Enhancing Air Quality Index Forecasting through Machine Lear...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Kamalapuram Vigneswara Reddy Chimakurthy Mounika Begum Adhi Neeraja Jyotsna C. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Air pollution poses serious health risks, including respiratory issues and hospitalization. This study focuses on predicting the Air Quality Index (AQI) in Delhi, leveraging data from monitoring stations and machine learning models like linear regression, support vector regression, and tree-based methods such as decision trees and random forests. Results show that the Decision Tree Regressor outperformed other models, achieving the lowest Root Mean Squared Error (RMSE) and highest R-squared (R 2 ) values. Incorporating and advanced preprocessing enhanced accuracy, addressing seasonal variations in air quality data. The findings emphasize the value of machine learning in accurate AQI forecasting, supporting policy decisions, and improving urban air quality management. This research highlights the critical role of predictive models in mitigating air pollution’s impact and protecting public health while suggesting pathways for future advancements in real-world applications.

关键词： Support vector machines Accuracy Atmospheric modeling Predictive models Air pollution Indexes Forecasting Public healthcare Regression tree analysis Random forests

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Development of an Adaptive Smart Toy with Emotional Feedback

Development of an Adaptive Smart Toy with Emotional Feedback

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Gayathri Mohandas Sanay Santh V Keshav Padmakumar Thangam S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Traditional toys often do not adapt to the emotional and physical needs of children, particularly those who may have limited verbal communication abilities. The project's goal is to create a smart toy that is adaptive and can communicate with a child by using nonverbal signs to express his or her physical and emotional states. There are sensors like accelerometers to detect movement, a gesture sensor indicating light intensity, a buzzer sensor to produce sound, and an IR sensor to detect the child's hand when the child is near the toy. All these components will help the user to get in-time feedback with a set of visual indicators (LEDs) and tactile feedback in the form of vibration motors. This smart toy seeks to improve the experience of play and developmental support for children by being more responsive and engaging with individual needs.

关键词： Vibrations Accelerometers Hands Visualization Toy manufacturing industry Tactile sensors Motors Light emitting diodes Internet of Things Intelligent sensors

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Hybrid Metaheuristic Optimisation for Lung Cancer Image Classification: Leveraging MOEA, PSO, and ACO Algorithms

Hybrid Metaheuristic Optimisation for Lung Cancer Image Clas...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Thambi, Sharanya Vanraj Ishvarya, G. Kammari, Kavya Sree Sahay, Apurvanand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In the critical domain of cancer classification, accurately identifying lung cancer scans as malignant or benign is paramount. This study proposes a novel approach that leverages a hybrid metaheuristic combination of algorithms. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and the Knapsack Algorithm are utilized for feature selection. Subsequently, Convolutional Neural Networks (CNNs), recognised as state-of-the-art in medical image classification, are employed. Among the three algorithms, ACO performed the best. Following this, Multi-Objective Evolutionary Algorithms (MOEAs), Particle Swarm Optimisation, and the Simplex Algorithm were used, respectively, to fine-tune hyperparameters and enhance Classification accuracy. The best result was obtained by using ACO for feature selection and PSO for hyperparameter tuning, achieving an accuracy of 98.83%. The study conducts a comparative analysis of different algorithmic combinations to validate the proposed methodology's efficacy. This comparative analysis of optimisation algorithms significantly maximises the accuracy of lung cancer classification. © 2025 The Authors. Published by Elsevier B.V.

关键词： Particle swarm optimization (PSO)

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Real-Time Performance Analysis of Multi-Armed Bandit Solution for OBSS and Dynamic Channel Bonding in WLAN

Real-Time Performance Analysis of Multi-Armed Bandit Solutio...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Remya P. R J. Govindarajan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Rapid expansion of dense Wi-Fi network deploy-ments creates challenges in optimizing performance. Existing multi-armed bandit (MAB) solutions for dynamic channel bonding (DCB) and overlapping basic service set (OBSS) issues are tested using simulations. This research aims to identify the gaps in current solutions through real-time performance analysis. Along-side this primary objective, we proposed a modified exploration-first algorithm to minimize convergence time. This secondary objective helped us achieve the primary objective. The results demonstrate that our algorithm can adapt to dynamic Wi-Fi environments and improve the user experience. We also present a summary of the inferences from the primary objective study.

关键词： Heuristic algorithms IEEE 802.11ax Standard Real-time systems Inference algorithms User experience Performance analysis Bonding Wireless fidelity Standards Convergence

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Automated Regional Language News Article Classification System for Kannada and Telugu

Automated Regional Language News Article Classification Syst...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Deepa Gupta K Thejaa Aarathi Rajagopalan Nair Panduranga Katti Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The increasing amount of digital news content has created a pressing need for automated systems that can efficiently organize and categorize articles. This study focuses on developing an automated classification system for Kannada and Telugu news articles, targeting predefined categories. For Kannada, the categories include Entertainment, Sports, and Lifestyle, while for Telugu, the categories are Sports, Business, and Entertainment. With the help of the techniques available in natural language processing and the available models in machine learning, the system preprocesses the textual data in both languages to extract meaningful features for classification. Several classification algorithms are explored, with a focus on evaluating model performance using standard metrics. The results demonstrate that embeddings like KannadaSBERT and TeluguSBERT significantly improve classification accuracy, showing a clear advantage in understanding and categorizing regional language text. This work addresses a critical gap in the field of NLP for regional Indian languages by providing a structured approach to classifying Kannada and Telugu news articles. It contributes to enhancing the organization and accessibility of information for Kannada - and Telugu-speaking communities, thereby advancing research in low- resource language processing.

关键词： Measurement Deep learning Standards organizations Pressing Organizations Feature extraction Natural language processing Data models Data mining Sports

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