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Development of an Adaptive Smart Toy with Emotional Feedback 3

Development of an Adaptive Smart Toy with Emotional Feedback

引用

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

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

ISBN: (纸本)9798331527549

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. © 2025 IEEE.

关键词： Intelligent robots

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Landslide Detection in Satellite Images using InceptionU-Net and Convolutional Block Attention Module 3

Landslide Detection in Satellite Images using InceptionU-Net...

引用

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

作者： Nigelesh, T.M. Shruthik, V. Sai Reddy, Valluru Sateesh Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In the contemporary world, Landslides pose huge risks to communities and infrastructure globally. One of the reasons it occurs is due to slope movement caused by urbanization and climate change in recent years. Landslide Detection Systems (LDS) are an evolving technology that is very crucial for handling the impacts of these natural disasters. It is essential for detecting and monitoring landslide occurrences across diverse terrains. This paper aims to propose one such automated Landslide Detection System based on the novel idea of integrating InceptionU-Net and Convolutional Block attention module that can efficiently detect landslides in satellite imagery using a benchmark dataset called LandSlide4Sense. The motive is to determine the most effective methods by training them on huge amounts of data which enhances accuracy and ensure quick detection and response. Deep Learning techniques like U-Net, ResUNet, U-Net++, InceptionU-Net, Inception, DenseUNet, and U2Net are utilized and an additional contribution is made using attention mechanisms on these models. After testing these models on the test data and comparing the models using metrices such as Loss, Accuracy, F-score, IOU, Precision, and Recall. With a particular emphasis on F-score and IOU, it was found that the Inception-UNet model integrated with attention mechanisms proves to perform the best with the highest Fscore of 72.8% and IOU of around 60%. Through this research, the advancements in DL-based semantic segmentation for satellite imagery analysis are highlighted. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Semantic Segmentation

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Efficient Quality Controllable Neural Image Compression based on QD-Model

Efficient Quality Controllable Neural Image Compression base...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shaokang Xiang, Guoqing Xu, Jinchang Zhang, Shanghang Xie, Xiaodong School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (纸本)9798350368741

Neural image compression has achieved significant advancements, consistently outperforming traditional codecs in terms of performance. However, research on quality control algorithms for neural image compression is still lacking. In this paper, we propose a framework designed to control the quality of compressed images through a one-pass pre-analysis. First, we construct a foundational relationship between the quantization factor and compression distortion, utilizing variable rate neural image compression as the basis for quality control. Second, we introduce the image Content-Compression features-based Distortion Estimation Network (C2DEN) to efficiently fit the sample-adaptive Quantization-Distortion (QD) model. Leveraging the QD model, we convert the target quality into a quantization factor to control the compression model, enabling quality-controllable compression of samples. Experimental results show that the average quality errors on four different datasets are only 0.89%, 1.79%, 1.73%, and 1.61%. Compared with existing control methods, our method reduces the algorithm time complexity by 98.58%, 98.52%, 98.85%, and 98.50% while ensuring accuracy, which further demonstrates the superiority of our method. © 2025 IEEE.

关键词： linear model Neural image compression quality control quantization factor

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Prediction of Cardiovascular State Using Baroreflex Sensitivity in Type 2 Diabetic Patients

Prediction of Cardiovascular State Using Baroreflex Sensitiv...

引用

Congress on Smart computing Technologies, CSCT 2023

作者： George, Meenu Ann Pallavi, M.S. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysore Campus Mysore India

ISBN: (纸本)9789819780952

Cardiovascular disease plays a crucial role in the morbidity and mortality of type 2 diabetic patients. These individuals often fight with regulating their autonomic and cardiovascular systems, mainly elevating the risk of cardiovascular incidents. Considering the various complications connected to diabetes, cardiovascular autonomic neuropathy (CAN) stands out as one of the main yet poorly understood outcomes. Most research has utilized the baroreflex sensitivity index as a parameter for prediction using statistical methods. This research aims to predict the probability of autonomic dysfunction by discovering the connection between cardiovascular autonomic neuropathy and diabetes mellitus, using the baroreflex sensitivity index obtained from heart rate and blood pressure (BP) data variability in diabetic patients. With deep learning, machine learning, and considering various other factors, the prediction efficiently found out the probability of cardiovascular disease appearance in type 2 diabetic patients. The research outcome gave logistic regression and SVM accuracies of 85% and 90%, respectively, in predicting cardiovascular disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Explainable Machine Learning Approach for Ventriculomegaly Classification in Brain MRI Images 6

Explainable Machine Learning Approach for Ventriculomegaly C...

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6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Gopikrishna, K. Niranjan, N.R. Maurya, Siddharth Uday Krishnan, V.G. Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Ventriculomegaly (VM) is one of the most common brain abnormalities that may cause risks such as developmental delays in children. Automatic classification of VM cases helps physicians in delivering accurate diagnoses and offering evidence-based recommendations. This study introduces an explainable machine learning (ML) framework for classifying MRI images into VM and non-VM *** augment a publicly available dataset of normal transventricular MRI images to generate VM cases and extract the key features. These extracted features are trained using different ML models among which Random Forest achieved the highest accuracy. To ensure trust and transparency in the diagnostic tool, we employ explainable AI models, LIME and SHAP, to interpret the ML predictions and highlight the importance of each feature. The results show that the ventricular area is the most significant factor for accurate classification. This interpretability is essential for earning the trust of medical professionals and patients for a reliable and transparent diagnostic aid. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Electroencephalography

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Effective Complaint Detection in Financial Services through Complaint, Severity, Emotion and Sentiment Analysis 3

Effective Complaint Detection in Financial Services through ...

引用

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

作者： Athira Adith, M. Gupta, Deepa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Sentiment analysis plays a crucial role in understanding customer feedback and opinions within the financial domain. This research addresses the problem of sentiment-aware complaint identification using the FINancial Complaint CORpus (FINCORP) dataset, which provides annotated complaints along with sentiment, emotion and severity labels. The dataset encompasses various sentiment classes including Negative, Neutral, and Positive, enabling a wide understanding of customer sentiment towards financial products and services. Leveraging natural language processing (NLP) techniques such as text classification and sentiment analysis, this study aims to develop models capable of accurately detecting and categorizing sentiment, emotion of customers and the severity level of financial complaints. Effectively analyzing these factors allows financial institutions to gain valuable insights into customer satisfaction levels, pinpointing areas for improvement and enhance both customer experience and service quality. This research contributes to the advancement of sentiment analysis methodologies in the financial domain, facilitating better decision-making and customer relationship management strategies. DistilBERT, Glove and RoBERTa has outperformed in classifying the sentiment, emotion and severity of complaints, achieving F1-scores of 0.71, 0.74, and 0.86, respectively using logistic regression and Bi-LSTM classifiers. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Emotion Recognition

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Speech Emotion Recognition Using Machine Learning 5th

Speech Emotion Recognition Using Machine Learning

引用

5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Veeramaneni, Asritha Samitha, V. Charitha, Talluri Shriya, Sagi Singh, Tripty Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

Speech emotion recognition (SER) is a technique for accurately determining a person’s emotion from their speech. Enhancing communication between people and machines. Even though emotions are arbitrary, it can be hard to annotate audio and it’s still hard to gauge someone’s emotional state. It now plays a crucial part in technological research as new technologies for human machine or medical applications are developed. It is obvious that surprise and rage feelings are more accurately predicted by our model than other emotions, It is understandable given how significantly the pitch, speed, and other qualities of the audio files for these emotions differ from those of other audio files. The system can capture and analyze various acoustic elements contained in voice signals using the SVM-based architecture and enabling the identification and categorization of various moods. Mel frequency cepstral coefficients (MFCCs), pitch, energy, and spectral features are some of these features, and they all work together to reveal important details about the emotional content that is present in speech. The deployed models attain a remarkable accuracy of 80% in identifying various emotional states from speech data after extensive training and optimization. This level of accuracy shows how well the suggested method works in properly classifying emotions based on auditory characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and XceptionNet Fusion Network 5th

From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and ...

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5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Abhinandhan, S. Sreedevi, A.G. Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819773701

The growth of deepfakes, or hyper-realistic synthetic media, demands effective detection algorithms to ensure the validity of visual information. This study presents a unique deepfake detection model based on a hybrid architecture that takes advantage of the characteristics of both Meso-5 and XceptionNet. Meso-5, which is well-known for its capacity to efficiently capture mesoscopic picture characteristics, is carefully paired with Xception-Net’s outstanding feature extraction capabilities. This novel strategy tries to achieve higher deepfake detection accuracy than previous approaches. We assess the proposed model’s performance using the publicly available Deepfake Detection Challenge (DFDC) dataset, which was published by Facebook in 2018. Our results show that this hybrid technique works well, with an impressive accuracy of 81.24% in detecting deepfakes. These findings demonstrate our model’s ability to mitigate the deepfake danger and build a more dependable future for visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deepfake detection DFDC dataset Hybrid architecture Hyper-realistic synthetic visuals Image detection Meso-5 Visual threat XceptionNet

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Enhanced Small Object Detection in Remotely Sensed Images using Hybrid ResNet50 and Feature Pyramid Network 3

Enhanced Small Object Detection in Remotely Sensed Images us...

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

作者： Vishal, K.S. Srinidhi, Srivageesh K. Shashank, U Someswara Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Developing automated systems for detecting small objects in aerial images is a challenging task due to the complexity of the scenes and the minute size of the objects. The problem is critical for applications in surveillance, tracking movements on sea and land borders, wildlife monitoring, disaster management and urban planning like infrastructure analysis, traffic management, green space management, etc. However this automated detection is hindered due to the tiny-sized objects, occlusions, camouflages, noise clutters, minimal pixel information, etc. To address these challenges, the work proposes a deep learning approach utilizing ResNet50 integrated with a Feature Pyramid Network (FPN). The main goal of this approach is to improve detection accuracy and robustness for small objects amidst complex aerial scenes. The work presents a comprehensive analysis covering data preprocessing, model architecture, training, and performance evaluation. Notably, the ResNet50-FPN model exhibits superior performance with an accuracy, precision, recall, and F1-score of 84.12%, 84.52%, 85.69%, and 85.10% respectively. Using the SODA-A dataset tailored for small object detection, we provide detailed insights into the implementation and comparative analysis of this model, highlighting its effectiveness in small object detection tasks and illustrating its practical utility in diverse real-world scenarios. © 2025 The Author(s).

关键词： Deep learning

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ArtLens: User-Driven Customization in AI-Powered Art Generation with Blip2 and Stable Diffusion 6

ArtLens: User-Driven Customization in AI-Powered Art Generat...

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6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Ranjit, Anargha Gaadha, V.S. Sreerag, Aswin Bonthu, Yasaswini Geetha, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

AI is changing the interpretation and teaching of art in the digital humanities with models like Blip2 and Stable Diffusion. This paper evaluates these models but particularly looks at how Blip2 can integrate visual and textual data to caption artworks, classify styles, interpret themes, and conduct contextual analysis, while Stable Diffusion can generate high-resolution images from literary prompts. The proposed method involves the use of Blip2 in creating descriptive captions for artworks and then modified with zero-shot prompting based on specific user demands. This updated text combined with the original input is then fed into the Stable Diffusion model to create new visuals reflecting the changed aspects. This method permits greater flexibility and user-driven customisation in creative image production. This work is a demonstration of the use of AI in interpretation with dynamic and interactive art and its ability to provide user-oriented, more subtle and personalized outputs. The embedding of user-specific adjustments into the workflow has enhanced the creative expression and adaptability of the generated content by AI, offering new avenues of applications in digital humanities, art education, and curatorial practices. This study marks the revolutionary potential of AI in most sectors, reflecting how it may inspire new involvement with art and culture. © 2025 The Author(s). Published by Elsevier B.V.

关键词： User centered design

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