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Cognitive Disorder Detection in Adolescents using Mental Health Data 2

Cognitive Disorder Detection in Adolescents using Mental Hea...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Gowtham, R. Ramaraj, Vignesh Dayanand, V. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350354461

Cognitive disorders among adolescents pose significant challenges to their overall well-being and prospects. Timely detection and intervention are crucial to mitigating the potential long-term impacts of these disorders. This research aims to develop a framework that can detect multiple cognitive disorders in adolescents by first collecting subjects' responses to various survey questions, which are derived from standard questionnaires used to assess the brain's functioning, then utilizing the Conditional Tabular Generative Adversarial Network (CTGAN) for treating the imbalance in the dataset, and finally using the TabNet deep neural network for classifying disorders. The proposed framework outperforms traditional machine learning (ML) models and is a promising tool for the online detection of cognitive disorders in adolescents. © 2024 IEEE.

关键词： Adversarial machine learning

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos 15

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, V. V. V. Bhagya Bharathi Mohan, G. Prasanna Kumar, R. Rithani, M. Pallavi, Gundala Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn't much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain. © 2024

关键词： AMOSI Attention CNN LSTM MultiModal

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Modeling Plants vs Zombies Gameplay using Finite automata 3

Modeling Plants vs Zombies Gameplay using Finite automata

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Archana Reddy, P. Divya Jyothi, G. Varshita, Velumury Akshitha, Chennupati Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Playing video games on personal computers is known as PC gaming. PC games are available in a variety of genres, offer both single-player and multiplayer options, and have spawned an international e-sports industry. Modding communities are active, digital distribution systems are essential, and hardware can be customized. Future developments in gaming, such as virtual reality, cloud gaming, and artificial intelligence, portend more innovation in the industry. The game, Plants vs Zombies is represented by Finite Automata and it is ruled by DFAs and NFAs which describe the actions and behaviours of both the plants and the zombies and the result of their actions. In the proposed work you can notice how the NFA will be converted to DFA and vice versa due to its handy functions. Overall, "Plants vs Zombies"is a fun and interactive way for players to explore computational thinking while learning about the complex ideas of NFA and DFA in Automata Theory. The advantages are Synchronize State Management, Consistent State Transitions, Easier Development, and Unambiguous Animation Staging. © 2024 IEEE.

关键词： Virtual reality

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Inference of Missing Attributes in Multilevel Complex Network 5

Inference of Missing Attributes in Multilevel Complex Networ...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nettem, Nikhil Chowdary Nukala, Aashish Gopal Kaushal, Siruguppa Vishnu Sai Deepthi, L.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

Social networks exhibit a hierarchical structure where users are grouped at multiple levels based on communities, interests, or behaviors. The attributes associated with users provide additional information for various social network analysis tasks. However, the attributes may be missing for multiple reasons: incomplete data collection, or privacy concerns. Inferring missing attribute values within a hierarchical social network poses a challenge for accurate analysis and modeling. The state-of-the-art algorithm infers the attributes in a single level and less focus is given to infer the attributes at multiple levels. In this paper, the missing attributes are inferred by employing hierarchical clustering followed by CNN and Hierarchical Graph Neural Network. Our method's accuracy of attribute inference is compared with traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The results demonstrate that the hierarchical clustering with the CNN approach consistently outperforms traditional machine learning models in terms of attribute inference accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Hand-Written Bramhi Script Recognition using Deep Learning

Hand-Written Bramhi Script Recognition using Deep Learning

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Swaroop, Bandi Vishnu Prasad, Angala Hari Reddy, Busa Thanuj Sathwik Vardhan, Puvvadi Harsha Singh, Rimjhim Padam Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350374933

This work involves the classification of handwritten script using deep learning with a special interest in the comparison of different pre- trained models. As one of the oldest writing systems in the world, derived from the Dravidian and southeast Semitic scripts that were developed 4,500 years ago in India and Central America, Brahmi script also has variability due to the hand-written accented forms. To address these obstacles, different pre-trained models viz., Vision Transformer (ViT), MobileNetV2, VGG16, ResNet50, DenseNet, and EfficientNet with the CNN are implemented and compared. The result of the implementation infers that all of these pre- trained models yield a higher accuracy than the custom CNN traditional CNN approach, with a peak improvement in performance shown by MobileNetV2. This lays the ground for future utilization of deep learning architectures which have the capability of preserving and understanding handwritten symbolic scripts. © 2024 IEEE.

关键词： Deep learning

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Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques 5

Adaptive Real-Time Malware Detection for IoT Traffic Streams...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bharani, D. Lakshmi Priya, V. Saravanan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

As IoT expands dynamically, cybersecurity threats are evolving with time;thus, classical malware detection systems are no more appropriate, due to the phenomenon of concept drift and the necessity of handling large amount of data while detecting malware. Concept drift is used to refer to the statistical properties of malware that change over time as the malware behavior changes. We henceforth propose a real-time system to detect malware within a large-scale network traffic streams in the presence of concept drift. The system is capable of processing continuous large volumes of IoT traffic in real-time as it is implemented using Apache Spark and Apache Kafka. The proposed system also compares the performance of six different concept drift detection techniques: KS test, CUSUM, ADWIN, KSWIN, DDM, Page Hinkley on NSL-KDD and IoT -23 datasets. The comparison results show that KS test correctly identified concept drift in both the datasets. The proposed system makes an IoT environment with better malware detection capabilities by integrating the adaptive mechanism in real-time data processing tools apart from achieving high performance and scalability. © 2024 IEEE.

关键词： Data integration

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MediLocker: Centralized Electronic Health Records System 2

MediLocker: Centralized Electronic Health Records System

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Harini, N. Karishma, R.S. Pranav, M. Duvvuri, Ananya Akhil, Marrivada Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The transition from traditional paper-based patient records to advanced digital formats has not been without its challenges. While Electronic Health Records (EHRs) offer instantaneous access to detailed patient health information, there are drawbacks too. One significant drawback that was identified from the literature survey was the lack of interoperability and uniformity in patients' medical information across different healthcare facilities. This fragmentation hampers communication and collaboration among healthcare providers, leading to inefficiencies and gaps in patient care. Additionally, concerns about the security and privacy of patient data in EHRs persist, with instances of data breaches and unauthorized access raising alarm within the healthcare community. In response to these challenges, this paper proposes the development of a secure, privacy-ensuring Medilocker system aimed at investigating electronic health management systems comprehensively by assessing the status of EHR systems and efficiently managing electronic health records while safeguarding patient data. The implementation of role-based access control, coupled with data anonymization for preserving confidentiality, results in the development of a cohesive solution that facilitates the systemization, integration, and fortification of the healthcare system, while ensuring prompt access to information in compliance with privacy and security standards. © 2024 IEEE.

关键词： Authentication

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Machine Learning Algorithm for Maternal Health Risk Classification with SMOTE and Explainable AI 9

Machine Learning Algorithm for Maternal Health Risk Classifi...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Maheswari, B. Uma Dixit, Aniket Karn, Alok Kumar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Targeted obstetric care refers to being specific with the caretaking of a pregnant woman based on her symptoms and risk levels. It is a crucial task because pregnancy-related complications can be life-threatening. Pregnant women's body undergo a variety of changes, which may be mental, emotional, or physical. It is possible to analyze these changes to assess the health complications. These include blood pressure and blood sugar readings, which can be examined to determine a pregnant woman's health risk level. Machine Learning (ML) algorithms are employed to determine maternity health risks based on a woman's health statistics. In this work, several ML algorithms, have been applied to the maternal health risk dataset to predict the mother's risk level. Moreover, parameterized tuning has been applied to improve the performances of these conventional algorithms. The performance of these algorithms is studied before and after applying the Synthetic Minority Over-sampling Technique (SMOTE) technique to balance the data. The results exhibit that after SMOTE enhancement, the XGBoost upon hyperparameter tuning gives an accuracy of 88.89% in predicting mothers' risk level. Hence, XGBoost can be deployed in the field of providing targeted obstetric care by accurately predicting health risk levels. This work also embeds the Explainable AI (XAI) using SHapley Additive exPlanations (SHAP) and LIME (Local Interpretable Model-agnostic Explanations) to provide trust in the ML model to gynecologists. Various plots are generated using LIME, SHAP, and ELI5 to showcase the ML model's interpretability on global and local levels. © 2024 IEEE.

关键词： Health risks

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Advanced Knee Osteoarthritis Detection using Deep Learning 9

Advanced Knee Osteoarthritis Detection using Deep Learning

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Krishna, Chetlapalli Amritha Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350394474

This present research investigates the use of machine learning and deep learning models for the detection of knee osteoarthritis using the X-Ray images in depth. knee osteoarthritis is one of the most common joint disorders which affects numerous percentages of people across the world. And its detection of it at an early stage is very critical for effective management. This paper presents valuable dataset of X-ray images with a dual focus on detection of knee joint and KellgrenLawrence grading [kl]. The methodology which is proposed in this paper includes basic steps such as data loading and preprocessing. It also includes feature extraction and utilization of models of both machine and deep learning. In the phase of machine learning models which are used are Decision tree, KNN, MLP, Random Forest and ensemble methods. While in deep learning models used for evaluation are Nasnetmobile, VGG19, Darknet53, Darknet19 and Googlenet. The output of all the above-mentioned models are compared and evaluated based on four most important metrics of performance. The final output of the research shows that Random Forest and the Ensemble methods have outperformed in the phase of machine learning and when it comes to deep learning Googlenet and Darknet53 have done well in comparison. This study helps in the early detection of the knee osteoarthritis and also helps in management of it by giving valuable insights to medical practitioners and researchers. © 2024 IEEE.

关键词： Grading

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License Plate Recognition-Based Automatic Gate Opening System

License Plate Recognition-Based Automatic Gate Opening Syste...

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

作者： Revanth, Yellanki Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350382693

This study proposes an automatic electronic gate system that uses three main modules for authenticity confirmation: vehicle make and model, validation of license plate, and facial recognition. When a vehicle arrives at a gate or entrance, the system detects the vehicle, recognizes the license plate, and make and model, cross-references it with the database, and validates the driver's face against stored data. When there is a match, the Raspberry Pi signals the servo motor to open the gate;if not, an alert system is triggered. The proposed system is intended to improve security and convenience while decreasing the need for traditional access control measures such as stickers or security guards. Experimental results show that the system is capable of successfully identifying vehicles and number plate readings, with high precision and recall. The integration of several deep learning models, such as YOLO v7, YOLO v7-Tiny, and ResNet, contributes to the overall efficiency and performance of the system. © 2024 IEEE.

关键词： Deep learning

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