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Enhanced Alzheimer's Disease Classification: A Stacked Model Fusion with Brain MRI Imaging 2

Enhanced Alzheimer's Disease Classification: A Stacked Model...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Chaitanya, Tekumudi Vivek Sai Surya Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

Alzheimer's disease (AD) is still a major global health issue that requires sophisticated staging and classification techniques. This paper introduces a novel approach to AD categorization, employing a stacked network architecture and feature extractions from InceptionV3, EfficientNetB0, and MobileNet models. The primary objective is to address the challenge of imbalanced datasets while minimizing the risk of overfitting. The proposed method utilizes brain MRI scans, extracting features and incorporating a Global Average Pooling (GAP) layer to improve the identification of critical features that correspond to AD phases. Combining the retrieved features results in a single set that helps with stage discrimination, followed by finishing via a SoftMax classifier. Model evaluation and validation are essential steps in determining the ideal model to minimize the risk of overfitting. A comprehensive evaluation is ensured by cross-validation (CV) using a 3-fold technique, which shows that the suggested Stacked Model has a better accuracy of 94.58%, closely followed by EfficientNetB0 at 90.70%. Both InceptionV3 and MobileNet exhibit competitive results, with accuracy rates of 89.46% and 87.60%, respectively. By presenting a strong approach to AD classification, this study advances the field of early detection and staging of AD by demonstrating the effectiveness of the Stacked network in identifying complex features that are indicative of different stages of AD progression within brain MRI datasets. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Exploring Compiler Optimization: A Survey of ML, DL and RL Techniques 8

Exploring Compiler Optimization: A Survey of ML, DL and RL T...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mithul, C. Abdulla, D. Mohammad Virinchi, M. Hari Sathvik, M. Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

The past few years, traditional compiler optimization methods have been found to be further enhanced by machine learning (ML), deep learning (DL) and reinforcement learning (RL). These differ from classical techniques that often use rule of thumb based decision making. Rather, ML/DL/RL based approaches provide a means for learning from data thus improving performance in different dimensions such as code generation, resource allocation and runtime. In this paper we give an overview of current research and methodologies utilizing ML, DL and RL for compiler optimization purposes. We analyze the major models in terms of their employed learning strategies and desired optimizations within a compiler framework. Moreover, we highlight some of the difficulties faced when these compilers are embedded with these learning models such as adaptability, generalization and overhead trade-offs. Additionally, our survey presents case studies demonstrating Quantitative improvements on well-known benchmarks mainly focusing on models' adaptability to different architectures and their role in supporting the decision-making process of compilers. We conclude outlining open research questions as well as possible future directions for further investigations into this emerging interdisciplinary field. © 2024 IEEE.

关键词： Program compilers

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Smart Refrigerator for Enhancing User Convenience and Food Management 2

Smart Refrigerator for Enhancing User Convenience and Food M...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Thangam, S. Gurupriya, M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350372892

Many individuals face challenges in effectively managing their household food storage due to limitations in traditional refrigerator technology. These limitations include challenges in monitoring the weights of food, receiving timely reminders for restocking, and incorporating location-based services. Existing solutions lack real-time inventory information, fail to provide timely restocking reminders, and lack integration with location-based services. The proposed Smart Refrigerator solution offers accurate inventory management, remote monitoring and control, location-based notifications, Peltier cooling system, and enhanced user experience through IoT technology and innovative sensor systems. A mobile application complements the system, providing users with remote monitoring, control, and locationbased notifications. Societal benefits include reduced food waste, improved food safety, enhanced user experience, and efficient household inventory management, contributing to sustainability and consumer well-being. The proposed Smart Refrigerator results demonstrated significant improvements in inventory management accuracy and user convenience with DS18B20 temperature sensors, load cells, and gas sensors, reducing food *** remote monitoring, control capabilities, and location-based notifications contributed to increased user satisfaction. © 2024 IEEE.

关键词： Location based services

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Adding Constructs to 'C' Through Flex and Bison 8

Adding Constructs to 'C' Through Flex and Bison

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Arroju, Nachiketh Vamsi, Nallabothula Reddy, N. Yeshwanth Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Developing a unique language for coding if and for loops involves using creatively designed markers such as 'ifff' and '4'. Meeting the three acceptance criteria requires coding loops using these markers, where 'ifff' signifies an if loop and '4' represents a for loop. Coding loops conventionally with '0' and '4' is considered unacceptable. Additionally, the language requires modifiers for loops and their respective lengths to be placed on one line. The language also encompasses formatting rules, such as combining multiple lowercase letters and ensuring that the letter 'c' appears three times for successful compilation. Repeated occurrence of certain letters is allowed and accepted within the language. Furthermore, specific responses like 'np' for 'no problem,' 'sry' for 'sorry,' and 'fq' for 'thank you' have been formulated based on input. © 2024 IEEE.

关键词： Codes (symbols)

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Emotion Recognition Using Meta-Learning based on Facial Expressions 5

Emotion Recognition Using Meta-Learning based on Facial Expr...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prashanth, V.J. Sri Naga Jathin, P. Gopinath, Suraj Udith, S. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Computation Offloading In Wireless-Powered Mobile-Edge computing Networks With Deep Reinforcement Learning For Online Optimization 4

Enhancing Computation Offloading In Wireless-Powered Mobile-...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, R. Prasanna Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sai, Gudivada Dhyana Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Edge computing at the mobile frontier, enhanced by the integration of wireless energy, represents a cutting-edge strategy to boost processing performance in networks with limited energy resources, such as wireless sensor networks and the Internet of Things (IoT). This study investigates a mobile edge computing (MEC) framework powered by wireless energy, employing a dual-mode offloading scheme. In this paradigm, tasks from wireless devices (WD) may either be processed on-device or entirely shifted to an MEC server. In this approach, tasks from a wireless device (WD) are either processed locally or completely transferred to an MEC server. The objective is to create an online algorithm that can adjust task offloading and wireless resource allocation adaptively according to the variable conditions of the wireless channel. Conventional numerical optimization methods are inadequate due to the swift variations within the channel's coherence time. Our aim is to develop an algorithm that operates online and can dynamically adjust both offloading and resource distribution in response to the fluctuating state of the wireless channel. Traditional numerical optimization approaches fall short because they cannot swiftly adapt to the rapid changes in the channel's coherence. The solution we propose is a framework based on Deep Reinforcement Learning for Online Offloading that utilizes deep neural networks to incrementally learn from offloading decisions, thereby circumventing the need for complex combinatorial optimization. This leads to a significant reduction in the computational load, particularly in expansive networks. We've further enhanced this system with a method that enables real-time modification of the DROO algorithm's parameters. Our experiments demonstrate that this novel algorithm nearly achieves optimal efficiency and significantly reduces computation times - by more than ten times relative to existing techniques. For example, in a network with 30 users, DROO achiev

关键词： access points component Mobile-edge computing radio-frequency reinforcement learning resource allocation Wireless Devices WPT

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Automated Classification and Size Estimation of Fetal Ventriculomegaly from MRI Images: A Comparative Study of Deep Learning Segmentation Approaches 5

Automated Classification and Size Estimation of Fetal Ventri...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 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

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning. © 2024 The Authors. Published by Elsevier B.V.

关键词： Clinical research

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Can Technical Festivals Help in Attaining Undergraduate engineering Programme Outcomes? 13

Can Technical Festivals Help in Attaining Undergraduate Engi...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dhanalakshmy, Dhanya M Murali, Ritwik Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376234

The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively. © 2024 IEEE.

关键词： Project management

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A Comparative Analysis of ML & DL Models for Fetal Health Classification Using Cardiotocographic Data

A Comparative Analysis of ML & DL Models for Fetal Health Cl...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kaushik, Tata Sri Vani, Tunuguntla Aasritha Vithyatharshana, N. Khare, Sangita Amrita Vishwa Vidyapeetam Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350361537

Traditional diagnostic methods in fetal health are frequently hindered by class imbalance and complex data, which puts early intervention and optimal perinatal outcomes at risk. This study fills this important gap by conducting a thorough comparative analysis of several ML and DL models that have been effective in fetal health research. This project goes beyond the obvious applications and investigates how model performance is affected by Principal Component Analysis (PCA) and Chi-Square for feature extraction and feature selection respectively and Conditional Generative Adversarial Networks (CGANs) for data augmentation. This project seeks to determine the best model or ensemble for dependable and accurate fetal health prediction by methodically assessing model performance across all its forms, CGAN-augmented versions, integrated CGAN - PCA and CGAN - Chi-Square configurations. This will provide medical professionals with a data-driven tool for early diagnosis and well-informed clinical decisions. This thorough study has the potential to greatly enhance perinatal outcomes and improve the health of moms and their unborn children. It is positioned to transform prenatal care. In addition to identifying the best model, this comparative analysis lays the foundation for future research examining the integration of explainable AI techniques to gain deeper insights into fetal health dynamics and to understand the internal decision-making processes of the model. © 2024 IEEE.

关键词： Generative adversarial networks

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Exploring the Essence of Prompt Tone: A Comparative Study of Large Language Model Security 3

Exploring the Essence of Prompt Tone: A Comparative Study of...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Nair, Aiswarya Vithayathil, Jose Nambiar, Sandeep Prem Snehasree, S. Murali, Shalu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369083

Chatbots powered by Large Language Model(LLM) can be manipulated by malicious prompts, generating harmful content and biased responses which would raise security concerns. Growing dependence on chatbots demands robust security for ethical development and user trust, which makes the work relevant in today's world. The motivation behind the work is to let the user have a safe experience with no negative responses being displayed while using the chatbot, which paved the way to arrive at the goal of developing a security filter that could be integrated into any LLM feature integrated application to mitigate the risk of having security vulnerabilities like prompt injection and jailbreaking, which could be achieved by converting malicious prompt into safer prompts by the method of eliminating negative sentiment phrases. The work focuses on building and implementing the security filters to popular in-production LLMs like Large Language Model Meta AI-2 (LLaMA2) and Generative Pre-trained Transformer - 3.5 turbo (GPT-3.5) to see how they handle against prompt injection and jailbreaking before and after the security filter being integrated. A large database of 200,000 prompts has been collected and preprocessed to train on a machine learning model using binary classification algorithm having 99.7% accuracy for classification of prompts into malicious or non-malicious and further checks are being done by breaking the prompt into smaller phrases and individually analyzing their compound sentiment score using Natural Language Toolkit (NLTK) Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm to detect and drop the negative sentiment phrases for the modification of the user prompt to eliminate the possibility of malicious prompt being passed to LLM. It is difficult to determine the sentiment of prompts in a detailed way and convert it into an efficient design that will perform well with models. Once this hurdle is overcome, chatbots will become even more reliable,

关键词： Economic and social effects

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