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Interactive Code Parsing and Expression Tree Visualization

Interactive Code Parsing and Expression Tree Visualization

2024 International Conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Akathya, Ve.Ram Suresh, Yadukul Shivadutt, Sahen Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331543617

Binary information trees are key visualization and evaluation tools, particularly during the passing of algorithms and synthesizing designs. This paper is an interactive tool for producing binary information trees over time. This tool will allow the user to input variables, selected, truncating and then show the same as a binary tree. Using Network X and Matplotlib for graphing, this presents the user with an interactive pie chart to enable operator control and variable assignment. Dynamic information evaluation and substitution is supported;this allows for an immediate view of change when variables and functions are altered. It is aimed at user interaction with a didactic value, and with this in mind, it contributes to easier understanding of the fundamental issues of distributed algorithms, clustering, and information evaluation. Though derived from such existing tools as VMEXT and PAVT, it offers a simple, and thus very practical approach, becoming suitable for educational environments where a visualization shows better understanding of the abstract computer concepts. © 2024 IEEE.

关键词： Trees (mathematics)

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Leveraging Confidence Analysis and Classification Using BiLSTM for Verbal Evaluations 5

Leveraging Confidence Analysis and Classification Using BiLS...

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

作者： Katariya, Priyanka Prakash Shree S, Shreya Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Understanding the emotion and confidence level of a respondent's answer helps formulate the response as well as the next question in an oral examination or viva. Automatic detection of these parameters is essential to develop appropriate next statements, provided the relevant tone of a Viva chatbot curated for assistance and analysis of human speech in interview preparations. Supporting this motivation, this work analyzes the effectiveness of using various spectral attributes such as Mel Frequency Cepstral Coefficients (MFCC), Chroma, Mel Spectrogram & Spectral Contrast alongside CNN, RNN, and Bi-LSTM networks to identify confidence & emotion in speech signals. It is observed that MFCC features along with a BiLSTM network provide the best results with 63.76% test accuracy and 58.82% train accuracy by considering minimal variance. © 2024 IEEE.

关键词： Speech analysis

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Analysis and Implementation of Crypto currency Arbitrage Strategies: A Data Structures Approach 3

Analysis and Implementation of Crypto currency Arbitrage Str...

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

作者： Madhuja, C. Yadav, Sneha Sidhu, Samreen Aiswariya Milan, K. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350369083

An arbitrage finder is a tool that helps users to earn profit from price differences in cryptocurrency trading across different markets or platforms. It offers opportunities to purchase an asset at a reduced cost in one location and resell it for a greater cost in another, enabling you to earn a profit. This project "ArbitrageXplorer"is a website that aims to educate users about cryptocurrencies and the market. It offers resources to help traders make informed choices without risking real money. A user-friendly simulation interface is used to provide users with the necessary knowledge to comprehend the dangers and the functioning of markets. The arbitrage finder is one of the primary features of "ArbitrageXplorer". To assist users in making strategic trading decisions, the "ArbitrageXplorer"shows the highest and lowest prices of assets in various markets. Users can also build and maintain their profiles enabling them to customize their trade journey and monitor their progress over time. The proposed project also focuses on educating users to confidently manage their assets and stay informed about the state of the market with the help of tools for managing notifications and portfolios. Being a reliable companion for both new and experienced traders, "ArbitrageXplorer"helps users as they prepare themselves towards the path to financial success. © 2024 IEEE.

关键词： Profitability

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Unveiling Hidden Patterns: Clustering Algorithms on C Code embedding 9

Unveiling Hidden Patterns: Clustering Algorithms on C Code e...

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

作者： Spoorthi, M. Savant, Richa Vivek Seshadri, Samarth Narmada, Nakka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Automatic grading can help streamline assessment by using advanced algorithms to evaluate embedded C programs swiftly and objectively. This technology ensures standardized evaluations, providing quick and objective feedback on programming proficiency, enabling educators and employers to gauge participants' skills accurately. This paper proposes leveraging the application of machine learning clustering methods on embedding of C programs that have been examined by subject matter experts (SMEs) to uncover hidden patterns in the input vectors. A variety of clustering techniques are applied at the score level to capture the patterns in the data and analyze if they lie at par with the SMEs' evaluations. Machine learning models can at best capture clusters at the questions level, i.e., it can capture similarities amongst text, but cannot comprehend the logic behind it and classify the types of errors in code solutions. K-means clustering proves to provide the best compact clusters at a question level of the chosen dataset with biased data distribution in the markings. © 2024 IEEE.

关键词： Embeddings

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EOG and PPG Fusion for Subject Independent Multimodal Emotion Recognition: A Prototypical Networks Approach 2

EOG and PPG Fusion for Subject Independent Multimodal Emotio...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ramaswamy, Manju Priya Arthanarisamy Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

Multimodal emotion recognition is recognizing the emotion from more than one modality like facial expression, gestures, speech, text and physiological signals. The prototypical networks is a well-known few-shot distance based machine learning algorithm, in which data point is classified based on the proximity to the prototype. In this work, the prototypical network algorithm is modified for multimodal emotion recognition using EOG and PPG, in which Smooth L1 loss is explored for learning the embedding space. This study also explored correlation, Euclidean, squared Euclidean and chord distance as distance metrics to learn the embedding space. The proposed method obtained a prediction accuracy of 60.00% and 61.00% in arousal and valence dimensions respectively which is higher than the accuracy reported in the literature. © 2024 IEEE.

关键词： Photoplethysmography

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Advancing Marine Plastic Detection with CLAHE-Enhanced Underwater Images Using YOLOv8 Integrated Attention Mechanism 6th

Advancing Marine Plastic Detection with CLAHE-Enhanced Under...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Krishna, N. Hasmitha Kumar, R. Prasanna Department of Computer Science and Engineering—Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819734412

The accumulation of plastic garbage in aquatic ecosystems puts at risk aquatic habitats as well as the larger global ecosystem health, posing serious environmental and financial dangers. By introducing hazardous chemicals and upsetting fragile marine ecosystems, this pollution has an impact on human health and marine life via the food chain. The current approaches to identifying marine plastic are labor-intensive and ineffective in the face of the worldwide problem of aquatic garbage. The custom-trained YOLOv8 deep learning model is presented in this work, increasing the identification of marine and undersea trash. Furthermore, an Efficient Channel Attention (ECA) module is integrated with YOLOv8. Our proposed YOLOv8 model outperforms YOLOv7 and YOLOv6 in accuracy, precision, recall, and F1-score, even in the face of difficulties such as decreased visibility and warped object shapes. By tackling the serious environmental and financial issues raised by plastic pollution, deep learning demonstrates notable advancements in real-time detection. This research represents a significant advancement in automated, accurate, environmentally dangerous marine plastic identification. The results indicate that our proposed attention mechanism improved YOLOv8 model performed better than remaining state-of-the-art models with an F1-score of 97.8%, a precision of 98.12%, and an mAP score of 95.4%, highlighting its robustness in reliably identifying particular targets across a range of assessment metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marine pollution

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Revised IEEE 802.11 MAC protocol for an Energy Efficient and Optimized Wireless Communication in Wireless Sensor Networks

Revised IEEE 802.11 MAC protocol for an Energy Efficient and...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Rajesh, M. Sreevidya, B. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350372502

In the world of wireless communication, the Medium Access Control (MAC) protocol sports a pivotal task in managing access to the shared communication medium. This project proposes a change to the existing IEEE 802.11 MAC protocol by introducing a modification in the sensing process. The conventional algorithm incorporates Short Inter-Frame Space (SIFS), DIFS (Distributed Inter-Frame Space) and backoff periods to regulate channel access. The proposed modification seeks to experiment the protocol by eliminating SIFS, thereby focusing solely on backoff and DIFS. This will reduce the delay each node waits before transmitting the packet. Eventually the reduction in wait time, reduces the energy consumed thus optimized the energy usage of nodes. To evaluate the efficacy of the modified protocol, a wireless network simulation is conducted in NS2. The simulation provides insights into the behavior of the network under the altered MAC protocol, shedding light on its performance and potential advantages. © 2024 IEEE.

关键词： Sensor nodes

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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...

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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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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions

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Multimedia Tools and Applications 2024年 1-58页

作者： Abdullahi, Mohammed Oyelade, Olaide Nathaniel Kana, Armand Florentin Donfack Bagiwa, Mustapha Aminu Abdullahi, Fatimah Binta Junaidu, Sahalu Balarabe Iliyasu, Ibrahim Ore-ofe, Ajayi Chiroma, Haruna Department of Computer Science Ahmadu Bello University Zaria Zaria Nigeria Department of Mechanical Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria College of Computer Science and Engineering University of Hafr Al Batin Hafar Al-Batin31991 Saudi Arabia School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models. © The Author(s), under exclusive licence to

关键词： computer vision

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