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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Asmitha, M. Panda, Niharika Amrita School Of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350371567

Compiler design represents the intersection of art and science, where intricate algorithms and creative problem-solving converge to translate abstract human-readable code into machine-executable instructions. At the heart of this complex process lie the essential components of the scanner and parser, working in harmony to decode the syntax and semantics of programming languages. In this exploration of compiler design, embark on a journey into the realms of scanning and parsing, unraveling the intricacies that transform code into executable software. The scanner is the lexical analyzer, serves as the gate-keeper in the compiler's journey. Its main task is to meticulously break-down the source code into manageable units called as the tokens. From identifying keywords to recognizing variables and constants, the scanner lays the groundwork for the subsequent phases of the compilation process. In this study we are going to focus on how any model of scanners work based on given input. The parser, responsible for syntax analysis, ensures that these sequence of tokens adheres to the grammatical rules of the particular programming language which is being used. This study focus on how a token is treated in Bottom up and Top down like left recursion, left factoring, augmentation, parse tree, DFA etc. The choice of parsing algorithm, whether LL(1) A top down approach or SLR, LALR bottom up parser impacts the efficiency and accuracy of this process. This process follows checking first, follow, parse table, shift reduce conflicts the acceptance. Compiler design is more than a technical endeavor, it is a craft that demands precision, creativity, and a deep understanding of both programming languages and the intricacies of hardware architecture. The scanner and parser stand as gatekeepers at the entrance to a realm where human intent transforms into machine action, making compiler design a captivating field where innovation and standards converge. © 2024 IEEE.

关键词： Semantics

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Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm

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International Journal of Wireless and Mobile computing 2025年第2期28卷 135-148页

作者： Devi, P. Yamini Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India

The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： authentication blockchain drug traceability key generation medical supply chain

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Forecasting and Analysis of Transient Voltage with Random Forest Regressor 5

Forecasting and Analysis of Transient Voltage with Random Fo...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Thakur, Arnav Arunbalaji, C.G. Pati, Peeta Basa Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350361155

This research addresses the critical challenge of predicting transient voltage events in electrical appliances. Transient voltages are temporary deviations from normal voltage levels that can damage electrical appliances and cause power interruptions if not addressed preemptively. Currently, limitations exist in detecting and forecasting these events across various electrical systems. This study investigates the value of predictive strategies to minimize transient voltage risks. Utilizing simulated data from RLC circuits, we train multiple machine learning regressor models to estimate transient onset times and predict voltage at specific timestamps using time series analysis and lag-based forecasting. Our approach demonstrates promising accuracy and lower error rate as measured by key performance metrics, opening doors for preventive measure of appliances. These findings provide essential insights into data-driven forecasting of transient voltages, paving the way for the development of a more resilient and reliable electrical infrastructure. © 2024 IEEE.

关键词： Time series analysis

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Dual-Attention Protein Secondary Structure Prediction (DAPSS-Pred) 15

Dual-Attention Protein Secondary Structure Prediction (DAPSS...

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

作者： Pallavi, Gundala Prasanna Kumar, R. Oviya, I.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Protein secondary structure prediction is a critical task in bioinformatics, essential for understanding protein function and aiding drug discovery. Traditional methods have achieved significant milestones, but there remains a need for further accuracy and efficiency improvements. In this paper, we propose a novel approach that combines Bidirectional Long Short-Term Memory BiLSTM networks and Transformer models to enhance prediction accuracy. Our dual-model strategy leverages the sequential processing strengths of BiLSTM and the robust attention mechanisms of Transformers, enabling a more comprehensive analysis of protein sequences. Our extensive experiments demonstrate that this hybrid approach achieves superior performance, with Q3 accuracy reaching 92.03% and Q8 accuracy at 85.26%, significantly surpassing existing methods. This enhancement is ascribed to the capacity of BiLSTM to capture long-range dependencies and the Transformer's capability to focus on relevant parts of the sequence. The integration of these models provides a powerful tool for predicting protein secondary structures with high precision. The findings suggest that our approach not only advances the current state of PSSP but also offers valuable insights that can accelerate bioinformatics research and drug discovery processes. This work underscores the potential of combining deep learning models to tackle complex biological challenges more effectively. © 2024 IEEE.

关键词： Bioinformatics Deep Learning LSTM Protein Secondary Structure Transformer Models

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Advancing Personality Detection through Word Embedments and Deep Learning: An Examination Using the MBTI Dataset 6

Advancing Personality Detection through Word Embedments and ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Shanmukha, Aditya G Shamyuktha, R.S. Karan, S. Gupta, Deepa Palaniswamy, Suja Amrita School of Computing Bengaluru Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350381689

Personality detection and classification hold significant potential for enhancing decision-making processes and optimizing outcomes across various sectors. This interdisciplinary field finds applicative relevance in numerous domains such as business, healthcare, education, and personal development. In an effort to contribute to this burgeoning area of research, this paper introduces a comprehensive model specifically designed for personality detection. The model is rigorously tested on the Myers-Briggs Type Indicator (MBTI) dataset, a widely recognized framework in personality typology. The core of this proposed model lies in its innovative integration of four advanced word embeddings: Keras, Word2Vec, Glove, and FastText. These embeddings are coupled with cutting-edge deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BI-LSTM), a hybrid of CNN and LSTM, and Gated Recurrent Units (GRU). This amalgamation results in a suite of 18 distinct models, each offering unique insights into personality classification. The implementation of these models is meticulously undertaken, and the outcomes are documented in detail. The results of this research which is 82% of accuracy, not only demonstrate the efficacy of combining various word embeddings with deep learning techniques but also pave the way for future explorations in the field of personality detection and classification. © 2024 IEEE.

关键词： Convolutional neural networks

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EBSDNET - Endanger Bird Species Detection Net using ResNet50 2

EBSDNET - Endanger Bird Species Detection Net using ResNet50

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

作者： Dharshan, R Divya Diviya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372892

In India Numerous individuals enjoy bird watching, but identifying bird species often requires consulting reference books. However, many bird species are in the verge of extinction because of climatic change and environmental change. These Changes results in many problems like, forest degradation, urbanization and avian disease. It is a threat to biodiversity because the destruction of birds are happening in rapid phase so it must be safeguarded. The use of hidden camera in wildlife for monitoring these species is skyrocketed in todays world but manually examining and taking information from vast data gathered by hidden camera be tiresome and time consuming. So to overcome this we have proposed a ResNet-50 to detect and classify these endanger bird species using image processing. This model was trained using a dataset comprising 3520 images, each depicting one of 38 unique bird species. The images had a resolution of 224 by 224 pixels. The proposed model in this work attained an accuracy level of 98.64 %. Various performance measures also evaluated to test the efficacy of the proposed work. Comparison study with the existing work also reveals that the proposed methodology yields fine results. © 2024 IEEE.

关键词： Endanger Bird species Image processing pre-trained model Resnet 50

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Chatbot to chat with medical books using Retrieval-Augmented Generation Model 3

Chatbot to chat with medical books using Retrieval-Augmented...

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

作者： Bhavani Peri, Sai Deepa Santhanalakshmi, S. Radha, Radha Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350364569

This research paper presents a novel AI tool aimed at revolutionizing medical education by addressing the specific needs of medical students, fostering effective learning, and managing stress. The implementation leverages cutting-edge technology known as Large Language Models (LLMs), with a focus on utilizing ChatGPT4, Streamlit, and Trulens. The digital assistant serves as a friendly and accurate resource, responding promptly to complex medical queries, thereby optimizing study time. The study meticulously examines the balance between technical innovation and practical benefits, emphasizing the project's goal of not only creating a sophisticated tool but also providing tangible assistance to medical *** report outlines the successful development and deployment of the AI-powered study buddy, showcasing its potential to significantly improve learning environments, particularly in demanding fields such as medicine. Notably, the project empha-sizes the importance of groundedness in its chatbot, achieving an impressive accuracy rate of approximately 84% for a single textbook. The integration of Streamlit, ChatGPT4, and Trulens contributes to creating a seamless and user-friendly experience for medical *** research underscores the broader implications of AI in education, demonstrating how advancements in technology can positively impact learning outcomes. By reducing the time spent on information retrieval, the AI study buddy empowers medical students to concentrate more on core learning objectives, ultimately facilitating their journey to becoming proficient and successful medical practitioners. © 2024 IEEE.

关键词： Medical education

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Securing Smart Home IoT devices using Hyperledger Fabric 15

Securing Smart Home IoT devices using Hyperledger Fabric

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

作者： Pranesh, R. Lalith Guptha, B. Aswinkumar, K. Pramoth, B. Abirami, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid growth of Smart Home applications has broadened the scope for a wide range of security concerns. Blockchain technology, specifically Hyperledger Fabric, offers a promising solution. The proposed model explores its application, leveraging the Fablo toolkit to enhance the security of Smart Home IoT devices. The decentralized nature of Hyperledger Fabric, with features such as its consensus mechanism and chaincode, helps mitigate common security issues like unauthorized access and device tampering. The resultant tamperproof network, coupled with favorable metrics like latency and transaction throughput, underscores the robustness of the proposed model. Our model promises low latency and high transaction throughput. The metrics assist the model in securely and efficiently handling a large number of transactions without compromising on speed and security. © 2024 IEEE.

关键词： Smart homes

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News Topic Categorization on Telugu Dataset Using Deep Learning Models 15

News Topic Categorization on Telugu Dataset Using Deep Learn...

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

作者： Deepika, M. Sree Subramanyam, N. Sri Lekhya Pranav, P. Shiva Reddy, Pulaputhuru Subramanyam Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The digital age has ushered in an unprecedented era of data proliferation across various domains, with the news sector being no exception, accumulating vast amounts of information, often measured in petabytes. Manual classification of such voluminous data is impractical, particularly considering the complexity of languages like Telugu, belonging to the Dravidian family, renowned for its intricate morphological characteristics. Natural Language Processing (NLP) offers a promising avenue for tackling textual data, leveraging diverse word embedding techniques to enrich model representations. Notably, prior research in this domain has predominantly relied on count-based algorithms for word embeddings. However, in this investigation, we explore alternative approaches, specifically custom-built Deep Learning models integrated with word embeddings, to optimize the classification of newspaper articles. Through a series of methodical experiments, we endeavor to identify the most effective model architecture for this task, aiming to enhance the accuracy and efficiency of article categorization in Telugu. The proposed model has experimented with word embeddings such as FastText and GloVE with deep learning models like LSTM, Bi-LSTM, CNN. Among the models experimented, FastText in combination with CNN have reported the highest F1-Score of 0.93. © 2024 IEEE.

关键词： Categorization Deep Learning News data Word Embedding

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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