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Protein Sequence Classification Using Bidirectional Encoder Representations from Transformers (BERT) Approach

SN computer science 2023年第5期4卷 481页

作者： Balamurugan, R. Mohite, Saurabh Raja, S.P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

Proteins play a vital role by booming out a number of activities within an organism to withstand its life. The field of Natural Language Processing has successfully adapted deep learning to get a better insight into the semantic nature of languages. In this paper, we propose semantic approaches based on deep learning to work with protein amino acid sequences and compare the performances of these approaches with traditional classifiers to predict their respective families. The Bidirectional Encoder Representations from Transformers (BERT) approach was tested over 103 protein families from UniProt consortium database. The results show the average prediction accuracy to 99.02%, testing accuracy to 97.70%, validation accuracy to 97.69%, Normalized Mutual Information (NMI) score on overall data to 98.45, on test data to 96.99, on validation data 96.93 with high weighted average F1 scores of 99.02 on overall data, 97.72 on test data and 97.70 on validation data, and high macro average F1 scores of 99.00 on overall data, 98.00 on test data and 98.00 on validation data. From the results, it is justified that our proposed approach is outperforming well when compared to the existing approaches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Document embedding Logistic Regression Natural Language Processing Protein family

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Secure and Efficient Intrusion Detection System in Medical Cyber-Physical Systems 3

Secure and Efficient Intrusion Detection System in Medical C...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Divina, T.P. Kathrine, Jaspher W. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9798331532420

As technology advances toward Cyber-Physical Systems (CPS), there is a significant growth in the development of Medical Cyber-Physical Systems (MCPS). In this 21st century, the cyber-attacks eventually increase as technology increases. Cyberattacks in medical cyber-physical systems cause a great disruption in the patient’s life as well as in the patient's information. A novel Intrusion Detection System (IDS) has been created using Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs manage relational dependencies by analyzing the behavior of nodes, which represent the data. On the other hand, Recurrent Neural Networks handle temporal dependencies and sequential data like sensor reading, communication logs, and timestamped series. ICU healthcare security dataset is used in this study to train the model. This dataset is constructed as a graph, then the graph has been fed to the Graph Neural Networks and Recurrent Neural Networks (GNN+RNN) framework. The proposed model has demonstrated a strong performance with 99% detection accuracy, 100% precision, 98% recall, and 99.13% f1-score. © 2024 IEEE.

关键词： Network intrusion

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Leveraging Sentiment Analysis and Emotion Detection for Mental Health Insights

Leveraging Sentiment Analysis and Emotion Detection for Ment...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Jose, Joysh Jayaron Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391213

This paper aims to establish a comprehensive analysis of mental health patient reviews using various NLP models, to derive insights and propose improvements that will lead to the enhancement of various mental health facilities. These NLP techniques were further used to categorize key themes such as doctor behaviour, communication and cost. A thorough correlation analysis was also conducted, understanding the relationship between various themes and how their frequency affects the overall patient satisfaction. The findings indicate that attentive listening, respectful staff behavior, and affordable services are critical to improving patient satisfaction. While speaking of mental health is still a taboo in various places, it is important to understand the frequency of the rising mental health related cases such as depression, anxiety, stress and social stigma associated with it. It is indeed much more important to provide better resources and lend understanding hand to the unaddressed mental health problems that are prevalent in various countries and places. © 2024 IEEE.

关键词： Electronic health record

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A Study on Lightweight Algorithms for Detecting Helmet-Wearing in Electric Scooters 6

A Study on Lightweight Algorithms for Detecting Helmet-Weari...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Hu, Po Wang, Haihui Wang, Yuzhuo Wang, Chenghui School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In urban transportation, electric bicycles serve as a primary mode of non-motorized transport, leading to increasing concerns about traffic safety, especially the helmet-wearing habits of riders. Helmets are crucial for rider safety, effectively reducing injuries in traffic accidents. Existing helmet detection algorithms struggle with small objects, occluded targets, and complex environments. To address these issues, we propose a helmet-wearing detection algorithm based on YOLOv8n. We enhance the model's feature extraction by incorporating the BiFormer attention mechanism before the first detection head, P3, improving the detection of small objects and overall performance. Additionally, in the Neck part of YOLOv8n, we replace traditional convolution modules with the lightweight G-GSConv module for efficient feature aggregation without sacrificing performance. Building on G-GSConv, we introduce the G-Ghost-Bottleneck layer, using a one-shot aggregation method to design the cross-level part network (GSCSP) module, G-GhostCSP-Bottleneck, replacing the original three CSP modules to reduce model parameters. We further integrate the BSAM attention mechanism based on the Transformer architecture to enhance the detection of dense small objects, thereby improving model accuracy. We also refine the loss function using the MDPIOU method to enhance detection flexibility and robustness. Experimental results on the SHWD dataset show that our improved model achieves an average precision of 88.9%, a 3.6% improvement over the original model. The proposed algorithm effectively improves helmet-wearing detection accuracy in complex traffic scenarios, demonstrating its potential for enhancing urban traffic safety management. © 2024 IEEE.

关键词： Nonmotorized transportation

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Impact of Equivalence Assessment in the Education Sector using the XAI Model of Blockchain with ECTS 3

Impact of Equivalence Assessment in the Education Sector usi...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, Sumathy Surendran, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The procedure for obtaining an equivalency certificate for international educational recognition is typically complicated and opaque, and differs depending on the nation and system. To overcome these issues and empower students, this study suggests a revolutionary assessment tool that makes use of blockchain technology, chatbots, the European Credit Transfer and Accumulation System (ECTS), and Explainable Artificial Intelligence (XAI). Educational equivalency assessments frequently face difficulties and lack of openness in a variety of settings. The suggested solution uses blockchain for tamper-proof record keeping and secure data storage, based on the capabilities of each component. This improves the blockchain's ability to securely store application data and evaluation results, fostering immutability and trust. Using the distributed ledger feature of blockchain promotes fairness in evaluations by preventing tampering and guaranteeing data integrity. The blockchain ensures data security and privacy by encrypting and storing data. Discuss how XAI might explain AI-driven equivalence choices, promoting fairness and trust, by reviewing pertinent material in each domain. Chatbots can improve accessibility by streamlining data collection and assisting students along the way. Transparency and efficiency are provided via ECTS computations that integrate XAI and chatbots. Emphasizing the availability of multilingual support for international students, we also address issues such as data privacy and system adaption. The study recommends further research to assess the multifaceted method in practical contexts and improve the technology for moral and efficient application. In the end, both students and institutions will benefit from this, as it can empower individuals and promote international mobility of degree equivalization. © 2024 IEEE.

关键词： Blockchain

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Application of Species Distribution Modelling in Agriculture: A Review 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Agbezuge, Eric Yaw Balakrishnan, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Identification of the right abiotic factors for an ambient environment, soil, and water is critical for successful agricultural activities. However, these factors have been influenced by climate change, and other factors as such are no longer in their natural state of equilibrium. This has affected the ecosystem and has made previously suitable environments currently unsuitable for most organisms and crop production. To have production of healthy crops with high yield and quality, precise agricultural inputs are required, including the right environmental abiotic factors. This goal has been achieved to some extent via extensive studies carried out by various scholars to ascertain their impact and possible mitigating action. In this paper, we first investigated the factors that influence the environment adversely and the application of SDM as a solution depicted in other studies and further presented an overview of some of the SDM-based software packages and their applications, with much emphasis on agronomy. This review aims to provide insight into, how effective computer-based SDM has been or otherwise in resolving the challenges in the agriculture sector and the future direction for the application of SDM-based solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Climate change

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Application of a three-phase unified power quality conditioner in a microgrid

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IET Smart Grid 2024年第6期7卷 872-890页

作者： Wu, Tsung-Hsun Huang, Shu-Syuan Chen, Pei-Yin Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Power Electronics Industrial Technology Research Institute Tainan Taiwan

In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the power system. As an advanced power electronics device, the unified power quality conditioner (UPQC) can effectively address these problems. Therefore, the integration of UPQC into power distribution lines can enhance the stability and reliability of the power system, providing higher-quality power supply to meet the needs of both users and suppliers for PQ. The authors utilise the TI TMS320F28377D chip as the control core to construct a simple yet highly efficient UPQC system. Through simulation, the correctness of the voltage and current compensation algorithms has been verified, and the overall system feasibility has been validated using a 100-kVA series-shunt inverter. The experimental results show that the voltage compensation range of the UPQC is 380V ± 10%. During sudden current drops, the imbalance rate can be reduced from 85.5% to 1.32% after controlled compensation. During sudden current rises, the imbalance rate can be reduced from 13.1% to 1.56% after controlled compensation. These findings demonstrate the effectiveness of the UPQC system in improving PQ and system stability in power distribution networks. © 2024 The Author(s). IET Smart Grid published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Power quality

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Predictive Analytics for Power Transformers and Energy Consumption with Machine Learning 5

Predictive Analytics for Power Transformers and Energy Consu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Muthukumar, S. Swetha, S. Us, Sutharshanagayathri St. Joseph's Institute of Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331530013

The power-generating plants, along with their long transmission lines, transformers, and the network of distribution systems, result in quite a complex mesh required for the smooth delivery of electricity over long distances. Power transformers, which step up or lower voltages to facilitate effective transmission, result in failure due to wear and tear, causing disruptions in supply and huge losses in terms of economy. This project enhances grid reliability through the prediction of transformer failures and forecasting energy consumption. Historical maintenance data, operational parameters, sensor readings, and external factors in terms of weather conditions predict the health of a transformer as well as the forecast the energy demand. Machine learning models applied were Linear Regression for failure prediction, while the decision tree and Random Forest were applied for energy forecasting. The other feature which was selected on the Random Forest and the Decision Tree models shows the highest performance ever achieved and could definitely help to better improve the resilience of the grid. Even a 1% reduction in outages can save high amounts every year, making the true worth of precise predictive analytics in the power industry eminent. © 2024 IEEE.

关键词： Distribution transformers

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Supervised Deep Learning based Leaf Disease and Pest Detection using Image Processing 7

Supervised Deep Learning based Leaf Disease and Pest Detecti...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Isravel, Deva Priya Somasundaram, Kumaresan Jestin Josephraj, M. Christopher Paul, L. Johnson, J.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Sivaganga India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Kumbakonam India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Dharmapuri India

ISBN: (纸本)9798350397253

The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of Deep Learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. Real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection. © 2023 IEEE.

关键词： Image processing

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Data Leakage and Validation Bypass in SHACL 20

Data Leakage and Validation Bypass in SHACL

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Joint of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems, SEMANTiCS-PDWT 2024

作者： Dao, Davan Chiem Debruyne, Christophe Montefiore Institute of Electrical Engineering and Computer Science University of Liège Liège Belgium

This paper describes security vulnerabilities when using SHACL for data validation in knowledge graphs (KGs) and enhance security awareness in SHACL and KG *** employ a KG developed in a prior study in the social security domain to illustrate potential security *** vulnerability identified is SHACL's inability to distinguish user input from historical information in the KG, resulting in two potential exploits: validation bypass and data *** study improves our understanding of security risks in SHACL and KG technologies, providing insights in this underexplored area in literature. © 2024 Copyright for this paper by its authors.

关键词： Information leakage

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