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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kumar, Amarjeet Mittal, Aanya Dixit, Abhishek Agrawal, Pooja School of Robotics Defence Institute of Advanced Technology Pune India Manipal University Jaipur Department of Computer Science Rajasthan India Defence Institute of Advanced Technology Department of Aerospace Pune India

ISBN: (纸本)9798350355611

This paper focuses on the safety of airport runways, which is crucial for aviation industries. Foreign Object Debris (FOD) i.e. objects like nails or bolts can cause significant harm to aircraft and potentially lead to catastrophic incidents and fatalities. This study delves into detecting objects on runways by using versions of the YOLO (You Only Look Once) model. Further, it also presents the differences in detecting objects and comparing the accuracy across a range of objects, paying attention to their ability to identify even the smallest things that have a potential danger. In order to identify the model variation and enable real-time runway surveillance, the proposed methodology considers the trade-off between detection speed and precision. The results illustrate the effectiveness of the latest YOLO model in object recognition over existing models, this can support the improvement of runway safety and the development of automated systems that monitor runway conditions. © 2025 IEEE.

关键词： Aircraft accidents

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Quantum routing with teleportation

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Physical Review Research 2024年第3期6卷 033313页

作者： Dhruv Devulapalli Eddie Schoute Aniruddha Bapat Andrew M. Childs Alexey V. Gorshkov NIST/ Computer Computational and Statistical Sciences Division Institute for Advanced Computer Studies Department of Computer Science Berkeley California 94720 USA

We study the problem of implementing arbitrary permutations of qubits under interaction constraints in quantum systems that allow for arbitrarily fast local operations and classical communication (LOCC). In particular, we show examples of speedups over swap-based and more general unitary routing methods by distributing entanglement and using LOCC to perform quantum teleportation. We further describe an example of an interaction graph for which teleportation gives a logarithmic speedup in the worst-case routing time over swap-based routing. We also study limits on the speedup afforded by quantum teleportation—showing an O(NlogN) upper bound on the separation in routing time for any interaction graph—and give tighter bounds for some common classes of graphs.

关键词： Entanglement manipulation Measurement-based quantum computing Quantum algorithms Quantum circuits Quantum computing models Quantum networks Quantum repeaters Quantum teleportation

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Decision-Level Fusion for the Early Dementia Identification by Hand-Writing and -Drawing 3

Decision-Level Fusion for the Early Dementia Identification ...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Dentamaro, Vincenzo Impedovo, Donato Pirlo, Giuseppe Semeraro, Gianfranco Universities of Bari 'Alda Moro' Department of Computer Science Bari Italy University School for Advanced Studies Iuss Pavia Pavia Italy

ISBN: (纸本)9798350378009

Neurodegenerative diseases are incurable diseases whose decline impairs the normal life of a human being. A timely diagnosis increases the chance of getting access to modern treatments with the aim of increasing the quality of life. This work presents the application of a novel decision-level fusion framework. This framework fuses classification results of different handwriting tasks with the aim of increase accuracy in detecting patients suffering of neurodegenerative disease in their early stage. The framework is structured in three layers: as first layer there are different classifier for each task performed by a patient, in the second layer there is an ensemble algorithm applied on decision fused scenario that come from the previous layer. The third layer, instead, computes accuracies. The results show that fusing decisions of a sub-group of the tasks which makes use of the Random Hybrid Stroke (RHS), outperforms the result of the model without the RHS achieving an Fl-Score of 0.7850. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Leveraging Transfer Learning: Fine-Tuning methodology for Enhanced Text Classification using BERT 7

Leveraging Transfer Learning: Fine-Tuning methodology for En...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Ajay Ware, Nilesh Gupta, Shaurya Technology Management Defence Institute of Advanced Technology Pune Pune India University of Petroleum and Energy Studies School of Computer Science Dehradun India

ISBN: (纸本)9798331527822

Text Classification is the most widely studied problem area in Natural Language Processing (NLP). BERT is the most popular NLP model based on Transfer Learning with its pre-trained model being widely used for such tasks. It generates contextualized word/ sentence embeddings by applying "Self-Attention and Feed Forward"mechanism through multiple layers. The [CLS] token is the only token in BERT which captures semantically-relevant context for the complete input sentence. BERT-based language models output [CLS] token embedding which represents the input sentence effectively, however, the need for more efficient and accurate sentence embedding still remains a research area in NLP. This paper is to explore the effectiveness of [CLS] token pooled from intermediate encoder layers of the BERT model using Mean and Max pooling methods on Amazon product review dataset. Results obtained during the experiment clearly shows the superiority of CLS pooling strategies over default [CLS] tokens. © 2024 IEEE.

关键词： Transfer learning

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A Research on the Perspective of Exploring Restricted Decentralized Blockchain by Applying PoFE: Proof of Familiarity and Existence to Reinforce Multiple Domains 3

A Research on the Perspective of Exploring Restricted Decent...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sheela, K. Priya, C. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Incorporation of Blockchain technology into our real-life scenarios serves in many aspects to improvise their services. Blockchain is a node-to-node decentralized distributed ledger system which provides clarity and unchangeable records of any digital assets while avoiding the intervention of a middleman. It is a new and revolutionary technology that is gaining a lot of attention because of its ability to eliminate threats and frauds on a large scale. The complete decentralized nature of blockchain has certain drawbacks wherein a restricted decentralized network can be accomplished in order to direct the overall process. The centralized server will be maintained along with its basic distributed nature with an authorized controller or miner. Every transaction will be stored in all the nodes and one node will be considered as a chief node wherein certain authenticating powers are provided to monitor and direct the operation in a successful manner. This process eliminates the involvement of multiple parties called miners to mine the block which reduces certain complications like time consumption for choosing miners, finding a fault node in case of breakdown and so on. Certain procedures are implemented to prove the presence of the records along with a process which encourages synergistic decision making for users using the Proof of Familiarity and Existence algorithm. Also, an approach of utilizing the nodes of any sector will be considered for smooth maintenance of an entire data in a blockchain platform. An approach of collaborating Proof of Familiarity and Proof of Existence will be carried out to enrich the decision-making system in both private and public sectors. © 2022 IEEE.

关键词： Blockchain

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence Controlled Manufacturing Strategies for New Energy Vehicles

Artificial Intelligence Controlled Manufacturing Strategies ...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Paul Sundar Singh Priya R Department of Computer Science Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Applications Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Manufacturing strategies which focus on New Energy Vehicles (NEVs) need to innovate because their rapid growth requires more efficient and sustainable production methods. This research studies flexible manufacturing since it represents the fundamental solution for NEV production difficulties. The article evaluates three manufacturing approaches including contract manufacturing together with skateboard platform sharing and modular production lines which show advantages in cost reduction and faster production times and better market responsiveness. Real examples from Tesla, Canoo and Toyota demonstrate how flexible manufacturing works in the NEV sector. Some interconnected obstacles including elevated start-up expenses and supplier connection requirements and employee skill development remain despite productive benefits. The research presents forecasts regarding AI-controlled intelligent manufacturing sites and environmentally-friendly production practices which will facilitate the development of an optimized NEV production network. The NEV industry will push global sustainability in transportation by using flexible manufacturing to solve production challenges.

关键词： Industries Transportation Production Manufacturing Batteries Artificial intelligence Sustainable development Contracts Smart manufacturing Automotive engineering

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advanced Optimizing Network Security in Manets: A Comprehensive Multilevel-Optimization and Monitoring Mechanism (Multi-Omm)

Advanced Optimizing Network Security in Manets: A Comprehens...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Aravind, A. Poongodi, A. School of Computing Sciences Vels Institute of Science Technology & Advance Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

With the proliferation of the Internet of Things (IoT), mobile ad hoc networks (MANET) have become an essential component of contemporary wireless communications. These networks, which rely on multi-hop data transmission, offer significant flexibility and adaptability. However, their open and dynamic nature makes them vulnerable to various internal and external security threats. Among these, Black hole attacks by malicious nodes present serious challenges. Traditional security measures, such as firewalls and encryption, often fall short in addressing these threats. Therefore, there is an urgent need for advanced intrusion detection systems tailored for these environments. Additionally, node selfishness-where nodes conserve their resources at the expense of others-further disrupts network performance and violates protocols. To address these issues, we propose the Multilevel Optimization and Monitoring Mechanism (Multi-OMM), a novel algorithm designed for real-time detection and prevention of malicious activities. Multi-OMM employs a multi-phase approach to enhance network security through traffic pattern analysis, dynamic topology inspection, and targeted attack detection. By implementing and evaluating Multi-OMM through comprehensive experiments, we aim to demonstrate its effectiveness in identifying and mitigating both selfish and malicious behaviors, thereby improving overall network security and performance. © 2024 IEEE.

关键词： Internet of things

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Building NIDS for IoT Network using Ensemble Approach 7

Building NIDS for IoT Network using Ensemble Approach

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Sivasankari, N. Kamalakkannan, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The term 'IoT' is a trendy word in current era due to its usage in day to day live of human beings. Its applications are widespread across many areas such as home, agriculture, health care, transportation, etc., Due to the vast development of IoT services, cyber-attacks against IoT application and devices also increased. Thus, Security and Privacy are two major concerns in IoT environment. Building a Network Intrusion Detection System (NIDS) is one of the ways for securing the IoT system against cyber-attacks. So, an Ensemble based Network Intrusion Detection System using machine learning algorithms has been proposed to detect botnet attacks against HTTP and DNS protocol in network traffic. The goal of this proposed work is to higher the detection rate and decrementing the false positive rate. UNSW_NBl5 dataset was used for evaluating the performance of the proposed NDS. In this work, an Io T environment architecture using fog node proposed and the performance of various classifiers included in the framework compared with the ensemble methodbased on detection rate and false positive rate. © 2022 IEEE.

关键词： Internet of things

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Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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