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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Jogekar, Ravindra Kadu, Mayur Patel, Bhavarth Rai, Jaskaran Kaur Kawale, Tejas Sapolia, Karan Thakur, Priyanka S. B. Jain Institute of Technology Management & Research Department of Computer Science and Engineering Maharashtra Nagpur441501 India

ISBN: (纸本)9798350348460

This research explores the application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm in training an artificial intelligence (AI) model to play the popular mobile game Flappy Bird. The primary objective is to investigate the effectiveness of NEAT in enabling an AI agent to learn and adapt to the dynamic challenges presented by the game environment. The Flappy Bird game serves as an ideal testing ground for evaluating the adaptability and learning capabilities of the NEAT *** methodology involves implementing the NEAT algorithm within the game framework, allowing the AI to learn from scratch without prior knowledge of the game dynamics. Various parameters and configurations are fine-tuned to optimize the learning process. The results indicate the AI's ability to navigate through the game obstacles and improve its performance over *** addition to detailing the NEAT algorithm and its application in the Flappy Bird context, this paper provides insights into the learning process, challenges encountered during implementation, and a discussion on the obtained results. Comparative analyses with other AI approaches highlight the unique advantages and contributions of the NEAT algorithm in the domain of gaming *** findings contribute to the growing body of research on AI in gaming and showcase the potential of evolutionary algorithms, specifically NEAT, in training agents for complex and dynamic environments. The implications of this research extend beyond the realm of gaming, offering valuable insights into the adaptability of evolutionary algorithms in real-world scenarios. © 2024 IEEE.

关键词： DQN evolutionary fitness flappy bird NEAT NeuroEvolution of Augmenting Topologies

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CNN Models Transfer Learning Based Ship Classification for Intelligent Ship Routing

CNN Models Transfer Learning Based Ship Classification for I...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Tiwari, Brij Kishor Munagala, Rohini Sai Mannarugudi, Sai Dheeraj Vignan's Foundation for Science Technology and Research Department of Advanced Computer Science and Engineering Guntur522213 India

ISBN: (纸本)9798350376876

Collision free autonomous navigation is challenging area of interest for the shipping industry, and an accurately classifying the ships according to their size and type classes is a crucial undertaking in marine operations. Accurate prediction and identification will improve the maritime security and navigation safety. However, automatic ship classification is a difficult subject because of the wide variety of ship designs and the complexity of visual data. We developed a deep learning model using transfer learning from pre-trained models in order to classify five different kinds of ships. We performed the experiment using different pre-trained convolution neural network model of the deep learning. In the transfer learning we considered the training of the dataset for 100 epochs. In this study, we investigated four deep learning model ResNet 50, VGG 16, VGG 19, and Inception V3. We carried out the identical procedure for training of each model by removing the top layer of pre-trained model and adding three dense layers for the top layers. The neuron counts are 1024, 512, 256 respectively followed by dropout layer of 0.5. Results of our model shows the maximum obtained accuracies are 86.66 % and from these we can observed that the developed model can be used for ship identification and classification and to take the decisions for collision free and smooth navigation. © 2024 IEEE.

关键词： Transfer learning

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An ultra efficient 2:1 multiplexer using bar-shaped pattern in atomic silicon dangling bond technology

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Journal of Supercomputing 2024年第13期80卷 18347-18364页

作者： Rasmi, Hadi Mosleh, Mohammad Jafari Navimipour, Nima Kheyrandish, Mohammad Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

As CMOS technology approaches its physical and technical limits, alternative technologies such as nanotechnology or quantum computing are needed to overcome the challenges of lithography, transistor scaling, interconnects, and miniaturization. This article introduces a novel nanotechnology that uses atomic-scale silicon dangling bonds (ASDB) to create high-performance, low-power, nanoscale logic circuits. DBs are atoms that can form basic logic gates on a silicon surface using a scanning tunneling microscope device. ASDB can also be an alternative to the existing complementary metal oxide semiconductor (CMOS) technology. The article also proposes a new bar-shaped pattern to design gates and logic circuits with ASDB nano tecnolgoy. The bar-shaped pattern improves the reliability of the output, reduces the area and power consumption, and solves the problem of interatomic energy effects of ASDB. The article demonstrates the efficiency of the bar-shaped pattern by implementing two-input gates such as AND, NAND, OR, NOR, XOR, XNOR, and a 2:1 multiplexer with ASDB. The article also uses a powerful tool called SiQAD to simulate and verify the performance of the proposed structures with ASDB. According to the simulation results, the proposed logic gates are more energy efficient, stable, and compact than the previous structures. They consume 35% and 24.34% less energy and have 14.18% more stability, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Logic gates

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GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles 27th

GUARDEV: Guided Unified Adaptive Response for Defending Elec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mondal, Koustav Kumar Das, Debasis Inter-Disciplinary Research Division - IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783821

In the rapidly evolving landscape of electric vehicles (EVs), integrating the Internet of Vehicles (IoV) has opened up new dimensions in automotive technology. While enhancing vehicle performance, safety, and security, this advancement also exposes EVs to heightened cyber threats. To address this critical concern, we introduce GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles, a novel Machine Learning (ML)-enhanced conflict resolution-based ensemble Intrusion Detection System (IDS) architecture. GUARDEV leverages the power of adaptive ML techniques to provide a unified and guided response in safeguarding EVs against sophisticated cyber-attacks. Our framework evaluates the effectiveness of three prominent ML models - XGBoost, Random Forest, and Extra Trees - in detecting specific categories of cyber threats. By harnessing the prediction confidence scores from these models, GUARDEV offers a nuanced and intelligent approach to recognizing and thwarting diverse cyber-attacks. Our research, grounded in rigorous experimentation using public IoV security datasets, demonstrates GUARDEV’s exceptional accuracy of 99.47% in intrusion detection. This remarkable achievement not only underscores the potential of ML in enhancing vehicular cybersecurity but also establishes GUARDEV as a powerful tool for creating safer, more secure EVs. GUARDEV paves the way for robust protection of internet-connected electric vehicles in today’s digital age by providing a guided, unified, and adaptive response to cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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A Comprehensive Review of Recent Artificial Intelligence Techniques and IOT Applications in Dairy Farms 10

A Comprehensive Review of Recent Artificial Intelligence Tec...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anusree, K. Rajeswari, M. Priyadharshini, C. Karunya Institute of Technology and Science Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sahrdaya College of Engineering and Technology Kerala Kodakara India

ISBN: (纸本)9798350384369

Each and every aspect of daily life has undergone a change thanks to the Internet of Things technology that renders everything intelligent. The wide range of IoT applications has captivated numerous researchers, and IoT enabled dairy farming is being leveraged to undertake innovative studies integrating IoT and Artificial Intelligence (AI) technology. More prospects for intelligent dairy farming are being created by the IoT and data-driven methods. Since the global population is increasing, there is a constant appetite for milk. Developed countries use a greater amount of dairy products than developing nations do. Enhanced technological approaches are required to elevate milk production and meet the escalating demand for dairy products. It is anticipated that a farmer would be able to significantly increase milk output and conquer several traditional farming obstacles with the help of IoT and various AI solutions. This study discusses various difficulties that dairy farmers deal with on every day of their lives. The primary objective of this paper is to undertake a comprehensive review on various Io'T, machine learning(ML) and deep learning(DL) technologies used in the field of smart dairy farming. It also looks into the difficulties and issues that traditional methods encounter as well as their results. Furthermore, the development of an IoT -enabled automated system for dairy farming may benefit further from this evaluation of research. © 2024 IEEE.

关键词： Smart agriculture

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Monitoring Clinical Parameters through Machine Learning and Image Processing

Monitoring Clinical Parameters through Machine Learning and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Aditya Pai, H. Agarwal, Jyoti Chandrappa, S. Mittal, Tanusha Joshi, Kapil School of Computer Science and Engineering Bengaluru India Computer Science and Engineering Dehradun India Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India

ISBN: (纸本)9798350394412

The purpose of the paper is to find the clinical parameters of an individual using normal webcam. The paper aims to help the healthcare domain. In recent times there has been emergence of smartphone devices which monitor vital information's. These devices have special sensors like Electrocardiogram (ECG) and Photoplethysmography (PPG) to check heart and respiratory rate. The smartphone camera reads the heart and respiratory data from the user's face. The method used in the paper mentions about the variation in the Hue that reads both heart and respiration rate from the face of a user. The result in the paper explains how the proposed approach gives more accurate results than the other detection algorithms. © 2024 IEEE.

关键词： Photoplethysmography

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Flow Behavior and Dynamic Recrystallization Mechanism of CSS‑42L Bearing Steel During Hot Compression Deformation

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Acta Metallurgica Sinica(English Letters) 2025年第3期38卷 465-480页

作者： Tianyi Zeng Zirui Luo Hao Chen Wei Wang Ke Yang School of Packaging and Materials Engineering Hunan University of TechnologyZhuzhou 412007China Shi-Changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China CAS Key Laboratory of Nuclear Materials and Safety Assessment Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China

In this work,fow behavior and dynamic recrystallization(DRX)mechanism of a low carbon martensitic stainless bearing steel,CSS-42L,were investigated using a thermomechanical simulator under the temperature and strain rate ranges of 900 to 1100℃ and 0.1 to 20 s^(−1),*** Arrhenius-type constitutive equation was established based on the fow stress ***,the peak stress decreased with the increase in deformation temperature and the decrease in strain *** were two DRX mechanisms during hot deformation of the current studied steel,the main one being discontinuous dynamic recrystallization mechanism,acting through grain boundary bulging and migration,and the auxiliary one being continuous dynamic recrystallization mechanism,working through the rotation of *** the basis of microstructural characterizations,power dissipation maps and fow instability maps,the optimized hot deformation parameters for CSS-42L bearing steel were determined as 1050℃/0.1 s^(−1) and 1100℃/1 s^(−1).

关键词： CSS-42L bearing steel Hot deformation Flow stress Constitutive equation Discontinuous dynamic recrystallization Continuous dynamic recrystallization

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Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

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IEEE Transactions on Network science and engineering 2023年第5期10卷 2439-2454页

作者： Yang, Chao-Lung Tampubolon, Hendrik Setyoko, Aji Hua, Kai-Lung Tanveer, Mohammad Wei, Wei National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore453552 India Xi'An University of Technology School of Computer Science and Engineering Shaanxi 710048 China

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second. © 2013 IEEE.

关键词： Authentication

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DP-BERT: A pre-trained deep language model for depression prediction using microarray data

DP-BERT: A pre-trained deep language model for depression pr...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Junyu Zeng, Min Li, Yiming Wang, Fang Zheng, Ruiqing Liu, Jin Guo, Fei Li, Min Central South University School of Computer Science and Engineering Changsha410083 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China

ISBN: (纸本)9798350386226

In recent years, the increasing number of individuals diagnosed with depression and the growing awareness of its impact on modern society have highlighted the significance of accurate depression diagnosis. Microarray data has played a crucial role in uncovering the genetic mechanisms underlying depression. However, existing methods for depression prediction using microarray data often rely on the selection of differentially expressed genes. This approach disregards important information from other genes and is susceptible to batch effects, thereby limiting generalizability and model stability. To address these limitations, we propose DP-BERT, a depression prediction model based on Bidirectional Encoder Representations from Transformers (BERT). DP-BERT follows a pre-training and fine-tuning paradigm, leveraging a large amount of unlabeled microarray data from diverse sequencing platforms for pretraining to extract comprehensive genetic-level representations of psychiatric disorders. Subsequently, supervised fine-tuning is performed for depression prediction. Experimental results demonstrate that the pre-trained model achieves superior performance in depression prediction. The source code can be obtained from https://***/CSUBioGroup/DP-BERT. © 2024 IEEE.

关键词： Prediction models

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Drilling-based measuring method for the c-φ parameter of rock and its field application

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International Journal of Mining science and technology 2024年第1期34卷 65-76页

作者： Bei Jiang Fenglin Ma Qi Wang Hongke Gao Dahu Zhai Yusong Deng Chuanjie Xu Liangdi Yao State Key Laboratory for Tunnel Engineering China University of Mining and Technology-BeijingBeijing 100083China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China Research Center of Geotechnical and Structural Engineering Shandong UniversityJinan 250061China

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

关键词： Digital drilling Rock crushing zone c-u parameter Measurement method Field application

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