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Precise Localization and Path Planning of Automatic Guided Vehicles Based on LiDAR-Assisted Landmark Processing 3rd

Precise Localization and Path Planning of Automatic Guided V...

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3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Wang, Jun Wang, Kailin Ma, Lei Xu, Jiawei Li, Yijun Xie, Fei Nanjing 30000iot Technology Co. Ltd. Nanjing211106 China School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China Nanjing Wanhe Robot Technology Co. Ltd. Nanjing211106 China

ISBN: (纸本)9789819616930

In recent years, the widespread application of Automatic Guided Vehicles (AGVs) in industrial transportation has significantly propelled the modernization of industrial production. However, traditional AGVs’ reliance on pre-laid magnetic tracks for navigation poses several limitations, including vulnerability to damage, inconvenience in construction and path modification, and potentially high manufacturing costs. To address these challenges, this paper proposes an innovative LiDAR-based positioning scheme. Firstly, this scheme breaks free from the constraints of traditional magnetic track laying and utilizes landmarks as key information sources to provide AGVs with accurate position data. This innovative approach not only improves positioning accuracy but also enables AGVs to navigate in more complex industrial environments. Secondly, based on these precise location data, we can calculate the virtual path that the AGV traverse. This step significantly simplifies the process of landmark layout and accelerates the layout speed, thereby enhancing the practical convenience of the system. Finally, experiments have been conducted to verify and analyse the effectiveness of the proposed method. The results demonstrate that the approach not only achieves coordinate positioning and path visualization for AGVs on open paths, but also offers an ideal positioning and path planning solution for industrial transportation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic guided vehicles

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Disease Detection in Grape Leaves Using Convolutional Neural Networks 3rd

Disease Detection in Grape Leaves Using Convolutional Neural...

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3rd international conference on intelligent Systems and Applications, ICISA 2024

作者： Naik, Varsha Baraskar, Trupti Khatri, Viren Kolhe, Parimal Katyarmal, Mitheelesh School of Computer Engineering and Technology MIT World Peace University Dr. Vishwanath Karad Maharashtra Pune411038 India

ISBN: (纸本)9789819798384

Grape cultivation is vital to the global economy, but various diseases threaten grapevine health and yield. Early and accurate disease detection is crucial for implementing effective control measures. Traditional methods rely on visual inspection by experts, which is subjective and time-consuming. automated approaches using deep learning offer a promising alternative. The goal of this study was to create a convolutional neural network (CNN) model that can identify grapevine leaf diseases reliably. The aim of the study was to train the model for distinguishing between healthy and infected leaves by three diseases that are common in them such as black rot, esca, and leaf blight. We collected a dataset of images containing healthy and diseased grape leaves. The dataset was pre-processed and augmented to increase data diversity and ensure consistency. The CNN model was developed using TensorFlow and Keras and consisted of convolutional, pooling, dense, and activation layers. Optimizations were implemented using cross-entropy loss function, adam optimizer, and Dropout layers. Model performance was evaluated using metrics such as accuracy of the model, the precision at which it cannot wrongly label a negative sample, the recall at which it can find all the positive outputs, and F1-score which is the ratio between precision and recall. The developed CNN model achieved high accuracy in disease identification. We observed significant accuracy, precision, F1-score, and recall values across all disease categories, demonstrating the model's effectiveness in distinguishing between healthy and diseased leaves. This study successfully demonstrates the potential of CNNs for automated grape leaf disease identification. The developed model offers a promising tool for viticulturists to improve early disease detection and ultimately enhance grape yield and quality. Future research could focus on expanding the model's capabilities to identify a wider range of diseases and exploring its

关键词： Convolutional neural networks

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Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring 23rd

Synonym-Based Essay Generation and Augmentation for Robust A...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tashu, Tsegaye Misikir Horvath, Tomas Eotvos Lorand Univ Fac Informat Dept Data Sci & Engn Telekom Innovat Labs Pazmany Peter Setany 1-C H-1117 Budapest Hungary Wollo Univ Kombolcha Inst Technol Coll Informat Kombolcha 208 Ethiopia Pavol Jozef Safarik Univ Fac Sci Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such that the output of a model changes easily with small changes in the input. We proposed to use keyword-based lexical substitution using BERT that generates new essays (adversarial samples) which are lexically similar to the original essay to evaluate the robustness of AES models trained on the original set. In order to evaluate the proposed approach, we implemented three NN-based scoring approaches and trained the scoring models using two stages. First, we trained each model using the original data and evaluate the performance using the original test and newly generated test set to see the impact of the adversarial sample of the model. Secondly, we trained the models by augmenting the generated adversarial essay with the original data to train a robust model against synonym-based adversarial attacks. The results of our experiments showed that extracting the most important words from the essay and replacing them with lexically similar words, as well as generating adversarial samples for augmentation, can significantly improve the generalization of NN-based AES models. Our experiments also demonstrated that the proposed defense is capable of not only defending against adversarial attacks, but also of improving the performance of NN-based AES models.

关键词： Adversarial attack data augmentation Automatic essay scoring

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Dragon Fruit Maturity Detection and Quality Grading with Scalable Deep Neural Networks

Dragon Fruit Maturity Detection and Quality Grading with Sca...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Aishwarya N Devisowjanya P Deshana Vikas Shah Raavi Niharika Department of ECE Amrita Vishwa Vidyapeetham Amrita School of Engineering Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper addresses the critical need for an automated system to accurately determine the ideal harvest time for dragon fruit. It effectively differentiates between fresh and defective fruit, as well as mature and immature fruit, to maintain high-quality standards. The study presents an advanced approach for detecting the maturity and freshness of dragon fruit using pre-trained deep learning models, including VGG16 (a lightweight model), MobileNetV3 (a moderate-weight model), and EfficientNetBO (a heavy-weight model). Each model utilizes three different activation functions: Exponential Linear Unit (ELU), Rectified Linear Unit (ReL U), and Scaled Exponential Linear Unit (SELU). The features extracted by these models, using the best activation function, were then employed to train five different machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Random Forest Classifier, AdaBoost, and CatBoost. Experimental results indicate that for maturity detection, the combination of EfficientN etBO with the SVM Classifier yields the most favorable results. For quality grading, the optimal outcomes are achieved with the MobileNetV3-SVM combination. This hybrid approach achieves a precision and recall of 99%, along with 99% accuracy for both maturity and freshness detection, thus facilitating the assessment of dragon fruit.

关键词： Support vector machines Visualization Accuracy Computational modeling Surface contamination Transfer learning Computer architecture Feature extraction Real-time systems Standards

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Detection of Brain Tumors from MRI Images using Novel Deep CNN 3

Detection of Brain Tumors from MRI Images using Novel Deep C...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Mohebbanaaz Deepthi Jordhana, P. Vamsi Krishna, G. Ravindra College of Engineering for Women Dept of ECE Andhra Pradesh Kurnool India PVKK Institute of Technology Dept of ECE Andhra Pradesh Anantapur India VNR Vignana Jyothi Institute of Engineering and Technology Department of EIE Hyderabad India

ISBN: (纸本)9798331523893

Brain tumors detection is the most crucial aspects in neuro-oncology. Early detection of tumors can significantly prevent critical health issues, improving the survival rates. Magnetic Resonance Imaging (MRI) has proven to be a effective tool in diagnosing brain tumors. This paper presents a deep learning (DL) methodology for the detection of brain tumors using Convolutional Neural Networks (CNNs). Given the complexity and high dimensionality of medical imaging data, transfer learning allows us to leverage pre-trained architecture as backbone and accelerate the process of training. The developed architecture is further is fine-tuned with different optimizing parameters. Our proposed approach achieved an accuracy of 98.70%. Experimental results demonstrate the success of developed models in detecting tumors with high accuracy, offering a promising solution for automated medical diagnostics. © 2025 IEEE.

关键词： Neuroimaging

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Machine learning based Classifier Models for Detection of Celestial Objects

Machine Learning based Classifier Models for Detection of Ce...

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intelligent Technologies (CONIT), international conference on

作者： Vikrant Sharma Sandeep Goel Anuj Kumar Jain Amit Vajpayee Rahul Bhandari Raj Gaurang Tiwari GraphicEra Hill University Dehradun Uttarakhand India Department of Information Technology Panipat Institute of Engineering & Technology Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Apex Institute of Technology (CSE) Chandigarh University Mohali Punjab India Department of Computer Science & Engineering Chandigarh University Mohali Punjab India

The classification of celestial objects such as stars, galaxies, and quasars is one of astronomy's most difficult and fundamental problems. Due to the technological advancement of telescopes and observatories, the classification of large volumes of data must be automated. Various machine learning techniques are currently employed for accurate classification. In this paper, a comparison of the efficacy of various classification algorithms is presented. XGBoost seems to be the most effective.

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AI-Based automated Subtitle Generation System for Multilingual Video Transcription and Embedding

AI-Based Automated Subtitle Generation System for Multilingu...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K. Penyameen G.M Siva Suriya Rajan A. Arshath Ahamed S. Yugesh Ram J John Shiny A Periya Nayaki Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The subtitle generator system proposed in this project is an intelligent tool towards automation of subtitle production for video content., using the most advanced speech recognition and transcription and video processing technologies. U sing integration with machine learning models such as Whisper by OpenAI with multimedia tools including FFmpeg and MoviePy., it manages the production of subtitles that track up and are perfectly synchronized with spoken content within video files. It gives standard SRT file generation with direct embedding abilities into videos., hence reducing the total manual effort and time taken in the production of subtitles. Support to transcribe into multiple languages is also given with an easy Graphical User Interface GUI that provides ease of interaction with the application. This solution to video accessibility., particularly for the purpose of using more than one language., is scalable and automated to provide the best possible approach in media industry subtitle generation.

关键词： Accuracy Production Speech recognition Machine learning Streaming media Media Generators data models Multilingual Graphical user interfaces

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Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference 23rd

Performance/Resources Comparison of Hardware Implementations...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Lozada, Randy Ruiz, Jorge Gonzalez, Manuel L. Sedano, Javier Villar, Jose R. Garcia-Vico, Angel M. Skibinsky-Gitlin, E. S. Inst Tecnol Castilla & Leon Burgos Spain Univ Oviedo Oviedo Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain

ISBN: (纸本)9783031217524;9783031217531

Fully Connected Network inference is a complex algorithm that can be accelerated using edge devices like Field Programmable Gate Array (FPGA). One commonly known performance improvement for Fully Connected Network inference is quantization. This technique replaces the floating points weights of the network by integers. Frameworks like Open Neural Network Exchange (ONNX) and Tensorflow Lite provide solutions for this procedure. However, these frameworks have different inference algorithms with different operations and data types. In this article inference algorithms of common Fully Connected Networks in ONNX and Tensorflow Lite have been analysed. A performance and resource usage comparison is tested on Xilinx((R)) Zynq UltraScale+(TM) PSoC. Results show that to achieve lower latency is better to avoid floating point operations in the inference algorithm. In terms of FPGA resource usage, an increase is observed when the neural network becomes more complex regardless of the algorithm. This growth in resource usage is framework-dependent.

关键词： FPGA Fully connected network Quantization

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices

Integrating Artificial Intelligence and Educational Technolo...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Lathika Ilayaraja Yukthika S S Subramani Prabakaran S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation.

关键词： Training Ethics Technological innovation Large language models Education Educational technology learning (artificial intelligence) Internet of Things Artificial intelligence Guidelines

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Alzheimer's Disease Classification Approach Using MRI Imaging 3

Alzheimer's Disease Classification Approach Using MRI Imagin...

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3rd international Mobile, intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Khaled, Eman Amr, Farah Essam, Farah Nouh, Maryam Sherif, Youssef Yakan, Zyad Gayar, Ziad El Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Scinece Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped dataset with features extracted from the CNN model we provided an

关键词： Magnetic resonance imaging

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