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YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detection and Classification 9

YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detecti...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Thapliyal, Nitin Aeri, Manisha Namdev, Dheeraj Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science And Engineering Uttrakhand Dehradun248002 India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

This research paper considers the You Only Look Once version 8 (YOLOv8) model as a tool for traffic sign detection and classification, which is an important feature of the autonomous driving system and the intelligent transportation system. Analyzing the latest version of the YOLO series, which seeks to maintain a suitable balance between object detection speed and accuracy, is the aim of this study since precise and timely traffic sign recognition is a critical factor for road safety and traffic management. The YOLOv8 model was trained and the accuracy of it was tested using a thorough dataset of traffic signs under different environmental conditions. The approach relied on exploiting YOLOv8's advanced deep learning architecture, transfer learning, and data augmentation to improve the model performance. Statistics revealed that YOLOv8 surpasses the previous versions with its mAP of 95.2% and processing speeds that match real-time application demands. The model had high precision and recall across multiple classes of traffic signs, especially those that are unique in shape or color. This research indicates that YOLOv8 can be an effective tool to enhance the performance of autonomous driving systems by its robust sign detection and classification. The model has opened a new frontier for research on improving the model for more accuracy and further application of the model in more elaborate areas of road safety and intelligent transportation systems. © 2024 IEEE.

关键词： Object detection

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Fused Transformers: Fused Information of Arabic long Article for Summarization 6

Fused Transformers: Fused Information of Arabic long Article...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Ezzat, Zeyad Khalfallah, Ayman Khoriba, Ghadad Center for Informatics Science Information Technology and Computer Science School Nile University Giza Egypt Computer and Systems Department Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Centre Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

This paper introduces a novel approach for extending the applicability of pre-trained models to accommodate longer texts. Addressing the inherent limitation of quadratic performance in attention models within transformers, we propose the concept of fused transformers. By integrating new adaptor techniques, we enhance the encoding of lengthy text segments by breaking them into shorter spans. Subsequently, these segments are fused to increase the model's effectiveness in processing extended texts. This fusion mechanism serves to fortify the model's capacity for fine-tuning. Furthermore, we implement a length-based curriculum for expedited training Our experiments yielded 16 Rouge-2 points, representing a doubling of the score achieved by the vanilla fine-tuning method on the newly introduced "Mukhtasar" dataset for summarization. This highlights the effectiveness of managing complex relations among text segments and confirms that our method can outperform conventional training approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Distribution transformers

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A Comprehensive Review of Mojo: A High-Performance Programming Language

A Comprehensive Review of Mojo: A High-Performance Programmi...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

As artificial intelligence continues to advance at an unprecedented pace, the selection of programming languages significantly affects development processes, workflows, and outcomes. Mojo, a novel programming language, has gained attention through its integration of Python-like syntax, Rust-inspired safety features, and superior performance characteristics. This paper evaluates Mojo across four critical dimensions: distinctive features, performance metrics, ecosystem maturity, and practical applications, while examining potential adoption barriers. Positioned at the intersection of high-level programming simplicity and low-level performance optimization, Mojo demonstrates particular utility for artificial intelligence developers requiring both flexibility and computational efficiency. Through analysis of prominent libraries, including Endia, Lightbug HTTP, Basalt, and ***, this study examines Mojo's capabilities and limitations. By presenting a comparative analysis of Mojo's features, emerging ecosystem, and applicability to artificial intelligence development, this research addresses the current literature gap and provides substantive insights into this emerging programming language.

关键词： Reviews Ecosystems Syntactics Programming Libraries Real-time systems Safety artificial intelligence Optimization Python

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation artificial intelligence Prognostics and health management Cancer

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Security Enhancement using Image verification method to Secure Docker Containers 4

Security Enhancement using Image verification method to Secu...

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4th International Conference on Information Management and Machine intelligence, ICIMMI 2022

作者： Saxena, Vivek Saxena, Deepika Singh, Uday Pratap Department of Computer Engineering Poornima Institute of Engineering and Technology India Department of Computer Science Engineering Poornima University India Department of Artificial Intelligence and Data Science Poornima Institute of Engineering and Technology India

ISBN: (纸本)9781450399937

Now with the dawn of the internet, cloud computing has been reformed by opening new horizons at an inclusive level with auspicious opportunities. With the rise of opportunities, popularity, and public connectivity by the internet, it is the next foremost edge for Trojans, worms, viruses, hackers and cyber-attacks will broaden because a hacker would see this as a new edge to try to steal confidential interrupt, information, services and route damage to the originality cloud computing Docker system. Security in Docker container over the cloud is of great anxiety hence care needs to be taken to provide secure Docker containers and secure cloud services. Docker is usual within the software development community due to the portability, versatility, and scalability of containers. In this paper, we analyze the Docker storage-based security and by applying the methods of Docker image verification, checking capabilities, and storage selection, improve the security in Docker containers. © 2022 ACM.

关键词： Containers

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The Quest for Fairness: A Comparative Study of Accuracy in AI Hiring Systems 3

The Quest for Fairness: A Comparative Study of Accuracy in A...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： D'Souza, Jason Kadam, Vedant Shinde, Pranali Saxena, Kumkum Thadomal Shahani Engineering College Information Technology Department Mumbai India Thadomal Shahani Engineering College Artificial Intelligence and Data Science Department Mumbai India Thadomal Shahani Engineering College Computer Engineering Department Mumbai India

ISBN: (纸本)9798350302288

artificial intelligence and Machine Learning based models are increasingly being used in developing models for automating recruitment systems in various Multinational Companies as well as small-scale and developing companies and industries. With the advent of Generative artificial intelligence models, many companies are turning towards automatic and AI-based hiring software. J.P. Morgan uses HireVue, Deloitte incorporates Hiretual for talent sourcing and engagement, IBM incorporates *** for automated interview scheduling and many more. But in all these systems a major factor to consider is the biasness in the models and how to detect them and efficiently mitigate them. Some models unwillingly sometimes amplify the biases in the models and results in faulty and biased predictions which can lead to the inaccurate matching of profiles to the job description and result in wayward hiring processes. The biasness can be based on the parameters such as gender, address, etc., or biasness transferred from the programmer into the model while creating the model which is called unconscious bias. The models implemented in the research are Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost. The accuracies for the models Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost were 0.80, 0.81, 0.81, 0.78, and 0.84 respectively. © 2023 IEEE.

关键词： Decision trees

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Feature fusion-based weighted broad learning system for imbalanced data 8

Feature fusion-based weighted broad learning system for imba...

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8th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2025

作者： Li, Yanting Wang, Yusha Jin, Junwei Philip Chen, C.L. Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798331517915

Broad Learning System (BLS), characterized by its flat architecture as an efficient neural network, has garnered significant interest for its benefits in terms of training velocity and network scalability. However, BLS does not perform well when dealing with imbalanced classification problems. First, conventional BLS neglects the intrinsic logical information contained in the original input data, which may be critical for classification and prediction. Second, BLS fails to emphasize the significance of minority classes in imbalanced data scenarios. In this paper, we propose a feature fusion-based weighted broad learning system. Unlike traditional BLS, we combine the original features and broad features as fused features for training. Additionally, we introduce a weight generation mechanism that assigns higher weights to minority classes, enhancing the model's focus on these classes. A closed solution is available for the issue, and the transformation matrix can be effectively resolved. The experimental results on multiple imbalanced datasets show that our method is superior to other imbalanced methods. © 2025 IEEE.

关键词： Calibration

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MPFFNet:Semantic Segmentation of Remote Sensing Image Buildings Based on Multipath Feature Fusion 8

MPFFNet:Semantic Segmentation of Remote Sensing Image Buildi...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Dong, Jie Mao, Jingkun Liu, Kun Cheng, Liangyong School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Interplanetary Space Technology Development Co. LTD Tianjin China

ISBN: (纸本)9798350305838

The buildings with variable scales and complex background information in remote sensing images often result in missed and misclassified segmentation of buildings. To address this issue, this paper designs an multipath feature fusion network (MPFFNet). MPFFNet has an overall encoding and decoding structure, embedded with an residual depth Inception block(RDI) module to extract multi-scale features. At the same time, MPFFNet uses the multipath residual channel attention (MPRCA) module to expand the Receptive field of the feature map to obtain rich multi-scale context information. The validity of the proposed algorithm is proved on the WHU Buildings data set, with F1 reaching 94.70%, IOU reaching 89.93%, and Recall reaching 94.98%. © 2023 IEEE.

关键词： Buildings

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Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach for Multidimensional Weed Disease Classification 5

Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Upadhyay, Deepak Manwal, Manika Deep, Pragya Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

A new category of weed disease classification in precision agriculture based on a hybrid model using convolutional neural networks (CNN) and support vector machine (SVM) has been presented in the proposed study. In line with overcoming the shortcomings of conventional disease detection approaches, this study introduces a machine learning solution that improves accuracy and productivity in the identification of weed diseases. The hybrid model utilizes the strengths of CNNs for feature extraction and its power with SVM in classification, yielding a stable system that enables multilevel classification of weed disease severity. Creation of a complete collection of weed disease image datasets for the training, validation, and testing phase. The findings from the study show that the use of the model outperforms standalone CNN & SVM models as it achieves a substantial increase in 96.21% accuracy across all the severity levels. The model also uses Grad-CAM visualizations to ensure interpretability and enable a better understanding of the decision-making process. The results imply that the proposed hybrid CNN-SVM model is both efficient in terms of weed disease classification and feasible for use across a range of agricultural settings. This research provides a valuable tool for the development of precision agriculture, which enables early and precise intervention in plant diseases that positively impact sustainable farming. Future work may include increasing the application potential of the model and investigating its integration into automated agricultural systems. © 2024 IEEE.

关键词： Deep Learning Performance Analysis Support Vector Machine Weed Diseases

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A Fuzzy based CNN Method for Covid 19 Classification in Noisy Chest X-ray Images

A Fuzzy based CNN Method for Covid 19 Classification in Nois...

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2023 International Conference on Integrated intelligence and Communication Systems, ICIICS 2023

作者： Mitra, Ankita Esakki Madura, E. Kiruba, B. Basker, N. Grace, N.Vini Antony Glory, E. Dr. B. C. Roy Engineering College Department of Ece Durgapur India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sona College of Technology Department of Cse Salem India R.M.D. Engineering College Department of Ece Kavaraipettai India Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350315455

The COVID-19 pandemic has caused a global health crisis. Chest X-ray (CXR) imaging is most commonly utilized techniques for diagnosing COVID-19. However, accuracy of CXR-based diagnosis is limited by the presence of noisy labels. Noisy labels are labels that are incorrect or inaccurate. They can be caused by a variety of factors, such as human error, image artifacts, and variations in imaging protocols. In this paper, a novel method is introduced for COVID-19 CXR image classification in the presence of noisy labels. The primary objective of this research is to improve the accuracy of COVID-19 detection in X-ray images that are characterized by the presence of noise. To achieve this, proposed method incorporates direction-based fuzzy rules into our methodology, which allows to effectively identify and analyse edges and textures within the X-ray images. The labels are then subjected to undergone manifold learning and multidimensional scaling to eliminate noise. High confidence samples are further chosen as training set for label-recovered dataset in order to increase constancy and precision of a classification framework without compromising its intrinsic performance. The proposed method is evaluated on three publicly available COVID-19 CXR datasets. The results showed that our method can achieve an accuracy of 96.59% on the large dataset, 97.12% on the medium dataset, 98.79% on the small dataset. These results are comparable to the state-of-the-art methods for COVID-19 CXR image classification. This can help to improve the early diagnosis and treatment of COVID-19 patients. © 2023 IEEE.

关键词： chest X-ray COVID-19 Fuzzy membership noisy labels semisupervised learning

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