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Advances in Computing Research on Science engineering and Technology (ACROSET), International Conference on

作者： Reshmi Tatikonda Jainath Ponnala Ramya Tatikonda Dileep Kumar Yendluri M. Kempanna A. Bhuvanesh Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderabad India Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Science University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This article examines the complex realm of search engine optimization and its importance in the era of digitalization. With an emphasis on different facets of search engine optimization, including optimizing on-page elements, off-page optimization, technology optimization, and optimizing content, this document thoroughly comprehends how enterprises can boost their online appearance and generate natural website visitors. Moreover, it explores the complexities of search engine optimization algorithms and advancements and reviews the available resources and methods for enhancement. Additionally, it covers recommended approaches and plans for the successful implementation of SEO. This article further explores upcoming patterns within search engine optimization, including enhancing voice search and incorporating AI and ML. Furthermore, it discusses potential hurdles and potential openings in SEO's future. Lastly, the study provides suggestions to explore further and improve optimizing search engines. It includes regional SEO, UX optimization, sound search, and socially conscious SEO practices.

关键词： Reviews Search engines Complexity theory artificial intelligence Optimization

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

arXiv

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

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Innovative Solutions for Cervical Cancer: Deep learning Models for Accurate Stage Prediction

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Roshan Kumar Yadav Divyam Kumar Kalyani Kadam Seeta Devi Rupali Gangarde Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International University (SIU) Pune India Department of Obstetrics and Gynecological Nursing Symbiosis College of Nursing Symbiosis International University (SIU) Pune India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University (SIU) Pune India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques–Local Binary Pattern (LBP), ResNet, and VGGNet–an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset.

关键词： Support vector machines Accuracy Image resolution Neural networks Predictive models Skin Cervical cancer

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Safe Fly Prediction Model for Unmanned Aerial Vehicle

Safe Fly Prediction Model for Unmanned Aerial Vehicle

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Divyesh Divakar Rajalakshmi Samaga B L Kanmani Ramesh E Prathibha M Artificial Intelligence and Machine Learning Department Canara Engineering College Visvesvaraya Technoloical University Belagavi Karnataka India Electrical and Electronics Engineering Department NMAM Institute of Technology Nitte Deemed to be University Karkala India Electronics and Communication Engineering Department Canara Engineering College Visvesvaraya Technoloical University Belagavi Karnataka India Computer Science and Engineering Department Canara Engineering College Visvesvaraya Technoloical University Belagavi Karnataka India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

In tropical regions like India, the weather is highly variable and can change rapidly, creating challenging conditions for Unmanned Aerial Vehicle operations. The lack of accurate, real-time predictive tools for assessing Unmanned Aerial Vehicle safety in such environments exacerbates the risk of accidents. Moreover, as Unmanned Aerial Vehicle become more integrated into commercial and public services, the demand for reliable safety protocols becomes even more urgent. This study evaluated the performance of various machine learning algorithms in predicting flight safety based on meteorological data. The Safe Fly Prediction Model, which employs machine learning algorithms to forecast flight safety based on meteorological data, obtained excellent accuracy and shown its potential for real-world use.

关键词： Support vector machines Accuracy Atmospheric modeling Predictive models Autonomous aerial vehicles Data models Robustness Safety Random forests Testing

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Adapting VGG16: Exploring Strategies for Real-World Image Classification Challenges using CIFAR-10 to CIFAR-100

Research Square

引用

Research Square 2025年

作者： Abidin, Shafiqul Izhar, Mohd Sawhney, Ruchi Abbas, Haider Department of Computer Science Aligarh Muslim University Uttar Pradesh India Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Professional Studies GGSIP University Delhi India Department of Computer Science Bosco Technical Training Society GGSIP University Delhi India Department of Artificial Intelligence and Machine Learning Dr. Akhilesh Das Gupta Institute of Professional Studies GGSIP University Delhi India

The study aims to address the complexities associated with deploying VGG16 in dynamic, real-world scenarios, where environmental conditions, object scales, and backgrounds exhibit significant variability. In the pursuit of enhancing the adaptability of the Visual Geometry Group 16 (VGG16) convolutional neural network (CNN) for real-world image classification challenges, this research delves into comprehensive strategies using the CIFAR-10 and the CIFAR-100 dataset. Leveraging advanced data augmentation techniques, including rotation, shifting, shearing, zooming, and horizontal flipping, the model is subjected to more realistic challenges, simulating the intricacies encountered in diverse environments. The VGG16 model, pre-trained on ImageNet, undergoes fine-tuning to tailor its features to the CIFAR dataset. Through systematic experimentation, this research explores the impact of different architectural modifications, including an upsampling layer and dense layers, on the model's adaptability and robustness. The training process involves a 10-15 ages preparing regimen with a carefully crafted data augmentation pipeline, facilitating the exploration of adaptive strategies for real-world scenarios. Evaluation on a test set unveils the model's accuracy in classifying CIFAR images. The training history, depicts through accuracy curves, provides insights into the model's learning trajectory over epochs. The findings contribute valuable insights into the nuanced strategies required to adapt VGG16 to real-world challenges, providing a foundation for practitioners seeking to optimize deep learning models for image classification in dynamic and varied environments. © 2025, CC BY.

关键词： Convolutional neural networks

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Ball Detection for a Lightweight Mobile Platform

Ball Detection for a Lightweight Mobile Platform

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hamish C. Pratt Bernard J.E. Evans Ian D. Reid Steven D. Wiederman Australian Institute for Machine Learning The University of Adelaide South Australia Australia School of Biomedicine The University of Adelaide South Australia Australia The School of Computer and Mathematical Sciences The University of Adelaide South Australia Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi Emirate United Arab Emirates

While convolutional neural networks have excelled in recent years in the domain of object detection, many of the benchmark datasets focus on large objects in static frames and neglect the importance of small moving object detection. Furthermore, most state-of-the-art detectors are comprised of many millions of parameters, making them too large to be efficiently deployed onto a computationally constrained mobile platform. To address the challenge of detecting small moving targets from a robotic platform, we introduce a lightweight temporally-aware ball tracker, with a reduced computational footprint and enhanced motion understanding. To better understand how different model variants utilise motion, we analyse the detection performance of different architectures under varying forms of motion information and find that multiple-input-multiple-output models learn to favour motion information in sequences more strongly than multiple-input-single-output models.

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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learning Physics-Informed Neural Networks without Stacked Back-propagation

arXiv

引用

arXiv 2022年

作者： He, Di Li, Shanda Shi, Wenlei Gao, Xiaotian Zhang, Jia Bian, Jiang Wang, Liwei Liu, Tie-Yan National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research Center for Data Science Peking University China

Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE). But, facing high-dimensional second-order PDE problems, PINN will suffer from severe scalability issues since its loss includes second-order derivatives, the computational cost of which will grow along with the dimension during stacked back-propagation. In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks. In particular, we parameterize the PDE solution by the Gaussian smoothed model and show that, derived from Stein's Identity, the second-order derivatives can be efficiently calculated without back-propagation. We further discuss the model capacity and provide variance reduction methods to address key limitations in the derivative estimation. Experimental results show that our proposed method can achieve competitive error compared to standard PINN training but is significantly faster. Our code is released at https://***/LithiumDA/PINN-without-Stacked-BP. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making Data visualization Prediction algorithms Real-time systems Planning

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression machine learning algorithms Decision making Transportation Receivers Data models Classification algorithms Random forests

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