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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

Lasers in Engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Improved chaotic fruit fly optimization with elman neural network based data-driven approach on product reviews classification

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Alexandria Engineering Journal 2025年 127卷 628-641页

作者： Alghamdi, Mohammed Al-shathry, Najla I. Said, Yahia Beyari, Hasan Sorour, Shaymaa Sattar, Khalid Nazim Abdul Alallah, Fouad Shoie Alshareef, Abdulrhman Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Language Preparation Arabic Language Teaching Institute Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Administrative and Financial Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Science and Information College of Science Majmaah University Majmaah11952 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

In the digital era, business platforms have considerably evolved towards online stores on the internet. Through the internet-based platform, customers can easily buy products through their smartphones and receive delivery at the place without going to the shopping mall. On the other hand, the disadvantage of these platforms is that customers need to learn about the product quality. Thus, these platform services frequently offer customer feedback to give feedback about the shipped items. Feedback is a valuable source for analyzing customer satisfaction. Customer satisfaction is effectively enhanced by performing a Sentiment Analysis (SA) of many customer ratings on E-commerce platforms. Yet, predicting the correct sentiment difference in customer feedback is challenging due to complicated logic, sequence length, and text pattern adjustments. This article focuses on designing and developing Enhanced NLP with Data-driven Product Reviews Classification using the improved chaotic fruit fly optimization (ENLPPR-ICFFO) method. The major goal of the ENLPPR-ICFFO method is to detect and classify the sentiments existing in product ratings. To accomplish this, the presented ENLPPR-ICFFO method employs data pre-processing to be consistent. Then, the Continuous Bag-of-Words (CBoW) method is utilized for the feature extraction. The extreme learning machine (ELM) method is used for the product review classification. The ENLPPR-ICFFO methodology includes an ICFFO-based hyperparameter selection technique for improving the overall product review classification technique. The empirical findings of the projected ENLPPR-ICFFO methodology are tested using a set of datasets. The performance validation of the ENLPPR-ICFFO technique portrayed superior accuracy values of 98.04 % and 96.57 % over existing methods under Amazon-Fine-Food-Reviews and Amazon-Products datasets. © 2025 The Authors

关键词： User experience

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Evolving reservoir computers reveals bidirectional coupling between predictive power and emergent dynamics

arXiv

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arXiv 2024年

作者： Tolle, Hanna M. Luppi, Andrea I. Seth, Anil K. Mediano, Pedro A.M. Department of Computing Imperial College London United Kingdom Department of Psychiatry University of Oxford United Kingdom Division of Information Engineering St John’s College University of Cambridge United Kingdom Department of Informatics University of Sussex United Kingdom Sussex Centre for Consciousness Science University of Sussex United Kingdom Division of Psychology and Language Sciences University College London United Kingdom

Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating the contributions of single neurons, here we argue that a deeper understanding requires considering emergent dynamics – dynamics that make the whole system "more than the sum of its parts". Specifically, we examine the relationship between prediction performance and emergence by leveraging recent quantitative metrics of emergence, derived from Partial Information Decomposition, and by modelling the prediction of environmental dynamics in a bio-inspired computational framework known as reservoir computing. Notably, we reveal a bidirectional coupling between prediction performance and emergence, which generalises across task environments and reservoir network topologies, and is recapitulated by three key results: 1) Optimising hyperparameters for performance enhances emergent dynamics, and vice versa;2) Emergent dynamics represent a near sufficient criterion for prediction success in all task environments, and an almost necessary criterion in most environments;3) Training reservoir computers on larger datasets results in stronger emergent dynamics, which contain task-relevant information crucial for performance. Overall, our study points to a pivotal role of emergence in facilitating environmental predictions in a bio-inspired computational architecture. © 2024, CC BY.

关键词： Recurrent neural networks

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FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information 35

FEVEROUS: Fact Extraction and VERification Over Unstructured...

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35th Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： Aly, Rami Guo, Zhijiang Schlichtkrull, Michael Thorne, James Vlachos, Andreas Christodoulopoulos, Christos Cocarascu, Oana Mittal, Arpit Department of Computer Science and Technology University of Cambridge United Kingdom Amazon Alexa United Kingdom Department of Informatics King’s College London United Kingdom Facebook United States

Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation. Existing large-scale benchmarks for this task have focused mostly on textual sources, i.e. unstructured information, and thus ignored the wealth of information available in structured formats, such as tables. In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes, or does not provide enough information to reach a verdict. Furthermore, we detail our efforts to track and minimize the biases present in the dataset and could be exploited by models, e.g. being able to predict the label without using evidence. Finally, we develop a baseline for verifying claims against text and tables which predicts both the correct evidence and verdict for 18% of the claims. © 2021 Neural information processing systems foundation. All rights reserved.

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Position: Topological Deep Learning is the New Frontier for Relational Learning 41

Position: Topological Deep Learning is the New Frontier for ...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc. United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai'i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Tool Condition Prediction in High-Speed Milling Using Deep Q-Networks (DQN) and Sensor Fusion Data 3

Tool Condition Prediction in High-Speed Milling Using Deep Q...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Dilli Ganesh, V. Duklan, Nitin Alabdeli, Haider Johri, Prashant Mohanraj, T. Venkatesh, R. Department of Mechanical Engineering Chennai India Uttaranchal University Dehradun India College of Technical engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Department of Physics Dindigul India

ISBN: (纸本)9798331542375

Highly effective and reliable high-speed milling depends on the timely tool condition monitoring (TCM) check. In this paper, we propose a novel approach for tool condition prediction based on DQN with sensor fusion data. Both contact and non-contact sensors such as, vibrational, force, and acoustic emission sensors are utilized to gather data in real time during milling. The information collected by these sensors are integrated into a number of important features to be used in DQN model to learn the prediction of tool wear and possible failure. The DQN model is built to learn from the response of the milling environment characterized by tool condition in terms of wear and the remaining useful life (RUL). The results derived from experiments tend to show that the utilised DQNbased model has better quantitative values for accuracy, precision, recall, and F1-score than classical machine learning models like artificial neural networks (ANN), support vector regression (SVR), decision trees and etc. The integration of the sensor data enhances the overall prediction reliability, a factor which is highly achieved from the fusion of the data received from the vibration, force and acoustic emission sensors. The overall system proposed has significant application for real time implementation and demonstrates relatively short prediction time and is less sensitive to changes in the machining conditions. This proposed methodology is a feasible approach for improving tool life and reducing time needed for high speed milling applications. © 2025 IEEE.

关键词： Milling (machining)

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Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks

arXiv

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arXiv 2024年

作者： Yu, Qixuan Tang, Xirui Li, Feiyang Cao, Zinan College of Computing Georgia Institute of Technology AtlantaGA30332-0280 United States College of Computer Sciences Northeastern University BostonMA02115 United States Department of Computer Science University of Illinois Urbana-Champaign ChampaignIL61820 United States Department of General Systems Studies The University of Tokyo Tokyo113-8654 Japan

This paper explores an improved Adaboost algorithm based on Long Short-Term Memory Networks (LSTMs), which aims to improve the prediction accuracy of user clicks on web page advertisements. By comparing it with several common machine learning algorithms, the paper analyses the advantages of the new model in ad click prediction. It is shown that the improved algorithm proposed in this paper performs well in user ad click prediction with an accuracy of 92%, which is an improvement of 13.6% compared to the highest of 78.4% among the other three base models. This significant improvement indicates that the algorithm is more capable of capturing user behavioural characteristics and time series patterns. In addition, this paper evaluates the model's performance on other performance metrics, including accuracy, recall, and F1 score. The results show that the improved Adaboost algorithm based on LSTM is significantly ahead of the traditional model in all these metrics, which further validates its effectiveness and superiority. Especially when facing complex and dynamically changing user behaviours, the model is able to better adapt and make accurate predictions. In order to ensure the practicality and reliability of the model, this study also focuses on the accuracy difference between the training set and the test set. After validation, the accuracy of the proposed model on these two datasets only differs by 1.7%, which is a small difference indicating that the model has good generalisation ability and can be effectively applied to real-world scenarios.. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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DEVELOPMENT OF CONTEXT-SENSITIVE FORMULAS TO OBTAIN CONSTANT LUMINANCE PERCEPTION FOR A FOREGROUND OBJECT IN FRONT OF BACKGROUNDS OF VARYING LUMINANCE

arXiv

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arXiv 2024年

作者： Akleman, Ergun Akgun, Bekir Tevfik Alpkocak, Adil Visual Computing & Computational Media SPVFA Computer Science and Engineering COE Texas A&M University College StationTX77831 United States Faculty of Computer and Information Sciences Yeditepe University Istanbul Turkey Department of Computer Engineering Izmir Bakırçay University Menemen Turkey

In this article, we present a framework for developing context-sensitive luminance correction formulas that can produce constant luminance perception for foreground objects. Our formulas make the foreground object slightly translucent to mix with the blurred version of the background. This mix can be used to quickly produce any desired illusion of luminance in foreground objects based on the luminance of the background. The translucency formula has only one parameter;the relative size of the foreground object, which is a number between 0 and 1. We have identified the general structure of the translucency formulas as a power function of the form y = sf(s) where s ∈ [0, 1] is the relative size of the foreground object, f(s) is a polynomial, and y is the opacity of the foreground object. We have implemented a web-based interactive program in Shadertoy. Using this program, we determined the coefficients of the polynomials f(s). To intuitively control the coefficients of the polynomial functions, we have used a Bézier form. Our final translucency formula uses a quadratic polynomial and requires only three coefficients. We also identified a simpler affine formula, which requires only two coefficients. We made our program publicly available in Shadertoy so that anyone can access and improve it. In this article, we also explain how to intuitively change the polynomial part of the formula. Using our explanation, users change the polynomial part of the formula to obtain their own perceptively constant luminance. This can be used as a crowd-sourcing experiment for further improvement of the formula. © 2024, CC BY.

关键词： Luminance

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Neural Networks and Deep Learning: Innovations and Applications in Soft computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary computing and informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems informatics Biological neural networks Robots

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MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

MCCNet: Multi-Color Cascade Network with Weight Transfer for...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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