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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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Face2Wear: An automatic and user-friendly facewear personalization framework with 3D symmetry-aware face registration using RGB-D selfies

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CAD Computer Aided Design 2025年 185卷

作者： Zhang, Jie Chen, Luwei Ren, Hang Luximon, Yan Li, Ping Faculty of Applied Sciences Macao Polytechnic University China School of Design The Hong Kong Polytechnic University Hong Kong Shenzhen Pingle Orthopedic Hospital Shenzhen China Laboratory for Artificial Intelligence in Design Hong Kong Science Park Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong

The realm of personalized human wearable has undergone significant evolution. However, existing methodologies often lack in addressing crucial factors essential for comprehensive facewear customization, including automation, accuracy, convenience, and comfort. To bridge this gap, we propose an automated and user-friendly approach for ergonomic facewear personalized based on 3D face modeling and registration. Our approach involves the development of a user-friendly 3D face scanning technique utilizing RGB-D selfies captured by smartphone depth cameras, simplifying the process of personal data collection. Subsequently, we present a precise 3D symmetry-aware face registration method integrated with automatic facial landmark detection to produce parameterized 3D facial meshes featuring accurate symmetry panels. Additionally, we introduce a comfort-centric eyeglasses customization process to showcase its practical applications. Qualitative and quantitative comparisons with state-of-the-art techniques highlight the efficacy of our symmetry-aware registration method. Experimental findings also showcase the versatility of our approach across various scanning data. Evaluation results underscore the practical utility of our method in creating personalized eyeglasses with significantly enhanced comfort levels. Moreover, our method can be extended to other symmetric facewear designs, such as sunglasses, swimming goggles, and respirators. © 2025 Elsevier Ltd

关键词： Ergonomics

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House Price Prediction using Random Forest Machine Learning Technique

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Procedia Computer science 2022年 199卷 806-813页

作者： Abigail Bola Adetunji Oluwatobi Noah Akande Funmilola Alaba Ajala Ololade Oyewo Yetunde Faith Akande Gbenle Oluwadara Department of Computer Science Faculty of Computing and Informatics Ladoke Akintola University of Technology Nigeria Computer Science Department College of Pure and Applied Sciences Landmark University Nigeria Accounting Department College of Business Sciences Landmark University Nigeria

Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat-sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. UCI Machine learning repository Boston housing dataset with 506 entries and 14 features were used to evaluate the performance of the proposed prediction model. A comparison of the predicted and actual prices predicted revealed that the model had an acceptable predicted value when compared to the actual values with an error margin of ±5.

关键词： Sales forecasting House Price Prediction Machine Learning Random Forest Algorithm

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Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

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Computers, Materials & Continua 2021年第4期67卷 1317-1332页

作者： Samina Amin M.Irfan Uddin M.Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H.Alkinani Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Articial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddahSaudi Arabi

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are available to infer a variety of user sentiments in posts on social *** amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing *** paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning *** articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of *** proposed method classies tweets related to the outbreak of dengue into positives and *** were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue *** proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

关键词： Articial neural network classication social media GloVe social networking sites

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases

Machine Learning Models for Brain Signal Classification: A F...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Ammar Almomani Mahmoud Al Sakhnini Amjad Aldweesh Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities.

关键词： Neurological diseases Machine learning algorithms Scalability Epilepsy Transforms Feature extraction Electroencephalography

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A Review of Effectiveness and Efficiency Methodology of Decision Support System for Selecting Suppliers

A Review of Effectiveness and Efficiency Methodology of Deci...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Sharaf Alzoubi Khalid Thaher Amayreh Mazen Mohammed Farea Yousef A. Baker El-Ebiary Syarilla Iryani Ahmad Saany Neeraj Bisht Information Technology department College of Computer Science and Informatics Amman Arab University (AAU) Jordan Business Administration Department Faculty of Finance Science and Business Irbid National University Jordan Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Faculty of Informatics and Computing UniSZA Malaysia Birla Institute of Applied Sciences Bhimtal India

In today's highly competitive global business environment, effective supplier selection plays a crucial role in the success and sustainability of organizations. Decision Support Systems (DSS) have emerged as powerful tools to aid in this complex process by providing valuable insights and data-driven recommendations for selecting suppliers. This research paper presents a comprehensive review of the methodologies used to assess the effectiveness and efficiency of Decision Support Systems employed in supplier selection processes. The primary objective of this study is to evaluate and compare the various methodologies applied in Decision Support Systems, focusing on their effectiveness and efficiency in assisting decision-makers during supplier selection. The research critically analyzes a range of published literature, academic papers, and case studies to highlight the strengths and limitations of different DSS methodologies in this context. The paper commences by elucidating the fundamental concepts of supplier selection and the significance of Decision Support Systems in streamlining this multifaceted process. Subsequently, a systematic review of existing literature is conducted to identify the key methodologies employed in DSS frameworks. Furthermore, the research explores the novel integration of emerging technologies, such as Artificial Intelligence, Machine Learning, and Big Data analytics, within Decision Support Systems for supplier selection. This investigation aims to discern how these advanced techniques contribute to the enhancement of decision-making precision and the overall efficiency of the selection process. By critically analyzing and comparing the effectiveness and efficiency of various Decision Support System methodologies, this research seeks to provide valuable insights for both academics and practitioners in supply chain management. The findings of this study will enable decision-makers to make informed choices regarding the adoption and customiz

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A Review Paper on Student-Graduate Advisory Expert system

A Review Paper on Student-Graduate Advisory Expert system

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2020 International Conference on computing and Information Technology, ICCIT 2020

作者： Tawafak, Ragad M Alfarsi, Ghaliya Romli, Awanis Jabbar, Jasiya Malik, Sohail Iqbal Alsideiri, Abir AlBuraimi College Information Technology Department Buraimi Oman University Malaysia Pahang Faculty of Computing College of Computing and Applied Science Malaysia

ISBN: (纸本)9781728126807

Academic advising of the postgraduate students is considered as a fundamental rule and one of the significant positive services and responsibilities for the academic staff follow up in most of the e-leading educations. The expert system is considered as one of most achievement areas in artificial intelligence and highly appreciated for application with e-learning and teaching methods. The aim of this paper, is to survey the most expert systems used in advisory model and replaced with the manual registration and advising system of face to face communication and discussion in Instructor office. The method used of determined the key factors related to student advising and expert system models though open access databases. The finding of this section reviews out nine selected expert systems in order to explore the strengths, and limitations of existing expert system approaches to advising undergraduate students. © 2020 IEEE.

关键词： Expert systems

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Evaluation Methodologies in Software Protection Research

arXiv

引用

arXiv 2023年

作者： Sutter, Bjorn D.E. Schrittwieser, Sebastian Coppens, Bart Kochberger, Patrick Computing Systems Lab Ghent University Belgium Christian Doppler Laboratory for Assurance and Transparency in Software Protection Faculty of Computer Science University of Vienna Austria St. Pölten University of Applied Sciences Austria

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 571 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks and formulate a number of concrete recommendations for improving the evaluations reported in future research papers. Copyright © 2023, The Authors. All rights reserved.

关键词： Malware

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Sine Cosine Optimization with Deep Learning-Based applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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Computers, Materials & Continua 2023年第6期75卷 4767-4783页

作者： Abdelwahed Motwakel Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life *** new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the ***,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable ***,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus *** has become one of the popular social media platforms and allows anyone to post *** study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 *** SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 *** SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish ***,the BGRU model is utilized to recognise and classify sen-timents present in the ***,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification *** experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

关键词： applied linguistics deep learning sentiment analysis COVID-19 pandemic sine cosine optimization twitter

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