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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

Medical Image Analysis of Lung Tumor Diagnosis Based on Gene...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zwaid, Jassim Mohammed Ahmed Murad, Masrah Azrifah Azmi Khalid, Fatimah Binti Manshor, Noridayu Al-Jumaily, Abdulmajeed Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Affiliated to University of Anbar Ramadi Iraq Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Multimedia Department Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés Madrid28911 Spain

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Data-driven Online Decision Support for Hotel Site Selection 9

Data-driven Online Decision Support for Hotel Site Selection

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Kumarage, Imal Weerakeshara, Nuwanga Chamika, Thanuja Rathnapala, Heshan Nawinna, Dasuni Gamage, Narmada Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665432405

Location is one of the fundamental factors that determine hotel success. The location, once selected, cannot be changed without a significant investment. This research aims to identify the location-specific factors that affect Sri Lankan coastal hotels. The factors that affect the location rating have been assessed under location attraction, accessibility, and popularity. An ensemble learning model has been trained to predict the location score of a hypothetical location, assess the manner accessibility affects hotel performance, and predict location popularity based on the surrounding competition. The results show that this method can assess hotel location and performance, with significant accuracy and the identified location-related factors that contribute to a hotel's success can be used by hoteliers and investors to improve decision making. © 2021 IEEE.

关键词： Location

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Sentiment-Based Neural Network Approach for Predicting the Severity of Bug Reports 5

Sentiment-Based Neural Network Approach for Predicting the S...

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5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021

作者： Baarah, Aladdin Aloqaily, Ahmad Zyod, Hala Mustafa, Nasser The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah II for IT Department of Software Engineering P.O. Box 330127 Zarqa Jordan The Hashemite University Department of Software Engineering Zarqa Jordan Wenzhou-Kean University Department of Computer Science Wenzhou China

ISBN: (纸本)9781665442381

During the software maintenance process, bugs encountered by software users need to be solved according to their severity level to improve the quality of the software. Therefore, bug reports with high severity should have the highest priority to be fixed. A numerous number of bug reports are submitted daily through Bug Tracking Systems (BTS) such as Bugzilla. The bug triager examines the incoming bug reports manually and verifies whether the assigned severity level is correct or not. This manual process is time-consuming, requires much effort and is possibly error-prone. However, a limited number of research works have considered the sentiments of the bug reporters in predicting the severity of bug reports. This paper proposes two approaches based on sentiment analysis, the Lexicon-based and Multilayer Perceptron (MLP) neural network approaches. The sentiment analysis process determines and measures the sentiment words and their sentiment scores according to the popular sentiment lexicon called SentiWordNet. The proposed approaches are validated on the Eclipse open-source project, and the sentiment-based models performance is evaluated. According to the experimental results, the sentiment-based MLP outperforms the Lexicon-based approach (baseline approach). The F-measure has been improved significantly from 0.52 for the Lexicon-based approach to 0.81 when applying the MLP approach. © 2021 IEEE.

关键词： Data mining

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AI-Infused Telemedicine for Rural Wellness: A Comprehensive Approach

AI-Infused Telemedicine for Rural Wellness: A Comprehensive ...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Nidhi B Kurpad S Dhanyashree V Shivank T A S Surya Shobith S Vinila Jinny Computer Science and Engineering Vellore Institute of Technology Vellore India Electronics and Communication Engineering S J B Institute of Technology Bengaluru India Electrical and Electronics Engineering B N M Institute of Technology Bengaluru India Department of Software Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The paper introduces a transformative Telemedicine Kiosk designed to enhance healthcare in remote areas by harnessing the synergy of computational intelligence and telemedicine. This advanced kiosk utilizes real-time video conferencing and comprehensive patient monitoring systems, all underpinned by Responsible AI practices that ensure ethical data management and the protection of patient privacy. Equipped with an ESP32 Wi-Fi module, the kiosk provides seamless communication between patients and medical officers, facilitating immediate care and consultation. The system goes beyond diagnostics: it supports healthcare professionals with an integrated medication dispensing mechanism powered by IoT technology. Our investigation delves into the implications of human factors, economics, and technological advances for the future of telemedicine. We present an innovative solution that not only advances healthcare accessibility but also embodies a commitment to responsible and ethical healthcare service provision in underserved regions.

关键词： Ethics Data privacy Patient monitoring Telemedicine Medical services Real-time systems Protection

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Malicious Clients and Contribution Co-Aware Federated Unlearning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Wang, Yang Li, Xue Chen, Siguang Nanjing University of Posts and Telecommunications Jiangsu Engineering Research Center of Communication and Network Technology Nanjing China Women’s Hospital of Nanjing Medical University Nanjing Women and Children’s Healthcare Hospital Department of Dermatology Nanjing China Hohai University College of Computer Science and Software Engineering Nanjing China

Existing federated unlearning methods to eliminate the negative impact of malicious clients on the global model are influenced by unreasonable assumptions (e.g., an auxiliary dataset) or fail to balance model performance and efficiency. To overcome these shortcomings, we propose a malicious clients and contribution co-aware federated unlearning (MCC-Fed) method. Specifically, we introduce a method for detecting malicious clients to reduce their impact on the global model. Next, we design a contribution-aware metric, which accurately quantifies the negative impact of malicious clients on the global calculating their historical contribution ratio. Then, based on this metric, we propose a novel federated unlearning method in which benign clients use the contribution-aware metric as a regularization term to unlearn the influence of malicious clients, and restoring model performance. Experimental results demonstrate that our method effectively addresses the issue of excessive unlearning during the unlearning process, improves the efficiency of performance recovery, and enhances robustness against malicious clients. © 2020 IEEE.

关键词： Federated learning federated unlearning malicious clients

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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International Conference on Advanced Communication Technology (ICACT)

作者： Saurabh Agarwal Rajeev Kumar Abha Devi Ki-Hyun Jung Department of Information and Communication Engineering Yeungnam University Gyeongsan Republic of Korea Department of Computer Science and Engineering Blockchain Technology Research Lab Delhi Technological University New Delhi India Department of Software Convergence Andong National University Gyeongbuk Republic of Korea Department of English JS(PG) College Sikandrabad Uttar Pradesh India

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics; hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field.

关键词： Industries Technological innovation Privacy Image forensics Digital forensics Organizations Media Market research Security Research and development

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Code Prediction Based on Graph Embedding Model 16th

Code Prediction Based on Graph Embedding Model

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Kang Yu, Huiqun Fan, Guisheng Yang, Xingguang Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Shanghai China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9783030675394

Code prediction aims to accelerate the efficiency of programmer development. However, its prediction accuracy is still a great challenge. To facilitate the interpretability of the code prediction model and improve the accuracy of prediction. In this paper, the source code’s Abstract Syntax Tree (AST) is used to extract relevant structural paths between nodes and convert them into training graphs. The embedded model can convert the feature of the node sequence in the training graph into a vector that is convenient for quantization. We calculate the similarity between the candidate value and the parent node vector of the predicted path to obtain the predicted value. Experiments show that by using prediction data to increase the weight of related nodes in the graph, the model can extract more useful structural features, especially in Value prediction tasks. Adjusting the parameters embedded in the graph can improve the accuracy of the model. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Trees (mathematics)

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Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination

arXiv

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arXiv 2022年

作者： Joy, Alen Poullis, Charalambos The Immersive and Creative Technologies Lab The Department of Computer Science and Software Engineering Concordia University Canada

This paper presents a process for estimating the spatially varying surface reflectance of complex scenes observed under natural illumination. In contrast to previous methods, our process is not limited to scenes viewed under controlled lighting conditions but can handle complex indoor and outdoor scenes viewed under arbitrary illumination conditions. An end-to-end process uses a model of the scene's geometry and several images capturing the scene's surfaces from arbitrary viewpoints and under various natural illumination conditions. We develop a differentiable path tracer that leverages least-square conformal mapping for handling multiple disjoint objects appearing in the scene. We follow a two-step optimization process and introduce a multi-view gradient consistency loss which results in up to 30-50% improvement in the image reconstruction loss and can further achieve better disentanglement of the diffuse and specular BRDFs compared to other state-of-the-art. We demonstrate the process in real-world indoor and outdoor scenes from images in the wild and show that we can produce realistic renders consistent with actual images using the estimated reflectance properties. Experiments show that our technique produces realistic results for arbitrary outdoor scenes with complex geometry. The source code is publicly available at: https://***/***/multi-view-gradient-consistency-for-svbrdf-estimation-of-complex-scenes-under-natural-illumination. © 2022, CC BY-NC-SA.

关键词： Conformal mapping

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Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 39

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Haodi Huang, Wenxi Su, Zhenhan Chen, Junyang Jiang, Di Fan, Lixin Zhang, Chen Lian, Defu Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Shenzhen University WeBank Institute of Financial Technology China Webank Co. Ltd. China Hong Kong Polytechnic University Department of Computing Hong Kong University of Science and Technology of China School of Data Science China

ISBN: (纸本)9798350322279

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs. © 2023 IEEE.

关键词： Approximation algorithms

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