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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Object on Use Case Description: Sequence Diagram Conformance based on Step Performed using Text Pre-Processing on Sipranta Application SRS

Object on Use Case Description: Sequence Diagram Conformance...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Abibi, Muhammad Alfath Priyadi, Yudi Dian Martha, Ati Suci Telkom University Department of Informatics Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350337617

In the Use Case Diagram, a Step Performed in Tabulation contains an object's explanation flow. A sequence diagram attempts to depict in detail the interaction of objects in a system, the messages or commands sent, and the implementation time. In other words, the Sequence Diagram, like the Use Case Diagram, encapsulates an object's explanation flow in a graphical diagram. The Step Performed and the Sequence Diagram should be related, yet the Step Performed and Sequence Diagram diverge while creating the Sipranta Application SRS. By adopting Text Pre-processing, this research intends to quantify the value of conformance between the Sequence Diagram and the Step Performed in Use Case Description. This study identifies the Step Performed and the Sequence Diagram using 22 documents, 11 of which are Step Performed and 11 of which are Sequence Diagrams. In addition, the Text Analysis process produces two documents with the highest conformity: d1 with d12 of 0.933461. Cohen Cappa Score produces a validation value of 0.29195, which is included in the "Fair Agreement"category. While the reliability results from 2 groups of experts, which is 0.8275, so it is included in the "Substantial agreement"category. The results differ between the two methods, which is 0.536. © 2023 IEEE.

关键词： Sequence Diagram Step Performed Text Pre-Processing

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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Segmentation Based Approach For Off-line Handwritten Sinhala Word Recognition From Touch Screen Gestures 8

Segmentation Based Approach For Off-line Handwritten Sinhala...

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8th International Multidisciplinary Moratuwa engineering Research Conference, MERCon 2022

作者： Mahesh, Hashan Priyankara, Chathura University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (数字)9781665487863

ISBN: (纸本)9781665487863

The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to recognize and validate the words written on the touch screens facilitates further capabilities to the users. Hence, in this paper, we describe a segmentation-based approach combined with an n-gram model for the recognition and validation of the Sinhala words written on touch screens. We compare the results of 6 commonly used machine learning models to find the best performing classifier for recognizing individual characters of words. The classifiers are trained to identify 19 different Sinhala characters. Based on the results, Convolution Neural Network (CNN) based word classifier stands ahead of other classifiers. © 2022 IEEE.

关键词： Touch screens

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Mobile Devices Interface Adaptivity Using Ontologies

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Computers, Materials & Continua 2022年第6期71卷 4767-4784页

作者： Muhammad Waseem Iqbal Muhammad Raza Naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo Department of Software Engineering The Superior UniversityLahore53700Pakistan INP-ENIT University of ToulouseTarbes65000France Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of *** context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the *** this purpose,the ontological modeling has been made for specific context and *** type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied *** contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and *** competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context *** consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined *** semantic context model is focused to bring in the usage of adaptive *** exploration has finished up with a versatile,scalable and semantically verified context learning *** model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

关键词： User context adaptive interfaces human computer interaction

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A Disambiguation of Security-based software Testing 7

A Disambiguation of Security-based Software Testing

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7th International Conference on Information and Computer Technologies, ICICT 2024

作者： Dilworth, Robert Gudla, Charan Rana, Md Shohel Ahmed, Feroz Mississippi State University Department Of Computer Science And Engineering StarkvilleMS United States Florida Gulf Coast University Department Of Computing And Software Engineering Fort MyersFL United States Prediction 3D

ISBN: (纸本)9798350385625

This paper reviews security testing, covering the concepts and notions essential to the discipline. The following analysis uses an inverted funnel approach to reduce the broad topic of software testing into its constituent parts. With this framework in mind, the paper will attempt to answer the following questions. Why is software testing significant? What are the roles of users and developers as they relate to software? What techniques are commonly used when testing software? Is the discipline of software testing unified;if not, what controversies exist, and what do they concern? What is security testing? What is the purpose of security testing? What metrics are used to guide security testing? What are common threats that hamper or impede security testing? How can developers mitigate the risk posed by security threats;what defensive options are available? What improvements can be made to software testing? What can occur in the absence of software/security testing? © 2024 IEEE.

关键词： software testing

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Recent trends in crowd management using deep learning techniques: a systematic literature review

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Journal of Umm Al-Qura University for engineering and Architecture 2024年第4期15卷 355-383页

作者： Alasmari, Aisha M. Farooqi, Norah S. Alotaibi, Youseef A. Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia Dar Al-Hekma University Jeddah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia

Crowd management has become an integral part of urban planning in abnormality in the crowd and predict its future issues. Big data in social media is a rich source for researchers in crowd data analysis. In this systematic literature review (SLR), modern societies. It can organize the flow of the crowd, perform counting, recognize the related works are analyzed, which includes crowd management from both global and local sides (Hajj events—Saudi Arabia) based on deep learning (DL) methods. This survey concerns crowd management research published from 2010 to 2023. It has specified 45 primary studies that accomplish the objectives of the research questions (RQs), namely, investigation of the taxonomies, approaches, and comprehensive studies of crowd management both globally and locally and focusing on the most commonly used techniques of DL. We found both supervised and unsupervised DL techniques have achieved high accuracy, with different strengths and weaknesses for each approach. A lot of these studies discuss aspects of scene analysis of crowds, that are captured by installed cameras in the place. However, there is a dilemma regarding exploiting data provided on social media to use in the crowd analysis domain. Which we believe that the analysis of big data may raise crowd management to the upper level of enhancement. To this end, motivated by the findings of this SLR. The primary purpose of this review is strived to illustrate obstacles and dilemmas in crowd analysis fields to provide a road map for future researchers. Furthermore, it aims to find research gaps existing to focus on it in the future studies. The results indicate that the lack of Hajj research, especially in sentiment analysis and the study of the pilgrims' behavior. © The Author(s) 2024.

关键词： Crowd analysis Crowd behaviors analysis Crowed management Hajj Social media

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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