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Signet Ring Cell Detection from Histological Images Using Deep Learning

Computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

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Assessing the Symbiotic Organism Search Variants using Standard Benchmark Functions 3

Assessing the Symbiotic Organism Search Variants using Stand...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Asyikin Zainal, Nurul Din, Fakhrud Zamli, Kamal Z. Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan26600 Malaysia Department of Computer Science and IT University of Malakand KPK Pakistan

Symbiotic Organism Search (SOS) is one of the latest meta-heuristic algorithms created to solve optimization problems. Combining the fact that this new algorithm is parameter-less (no need for tuning) and having a superior performance compared with other meta-heuristic algorithms, the interest to investigate and enhanced this algorithm had emerged. In this paper, we present a new version of SOS by looping the current algorithm rather than doing it one after the other. The target of this paper is to find the effect of changing the structure of algorithm from the original SOS using selected benchmark functions. Our findings indicate that using a loop structure can improve the solution in some of the benchmarks functions as compared to the original SOS. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic algorithms

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Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

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Computer Systems science & Engineering 2023年第3期44卷 2309-2322页

作者： Mahmoud Ragab Jaber Alyami Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Diagnostic Radiology Faculty of Applied Medical SciencesKing AbdulazizJeddah21589Saudi Arabia Imaging Unit King Fahd Medical Research CentreKing AbdulazizJeddah21589Saudi Arabia

Liver cancer is one of the major diseases with increased mortality in recent years,across the *** detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its ***,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response ***,there is a need exists to develop automated tools for liver cancer detection in a precise *** this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)*** proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation ***,Kapur’s entropy-based segmentation technique is used to identify the affected regions in ***,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature *** last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer *** order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different *** comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.

关键词： Liver cancer image segmentation artificial intelligence deep learning CT images parameter tuning

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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Proceedings of the International Convention MIPRO

作者： M. Horvat P. Jerčić Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey.

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Exsolution of CoFe(Ru)nanoparticles in Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)for efficient oxygen evolution reaction

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Nano Research 2022年第8期15卷 6977-6986页

作者： Yi Liang Yu Cui Yang Chao Ning Han Jaka Sunarso Ping Liang Xin He Chi Zhang Shaomin Liu School of Applied Physics and Materials Wuyi UniversityJiangmen 529020China Department of Materials Engineering KU LeuvenLeuven 3001Belgium Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching 93350SarawakMalaysia College of Chemical Engineering Beijing University of Chemical TechnologyBeijing 100029China

The rational modification of perovskite oxides(ABO3−δ)is essential to improve the efficiency and stability of oxygen *** engineering represents a facile approach to modify perovskites for enhanced *** compositional design and in situ exsolution,a Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)(LSCFR)perovskite anchored with CoFe(Ru)alloy particles on the surface was fabricated for oxygen evolution reaction(OER)in this *** results and calculations indicate that Ru-doping promotes the exsolution of CoFe(Ru)from the perovskite *** exsolution in the reduced atmosphere for 3 h,the catalyst(LSCFR-3)exhibited superior OER performance with an overpotential of 347 mV and a Tafel slope of 54.65 mV·dec^(−1),and showed good stability in contrast to the pristine *** exsolution of CoFe(Ru)particles,Ru doping,and the increase of surface oxygen vacancies are responsible for the enhancement of OER *** findings obtained in this study highlight the possibility of controlling exsolution and composition of nanoparticles by element doping and prove that in situ exsolution is an effective strategy for designing OER catalysts.

关键词： perovskite oxides in situ exsolution oxygen evolution reactions CoFe nanoparticles B-site cation doping

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Robust Federated Learning for Energy Storage Systems

Robust Federated Learning for Energy Storage Systems

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IEEE Conference on Wireless Communications and Networking

作者： Xu Wang Yuanqi Liang Yuanzhu Chen Octavia A. Dobre School of Computing Queen's University Kingston ON Canada Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

One of the Sustainable Development Goals of the United Nations is affordable and clean energy. True utilization of renewable energy is only possible via battery-based energy storage systems. Overseeing the operation of battery-based energy storage systems and diagnosing abnormal batteries are of the utmost importance for their durability and stability. Because of inadequate anomalous samples and privacy considerations, we jointly train a global autoencoder on various battery-based energy storage systems to detect anomalous batteries. Due to potentially unstable network connectivity in energy storage systems, a chunk of model parameters may be lost during model transmission, leading to dramatic performance deterioration. The trained model tends to classify all measurements as anomalies. To solve this problem, we propose a robust federated learning scheme to mitigate negative impact caused by packet loss during model transmission. By permuting and unpermuting model parameters before and after model transmission, we are able to distribute the lost parameters across the entire model. Such a loss can no longer have a significant negative impact on anomalous battery detection. Experimental results illustrate that the proposed algorithm is robust against packet loss during the model exchange between the cloud server and battery-based energy storage systems.

关键词： Privacy Federated learning Packet loss Stability analysis Battery charge measurement Batteries Servers

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

arXiv

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arXiv 2024年

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Faculty of Engineering and Applied Science Memorial University Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAI) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAI in improving energy harvesting wireless networks for IoT in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting wireless networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting wireless networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data collection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some important open directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Resilience Techniques for an Automatic Car Parking System

Resilience Techniques for an Automatic Car Parking System

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Bunyakiat, Ganlayarat Chaowalit, Orawan Visutsak, Porawat Department of Computer and Information Science Faculty of Applied Science KMUTNBBangkok10800 Thailand Department of Computing Faculty of Science Silpakorn University Muang Nakhon Pathom Nakhonpathom 73000 Thailand

ISBN: (纸本)9781665416566

One example of the advancement of information technology that exhibits to the usual life in the city is an automatic car parking system. The automatic car parking model consists of many different parking modes and situations. Thus, in the design of software structure, it is exceedingly difficult to find defects which might be occur during the design process because the software system is complicated. This paper presents a method to design the structure of the software to be suitable and easy to find faults and errors using the software resiliency techniques. The objective of this work is to make the structure of the system more appropriate and easier to maintain the reliability of software. It also includes the finding of software defects easier, reducing cost and increasing system performance. The major contribution of this work is to improve the resiliency of the software with reasonable costs and test the performance of software testing in the large systems which can be tested continuously and show test results efficiently. In the case of problems are encountered during the test, the information will be collected and the results of software errors will be generated in the form of log file. It also indicates the cause of errors in each point of the software;therefore, it is easy to diagnose and solve problems by using less time to find the cause of the software defect. The experiments show that our resilience design pattern can reduce the number of LOC executed by 1.5 times as compared to the original design pattern, while the number of statements executed is reduced by 18.92%. © 2021 IEEE.

关键词： Software testing

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What StudentsWant to Have in the Post-Pandemic?: A Study about computing science Study Preferences and Mode of Study in Southeast Asia 5

What StudentsWant to Have in the Post-Pandemic?: A Study abo...

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5th International Conference on Education Technology Management, ICETM 2022

作者： Yau, Peter ChunYu Kim, Yongmin Wong, Dennis Pang, Patrick Kan, Stanley Cao, Qi School of Computing Science University of Glasgow United Kingdom School of Engineering University of Glasgow United Kingdom Department of Computer Science State University of New York Korea Republic of Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450398015

After two years of online learning, minor changes happened in some tiny places which we may not always notice. These changes may impact the school management, education arrangement, and curriculum design: what do students expect from their teachers, and the school? Are they expecting forever online learning, and getting their degree completely online? In this study, we examined 93 final-year students from the school of computing science which mainly used work-based learning applied to learn pedagogy. We analyze their viewpoint on how COVID-19 changed their understanding of teaching and learning: what is the role of teachers and students? And how do they see the arrangement for the internship program? We found that over 92% of students agree that a creditable degree can better secure a job;over 68% of the students believed that face-to-face teaching is still a better choice, but at the same time, over 73% wish to have the materials recorded so that they can access them at their convenience time. We also examined some of the course design elements, especially those related to the internship program, to achieve a possible curriculum improvement for the students in the Asia Pacific region. © 2022 ACM.

关键词： Students

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