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Medical Imaging 2025: Digital and Computational Pathology

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

ISBN: (纸本)9781510686045

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 94%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. © 2025 SPIE.

关键词： Breast cancer Deep learning Tumor growth modeling Medical imaging Pathology Alternate lighting of surfaces computer engineering computer science Health informatics

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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A framework for object tracking in videos with complex backgrounds and obstructions

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Neural Computing and Applications 2025年 1-20页

作者： Chang, Tsui-Ping Chen, Tzer-Long Hsiao, Tsung-Chih Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Gaoxiong807 Taiwan Department of Radio Television and Film Shih Hsin University Taipei11604 Taiwan

The research of object tracking in videos utilizes computer vision and machine learning techniques to identify and track objects in the consecutive image frames of videos. The popular algorithms used in the research are YOLO, DeepSORT, StrongSORT, and ByteTrack. YOLO has gained recognition for its hardware efficiency and impressive performance in object detection tasks. It efficiently detects objects in the consecutive image frames of videos. On the other hand, DeepSORT, StrongSORT, and ByteTrack leverage the prediction capabilities to estimate the positions of objects in the next image frames. These algorithms play a significant role to enhance the capabilities of object tracking systems. Recently, many researchers have proposed their methods to enhance the accuracy in object tracking tasks. However, the division of videos into the consecutive image frames for tracking objects can pose challenges, especially when the videos have complex backgrounds and many obstructions in the image frames. In fact, the complex backgrounds with many obstructions in image frames decrease the accuracy of tracking objects in applications. In this study, we present TraObs, a framework for object Tracking in complex backgrounds with Obstructions in the image frames of videos. In TraObs, two novel mechanisms CFM and M-controller are proposed for detectors and trackers to enhance the accuracy of object tracking performance. CFM addresses the tracked objects which are missing in some image frames of videos, while M-controller makes sure the tracked objects with the correct object ids in the system. Some experiments with four testing datasets and benchmark datasets are designed and used respectively to analyze the performance of TraObs. Furthermore, the HOTA and MOTA metrics are used to evaluate our proposed methods. The experimental results also prove the effectiveness of CFM and M-controller in our framework TraObs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd.,

关键词： Image enhancement

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Bandwidth Optimization Techniques in Heterogeneous 5G Networks Using DUDe 6

Bandwidth Optimization Techniques in Heterogeneous 5G Networ...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Tsachrelias, Konstantinos Katsigiannis, Chrysostomos-Athanasios Kokkinos, Vasileios Gkamas, Apostolos Bouras, Christos Pouyioutas, Philippos University of Patras Computer Engineering and Informatics Department Greece University Ecclesiastical Academy of Athens Greece University of Nicosia Computer Science Department Cyprus

ISBN: (纸本)9798350329391

The deployment of 5G networks has led to a significant increase in demand for high-speed data rates and improved overall network performance. HetNets are a promising solution for providing coverage and capacity enhancements for 5G networks. However, one of the key challenges in HetNets is the optimization of bandwidth allocation. Traditional resource allocation techniques are limited in their ability to provide efficient and effective solutions for 5G networks. To address this challenge, a novel technology called DUDe (Downlink/Uplink Decoupling) has been proposed. DUDe optimizes bandwidth allocation in HetNets by dynamically adjusting the allocation of network resources to user demand and network conditions. The motivation of this paper is to highlight the importance of DUDe technology in macro cell offloading in 5G HetNets. The paper evaluates the performance of DUDe in comparison to traditional resource allocation techniques and provides insights into the key factors that influence its effectiveness. Moreover, this paper presents a detailed analysis of simulations results and provides recommendations for future research. The findings of this study will be useful for network operators and researchers in developing more efficient and effective resource allocation strategies for 5G networks. © 2023 IEEE.

关键词： 5G mobile communication systems

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Interactive Learning Through Virtual Reality Operating Training Simulations for Industry Operators 1

Interactive Learning Through Virtual Reality Operating Train...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Hanifati, Kirana Achmad, Zacky Maulana Arissabarno, Cahyo Fikri, Muhammad Ali Razandistiawan, Adzhani Muhammad, Farhan Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Pt Molca Teknologi Nusantara Surabaya Indonesia

ISBN: (纸本)9798350365191

In the manufacturing industry, factory operations encounter three primary conditions: normal, start-up, and shutdown, each necessitating specialized handling for efficiency and safety. Manual training to address these conditions often proves suboptimal, intricate, time-consuming, and poses high safety risks. To tackle this issue, this research proposes and develops a Virtual Reality (VR)-based Operator Training Simulator (OTS). The VR OTS enables interactive and realistic simulation training from the perspective of both field operators and control panel operators. he development process involved iterative design and testing phases, incorporating feedback from a sample of 50 operators to ensure usability and realism. Data analysis was performed to evaluate the simulator's impact on training efficiency and effectiveness. Research findings indicate that this VR application successfully generates highly realistic simulations for normal, start-up, and shutdown conditions, enhancing operator readiness and skills. Its performance is also adequate with a frame rate of 72 frames per second (FPS) and a manageable CPU and GPU load. Further studies are warranted to optimize OTS with other immersive technologies to bolster training efficiency. © 2024 IEEE.

关键词： Virtual environments

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Leveraging Artificial Intelligence for Innovative Technopreneurial Business Models 3

Leveraging Artificial Intelligence for Innovative Technopren...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Sudaryono Yusuf, Inayatul Izzati Diana Maulana, Sabda Pratiangga, Aptanta Apriliasari, Dwi University of Raharja Department of Informatics Engineering Tangerang Indonesia Bina Bangsa University Department of Informatics Engineering Serang Indonesia Bosindo Department of Digital Business Tangerang Indonesia Rey Department of Computer Science Tangerang Indonesia Pandawan Sejahtera Indonesia Department of Information System Tangerang Indonesia

ISBN: (纸本)9798350367492

This study examines the rapid growth of startups integrating artificial intelligence (AI) into their business models, aiming to analyze and compare these startups with traditional IT organizations to understand their differences. Analyzing 162 global startups, this research identifies four unique archetypal business models, introducing novel methodologies in AI integration and data utilization that contrast with conventional IT paradigms. The focus is on technologies providing data analytics solutions, AI products and services, and the expansion of AI use. Key components of AI startup business models are highlighted: (1) advantages of AI in new value propositions, (2) data use to create value, and (3) the impact of AI on business decision-making. This research enhances the understanding of AI startup business models and suggests future research directions in entrepreneurship. The developed taxonomy highlights four distinct AI business models: the Deep Tech Researcher, Data Analytics Provider, AI Product/Service Provider, and AI Development Facilitator, each offering unique value propositions and leveraging AI technologies in different ways to address specific market needs. The research framework fosters entrepreneurial efforts with taxonomies and models as useful instruments. © 2024 IEEE.

关键词： Artificial Intelligence Data Analytics Startup Business Models Technopreneurship

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Enhancing the Efficiency of Navigation Systems: Dijkstra Algorithm Optimization Through Integration of Douglas-Peucker Line Simplification Technique 23

Enhancing the Efficiency of Navigation Systems: Dijkstra Alg...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Haq, Muhammad Yasir Anshari Sianturi, Riswan Septriayadi Pinandito, Aryo Informatics Engineering Department Universitas Brawijaya Faculty of Computer Science Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

Smartphones have grown popular in the current digital era among people. Map-based applications are one of many that are used daily by people to acquire information about specific locations. Google Maps and Waze are two popular examples of map-based applications for travel and navigation, as they provide detailed information while the user navigates from one point to another. One of the most commonly used algorithms in map-based applications is the Dijkstra algorithm. Although the Dijkstra algorithm can determine the shortest route between two points, there is an issue when the selected route involves a large number of data points that need to be processed. The more data points involved, the longer it takes to do a route calculation in the Dijkstra algorithm. This can lead to a degradation in system performance, especially on smartphones with limited resources. To address this issue, the Douglas-Peucker algorithm is proposed as a solution to reduce the number of points involved in the calculation using the line simplification technique. Line simplification means reducing unnecessary points that are considered unimportant. This study suggested that integrating the Douglas-Peucker line simplification technique before Dijkstra could improve the system performance more than the system without integrating line simplification in terms of reducing both memory usage and the time required to compute the solution. © 2023 ACM.

关键词： Navigation systems

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Zero-Shot Sentiment Analysis Exploring BART Models 8

Zero-Shot Sentiment Analysis Exploring BART Models

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350373615

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique. © 2023 IEEE.

关键词： Sentiment analysis

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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