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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Aygün, Elif Nur Kaya, Mehmet Celal Bayar University Department of Software Engineering Manisa Turkey Firat University Department of Computer Engineering Elaziǧ Turkey

ISBN: (纸本)9798350369106

Today, it is known that, like other types of cancer, breast cancer cases are increasing every year. It is an indisputable fact that early diagnosis and correct diagnosis, which medical literature pays particular attention to, have positive effects on patient health in this field. Therefore, it is a great necessity to identify breast cancer cases correctly and not to confuse malignant masses with benign masses. At this stage, a study was carried out to support the physician's decision, and segmentation was carried out on images containing potential breast cancer with the U-Net model. In addition, a two-class classification model is proposed to determine whether ultrasound images containing masses are benign or malignant. While the detection rate of the current model for benign masses is 99.9%, it reaches 86.6% for malignant masses. In addition, various data augmentation techniques were used hybridly to increase malignant mass images containing fewer samples, and the overall prediction success was increased to 91.13%. © 2024 IEEE.

关键词： Lung cancer

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An Overview for Trustworthy and Explainable Artificial Intelligence in Healthcare 28

An Overview for Trustworthy and Explainable Artificial Intel...

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2024 28th International Conference on Information Technology, IT 2024

作者： Arslanoglu, Kubra Karakose, Mehmet The University of Firat The Faculty of Engineering Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798350369618

Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications to predict disease diagnosis and reduce health risks in a predetermined way. In addition to Artificial Intelligence (AI) techniques for processing data and understanding the results of this data, Explainable Artificial Intelligence (XAI) techniques have also gained an important place in the healthcare sector. In this study, reliable and explainable artificial intelligence studies in the field of healthcare were investigated and the blockchain framework, one of the latest technologies in the field of reliability, was examined. Many researchers have used blockchain technology in the healthcare industry to exchange information between laboratories, hospitals, pharmacies, and doctors and to protect patient data. In our study, firstly, the studies whose keywords were XAI and Trustworthy Artificial Intelligence were examined, and then, among these studies, priority was given to current articles using Blockchain technology. Combining the existing methods and results of previous studies and organizing these studies, our study presented a general framework obtained from the reviewed articles. Obtaining this framework from current studies will be beneficial for future studies of both academics and scientists. © 2024 IEEE.

关键词： Blockchain

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Classification and Segmentation of Gastroenterology Images Using Saliency Mapping in Convolutional Neural Network 21

Classification and Segmentation of Gastroenterology Images U...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Khan, Yusra Shaukat, Arslan Riaz, Farhan Department of Computer and Software Engineering Islamabad Pakistan School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798331516680

This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by experimenting as to how a computer aided design can help oncologists classify and segment GI Cancer using Gastroenterology (GE) Images. The concept of Attention Module by the name of Convolution Block Attention Module (CBAM) using Convolutional Neural Network (CNN) layers has been utilized to achieve saliency mapping in these images. The different layers of the CNN are tweaked and the hyper parameters are consistently checked and changed to achieve maximum accuracy. The CBAM Algorithm is divided into Channel Attention (CA) and Spatial Attention (SA) module. The Channel Attention has been implemented in the HSV Color Space for finding the best color space for segmentation purposes of the Chromo endoscopy medical images. Classification, followed by segmentation, is again implemented using CNN based model for normal and abnormal cancer images. For classification, 92% test accuracy has been achieved, while 70% Dice Similarity Coefficient has been obtained for segmentation. The results achieved are competitive as compared to the previous reported results on the GE images dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

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computers, Materials & Continua 2023年第3期74卷 6177-6193页

作者： Moeen Tayyab Ayyaz Hussain Usama Mir M.Aqeel Iqbal Muhammad Haneef Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Senior Member IEEEUniversity of WindsorN9B 3P4Canada Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Engineering Foundation University IslamabadIslamabad44000Pakistan

The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory *** this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news *** primary emphasis of this research is on ticker recognition methods and storage *** that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification *** proposed learning architecture considers the grouping of homogeneousshaped *** incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual ***,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested *** proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

关键词： Arabic text recognition optical character recognition deep convolutional network SegNet LeNet

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Navigating the Digital Twin Network landscape:A survey on architecture,applications,privacy and security

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High-Confidence Computing 2024年第4期4卷 129-149页

作者： Akshita Maradapu Vera Venkata Sai Chenyu Wang Zhipeng Cai Yingshu Li Department of Computer and Information Sciences Towson UniversityTowson21252USA Department of Software Engineering and Game Development Kennesaw State UniversityMarietta30060USA Department of Computer Science Georgia State UniversityAtlanta30303USA

In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twin(DT)*** widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical *** create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT *** high volume of user data and the ubiquitous communication systems in DTNs come with their own set of *** most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data ***,currently,there is not enough literature that focuses on privacy and security issues in DTN *** this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their ***,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective *** then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the *** also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the ***,we provide some open research issues and problems in the field of DTN privacy and security.

关键词： Digital Twin Networks Network architecture Privacy and security Federated learning Blockchain Internet of Vehicles 6G Supply chain

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Accuracy-Aware Low-Complexity Deep Learning Models for Automatic Modulation Recognition 1

Accuracy-Aware Low-Complexity Deep Learning Models for Autom...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Vaziri, Mobin Vakili, Shervin Pierre Langlois, J.M. Polytechnique Montréal Department of Computer and Software Engineering Canada Montréal Canada

ISBN: (纸本)9798350351736

This paper explores the design of Deep Learning (DL) models for Automatic Modulation Recognition (AMR) in wireless communications. The primary goal is to enhance the efficiency and hardware compatibility of convolutional neural networks (CNNs) for AMR through hyperparameter tuning and model compression. The paper first examines the effectiveness of applying quantization and pruning on the accuracy and compu-tational cost of two prominent CNN models from the literature. It then introduces a new CNN model that achieves superior accu-racy with lower computational complexity compared to previous work. The design flow integrates TensorFlow Lite for pruning and quantization, and NVIDIA TensorRT for benchmarking on GPUs specialized for machine learning computing. Experimental results show significant reductions in model size and computational complexity while maintaining accuracy, rendering the proposed DL models suitable for real-time applications on edge devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Understanding Blockchain Trilemma, Causes and Solutions 17

Understanding Blockchain Trilemma, Causes and Solutions

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Chaudhary, Monu Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351637

With extensive research and development in blockchain technology, the concern about scalability, security, and decentralization is still evident. Blockchain trilemma describes that it is realistically impossible to simultaneously achieve a high level of decentralization, security, and scalability. However, new developments in the sector of blockchain are making progress in simultaneous achievement of these three aspects. Layer 1 blockchain is the base level of blockchain network architecture. It includes blockchain networks like Bitcoin, Ethereum, etc. However, the Layer 1 blockchain network faces scalability issues. Layer 2 blockchain is a network built on top of Layer 1 blockchain to help eliminate scalability issues. This paper presents a comparative analysis of Layer 1 and Layer 2 blockchain networks and different approaches to solving the blockchain trilemma in Layer 1 and Layer 2 blockchain networks. From the study, we found that Layer 2 blockchain networks are easier and faster to implement. © 2024 IEEE.

关键词： Blockchain Blockchain Trilemma Decentralization Scalability Security

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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A Review of 3D Indoor Positioning and Navigation in Geographic Information Systems 8th

A Review of 3D Indoor Positioning and Navigation in Geogr...

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8th International Conference on Smart City Applications, SCA 2023

作者： Kazangirler, Buse Yaren Karas, Ismail Rakip Ozcan, Caner Department of Computer Technologies Kastamonu University Kastamonu Turkey Department of Computer Engineering Karabuk University Karabuk Turkey Department of Software Engineering Karabuk University Karabuk Turkey

ISBN: (纸本)9783031543753

Geographic information system (GIS) has become widespread with the management of geographically-based information, constituting a large part of the new data obtained and produced today in electronic platform. Three-dimensional (3D) models provide an overview of the more rapid establishment of new ideas that a two-dimensional (2D) visualization cannot present. Thus, 3D GIS demands data to be comprehensive and continious. 3D indoor positioning and 3D navigation are technologies that provide the ability to position and navigate indoors using these systems. The 3D navigation system is a field that can show covering objects in 3D along the route to the goal when precision is involved. In this study, navigation and indoor positioning studies in 3D GIS applications are mentioned, and particularly cutoff edge studies are included. It contains research on the use and benefits of 3D indoor modeling and navigation techniques. The literature examined for the study were evaluated, and it was concluded that 3D indoor positioning and navigation operations were generally performed with algorithms such as map-matching, location-based service, or ML/DL models. We comprehensively review positioning and navigation techniques in the 3D GIS field by combining existing studies, findings, and different approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Geographic information systems

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Detection of Conspiracy vs. Critical Narratives and Their Elements using NLP 25

Detection of Conspiracy vs. Critical Narratives and Their El...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Albladi, Aish Seals, Cheryl D. Department of Computer Science and Software Engineering Auburn University AuburnAL United States

The growing use of digital media or social media has given a platform to the people to deliver their ideas and viewpoints openly. It is facilitating the rapid spread of contrasting opinions openly. Ultimately, this has developed echo chambers or polarized platforms. These platforms either encourage conspiracy theories or critical narratives. So, nowadays, it has become crucial for people to be aware of being part of such narratives. Moreover, it is also necessary for the organisations to identify the conspiracy creators or the ones who do not possess a conspiracy mentality. This can be made possible by the use of natural language processing techniques to do this task automatically. This study presents the solutions to the two most pressing challenges in today's era. The subtask one of this study provides a solution for the identification of conspiracy narratives and critical narratives using the BERT model. Our model achieved an MCC score of 0.80. Secondly, the other subtask helps to recognize the span of the topic relevant to the important constituents of both categories of oppositional narratives, i.e., agent, facilitator, victim, campaigner, objective, negative effect. The overall accuracy of the model is 95%. These models can be used for the development of an algorithm that can automatically classify the conspiracy theories and critical narratives. © 2024 Copyright for this paper by its authors.

关键词： Social networking (online)

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