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Automated Organ Segmentation in 3D Biomedical Images Using Dual-Path 3D Attention U-ResNet 4

Automated Organ Segmentation in 3D Biomedical Images Using D...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhavana, J. Hameed, thushara Maruthakutti, Maheswari Sivaraman, R. Vijayanand, Sellamuthu Palanisamy Jegajothi, B. Sr University Department of Cs & Ai India College of Engineering and Technology University of Technology and Applied Sciences Nizwa Oman Dwaraka Doss Goverdhan Doss Vaishnav College Department of Mathematics Arumbakkam Tamil Nadu Chennai600 106 India Government of Engineering Department of Computer Science and Engineering Erode India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529635

Deep learning algorithms have made considerable strides in recent years, and these advancements have the potential to significantly improve multi-organ segmentation in three-dimensional biomedical images. this would result in improved diagnostic accuracy and efficiency in medical imaging. DA-TransUNet is an innovative architecture that successfully combines information on several scales and enhances feature extraction by utilizing the DA-Block encoder and the Convolutional Block Attention Module (CBAM). the assessed propsoed model using a variety of datasets, one of which being the LiTS dataset. Finally, were able to achieve a Dice coefficient of 92.56% and a Hausdorff Distance (HD95) of 28.09 mm for liver images. DA-TransUNet surpasses existing state-of-the-art segmentation models across a variety of medical imaging datasets, as demonstrated by the results of the experiments, which reveal considerable increases in accuracy and robustness. the discoveries that have obtained contribute to the advancement of automated segmentation approaches in medical imaging, which in turn facilitates increased diagnostic capabilities. © 2024 IEEE.

关键词： Image segmentation

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Graph Isomorphic Network with Denoising Attention Mechanism for Session-Based Recommendation 4

Graph Isomorphic Network with Denoising Attention Mechanism ...

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4th international conference on Computer science and Blockchain, CCSB 2024

作者： Lu, Dingkun Lu, Xiaoang Wang, Zhixin Huang, Jinyu Sichuan University of Science & Engineering Yibin China

ISBN: (纸本)9798350356502

the session-based recommendation system aims to predict items that users may interact with in the future by analyzing historical behaviors in anonymous sessions. this field has long faced two major challenges: first, the integration of non-adjacent item relationships is insufficient, which leads to the neglect of higher-order relationship information of items not directly connected;second, the uncertainty in user behavior introduces noise, negatively affecting prediction accuracy. these two issues collectively hinder the recommendation system's ability to accurately capture users' true intentions. To address these problems, this paper proposes a new model named IGANRec. IGANRec learns two-level item embeddings through session graphs and relational graphs, utilizing the powerful aggregation capabilities of Graph Isomorphism Networks (GIN) for initial encoding. Furthermore, by introducing multiple information highways, the model can select effective information before and after GIN and uses a self-attention mechanism to generate target representations. then, the model performs denoising on the global representation. In the relational graph, a target-aware global attention mechanism is used to generate the denoised relational representation. By integrating information from both graphs and considering the similarity of sessions from the perspective of user intent through the Intent Neighborhood Collaboration Module, the session's intent representation is obtained. Experimental results on three benchmark datasets show that the proposed IGANRec exhibits the improvement of performance compared to existing state-of-the-art models. © 2024 IEEE.

关键词： Graph neural networks

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Deadlock-Free Verification of the SACI-1 OBC Using Circus Time Action and FDR 4

Deadlock-Free Verification of the SACI-1 OBC Using Circus Ti...

引用

4th IEEE international Maghreb Meeting of the conference on sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Agina, Duaa Adel Masaud Elsherif, Adnan University of Tripoli Computer Science Department Tripoli Libya

ISBN: (纸本)9798350372632

Formal Specification and Validation of Computer Systems is an important development methodology in software engineering, especially in life-critical systems. Formal software development involves the use of formal specification languages to specify the system behavior and the implementation of the specification. Model checking tools and theorem proving tools can be used to validate if the system implementation satisfies the specification or, to explore specific characteristics such as liveness and deadlock free. Circus Time Action (CTA) is a formal specification language developed to add time related observations to the semantic model of Circus family of formal specification languages. A mapping between Circus semantic model, based on Unified theories of programming (UTP), and CTA semantic model exists. Such mapping permits a sound syntax mapping from CTA to the original Circus syntax. Additionally, a framework for validating the untimed properties of CTA has been created, enabling the validation of specifications properties written in CTA using a variety of model checking techniques and, therefore, including Failures Divergence Refinement (FDR). In the present work, we explored the use of such a framework by applying it to a case study. We used CTA to specify the implementation of First Brazilian Scientific Application Satellite (SACI-1) On-Board Computer (OBC). Further, we applied the mapping between Circus Time Action and Circus to obtain an untimed specification of the SACI1-OBC. FDR is used to check for deadlock-free property of the SACI1-OBC specification. the obtained results were fully consistent with the previous similar work of SACI-1-OBC. © 2024 IEEE.

关键词： Formal specification

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Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems 4

Neuromorphic-Driven Agentic AI for Autonomous Decision-Makin...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Samata Mehta, S. Akshaya, H.L. Shilpashree, N. Jadhav, Girish Mitra, Abhijit Department of Cyber Security Karkala India Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department Of Electrical Engineering Post Limda India Pune India

ISBN: (纸本)9798350352931

Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. this paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. the findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications. © 2024 IEEE.

关键词： Intelligent systems

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Automatic Diagnosis of Congenital Conditions in Pediatric Age Using Pupillometry with Machine Learning 4

Automatic Diagnosis of Congenital Conditions in Pediatric Ag...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Vijayakumari, E.N. Swetha, Kora. Kumar, Atul Tulasi, Rajesh Mlr Institute of Technology Computer Science and Engineering Hyderabad India The ICFAI University Department of Fst Jharkhand India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350300888

Children who suffer from inherited retinal illnesses have a significantly reduced ability to see. this can cause damage to either the inner or outer retina, which can lead to blindness in infants. the clinical measures that clinics use to screen children and young adults for visual impairments are insufficient. these are not the tactics that should be used with infants. Chromatic Pupillometry is a state-of-the-art method that has been developed to evaluate both the exterior and inner functioning of the retina. this study has used a technology called the Support Vector Machine (SVM), which is based on machine learning and makes use of pupillometry. Additionally, a system called 'Clinical Decision Support System (CDSS)' is used. It is guaranteed that the desired outcome will be accomplished. © 2023 IEEE.

关键词： Support vector machines

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Impact of Crypto art Sentiment on art Valuation 4

Impact of Crypto Art Sentiment on Art Valuation

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4th IEEE international Multidisciplinary conference on engineering Technology, IMCET 2023

作者： Othman, Mohamed Nasr, Elie Nasr, Joe Karam, Liliane American University of Science and Technology Computer Science Department Beirut Lebanon University of Massachusetts Computer Science Department BostonMA United States American University of Science and Technology Communication Art Department Beirut Lebanon

ISBN: (纸本)9798350313826

this paper examines the impact of sentiment on the value of Non-Fungible Tokens (NFTs) in the cryptocurrency market. Non-fungible tokens (NFTs) have gained significant attention in recent years as a new form of digital art investment. However, there is limited research available on the relationship between NFT sentiment and art value. In this paper, we sought to address this gap by using OpenSea, the largest NFT marketplace, and VADER (Valence Aware Dictionary and Sentiment Reasoner), a sentiment analysis tool, to gather and analyze NFT sentiments. the OpenSea API was utilized to extract over one million NFT transactions. the data collected included information such as NFT asset name, description, date of transaction, and transaction price. After the data was extracted, it was ingested into a data storage system for analysis. the NFT sentiment was then analyzed using the VADER sentiment analysis tool, which uses a lexicon of words to determine the sentiment polarity of a given text. Before analyzing the sentiment, the data was preprocessed to remove any irrelevant information and to ensure that the data was in a consistent format. this involved cleaning the data to remove any duplicates, outliers, and missing values. the results indicate that most artists tend to use neutral words when describing their art, but the average price of NFTs with negative sentiment is higher than those with positive sentiment. this paper contributes to a better understanding of the factors that influence NFT value and offers a valuable resource for those interested in NFT investment such as digital artists, digital art collectors, blockchain-based companies, or anyone with basic knowledge in blockchain and non-fungible token. the paper approach is unique in terms of topic, problem-solving and the count data used in the experiment. the impact of digital art sentiment on art value is not only examined in this paper, but it opens the way for future research. © 2023 IEEE.

关键词： Sentiment analysis

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A Discussion of Improving Efficiency and Addressing Obstacles with the use of Data mining and IoT for crop production 4

A Discussion of Improving Efficiency and Addressing Obstacle...

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bisht, Sandeep Alabdeli, Haider Alsailawi, H.A. Kirubananthan, P. Dhabalia, Dharmesh Attiwi, Madi Hashem Al-Khuzaie, Mohammed Y. Mezaal, Ali Abdulkareem Graphic Era Hill University School of Management Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur SRM Nagar Chennai India Vishwakarma Institute of Information Technology Pune India Al-kunooze University College Dental Department Basra Iraq National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

the advancement of industries such as supply chain management, smart grids, and healthcare has been made possible by the Internet of things (IoT), which also improves people's daily lives and interactions. However, the growing size and complexity of IoT networks pose challenges in security, authenticity, reliability, and scalability. Blockchain emerges as a solution due to its security, transparency, reliability, and traceability. this study examines the problems with Internet of things (IoT) systems, talks about how blockchain might help solve them, assesses the state of the art, and suggests a two-layer architectural solution that combines blockchain and IoT utilizing dew and cloudlet computing. this study presents an analysis of a blockchain-based trade origination and verification system, comparing its performance to the Hyperledger Fabric framework. the system comprises three phases: Trade Origination, Verification & Authentication, and Commit. through experiments, the system demonstrates superior performance in transaction validation, block closing times, and memory scalability as the number of trades and participating nodes increases. © 2024 IEEE.

关键词： Smart contract

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Open Standards for Integrating Datacube Backend, Catalog, and Visualization Tools: A Case Study 4

Open Standards for Integrating Datacube Backend, Catalog, an...

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4th international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Baumann, Peter Computer Science and Electrical Engineering Constructor University Bremen Germany

ISBN: (纸本)9798350391183

Today, service-oriented architectures dominate the field over monolithic systems. Manifold building blocks are available, with a likewise wide range of application programming interfaces (APIs). Standards can help here in that they give guidance for implementers and ease adaptation to further tools coming with the same interface, thereby widening opportunities for composing services. Some argue that open source is the key because the source code can be inspected anytime;the author's view, conversely, is that open standards are key to interoperability. this case study connects the thREDDS data catalog and metadata management server, the ncWMS Web mapping extension of the OGC Web Map Service (WMS), and the rasdaman array database management system, thereby providing an integrated approach to handle Big Geo Data. In particular, the resulting system combines efficient raster query processing of rasdaman with flexible metadata search in thREDDS and intuitive visualization through ncWMS. © 2024 IEEE.

关键词： Interoperability

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A Comparative Study on Different Optimization Algorithms for Solving Economic Dispatch Problem 4

A Comparative Study on Different Optimization Algorithms for...

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4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Foqha, Tareq Alsadi, Samer Refaat, Shady S. Arab American University Department of Electrical Engineering Jenin Palestine Palestine Technical University-Kadoorie Department of Electrical Engineering Tulkarm Palestine University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798350306262

Economic dispatch is one of the mathematical optimization problems in power system operation and planning. It aims to find the most efficient output for generating units that meets the demand of the load at the lowest possible cost while satisfy all operational constraints. this paper examines numerous methods to address the economic dispatch problem, including deterministic approaches like the Lagrange multiplier method, metaheuristic optimization algorithms such as the Genetic Algorithm, the Firefly Algorithm, the Harris-Hawks optimization algorithm, and their hybridizations. the study also utilizes PowerWorld Simulator, a software package that solves economic dispatch problems using sequential linear programming. Two different case studies have been conducted on IEEE 5-bus and 30-bus test systems for demonstrating the effectiveness of the proposed algorithms. the results of various case studies showed that the deterministic methods are the most effective for solving the economic dispatch problem. It was also shown that the hybrid algorithms, which combine the strengths of different optimization techniques, can achieve a significant enhancement in total cost compared to the conventional metaheuristic methods. © 2024 IEEE.

关键词： Genetic algorithms

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Interactive Machine Learning Framework for Predicting Asthma Health Conditions Using XGBoost 4

Interactive Machine Learning Framework for Predicting Asthma...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Edward, Chimba S. Ekpo, Raphael H. Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Crawford University Department of Computer and Mathematical Sciences Ogun StateFaith Igbesa Nigeria

ISBN: (纸本)9798350353266

Asthma is a far reaching non-communicable sickness that affects individuals of any age and is a significant danger to human wellbeing worldwide, especially in low and middle-income countries. Its prevalence is substantial, and it remains one of the major contributors to global death rate. A substantial number of asthma development prediction models, and machine learning based approaches, exist. the problem associated with conventional methods of asthma prediction is low predictive accuracy of the model. the objective of this study is to develop an interactive Machine Learning (iML) framework for predicting Asthma Health conditions in selected African countries. the model was tested using dataset obtained from National Survey of Children's Health (NSCH). the machine learning technique used was XGBoost, and model development including preprocessing, training, and tested were carried out using Python programming language on Google Collaboratory (Collab) environment. the results of benchmarking of the performance accuracy of XGBoost shows 90.6% which is higher than the existing results in literature for Decision Tree (90.49%), KNN (90.28%), Naïve Bayes (82.7%). the study found out that the model developed by the researchers performed comparatively well in predicting asthma when compared with some existing state-of-the-art machine learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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