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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Larsen, Thea Kim, Seho Kennedy, John Liu, Jingjing University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Earth Resources and Materials Geological and Nuclear Science Wellington New Zealand The University of Auckland Department of Chemical and Materials Engineering Auckland New Zealand

ISBN: (纸本)9798350376067

Hydrogen is a potential clean energy source that minimises environmental impacts but is currently economically prohibitive due to the low energy conversion efficiency. In order to improve the economic viability, this paper investigates improving the efficiency of hydrogen electrolysis. The prototype power electronics presented in this paper provide low-frequency AC or pulsed current to a proton exchange membrane (PEM) electrolyser to improve the operational efficiency up to 4.2% in contrast to the typical constant current or voltage excitation. © 2024 IEEE.

关键词： Clean energy

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By artificial intelligence algorithms and machine learning models to diagnosis cancer

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Materials Today: Proceedings 2023年 80卷 2969-2975页

作者： Agarwal, Seema Yadav, Ajay Singh Dinesh, Vennapoosa Vatsav, Kolluru Sai Sri Prakash, Kolluru Sai Surya Jaiswal, Sushma Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus U.P. Ghaziabad India Software Engineer Mechanical Engineering National Institute Of Technology Mizoram Aizwal Mizoram India Computer Science Engineering Gitam university Andhra Pradesh Visakhapatnam India Bilaspur India

Cancer is one of the world's most critical health issues. Many in the past decade More accurately, diagnostic tests and methodologies have been enhanced. Tests are categorised into imaging tests, endoscopic procedures Includes testing for biopsy and cytology. These diagnostic tests yield huge numbers. volumes of data that must be evaluated and differentiated by specialists Between tumours that are benign and malignant. Artificial intelligence now (AI) Large quantities of diagnostic imaging may be examined using applications Improved accuracy to enhance health system efficiency. New AI algorithms technical advances and computer enhancements Hardware may be used to train diagnostic neural artificial networks Experience with a wide range of scans. Already a thorough understanding and Machines can educate computers to compare and assess models Huge quantity of cancer scanning data. Highly diagnostic skills software has been evaluated and compared to the conventional Cancer specialists' diagnostic tools. Their precision is much increased and regarded highly effective in early diagnosis and extended forecasting Different cancers. Scientists have become systems of artificial intelligence (AI). that may exceed human specialists in the prognosis of breast cancer and A great deal earlier diagnosis. Similarly, informatics developed an AI algorithm. and profound learning algorithms that may forecast to which persons Develop lung cancer using low dose CT analysis (computerized tomography) Lung scans. Lung scans. Use of the convolutionary neural network recently (CNNs) was employed in the invasion depth diagnosis of Gastric endoscopy-based gastric cancer. Studies have also been shown AI techniques paired with imagery may have a significant influence early diagnosis with algorithm-guided identification of oral cancer results Heterogeneity of the oral lesion. This review gathered some pretty interesting information Research publications on the topic. © 2021

关键词： Cytology

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

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A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 2024 IEEE.

关键词： Adversarial machine learning

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An Online Integrated Development Environment for Automated Programming Assessment Systems 17

An Online Integrated Development Environment for Automated P...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9789897587467

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

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GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

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Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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AI-Based Discourse Analysis System (ADAS) for Improved STEM Education 14

AI-Based Discourse Analysis System (ADAS) for Improved STEM ...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Gottam, Varun Dehbozorgi, Nasrin Lee, Soon Kennesaw State University Department of Computer Science United States Kennesaw State University Department of Software Engineering United States Kennesaw State University Department of Elementary and Early Childhood Education United States

ISBN: (纸本)9798350352801

Recent advancements in Artificial Intelligence and Natural Language Processing play a key role in building systems that facilitate machine comprehension of human language. The integration of Natural Language Processing offers myriad educational benefits, including automated assessments, interactive tutorials, and sophisticated feedback systems, thereby enhancing the efficiency of teaching and learning endeavors. Particularly in science education, the analysis of dialogues between educators and students holds significant importance. Researchers have developed the Classroom Discourse Analysis Tool, a framework for categorizing discourse patterns in science classrooms, thereby enriching educational interactions. However, the manual application of this tool poses challenges due to its timeconsuming and resource-intensive nature. To address this challenge, we propose developing an AI-based Discourse Analysis System tailored for educational settings. The primary objective is to create a user-friendly web application accessible to educators nationwide, enabling them to upload classroom discussion transcripts and receive immediate, comprehensive feedback on discourse types. By harnessing the capabilities of AI, this system aims to provide insightful analyses of classroom dialogues, scientific discourse patterns, and student engagement levels. The incorporation of AI into the education sector marks a significant milestone in the advancement of educational technology, promising to revolutionize teaching methodologies and enhance learning outcomes. © 2024 IEEE.

关键词： Students

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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Towards agile collaborative multi-view modeling with inconsistency tolerance

Towards agile collaborative multi-view modeling with inconsi...

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2024 Symposium on software Technologies: Applications and Foundations, STAF 2024 Workshops: 2nd International Workshop on Agile Model-Driven engineering, AgileMDE 2024, 1st Large Language Models for Model-Driven engineering Workshop, AgileMDE 2024 and 4th International Workshop on Model-Driven engineering for Smart Internet-of-Things Systems, MeSS 2024

作者： Shahvari, Yaser Sharbaf, Mohammadreza Rahimi, Shekoufeh Kolahdouz Tehrani, Sobhan Yassipour MDSE Research Group Department of Software Engineering University of Isfahan Isfahan Iran School of Arts University of Roehampton London United Kingdom Computer Science Department Faculty of Engineering University College London London United Kingdom

Model-driven engineering (MDE) copes with the complexity of software development by using the principles of separation of concerns and automatic transformation. In MDE, stakeholders from diverse domains collaborate concurrently on different models to quickly analyze, design, and generate complex software-intensive systems. To adopt an agile development approach in MDE, an inconsistency tolerance framework is needed. This framework postpones the resolution phase for incompatibilities caused by online cooperation, allowing temporary incompatibilities to be automatically fixed while requiring resolution only for the remaining conflicts at the appropriate time. In this paper, we propose a general framework for identifying and diagnosing inconsistencies, making decisions regarding inconsistency tolerance or intervention for resolution, and outlining tolerance strategies in agile systems modeled as multi-views. Our framework comprises three primary phases: detection, analysis, and tolerance. It is designed to identify inconsistencies in multi-view models, which are inherently more complex than single-view models. The tolerance phase employs strategies to tolerate inconsistencies, enhancing the flexibility of the agile development approach for collaborative multi-view modeling. © 2024 Copyright for this paper by its authors.

关键词： software design

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