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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bore, Joy Dhale, Trishul Kumar, Praveen Deshmukh, Gauri S. Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Meghe Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Department of Applied Chemistry Nagpur India

ISBN: (纸本)9798331528713

The paper provides a synoptic view of portable biomedical point-of-care devices for blood coagulation detection, emphasising the state-of-the-art technology adopted and its use in the medical industry. These devices give fast and precise results at the point of care and are indispensable for diagnosing coagulation and perceiving thrombotic states. It brought into focus the issues of monitoring blood coagulation across different areas of practice, such as the ICU, operative and perioperative services, and ambulatory care. This paper also highlights new trends in the development of sensors, miniaturisation and the integration of digital health solutions. Further, it explains how these devices are advantageous and disadvantageous in terms of speed, sample requirement, standardisation, calibration, and user-friendliness. Last but not least, the regulation and market uptake issue is discussed, focusing on how portable coagulation detection devices can revolutionise patient care and access to healthcare. Finally, the authors state that the paper should inspire further research to develop new solutions and collaboration to address obstacles and underpin advancements in the field. © 2024 IEEE.

关键词： Portable equipment

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VGG Models in Image Captioning: Which Architecture Delivers Better Descriptions? 18

VGG Models in Image Captioning: Which Architecture Delivers ...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Maaz, Ahmad Abbas, Shaheer Ali, Raja Hashim Ahmed, Iftikhar Khan, Talha Ali University of Europe for Applied Sciences Department of Business Think Campus Potsdam14469 Germany GIK Institute of Engineering Sciences & Technology Faculty of Computer Science & Engineering Topi23460 Pakistan

ISBN: (纸本)9798331508692

The recent advancements and a flurry of deep learning architectures in the fields of computer vision and natural language processing have greatly benefited the task of creating natural language descriptions for images in recent years. These advancements have proved to be the turning point with popular practical applications in artificial intelligence, as well as for driving cross-disciplinary developments. However, not many studies have investigated the performance of different convolutional and LSTM architectures for image captioning, motivating our study to bridge this gap. This study compares the performance of VGG16 and VGG19 models on the Flickr8k dataset. Image features are extracted using pretrained VGG16 and VGG19 models, textual input is tokenized, and a unique Encoder-Decoder architecture is built. We determine the effect of training length on captioning proficiency by conducting comprehensive tests with varied epochs. The BLEU ratings reveal detailed insights into the models' language generating capabilities, demonstrating comparable performance across VGG16 and VGG19. Our work fills this gap by assessing the efficiency of several neural designs on a given dataset. The findings highlight the complexities of model convergence, and the need for different evaluation measures and defined benchmarks in the picture captioning sector. We determine the relationship between training epochs and model performance in this study, and provides useful observations for researchers and practitioners. We systematically examine the design of various convolutional neural networks, and provide a basis for future research in the ever-advancing world of image captioning. © 2024 IEEE.

关键词： Convolutional neural networks

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems science & Engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Automatic Timetable Generation using Neural Networks Trained by Genetic Algorithms 18

Automatic Timetable Generation using Neural Networks Trained...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Abbas, Shaheer Maaz, Ahmad Ali, Raja Hashim Khan, Talha Ali Ahmed, Iftikhar University of Europe for Applied Sciences Department of Business Think Campus Potsdam14469 Germany GIK Institute of Engineering Sciences & Technology Faculty of Computer Science & Engineering Topi23460 Pakistan

ISBN: (纸本)9798331508692

Scheduling is a crucial task in various real-life situations, including educational institutions where creating a feasible timetable is a complex challenge due to numerous constraints. The educational timetable scheduling problem is NP-hard, making it a difficult problem to solve optimally within reasonable time frames. Traditional methods like Genetic Algorithms (GAs) and Evolutionary Algorithms (EAs) have been applied to this problem with varying degrees of success. In this paper, we introduce a novel approach for automatic timetable generation using neural networks trained by genetic algorithms. Our method leverages the strengths of neural networks in pattern recognition and GAs in optimization to effectively handle the constraints and complexities of timetable scheduling. Experimental results demonstrate a significant improvement in scheduling accuracy and computational efficiency. We compare the performance of our proposed method with traditional GAs and demonstrate that the neural network approach trained by GAs achieves superior results in terms of speed and solution quality. This research paves the way for more efficient and intelligent scheduling systems in educational environments. © 2024 IEEE.

关键词： Genetic algorithms

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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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The Blockchain technology Integration with Internet of Things for Digital supply Chain Transformation 2

The Blockchain Technology Integration with Internet of Thing...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Shehadeh, Maha Al-Gasaymeh, Anwar S. Ahmad Almahadin, Hamed Rustom Al Nasar, Mohammad Al-Trad, Esra'A B. Applied Science Private University Finance and Banking Science Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Applied Science Private University Accounting Department Amman Jordan

ISBN: (纸本)9798350335644

Numerous global supply chains lack the tools necessary to survive in the world. Because supply chain managers must change their focus from cost-cutting to enabling new processes, as well as making businesses more connected and flexible, in order to create value across the enterprise. Digitalization controls the networks that transforms today's supply chains. However, supply chain management consists of intricate systems and procedures that span numerous ecosystems, partners, and geographical regions and are managed by a number of different stakeholders. The complexity of current supply chain management systems has resulted in a lack of effectiveness along the whole value chain, which delays transactions and erodes customer and organization trust globally. Therefore, in order to consider this concept to be examine, empirical research was conducted to assess the blockchain technology integration and internet of things and their impact on digital supply chain transformation. This research used a quantitative approach, descriptive and analytical methods used for assessment as well as a convenient random sampling technique. Data of 187 respondents used after screening 390 received responses from hardware manufacturing companies located in Dubai UAE. Data was tested using SmartPLS 4.0 by applying, reliability, validity, discriminant validity and hypothesis testing. The findings revealed a positive significant relationship between blockchain technology and digital supply chain transformation utilizing internet of things in an upgrading way. © 2023 IEEE.

关键词： Internet of things

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Compound AI System for Personalized Learning: Integrating LLM Agents with Knowledge Graphs

Compound AI System for Personalized Learning: Integrating LL...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Eduard Antonov Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Russia

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Personalized learning plays a critical role in enhancing educational outcomes by customizing experiences to meet individual learners' unique needs, preferences, and abilities. Although Large Language Models (LLMs) offer promising opportunities for personalization through natural language interactions, they face challenges related to maintaining long-term context and ensuring traceability of a learner's evolving knowledge. This paper introduces a composite AI framework that merges an LLM-powered conversational agent with Knowledge Graphs (KGs) structured according to Knowledge Space Theory (KST), enhanced by advanced retrieval tools and a robust long-term memory mechanism. This system autonomously orchestrates meaningful, context-aware dialogues while utilizing function calling to access various tools, effectively bridging the gap between dynamic interactions and structured knowledge representation. Our empirical evaluations indicate that the proposed system excels in maintaining coherent dialogues, accurately tracking learner progress, and providing reliable, personalized educational support. By addressing the limitations of standalone LLMs and traditional adaptive learning systems, this integrated approach signifies a notable advancement in intelligent educational technology.

关键词： Large language models Retrieval augmented generation Natural languages Knowledge graphs Educational technology Memory modules Learning (artificial intelligence) Reliability theory Robots Intelligent control

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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