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Virtual Training Simulator and Medical Decision Support for Personalized Assessment of Severity and Prediction of the Patients' State (the Case of Acute Pancreatitis): Investigation of the Problem Area and Identification of the Problem 4

Virtual Training Simulator and Medical Decision Support for ...

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4th International conference on Technology Enhanced learning in Higher Education, TELE 2024

作者： Rumovskaya, Sophiya Paramzin, Fedor The Federal Research Center 'Computer Science and Control' The Russian Academy of Sciences Kaliningrad Branch Kaliningrad Russia Central City Clinical Hospital Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9798350353532

Multimorbid, polyetiological diseases (including acute pancreatitis) have a diverse and often unpredictable clinical picture intersecting with a number of other diseases. they are often characterized by a phased course and a high probability of developing severe complications and mortality. this changes and complicates the clinical picture and clinical course of the disease as well as the process of personalized assessment of severity and prediction of the condition of a complex object in medicine (the patients' body). As a result, education, training and advanced training in this area are also complicated. In order to improve the quality of medical intelligent and training systems, a new class of intelligent systems is proposed, namely cooperative self-adjusting hybrid intelligent systems 'Virtual councils for personalized assessment of severity and prognosis of patients' condition' (the case of acute pancreatitis). they are supposed to be developed within the framework of the problem-instrumental methodology of Professor A.V. Kolesnikov et al. this paper presents the results of the first stage of this methodology, namely: the results of an analysis of the specifities of the problematic environment for the diagnostic and prognostic assessing of the state of the patients' body using the example of acute pancreatitis;a functional model of the decision-making process regarding the problem of the personalized assessment of severity and predicting the condition of a patient with suspected acute pancreatitis as well as the results of its identification. © 2024 IEEE.

关键词： intelligent systems

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Determination of Fault Location in Active Distribution System Using Machine learning with HT Data Extraction Method 4

Determination of Fault Location in Active Distribution Syste...

引用

4th IEEE International conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Tamrakar, Archana Mishra, Arpit Shivam Sarangi, Saumendra Mnnit Allahabad Electrical Engineering Department Prayagraj India

ISBN: (纸本)9798350383997

Reliable power supply in active distribution system demands restoration of power within minimum time following an outage due to fault. However, recent changes in the DS with penetration of renewables, use of underground cables, smart loads and power quality improvement using smart controllers, limits the application of conventional methods to locate the fault. Further, to minimize the losses and enhance the energy management, smart intelligent meters are added which provides large sets of data and better observability is achieved even during fault. In this work, an intelligent fault location algorithm is proposed to handle huge data sets and deriving sequence components during the fault from these data. Hilbert Transform is applied to the raw voltage and current data collected from smart meters to compute the sequence components which is considered as input to the machine learning based intelligent Random Forest (RF) algorithm. the efficacy of this method is verified through simulation of modified IEEE 33 bus system with renewables in MATLAB/ SIMULINK platform and the RF algorithm is realized through JUPITER NOTEBOOK. the results reveal, the method accurately identifies the faulty section and fault location for different types of faults with and with without high resistance and DGs even with unbalance in the system. © 2024 IEEE.

关键词： Power distribution faults

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Research on Heterogeneous Network Data Fusion based on Deep learning 4

Research on Heterogeneous Network Data Fusion based on Deep ...

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4th International conference on intelligent Communications and Computing

作者： Liu, Minghao Liu, Lipeng Fu, Lei Hu, Zhuohuan Mao, Yu Tang, Xirui Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Peking Univ Coll Engn Beijing Peoples R China Changshu Inst Technol Sch Comp Sci & Engn Suzhou Peoples R China Northeastern Univ Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9798331530372;9798331530365

the advent of the era of big data has led to the emergence of heterogeneous network data fusion as a prominent area of research. Heterogeneous network data is characterised by multi-modality, multi-source, and high dimensionality, which presents significant challenges for traditional data fusion methods. these methods often encounter difficulties in processing such data, including issues such as information redundancy, data inconsistency, and high computational complexity. this paper proposes a heterogeneous network data fusion model based on a deep neural network. the model employs the Multi-Layer Perceptron (MLP) as its fundamental framework, utilising the deep neural network to facilitate joint feature representation learning on data from disparate modalities. the Adaptive Feature Reconstruction Module enables the model to learn the interrelationships between different modalities and to balance the importance of different modal features in the fusion process in a dynamic manner. Furthermore, we introduce an innovative cross-modal attention mechanism, which is capable of effectively capturing the coupling relationship between deep features in heterogeneous data, thereby enhancing the expressiveness and data fusion efficacy of the model. the experimental results demonstrate that the proposed model markedly enhances the accuracy of classification and regression tasks in comparison to traditional methodologies.

关键词： Heterogeneous Network Data Data Fusion Deep Neural Network Cross-Modal Attention Mechanism

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Review of intelligent Methods for Sign Language-to-Speech Translation 4

Review of Intelligent Methods for Sign Language-to-Speech Tr...

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4th IEEE International conference on Advanced learning Technologies for Education and Research, ICALTER 2024

作者： Trujillo, Edita Chafloque Cajusol Castillo, Luis Felipe Aquino, Juri Universidad Tecnológica del Perú Systenms and Computer Engineering Chiclayo Peru Universidad Tecnológica del Perú Dirección de Investigación Chiclayo Peru Universidad Tecnológica del Perú Docente of Systems and Computer Engineering Chiclayo Peru

ISBN: (纸本)9798331510244

In today's digital age, technology has significantly transformed communication. However, for the deaf community, the communication barrier remains a persistent challenge. this article addresses the critical need to develop intelligent systems that translate sign language into speech, highlighting its potential to improve the social inclusion of people with hearing impairment. the study conducts a systematic review of advanced methods, such as the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to identify promising techniques and persistent challenges in this field. the research is based on the analysis of 32 articles selected using strict inclusion and exclusion criteria, following the PRISMA statement. the results reveal that these systems can improve the autonomy of deafblind people, facilitating their access to essential services and promoting their social and occupational inclusion. Furthermore, it highlights the need for solutions adapted to specific local contexts due to the geographical variability in the prevalence of hearing impairment. this work not only contributes to scientific knowledge, but also lays the foundation for the development of more effective and accessible technologies, potentially transforming areas such as education, healthcare and employment for the deaf community. © 2024 IEEE.

关键词： intelligent systems

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Path planning of mobile robot based on improved ant colony optimization algorithm 4

Path planning of mobile robot based on improved ant colony o...

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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhu, Baiqiang Luo, Xi Huang, Rongxin Lu, Fangyao Yanshan University School of Electrical Engineering Qinhuangdao China Beijing Institute of Technology School of Physics Beijing China

ISBN: (纸本)9798331531225

Path planning for mobile robots is a critical research area in intelligent control and automation. To address the issues of slow convergence and susceptibility to local optima in traditional ant colony algorithms within complex environments, this paper introduces a series of improvement strategies. An attenuation function is incorporated to optimize heuristic information, enabling ants to rely more on pheromone-based decision-making in the later stages. Directional guidance angles are added to enhance the directivity of state transition probabilities, accelerating the path search process. Additionally, cubic B-spline curves are employed to smooth the paths, while the 2-opt algorithm is integrated to further minimize path lengths. these enhancements significantly improve the algorithm's efficiency and stability. Simulation results validate the superiority of the proposed algorithm in path planning performance, offering an effective solution for optimizing mobile robot navigation in complex environments. © 2024 IEEE.

关键词： Ant colony optimization

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Application of big data analysis technology in industrial intelligent inspection system 4

Application of big data analysis technology in industrial in...

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4th International conference on Testing Technology and Automation Engineering, TTAE 2024

作者： Chen, Yiming College of Business and Management Jilin University Jilin Province Changchun130012 China

ISBN: (数字)9781510686656

ISBN: (纸本)9781510686649

this paper discusses the current status of the application of big data analytics technology in industrial intelligent inspection system as well as the challenges and opportunities. through combing the related literature, it summarizes three major application scenarios of big data analytics in intelligent inspection system: equipment fault diagnosis, production process monitoring and efficiency management, and safety risk identification and early warning, which provide strong support for the safety, efficiency and quality of industrial production. In addition, we summarize the key technologies applied in this field, including machine learning and deep learning, big data analysis and processing technology, as well as the Internet of things and sensor technology. the application of these key technologies effectively enhances the intelligent level and practical value of the intelligent inspection system. © 2024 SPIE.

关键词： Inspection Intelligence systems Computing systems Inspection equipment Data analysis Industrial applications Deep learning Design Internet of things Safety

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Design of an intelligent Asset Recovery System Based on Deep learning 4

Design of an Intelligent Asset Recovery System Based on Deep...

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4th International conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yang, Fang Guizhou University Faculty of Public Administration Guizhou Guiyang550001 China

ISBN: (纸本)9798331528850

the increasing complexity of financial crimes poses significant challenges for traditional asset recovery methods, which often fail to effectively trace covert fund transfers and complex money laundering schemes. To address these limitations, this paper proposes and implements an intelligent asset recovery system based on deep learning, incorporating advanced computational techniques to enhance the detection and analysis of complex financial transactions. the system utilizes a hybrid CNN-LSTM model to extract both spatial and temporal features from transaction data, while a self-attention mechanism dynamically highlights critical transaction patterns, improving the model's interpretability and recognition capabilities. A multimodal data fusion approach integrates diverse sources, including bank transaction records, social network data, and public financial statements, enabling comprehensive analysis of heterogeneous data. Experimental results demonstrate that the proposed system significantly outperforms traditional methods in key metrics, achieving higher accuracy, precision, recall, and Fl scores. Moreover, it exhibits superior performance in identifying intricate fund transfer chains and adapting to diverse financial crime scenarios. the study not only provides a robust, automated tool for asset recovery but also establishes a foundation for the development of intelligent systems aimed at preventing and combating financial crimes. Future research will focus on enhancing system scalability, optimizing computational efficiency, and integrating additional data sources to further expand its application scope. © 2024 IEEE.

关键词： Decentralized finance

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intelligent In-Car Emotion Regulation Interaction System Based on Speech Emotion Recognition 4

Intelligent In-Car Emotion Regulation Interaction System Bas...

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4th International conference on Computer, Control and Robotics (ICCCR)

作者： Yang, Yuhan Zhang, Yan Zhong, Zhinan Dai, Wan Chen, Yunfei Chen, Mo Southeast Univ Sch Mech Engn Nanjing Peoples R China Nanjing Tech Univ Coll Art & Design Nanjing Peoples R China

ISBN: (纸本)9798350373141;9798350373158

With driving emerging as a common mode of transportation, the automotive industry has increasingly prioritized driving safety and experience. A substantial body of research focused on driving safety and the overall travel experience has underscored the pivotal role of emotions. In this article, we introduce an innovative in-car emotion recognition and interaction system, carefully crafted to intelligently respond to the emotional states of drivers. this system captures real-time emotional data through its user input layer and seamlessly integrates it into the technological architecture layer, residing within the vehicle's CPU. Leveraging cutting-edge deep learning models for emotion recognition, the system's outcomes trigger tailored emotion regulation strategies within the interaction feedback layer. Notably, our study introduces a groundbreaking speech fusion feature, MFCCs+, meticulously crafted for driving contexts. Furthermore, we have optimized the driving speech emotion recognition model using 1D-CNN, resulting in a remarkable 10% improvement in recognition accuracy. Subsequent validation experiments affirm the system's effectiveness in enhancing driving safety. In conclusion, the integration of emotion-based interaction solutions holds immense potential for elevating both driving safety and the overall travel experience within intelligent driving scenarios. this innovation promises to shape the future landscape of automotive travel, offering a safer and more enjoyable journey for all.

关键词： Human machine interaction (HMI) automotive travel feature selection speech emotion recognition machine learning emotion regulation

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CNN Brain Tumor Classification based on Masked Image Using U-Net Segmentation 4

CNN Brain Tumor Classification based on Masked Image Using U...

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4th International Mobile, intelligent, and Ubiquitous Computing conference

作者： Abdelreheem, Tarneem Alaa Salem, Mohammed A-M Elnabawy, Reham H. GUC Media Engn & Technol Cairo Egypt New Giza Univ Sch Informat Technol Giza Egypt

ISBN: (纸本)9798350367782;9798350367775

Brain tumors are a serious issue due to late detection and potential misdiagnosis from varying sizes and locations. the current state-of-the-art in brain tumor detection uses deep learning models, while classification is often achieved through transfer learning. However, these approaches lack sufficient validation on larger datasets. this work improves this by using U-Net for segmentation, transfer learning for classification, and validating on a larger dataset to enhance the accuracy and generalizability. this study utilizes two datasets: Figshare Brain Tumor Dataset and the Br35H dataset. After preprocessing, a U-Net model detects tumor regions with notable 99% accuracy. Transfer learning was used to classify tumors to glioma, meningioma, pituitary or no tumor using VGG16, VGG19, DenseNet121 and InceptionV3. VGG16 and DenseNet121 achieved highest accuracy around 94%. this approach enables more accurate detection and diagnosis, leading faster treatment and better patient outcomes.

关键词： Deep learning Brain MRIs Brain tumor detection Brain tumor segmentation Brain tumor classification U-Net Transfer learning Medical image

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2023 IEEE 4th Annual Flagship India Council International Subsections conference: Computational Intelligence and learning Systems, INDISCON 2023

2023 IEEE 4th Annual Flagship India Council International Su...

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4th IEEE Annual Flagship India Council International Subsections conference, INDISCON 2023

ISBN: (纸本)9798350333558

the proceedings contain 172 papers. the topics discussed include: reconstruction optimization of economic operation of distribution network based on data mining algorithm;ROI segmentation for breast cancer classification : deep learning perspective;evaluation of hotspot performance in IoT enabled wireless sensor network using special intermediate nodes in the network;a university innovation and entrepreneurship information sharing platform based on data mining and classification algorithms;data governance based on full-service data analysis domain of power grid;minimum utilization of energy resource via MAC routing protocol for mobile sensor network;sentimental analysis of movie review based on naive bayes and random forest technique;parallel algorithm of high precision surface modeling based on differential geometry;maximization of achievable sum rate and improvement in outage probability in intelligent reflecting surface (IRS) driven NOMA networks;automated 20-20-20 timers using facial detection and facial recognition;enhancement of routing flexibility by a novel distributed approach for WSN;and improvement of reliable data transmission signal by space diversity technique in wireless communication.

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