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A New Database of Houma Alliance Book Ancient Handwritten Characters and Its Baseline Algorithm 23

A New Database of Houma Alliance Book Ancient Handwritten Ch...

引用

8th International Conference on Multimedia systems and Signal Processing, ICMSSP 2023

作者： Yuan, Xiaoyu Zhang, Zhibo Sun, Yabo Xue, Zekai Shao, Xiuyan Huang, Xiaohua School of Computer Engineering Nanjing Institute of Techology Nanjing China School of Economics and Management Southeast University Nanjing China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing Jiangsu China

ISBN: (纸本)9798400700606

The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and difficult to display, study and publicize. Therefore, the digitization of the recognized ancient characters of Houma League can effectively improve the efficiency of recognizing ancient characters and provide more reliable technical support and text data. This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters. In the database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten characters are collected from the original book collection and by human imitative writing. Furthermore, the decision-level classifier fusion strategy is applied to fuse three well-known deep neural network architectures for ancient handwritten character recognition. Experiments are performed on our new database. The experimental results first provide the baseline result of the new database to the research community and then demonstrate the efficiency of our proposed method. © 2023 ACM.

关键词： Database systems

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AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach

AI Framework for Early Diagnosis of Coronary Artery Disease:...

引用

IISE Annual Conference and Expo 2023

作者： Nasarian, Elham Sharifrazi, Danial Mohsenirad, Saman Tsui, Kwok Alizadehsani, Roohallah Grado Department of Industrial and System Engineering Virginia Tech BlacksburgVA24061 United States Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia

ISBN: (纸本)9781713877851

The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others. In this context, artificial intelligence (AI) can help clinicians identify high-risk patients early in the diagnostic process, by synthesizing information from multiple factors. To this aim, Machine Learning algorithms are used to classify patients based on their CAD disease risk. In this study, we contribute to this research filed by developing a methodology for balancing and augmenting data for more accurate prediction when the data is imbalanced and the sample size is small. The methodology can be used in a variety of other situations, particularly when data collection is expensive and the sample size is small. The experimental results revealed that the average accuracy of our proposed method for CAD prediction was 95.36, and was higher than random forest (RF), decision tree (DT), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN). © IISE and Expo *** rights reserved.

关键词： Decision trees

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Edge Computing Resource Management Based on Genetic Algorithm 13

Edge Computing Resource Management Based on Genetic Algorith...

引用

13th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2023

作者： Cai, Lei Yao, Kangning Xian, Wenbin Gong, Chao North China Institute of Computing Technology The 4th System Department Beijing China University of Science and Technology Beijing Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350331714

Resource management in edge computing has always been a hot research topic. In the scenario of multiple edge servers and multiple tasks, this paper proposes a task offloading model that optimizes the total cost by weighting energy consumption and latency to reduce energy consumption and latency. Based on the proposed task offloading model, an improved genetic algorithm is proposed to solve the optimal task offloading strategy. Simulation experiments have shown that applying improved genetic algorithms to task offloading models can accelerate convergence speed, find the optimal computing offloading strategy, and improve system performance. © 2023 IEEE.

关键词： Genetic algorithms

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Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

引用

2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Malik, Rida Ur-Rehaman, Atta Razzaq, Hamza Bhatt, Chandradeep Kaushik, Keshav Khan, Inam Ullah Riphah Institute Of System Engineering Department Of Information Security Islamabad Pakistan Riphah Institute Of System Engineering Department Of Data Science Islamabad Pakistan Graphic Era Hill University Department Of Cse Uttarakhand India Graphic Era Deemed To Be University Uttarakhand Dehradun India School Of Computer Science University Of Petroleum And Energy Studies Dehradun India Seas Isra University Department Of Electronic Engineering Islamabad Campus Pakistan

ISBN: (纸本)9798350366846

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored. © 2024 IEEE.

关键词： Diagnosis

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Improved Sparrow Search Algorithm with Support Vector Machine for Wind Turbine Fault Detection 2

Improved Sparrow Search Algorithm with Support Vector Machin...

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2nd International Conference on Data Science and Information system, ICDSIS 2024

作者： Nagendar, Y. Alabdeli, Haider Shruthi, B.S. Kamesh, M.R. Sudha, N. Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Dayananda Sagar College Of Engineering Department Of Mechanical Engineering Bengaluru India Angel College Of Engineering And Technology Embedded System Technologies Tamil Nadu India

ISBN: (纸本)9798350381665

Wind turbines are widely used across the globe as a primary means of renewable energy by using the power of wind, it converts kinetic energy into electrical energy, contributing to generating electricity. However, turbines are positioned far from control centers, operating in environments that continuously change and have uncontrollable factors and conditions lead to wind turbine failures. The proposed method using an Improved Sparrow Search Algorithm (ISSA) optimizer along with also used a Support Vector Machine (SVM) classifier helps to detect the faults in turbines. This method uses the Supervisory Control and Data Acquisition (SCADA) dataset for wind turbine fault detection, the optimization using ISSA which is improved by Uniformity Diversity Orientation Strategy (UDOS) to optimize model's internal parameters. This helps to learn pattern during training period and helps to classify the normal and fault patterns. Then in the SVM classification process using the parameters this compares the obtained values with the current fitness value to classify patterns. The performance of the ISSA is analyzed by using accuracy, precision, f1-score, and recall. This shows that the proposed method outperforms existing methods such as K Nearest Neighbors (KNN), stacking classifier, and Feature Simplification Random Forest (FS-RF) in terms of accuracy 98.78%, precision 98.97%, recall 98.24%, and f1-score 98.00%. © 2024 IEEE.

关键词： Wind turbines

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Improved Multistep Model Predictive Control for Six-Step Operation of Traction PMSM Drives

Improved Multistep Model Predictive Control for Six-Step Ope...

引用

2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

作者： Sun, Zhenyao Xu, Shuai Ma, Guangtong Jatskevich, Juri Southwest Jiaotong University State Key Laboratory of Rail Transit Vehicle System Chengdu China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350396867

Model predictive control (MPC) has recently been considered for many permanent magnet synchronous machine (PMSM) drive applications. However, the conventional MPC lacks accurate current tracking and fast dynamic responses in the overmodulation region and the six-step operation, which limits its application areas. This paper proposes a six-step operation scheme based on MPC for PMSM drives in traction applications. The current trajectory long-horizon prediction and the average current-based objective function are introduced to determine the optimal switching instants. To enhance the method's precision, a trapezoidal integration rule has been used in the prediction step. The voltage angle control principle is introduced to clamp the voltage vectors. The effectiveness of the proposed method has been validated by detailed studies. The proposed method is demonstrated to have excellent current tracking accuracy and fast dynamic response in six-step operation, which is an advantage over existing methods. © 2023 IEEE.

关键词： Model predictive control

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COMSOL Modeling of ISFET for pH Sensing and Antigen-Antibody Detection 66

COMSOL Modeling of ISFET for pH Sensing and Antigen-Antibody...

引用

2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Noyan, Utku Ray, Glenn Cobb, Avery Snow Abshire, Pamela Shah, Sahil University of Maryland Department of Electrical and Computer Engineering College ParkMD United States Institute of System Research University of Maryland College ParkMD United States

ISBN: (纸本)9798350302103

This paper describes the development and validation of a comprehensive COMSOL Multiphysics ® simulation model for Ion-Sensitive Field Effect Transistors (ISFETs) used for pH sensing. The model accurately captures the performance of fabricated ISFETs in measuring multiple pH electrolytes and also enables exploration of different sensing membrane materials and their thicknesses and impact on device sensitivity. The study validates the accuracy of the simulation model through laboratory experiments using a commercial ISFET with a Si3N4 layer as the sensing membrane. Based on the optimized ISFET model, the study further extends its scope to develop a simulation model for antigen-antibody detection, enabling the development of point-of-care devices. © 2023 IEEE.

关键词： COMSOL Multiphysics DNA detection ISFET pH sensing sensitivity simulation methodology surface functionalization

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Fine-Tuning Large Language Models for Sentiment Classification of AI-Related Tweets

Fine-Tuning Large Language Models for Sentiment Classificati...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Riad, Md Jahid Alam Debnath, Rahul Shuvo, Mahfuzur Rahman Ayrin, Fateha Jannat Hasan, Nobanul Tamanna, Ayesha Akhter Roy, Prosenjit United States Prairie View A & M University Department of Computer Science TX United States International American University Department of Management Information System Los AngelesCA United States Department of Computer Science and Engineering Bangladesh University of Aiub Department of Csse Bangladesh

ISBN: (纸本)9798331535476

Fine-tuning large language models (LLMs) for sentiment analysis presents a promising avenue for enhancing the understanding of public sentiment surrounding artificial intelligence (AI) topics. This study explores the fine-tuning of Large Language Models (LLMs) for sentiment analysis on AI-related topics using social media data. We collected approximately 20,000 tweets containing hashtags and comments related to AI concerns, including 'AI Danger,' 'AI Ethics,' 'AI Problems,' 'AI Creating Problem on Jobs,' and 'AI Threat.' The dataset captures public sentiment surrounding potential negative impacts of artificial intelligence. Three cutting-edge LLMs, Llama 2, Gemma, Mistral, and Phi 3, served as our foundational models for fine-tuning. To enhance model performance, we implemented advanced prompt engineering techniques, including Chain-of-Thought (COT), Algorithm-of-Thoughts (AOT), Fewshot and EmotionPrompt. Our results demonstrate that Llama 2 achieves consistent performance across all techniques with accuracies ranging from 82.3% to 83.9%. Phi-3 showed the best performance with EmotionPrompt and Fewshot techniques, achieving accuracies of 87.8% and 87.4% respectively. Gemma 7B performed optimally with Fewshot at 84.0% accuracy, while Mistral achieved 85.0% accuracy using the same technique. The research reveals that Fewshot and AOT consistently produce reliable results across models, with Emotion-Prompt showing model-specific effectiveness. Our findings have applications for enhancing the precision and dependability of sentiment analysis instruments that track public opinion and AI-related speech. © 2024 IEEE.

关键词： Tweets

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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VMAP: Matching-based Efficient Offloading in IoT-Fog Environments with Variable Resources 17

VMAP: Matching-based Efficient Offloading in IoT-Fog Environ...

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17th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Morey, Jui Satpathy, Anurag Addya, Sourav Kanti Cloud and Smart System Services Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Missouri University of Science and Technology United States

ISBN: (纸本)9798350307672

Fog computing is a promising technology for critical, resource-intensive, and time-sensitive applications. In this regard, a significant challenge is generating an offloading solution that minimizes the latency, energy, and number of outages for a dense IoT-Fog environment. However, the existing solutions either focus on a single objective or mainly dedicate fixed-sized resources as virtual resource units (VRUs). Moreover, these solutions are restrictive and not comprehensive, resulting in poor performance. To overcome these challenges, this paper proposes a VMAP model addressing the lacunas above. Offloading problem is abstracted as a one-to-many matching game between two sets of entities - tasks and fog nodes (FNs) by considering both preferences. Moreover, the preferences and weights of the parameters are generated using the Analytic Hierarchy Process (AHP). Exhaustive simulations indicate that the proposed strategy outperforms the baseline algorithms, considering average task latency and energy consumption by 35% and 22.2%, respectively. Additionally, resource utilization also experiences a boost by 28.57%, and 97.98% of tasks complete their execution within the deadlines. © 2023 IEEE.

关键词： Fog

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