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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Dahanukar, Soham Ajit Nagarhalli, Tatwadarshi P. Pawar, Shruti Sunil Vartak, Rujuta Vidyavardhini's College of Engineering and Technology Dept of Artificial Intelligence and Data Science Mumbai India Vidyavardhini's College of Engineering and Technology Mumbai India

ISBN: (纸本)9798350368413

White blood cells (WBCs) must be accurately classified in order to be used in medicine for both therapeutic and diagnostic purposes. The performance of two domains that are very efficient in classifying blood cells are examined in this work, with particular emphasis on the models' robustness and accuracy of classification. The development of Convolutional Neural Networks (CNNs) and, more recently, Large Image Models (LIMs) has drastically changed the landscape of image recognition. This study examines how well these two architectures perform in comparison when used for image-based tasks with uncommon datasets. Although CNNs have proven to be exceptionally powerful in certain fields, LIMs hold the potential to achieve more broad picture understanding. To do this, this research study compares a pre-trained CNN and a cutting-edge LIM. These results clarify the classification of each strategy, showing that CNNs can potentially beat LIMs at jobs requiring specialized expertise. This work adds to the current discussion on model selection in image analysis by highlighting the significance of dataset properties in choosing the best architecture. This research study intends to direct the creation of more efficient image recognition systems for a variety of applications by comprehending the trade-offs between generalization and specialization. © 2024 IEEE.

关键词： Convolutional neural networks

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A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

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Computers, Materials & Continua 2024年第6期79卷 4149-4167页

作者： Yan Xiang Xuedong Zhao Junjun Guo Zhiliang Shi Enbang Chen Xiaobo Zhang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming650500China Kunming Enersun Technology Co. Ltd.Kunming650217China

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information *** previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous ***,a unified CNER is often needed in real-world *** studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified ***,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved *** this study,we enhance the character-pair grid representation by incorporating both local and global ***,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation *** devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character *** approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition *** conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and *** results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

关键词： Chinese named entity recognition character-pair relation classification grid tagging U-shaped segmentation network

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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Computer Modeling in engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第2期5卷 698-708页

作者： Dutta, H Pallab Jyoti Bhuyan, M.K. Neog, Debanga Raj Macdorman, Karl F. Laskar, R.H. Department of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati781039 India Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Assam Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute ofTechnology Assam Guwahati781039 India School of Informatics and Computing Indiana University IN46202 United States Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India

In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: 1) OUHANDS and 2) NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved. © 2023 IEEE.

关键词： Deep neural networks

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Robust design of damping controller for power system using a combination of snake optimisation algorithm and optimal control theory

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International Journal of Energy technology and Policy 2024年第1-2期19卷 171-215页

作者： Agrawal, Niharika Khan, Faheem Ahmed Gowda, Mamatha Department of Electrical and Electronics Engineering Ghousia College of Engineering Karnataka Ramanagaram District 562 159 India Department of Artificial Intelligence and Data Science BGS College of Engineering and Technology Mahalakshmi Puram Karnataka Bengaluru560 086 India

Low-frequency oscillations (LFO) are created in the power system due to various disturbances. The LFO if not controlled, grows and causes the system separation. There is a huge financial loss due to the interruption of the power supply caused by disturbances. With the increasing complexity of the modern power system, there is a need for the design of a more accurate and detailed modelling. An Advanced Heffron Phillips Model (AHPM) is developed with a higher order Synchronous Generator Model 1.1, based on ten K-Constants for stability improvement. This AHPM employs the combination of snake optimisation algorithm (SOA) and linear quadratic regulator (LQR) from optimal control theory. The highest damping ratio (99.98%) is obtained by AHPM in coordination with PSS, and TCSC based on SOA and LQR. For various parameters, the settling time ranges from 1.5 to 2.0 seconds. This AHPM is robust and capable of meeting the challenges of grid integration with renewables. © 2024 Inderscience Enterprises Ltd.

关键词： Damping

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Parallelism in Cloud Architecture using Implicit Partition Clustering Framework 4

Parallelism in Cloud Architecture using Implicit Partition C...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sudharson, D. Diwakaran, M. Senthil Kumar, V. Sukirtha, S. Afsheen Zaahrah, A. Karthick, A. Artificial Intelligence and Data Science Kumaraguru College of Technology Tamil Nadu Coimbatore India Sri Krishna College of Eng & Tech Dept of IT Tamil Nadu Coimbatore India Kumaraguru College of Technology Dept of CSE Tamil Nadu Coimbatore India Shiv Nadar University Chennai Artificial Intelligence and Data Science Tamil Nadu Chennai India Kumaraguru College of Engineering Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Kumaraguru College of Technology Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

To overcome the reliability issues concerning data's exponential growth, many optimization techniques are deployed in artificial intelligence. Parallelism in a distributed environment provides an effective solution to handle this expansion efficiently. Catering to real-Time and batch-Time requirements in distributed environments, Spark and Flink are two prominent, powerful, versatile, fault-Tolerant, open-source frameworks providing a unified platform for large data processing. Primarily analyzing the efficiency optimization through implicit partitioning clustering algorithms and adaptable K-means. Combining a few methods for adaptable K value enhances the robustness of the clustering solution, which effectively yields optimized performance. © 2024 IEEE.

关键词： Cloud platforms

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A Comparative Analysis of Machine Learning Algorithms for Healthcare Applications 4

A Comparative Analysis of Machine Learning Algorithms for He...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Prabha, B. Vishnu Manikanda Kumaran, K. Manikandan, S. Murugan, S. Palani Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Chennai India E.G.S. Pillay Engineering College Department of Information Technology Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence and Data Science Nagapattinam India

ISBN: (纸本)9798350364729

Healthcare has increasingly embraced machine learning (ML) algorithms, which have been shown to enhance diagnostic accuracy, treatment effectiveness, and ultimately improve patient outcomes. Despite the diversity of medical data and application requirements, it is still difficult to determine which algorithm will work best for a given healthcare task. The focus of this paper is on comparing various medical laboratory (ML) algorithms that are commonly used in the healthcare domain. The performance of algorithms is evaluated across a range von der Gesundheitswesen Aufgabenstellungen (disease forecasting, medical imaging analysis, treatment recommendation), and are then used to evaluate them using publicly available datasets. The efficacy of algorithms is measured using performance metrics like accuracy, sensitivity (for some algorithms even faster than the Precise Lotion Function), specificity and area under the receiver operating characteristic curve (AUC-ROC) Our analysis provides insight into the strengths and limitations of each algorithm and helps healthcare professionals and researchers choose algorithms for specific healthcare applications. © 2024 IEEE.

关键词： Contrastive Learning

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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