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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

the proceedings contain 102 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Comparative Analysis Between Fuzzy theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and Image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT Images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images;non-invasive Technique for Detecting Glycosuria through Image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;thz Wideband Metamaterial Absorber for Different Applications.

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Enhancing Alzheimer's Disease Diagnosis through Deep Learning-Leveraging VGG-16 Architecture and Adaptive Boosting 5

Enhancing Alzheimer's Disease Diagnosis Through Deep Learnin...

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5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Irrigisetty, Harshavelu Madhavi, K. Reddy Avanija, J. thokala, Rishi Reddy Varma, K. S. S. Rishi Sree Vidyanikethan Engineering College Department of CSE Tirupati India Department of AI&ML Tirupati India School of Computing Tirupati India SRM Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798350367171

the key to early intervention and patient care with Alzheimer's disease is early detection. the World Health Organization estimates that 50 million people worldwide suffer from dementia, with 60-70 % of cases being Alzheimer's disease (AD). Moreover, the WHO projects that the number of people affected by AD will be nearly tripled by the year 2050, thus, the development of effective detection and management strategies. Our Research suggests a complete method that combines Convolutional Neural networks (CNNs) with transfer learning using the VGG16 architecture, on the one hand, and Adaptive Boosting on the other, in order to improve the accuracy. the dataset includes NonDemented, VeryMildDemented, MildDemented, and ModerateDemented classes which makes the VGG16 model suitable for extraction of features and classification after the preprocessing steps, such as image resizing, conversion to arrays and pixel value normalization, are performed. From the problems faced in the earlier AD detection methodologies we use CNNs, transfer learning and adaptive boosting to improve the task performance. In conclusion, the combination of automated computational methods and sophisticated algorithms such as CNNs and AdaBoost is a progressive approach in AD detection. It provides services like efficiency, consistency, and improved diagnosis that can be applied to the patient care and the healthcare management. © 2024 IEEE.

关键词： Convolutional neural networks

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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Chinese Named Entity Recognition for IC Patent Domain Based on RoBERTa-wwm-ext, GCN and Efficient Global Pointer 24

Chinese Named Entity Recognition for IC Patent Domain Based ...

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5th international conference on Computing, networks and Internet of things (CNIOT)

作者： Lin, Yunxiao Tang, Jiahao Huang, Wenjun Ding, Yanyu Hu, Jianguo Sun Yat Sen Univ Zhuhai Peoples R China Sun Yat Sen Univ Shenzhen Res Inst Shenzhen Peoples R China Dev Res Inst Guangzhou Smart City Guangzhou Peoples R China Dongguan Univ Technol Dongguan Peoples R China

ISBN: (纸本)9798400716751

In the domain of Natural Language processing (NLP), the task of Named Entity Recognition (NER)for Integrated Circuits (IC) Intellectual Property (IP) presents a substantial challenge. this challenge stems from the intricacies of the Chinese language, characterized by its complexity and character-based nature, the unique structural attributes of IP data, and the difficulties in identifying lengthy and nested entities. To tackle these challenges, our study introduces a novel Chinese Entity Recognition approach named RGEGP, meticulously crafted for the IC IP domain. this approach integrates RoBERTa-wwm-ext, Graph Convolutional networks (GCN), and Efficient Global Pointer (EGP) techniques into a cohesive framework. Specifically, RoBERTa-wwm-ext, which is designed for Chinese character attributes through a whole word masking pretraining approach, considerably improves the capability of model in recognizing Chinese entities. Additionally, the incorporation of GCN enhances the ability of model to exploit entity relationships and structured Chinese textual information, markedly boosting its performance in entity recognition within complex Chinese texts. Addressing the occurrence of elongated and nested entities, the EGP method utilizes global pointers coupled with an efficient encoding strategy to directly predict entity boundaries and categories. Experimental results on two datasets, particularly concerning IC domain, show that our methodology outperforms existing leading NER models in this field, achieving significant advancements in precision, recall, and F1 scores.

关键词： Natural Language processing Named Entity Recognition Machine Learning Large Language Model

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Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期130卷 2407-2440页

作者： Mishra, Rashmi Yadav, Rajesh K. K. Delhi Technol Univ Delhi India

WSN consist of tiny sensors that are distributed over the entire network and have the capability of sensing the data, processing it, and conveying it from one node to another. the purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, nature-inspired mechanisms are used to minimize the power utilization of the network. In the proposed study, the Butterfly Optimization Algorithm (BOA) is used to choose the optimal quantity of cluster heads from the dense nodes (available nodes). the parameters to be considered for the choice of the cluster head are: the remaining power of the node;distance from the other nodes in the network;distance from the base station;node centrality;and node degree. the particle swarm optimization (PSO) is used to form the cluster head by choosing certain parameters, such as distance from the cluster head and the BS. the path is chosen by means of the Ant Colony Optimization (ACO) Mechanism. the route is optimized by the distance, node degree, and the chosen remaining power. the inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the base station, and overall power utilization of the network. the results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, and EDEEC (Khan et al. in World Appl Sci J, 2013;Arora and Singh in Soft Comput 23:715-734, 2019;Saini and Sharma in 2010 First international conference on parallel, distributed and grid computing (PDGC 2010), 2010;Elbhiri et al. in 2010 5th international symposium on I/V communications and mobile network, 2010) and correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, and FLION. this review will help investigators discover the applications in this arena.

关键词： Wireless sensor networks Energy efficiency throughput Delay Nature inspired algorithm

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A Comparative Analysis of Trust Management Models for Wireless sensor networks 5

A Comparative Analysis of Trust Management Models for Wirele...

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5th IEEE international Flexible Electronics Technology conference, IFETC 2023

作者： Gangwani, Pranav Perez-Pons, Alexander Upadhyay, Himanshu Florida International University Department of Electrical & Computer Engineering MiamiFL United States

ISBN: (纸本)9798350332094

Wireless sensor networks (WSNs) have diverse applications that are progressively becoming a part of our daily lives. WSNs are currently employed in healthcare, smart homes, traffic monitoring, and aircraft to enhance safety. However, due to the significant number of sensor nodes in WSNs, they are often the target of malicious attacks. thus, there is a necessity for a trust management model that can function as a security mechanism for WSNs by detecting defective or malicious sensor nodes. Once misbehaving nodes are identified, their neighboring nodes can utilize this information to refrain from cooperating with them in functions such as data forwarding and aggregation. Several trust management models have been proposed in the literature to evaluate the trustworthiness of sensor nodes by utilizing their direct and indirect interactions. However, the implementation and interpretation of the trust evaluation criteria in the literature can be challenging due to the lack of available data and the complexity of the trust evaluation scheme. In this paper, we experimentally evaluate three trust management models from the literature. We analyze and compare the effectiveness of these models using different scenarios and determine their effectiveness. the proposed comparative analysis solution serves as a benchmark for future trust management models. © 2023 IEEE.

关键词： sensor nodes

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Machine Learning Approaches for Adaptive Signal processing in 6G networks 5

Machine Learning Approaches for Adaptive Signal Processing i...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Shobarani, R. Kumaresh, Sakthi Dhivya, P. Bharathi, M.J. Santhi, S. Srividhya Dr. M.G.R Educational and Research Institute Department of Cse Chennai India M. O. P. Vaishnav College for Women Dept of Information Technology Chennai India St. Thomas College of Arts and Science Department of Computer Science Chennai India Dr.M.G.R Educational and Research Institute Department of Computer Applications Chennai India

ISBN: (纸本)9798350383522

Due to the very rapid growth of wireless communication networks, the arrival of the 6th Generation (6G) signals the beginning of groundbreaking experiences and new opportunities. the success of 6G networks hinges on their capacity to adapt to complicated and constantly evolving communication environments. this study investigates the critical role of ML in enabling adaptive signal processing in 6G networks. In conclusion, our study proves that ML is crucial for adaptive signal processing in 6G networks and suggests that it could impact the design of future wireless communication systems. the advent of 6G networks, enabled by machine learning, will usher in a new age of connectedness and innovation by satisfying the need for very fast, extremely reliable, and extremely efficient communication. © 2024 IEEE.

关键词： 6G networks Machine Learning Applications Massive MIMO Signal processing

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Retinal Scan Denoising Using Generative Adversarial networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

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5th international conference on IoT Based Control networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. these methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. this paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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Hand Gesture Recognition for Smart Television Using GRU 5th

Hand Gesture Recognition for Smart Television Using GRU

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5th international conference on Data Science, Machine Learning and Applications

作者： Suresh, Garugu Yaswanth Lakshmi Sravani, Ravinuthala Gayatri Venkata VIT AP Univ Sch Comp Sci & Engn Inavolu AP Secretariat Amaravati Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Even with the rapid advancement of technology, using a television still requires a physical remote control. Apart from occasionally losing sight of the television remote control, we also sometimes run out of batteries. the goal is to discover an effective way of controlling television and develop 3D hand gesture-based smart television control using Gated Recurrent Unit (GRU). the results of the research show that hand gesture recognition-based interface technology is capable of performing the majority of smart TV operations. It is a comfortable and delightful experience for consumers. the existing research features static image gesture recognition with predefined models for training in addition to the existing research the proposed model features sample video dataset, Custom design with Gated Recurrent Unit. the suggested model is trained using five hand gestures. the camera positioned on the TV continually records the motions. Each gesture is associated with a certain command. the proposed model has achieved an accuracy of about 94% in recognizing the gestures.

关键词： Smart Television Image processing Deep Learning Convolutional Neural networks Gated Recurrent Unit (GRU)

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Research on Outdoor Mobile Robot Localization and Mapping Algorithm Based on Multi-sensor Fusion 5

Research on Outdoor Mobile Robot Localization and Mapping Al...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Liu, Biao Xiang, Shuqin Guangzhou Gosuncn Robot Co.Ltd Guangdong China Chongqing University Chongqing China

ISBN: (纸本)9798350357738

An accurate localization and mapping method based on multi-sensor data fusion is proposed for mobile robots in outdoor open scenes. By collecting and pre-processing multi-sensor data, combining with LIO-SAM to build 3D point cloud maps and optimizing point cloud maps, it helps mobile robots to complete high-precision localization and mapping in outdoor environment. Finally, the mobile robot platform is built and combined with the robot operating system to verify the effectiveness and accuracy of the scheme in outdoor environments. © 2023 IEEE.

关键词： Mobile robots

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