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

作者： Yadav, Nikhil Kumar Tiwari, Amit Gaurav Verma, Harsh Kumar Saxena, Arpna Ajay Kumar Garg Engineering College Varanasi India Ajay Kumar Garg Engineering College Muzaffarpur India Ajay Kumar Garg Engineering College Meerut India Ajay Kumar Garg Engineering College Ghaziabad India Ajay Kumar Garg Engineering College Master of Computer Application Ghaziabad India

ISBN: (纸本)9798350364729

This research highlights the critical role of the skin as the body's primary defense against damage and infection. Despite its importance, the skin can be vulnerable to serious infections caused by fungi, viruses, or even dust, leading to conditions ranging from acne to eczema. Some skin diseases are highly contagious and can be spread by simple contact, underscoring the need for proper diagnosis and treatment to alleviate suffering and prevent transmission. The study focuses on developing a prototype for identifying skin diseases using convolutional neural networks (CNNs), a choice based on their effectiveness in image processing tasks. This research uses CNNs to classify specific skin conditions, including hand dermatitis, hand eczema, subacute eczema, lichen simplex, stasis dermatitis, and ulcers are all different skin conditions.. The methodology integrates image processing techniques with machine learning and uses the processed images to train a CNN model. The training data consists of five classes of skin conditions from the Dermnet dataset, which includes 19600 images. The prototype achieved an accuracy of 92%, highlighting the system's potential. Research suggests that with further improvements and a larger data set, the system could significantly improve early detection and treatment of skin ailments representing a substantial advance in dermatology care. © 2024 IEEE.

关键词： Dermatitis

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Empirical Analysis of Dialogue Relation Extraction with Large Language Models

arXiv

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arXiv 2024年

作者： Li, Guozheng Xu, Zijie Shang, Ziyu Liu, Jiajun Ji, Ke Guo, Yikai School of Computer Science and Engineering Southeast University Bangladesh Beijing Institute of Computer Technology and Application China

Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun frequency and lower information density in dialogues. However, existing DRE methods still suffer from two serious issues: (1) hard to capture long and sparse multi-turn information, and (2) struggle to extract golden relations based on partial dialogues, which motivates us to discover more effective methods that can alleviate the above issues. We notice that the rise of large language models (LLMs) has sparked considerable interest in evaluating their performance across diverse tasks. To this end, we initially investigate the capabilities of different LLMs in DRE, considering both proprietary models and open-source models. Interestingly, we discover that LLMs significantly alleviate two issues in existing DRE methods. Generally, we have following findings: (1) scaling up model size substantially boosts the overall DRE performance and achieves exceptional results, tackling the difficulty of capturing long and sparse multi-turn information;(2) LLMs encounter with much smaller performance drop from entire dialogue setting to partial dialogue setting compared to existing methods;(3) LLMs deliver competitive or superior performances under both full-shot and few-shot settings compared to current state-of-the-art;(4) LLMs show modest performances on inverse relations but much stronger improvements on general relations, and they can handle dialogues of various lengths especially for longer sequences. © 2024, CC BY.

关键词： Extraction

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of engineering & Technology 2023.

关键词： Chirp modulation

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Apple Variety Classification: Leveraging CNN and Random Forest for Precision 3

Apple Variety Classification: Leveraging CNN and Random Fore...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yashu Kukreja, Vinay Brar, Tejinder Pal Singh Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh Group of Colleges Department of Computer Application Mohali Landran India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350319125

An important difficulty in computer vision and agricultural technology is the automatic classification of apple cultivars based on their shape, texture, and colour properties. In this article, we suggest a unique method for tackling this problem that combines Random Forest classifiers and Convolutional Neural Networks (CNNs). In total, 5,510 high-resolution photos of the five well-known apple cultivars Gala, Cortland, Pink Lady, Honeycrisp, and Fuji were gathered in our extensive collection. In the first step of our process, a CNN automatically extracts distinguishing features from these photos. Four convolutional layers with ReLU activation make up the CNN architecture, which is followed by layers with maximum pooling to reduce spatial dimension. The Random Forest classifier then uses the output probabilities from the CNN to determine the final classification. We rigorously experimented to show the accuracy and resilience of our model in identifying these apple types. Deep learning and ensemble approaches work together to successfully capture minute variations in shape, texture, and colour. The agriculture and food industries stand to benefit significantly from this research in terms of automated quality control, precise inventory management, and increased customer satisfaction. Our method opens up new directions for future study in automated fruit classification and agricultural automation while demonstrating the potential of contemporary machine learning techniques to simplify difficult agricultural operations. © 2023 IEEE.

关键词： agriculture apple cnn-svm deep learning fruit type detection

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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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Loss Functions in Link Prediction Based on Graph Neural Networks 6

Loss Functions in Link Prediction Based on Graph Neural Netw...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sood, Vivek Rani, Sangeeta Rustagi, Tanvi Ghakkar, Garima Rastogi, Mitanshi World College of Technology and Management Department of Computer Science & Engineering Gurgaon India Dr. Akhilesh das Gupta Institute of Technology & Management Department of Computer Science & Engineering New Delhi India Vivekanand Institute of Professional Studies Department of Computer & Application New Delhi India

ISBN: (纸本)9798350304480

GNNs are neural networks that can be used to analyse graphs directly. We're going to discuss utilising GNNs for link prediction even though they can also be used for node-level tasks (like giving each website on the internet a score) and graph-level tasks (like predicting characteristics given a molecular structure). It is feasible to predict if a link will most likely connect two network nodes. Given the pervasiveness of networks, it has a wide range of uses, including reconstructing metabolic networks and recommending friends, movies, and knowledge graphs. To train these link prediction networks. We mostly use the binary categorical cross-entropy (CE) loss with adaptive moment estimation to train these link prediction networks. A loss function, cost function, or error function is a calculation formula that returns an event or the value of one or more variable to its true number, which in turn represents some 'costs'associated with this event. In this study, we evaluate the effects of various loss functions on the effectiveness of GNNs for link prediction with specified labels and various sample sizes. Our findings for GNNs demonstrate that in both of the aforementioned circumstances, there is no discernible difference between models trained using CE and other conventional loss functions. © 2023 IEEE.

关键词： Graph neural networks loss functions semi-supervised node classification

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Deep Learning-Based Detection of Foliar Diseases in Apple Plants Using an Assembled CNN Model

Deep Learning-Based Detection of Foliar Diseases in Apple Pl...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Yatendra Sahu Arpita Bhargava Ghanshyam Singh Thakur Rishi Jain Department of Computer Science and Engineering IIIT Bhopal India Department of Computer Application MANIT Bhopal India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%.

关键词： Productivity Industries Accuracy Biological system modeling Computational modeling Hardware Time factors

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Kernel Based Fuzzy Simulation Model for Early Detection of Cancer to Avoid Metastasis 3

Kernel Based Fuzzy Simulation Model for Early Detection of C...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Deyasi, Arpan Techno International New Town Department of Computer Application Kolkata India RCC Institute of information Technology Department of Computer Science and Engineering Kolkata India B. P. Poddar Institute of Management & Technology Department of Electronics and Communication Engineering Kolkata India RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

In this paper, novel cancer detection method using Kernel Based Fuzzy Simulation Model (KBFSM) represented with enhanced accuracy of 12%, considering MRI based breast cancer images. Developed framework, as suggested in this paper, Effectiveness and superiority of the suggested approach are proven by thorough testing on a variety of datasets and thorough assessment metrics, highlighting its capacity to produce accurate and nuanced segmentation results. Fuzzification and defuzzification of images using Fuzzy C means algorithm, speaks in favor of the proposed analysis, as tested over several dataset, owing to minute pixel intensity modification when associated with malignant tissues. Result is supported by histogram analysis, which paves the way of future research in the field of lung cancer using the same technique. © 2024 IEEE.

关键词： Lung cancer

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SketchConf: A Framework for Automatic Sketch Configuration 39

SketchConf: A Framework for Automatic Sketch Configuration

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Miao, Ruijie Dong, Fenghao Zhao, Yikai Zhao, Yiming Wu, Yuhan Yang, Kaicheng Yang, Tong Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github. © 2023 IEEE.

关键词： Frequency estimation

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Remote Sensing Target Recognition Model Based on Small Sample Image Generation 4

Remote Sensing Target Recognition Model Based on Small Sampl...

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4th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2023

作者： Chen, Lei Sun, Liting Space Engineering University School of Space Information Beijing China Beijing Institute of Computer Technology and Application General Department of Network and Information Security Beijing China

ISBN: (纸本)9798350306323

In recent years, remote sensing object recognition methods based on deep learning have received extensive attention. Deep learning methods need to use a large number of labeled data to carry out network training. However, the cost of labeling remote sensing image data is very high. Therefore, how to use a small number of labeled target images to train the remote sensing target classifier with high performance is an urgent problem to be solved. In view of the above problems, this paper has carried out the following work. Firstly, based on WGAN-GP, this paper optimizes the constraint conditions of neural network and proposes a depth generation model, namely CCWGAN-GP, for remote sensing image generation. Secondly, this paper proposes a remote sensing target classification model SCLNet, which makes full use of the original one-hot label and the generated soft classification label to train the neural network. Experimental results show that the proposed model can generate highly similar images to real images, and can significantly improve the performance of the classifier under training conditions with only a few labels. © 2023 IEEE.

关键词： Remote sensing

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