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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Singh, Navdeep Narang, Harshit Rawat, Daksh Vats, Satvik Sharma, Vikrant Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350371802

Breast cancer is a major global health concern that is more common among women in the modern period. Many methods have been investigated for the diagnosis of breast cancer to address this problem, including Convolutional Neural Networks (CNN) and pre-trained models. Our study aims to enhance the efficacy and precision of breast cancer diagnosis by employing deep learning with transfer learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced Encryption Standard (AES)-Based Text Encryption for Near Field Communication (NFC) Using Huffman Compression

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SN computer science 2024年第1期5卷 156页

作者： Ajagbe, Sunday Adeola Adeniji, Oluwashola David Olayiwola, Adedayo Amos Abiona, Seun Femi Department of Computer Engineering First Technical University Ibadan Ibadan 200255 Nigeria Computer Engineering Department Ladoke Akintola University of Technology LAUTECH Ogbomoso Nigeria Computer Science Department University of Ibadan Ibadan Nigeria

Data encryption which is associated with cryptography is necessary to prevent the compromise of Personally Identifying. Multi-level security is ensured by combining the Huffman code with certain cryptographic techniques, such as symmetric encryption algorithms. In order to decode the message, Huffman code can access both the code wordlist and the encoded bits. Inadequate error-correcting techniques with encoded bits can be harmed in this way, leading to the complete loss of information. Nevertheless, sending code wordlists and bits of code to the recipient takes a lot of transmission time. It is necessary to offer strategies for countermeasures to secure and fix the broken encoded bit. So, the problem is to safeguard the original wordlist from tampering without compromising the current code bit. Information. The goal of the investigation is to reduce interference in the levels of communication between the reader and the card. Due to the card's susceptibility, a Mifare classic 1K and Radio Frequency Identification (RFID) were used in this study's simulation. The experiment when N = 200 was conducted and the result below was obtained. The number of unique characters at 0.5 in AES running mode with 0.023 ms at CBC optimal and ECB 0.023 ms. It was possible to simulate the Advanced Encryption Standard (AES) using changing bytes and a number of unique characters. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Advanced Encryption Standard Applied informatics Cryptographic algorithms Huffman NFC RFID Security

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Real-Time Crop Monitoring System Using Deep Learning Techniques for Increasing Production

Real-Time Crop Monitoring System Using Deep Learning Techniq...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dutta, Chiranjit Moudgil, Suveg Aluvala, Srinivas Saxena, Virat Raj Devi, K. Singh, Jagendra SRM Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Galgotias University School of Computer Science Engineering Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India DAV Autonomous College Department of Commerce Titilagarh India Bennett University School of Computer Science Engineering & Technology Greater Noida India

ISBN: (纸本)9798350394412

This study applies machine learning to determine rice crop production using sensor information from temperature, humidity, and water levels. This project looks forward to providing insights to maximize agricultural practices and allow data-driven decision-making in rice cultivation. This is in line with how well these disparate machine learning models can predict rice crop production when measured against precision, accuracy, recall, and F1 score. To some extent, this is evident, as this study found that the machine learning models can predict yield quite satisfactorily, of which the model Random Forest performs very well in terms of precision and accuracy. Among them were the exactitude of sensor data and the algorithm selection, along with subsequent model assessment, refinement, and the ingression of great datasets inclusive of a broader set of environmental characteristics and crop yields - that were considered to be crucial in improving the precision of yield prediction algorithms. Additionally, the use of machine learning in farming has a number of benefits, such as enabling farmers to make decisions about crop management, irrigation, and optimization of yield. Combining machine learning methodologies with sensor technology can optimize resource allocation, save water, and improve overall agricultural productivity. Further, this study will facilitate the implementation of smart irrigation systems and set the stage for data-driven decision-making in agriculture for better farming practices and food production worldwide. © 2024 IEEE.

关键词： Smart agriculture

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Diabetic Retinopathy Diagnosis Using Retinal Fundus Images through MobileNetV3 1

Diabetic Retinopathy Diagnosis Using Retinal Fundus Images t...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sejal, A Yuktha Thiruthuvanathan, Michael Moses Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335774

Diabetic Retinopathy (DR) is a major disease throughoutthe world. Diagnosis of diabetes at an early stage is so critical and could help save several lifestyles. One out of two individuals experiencing diabetes has been determined to have some phase of DR. Recognition of DR symptoms in time can turn away the vision weakness inmost the cases, nonetheless, such disclosure is troublesome with present devices and strategies. Existingmethods for determining whether a person is suffering from diabetes or maybe the chances of acquiring diabetesrely heavily on examiners. Most of the time, it can be treated if caught during the early stages. There is a need for creating models that are efficient and robust to detect DR holistically. In recent times the advent of Deep learning models has been used extensively in various Bio medical applications. In this work, we utilize a Hyper parameter tuned MobileNet-V3 model based on a multi-stage Convolutional Neural Network (CNN) to efficiently classify images from the IRDID dataset. A Multiclass classification model involving images collated from various sources were trained, validated and tested for classification accuracy. The network was evaluated based on parameters and the network was able to achieve an accuracy of 88.6% © 2023 IEEE.

关键词： Medical applications

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Water Pumping Requirement Prediction in Irrigation System Using Internet of Things-Assisted Machine Intelligence-Based Approach 2nd

Water Pumping Requirement Prediction in Irrigation System Us...

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2nd International Conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Roy, Asish Kumar Jena, Kalyan Kumar Mohapatra, Debasis Biju Patnaik University of Technology Odisha Rourkela India Nodal Centre Department of Computer Science and Engineering Parala Maharaja Engineering College Berhampur India School of Computer Science and Engineering NIST Institute of Science and Technology Berhampur India Department of Computer Science and Engineering Parala Maharaja Engineering College Berhampur India

ISBN: (纸本)9789819747269

Irrigation system (IS) is considered as a crucial component in the human society. It plays a crucial role for the supply of water to the cultivation fields. So, it is very much essential to predict the water pumping requirement in IS. In this work, an Internet of Things (IoT)-assisted machine intelligence (MI)-based approach is proposed for the prediction of water pumping requirement in IS. This work is focused on the machine learning (ML)-based models such as random forest (RF), CatBoost (CB), K-nearest neighbors (KNN), and stochastic gradient descent (SGD) to perform the prediction. In this work, the water pumping requirement is monitored using IoT-assisted sensors. Here, the water pumping requirement and non-requirement cases are represented using 1 and 0, respectively. This work is carried out using cross-validation (CRV) by taking the number of folds (NFL) as 3, 5, and 10. These models are evaluated using classification accuracy (CA). This work is implemented using Python-based Orange 3.32.0. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第7期20卷 1-26页

作者： Wu, Huisi Wang, Zhaoze Li, Yifan Liu, Xueting Lee, Tong-Yee College of Computer Science and Software Engineering Shenzhen University No. 3688 Nanhai Road Nanshan District Guangdong Shenzhen518060 China Dept. of Computer Science and Information Engineering National Cheng-Kung University No. 1 University Road Tainan70101 Taiwan

Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE 3

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Selvam, Prabu Nevgi, Akshaj Gunasundari, C. Sowrish, V.K. Natarajan, B. Jessika, S. Sharon School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Chennai Vellore India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India VIT University Department of Computer Science and Engineering Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

ISBN: (纸本)9798350317022

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-Processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results. © 2023 IEEE.

关键词： connected component edge detector image processing MSER Text detection

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Peer-to-Peer File Streaming Using Web Sockets Protocol

Peer-to-Peer File Streaming Using Web Sockets Protocol

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Geetha Rani, E. Roshan Jose, S. Chacko, Joel Thomas Joshua Paul, C. Jeanette Krizelda, K. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350396195

This work studies a peer-to-peer file streaming system with the following characteristics. The file streaming capacity is dynamic and depends on the storage capacity of the individual peers' devices. The large file is split into multiple chunks to stream it across peers. These chunks are not stored on the cloud or any intermediate channel, eliminating the need for a cloud storage bucket. To achieve this real-time streaming among peers, the Secure WebSocket (WSS) protocol is used, which has a minimal buffering delay. Depending on the internet bandwidth of the sender, the chunk size is dynamically modified to achieve a faster streaming rate. Before the chunks are received, the system allocates the required storage space for the incoming file stream. As these chunks arrive, they are written directly to the native storage of the device. Each room's name is unique. The creator of the room shares the name with his peers to start a streaming session. From this point, any peer in the room can act as a sender or a receiver. © 2023 IEEE.

关键词： Data transfer

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