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2024 International Conference on Cloud Computing and Big Data, ICCBD 2024

作者： Qiu, Wenfeng Ma, Chihao Li, Jianhua Qian, Ren Zhao, Yun Fan, Yujun Zhao, Yong College of Computer Science and Technology Guizhou University China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University China College of Biomedical Engineering Guangdong Medical University China Co. Ltd China

ISBN: (纸本)9798400710223

Addressing the generalization issue in stereo matching, we propose the CSD4Stereo network, featuring style-content information decoupling. Through the design of a style normalization module, instance-level style information is stripped away, while a content restoration module adaptively preserves and recovers content details. The bidirectional compensation loss significantly enhances the decoupling effect. Utilizing generalized cosine similarity in a decoupled space to construct the cost volume further boosts the generalization and discriminative power of stereo matching. Cross-domain evaluations on the Middlebury and KITTI datasets demonstrate respective performance improvements of 23% and 17.3%. Similarly, notable generalization capabilities are achieved on the complex scene autonomous driving dataset DrivingStereo. © 2024 ACM.

关键词： Stereo image processing

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SVM Classifier for Hygienic Food Handling with Cloud-Connected UV-C Sterilization Solutions 10

SVM Classifier for Hygienic Food Handling with Cloud-Connect...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Arunfred, N. Ranganathan, Chitra Sabapathy Kumaresh, P.S. Maruthukannan, B. Gobinath, D. Arunagiri, Bhuvaneswari Srm Institute of Science and Technology Faculty of Management Studies Chengalpattu Chennai Tamil Nadu Kattankulathur India Mphasis Corporation ChandlerAZ United States K.L.N.College of Engineering Department of Electronics and Communication Engineering Sivagangai Tamil Nadu Pottapalayam India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research integrates a support vector machine (SVM) classifier with cloud-connected ultraviolet-C (UV-C) sterilization systems to improve food handling hygiene. To ensure food safety, this system uses machine learning to analyze ambient conditions, handling processes, and equipment cleanliness. The SVM classifier classifies hygiene levels based on input data for real-time monitoring and decision-making. Cloud-connected UV-C sterilization technologies automate and streamline food industry microorganism control. Remote monitoring and control via the cloud lets stakeholder's access vital data and take immediate action. This increases food handling cleanliness, system speed, and responsiveness. The research examines the reduction of microbial contamination in various settings by using the SVM classifier and the cloud-connected UV-C sterilization solutions. Results show a considerable increase in hygiene level predictions and a significant decrease in the presence of bacteria in food processing. The SVM classifier and cloud-connected UV-C sterilization systems might improve food safety standards. Machine learning and cloud technologies provide a complete and adaptable solution to sanitary food handling issues, providing customers with safe and high-quality food items. © 2024 IEEE.

关键词： Remote control

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A Survey of Deep Learning Models to Detect and Classify Eye Disorders

A Survey of Deep Learning Models to Detect and Classify Eye ...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Keerthana, I.P. Satheesh Kumar, R. Apj Abdul Kalam Technological University Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350333602

Various advancements in the machine learning methods helps in early identification of eye diseases by using an automated system. It has significant advantages over the manual detection. This article offers a thorough analysis of deep learning and image processing-based automated methods for detecting eye diseases. Deep learning has recently risen to prominence as one of the methods used most frequently to improve performance in a variety of fields, including medical image analysis and classification. In the analysis of medical images, convolutional neural networks are a more prominent and effective deep learning technique. Deep learning strategies for eye illness detection and classification have been researched and examined for this paper. The accessible datasets for the colour fundus retina have also been examined. Readers will be able to recognise gaps in existing research and comprehend the difficulties that eye disease presents from the perspectives of image processing and machine learning. © 2023 IEEE.

关键词： Image enhancement

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A Multi-feature Fusion Method for Smart Contract Classification 4

A Multi-feature Fusion Method for Smart Contract Classificat...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Tian, Gang Wang, Xiaojin Yang, Hongjuan Wang, Rui College of Computer Science and Engineering Shandong University of Science and Technology China

ISBN: (纸本)9781450398343

The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Compared with other baselines and variants, experimental results on a real-world dataset show that MFF-SC achieves an accuracy rate of 83.9%. © 2022 ACM.

关键词： Smart contract

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An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and computer Vision 3rd

An Analysis of Identification of Plant Leaf Diseases and Cla...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Surya Prakash, Y. Archana, U. Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering MVGR College of Engineering Andhra Pradesh Vizianagaram India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of CSE GIET University Gunupur India

ISBN: (纸本)9789819705726

Agriculture is a significant source of revenue for Indians. Farmers can grow a wide range of crops. However, crop growth is hampered by illnesses. Plant pests are one of the reasons for the damage to plant crops. Different diseases affect different plants. The proposed work discussed a detailed investigation of disease recognition and classification. Implementing reliable methods with high recognition rates for diagnosing plant diseases is still on hold. Supervised learning techniques are mostly employed for the identification of diseases in plants. Unsupervised learning is an alternative, though. This suggested illness detection system includes image acquisition, pre-processing, segmenting the images, extracting features, and classification. As a result, image processing and feature extraction technologies have mainly been used to detect plant illnesses. This paper was classified based on two important concepts: leaf feature identification and classification. A detailed in-depth disclosure of disease identification and classification efficiency is depicted here, considering and evaluating certain well-defined suggested techniques by various authors from the last few decades. This paper also focuses on the state-of-the-art methodologies that came into the picture the in the present day in the boom of AI. Finally, examine and categorize the obstacles and potential future improvements in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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Machine Learning Solution for Police Functions

Machine Learning Solution for Police Functions

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Anandan, P. Kumar, A.M. Senthil Vignesh, C. Chandru Kokila, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vivekanandha College of Engineering for Women Namakkal India

ISBN: (纸本)9781665474511

In our society, crime has become one of the most prevalent problems. Preventing it is of paramount importance. In order to prevent it, crimes must be tracked and a public database must be maintained so that case histories can be retrieved when necessary in the future. This project is being proposed to reduce criminal activities by developing predictive models. From past experiments, many people have used deep learning methods to identify patterns of only one particular crime from past criminal activities that took place in different countries. In this research work, machine learning is being applied to predict the states which have high crime rates by analyzing the past crime data. Consequently, the police can focus on those areas where crimes are likely to occur. To predict this, machine learning algorithms like Decision trees and Random Forest algorithms were being used to classify and give accurate information. © 2023 IEEE.

关键词： Crime

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PolluSenseCheck: Cost Effective TinyML-based Air Quality Monitoring System with In-Built Sensor Fault Detection 17

PolluSenseCheck: Cost Effective TinyML-based Air Quality Mon...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Datta, Arko Chakraborty, Joyita Majhi, Pabitra Mondal, Astom Roy, Satyaki Nandi, Subrata Saha, Sujoy National Institute of Technology Department of Computer Science and Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India University of Alabama in Huntsville Department of Mathematical Sciences AL United States

ISBN: (纸本)9798331531195

Air pollution is a significant contributor to respiratory diseases, posing severe health risks globally. Traditional air quality monitoring stations, while effective, are too expensive, particularly in developing nations like India, where numerous monitoring sites may be required to cover diverse urban and industrial regions. Existing cost-effective air quality monitoring systems are rendered inadequate by their reliance on sensors that are prone to drift, which necessitates frequent recalibration. This study addresses the gap by developing a cost-effective air quality monitoring device capable of detecting particulate matter, carbon monoxide (CO), temperature, and humidity, and incorporating self-diagnostic capabilities. It enhances reliability through a dual-sensing mechanism: it calculates the Air Quality Index (AQI) directly and uses a TinyML model to provide secondary-sensed AQI estimations. Deviations between these two AQI values signal sensor malfunction, which is tracked by a proposed algorithm, Interocept, that assigns fault scores to the sensors. Models were created for eight cities, showing accuracies from 0.69-0.89 for dust and 0.76-0.95 for CO. These models were optimized for real-time performance by converting them into TensorFlow Lite micro-models, allowing deployment on the device itself for continuous, autonomous sensor health diagnostics and improved long-term air quality management systems. © 2025 IEEE.

关键词： Fault detection

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Towards Efficient Anomaly Detection Using Memory Broad Learning System 9

Towards Efficient Anomaly Detection Using Memory Broad Learn...

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9th International Conference on Control science and Systems engineering, ICCSSE 2023

作者： Zhong, Zhijie Yang, Kaixiang Yu, Zhiwen Shi, Yifan Philip Chen, C.L. South China University of Technology Computer Science and Engineering Guangzhou China College of Engineering Huaqiao University Quanzhou China

ISBN: (纸本)9798350339055

Anomaly detection aims to identify data points in real-world data that do not conform to expected patterns. The lack of labels, large sample size, and numerous features make it challenging to develop an effective and straightforward algorithm. This paper proposes a new approach called Memory Broad Learning System for Anomaly Detection (MemBLSAD) that can be trained end-To-end. MemBLSAD seamlessly integrates a memory module and a Double BLS-decoder module to achieve hybrid anomaly detection scores, thereby enhancing its capability. Despite the added complexity, MemBLSAD maintains the fast training ability of classic BLS and ensures an end-To-end training process. Our experiments on MNIST and CIFAR10 datasets demonstrate that MemBLSAD outperforms the comparison algorithms on the AUC measure. We also provide reliable visualizations to analyze MemBLSAD's outstanding performance and explain why it works better. © 2023 IEEE.

关键词： Learning systems

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Advancements and Challenges in Networking Technologies: A Comprehensive Survey 7

Advancements and Challenges in Networking Technologies: A Co...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Shihab, Mustafa A. Aswad, Salma Abdullah Othman, Rawshan Nuree Ahmed, Saadaldeen Rashid Sciences College of Sharia Tikrit University Department of Banking and Finance Salahalden Iraq College of Science University of Duhok Department of Computer Science Duhok42001 Iraq College of Engineering and Computer Science Lebanese French University Department of Information Technology Erbil Iraq College of Engineering and Science Bayan University Dept Computer Science Erbil Iraq

ISBN: (纸本)9798350342154

This survey paper provides a comprehensive overview of emerging technologies in networking, focusing on caching in Information-Centric Networking (ICN), context-aware radio access technology (RAT) selection in 5G ultra-dense networks, cryptocurrency adoption, and mobility support for routing in Low Power and Lossy Networks (LLNs). Adaptive RAT selection mechanisms are stressed in 5G context-aware RAT selection. RAT selection processes address context acquisition, decision-making algorithms, and resource allocation. 5G context-aware RAT selection study is suggested. The report then discusses bitcoin adoption, including its current state and benefits. Scalability, security, and regulation are difficulties. Future studies should address these issues and increase cryptocurrency adoption. Finally, LLN mobility support investigates routing techniques for mobility needs. LLN reactive and proactive routing protocols are analyzed. LLN routing mobility support research is suggested. This survey article discusses techniques, difficulties, and future research by summarizing each technology. To help researchers and practitioners make educated judgments and enhance networking technologies. © 2023 IEEE.

关键词： Decision making

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