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Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1892-1895页

作者： Leyuan Fang Dingshun Zhu Jun Yue Bob Zhang Min He IEEE the College of Electrical and Information Engineering Hunan University the Peng Cheng Laboratory the Department of Geomatics Engineering Changsha University of Science and Technology the PAMI Research Group the Department of Computer and Information ScienceUniversity of Macao

Dear editor,This letter presents an open-set classification method of remote sensing images(RSIs)based on geometric-spectral reconstruction *** specifically,in order to improve the ability of RSI classification model to adapt to the open-set environment,an openset classification method based on geometric and spectral feature fusion is proposed.

关键词： editor letter Open

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Analyzing the Benefits of Applying Machine Learning to Real-Time Data Analysis 3

Analyzing the Benefits of Applying Machine Learning to Real-...

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

作者： Yadav, Rakesh Kumar Mahesh, T.R. Kumar, Ajay Jayadurga, R. Vishal, A. Sood, Gourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The application of device learning to actual-time data analysis has the potential to revolutionize the way businesses make decisions. With the capacity to procedure big volumes of data fast and correctly, system learning can provide insights that tell strategic decisions at the same time as promoting information-pushed choice making. This evaluation can assist agencies increase efficiency, lower expenses, and create a more aggressive area. By means of leveraging system learning to higher recognize patron behavior, operational operations, and traits in the market, organizations can create proactive procedures to assume customer wishes and capitalize on opportunities. This paper will discover the capacity advantages of making use of device studying to actual-time records analysis and talk how businesses can satisfactory leverage it. The paper can even speak exceptional varieties of device gaining knowledge of strategies and do not forget some problems businesses need to do not forget before integrating system gaining knowledge of into their records evaluation systems. © 2023 IEEE.

关键词： businesses Evaluation exceptional forget records

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Siddiqi, Hafied Nur Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Technology PO Box 84428 Riyadh11671 Saudi Arabia Shenzhen University College of Computer Science & Software Engineering Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Prince Sultan University EIAS Data Science and Blockchain Laboratory CCIS Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a security mechanism that exploits the unique, unrepeatable physical characteristics of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The unique identification of IoT devices along the supply chain is implemented by using PUFs as tamper-resistant IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its potential to combat several attack scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing

关键词： Supply chain management

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Smart Bus Ticketing System with RFID Using IoT 6

Smart Bus Ticketing System with RFID Using IoT

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Balla, Vijay Kumar Nandibhatla, Haritha Santhoshi, Guna Krishna, A.Vijaya Mohan, M. Purushotham, Pidugu Gnits Department of Information Technology Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology CSE- Aiml & IoT Department India Gnits Department of Computer Science & Technology Hyderabad India

ISBN: (纸本)9798331505790

This paper proposes the design and implementation of a comprehensive Smart Bus Ticketing System leveraging Radio-Frequency Identification (RFID), Global System for Mobile Communications (GSM), Global Positioning System (GPS), and Internet of Things (IoT) technologies. The integration of these advanced technologies aims to revolutionize the traditional bus fare collection process by introducing a seamless, efficient and transparent system. In this system, RFID tags or cards are used to enable contactless fare payment, enhancing passenger convenience and reducing boarding time. By leveraging RFID and GSM technologies, the system introduces a secure, efficient, and modern approach to bus fare collection that aligns with the demands of contemporary urban transportation. Additionally, GPS technology enables accurate tracking of entry and exit locations. The IoT framework enables seamless connectivity between onboard devices, backend servers, and central control centres. IoT-enabled ticketing devices equipped with RFID readers can communicate with onboard sensors and controllers in enchancing the efficiency of the system. Through this integrated approach, the proposed Smart Bus Ticketing System offers a holistic solution to modernize public transportation systems, improve the operational efficiency, and enhance passenger experience in Bus Transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Motor Imagery Features Selection with Grey Wolf Optimization for Brain computer Interface

Motor Imagery Features Selection with Grey Wolf Optimization...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Pawan Dhiman, Rohtash Saini, Jasbir Singh Saxena, Ankur Sharma, Vikas Meerut Institute Of Engineering Technology Department Of Computer Science And Engineering Uttar Pradesh Meerut India Deenbandhu Chhotu Ram University Of Science And Technology Department Of Electrical Engineering Haryana Murthal India Panipat Institute Of Engineering And Technology Panipat Institute Of Engineering And Technology Haryana Samalkha India Delhi Technological University Department Of Information Technology Delhi India

ISBN: (纸本)9798350306446

Feature selection is a significant process in brain-computer interfaces (BCIs) that has the potential to significantly enhance system performance, especially if a large number of features are present. Feature selection can help decrease noisy data, overtraining effects, storage needs, and computational complexity thereby enhancing classifier performance. The various feature selection strategies have been reported to attain these objectives. In this work, a hybrid feature selection technique is suggested. It uses a wavelet packet decomposition technique to extract features and grey wolf optimization (GWO) technique to obtain the best subset of features. It is then evaluated with support vector machine (SVM) classifier and obtains maximum classification accuracy of 82.88% for the approximate entropy feature. Experimental findings reveal that the suggested approach outperforms the contemporary MI-BCI systems even with relatively small training data sets. © 2024 IEEE.

关键词： Support vector machines

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. Based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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Exploring the Impact of Machine Learning on Real-Time Data Analysis 3

Exploring the Impact of Machine Learning on Real-Time Data A...

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

作者： Singh, Swati Nanda, R.T. Sathya, S. Banchhor, Chitrakant O. Pandey, Pratik Rampal, Sourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The emergence of system mastering has enabled actual-time information analysis to come to be extra accurate, efficient, and comfy. ML can rapidly examine and classify excessive-volume streams of raw records, allowing companies to reply to quickly changing environments. This technology has had a considerable impact on the way groups and developers manner complicated datasets and make decisions in realtime. It has revolutionized how agencies use and combine information to gain new insights and understand trends. ML performs a vital position in helping businesses efficiently technique large quantities of data while reducing human errors. At the same time, it provides security and integrity of the data, lowering the capacity for change and loss. In this paper, we discover the effect of ML on actual-time information evaluation and talk about its diverse applications. We additionally offer an overview of different methods of ML and their advantages and disadvantages. ultimately, we speak of future traits for mixing ML with actual-time records analytics and the consequences of this integration. © 2023 IEEE.

关键词： disadvantages environments organizations revolutionized understand

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Dual-Stream Heterogeneous Graph Neural Network Based on Zero-Shot Embeddings for Predicting miRNA-Drug Sensitivity

Dual-Stream Heterogeneous Graph Neural Network Based on Zero...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Li Wang, Wang Yang, Cheng Xiao, Wenhui Fu, Xiangzheng Chen, Yifan Hunan University of Science and Technology College of Computer Science and Engineering Xiangtan China Hong Kong Baptist University College of Chinese Medicine Hong Kong Hong Kong Central South University of Forestry and Technology College of Computer and Information Engineering Changsha China

ISBN: (纸本)9798350386226

MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and drugs. DSHGZS first constructs two heterogeneous graphs with different isomorphic subgraphs based on zero-shot embeddings obtained from large language models (LLMs) and known miRNA-drug association data. It then utilized the enhanced LLM-derived node feature representations, embedding them into the layer feature learning process of the two heterogeneous graphs to generate high-quality vector representations of miRNAs and drugs. The learned high-quality feature embeddings are subsequently used in a segmented inner product decoder to evaluate the sensitivity association scores between miRNAs and drugs. To address the model's excessive reliance on high-quality feature representations, we employed PCA to extract the core representations of the LLM-derived node features for data augmentation. Case studies demonstrated that DSHGZS is an effective tool for predicting potential sensitivity scores between miRNAs and drugs. © 2024 IEEE.

关键词： Graph neural networks

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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