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A survey on graph neural network-based next POI recommendation for smart cities

Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

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Stock Trend Analysis using Time Series Techniques & Deep Neural Network with data Visualization

Stock Trend Analysis using Time Series Techniques & Deep Neu...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Jangada, Moli Tarale, Nandini Dhule, Chetan Morris, Nekita Chavan Agrawal, Rahul G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Nagpur440016 India

ISBN: (纸本)9798350319019

Investing capital requires cautious thought when navigating the stock market. Analysis carried out throughout the project's design phase concentrated on handling large amounts of data and the effects of pattern recognition on datasets. Minimizing the error ratio over all potential trends is the aim. In order to overcome the shortcomings of the existing Deep Neural Network-based systems (which allow for improved stock tracking and pattern detection), this white paper presents a time series model. The method looks for novel patterns that can be used to buy stocks at a discount and sell them at a premium in order to maximize user gains. © 2024 IEEE.

关键词： data visualization

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Cryptocurrency Price Prediction Using Deep Learning 9th

Cryptocurrency Price Prediction Using Deep Learning

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9783031440830

As technology advances daily, are advancing our lives into the digital sphere. The introduction of these cryptocurrencies aims to prevent the financial crisis. Due to its decentralized nature, high level of security, and restrictions on the number of coins that may be created, cryptocurrencies have attracted investors. Predicting the future price includes several limits and determinants because it involves capital. It varies according to market share. Using block chain technology and encryption, the transactions are encrypted from the beginning to end. Predicting prices to encourage consumers to invest during a specific period and earn a profit. They include a variety of elements, such as market analysis, sentiment analysis on Twitter, trading volume, and open and closing prices. Typical models that can be used to forecast bitcoin prices include regression techniques, neural networks, and support vector machines. Predictions of cryptocurrency prices based on their closing prices give investors additional insight into whether to wait until the closing period if prices are low for the entire day or to invest the following day. Using deep learning and bidirectional Long Short-Term Memory (LSTM) suggested this model to anticipate the price of digital currencies including Bitcoin, Litecoin, Ethereum, and Cardano. In this model, predictions are made using historical price statistics, and the graph is created by evaluating several performance indicators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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A Robust Approach to E-Banking Phishing Detection using Ensemble Methods and LSTM 1

A Robust Approach to E-Banking Phishing Detection using Ense...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Siva Reddy, Naga Venkata Rohith Saai, A.R. Ramanujan, T.V. Reddy, N Rajesh Kumar Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350372748

Recently, the integration of Internet and banking technologies has shifted financial transactions primarily to online platforms. However, this transformation has exposed the financial sector to cyber threats, particularly phishing, resulting in substantial annual losses and widespread data breaches. Despite ongoing efforts to combat phishing, a comprehensive solution remains elusive, creating a vulnerability for cyber attackers to unlawfully access sensitive personal and financial data. In this research, a novel method for successfully recognizing phishing attempts is presented. This proposed methodology includes components such as URLs, HTML, and recurring themes in phishing efforts, which are thoroughly analyzed. These characteristics are consistently similar, resulting in a significant amount of data being collected and sent over the Internet. To preserve the integrity of online financial transactions and shield people from data breaches, a robust phishing attack detection system must be developed. This study applies machine learning techniques such as LSTM, Boosting, Random Forests, and Ensemble models for intelligent phishing detection, attaining a remarkable 97 percent accuracy. To capture complicated data relationships, minimize overfitting, and improve generalization performance, a voting classifier combines predictions and stacking and uses feedback from several models. By adopting this strategy, defenses against the constantly changing cyber threat landscape in the digital financial transaction environment are strengthened. © 2024 IEEE.

关键词： Learning systems

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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A Survey of Digital twins in Smart Farming Systems

A Survey of Digital twins in Smart Farming Systems

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Shashidhar, S. Sunil, C.K. Vineetha, B. Govt. First Grade College Department of Computer Science Karnataka Manvi India Indian Institute of Information Technology Department of Computer Science and Engineering Karnataka Dharwad India Presidency University Presidency School of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798331505776

Digital twins (DTs) have developed as a transformative technology in smart farming, facilitating real-time simulation, monitoring, and optimization of agricultural processes. This survey explores DTs' definition and their various applications in agriculture, underscoring their ability to enhance decision-making and upsurge farm productivity. It analyses the existing surveys and explains different use cases, highlighting the projects that exemplify the DT's practical integration in smart farming systems. The role of the Internet of Things (IoT) and machine learning (ML) in DT is also discussed for creating accurate and efficient DTs for precision agriculture. To identify gaps and areas for further research, a comparative analysis of these technologies is presented. This comprehensive survey intends to deliver understandings into the present state of DTs in smart farming, addressing challenges and providing directions for future development to improve agricultural practices. © 2024 IEEE.

关键词： Smart agriculture

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction 20th

Automatically Choosing Selection Operator Based on Semantic...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Zhang, Hengzhe Chen, Qi Xue, Bing Banzhaf, Wolfgang Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand Department of Computer Science and Engineering Michigan State University East Lansing48824 United States

ISBN: (纸本)9789819970216

In recent years, genetic programming-based evolutionary feature construction has shown great potential in various applications. However, a critical challenge in applying this technique is the need to select an appropriate selection operator with great care. To tackle this issue, this paper introduces a novel approach that leverages the Thompson sampling technique to automatically choose the optimal selection operator based on semantic information of genetic programming models gathered during the evolutionary process. The experimental results on a standard symbolic regression benchmark containing 37 datasets show that the proposed adaptive operator selection algorithm outperforms expert-designed operators, demonstrating the effectiveness of the adaptive operator selection algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic programming

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Impact of Random Forest and XGBoost Algorithms on Improving Patient Outcomes Compared to Standard Decision-Making Methods in Healthcare Predictive Analytics

Impact of Random Forest and XGBoost Algorithms on Improving ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Senthilvadivu, S. Ramesh, P.S. Narang, Sudha Praveena, N. Shakila, J. Sudha, I. Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE TamilNadu Chennai India Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Jeppiaar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering SIMATS University Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

In the field of healthcare, it is important to bring quick, accurate and prompt predictions in order to serve the needs of patients better, avoid re-hospitalizations, and better allocate resources. With this, this paper proposes an ML framework for enhancing decision-making in healthcare predictive analytics employing Random Forest and XGBoost algorithms. The first and major problem solved is the inefficiency of the existing decision-making approaches which is caused by the impossibility to analyze and process big and big complex data. The proposed solution uses these machine learning models for activities such as estimating mortality, critical illness, readmission, and length of stay using massive healthcare data set. Due to the utilization of sophisticated mechanisms of machine learning, the models provide much more precise predictions to reach better results in terms of early interventions and the usage of resources. The outcomes reveal that Random Forest and XGBoost improve the prediction accuracy and XGBoost can improve most of the measures compared with Random Forest. The findings of the models suggest their usefulness for providing daily operational decision making in clinical contexts, helping enhance the delivery of care and management of resources in healthcare facilities. © 2024 IEEE.

关键词： Predictive analytics

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