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9th International Conference on Water Resource and Environment, WRE 2023

作者： Sheng, Zheng Zheng, Mengting Computer Applications and Software Huzhou University Zhejiang Huzhou313000 China

ISBN: (纸本)9789819709472

In this paper, a pipe water level prediction platform is proposed to address the low predictability issue of traditional drainage systems in combating waterlogging. Based on the drainage pipe network, the study integrates real-time data collected through the Internet of Things (IoT) sensor system and predictive data from the Storm Water Management Model (SWMM) and Long Short-Term Memory (LSTM) neural network models to enhance the accuracy and speed of waterlogging forecasts. Additionally, a web-based and WeChat Mini Program application platform has been developed. The platform was tested and validated in a specific region in Huzhou. Experimental results demonstrate the platform’s high prediction accuracy and its potential for urban waterlogging risk monitoring and early warning, offering a novel approach to smart water management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第5期17卷 71-83页

作者： Aljammal, Ashraf H. taamneh, Salah Qawasmeh, Ahmad Salameh, Hani Bani Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Software Engineering The Hashemite University Zarqa Jordan

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset;the performance of the classifiers has been affected © 2023, International Journal of Interactive Mobile *** Rights Reserved.

关键词： Network security

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Two-branch Convolutional Networks for Stress Detection from Biomedical Data 27

Two-branch Convolutional Networks for Stress Detection from ...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Dobrokhvalov, Maksim O. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Stress is considered one of the main causes of health problems. Combining physiological signals from multiple modalities is a promising method for more accurately determining a person's condition. This paper proposes single-branch and two-branch convolutional neural networks for stress detection using biomedical data (electrocardiogram, blood pulse pressure, electrodermal activity). The two-branch model independently processes heart data (electrocardiogram or blood volume pulse) and electrodermal activity, and then merges the results of the branches using a predictor. The work examines various predictors. In some predictors, only the final result of the network is transferred to the loss function, and in one, the results of each branch and the whole network are taken into account. On the WESAD dataset, the single-branch model achieves an accuracy 0.983 and an F1 score 0.976, and the two-branch model achieves an accuracy 0.994 and an F1 score 0.992. On the UBFC-Phys dataset, the single-branch model achieves an accuracy of 0.922 and an F1 score of 0.945. © 2024 IEEE.

关键词： Electrocardiograms

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Improving Transfer Learning Performance for Abnormality Detection in Brain MRI Images Using Feature Optimization Techniques 27

Improving Transfer Learning Performance for Abnormality Dete...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Nizamli, Yasser A. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Accurate and early diagnosis of brain tumors plays a vital role in determining appropriate treatment and increasing survival rates. Manual diagnosis using MRI images is often prone to errors and can be a tedious and time-consuming procedure. As a result, automated computer-aided systems that utilize artificial intelligence and machine learning techniques are widely used. However, accurately diagnosing the type of brain tumor remains a significant and complex challenge for today's medical imaging research community. In this paper, we present an enhanced transfer learning approach that aims to differentiate brain tumor types using MRI scans while achieving state-of-the-art performance. In the proposed approach, the pre-trained VGG-19 network with fixed weights is used to map MRI scans to a high-level numerical representation. The extracted features are then passed to a support vector machine algorithm to classify brain tumors into one of three major categories: glioma, meningioma, and pituitary. To further improve the performance of the model, the effectiveness of utilizing feature optimization techniques based on the radial basis similarity function and L1-penalty logistic regression was investigated. For evaluation, the benchmark Figshare dataset containing 3064 MRI images was used after a series of processing operations. The proposed system achieved a high overall accuracy of 98.53%, outperforming other analogues. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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Knowledge Graph Completion Method Based on Using Multi-Hop Reasoning 10

Knowledge Graph Completion Method Based on Using Multi-Hop R...

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Golovin, Aleksei Zhukova, Nataly Kulikov, Igor Saint Petersburg Electrotechnical University 'LETI' Department Of Software Engineering And Computer Applications Saint-Petersburg Russia Saint-Petersburg Russia

ISBN: (纸本)9798350372939

In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data. Within the research, the method for restoring the structure of a knowledge graph using sequence-to-sequence multi-hop reasoning, based on the transformer architecture, is proposed. To evaluate the effectiveness of the proposed method, the study of its properties was carried out and the results were compared with the results of the existing methods used for predicting relations in the knowledge graphs. The proposed solution almost achieves the same results as methods based on embeddings. The evidential paths generated by the proposed method ensure the interpretability of the results. Our method does not rely on existing relations to create an evidential path and has the flexibility to restore missing relations in the path. © 2024 IEEE.

关键词： Knowledge graph

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Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 1189-1203页

作者： T.Edwin Ponraj J.Charles Department of Computer Applications Noorul Islam Centre for Higher EducationKumaracoil629180India Department of Software Engineering Noorul Islam Centre for Higher EducationKumaracoil629180India

The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical *** wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are *** work presents a robust methodology to analyze and identify the effects of mutation on a single protein ***,the context in a collection of words is determined using a knowledge graph for feature selection *** proposed prediction is based on an easier and sim-pler logistic regression inferred binary classiﬁcation *** approach can able to obtain a classiﬁcation accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy ***,for each cross-fold validation,the precision,recall,and F-Score are *** results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

关键词： Proteins data science mutation analysis random forest neighbor proteins single and double mutations

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Lung Cancer Analysis and Classification using Enhanced Efficient Net V3 6

Lung Cancer Analysis and Classification using Enhanced Effic...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ramya, J. Poongodi, A. Department of Software Applications Agurchand Manmull Jain College Chennai India Department of Computer Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in diagnostic imaging has garnered a lot of attention in recent years, particularly for the detection of lung cancer. On the other hand, manual CT scan analysis takes a lot of time and is inaccurate or prone to mistakes. Given these limitations, the accurate categorization of CT images as either malignant or non-cancerous is accelerated by the application of computer approaches, including machine learning and deep learning algorithms. In this work, we provide a new and improved CNN model architecture with FCN and Deeplab V3+ for segmentation and classification using hybrid efficient net B0 to investigate lung cancer. The suggested model achieves superior results in both segmentation and classification. © 2025 IEEE.

关键词： Lung cancer

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DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

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Frontiers of computer Science 2023年第5期17卷 197-205页

作者： Long LI Jianbo HUANG Liang CHANG Jian WENG Jia CHEN Jingjing LI College of Cyber Security Jinan UniversityGuangzhou 510632China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Applications Guilin University of Technology at NanningNanning 530000China

Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy *** particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment ***,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous *** address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection ***,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction ***,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are *** validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.

关键词： location-based services privacy-preserving hidden Markov model k-anonymity query probability

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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