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6th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Yan, Danni Chang, Menglin Chen, Shuxin Wang, Yingkui Qiqihar University Mechanical and Electrical Engineering College Qiqihar China Tianjin Renai College Department of Computer Science and Technology Tianjin301636 China

ISBN: (纸本)9781665460033

With the rapid development of the Internet and the Internet of Things, smart city and smart home are gradually penetrating into people's lives. Through the cognition of existing smart furniture models in the market, research is conducted on the problem that the wisdom of furniture is only reflected in the electrical appliances, while the wisdom of resting sofas is underdeveloped. The wisdom of the smart sofa model lies in its ability to understand the user's habits and preferences through deep learning and to realize the user's living needs in terms of movement or temporal characteristics. Using a combination of qualitative and quantitative analysis, the metaverse virtual model of intelligent furniture to realize the contradiction between user needs and real furniture defects. The results of the SPSS modeling experiments demonstrate the reliability and rationality of the blood pressure monitoring, heart rate monitoring, stand up assistance and childcare functions of the smart sofa. The innovative construction of a virtual model of a smart sofa provides thinking and data support for the development of smart furniture products. © 2023 IEEE.

关键词： Deep learning

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A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bai, Tian Li, Chu Peng, Xinyue Guan, Haotian Zhang, Zefan Wang, Guishen Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Jilin University School of Nursing Changchun China Jilin University School of Life Sciences Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy. © 2023 IEEE.

关键词： drug-drug interaction prediction graph-level classification line graph line subgraph strategy node feature enhancing method

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Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

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computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood Ali Mustafa Qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single *** is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing ***,the support to configure LDTs design needs to be made available even in standard LIMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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Smart Agricultural Water Management Scheme in Saudi Arabia Based on IoT and Cloud Computing Techniques

Smart Agricultural Water Management Scheme in Saudi Arabia B...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Abbas, Saad Qasim Abdulsada, Zainab.R. Sheel, Shaid Mahmood, Sarmad Nozad Khalid, Raed Ali, Mohsin Al-Fatlawy, Ramy Riad Al- Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah DhiQar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Irrigating fields, collecting rainwater for use on the farm, and delivering agricultural goods to markets throughout Saudi Arabia all rely heavily on water conservation measures known together as the agriculture water management scheme (A WMS). The dates grown in Saudi Arabia are used both at home and abroad. Management, user operation, and environmentally sound farming are all the provinces of the Ministry of Agriculture. Collectively, these practices are known as agricultural water management. Saudi Arabia's agricultural and energy sectors employ Internet of Things (loT) sensors to gather real-time data on their infrastructures for immediate analysis and response. Cloud computing (CC) may assist farmers in predicting the quality of their future harvests. The cloud may hold both long-term predictions and local weather information. Keeping realistic choices open and increasing the efficiency of water reserves are aided by technology. In addition to the problems of excess fertilizers and pesticides, using water is one of the most resource-intensive human endeavours in history. However, if water use continues to rise, the associated cost of water contamination might become unsustainable. The A WMS smart water management system employs electronic and computerized engineering to improve data collecting and analytics in Saudi Arabia. The agricultural loT encompasses specialized machinery, wireless networks, applications, and services. In Saudi Arabia, agricultural fields are monitored through a technology called loT smart farming solutions, which makes use of sensors. The online monitoring of agricultural water management and the management of complicated supply chain ecosystems are two examples of the many areas that need to be integrated to use loT in agriculture in Saudi Arabia. Experiments reveal that compared to other approaches, the A WMS leads to much higher ratios of crop production of 98.2 %, water analysis of 90.2 %, food production of 97.30/0, and economic dev

关键词： Agriculture Cloud Computing Integration Internet of Things Water management

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Detection of Anomaly in Internet of Things using Gated Recurrent Unit with Artificial Bee Colony 1

Detection of Anomaly in Internet of Things using Gated Recur...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Karthik, M. Ganesh Divya, N. Sree Shnaeen, Amaar Haamid Alagarraja, K. Praveena, S. Gitam University Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Now a days, development in Internet of Things (IoT) networking generates large attack surface for cybercriminals, that impact the industry has resulted with exponential increase in cyber-attacks. To develop a robust anomaly detection system, a Gated Recurrent Unit Anomaly Detection (GRUAD) is proposed in this research. Initially, the data is collected from the Toyama IoT Network Traffic Dataset (TON-IoT) dataset which includes samples of packet capture data (PCAP files) and network logs (text files). Then, the collected data is pre-processed using data cleaning technique which removes noisy and irrelevant data, then label encoding and data normalization assist to transform data into a suitable format while data splitting. Further, features are selected using Mutual Information (MI) in which features with high mutual information with the target variable are selected. Then, Pearson Coefficient Correlation (PCC) identifies features with strong linear correlations. At the end, K-Best selects the top-ranked features. Finally, the Multistep-Prediction is performed using GRUAD by hyperparameter tuning the Artificial Bee Colony (ABC). The proposed model gained better results in terms of precision with value 98.54%, f1 score of 98.74%, Accuracy of 98.62%, recall of 98.77% comparing with existing method Logistic Regression (LR) respectively. © 2025 IEEE.

关键词： Logistic regression

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A Comparative Analysis of Three Multispectral Images in Classification with Different Methods 24

A Comparative Analysis of Three Multispectral Images in Clas...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Chen, Feng Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Jiangxi Key Laboratory of Watershed Soil and Water Conservation Jiangxi Academy of Water Science and Engineering Nanchang China

ISBN: (纸本)9798400717482

Benefited from the increased satellite observations with medium spatial resolution, land use/cover mapping at local to global scales has provided important supports for society and academy community. For specific purposes (e.g., disaster assessment, detecting surface abrupt change), it is needed to increase the observation frequency, through combining different satellite sensors (i.e. as virtual constellation). However, many factors challenge this combination in practice, in which the comparability issue in sensor settings should be considered among various payload platforms. In this study, from the perspective of practical application, a comparative analysis of land use/cover mapping was investigated, with focusing on three observations by the Gaofen-1 (GF-1) wide field of view (WFV), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI) respectively. Four methods were implemented in classification experiments. Preliminary results showed that the Sentinal-2A MSI outperformed the GF-1 WFV and the Landsat-8 OLI in classification, no matter which classifier was considered. Meanwhile, the higher spatial resolution of the GF-1 WFV certainly compensated its lack of spectral channels compared against the Landsat-8 OLI. The choice of methods affected classification for a specific observation, while the more suitable classifier varied among sensors. This study underlines the compatibility issue in land use/cover mapping with different observations. © 2024 Copyright held by the owner/author(s).

关键词： Mapping

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Crash Injury Prediction using Bidirectional Gated Recurrent Unit with Multi Head Attention Network 2

Crash Injury Prediction using Bidirectional Gated Recurrent ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rana Veer Samara Sihman Bharattej, R. Shnain, Ammar H. Al-Fatlawy, Ramy Riad Prabhakar, P.B. Edwin Chakraborty, Subhra Department of Computer Science Saint Leo University St Leo United States Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

In this era, Crash injury prediction is a crucial area of research aimed at reducing road traffic mortalities and injuries, which remain a leading global cause of death. Previous researchers have suggested various methods but still there is need to improve the accuracy and reliability of these predictions by analyzing large, multi-dimensional datasets from various sources, road conditions, and driver data. Therefore, this paper suggests an approach to predict the severity of injury occurred during the vehicle crash. Firstly, the data is collected from the Ministry of transport and logistics services. Then, the accumulated data is processed through a sampling technique namely Adaptive Synthetic (ADASYN). The sampled data is embedded into the model combined with correlation based selection with Recursive Feature Elimination (RFE) techniques for extraction of suitable features and selection of optimal attributes based on ranking method. Then based on the selected optimal features, injury prediction is initiated by employing Bidirectional Gated Recurrent Unit (BiGRU) with Multi-head Attention Network (MAN) technique. Finally the overall interpretation of the model is developed by SHapley Additive exPlanations (SHAP). The proposed model namely BiGRU-MAN-SHAP resulted with 93.63% accuracy. © 2024 IEEE.

关键词： Highway accidents

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer 46

STFormer: Learning to Explore Spot Relationships for Spatial...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhan, Gan Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies. © 2024 IEEE.

关键词： WSI circuits

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Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network 3

Optimized Detection of Continuous Gravitational-Wave Signals...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Niranjani, V. Parvathy, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350320749

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from '***/datasets/andradaolteanu/g2net-gravitational-wave-dataset' that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal's features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN's convolutional processing. © 2023 IEEE.

关键词： Gravity waves

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Expensive Optimization Based on Evolutionary Multi-Tasking and Hybrid Restart Strategy

Expensive Optimization Based on Evolutionary Multi-Tasking a...

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Congress on Evolutionary Computation

作者： Zhenyuan Li Xiaoliang Ma Zexuan Zhu Yueyue Li College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Evolutionary Algorithms (EAs) can not handle expensive optimization problems (EOPs) well due to the limited function evaluations in EOPs. To address this challenge, surrogate-assisted evolutionary algorithms (SAEAs) have been widely used and obtained good performance. With the problem dimension increases, SAEAs encounter some challenges in relatively high complexity on the training time and prediction time. To address this, this article proposes a novel expensive optimization algorithm with evolutionary multi-tasking and hybrid restart strategy (HRS-EMT). In the surrogate model construct, two radial basis function (RBF) models with different kernel functions are trained on all evaluated data to provide diversity and then are solved by a multi-tasking optimizer to a better optimization performance. In the surrogate model management, HRS-EMT combines multiple RBF models into an ensemble RBF (ERBF) model, strategically applied in the initial population pre-selection of surrogate model. Based on the prediction of ERBF surrogate model, HRS-EMT can obtain a better initial population in high dimensions. HRS-EMT is validated on twelve benchmark functions and compared with other state-of-the-art SAEAs. Experimental studies have shown the superior or comparable performance to other popular SAEAs in addressing EOPs.

关键词： Computational modeling Sociology Evolutionary computation Predictive models Benchmark testing Prediction algorithms Multitasking

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