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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Examining the Advantages of Dynamic Communications Software Design in Networks 5th

Examining the Advantages of Dynamic Communications Software ...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Raghavendra, R. Das, Neeraj Inzimam Kurmi, Raviraj Singh Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper examines the blessings of dynamic communications software program design in networks. By leveraging advances in generation, dynamic communications provide an ever-evolving method of connecting, communicating, and sharing facts inside networks without relying on static, often outdated, hardware. The principle blessings related to this community layout are scalability, flexibility, and excellent reliability. As networks extend, dynamic communications software can auto-regulate accordingly to ensure the overall capability of the general gadget. Moreover, users can count on operational performance upgrades and extended capability to address protection and privacy worries. Moreover, using dynamic software allows for more green aid usage, which facilitates lessening expenses related to the control and operation of the network. Moreover, dynamic communications have also helped reduce downtime and sell extra resilience in the face of surprising changes or unanticipated threats. Standard, this paper provides an in-depth knowledge of the advantages of dynamic communications software layout in networks. Dynamic Communications software program design and its various benefits in networking have been gaining substantial attention in recent years. Dynamic Communications software program layout additionally allows protocols to be modified on an as-needed foundation. That is useful for networks that might be constantly changing and want to adapt, which will take care of the converting environment. New protocols may be introduced or eliminated as wished, and the gadget will adjust to make the most efficient performance. In addition, using Dynamic Communications software design allows for a more straightforward integration of different systems. It is useful for networks that require a couple of systems to work together in order for them to function well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Hierarchical online streaming feature selection based on adaptive ReliefF 12

Hierarchical online streaming feature selection based on ada...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Ren, Mengli Chen, E. Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm. © 2022 IEEE.

关键词： Feature Selection

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Leveraging Machine Learning to Optimize Real-Time Data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. This technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. Through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data Processing Fusion Model 24

BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhang, Xiaodan Wang, Hongyu You, Shaojie Zhao, Tong Li, Zi School of Computer Technology and Applications Qinghai University Qinghai Xining China Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai Xining China Qinghai Provincial Institute of Meteorological Sciences Qinghai Xining China

ISBN: (纸本)9798400710094

Qinghai Province shoulders the significant mission of maintaining ecological security, protecting the three-river source region, and safeguarding the ‘Chinese Water Tower’. To improve the accuracy of precipitation data is a great importance for studying regional ecological changes. The paper takes Qinghai Province as the research area and develops a multi-source precipitation data fusion model: BP-LSTM-ATTENTION, which is based on BP network, LSTM network and attention mechanism. The model uses satellite radar precipitation data and auxiliary data such as vegetation normalized difference index as feature data, and ground observation station data as label values, and through training and evaluation, improving the accuracy of data fusion. Then, a regional long-term and high spatial resolution precipitation fusion dataset with a spatial resolution of 1Km, a temporal resolution of 3 hours, and a time series from 2000 year to 2020 year, was generated by the data fusion model. Taking the fusion processing of precipitation data from 2016 to 2020 as an example, the fusion model successfully reduced the average RB of precipitation fusion data in the study area to 14.6%, the average RMSE to 0.1566, the average MAE to 43.706, and the average CC to 0.906. The experiment shows that the precipitation data fusion model studied in this paper has high data fusion processing accuracy and good precipitation data prediction performance. In terms of the model’s ability to capture precipitation events, the precipitation data fused by the model has an average POD of 0.818, an average FAR of 0.318, and an average MISS of 0.182 from 2016 to 2020, demonstrating its strong capability in monitoring precipitation events and good performance in reducing false alarms and missed reports. The BP-LSTM-ATTENTION model proposed in this study solves the problems of spatiotemporal correlation and time series in precipitation data of Qinghai Province, and has produced high-resolution, high spatiotemporal

关键词： Water towers

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Dual-Branch Contrast Enhancement for Drug Repositioning

Dual-Branch Contrast Enhancement for Drug Repositioning

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haifeng Long, Qiuyu Zhao, Nan Nanjing Normal University School of Computer and Electronic Information China Nanjing University of Chinese Medicine Department of Big Data Management and Application China Nanjing University of Chinese Medicine School of Artificial Intelligence and Information Technology China

ISBN: (纸本)9798350386226

Drug repositioning offers a promising strategy to identify novel therapeutic applications for existing drugs. Despite the frequent use of graph neural network in this domain, their efficacy is often hampered by the sparsity of known drug interaction networks. Furthermore, diseases within the same subclass frequently share structural and clinical traits, leading to similar complications and responses to specific treatments among related *** tackle the challenges of sparse drug interaction networks and leverage the shared treatment potential among similar diseases, we propose Dron, a dual-branched contrast-enhanced method for drug repositioning. Dron enhances drug and disease representations using a branched contrastive loss strategy, and improves identification accuracy by aggregating features from related categories. Validation across multiple public datasets shows that Dron significantly enhances drug repositioning performance. Molecular docking experiments on Alzheimer's disease further confirm Dron's effectiveness in addressing complex diseases. © 2024 IEEE.

关键词： Contrast Learning Drug repositioning Graph Neural Network

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch 4

Stage Wise Classification and prediction of Alzheimer diseas...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Prabagaran, A. Dwivedi, Vinay Neelam Rani, Jyoti Angelin Blessy, J. Padmanabhan, P. Gupshup Technology India Pvt Ltd Bengaluru India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Galgotias Univeristy School of Computing Science and Engineering Dept of Computer Application India Greater Noida Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350349900

Alzheimer's, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals' independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer's disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer's disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD. © 2024 IEEE.

关键词： Convolutional neural networks

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Surface flashover in 50 years:Theoretical models and competing mechanisms

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High Voltage 2023年第5期8卷 853-877页

作者： Zhen Li Ji Liu Yoshimichi Ohki George Chen He Gao Shengtao Li Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbinChina State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'anChina Research Institute for Materials Science and Technology Waseda UniversityShinjuku‐kuJapan School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Surface flashover is a devastating electronic avalanche along the gas–solid interface when a high electric field is applied,which is a potential issue that threatens the safe operations of advanced power electronic,electrical,and spacecraft ***,the underlying physical mechanisms for surface flashover development are still under investigation owing to the complex charge transport processes through the gas phase,solid phase,and gas–solid *** this study,the history of surface flashover theory in the last 50 years is introduced,and several key questions are reviewed from the perspective of the competing mechanisms of charge transport:the role of each phase in a surface flashover,the origin of surface charging,and effects of traps in solid on surface ***,some suggestions involve charge transport processes in each phase,and their correlations are put forward,and a predictable‘charge transport competitive flashover model’is proposed by clarifying the competing mechanisms of charge transport processes through multiple *** study summarises the history and hot topics of physical mechanisms of surface flashover proposed based on classic and recent progress and offers promising routes for developing a more precise surface flashover theory and improving surface flashover performances.

关键词： flash transport charge

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Representing Multiview Time-Series Graph Structures for Multivariate Long-Term Time-Series Forecasting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2651-2662页

作者： Wang, Zehao Fan, Jin Wu, Huifeng Sun, Danfeng Wu, Jia Hangzhou Dianzi University Department of Computer Science and Technology Hangzhou310018 China Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries Hangzhou310018 China Zhejiang Normal University Laboratory of Intelligent Education Technology and Application of Zhejiang Province Hangzhou310018 China Macquarie University School of Computing SydneyNSW2113 Australia

Multivariate long-term time-series forecasting tasks are very challenging tasks in many real-world application areas. Recently, researchers focus on designing robust and effective methods, and have made considerable progress. However, there are several issues with existing models that need to be overcome. First, the lack of a relationship structure between multivariate variables needs to be addressed. Second, most models only have a weak ability to capture local dynamic changes across the entire long-term time-series. Third, current models suffer from high computational complexity and unsatisfactory accuracy. To figure out the abovementioned issues, we propose an effective and efficient method called multiview time-series graph structure representation (MTGSR). MTGSR uses GCNs to construct topological relationships in the multivariate time-series from three different perspectives: time, dimension, and crossing segments. Variation trends in different dimensions are extracted through a difference operation to construct topological maps that reflect the correlations between different dimensions. To capture the dynamically changing characteristics of fluctuation correlations between adjacent local sequences, MTGSR constructs a cross graph by calculating correlation coefficients between adjacent local sequences. Extensive experiments show that MTGSR reduces errors by 17.41% over the state of the art. In addition, memory use is decreased by 66.52% and the running time is reduced by 78.09%. © 2020 IEEE.

关键词： Forecasting

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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