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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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Transversal Skills that Matter to computer engineering and information Technology Undergraduates and Strategies for their Development in English Classroom 15

Transversal Skills that Matter to Computer Engineering and I...

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15th international Scientific and Practical conference on Environment. Technology. Resources, ETR 2024

作者： Aristova, Nataliia Malykhin, Oleksandr Dybkova, Liudmyla Department of International Relations and Research Cooperation Institute of Pedagogy of NAES of Ukraine Kyiv Ukraine Department of Didactics Institute of Pedagogy of NAES of Ukraine Kyiv Ukraine Department of Informatics and Systemology Kyiv National Economic University Named after Vadym Hetman Kyiv Ukraine

ISBN: (纸本)9781713899570

The importance of transversal skills for employees who work in the field of computer science is undeniable as transversal skills complement technical computer science skills and play a key role in their professional development and success. It means that while studying at higher educational institutions computer engineering and information Technology undergraduates should have all the opportunities to develop these skills. And the English language classroom is not an exception since English teachers can use a wide range of strategies for developing transversal skills among computer engineering and information Technology undergraduates. The research is aimed at finding out computer engineering and information Technology undergraduates’ points of view on the importance of transversal skills for their future work and identifying effective strategies for their development in the English language classroom. 217 computer engineering and information Technology undergraduates were participants in the research conducted by the team of researchers. To find out computer engineering and information Technology undergraduates’ points of view of the importance of transversal skills for their future work and to identify effective strategies for their developing in the English language classroom, the researchers developed a questionnaire which consisted of close-ended and open-ended questions and items on a 5-Likert scale. The study may contribute to the improvement of computer engineering and information Technology undergraduates’ training by identifying effective strategies for the development of transversal skills in the English language classroom. © 2024 Nataliia Aristova, Oleksandr Malykhin, Liudmyla Dybkova.

关键词： engineering research

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Optical reservoir computing based on the bagging trees 4

Optical reservoir computing based on the bagging trees

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4th international conference on advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Xiao, Mujun Zhang, Ailing Wang, Chao Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China School of Engineering and Digital Arts University of Kent Canterbury United Kingdom

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

In traditional optical reservoir computing, the least squares method are commonly used to train the output weights for regression tasks. Although these algorithms are highly versatile, their training efficiency and accuracy are somewhat inferior compared to current algorithms. Bagging trees, an ensemble learning method, works by resampling the dataset to train multiple models and then combining the predictions of these models, thus improving the stability and accuracy of the final prediction. By combining optical reservoir computing with the bagging trees, both the prediction accuracy and training efficiency are greatly improved, with the highest R-squared prediction reaching 99.56%. © 2025 SPIE.

关键词： Prediction models

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Optimization of DMA Operations for RFSOC in Radar Systems 4

Optimization of DMA Operations for RFSOC in Radar Systems

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4th international conference on Electronic information engineering and computer science, EIECS 2024

作者： Dai, GanE Xie, Pengfei School of Electronic and Information Engineering Foshan University Foshan China

ISBN: (纸本)9798331531409

Radio Frequency System-on-Chip (RFSoC) technology, with its high integration and powerful processing capabilities, is widely used in modern radar systems. Its integrated wideband Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs) handle substantial data streams. In practical applications, data transfer largely relies on Direct Memory Access (DMA) operations. However, DMA faces inherent limitations, particularly when the data rates of ADC/DAC are comparable to the memory bandwidth of DDR, which can lead to transmission interruptions and increase buffer pressure within the system. This paper presents an optimized approach to DMA operations, incorporating advanced buffer management and enhancements to the Scatter-Gather (SG) mode. These optimizations mitigate the risk of buffer overflow and data transmission interruptions, thereby improving overall system performance. Experimental results demonstrate that the proposed optimizations significantly reduce transmission interruptions and enhance data processing efficiency in radar systems. © 2024 IEEE.

关键词： System-on-chip

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An empirical IoT and cloud-based customizable healthcare surveillance system

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international Journal of information Technology (Singapore) 2024年第8期16卷 5317-5323页

作者： Meti, Subhash Razauddin, S. Nallakumar, R. Mansingh, P. M. Benson Sameen, Aws Zuhair Pandey, Sakshi Bhatt, Sanjeev Kumar Jayabalan, Bhuvana Department of Electronics and Communication Engineering SKSVMACET Lakshmeshwar Karnataka Gadag India College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India College of Medical Techniques Al-Farahidi University Baghdad Iraq Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Software Architect and Research Scholar Digital Innovation College PDM University Bahadurgarh India Department of Computer Science and Information Technology JAIN (Deemed-to-Be University) Karnataka Bengaluru India

In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： BPNN Cloud computing Healthcare surveillance IoT Particle swarm intelligence (PSI)

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A Parallel Fusion of CNN and Transformer for CT Lung Nodule Detection 4

A Parallel Fusion of CNN and Transformer for CT Lung Nodule ...

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4th international conference on Electronic information engineering and computer science, EIECS 2024

作者： Liu, Wei He, Jin School of Communication and Information Engineering Shanghai Technical Institute of Electronics & Information Shanghai China School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331531409

To improve the precision of CT lung nodule detection, this paper presents a parallel fusion model based on CNN and Transformer network, which integrates features of the two networks to fully utilize the high-resolution spatial information of CNN and the global semantic information of Transformer, thereby enhancing the model's expressive capability and providing high-quality input for downstream tasks. Experimental results show that the proposed improves the sensitivity of detection and reduce the false positive rate based on anatomical features, while also considering algorithm efficiency. © 2024 IEEE.

关键词： computerized tomography

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SpikeYOLO-NDFEB: Integrating NeuroDynamic Enhancement and Dynamic Pruning for advanced Energy-Efficient Spiking Object Detection 6

SpikeYOLO-NDFEB: Integrating NeuroDynamic Enhancement and Dy...

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6th international conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Lu, Ruizhi Li, Linjing Xie, Jinda Zuo, Fenglin Chen, Bo Huo, Jianwen Tan, Liguo Li, Beibei School of Information Engineering Southwest University of Science and Technology Mianyang China School of Mechanical and Electrical Engineering Chengdu University of Technology Yibin China School of Clinical Medicine Tsinghua University Beijing China School of Astronautics Harbin Institute of Technology Haerbin China School of Cyber and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798331541750

Spiking Neural Networks (SNNs) offer energy efficiency and bio-plausibility advantages over traditional Artificial Neural Networks (ANNs). However, their application in complex tasks like object detection is limited by inadequate feature extraction, insufficient temporal processing, and high computational demands. To address these challenges, we present SpikeYOLO-NDFEB, which integrates a novel NeuroDynamic Feature Enhancement Block (NDFEB) and employs Dynamic Pruning and Adaptive Pruning (DPAP) techniques. Experiments on our proprietary dataset demonstrate that Spike YOLO-NDFEB significantly improves detection performance and computational efficiency compared to the baseline SpikeYOLO, while maintaining high accuracy. SpikeYOLO-NDFEB effectively enhances SNNs for real-time, energy-constrained applications such as auto-nomous driving and edge computing. © 2024 IEEE.

关键词： dynamic pruning energy efficiency neurodynamic feature enhancement object detection spiking neural networks

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research on high frequency fault prediction of elevator based on combined model 6

Research on high frequency fault prediction of elevator base...

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6th international conference on computer information science and Application Technology, CISAT 2023

作者： Liang, Zuodong Guo, Zhiqiang School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781510668546

The safety hazards caused by elevator faults are increasing day by day, and it is urgent to study the problem of elevator fault prediction. In this paper, a combined model elevator fault prediction method based on CNN, LSTM and self-attentive mechanism is proposed. The operating parameters and high frequency faults of elevators are statistically determined, and a large amount of real-time elevator data is collected to build a data set. The advanced features are extracted by CNN and fed to LSTM for training, and then fed to softmax classifier for classification prediction after further feature extraction by the self-attention mechanism. The experimental results show that the method can effectively predict elevator faults. © 2023 SPIE.

关键词： Forecasting

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS international conference on computer and information science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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research on intellectual property protection algorithm of deep semantic segmentation model

Research on intellectual property protection algorithm of de...

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2024 international conference on advanced Image Processing Technology, AIPT 2024

作者： Chen, Na Li, Ying Zheng, Gang School of Computer Science & Artificial Lanzhou Institute of Technology Lanzhou730050 China School of Electronic Information Engineering Lanzhou Institute of Technology Lanzhou730050 China

ISBN: (纸本)9781510682542

In its judicial interpretation, the Supreme People's Court clarified that algorithmic information can be protected by trade secrets by way of enumeration, which reflects the increased importance of algorithmic trade secret protection. Deep models have high intellectual property value and commercial value, and have even become the core competitiveness of some individuals or small businesses, making them the target of attacks by some malicious competitors and lawless elements. Current digital watermarking technology has been extended to deep neural networks, but these methods are only suitable for protecting the intellectual property of classification models. Therefore, this article proposes research on the intellectual property protection of semantic segmentation models. This paper abandons the traditional method of selecting trigger sets in classification model protection, and proposes an adversarial generation method to independently design trigger sets, embed patterns or symbols with special markings into pictures, and then use a backdoor mechanism to embed trigger set digital watermarks into semantic segmentation In the model, the backdoor disadvantages of the model are converted into backdoor advantages, making the segmentation model more discriminative during the verification process. © 2024 SPIE.

关键词： Semantic Segmentation

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