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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Sindhu Madhuri, G. Somashekhara Reddy, D. Mahesh, T.R. Rajan, T. Vanitha, K. Shashikala, H.K. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Karnataka Bengaluru India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India School of Computer Science and Engineering Presidency University Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

Diabetes Mellitus (DM) is primarily defined by hyperglycemia, polyuria, and polyphagia and as a result of a complex interaction of hereditary and environmental variables, it has developed into a severechronic metabolic condition. Numerous diabetes problems might arise from uncontrolled high blood sugar. Long-term diabetes causes severe complications, some of which are fatal. Everywhere in the world, the prevalence of diabetes in patients is increasing at epidemic rates. Every year, diabetes and related disorders consume a sizable percentage of the national health budget. Several risk factors influence the etiopathogenesis of the disease and the emergence of the epidemic. The untreatable condition of diabetes may be managed by maintaining self-care in daily life, providing appropriate education about diabetes, and making significant advancements in knowledge, attitudes, skills, and management. Diabetes should be diagnosed as soon as possible because it can lead to several illnesses, including kidney failure, stroke, blindness, heart attacks, and lower limb amputation. This study aims to use relevant variables, create a prediction algorithm using machine learning, and choose the best classifier to produce results that are as close to clinical outcomes as possible with reduced entropy. The proposed approach is on selecting the characteristics that ail in the early detection of Diabetes Miletus utilizing Predictive analysis. The computational techniques K-Nearest Neighbour (KNN) and Decision Tree (DT) have been employed to detect DM at an early stage. The KNN outperforms with the best performance when evaluated against different performance metrics. © 2023 IEEE.

关键词： Decision trees

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Memory-enhanced text style transfer with dynamic style learning and calibration

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science China(Information sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of Computer Science and Technology National University of Defense Technology Department of Computer Science and Engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation Institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

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Translational Generative Retrieval via Potential Query Generation

Translational Generative Retrieval via Potential Query Gener...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Yihan Liu, Tingwen Sheng, Jiawei Ma, Duohe Sun, Ming Tian, Ling Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Document retrieval aims to find documents related to the query from all candidate documents. Existing studies develop the Generative Retrieval approach, which assigns a unique DocID to each document, and then measures document-query relevance based on the probability of generating the expected DocID for the given query. However, the generated DocID can have expressive limitation of different semantic topics, leading to semantic gaps in document retrieval. Besides, existing GR models usually suffer from the catastrophic forgetting when memorizing new documents incrementally, which makes real-world application impractical. To overcome these issues, we propose Translational Generative Retrieval, which translates each document into potential queries reflecting different topics, specifically by a sequence of token probability distributions. To better model the semantics of potential queries and effectively decode the target query from the distributions, we propose DirEcted Acyclic Graph Retrieval (DEAR) model, which reforms the distributions from a non-autoregressive generative model into a Directed Acyclic Graph. Experimental results demonstrate that DEAR outperforms existing retrieval models, setting a new state-of-the-art in generative retrieval. © 2025 IEEE.

关键词： Directed Acyclic Graph Generative Retrieval Non-autoregressive Generation Query Generation

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Adversarially Robust Object Detection via Deviation Calibration and Content Preservation

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5900-5911页

作者： Zhang, Xu Peng, Bo Lei, Jianjun Xue, Chao Yao, Yuxuan Huang, Qingming Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tiandy Technologies Tianjin300384 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China

Object detection has achieved a promising development in recent years and played an important role in various applications. However, the performance of object detection networks generally drops significantly when subjected to adversarial attacks. As an effective technique for defending against adversarial attacks, adversarially robust object detection has attracted increasing interest. In this paper, a novel deviation-calibrated and content-preserved network (DCCP-Net) is proposed for adversarially robust object detection by effectively exploring and mitigating the essential negative impact of noise disturbance in the feature space. Specifically, a deviation-calibrated robust feature enhancement module is designed to enhance the feature robustness of adversarial images by removing noise disturbance and supplementing rectified information. Besides, by enabling adversarial image features to imitate corresponding clean image features, a content-preserved consistency information imitation mechanism is proposed to obtain more accurate content information of adversarial images. Extensive experiment results have verified the superiority of the proposed DCCP-Net. © 1991-2012 IEEE.

关键词： Feature extraction Noise Training Object detection Detectors Robustness Perturbation methods Accuracy Calibration Convolution

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DB-YOLOv8 Model: Object Detection in Complex Scenarios 10

DB-YOLOv8 Model: Object Detection in Complex Scenarios

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10th International Conference on computer and Communications, ICCC 2024

作者： Yu, Bo Li, Shun Wang, Wei Meng, Yuanze Liu, Dongying Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

The improved DB-YOLOv8 model aims to address the issues of missed detection and false detection that are prone to occur in object detection tasks under complex scenarios, particu-larly in the case of overlapping objects and multiple identical ob-jects. It targets the limitations of the YOLO backbone network caused by fixed kernels that lead to restricted receptive fields and insufficient multi-scale feature learning capabilities due to the in-ability of the spatial and channel attention fusion mechanism to adapt to the input data feature distribution. The DB- YOLOv8 model introduces the deformable convolution C2f (D - C2f) module in the backbone network to achieve adaptive adjustment of the network's receptive field. Additionally, the BiFPN structure is in-troduced in the Neck network, allowing information to propagate bi-directionally between different resolution levels, thereby better fusing multi-scale information. Experimental results demonstrate the outstanding performance of the DB-YOLOv8 model on the VOC and COCO datasets. Validation confirms that its mAP50 and mAP50-95 metrics are improved by 3.3% and 4.7% compared to the YOLOv8 model, respectively. © 2024 IEEE.

关键词： Object detection

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Design of Cost Efficient VBIR Technique Using ICA and IVCA

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SN computer science 2024年第5期5卷 560页

作者： Pradeep Kumar, B.P. Srinidhi, N.N. Shiva Darshan, S.L. Naresh, E. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99% and 82.8% to 99.6%, respectively. The average recall and precision across datasets are 92.86% and 92.82%).All things considered, this study demonstrates the effectiveness of ACICA in evaluating intravascular responsiveness and presents viable paths for enhancing clinical results and diagnostic precision in the treatment of prostate cancer. © The Author(s) 2024.

关键词： Adaptive complex independent components analysis Computed tomography Dynamic contrast enhancement Magnetic resonance imaging Visual-based image retrieval

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Deep Learning Architectures Based Sentiment Analysis Systematic Literature Review 5

Deep Learning Architectures Based Sentiment Analysis Systema...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Anjana Thampy, S. Jane Rubel Angelina, Jeyaraj Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Lourdes Matha College of Science and Technology Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Recently, sentiment analysis has attracted a good deal of interest from researchers. Each day, FB, Twitter, Weibo, and other social media, in addition to huge e-trade websites, generate big feedback, which can be used to investigate humans' critiques or emotions. Sentiment analysis will correctly analyze subjective records from this information. Deep learning becomes an effective machine learning approach which learns from a couple of layers of representations or features of the records and outputs a cutting-edge prediction effect. Deep learning overcomes all other machine learning techniques for sentiment analysis as well as offers different architectures for sentiment evaluation. Recently, RNN abbreviated as Recurrent Neural Networks as well as CNN abbreviated as Convolutional Neural Networks has been replaced with Transformer Language models in deep learning *** survey deals with contemporary architectures and challenges in that are in Sentiment Analysis as well as Natural Language Processing. © 2023 IEEE.

关键词： Sentiment analysis

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Zero-Shot Cross-Domain Slot Filling with Retrieval Augmented In-Context Learning

Zero-Shot Cross-Domain Slot Filling with Retrieval Augmented...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Song, Mengxiao Liu, Tingwen Li, Quangang Ma, Duohe Sun, Ming Tian, Ling Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Zero-shot cross-domain slot filling is becoming increasingly important due to its ability to generalize to new domains without the need for annotating domain-specific data, which aligns well with the requirements of industrial deployments. Recent advanced works deal with this task through question answering framework and make remarkable progress. However, they always rely on human efforts to manually construct question templates or prompts for all slot types, which is not only labor consuming, but also experience context inconsistency issue between the manual example and the specific test instance. To alleviate this problem, we introduce a retriever designed to extract reference samples from the training sets, serving as demonstrations to guide the model in generating the target slot entity through in-context learning. Building upon this retriever, we propose a retrieval-augmented generative framework that automatically constructs and tailors prompts to each specific test instance, eliminating the need for manual efforts. Experiment results verify that our approach attains the state-of-the-art. © 2025 IEEE.

关键词： slot filling spoken language understanding zero-shot cross-domain

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A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

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International Journal of Information technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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