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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Joshy, Devika M Das, Abhijith Amijith, M.M. Sunil, Diya Theresa Safar, Seema Rajagiri School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350338607

Transformers have an encoder-decoder architecture which is widely used as an image captioning model. Identifying the key elements and the underlying relationships between them is the challenge of image captioning. The introduction of the Fourier transform in this architecture is expected to perform better than other state-of-the-art models. Fourier transform is a signal processing tool that allows us to decompose a signal into its frequency components. In signal processing, an image is often treated as a two-dimensional signal, where the brightness of each pixel represents the amplitude of the signal at that location. With this intuition, an additional Fourier layer is added to the transformer architecture to enhance its performance. The Flickr dataset is used for training the proposed model in its initial phase. The model is tested against state-of-art-model using various evaluation metrics like BLEU-n, meteor, rouge scores, etc. During the evaluation, the suggested approach produced better outcomes. © 2023 IEEE.

关键词： Fourier transforms

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Ultra-Wideband Technology for Healthcare Monitoring: A Machine Learning Approach

Ultra-Wideband Technology for Healthcare Monitoring: A Machi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Raj, Raunak Awasthi, Avinash Arora, Komal Singh, Shreshth Miranka, Hemant Rakhra, Manik School of Computer Science Engineering Lovely Professional Univeristy Punjab India Malviya National Institute of Technology Department of Computer Science and Engineering Rajasthan India Visvesaraya National Institute of Technology Department of Computer Science and Engineering Maharashtra India Lakshmi Niwas Mittal Institute of Information Technology Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798331542344

Ultra-Wideband (UWB) technology has emerged as an emerging solution for a variety of applications in clinical establishments., offering significant advantages such as precise localization capabilities., high data rates., and low power consumption. The paper systematically examines the utilization of UWB technology within clinical settings., focusing on its applications in health care monitoring., medical imaging., indoor positioning., and wireless communication. By leveraging a comprehensive dataset from the UCI Machine Learning Repository., the authors provide detailed empirical insights into the performance and effectiveness of VWB-based systems in enhancing healthcare outcomes. The authors also identify and discuss specific challenges., such as integration complexities and regulatory hurdles., as well as opportunities., including potential advancements in patient care and health care delivery through the adoption of UWB technology. Additionally., the authors have built a model using a Random Forest classifier and achieved an accuracy of 91.8%. © 2024 IEEE.

关键词： Electronic health record

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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On the Design of Cancer Tumor Type Prediction using Machine Learning Techniques for Efficient Diagnosis 2

On the Design of Cancer Tumor Type Prediction using Machine ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Thakkar, Hiren Kumar Kumar, Yogesh Kathirisetty, Nikhila School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat 382007 India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad Shamshabad India

ISBN: (纸本)9798350370188

In the recent past, cancer is designated as the life threatening disease causing significant deaths across the globe. Countries are putting efforts for early stage cancer detection, and mortality prediction. Machine Learning (ML) models are proven to efficiently find the correlation among the features for robust predictions over statistical approaches. In this paper, we used kaggle dataset that includes 569 cancer cells and around 30 features cancer cells' classification. The proposed study employs ML models (e.g., binary classifiers) for effective predictive analytics. The evaluation results indicate that Random Forest achieves better results with Area Under Curve (AUC) of 0.99 followed by Decision tree with AUC be 0.92. However, the Logistic Regression (LR) and Support Vector Machine (SVM) fails to capture the generalized behavior of the cancer data for predictive analysis. © 2024 IEEE.

关键词： Classification (of information)

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Optimized cross-corpus speech emotion recognition framework based on normalized 1D convolutional neural network with data augmentation and feature selection

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International Journal of Speech Technology 2023年第4期26卷 947-961页

作者： Barsainyan, Nishant Singh, Dileep Kumar Department of Computer Science & Engineering JLU School of Engineering and Technology Jagran Lakecity University Chandanpura Madhya Pradesh Bhopal462044 India

Human–computer interaction (HCI) improved via voice detection of emotions. Speech Emotion Recognition (SER) software typically detects the appearance of various feelings in the speaker. However, there are significant challenges in combining information from multidisciplinary domains, notably speech-emotion recognition and applied psychology. Some researchers have used handcrafted attributes to categorize emotions and obtained high classification accuracy. However, these attributes reduce the categorization accuracy for multi-lingual environments. Deep learning algorithms have been utilized to autonomously retrieve the local representation from supplied speech data. The given strategies can't extract the most valuable characteristics from challenging speech inputs. To address this constraint, we propose an innovative SER framework that employs data augmentation approaches before generating relevant feature sets from each utterance and selecting the most discriminative optimum features. And the chosen feature vector is sent into the Normalized 1D CNN for emotion recognition using multi-lingual databases. This study evaluates the effectiveness of an XGB classifier for multi-lingual emotion recognition by testing its performance on data from a corpus trained on a different corpus. The testing outcomes displayed that our proposed SER architecture functioned better than existing SER approaches. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

ConU: Conformal Uncertainty in Large Language Models with Co...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wang, Zhiyuan Duan, Jinhao Cheng, Lu Zhang, Yue Wang, Qingni Shi, Xiaoshuang Xu, Kaidi Shen, Hengtao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Drexel University United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761681

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Object Detection Algorithm Based on Multi-Scaled Convolutional Neural Networks 3

Object Detection Algorithm Based on Multi-Scaled Convolution...

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

作者： Nandhini, T.J. Thinakaran, K. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350320749

Object detection algorithms must first identify all the objects inside an image before machine vision can properly categorize and localize them. Many methods have been proposed to handle this problem, with most of the motivation coming from computer vision and deep learning methods. However, prevailing technologies have never effectively recognized tiny, dense things and often failed to detect objects that have undergone random geometric alterations. We analyze the current state of the art in object identification and propose a deformable convolutional network with adjustable depths to address these concerns. The results of our research suggest that they are better than the current best practices, blend deep convolutional networks with flexible convolutional structures to account for geometric variations, and get multi-scaled features. Next, we perform the remaining phases of object identification and region regress by up-sampling the fusion of multi-scaled elements. Experimental validation of our proposed framework demonstrates a considerable improvement in accuracy relative to time spent recognizing small target objects with geometric distortion. © 2023 IEEE.

关键词： Object detection

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An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning 31

An Efficient Dialogue Policy Agent with Model-Based Causal R...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Kai Wang, Zhenyu Zhao, Yangyang Fang, Bopeng School of Software Engineering South China University of Technology Guangdong China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798891761964

Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviate the sample inefficiency. However, traditional world model methods merely consider one-step dialogues, leading to an inaccurate environmental simulation. Furthermore, different users may have different intention preferences, while most existing studies lack consideration of the intention-preferences causal relationship. This paper proposes a novel framework for dialogue policy learning named MCA, implemented through model-based reinforcement learning with automatically constructed causal chains. The MCA model utilizes an autoregressive Transformer to model dialogue trajectories, enabling a more accurate simulation of the environment. Additionally, it constructs a causal chains module that outputs latent preference distributions for intention-action pairs, thereby elucidating the relationship between user intentions and agent actions. The experimental results show that MCA can achieve state-of-the-art performances on three dialogue datasets over the compared dialogue agents, highlighting its effectiveness and robustness. © 2025 Association for Computational Linguistics.

关键词： Deep reinforcement learning

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SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

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4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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PUF Based Security for the Physical Hardware Devices of IoT Using White-Box Cryptographic Technique 4th

PUF Based Security for the Physical Hardware Devices of IoT ...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Makhija, Jigar Anantha Narayanan, V. Department of Computer Science and Engineering Amrita School of Engineering Coimbatore India

ISBN: (纸本)9789811998874

Hardware security for IoT systems has always been a concern to establish trust among its users and till now it doesn’t meet its reliable outcome, though many solutions are provided also. Using physical unclonable functions with WBC as a solution to secure the embedded firmware of the IoT device hardware gives a nominal level of security. To provide the solution to these issues, our approach gives the solution by implementation of White-Box AES cryptographic technique that can be integrated with the physical unclonable function (PUF) based key generation, this dynamic hash-key will be used to encrypt the firmware of embedded hardware as well as for device authentication which makes IoT devices more secure and reliable on SOC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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