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2nd IEEE international conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Sunil, B. Sameer, S. Vaseekaran, K.V. Rishi, R. Department of Computer Applications SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9798331504960

Cancer detection is critical for early diagnosis and treatment, and non-invasive imaging methods like microwave imaging have shown promise in this domain. This research presents a CNN-based machine learning model to improve the speed and accuracy of microwave medical imaging for cancer detection. Leveraging large datasets, advanced preprocessing techniques such as noise removal, normalization, and data augmentation were applied. The proposed model includes a multi-layer CNN architecture, optimized using stochastic gradient descent (SGD) and categorical cross-entropy loss functions. Evaluation metrics, including accuracy, sensitivity, and specificity, were calculated from a confusion matrix. The system achieved 95.6% accuracy, 94% sensitivity, and 96% specificity, outperforming traditional models both in terms of accuracy and computational efficiency. The results suggest that the proposed model is well-suited for real-time clinical applications. Future work will focus on expanding the dataset and integrating multimodal imaging to enhance diagnostic precision. © 2024 IEEE.

关键词： Stochastic models

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mL-mB LBP Fused Net: multi-layer-multi-block LBP Image Fusion Facial Expression Recognition 5th

mL-mB LBP Fused Net: Multi-layer-Multi-block LBP Image Fusio...

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5th international conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mukhopadhyay, Moutan Ghosh, Ankush Shaw, Rabindra Nath Dey, Aniruddha Department of Computer Science & Engineering MAKAUT West Bengal Kolkata India Chandigarh University Punjab Mohali India Department of Computer Science & Engineering MSIT West Bengal Kolkata India

ISBN: (纸本)9789819791118

Facial emotion recognition (FER) systems have gained popularity due to their applications in various fields, including healthcare, cognitive science, video conferencing, and driver safety. However, recognizing facial expressions automatically from images presents challenges due to inter-person variability and the difficulty in accurately distinguishing between different emotions. The research investigates the application of a modified Local Binary Pattern (LBP) in facial emotion detection, focusing on the multilayer multi-block local binary pattern (mL-mB LBP). The process involves converting facial expression images into 3×3 identical blocks, which are then used to generate center pixels using the LBP descriptor block-wise. The final layer is partitioned into 3×3 blocks to generate central pixels. The study introduces a customized convolutional neural network (CNN)-based framework for emotion recognition, which uses the mL-mB LBP image data to accurately predict emotions. The CNN model is trained using multi-level multi-block images. The experimental study findings show that the accuracy of the CNN model is significantly improved when trained using multi-block multi-level images with all layers fused. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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AP2PNET: Attention P2PNET for Sparse 3D Leg Reconstruction 21

AP2PNET: Attention P2PNET for Sparse 3D Leg Reconstruction

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21st international computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hang, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Although the current point cloud upsampling techniques are already capable of attaining arbitrary upsampling rates af-ter one training. However, during the upsampling process, it may be difficult to preserve the fine geometric details in the original point cloud, resulting in insufficient detail in the up-sampled results and poor performance on our collected leg da-taset. Moreover, the network structure of this method is relatively complex, with high computational complexity and in-creased training costs. To tackle the issues mentioned above, we propose a new attention enhanced multi-scale feature extraction method. First, we extract local features through multilayer perceptron, and then extract important channel and spatial feature information through attention mechanisms to obtain multi-scale feature representations for learning point-to-point distances. Compared with the Grad-PU method, our method reduces the size of network parameters by 54.41%, experi-mental results demonstrate that our approach achieves the state-of-the-art upsampling performance. © 2024 IEEE.

关键词： Deep learning

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Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning

Enhancing Energy Efficiency in Wireless Sensor Networks usin...

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2025 international conference on multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raskar, Sandeep Dhasmana, Gulshan Lakshminarayana, M. Patil, Harshal Bhagya Shree, S. Natrayan, L. Terna Engineering College Department of Artificial Intelligence and Data Science Nerul Maharashtra Navi Mumbai400706 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India M S Ramaiah Institute of Technology Department of Medical Electronics Engineering Karnataka Bengaluru560054 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Brindavan College of Engineering Department of Physics Karnataka Bengaluru560063 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations. © 2025 IEEE.

关键词： Deep reinforcement learning

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multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

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5th international conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 IEEE.

关键词： Emotion Recognition

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One More Check For The New Tracker And Just Disappeared Tracker

One More Check For The New Tracker And Just Disappeared Trac...

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2023 international conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Yang, Hanzhi Feng, Xin Zhu, Zhao-Han College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Southwest Jiaotong University School of Electrical Engineering China

ISBN: (纸本)9781510665040

We propose a fast and accurate multi-target tracking method, Twice Check. This method is a multi-target tracking method, which matches the newly appeared track and the just disappeared track, and adds the low-frame quadratic verification and linear interpolation to repair the track. Our method, combined with the current SOTA method, can improve the effect of the original method on the open multi-target tracking challenge (MOT) dataset. The experimental results show that our Twice Check can effectively increase the effect of the current tracking model, and our method is simple and does not increase the tracking time. © 2023 SPIE.

关键词： Object detection

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to international Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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multi-Digit Handwritten Recognition: A CNN-LSTM Hybrid Approach with Wavelet Transforms 14

Multi-Digit Handwritten Recognition: A CNN-LSTM Hybrid Appro...

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14th international conference on computer and Knowledge engineering, ICCKE 2024

作者： Kazempour, Amin Tanha, Jafar Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9798331511272

Handwritten digit recognition remains a pivotal area in machine learning and computer vision, essential for applications like license plate identification, form processing, and historical document reading. Addressing the challenges of multi-digit and multi-language recognition, including variations in handwriting styles across different languages, we propose a novel model integrating convolutional and recurrent neural networks with an attention mechanism. Unlike conventional methods, our model employs wavelet transforms instead of max pooling to preserve image texture and edges. We created a comprehensive dataset containing both English and Persian digits, featuring 80,000 training and 20,000 test images with 1–5 digit numbers. To demonstrate the superiority of the proposed model, we conducted extensive experiments and compared it to some state-of-the-art models. Our model demonstrated remarkable accuracy, achieving 99.58% for single digits and 98.03% for sequences. Extensive experiments validated the efficacy of our approach, highlighting its potential for future research in multi-digit recognition systems across various languages. ©2024 IEEE.

关键词： Convolutional neural networks

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Fuzzy Concession Strategy for Emotional Human-computer Negotiation 36

Fuzzy Concession Strategy for Emotional Human-Computer Negot...

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36th IEEE international conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Luo, Xudong Luo, Ying Sun, Kaili Li, Yanling School of Computer Science and Engineering Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China

ISBN: (纸本)9798331527235

This paper proposes an emotion-based human-computer negotiation system. Existing systems handle basic dialogues but struggle with complex negotiations involving human emotions. To address this, we design a fuzzy concession strategy that adjusts tactics through sentiment analysis, managing states like anger, happiness, and anxiety. Experiments show significant improvement in negotiation success and overall gains when considering emotional factors. Results demonstrate that this model enhances customer satisfaction and user experience by achieving agreements that satisfy users. This research highlights the potential of integrating fuzzy reasoning, sentiment analysis, and natural language processing into AI systems, laying the foundation for more intelligent, human-like dialogue agents and improving human-computer interaction and e-commerce engagement. © 2024 IEEE.

关键词： Natural language processing systems

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Property Price and Rent Prediction System

Property Price and Rent Prediction System

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Singh, Atharava Pratap Rai, Abhinav Karnwal, Devansh Vats, Anupam Pandey, Tripti Abes Engineering College Department of Information Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331529833

Machine Learning (ML) has revolutionized various sectors including speech recognition, product recommendations, and healthcare. This research focuses on applying ML techniques to address challenges in real estate market prediction. The unpredictable nature of property prices creates significant difficulties for buyers in making informed decisions. This research proposes a multi-algorithm housing price prediction model, integrating SVM, Random Forest, and GBM approaches, with Random Forest demonstrating superior predictive performance. The model considers various factors influencing property prices, enabling buyers to make data-driven decisions without excessive reliance on intermediaries. This paper presents the methodology, implementation, and comparative results of these algorithms in the context of real estate price prediction. © 2025 IEEE.

关键词： Prediction models

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