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

作者： Gayathri, N. Anandakumar, H. Gowri, S. Keerthika, J. Nithyapriya, S. Sri Eshwar College of Engineering Computer Science and Engineering Tamilnadu Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350397376

The primary motivator after the evolution of cognitive radio is the lack of utilization of the spectrum. This technology adds new functionality in the physical layer, data link sub-layer, Transmission Control Protocol/Internet Protocol layer, and Transport layers of the hierarchy of software layers. The majority of the currently available research in cognitive radio has concentrated on security challenges at the physical, MAC layers. Over the past two decades, cyber security threats and problems have grown significantly, especially in cognitive radio networks. This study describes and analyzes various attacks that target each layer at its protocol stack. Each attack's specific detection techniques and defenses are also examined and contrasted. © 2023 IEEE.

关键词： Cognitive radio

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Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition

Causal fMRI-Mamba: Causal State Space Model for Neural Decod...

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

作者： Deng, Weihao Han, Fei Ling, Qinghua Liu, Qing Han, Henry School of Computer Science and Communication Engineering JiangSu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China School of Computer Jiangsu University of Science and Technology China School of Electronic and Information Engineering West Anhui University China School of Engineering and Computer Science Baylor University United States

ISBN: (纸本)9798350368741

Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal information due to high dimensionality, noise and inter-individual difference of fMRI, which also increase computational complexity and prior bias. To this end, a novel causal state space model, Causal fMRI-Mamba, is proposed for neural decoding and task state mapping. It effectively captures global spatiotemporal information via eliminating local redundancies and capturing long-distance dependencies. Meanwhile, a causal representation framework is designed to extract invariant high-order features and disentangle related causal features, enhancing model performance. Furthermore, a dense connection module is expanded to prevent significant causal information loss in hidden states of inter layers. On the HCP brain task state classification task, Causal fMRI-Mamba achieves better performance and generalization than comparison methods. © 2025 IEEE.

关键词： causal representation learning Neuro-decoding state space model task-based fMRI

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Multi-Label MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Building a Robust Machine Translation System for Punjabi to English Using Deep Learning Approaches 4

Building a Robust Machine Translation System for Punjabi to ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Nisha Chhabra, Megha Abidi, Ali Imam Setia, Sonia Maurya, Ajay Anjli, Km. Noida Institute of Engineering and Technology Department of Data Science U.P. Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science & Engineering U.P. Greater Noida India Poornima Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Rajasthan Jaipur India Delhi New Delhi India

ISBN: (纸本)9798350387490

In the dynamic realm of artificial intelligence, the evolution of Machine Translation Systems (MTS) for Indian languages faces unique challenges due to linguistic diversity. The diversity of morphology and the inherited dialects make it difficult to translate. The translation can be improved with pre-processing and optimization of MTS. Our MTS proposes the steps to improve the translation of Punjabi to English by employing two models, one with a Gated Recurrent Unit (GRU) layer and another with a Long Short-Term Memory (LSTM) layer. Another step involves Hyperparameter tuning to reduce complexity and increase model's efficiency. The experimentation shows accuracy rate on Samanantar dataset. The research aims to provide solutions to gaps identified in MTS for Punjabi, contributing to the advancement of machine translation technology in Punjabi's linguistic landscape. © 2024 IEEE.

关键词： Long short-term memory

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Understanding Student Behavior and Learning Patterns through Video Analysis: A Comprehensive Literature Review 4

Understanding Student Behavior and Learning Patterns through...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Gadad, Jyoti Asundi, Madhu Vijayalakshmi, M. Baligar, Preethi Kle Technological University Centre for Engineering Education Research Hubballi India Data Science and Intelligent Systems Iiit Ittigatti Dharwad India School of Computer Science and Engineering Kle Technological University Hubballi India

ISBN: (纸本)9798350387063

Technology-enhanced learning (TEL) has emerged as a powerful approach to facilitate and enhance learning experiences in diverse educational settings. The integration of TEL tools enables the collection of real-time and longitudinal data. Technology-enhanced learning provides the digital infrastructure and tools that enable educators to collect and process diverse data. It is fundamental for successfully implementing Multi-modal Learning Analytics (MMLA) in understanding student learning patterns. This literature paper presents a comprehensive review of research at the intersection of MMLA, video analytics, human gesture, and their relation to behavior and learning patterns. This study explores how video analysis techniques capture and analyze human gestures, revealing insights into underlying behavioral dynamics and learning patterns. This study conducts a scoping review of relevant literature from diverse fields such as computer science, education, human-computer interaction, and medical science. The authors utilized LitMap, a comprehensive search strategy to identify pertinent literature. In education context, understanding the role of gestures in learning can inform the development of instructional strategies that optimize knowledge acquisition and retention. By studying patterns in human gestures and behavior, particularly in the education context, researchers can gain insights into students' engagement, cognitive processes, and learning strategies. This scoping review presents an in-depth survey of research encompassing video analytics, human gestures, and their interplay with behavior and learning patterns. The study delves into the behavioral factors that impact student learning and explores how educators can leverage them to enhance their teaching practices. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring Novel CNN Architectures for Deep Learning-Based Liver Tumor Occurrence Prediction

Exploring Novel CNN Architectures for Deep Learning-Based Li...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kalaiselvi, P. Anusuya, S. Kumaravel, A. Esther, C. Gomathy, G. Aravind, Arun Sri Sairam Engineering College Department of Artificial Intelligence and Data Science India Simats Engineering Department of Computer Science and Engineering India Biher Department of Information Technology India

ISBN: (纸本)9798350394580

According to the authorized findings of,' Hepatocellular carcinoma (HCC)' a common in the human species, liver located, tumor,90 % of its occurrences demand any sort of support for predicting them in advance, if possible. For this purpose, Image Processing, Text Mining, and Deep learning techniques are handled with effective pre-processing methods in the recent research works to improve the results available so far. A majority of contribution by the researchers finds it challenging mostly in selecting suitable architectures for deep learning as most of the previous literature studies show the lacking of details on feasibility and justifiability in specifying the architectures. In this article, it is aimed at the primary goal of such studies as the first attempt on varying architectures in terms of profile and position of layers and observing the performance for recognizing the liver Tumor from the Image-data sets. The CNN architecture of neural network like AlexNet, VGGnet, ResNet, LeNet are standard architectures, based on this liver tumor dataset is implemented in customized architecture and its produced best performance with accuracy of 92.4 %. And It is compared with MeanderHandPD, Breast cancer and Liver Tumor datasets, the proposed architecture shows higher performance for Liver Tumor images. © 2023 IEEE.

关键词： AlexNet DeepLearning4j F-Measure Hepatocellular carcinoma Precision ROC SoftMax VGG

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Applying Competition-Based Learning to Stimulate Students' Practical and Competitive AI Ability in a Machine Learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. Research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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Modelling High-Order Social Relations for Item Recommendation (Extended Abstract) 39

Modelling High-Order Social Relations for Item Recommendatio...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Yang Chen, Liang He, Xiangnan Peng, Jiaying Zheng, Zibin Tang, Jie Sun Yat-sen University School of Computer Science and Engineering Guangzhou China University of Science and Technology of China School of Data Science School of Information Science and Technology Hefei China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350322279

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. © 2023 IEEE.

关键词： Online systems

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

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IEEE Sensors Journal 2025年第11期25卷 20547-20560页

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

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