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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhe Dai Xiaoshuang Shi Xiaofeng Zhu Tingrui Pan Kang Li School of Software Engineering University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital of Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024.

关键词： Training Analytical models Solid modeling Three-dimensional displays Annotations Magnetic resonance imaging Feature extraction Vectors Optimization Biomedical imaging

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Advancements in Cybersecurity Using Deep Learning Techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Hosanna Princye P Ahila A A. Anna Lakshmi N. Ragavendran K. E. Purushothaman G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore Anekal India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Software Analyst Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study.

关键词： Deep learning Training Accuracy Feature extraction Safety Trojan horses Convolutional neural networks computer crime Testing Python

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Robust Dynamic Broadcasting for Multi-Hop Wireless Networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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AI Personalized Mental Health Monitoring System using Machine Learning, and Natural Language Processing

AI Personalized Mental Health Monitoring System using Machin...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Mohamed Jamal Irfan G. Dhivya N. Raghavendran M. C. Babu T. Jasperline R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Dr. G. U. Pope College of Engineering Tuticorin Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study introduces an AI-driven customized mental health monitoring system that uses machine learning (ML) and natural language processing (NLP) to provide ongoing mental health evaluation and assistance. The system uses user-generated textual data, including journal entries, chat logs, and social media postings, to assess emotional states, stress levels, and mental health issues. The system uses sophisticated natural language processing methods to identify linguistic patterns associated with mental health problems such as depression, anxiety, and others. The machine learning algorithms customize monitoring by adjusting to the unique characteristics of each user and use past data to provide customized suggestions and preemptive alerts. The technology aims to enhance human interaction and function as a proactive tool in mental health treatment by providing real-time information. The system extracts emotional indicators from textual input using sentiment analysis methods and pre-trained natural language processing models such as BERT. The results are used with user demographics and history to build machine learning classifiers, such as Random Forest (RF) and Support Vector Machine (SVM), which predict mental health disorders. Supervised learning is used to develop the model, and suggestions are progressively improved using a tailored feedback system. The SVM and RF algorithms demonstrated an 88% and 92%accuracy rate in forecasting mood swings and early warning indicators of mental health disorders when evaluated on a set of anonymized mental health information. The 30% increase in user involvement demonstrated the efficacy of tailored suggestions in proactive mental health treatment.

关键词： Support vector machines Machine learning algorithms Accuracy Social networking (online) Supervised learning Mental health Natural language processing Monitoring Random forests Classification tree analysis

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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TPE-H2MWD:an exact thumbnail preserving encryption scheme with hidden Markov model and weighted diffusion

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Frontiers of Information Technology & Electronic engineering 2023年第8期24卷 1169-1180页

作者： Xiuli CHAI Xiuhui CHEN Yakun MA Fang ZUO Zhihua GAN Yushu ZHANG School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of ThingsHenan UniversityZhengzhou450046China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China School of Software Intelligent Data Processing Engineering Research Center of Henan ProvinceInstitute of Intelligent Network SystemHenan UniversityKaifeng475004China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China

With the substantial increase in image transmission,the demand for image security is ***-like images can be obtained by conventional encryption schemes,and although the security of the images can be guaranteed,the noise-like images cannot be directly previewed and *** on the rank-then-encipher method,some researchers have designed a three-pixel exact thumbnail preserving encryption(TPE2)scheme,which can be applied to balance the security and availability of images,but this scheme has low encryption *** this paper,we introduce an efficient exact thumbnail preserving encryption ***,blocking and bit-plane decomposition operations are performed on the plaintext *** zigzag scrambling model is used to change the bit positions in the lower four bit ***,an operation is devised to permute the higher four bit planes,which is an extended application of the hidden Markov ***,according to the difference in bit weights in each bit plane,a bit-level weighted diffusion rule is established to generate an encrypted image and still maintain the same sum of pixels within the *** results show that the proposed scheme improves the encryption efficiency and can guarantee the availability of images while protecting their privacy.

关键词： Hidden Markov model Weighted diffusion Balance between usability and privacy Image encryption

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Machine Learning Enabled Healthcare Balancing Patient Privacy and data Utility

Machine Learning Enabled Healthcare Balancing Patient Privac...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Manoj Ram Tammina Bhargavi Posinasetty Prabha Shreeraj Nair Santosh Kumar Pavithra G Harpreet Kaur Sr Software Developer Innovation Bread Financial Columbus Ohio Clinical Data Management Sr. Clinical Data Manager PROMETRIKA LLC Department of IT Noida Institute of Engineering and Technology Greater Noida Computer Science ERA University Lucknow Uttar Pradesh Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Lovely Professional University Punjab Phagwara

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

When applied to healthcare, machine learning ushers in a new age of data-driven medical practice that holds great promise for better patient outcomes and individualized treatment. However, this evolution isn't without significant difficulties, such as the difficulty of striking a balance between patient confidentiality and data use. In this study, we use -Differential Privacy as a privacy-protecting technique and a number of machine learning models to quantify the value of the data collected. Our research demonstrates a subtle trade-off, where more stringent privacy safeguards often result in less useful data, and vice versa. We stress the need for ethical frameworks, patient permission, and flexible privacy restrictions as means of negotiating this space. Achieving responsible and successful machine learning-enabled healthcare calls for a number of future steps, including optimization of privacy settings, adoption of federated learning, data ownership through blockchain, validations in the actual world, and extensive ethical advice.

关键词： data privacy Ethics Privacy Federated learning Medical services data models Blockchains

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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