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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Low-Overhead General-Purpose Near-data Processing in CXL Memory Expanders 57

Low-Overhead General-Purpose Near-Data Processing in CXL Mem...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Ham, Hyungkyu Hong, Jeongmin Park, Geonwoo Shin, Yunseon Woo, Okkyun Yang, Wonhyuk Bae, Jinhoon Park, Eunhyeok Sung, Hyojin Lim, Euicheol Kim, Gwangsun POSTECH Department of Computer Science and Engineering Korea Republic of POSTECH Graduate School of Artificial Intelligence Korea Republic of Seoul National University Graduate School of Data Science Korea Republic of SK Hynix Korea Republic of

ISBN: (纸本)9798350350579

Emerging Compute Express Link (CXL) enables cost-efficient memory expansion beyond the local DRAM of processors. While its *** protocol provides minimal latency overhead through an optimized protocol stack, frequent CXL memory accesses can result in significant slowdowns for memory-bound applications whether they are latency-sensitive or bandwidth-intensive. The near-data processing (NDP) in the CXL controller promises to overcome such limitations of passive CXL memory. However, prior work on NDP in CXL memory proposes application-specific units that are not suitable for practical CXL memory-based systems that should support various applications. On the other hand, existing CPU or GPU cores are not costeffective for NDP because they are not optimized for memorybound applications. In addition, the communication between the host processor and CXL controller for NDP offloading should achieve low latency, but existing ***/PCIe-based mechanisms incur μs-scale latency and are not suitable for fine-grained NDP. To achieve high-performance NDP end-to-end, we propose a low-overhead general-purpose NDP architecture for CXL memory referred to as Memory-Mapped NDP (M2NDP), which comprises memory-mapped functions (M2func) and memory-mapped μthreading (M2μthread). M2func is a ***-compatible lowoverhead communication mechanism between the host processor and NDP controller in CXL memory. M2μthread enables lowcost, general-purpose NDP unit design by introducing lightweight μthreads that support highly concurrent execution of kernels with minimal resource wastage. Combining them, M2NDP achieves significant speedups for various workloads by up to 128x (14.5x overall) and reduces energy by up to 87.9% (80.3% overall) compared to baseline CPU/GPU hosts with passive CXL memory. © 2024 IEEE.

关键词： Memory architecture

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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computer Modeling in engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Improved Deep Matrix Factorization for Signal Recovery in Random Frequency Diversity Array Radar 2

Improved Deep Matrix Factorization for Signal Recovery in Ra...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Guo, Dayan Lin, Zijian Huang, Tianyao Qi, Yue He, Jie Zhang, Xiao-Ping Wang, Xueqian Li, Gang University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua Shenzhen International Graduate School Institute of Data and Information Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random frequency diversity array (FDA) radars. While the existing deep matrix factorization approach is designed for real-valued situations and signals of FDA radars are complex-valued cases, we propose two methods to extend the existing approach into complex-valued framework: 1) adjusting the neural network structures and activation functions for complex-valued operations, and 2) constructing a real-valued representation of the original complex-valued input. Simulation results demonstrate that the proposed methods outperform traditional ones in multi-target scenarios. © 2024 IEEE.

关键词： Matrix factorization

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Identification of Breast Cancer using Jelly fish Based Deep Wavelet Autoencoder 9

Identification of Breast Cancer using Jelly fish Based Deep ...

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

作者： Anbumani, K. Balamanigandan, R. Kumar, T. Rajesh Sasi Kumar, A. Mahaveerakannan, R. Sri Sai Ram Engineering College Department of EIE Chennai India Saveetha School of Engineering Computer Science and Engineering India SIMATS Saveetha School of Engineering Computer Science and Engineering Tamil Nadu Chennai602105 India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Mangalore India

ISBN: (纸本)9798350397376

Mammography, an imaging technology used to detect breast cancer, has recently received more interest from scientists. Screening mammography is the best way to detect breast cancer and other abnormalities. Breast tumours often have two distinct characteristics: mass lesions and microcalcification. A gathering of appropriate picture pre-processing, proceduresare essential for accurate cancer detection and classification in mammograms. Image processing is widely employed in the medical field to improve the diagnostic and treatment processes. When it comes to classification and segmentation, deep neural networks (DNN) have excelled so far. With this in mind, the authors of this paper suggest a deep wavelet autoencoder (DWA) method for image compression by fusing the autoencoder's basic feature reduction function with the picture decomposition capabilities of the wavelet transform. The best attributes for identifying breast cancer are chosen in this study by using the Jelly Fish Search Algorithm (JFSA). When used together, they have a dramatic impact on reducing the size of the feature set required to persist through a subsequent classification job using DNN. The suggested DWA-DNN image classifier was evaluated on two public datasets of breast cancer pictures. The DWA-DNN classifier's performance criteria was compared to that of other existing classifiers, and it was found that the suggested technique excels in comparison across a range of measures. © 2023 IEEE.

关键词： Deep neural networks

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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An Intrusion Detection Scheme Based on Sequential Tri-Way Decisions of Multiple Granularity for Intelligent Connected Vehicles

An Intrusion Detection Scheme Based on Sequential Tri-Way De...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Yongzhong Wang, Qi Li, Yuheng Zhang, Shipeng School of Automotive Engineering Nantong Institute of Technology Nantong226010 China Internet of Things and Big Data Major of Macao Polytechnic University Macao China School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China

ISBN: (纸本)9789819639762

With the rapid developing technology of the Internet of Things, intelligent connected vehicles are facing more and more serious cybersecurity threats and challenges. With the developing of cybersecurity technology for intelligent connected vehicles it has posed a comprehensive challenge to the existing cybersecurity technologies. Intrusion detection is an important research field of cybersecurity. An intrusion detection scheme based on sequential tri-way decisions of multiple granularities (S3WD-MG) is proposed for Intelligent Connected Vehicles. The S3WD-MG method obtains feature sets of multi-granularities through the auto-encoder of deep learning. This method combines multiple granularities and sequential tri-way decisions to make decisions by the most appropriate for network behavior through by the decision threshold. The experiment results on NSL-KDD show that S3WD-MG method does have a good performance in intrusion detection. Compared with other models, our proposed S3WD-MG method has a higher detection’s rate and the robustness of algorithm is stronger. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Network intrusion

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Internet of Things IoMT data Aggregation Using Machine Learning 2

Internet of Things IoMT Data Aggregation Using Machine Learn...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Pulabaigari, Lakshmi Sagar Sivakumar, C. School of Computing Mohan Babu University Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Mohan Babu University Dept. of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350372847

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available. © 2024 IEEE.

关键词： Internet of things

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