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IEEE Conference on Wireless Communications and Networking

作者： Huapeng Yang Han Ji Zhangqin Huang Xiping Wu Beijing Engineering Research Center for IoT Software and Systems College of Computer Science Beijing University of Technology Beijing China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time.

关键词： Energy consumption Runtime Wireless networks LoRa Artificial neural networks Spread spectrum communication Complex networks Graph neural networks Topology Internet of Things

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Lung cancer

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WoKnack-A Professional Social Media Platform for Women Using Machine Learning Approach 3

WoKnack-A Professional Social Media Platform for Women Using...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shanmugarajah, Sutharshan Praisoody, Ashvini Uddin, Md Rakib Paskkaran, Vinojan De Silva, Hansi Tharmaseelan, Janani Thilakarathna, Thusithanjana Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Today's generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system. © 2021 IEEE.

关键词： Machine learning

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MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation 19th

MD-TransUNet: TransUNet with Multi-attention and Dilated C...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Jie Wan, Jian Zhang, Xin School of Computer Science and Technology Hangzhou Dianzi University Zhejiang310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Ministry of Education Beijing China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China

ISBN: (纸本)9783031545276

The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolution

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United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images

arXiv

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arXiv 2024年

作者： Sun, Yanguang Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: https://***/CSYSI/UDCNet. © 2024, CC BY.

关键词： Optical remote sensing

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Predicting software Functional Size Using Natural Language Processing: An Exploratory Case Study

Predicting Software Functional Size Using Natural Language P...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Hüseyin Ünlü Samet Tenekeci Can Çiftçi İbrahim Baran Oral Tunahan Atalay Tuna Hacaloğlu Burcu Musaoğlu Onur DemirǶrs Department of Computer Engineering Izmir Institute of Technology Izmir Turkiye Bilgi Grubu Izmir Turkiye Department of Information Systems Engineering Atilim University Ankara Turkiye Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Siskon Software & Automation Izmir Turkiye

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

关键词： Training Schedules Accuracy Estimation Project management Companies Predictive models Size measurement software Natural language processing

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Exploring the Impact of Drivers’ Emotion and Multi-task Learning on Takeover Behavior Prediction in Multimodal Environment Exploring Takeover Behavior Prediction in Multimodal Environment

Exploring the Impact of Drivers’ Emotion and Multi-task Lea...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Feng, Xinyu Gu, Yu Lin, Yuming Cai, Yaojun Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Province Chengdu China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangxi Nanning China

ISBN: (纸本)9798400713958

With the development of autonomous driving technology, drivers may be immersed in non-driving related tasks. Therefore, takeover safety has received increasing attention. Previous studies have focused on designing the empirical analysis to formulate preventive measures or constructing methods for a single-task learning strategy. However, due to the persistent gaps between modalities and ignoring the correlation between tasks, they make it difficult to capture similar information between modalities. In this paper, we propose the Multi-TBP, a general deep learning framework for takeover behavior prediction. Experiments show that our method achieves the best performance on all tasks. Through ablation studies and visualization, we find that each modality’s importance varies from each task, and there are also correlations between tasks. © 2025 Copyright held by the owner/author(s).

关键词： Multi-task learning

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ADATS: Adaptive RoI-Align based Transformer for End-to-End Text Spotting

ADATS: Adaptive RoI-Align based Transformer for End-to-End T...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zepeng Huang Qi Wan Junliang Chen Xiaodong Zhao Kai Ye Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Scene text spotting has attracted great attention in recent years. Compared with two-stage approaches that locate scene texts in the first stage and recognize them in the second stage, the advantages of joint location and recognition training are not fully explored. In this paper, we present an ADaptive RoI-Align based transformer for end-to-end Text Spotting (ADATS), which simultaneously locates and recognizes text with a single forward pass. By employing an Adaptive RoI-Align, the text features are extracted from the feature extraction network with the original aspect ratio, such that less information is lost during the alignment of arbitrarily-shaped scene text. Attention-based segmentation and recognition heads allow us to simultaneously optimize detection and recognition. Experiments on ICDAR 2015, MSRA-TD500, Total-Text, and CTW1500 demonstrate the effectiveness of our method.

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Organization-wide IOC Monitoring and Security Compliance in Endpoints using Open Source Tools 3

Organization-wide IOC Monitoring and Security Compliance in ...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Divya, P.J. George, Reenu Sara Madhusudhan, G. Padmasree, S. Jss Science and Technology University Department of Computer Science and Engineering Mysore India Indian Space Research Organization Information Technology Cell Laboratory for Electro-Optics Systems Bengaluru India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Endpoint Security is an important layer in cyber security and it is one of the first places organizations look to secure their enterprise network. Proactively and iteratively monitoring endpoints for threats and Indicators of Compromise (IOC), helps security analysts to detect malware/attacks and to respond to incidents quickly. An Indicator of Compromise (IOC) refers to data that indicates that a system may have been infiltrated by a cyber-threat. Cyber security agencies send regular cyber-alerts to various organizations nation-wide and provide IOC list in the form of hashes, IPs, URLs, domains, yara rules, etc. for monitoring the presence of IOCs in their respective network. Periodic auditing of endpoints for security compliance is also a challenging and time-consuming task for every organization. To overcome these challenges, a solution based on client-server architecture is proposed in organizational network using open-source tools named Osquery, Fleet, Filebeat and Graylog. Using these tools, endpoints can be monitored for IOCs on demand or on scheduled manner from a central server. Yara based scan can also be performed efficiently in endpoints to monitor for IOCs using this architecture. In addition to IOC monitoring, this paper explains verifying compliance of endpoints with the organization's security policy using the proposed setup. Impact on performance of the endpoints during query execution was also analyzed. This solution will contribute in strengthening the endpoint security which in turn enhances overall cyber-security posture of the organization. © 2022 IEEE.

关键词： Network architecture

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