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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Muhammad Azeem Muhammad Zaman Adnan Akhunzada Tanzila Kehkashan Imran Ashraf Abdul Rehman Department of Computer Science The University of Lahore Sargodha Pakistan Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Computer Science The University of Lahore Sargodha Pakistan

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Diabetic foot ulcer (DFU) are a severe complication in diabetic patients, requiring accurate and early diagnosis to prevent further complications such as infection or amputation. To address this, Research employed YOLO and SSD models for automated DFU detection from medical images, comparing their performance based on key evaluation metrics, including accuracy, precision, recall, and F1-score. Results demonstrate that both models perform exceptionally well, with SSD achieving an accuracy of 98.7%, surpassing YOLO's 97.5%. Additionally, SSD exhibited superior precision (99%), recall (98.9%), and F1-score (99.1%), indicating its robustness in detecting ulcerated regions of varying sizes and stages. These results were further contextualized through a comparison with previous studies, where SSD outperformed several CNN-based models, which achieved accuracy ranging from 93.7% to 97.0%. The SSD model's multi-scale object detection capabilities, combined with transfer learning and data augmentation techniques, contributed to its high performance. Overall, this study demonstrates the potential of deploying SSD and YOLO models in clinical settings for early and accurate DFU detection, paving the way for more reliable and timely interventions in diabetic care. The findings underline the significance of advanced object detection methods, offering promising avenues for improving patient outcomes in medical imaging

关键词： YOLO Accuracy Smart cities Computational modeling Transfer learning Robustness Distance measurement Diabetes Medical diagnostic imaging Robots

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yusei Ishimizu Jialong Li Takuto Yamauchi Sinan Chen Jinyu Cai Takanori Hirano Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions.

关键词： Large language models Semantics Manuals Aerospace electronics Real-time systems Cognition Complexity theory Drones

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Towards Resource-Efficient Reactive and Proactive Auto-Scaling for Microservice Architectures

SSRN

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SSRN 2024年

作者： Ahmad, Hussain Treude, Christoph Wagner, Markus Szabo, Claudia School of Computer and Mathematical Sciences University of Adelaide Australia School of Computing and Information Systems Singapore Management University Singapore Department of Data Science and AI Faculty of IT The Monash University Australia

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust microservice resources to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, further exacerbating these issues. In addition, the architectures of existing HPAs exhibit several challenges, including single point of failure and the absence of coordinated scaling operations. To address these concerns, we propose Smart HPA and ProSmart HPA, reactive and proactive horizontal pod auto-scalers. These auto-scalers integrate centralized and decentralized styles. Smart HPA optimizes resource exchange in constrained environments, while ProSmart HPA uses machine learning to manage resource demands proactively, reducing delays and enabling preemptive resource sharing. Our experiments show Smart HPA outperforms Kubernetes HPA, and ProSmart HPA surpasses both by reducing resource overutilization, overprovisioning, underprovisioning, and increasing resource allocation to microservice applications. © 2024, The Authors. All rights reserved.

关键词： Losses

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

Hybrid Semantic/Bit Communication Based Networking Problem O...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Le Xia Yao Sun Dusit Niyato Lan Zhang Lei Zhang Muhammad Ali Imran James Watt School of Engineering University of Glasgow Glasgow UK College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Clemson University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks.

关键词： Heuristic algorithms Packet loss Benchmark testing Throughput Semantic communication Resource management Global communication Optimization Queueing analysis Next generation networking

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Large Language Model (LLM)-enabled Graphs in Dynamic Networking

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IEEE Network 2024年

作者： Sun, Geng Wang, Yixian Niyato, Dusit Wang, Jiacheng Wang, Xinying Poor, H. Vincent Letaief, Khaled B. Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs in different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. © 2024 IEEE.

关键词： Generative adversarial networks

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CLPCare: Bootstrapping Multimodal Large Language Models for Ultrasound Cleft Lip and Palate Recognition and Analysis 10

CLPCare: Bootstrapping Multimodal Large Language Models for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Jiaru Luo, Guofeng Yan, Haolun Huang, Shiwang Liao, Zhenghan Zhang, Shiyi Wu, Jinzhun He, Xiaoqin Chen, Longbiao Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen China Xiamen University National Institute for Data Science in Health and Medicine Xiamen China Xiamen University Women and Children's Hospital Xiamen China Xiamen University Xiamen Maternity & Child Healthcare Hospital Women and Children's Hospital Department of Ultrasound Xiamen China

ISBN: (纸本)9798331520861

Cleft lip and palate is a common congenital malformations in fetuses, and early diagnosis is crucial for reducing the physical and psychological impact on pregnant women and their families. Traditional image processing methods for recognizing and analyzing cleft lip and palate rely on manual annotation and have limited interpretability. To address this issue, we propose a method for ultrasound image recognition and analysis of cleft lip and palate based on multimodal large language models. Firstly, we design a specific ultrasound image findings instruction finetuning template and fine-tune the multimodal large language model to enhance the model's recognition capability. Secondly, we conceive a specific retrieval-enhanced prompt template aimed at improving the model's understanding of cleft lip and palate analysis. Lastly, we evaluate our framework using a real dataset from Xiamen Maternal and Child Health Hospital in China. Additionally, we develop a medical system for the recognition and analysis of cleft lip and palate. © 2024 IEEE.

关键词： Ultrasonic imaging

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Staggered Routing in Autonomous Mobility-on-Demand Systems

arXiv

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arXiv 2024年

作者： Coppola, Antonio Hiermann, Gerhard Paccagnan, Dario Schiffer, Maximilian School of Management Technical University of Munich Germany Department of Computing Imperial College London United Kingdom School of Management Munich Data Science Institute Technical University of Munich Germany

In autonomous mobility-on-demand systems, effectively managing vehicle flows to mitigate induced congestion and ensure efficient operations is imperative for system performance and positive customer experience. Against this background, we study the potential of staggered routing, i.e., purposely delaying trip departures from a system perspective, in order to reduce congestion and ensure efficient operations while still meeting customer time windows. We formalize the underlying planning problem and show how to efficiently model it as a mixed integer linear program. Moreover, we present a matheuristic that allows us to efficiently solve large-scale real-world instances both in an offline full-information setting and its online rolling horizon counterpart. We conduct a numerical study for Manhattan, New York City, focusing on low- and highly-congested scenarios. Our results show that in low-congestion scenarios, staggering trip departures allows mitigating, on average, 94% of the induced congestion in a full information setting. In a rolling horizon setting, our algorithm allows us to reduce 90% of the induced congestion. In high-congestion scenarios, we observe an average reduction of 66% as the full information bound and an average reduction of 56% in our online setting. Surprisingly, we show that these reductions can be reached by shifting trip departures by a maximum of six minutes in both the low and high-congestion scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Integer programming

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Artificial Intelligence Strategies for Accurate Segmentation and Categorization of Unveiling Genetic Disorders in Bioinformatics

Artificial Intelligence Strategies for Accurate Segmentation...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： T. Sarathamani K. Kavitha C. Thirumoorthi K Jayanthi Vagini P. Manikandaprabhu P. Sumathi Department of Computer Science and Engineering (AI) Brainware University Kolkata India Department of AI and ML School of Computing Mohan Babu University Sree Sainath Nagar Tirupati India Department of Computer Science PSG College of Arts and Science Coimbatore Tamilnadu India Department of Computer science with Data Analytics KPR College of Arts Science and Research Coimbatore Tamilnadu India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Karnataka India Department of Computer Science Dr. G R Damodaran College of Science Coimbatore Tamilnadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Women all across the world are affected by the potentially fatal condition known as breast cancer. According to clinical experts, early cancer detection helps to save lives. Several machine learning algorithms have been developed to classify cancers in order to diagnose breast cancer, and medical image processing has been found to be a beneficial area for increasing classification accuracy. One of the most crucial signs for detecting breast cancer early is micro classifications. Machine learning methods have been employed in numerous studies to address this problem. Various classification algorithms are used to find the presence of breast cancer whether it is Benign or Malignant. For earlier cancer detection and diagnosis, an image enhancement approach is being developed. A machine learning technique is employed in this work to attempt to categorize breast cancer. The purpose of the present research was to increase the accuracy of machine learning methods for disease diagnosis using ML techniques.

关键词： Industries Image segmentation Accuracy Machine learning algorithms Machine learning Transforms Cancer detection Breast cancer Medical diagnosis Monitoring

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Intelligent Network Optimisation for Beyond 5G Networks Considering Packet Drop Rate 25

Intelligent Network Optimisation for Beyond 5G Networks Cons...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Mahmoud, Haitham Aneiba, Adel He, Ziming Tong, Fei Guo, Liucheng Asyhari, Taufiq Wang, Ziwei Gao, Zhen College Of Computing Birmingham City University Birmingham United Kingdom Samsung Cambridge Solution Centre System Lsi Samsung Electronics Cambridge United Kingdom London United Kingdom Monash University Data Science Department Tangerang Indonesia School Of Engineering Lancaster University Lancaster United Kingdom School Of Information And Electronics Beijing Institute Of Technology Beijing China

ISBN: (纸本)9798350340266

To meet the growing expectations for fast and dependable connectivity, Novel approaches such as reinforcement learning-based resource allocation and network slicing are essential to consider. Enhancing network intelligence through the use of deep reinforcement learning and machine learning may increase capacity, lower latency and congestion, improve energy efficiency, and open up new revenue opportunities and business models. However, the current body of research falls short in terms of thoroughly exploring network slicing datasets and taking packet drop likelihood into account when allocating resources. With reference to current benchmarks, this paper presents a network slicing strategy and illustrates its efficacy using seven machine learning algorithms (ANN, SVM, RF, DT, GNG, LDA, and RT). To prioritize packets that run the risk of being dropped, a priority algorithm is also developed. To improve network performance, a resource allocation technique is used that is based on the mathematical study of packet drop rate. Utilizing deep reinforcement learning and genetic algorithms, the distribution of tasks across Cloud and Edge resources is further improved according to network slice characteristics. In spite of high network traffic, this guarantees constant service availability. © 2024 IEEE.

关键词： Resource allocation

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Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter data

Divide-Conquer Transformer Learning for Predicting Electric ...

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IEEE General Meeting Power& Energy Society

作者： Fucai Ke Hao Wang Department of Human Centred Computing Faculty of Information Technology Monash University Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia Monash Energy Institute Monash University Australia

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a "divide-conquer" strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.

关键词： Load monitoring Accuracy Transportation Predictive models Charging stations Parallel processing Transformers Electric vehicle charging Smart meters Energy management

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