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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Chiru, Catalin-Mihail Nan, Mihai Trascau, Mihai Florea, Adina Magda National University of Science and Technology POLITEHNICA Computer Science Department Bucharest Romania

ISBN: (纸本)9798331532833

Lung cancer persists as a global leader in cancer-related deaths, highlighting the critical need for precise and efficient detection methods. This paper investigates the use of the Medical Segmentation Decathlon dataset to train neural networks for lung cancer segmentation in CT scans via semantic segmentation. We propose and evaluate four new data adaptation techniques specifically designed for this dataset, with each technique being assessed using U-Net-based architectures. Our approach incorporates a thorough exploratory data analysis to uncover the dataset's strengths and weaknesses, which in turn guided our data preprocessing and augmentation strategies. © 2024 IEEE.

关键词： Lung cancer

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Medical Recommendation System Based on Collaborative Filtering Algorithms

Medical Recommendation System Based on Collaborative Filteri...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Shakhmametova, Gyuzel Niiazgulov, Albert Ufa University of Science and Technology Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9798350395044

The article deals with the issues of designing a medical recommendation system based on the collaborative filtering algorithm. The problem under consideration is a large number of medical errors, which are one of the most common causes of patient deaths. In Russia alone, more than 70 thousand people suffer severe complications every year for this reason. The aim of the research is to analyze the possibility of designing and implementing software that reduces the probability of occurrence of medical errors and allows to realize the automation of prescribing by a doctor of the optimal treatment for a particular patient. The article analyzes the subject area and problem situation, identifies the causes of a large number of medical errors in public medical institutions in Russia. The problem statement is considered, mathematical support of the recommendation system based on the algorithm of collaborative filtering is proposed. The analysis of methods for solving the problem is given. The implementation of the recommendation system in the Python programming language is considered, the results of research in the form of a computational experiment are analyzed and interpreted. According to the results of the research, it is concluded that the proposed implementation of the medical recommendation system based on the collaborative filtering algorithm can be used in the further design of a full-fledged system and then used in medical institutions as a part of the system. © 2024 IEEE.

关键词： Collaborative filtering

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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Developing an Esports Assistant System for First Person Shooting Game Based on Multimodal Learning 14th

Developing an Esports Assistant System for First Person Shoo...

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14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Wei-Hsiang Hung, Jason C. Ma, Hao-Shang Huang, Chun-Hong Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

We plan to develop a specialized training system to enhance the competitive skills of players in the first-person shooter game "Valorant", aiming to improve their abilities and tactical understanding within the game. We will adopt a multimodal image-text fusion technique to enhance the model’s learning of sample similarity. Specifically, we will use the cross-entropy function to maximize the diagonal values of the similarity matrix, thereby capturing the features of similar samples and reducing the similarity between dissimilar samples. Through this process, we ensure that the model can map the same entity across different modalities into similar embedding representations, while distinguishing unrelated samples. Our goal is to find a way of embedding representation during the learning process that enables the model to better understand and represent multimodal data. We will utilize the YOLOv7 model for object detection to identify destructible skills. Based on the results of object detection, we will generate relevant textual descriptions for each object. This will enable novice players to quickly understand the function of diverse and complex skills and take appropriate actions when encountering them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

ConU: Conformal Uncertainty in Large Language Models with Co...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wang, Zhiyuan Duan, Jinhao Cheng, Lu Zhang, Yue Wang, Qingni Shi, Xiaoshuang Xu, Kaidi Shen, Hengtao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Drexel University United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761681

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification

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Multimedia Tools and Applications 2024年第39期83卷 86923-86954页

作者： Malik, Isra Yasmin, Mussarat Iqbal, Ahmed Raza, Mudassar Chun, Chang-Jae Al-antari, Mugahed A. Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt44000 Pakistan Department of Computer Science Sir Syed Case Institute of Technology Islamabad45230 Pakistan Department of Computer Science HITEC University Taxila Taxila47080 Pakistan Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of

Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurate and rapid KOA severity grading. The proposed CAD system leverages an ensemble transfer learning strategy to extract robust deep features by fusing multiple deep learning models. It combines features from two consecutive AI models: (1) AlexNet for implicit class-wise deep feature extraction from preprocessed data, and (2) a custom IsrNet for further feature depth. Unsupervised k-means clustering based on PCA dimensionality reduction decomposes each class into subgroups, further refining features. Finally, Ant Colony Optimization (ACO) selects the most informative features. Evaluated on the Osteoarthritis Initiative (OAI) dataset, the proposed system achieves high accuracy in classifying the five KOA severity grades. With 1000 optimized features, it reaches average overall accuracies of 89.89% and 85.44% using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, respectively. This surpasses recent deep learning methods, demonstrating significant improvement. This novel CAD system presents a promising solution for practical applications, offering an accurate AI-powered tool for KOA diagnosis and management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ant colony optimization

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Automated Scoring Model for Semi-Open-Ended Question in Scientific Explanation

Automated Scoring Model for Semi-Open-Ended Question in Scie...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Lertchaturaporn, Teevit Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathumthani Thailand

ISBN: (纸本)9798350370096

Scientific explanation is a crucial skill for analyzing data and drawing reasonable conclusions, especially in the context of semi-open-ended and open-ended questions. However, evaluating such questions requires significant effort from teachers, leading to a labor-intensive grading process influenced by class size and teacher expertise, potentially compromising the question's reliability. Consequently, students may lack valuable feedback, hindering the effective development of their skills. This paper presents our research focus on designing a system to automatically score students' responses. We leverage natural language processing techniques and machine learning algorithms to construct an automatic assessment model for scientific explanations in semi-open-ended questions. The responses were obtained from eighth-grade students in the fundamental science subject of the Thai curriculum, totaling 100 responses. Initially, Thai natural language processing is employed to preprocess the responses. Subsequently, N-Gram and word embedding techniques are utilized to represent the responses in a trainable format. Finally, the performance of various machine learning models trained with different word representation techniques is reported using F1-score. The experimental results demonstrate that a common method of word representation, combined with a simple machine learning model, can achieve high performance. © 2024 IEEE.

关键词： Students

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BanFish: A Dataset for Classifying Common Bangladeshi Fish Species 6

BanFish: A Dataset for Classifying Common Bangladeshi Fish S...

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6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Ataullha, Md Jisun, Soumik Paul Jahan, Ishrat Rahman, Mushfiqur Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350385779

As a riverine country like Bangladesh, where fish play an indispensable role in both cultural practices and dietary habits, they serve as a primary source of protein in rural households. However, the disheartening reality of the gradual disappearance of numerous traditional indigenous fish species poses an imminent threat to their survival. Compounded by the younger generation's waning familiarity with these culturally significant fish, urgent measures are imperative. Addressing this pressing concern, we propose the implementation of an automatic fish classification system, not just as a timely necessity but as a proactive initiative. Introducing BanFish, the largest fish classification dataset designed for commonly available Bangladeshi fish. Comprising 2,506 images spanning 18 distinct fish species, it aims to fill a critical void in preserving the diversity of indigenous fish. To assess the dataset's quality, we employ a Vision Transformer with a transfer learning model, achieving an impressive overall accuracy of 83%. Beyond contributing a valuable resource for fish classification, our work marks a significant step toward preserving the rich tapestry of traditional fish species in Bangladesh, envisioning a mobile application to complement our system and bridge generational knowledge gaps. © 2024 IEEE.

关键词： Fish

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Scheduling of Low-Latency Medical Services in Healthcare Cloud with Deep Reinforcement Learning

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Tsinghua science and technology 2025年第1期30卷 100-111页

作者： Hongfei Du Ming Liu Nianbo Liu Deying Li Wenzhong Li Lifeng Xu School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054China School of Information Renmin University of ChinaBeijing 100872China Department of Computer Science and Technology Nanjing UniversityNanjing 210093China People’s Hospital of Quzhou City Wenzhou Medical UniversityQuzhou 324000China

In the current landscape of online data services,data transmission and cloud computing are often controlled separately by Internet Service Providers(ISPs)and cloud providers,resulting in significant cooperation challenges and suboptimal global data service *** this study,we propose an end-to-end scheduling method aimed at supporting low-latency and computation-intensive medical services within local wireless networks and healthcare *** approach serves as a practical paradigm for achieving low-latency data services in local private cloud *** meet the low-latency requirement while minimizing communication and computation resource usage,we leverage Deep Reinforcement Learning(DRL)algorithms to learn a policy for automatically regulating the transmission rate of medical services and the computation speed of cloud ***,we utilize a two-stage tandem queue to address this problem *** experiments are conducted to validate the effectiveness for our proposed method under various arrival rates of medical services.

关键词： medical service tandem queue cloud computing Deep Reinforcement Learning(DRL) resource allocation

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