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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhao, Jiangfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518057 China

ISBN: (纸本)9781510681316

Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and challenging. Recent studies often rely on the static features of team members to evaluate a team’s capabilities and predict their likelihood of winning. However, such approaches fail to consider the critical game-contextual influences of interactions with teammates and opponents on individuals. During a match, an individual’s performance is consistently influenced by teammates and opponents, leading to variations in individual features across different games. Moreover, the measurement of team capabilities also needs improvement, as the single scalar rating employed is too simplistic for sophisticated MOBA games. To this end, we propose a novel Context-Enhanced Match Outcome Prediction model (CEMOP), which incorporates the effect of both teammates and opponents for a better measurement of team capabilities. Specifically, we designed a match context encoder for MOBA games to evaluates the impact of teammates and opponents on individuals separately and embeds the information into the individual representations. After that the single team member’s representations which contain the influence of teammates and opponents are aggregated based on their importance to create a comprehensive representation of the team capabilities, enables CEMOP to adaptively adjust team evaluations. Experimental results from three real-world MOBA games clearly verify the effectiveness of CEMOP compared with several state-of-the-art methods. © 2024 SPIE.

关键词： Data mining

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Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

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computer Modeling in engineering & sciences 2023年第3期134卷 2137-2162页

作者： Lei Yan Maode Ma Dandan Li Xiaohong Huang Yan Ma Kun Xie School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijingChina College of Engineering QatarUniversityDohaQatar

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate *** methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on *** these existing mechanisms,the identification of malicious flows depends on the IP ***,the IP address is easy to be changed by *** the IP address,the certificate ismore challenging to be tampered with or ***,the traffic trend in the network is towards *** certificates are popularly utilized by IoT devices for authentication in encryption *** proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN *** on DTLShps,the SDN controller can collect statistics on *** this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for *** goal is ***,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire ***,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay ***,for detecting the Crossfire attack accurately,a method based on graph connectivity is ***,several trust-based routing principles are proposed tomitigate the Crossfire *** principles can also encourage users to use certificates in *** performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting ***,our trust model has a more appropriate decay rate than the traditional methods.

关键词： Trust model certificate SDN Crossfire attack bayesian trust system forgetting curve IoT

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Anomaly Detection in Retinal Imagery Using Variational Autoencoders 24

Anomaly Detection in Retinal Imagery Using Variational Autoe...

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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Lebaka, Kagiso Blessing Sithungu, Siphesihle Philezwini Academy of Computer Science and Software Engineering University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

This paper situates itself within the broader medical imaging field, focusing on applying generative modelling techniques for anomaly detection in retinal images. Given the complexity of retinal structures, detecting subtle abnormalities that signal disease requires models that can learn intricate patterns and discern deviations. This study explores the use of a Variational Autoencoder (VAE) to reconstruct healthy retinal images and identify anomalies through reconstruction loss and latent space analysis. With the use of a dataset with an imbalance of healthy and abnormal samples, the model was trained exclusively on healthy images to capture a baseline of normal retinal features. The findings suggest that while the VAE can effectively reconstruct broader structural patterns, it needs to work on finer details, impacting its sensitivity in anomaly detection. These results highlight the challenges inherent in applying generative models to medical diagnostics and emphasise the need for advanced techniques, such as contrastive learning, that may enhance feature extraction. Thus, this paper contributes to the ongoing discourse on the suitability of generative models for medical imaging and supports continued research into more nuanced methods to meet the specific demands of retinal anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： Ophthalmology

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An aspect-based sentiment analysis model for Arabic game reviews based on hybrid transformers models

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Neural Computing and Applications 2025年 1-23页

作者： Hammad, Mahmoud AbuEnnab, Noor Al-Refai, Mohammed IT Department Ajman University Ajman United Arab Emirates Software Engineering Department Jordan University of Science and Technology Irbid Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data content generated by interested customers and reviewers motivated researchers and data scientists to conduct ABSA. ABSA has become increasingly popular in recent years due to its versatility in e-commerce, social media, and customer feedback analysis. However, ABSA faces several significant challenges, including determining the aspects and their sentiment polarities (positive, negative, or neutral) in a given text. Moreover, ABSA faces particular challenges in non-English languages such as Arabic due to the lack of resources and mature models. Typically, ABSA tackles one or more of the ABSA research tasks: (T1) aspect term extraction, (T2) aspect term polarity, (T3) aspect category identification, and (T4) aspect category polarity. To identify the aspects and their corresponding sentiment polarities in a given text, accurate and efficient NLP techniques are required. Despite growing interest in Arabic ABSA, the lack of annotated datasets and pre-trained models has hindered its development. In this research, we have collected a dataset of Arabic game reviews and annotated them using three annotators, and then we trained an ABSA deep learning model based on the BERT pre-trained model combined with zero-shot learning (ZSL) to tackle all the four aforementioned tasks. Our best performing model achieved a high accuracy on all four tasks with an accuracy of 91.61% on T1, 90.99% on T2, 79.08% on T3, and 88.17% on T4. Finally, we compared our model’s accuracy with the state-of-the-art Arabic-based ABSA models on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

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Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

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computers, Materials & Continua 2024年第7期80卷 61-103页

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science FAST School of ComputingNational University of Computer and Emerging SciencesLahore54000Pakistan Department of Software and Communications Engineering Hongik UniversitySejong City30016Republic of Korea

Wireless Ad Hoc Networks consist of devices that are wirelessly *** Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc *** is used in wireless ad hoc *** is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined *** fetches data from a fixed *** redundancy,mobility,and location dependency are the main issues of the IP network *** these factors result in poor performance of wireless ad hoc *** main disadvantage of IP is that,it does not provide in-network ***,there is a need to move towards a new network that overcomes these *** Data Network(NDN)is a network that overcomes these *** is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user *** NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and *** considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless *** cachingmechanism-based results are also *** the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

关键词： Content centric network Internet of Things mobile ad hoc network named data network vehicular ad hoc network

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Striking a Chord through a Mixed-Methods Study of Music-Based Learning to Leverage Music and Creativity to Bridge the Gender Gap in computer science 2024

Striking a Chord through a Mixed-Methods Study of Music-Base...

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55th ACM Technical Symposium on computer science Education, SIGCSE 2024

作者： Jamshidi, Fatemeh Bigonah, Maryam Marghitu, Daniela Computer Science and Software Engineering Auburn University AuburnAL United States

ISBN: (纸本)9798400704246

The persistent gender gap in technical fields, particularly in computer science (CS), remains a complex challenge. This paper draws upon recognized gender differences in aptitude, interest, and the educational environment. It builds upon established theoretical foundations, including Bandura's Social Cognitive Theory [9] and Eccles's Expectancy-Value Theory [3][2], to explore innovative strategies for engaging underrepresented groups, specifically female students, in CS. Central to our approach is an innovative curriculum that integrates the realms of math, computing, and music. Recognizing the intrinsic connection between music and mathematical patterns, our pedagogical model simplifies complex CS concepts, transforming topics like looping, algorithms, and conditional statements into engaging, relatable lessons. Additionally, our curriculum incorporates foundational elements of robotics, enhancing its practical applicability and real-world relevance. This groundbreaking educational paradigm not only simplifies technical concepts but also leverages students' innate passion for music, creating an intuitive and enjoyable learning environment. By blending artistic creativity with scientific precision, this approach sets a new standard for curriculum design and offers educators a pioneering roadmap to address the gender gap in CS. Our study aligns with SIGCSE's mission, marking a fresh, inclusive trajectory for the future of technology education. © 2024 Owner/Author.

关键词： Music

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

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Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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Rainfall Process Similarity Algorithm Research Based on Deep Learning by Using Precipitation Images 7

Rainfall Process Similarity Algorithm Research Based on Deep...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Yu, Yufeng Wu, Panqi Cai, Difei Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing210098 China

ISBN: (纸本)9798350375107

Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a challenging work. Based on the similarity search method for daily precipitation images, this paper proposes a rainfall process similarity search method, which extracts rainfall duration, cumulative precipitation, temporal distribution, spatial distribution and center movement trajectory as the features of rainfall process, and defines a set-weighting method based on the Grey Wolf Optimization algorithm (IGWO) to weight and fuse the five extracted features as the similarity measure of rainfall process. The similarity search results of rainfall process images on the Jialing Basin illustrate that the method proposed in this paper can efficiently and accurately identify similar rainfall processes in historical scenes, thereby providing a new technical support for intelligent rainfall-flood forecasting. © 2024 IEEE.

关键词： Rain

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EmoNet: A Convolutional Neural Network and Transfer Learning-Based Framework for Facial Micro-Expression Recognition and Emoticonification 5

EmoNet: A Convolutional Neural Network and Transfer Learning...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Gong, Rui Wang, Zhongyi Zhao, Jie Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China

ISBN: (纸本)9798350366174

With the rising popularity of online social interactions, emojis play a pivotal role in communication, effectively conveying people's emotions. Hence, accurately converting facial micro-expressions into corresponding emojis becomes crucial. However, current micro-expression recognition technology faces challenges of low accuracy and poor generalization. To address these issues, this paper innovatively proposes a method that combines transfer learning with convolutional neural networks. This approach significantly enhances the model's generalization capability and recognition accuracy. Experimental results demonstrate notable improvements in both accuracy and robustness in micro-expression recognition, effectively overcoming common problems found in traditional methods, such as overfitting and sample imbalance. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

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computer Modeling in engineering & sciences 2023年第6期135卷 1981-2006页

作者： Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

Travelling Salesman Problem(TSP)is a discrete hybrid optimization problem considered *** aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point,making it the shortest route *** paper employed a Farmland Fertility Algorithm(FFA)inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function(MCF).The neighborhood search operator can use this strategy to automatically select the best heuristic method formaking the best ***-Kernighan(LK)local search has been incorporated to increase the efficiency and performance of this suggested approach.71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and *** results indicated that the proposed algorithm outperforms comparable methods of average mean computation time,average percentage deviation(PDav),and tour length.

关键词： Travelling salesman problem optimization farmland fertility optimization algorithm Lin-Kernighan

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