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

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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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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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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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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Theoretical Analysis of an Adaptive Closeness Centrality-Based Algorithm for Dynamic Optimization of Transportation Networks

Theoretical Analysis of an Adaptive Closeness Centrality-Bas...

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2024 International Conference on engineering Management of Communication and Technology, EMCTECH 2024

作者： Mann, Michael Afeka - the Academic College of Engineering School of Software Engineering and Computer Science Tel Aviv Israel

ISBN: (数字)9798331507909

ISBN: (纸本)9798331507909

Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closeness Centrality (ACC) metric and the DynaTrans algorithm, providing formal proofs of correctness, convergence, and efficiency. The analysis employs graph theory, algorithmic complexity theory, and competitive analysis techniques. Findings: We prove that DynaTrans converges to a local optimal state in O(|V|/ϵ) iterations, with each iteration has limited computational and memory requirements. The algorithm achieves solutions within a factor of O(log|V|) from the global optimum, outperforming simple greedy approaches. Practical implications: DynaTrans offers a theoretically sound foundation for real-time traffic management systems, potentially improving urban mobility and reducing congestion. Originality/value: This work introduces a new paradigm for dynamic transportation network optimization, combining adaptive centrality measures with efficient graph algorithms. The rigorous theoretical analysis provides a solid basis for practical implementation and future research in urban traffic management. © 2024 IEEE.

关键词： Traffic congestion

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DECAY: Dynamic Evaluation and Component Analysis for Enhancing Trust Management

DECAY: Dynamic Evaluation and Component Analysis for Enhanci...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Huber, Brennan Kandah, Farah Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering United States

ISBN: (纸本)9798350324136

The Internet of Things (IoT) has gained widespread use across various domains, necessitating robust trust management for safeguarding data integrity and device reliability. Existing trust management algorithms often overlook the impact of a device's lifespan on trust adjustments. To address this shortcoming, we propose an innovative trust management strategy that introduces an 'engagement-level' component as a key feature. This component considers a device's past performance, granting established devices milder trust penalties than new ones and facilitating faster trust restoration for devices with low trust levels. Our approach also introduces a trust decay mechanism to handle inactive devices and cease contributing to the system, resulting in decreased trust levels. By discouraging passive behavior and encouraging active participation, this mechanism enhances IoT system trustworthiness. Through simulations and evaluations, we have demonstrated that our approach bolsters system security by scrutinizing malicious devices and rewarding active, trustworthy ones. It ensures vigilant threat monitoring, bolstering system security and improving trust assessment in the IoT environment. By integrating the engagement-level component and trust decay mechanism, our approach offers a dynamic trust management system that addresses existing algorithm limitations, enhancing accuracy and adaptability. © 2024 IEEE.

关键词： Internet of Things (IoT) Trust decay mechanism Trust management

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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A Global-Local Probsparse Self-Attention Transformer for LEO Satellite Orbit Prediction 23

A Global-Local Probsparse Self-Attention Transformer for LEO...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Huang, Guan Shu, Tao Auburn University Department of Computer Science and Software Engineering Auburn United States

ISBN: (纸本)9798350374889

In recent years, the proliferation of LEO (Low-Earth Orbit) satellites and the accumulation of space debris have made Near-Earth space more and more crowded, and hence significantly increased the risk of collisions in this space. As a result, precise orbit prediction becomes essential for LEO satellites to avoid collision, maintain the right constellation, and perform normal space operations. Although machine learning(ML)-based satellite orbit prediction methods have been extensively explored, most existing methods are trained on simulated/synthetic orbit data, which essentially assumes a stationary orbit process and cannot reflect the non-stationary dynamic orbit changes in real-world LEO satellite constellations that are caused by satellite flight status adjustment (e.g., for the purpose of collision avoidance). In this study, we propose a novel multi-range (global-local) self-attention transformer-based ML model, the GloLoSAT (Global-Local Probsparse Self-Attention Transformer), and train the model over real-world LEO satellite orbit data crawled from *** to give more precise orbit prediction in case of a series of orbit adjustments. Theoretical analysis demonstrates our Global-Local Probsparse Self-Attention mechanism can achieve O(Llog(L)) computational complexity with respect to the input sequence length. Extensive experiments conducted on real satellite orbit tracking datasets demonstrate the efficacy of GloLoSAT in achieving consistent performance improvements across various prediction scenarios compared to their counterparts. © 2024 IEEE.

关键词： Space debris

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