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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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NEGATIVELY CORRELATED ENSEMBLE REINFORCEMENT LEARNING FOR ONLINE DIVERSE GAME LEVEL GENERATION 12

NEGATIVELY CORRELATED ENSEMBLE REINFORCEMENT LEARNING FOR ON...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Ziqi Hu, Chengpeng Liu, Jialin Yao, Xin Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computing and Decision Sciences Lingnan University Hong Kong

Deep reinforcement learning has recently been successfully applied to online procedural content generation in which a policy determines promising game-level segments. However, existing methods can hardly discover diverse level patterns, while the lack of diversity makes the gameplay boring. This paper proposes an ensemble reinforcement learning approach that uses multiple negatively correlated sub-policies to generate different alternative level segments, and stochastically selects one of them following a dynamic selector policy. A novel policy regularisation technique is integrated into the approach to diversify the generated alternatives. In addition, we develop theorems to provide general methodologies for optimising policy regularisation in a Markov decision process. The proposed approach is compared with several state-of-the-art policy ensemble methods and classic methods on a well-known level generation benchmark, with two different reward functions expressing game-design goals from different perspectives. Results show that our approach boosts level diversity notably with competitive performance in terms of the reward. Furthermore, by varying the regularisation coefficient values, the trained generators form a well-spread Pareto front, allowing explicit trade-offs between diversity and rewards of generated levels. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-20页

作者： Islam, Md Mahamodul Das, Pobithra Rahman, Md Mahbubur Naz, Fasiha Kashem, Abul Nishat, Mosaraf Hosan Tabassum, Nujhat Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Leading University Sylhet Bangladesh Department of Industrial and Systems Engineering Lamar University Beaumont United States Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Civil and Environmental Engineering Shahjalal University of Science & amp Technology Sylhet Bangladesh Department of Civil and Environmental Engineering Islamic University Of Technology -IUT(OIC) Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Forecasting the compressive strength of high-performance concrete (HPC) is crucial for its practical applications. However, conducting experimental tests for this purpose demands significant resources and time. In recent times, data-driven modeling, particularly machine learning, has become prominent in this field. Thus, this study employed machine learning methods, including Gradient Boosting, Random Forest, and CatBoost algorithms, to predict the compressive strength of high-performance concrete (HPC). Furthermore, interpretable algorithms like SHapley Additive exPlanations (SHAP) were utilized in this study to assess the contribution of input variables to predictions. The dataset includes parameters such as cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, with compressive strength as the output variable for models development. The CatBoost model performance was superior to GBM and RF models, with R² values of 0.979 for the training stage and 0.959 for the testing stage. Moreover, it demonstrated minimal mean absolute errors (MAE) of 2.37 MPa and 3 MPa for compressive strength prediction in the training and testing stages, respectively. Furthermore, the SHAP analysis revealed that age, cement, and superplasticizer are the primary influencers in determining compressive strength. The machine learning models presented in this study exhibit promising potential for practical implementation in different engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Compressive strength High-performance concrete Machine learning SHAP analysis

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Cloud Computing based Smart Traffic Management System with Priority Switching for Health Care Services 2

Cloud Computing based Smart Traffic Management System with P...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Shiny, X.S. Asha Ravikumar, D. Chinnasamy, A. Hemavathi, S. Department of Computer Science and Engineering Telangana Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic. © 2023 IEEE.

关键词： Internet of things

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Exploring Advancements in 6G Wireless Networks for the Future of High-Speed Connectivity 2

Exploring Advancements in 6G Wireless Networks for the Futur...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Suresh, J. Kumari, V Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology ThandalamTamil Nadu Chennai India

ISBN: (纸本)9798350364699

High-speed connectivity technologies that aim to be faster and more secure have been explored so that there will be a change of the very nature of wireless communication. This article presents advances concerning the high-speed connection component of the sixth-generation wireless networks, which is simply denoted as 6G. Different performance parameters of 6G are compared with 4G (LTE) and 5G (5G) through multiplicity of research analyses done across disciplines. Our findings suggested that 6G networks are incredibly dynamic and will change how the internet is used. These SixG networks are due to accompany massive data transmission rates of 100 Gbps, five times higher than 5G's 20 Gbps. Latency also approaches same level as 6G where on average 1 millisecond is noticed compared to 5G's 10 and 4G's 50. Server speed and its response time in milliseconds will expose room for a number of real-time applications from only augmented reality imaging to the world of driverless automobiles and interactive games. Our study outlines the major consequences of the coming health shift. 4G allowed only for slow/limited improvements in healthcare whereas the 5G made telemedicine possible. On the other hand, 6G reportedly offers "precision remote surgery", allowing surgeons to be located remotely of a patient while still performing a high precision surgery. Attitude of people becomes completely unaware when they are briefed about concepts of latest generation known as 6G. Before studying about 6G, the number of those are eagerly delighted are 20%, having no idea on how it will be is 50%, while the other 30% are skeptics. Meanwhile, once people are better cognizant of 6G, fanatics raise up from 60 percent and sceptics drop till 10 percent. The significant shift is a reflection of 6G technology in the stakeholders' mind. © 2024 IEEE.

关键词： 5G mobile communication systems

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Challenges in Cyber Physical Social systems and Internet of Things 5

Challenges in Cyber Physical Social Systems and Internet of ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Revathi, S. Raja, J. Mohanraj, M. Malathi, K. Mallala, Balasubbareddy Vidhya, R.G. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sri Eshwar college of engineering Department of Computer Science and Business Systems Coimbatore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798331504403

This research analyses the complex dynamics of Cyber-Physical-Social systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems. © 2024 IEEE.

关键词： Cyber-Physical systems Cyber-Physical-Social systems (CPSS) Cybersecurity Internet of Things (IoT) Social Media

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Scanning the Issue - May 2025

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IEEE Transactions on Intelligent Transportation systems 2025年第5期26卷 5627-5648页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

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Extending OpenStack Monasca for Predictive Elasticity Control

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Big Data Mining and Analytics 2024年第2期7卷 315-339页

作者： Giacomo Lanciano Filippo Galli Tommaso Cucinotta Davide Bacciu Andrea Passarella Scuola Normale Superiore Pisa 56126Italy the Real-Time Systems Laboratory(RETIS) TelecommunicationsComputer Engineeringand Photonics Institute(TeCIP)Scuola Superiore Sant’AnnaPisa 56127Italy Department of Computer Science University of PisaPisa 56127Italy National Research Council Pisa56127Italy

Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption *** such rules at scale is cumbersome,especially when resources require non-negligible time to be *** paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the *** this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when *** proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard *** use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional *** assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)*** particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting *** implementation of our architecture is open-source.

关键词： OpenStack monitoring elasticity control auto-scaling predictive operations Monasca

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Automatic Learning for Commercial Registration Renewal - The Case of Camara de Comercio of Barranquilla-Colombia

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IEEE engineering Management Review 2023年第1期51卷 26-40页

作者： Rios-Henao, Cristian Ariza-Colpas, Paola Patricia De-La-Hoz-Franco, Emiro Aziz, Shariq Butt Melo, Marlon Alberto Pineres Perez-Coronell, Leidy Universidad de la Costa Cuc Department of Computer Science and Electronics Barranquilla08002 Colombia University of South Asia Department of Computer Science Lahore54000 Pakistan Universidad Del Norte Department of Systems Engineering Barranquilla081001 Colombia Universidad Simón Bolívar Facultad de Ingenierías Barranquilla08002 Colombia

This article shows the implementation of a prediction model of the payment behavior of the renewal concept of companies registered in the commercial registry of the Barranquilla Chamber of Commerce using machine learning techniques in a multilevel classification scenario, where it will offer the organization a tool that allows it to know in advance the behavior of the payment of the renewal of a company in such a way that it is able to design strategies to increase the success indicators in terms of the number of registration renewals, mercantile, and of the income collected for this concept. © 1973-2011 IEEE.

关键词： Behavioral research

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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