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Web Page Recommendation Using Distributional Recurrent Neural Network

computer Systems Science & engineering 2023年第4期45卷 803-817页

作者： Chaithra G.M.Lingaraju S.Jagannatha Department of Computer Science&Engineering Sapthagiri College of EngineeringBangaloreIndia Department of Information Science and Engineering Sahyadri College of Engineering&ManagementMangaluruIndia Department of Computer Applications MSRITBangaloreIndia

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the *** that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching ***,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation *** improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data *** this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire *** overall work was implemented for the application of the data recommendation *** are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching ***,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query *** was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature *** training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the *** are formed as clusters and paged with proper indexing based on the MPS parameter of similarity *** overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision

关键词： Ontology data mining in big data logarithmic directionality texture pattern metaheuristic pattern searching system distributional recurrent neural network query recommendation

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Adaptive Data Forwarding Scheme for Diverse Infotainment Services in Vehicular Named Data Networking

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 8012-8022页

作者： Khan, Sajjad Ahmad Lim, Huhnkuk Chaudhry, Shehzad Ashraf Kocaeli University Software Engineering Department Izmit41000 Turkey Hoseo University Computer Engineering Department Korea Republic of Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering & Architecture Nisantasi University Istanbul Turkey

Providing diverse infotainment services is crucial for enhancing road safety and improving the overall driving experience in vehicular networks. However, delivering these services within Vehicular Named Data Networking (VNDN) environments presents significant challenges, primarily due to the data broadcast phenomenon and the varying Quality of Service (QoS) requirements of different applications. This paper introduces an adaptive data forwarding scheme designed to effectively manage these challenges and optimize the delivery of infotainment services in VNDN. Our proposed scheme enhances the data packet format by incorporating additional attributes that prioritize data based on its criticality, specifically categorizing it into critical, non-critical, and entertainment data. Furthermore, we introduce an Enhanced Weighted Fair Queuing (EWFQ) policy to optimize the scheduling of data packets according to their importance. Two innovative data forwarding schemes, namely Advanced Push-based Data Forwarding (APDF) and Enhanced Pull-based Data Forwarding (EPDF), are developed to address the needs of critical and non-critical information and entertainment data, respectively. Simulation results demonstrate that our adaptive data forwarding scheme significantly outperforms legacy fragment schemes in delivering infotainment data within dynamic VNDN environments, ensuring reliability and efficiency. This paper contributes to advancing data dissemination strategies in vehicular networks and lays the groundwork for future research in adaptive QoS management. © 1967-2012 IEEE.

关键词： Entertainment industry Quality of service Adaptive systems Delays Data dissemination Vehicle dynamics Scalability Road safety Real-time systems Vehicles

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Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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computers, Materials & Continua 2024年第6期79卷 4603-4620页

作者： Mohammad Aldossary Hatem A.Alharbi Nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Group Decision-Making Method of Entry Policy During a Pandemic

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Tsinghua Science and Technology 2024年第1期29卷 56-65页

作者： Chunsheng Cui Baiqiu Li Xianfeng Chen College of Computer and Information Engineering Henan University of Economics and LawZhengzhou 450046China Tourism College Beijing Union UniversityBeijing 100101China

Omicron,the new mutant coronavirus,has spread rapidly globally,attracting close attention from different stakeholders *** complex and constantly changing epidemic situation has had a new impact on the ***,this paper focuses on the characteristics of the rapid spread of the COVID-19 variant ***,epidemic prevention experts conduct preliminary screening as part of the existing epidemic plan database according to the current local situation,after which they sort the alternatives deemed more suitable for the *** the decision-makers identify the most divergent expert group,plan for consultation and adjustments,and finally obtain the plan with the smallest *** article aims to integrate the experts'opinions with the method of minimizing the differences,which can maximize the expert consensus and help organize the schemes that best meet the epidemic *** experts'negotiation and iteration of the differences in the initial plan align with the current complex and dynamic epidemic situation and are of great significance to the rapid formulation of plans to achieve effective prevention and control.

关键词： epidemic prevention and control group decision-making alternative ranking control policy

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Advancing Smart Sensor Networks and Carbon-Based Biosensors through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation

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IEEE Access 2025年 13卷 86083-86109页

作者： Luo, Keliang Putra University of Malaysia Software Engineering Faculty of Computer Science and Information Technology Kuala Lumpur43400 Malaysia

This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices. © 2013 IEEE.

关键词： Deep learning

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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

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Alexandria engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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Confidence-based Class Relation Embedding for Few-shot Domain Adaptation

Confidence-based Class Relation Embedding for Few-shot Domai...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Jia, Baoxi Chen, Junyang Wu, Yirui College of Computer Science and Software Engineering Hohai University Nanjing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331505516

In visual tasks such as image classification, the presence of domain shift often renders deep neural network models trained solely on specific datasets unable to generalize to new domains. In practical applications, due to the high cost of annotating rare data, it is extremely challenging to label all source domain samples comprehensively. Moreover, existing unsupervised domain adaptation methods often lack modeling of class relationships. Therefore, we propose a few-shot unsupervised domain adaptation method based on confidence-guided class relationship embedding. This approach aims to identify high-confidence target samples corresponding to sparsely labeled source samples, forming an inter-domain cross-mixed dataset. By constructing inter-domain contrastive learning, we explicitly guide the transfer of knowledge from the source domain to the target domain, leveraging the domain invariance of class relationships. Additionally, considering the varying learning difficulties of different target samples, we have devised an intra-domain class relationship contrastive loss, which leverages easy samples to facilitate the learning of difficult-to-classify samples. Experiments demonstrate that this model contributes to enhancing the performance of domain adaptation tasks. © 2024 IEEE.

关键词： Contrastive Learning

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A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

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International Journal of Energy Technology and Policy 2024年第3-4期19卷 363-376页

作者： Tong, Wei Huang, Qi-Ping School of Computer Engineering Anhui Wenda Information Engineering College Hefei Anhui231201 China Electrical Engineering Department Anhui Electrical Engineering Professional Technique College Hefei Anhui230051 China

Different malfunctions may arise during the operation of power equipment, impacting the quality and dependability of the power supply. Conventional monitoring techniques face challenges, prompting the introduction of a novel power equipment monitoring and fault localisation method based on an enhanced ant colony algorithm. This approach entails gathering operational signals from the power equipment and amalgamating singular value decomposition with particle filtering methods to oversee the equipment's condition. Through enhancements to the pheromone configuration and update approach of the ant colony algorithm, a fault localisation assessment function is formulated to achieve precise fault localisation. Empirical findings have illustrated that this method has the capability to promptly monitor equipment status and attain fault localisation accuracy surpassing 90%. © 2024 Inderscience Enterprises Ltd.

关键词： Ant colony optimization

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Multi-Tree-Based Avoidance Multicast Routing for Wireless Sensor Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Sakai, Kazuya Sun, Min-Te Ku, Wei-Shinn Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Hino Tokyo Japan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Software Engineering Auburn University Auburn AL USA

Multi-path avoidance routing for wireless sensor networks (WSNs) is a secure routing paradigm against adversaries with unbounded computational power. The key idea of avoidance routing is to encode a message into several pieces by the XOR coding, and each piece is routed via different paths. Then, an adversary cannot obtain the original message unless she eavesdrops on all message pieces from all the paths. In this paper, we extend such an approach into secure multicast routing, which is a one-to-many communication primitive. To this end, we propose the multi-tree-based avoidance multicast routing protocol (AMRP) for WSNs, in which a set of adversary disjoint trees is discovered, i.e., a set of multicast trees with no common adversaries. When a set of multicast trees is adversary disjoint, no adversary can eavesdrop on all message pieces to recover the original message. In addition, optimized AMRP (OAMRP) is proposed in order to reduce the control overhead of AMRP, where additional multicast trees are used for only a subset of destination nodes with no single safe tree. The simulation results demonstrate that the proposed protocols achieve higher secure delivery rates than a simple extension of the existing unicast avoidance routing protocol. IEEE

关键词： Wireless sensor networks

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