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Modified Neural Network Used for Host Utilization Predication in Cloud computing Environment

Computers, Materials & Continua 2025年第3期82卷 5185-5204页

作者： Arif Ullah Siti Fatimah Abdul Razak Sumendra Yogarayan Md Shohel Sayeed Centre for Intelligent Cloud Computing Multimedia UniversityMelaka75450Malaysia Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client *** essential aspect of cloud computing that improves resource allocation techniques is host load *** difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response *** solve this issue and accurately predict cloud capacity,cloud data centers use prediction *** permits dynamic cloud scalability while maintaining superior service *** host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this ***,the suggested hybrid model is input is subjected to the vector auto regression *** data in many variables that,prior to analysis,has been filtered to eliminate linear *** that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing *** next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series *** key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant *** systems require accurate prediction due to the increasing degrees of unpredictability in data *** of this,two actual load traces were used in this study’s assessment of the *** example of the load trace is in the typical dispersed *** comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.

关键词： Cloud computing datacenter virtual machine(VM) predication algorithm

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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

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Computers, Materials & Continua 2024年第9期80卷 4663-4686页

作者： Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris Faculty of Computer Science and Information Technology Universiti MalayaKuala Lumpur50603Malaysia Faculty of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Center for Mobile Cloud Computing Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and *** paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN *** technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access *** proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy ***,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern ***,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

关键词： BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN multilayer dynamic adaptable flexibility bottlenecks performance policy conflict

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Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled Mobile Edge computing Networks

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China Communications 2024年第11期21卷 150-162页

作者： Sun Gangcan Sun Jiwei Hao Wanming Zhu Zhengyu Ji Xiang Zhou Yiqing School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Beijing Key Laboratory of Mobile Computing and Pervasive Device and the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

关键词： eavesdropping massive multiple input multiple output mobile edge computing partial offloading secure computation efficiency

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Stepwise monogamous pairing genetic algorithm method applied to a multi-depot vehicle routing problem with time windows

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Neural computing and Applications 2025年第16期37卷 9639-9668页

作者： Lim, Ting Yee Ng, Xin Ju Tan, Choo Jun Lim, Chee Peng Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology 77 Lorong Lembah Permai Tiga Penang Tanjong Bungah11200 Malaysia Department of Computing Technologies Swinburne University of Technology John Street HawthornVIC3122 Australia

Evolutionary computation (EC) is a powerful tool for global optimization across various domains, including healthcare logistics. However, the prevalence of local optima in complex problems often hinders the ability of the populations to find optimal solutions, leading to premature convergence. More daunting is that complex optimization problems motivated from real-world challenges continue to pose huge hurdles for EC, with many remaining unresolved. To fill this gap, we propose a new framework named SW-MopGA (Stepwise Monogamous Pairing Genetic Algorithm), which hinges on two innovations. First, a controller for diversity maintenance is introduced into MopGA to generate perturbed opposite solution via opposition-learning when necessary. In doing so, the algorithm is able to maintain population diversity effectively, which alleviates the risk of premature convergence. Second, SW-MopGA proposes a two-step framework: Initially, the population undergoes evolution on a simplified version of the originally complex problem. Solutions derived from the simplified problem serve as building blocks (stepping stones) upon which more intricate blocks are constructed for the final problem—a form of incremental evolution. Numerical analysis on two real-world cases in the logistics healthcare sector demonstrates the effectiveness of SW-MopGA. The problems are formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Overall, the combined effect of the two innovations resulted in up to 18% and 8% savings in terms of total travel time and distance, and vehicles used, respectively on an off-peak day scenario;while approximately 17% and 7% savings for the same criteria on a peak-day scenario, compared to the baseline MopGA. Additionally, SW-MopGA improved the best-known solution of three instances in the MDVRPTW benchmark dataset. Finally, algorithmic insights are provided. In general, our initial study shows encouraging outcomes for incremental evolution and its

关键词： Travel time

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Multi-Keyword Ranked Search in Cloud Environment

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Journal of Engineering Science and technology Review 2024年第4期17卷 23-30页

作者： Sharma, Vikas Sharma, Kapil Kumar, Akshi Pawan Department of Information Technology Delhi Technological University Delhi India Department of Computing Goldsmiths University of London United Kingdom

Nowadays, with the simplification of cloud storage complexity and expanding its capacity, the cloud has become more widely accessible. Data owners increasingly shift away from traditional systems to leverage cloud resources, enhancing capacity and geographic diversity. Despite these advancements, the need to safeguard confidential data prompts a transformation to encryption before outsourcing, establishing additional standards for data privacy. This shift renders conventional data utilization through standard keyword searching obsolete. Therefore, there is a crucial need for an encrypted cloud information search service. To address the challenge of privacy-preserving multi-keyword searches, this paper proposes an efficient solution that enhances the existing framework. The proposed system uses an advanced encryption standard (AES) to improve data security and management in a cloud environment. Each private server maintains a registered information file and deletes merged data from the free cloud after observation. Customers can manage order records in the cloud data warehouse by transparently logging observations. Access to server information requires client inspection requests and verification. Private servers validate capacity and issue tokens to the Key Distribution Center (KDC) for key distribution. Finally, the term frequency-inverse dense frequency (TF-IDF) equation is used for data positioning. This approach ensures secure, authenticated, and efficient data handling. This solution focuses on two key objectives: firstly, incorporating synonym-based search to facilitate multi-keyword synonym queries, and secondly, implementing a ranked search to yield more detailed search results. While existing search methods support only fuzzy keywords or precise queries over encrypted cloud data. The authors assess its performance by employing real-life datasets, and comprehensive experiments affirm the validity and practicality of the proposed scheme. © 2024 School of Scien

关键词： Cloud platforms

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A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction

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Computers, Materials & Continua 2022年第6期71卷 6089-6105页

作者： Altyeb Altaher Taha Sharaf Jameel Malebary Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood *** diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular *** learning methods can be very useful for disease identification,prediction,and *** paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic *** proposed approach consists of two ***,a baselearner comprising six machine learning algorithms is utilized for predicting ***,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of *** hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from *** predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction *** were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting *** compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.

关键词： Ensemble learning fuzzy clustering diabetes prediction machine learning

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Optimizing Food Security and Minimizing Waste: An Intelligent Agricultural Forecasting System for Qatar 21

Optimizing Food Security and Minimizing Waste: An Intelligen...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Benouar, Mohamed Salah Amine Al-Naemi, Salem Benlamri, Rachid Bouallegue, Seifeddine College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9798350378078

Qatar's reliance on food imports, limited agricultural land, and growing demand highlight the need for efficient resource management to ensure food security. This paper presents an AI-driven predictive system that forecasts agricultural yield using synthetic data encompassing commerce, hospitality, and agricultural sectors. By integrating these datasets through a Python-based ETL (Extract, Transform, Load) pipeline. These inputs were used to train multiple Recurrent Neural Network (RNN) models to predict future harvested quantities. This cross-sectoral data integration significantly enhances prediction accuracy, contributing to improved resource management, waste reduction, and sustainable agricultural practices. The system's modular design supports future expansion to include more data types, enabling comprehensive agricultural planning and bolstering food security in Qatar. © 2024 IEEE.

关键词： Cross-Sectoral Data Integration Food Security Predictive System Recurrent Neural Network (RNN) Resource Management

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BDI Agents Based Dynamic Resource Allocation in Emergency Scenarios

BDI Agents Based Dynamic Resource Allocation in Emergency Sc...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Shang, Junhui Yan, Jun Ren, Fenghui University of Wollongong School of Computing and Information Technology Wollongong Australia

ISBN: (纸本)9798331539917

This study proposes a dynamic resource scheduling framework based on a multi-agent system (MAS). The framework integrates the Belief-Desire-Intention model with fuzzy logic and time windows to enable efficient dynamic resource allocation. It is designed to address various uncertainties in emergency resource allocation, such as delays in data transmission and sudden changes in the external environment. These uncertainties impact the efficiency and accuracy of resource scheduling. Effective resource allocation is critical for ensuring the continuous operation of critical infrastructure, minimizing disaster-related losses, and expediting relief efforts. However, existing approaches often lack consideration for dynamic allocation and real-world constraints. The proposed framework aims to improve resource scheduling under these challenging conditions. © 2024 IEEE.

关键词： Fuzzy logic

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TCP Vegas Simulation and Analysis on Wireless Sensor Network for Building Structure Condition Monitoring Topology

TCP Vegas Simulation and Analysis on Wireless Sensor Network...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Kamillia, Revina Nuha, Hilal Hudan Mugitama, Satria Akbar Kirana, Disca Safa Information Technology School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350369106

This study aims to analyze the effect of TCP Vegas implementation in wireless sensor networks for building condition structure monitoring protocols. Wireless sensor networks are very effective because they are able to detect vibrations without using cables and at low cost. This research method involves a quantitative approach with an experimental type of research. The study will be conducted using NS-2 as a simulator tool. The data analysis to be carried out aims to identify the results of throughput, delay, and packet loss ratio. TCP Vegas is one of the transmission control protocols that is suitable for monitoring building structure conditions because it can obtain more consistent throughput while reducing delay. This study aims to provide insight into TCP Vegas on wireless sensor networks, especially for monitoring building structure conditions. © 2024 IEEE.

关键词： Packet loss

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