检索结果-内蒙古大学图书馆

The impact of cultural factors on digital marketing strategies with Machine learning and honey bee algorithm (HBA)

COGENT BUSINESS & MANAGEMENT 2025年第1期12卷

作者： Khan, Muhammad Ahmad, Masood Alidjonovich, Rakhmonov Dilshodjon Bakhritdinovich, Kalonov Mukhiddin Turobjonovna, Kurbanbekova Mohichehra Odilovich, Imomov Jamshidxon Abdul Wali Khan Univ Mardan Inst Business Studies & Leadership Marden 23200 KP Pakistan Abdul Wali Khan Univ Dept Comp Sci Mardan Pakistan Tashkent Int Univ Tashkent Uzbekistan Navoi State Univ Tashkent Uzbekistan Tashkent State Univ Econ Dept Commercializat Sci & Innovat Dev Tashkent Uzbekistan Tashkent State Univ Econ Dept Econ & Financial Secur Tashkent Uzbekistan

This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the honey bee algorithm (HBA) has been incorporated for better solutions. Cultural differences play a vital role as consumers behave differently based on some cultural differences, demanding sensitive marketing strategies. Experimental results demonstrate that machine learning models effectively capture cultural preferences, and HBA significantly enhances marketing effectiveness, leading to a 20% increase in engagement and a 15% improvement in click-through rates (CTR). Natural Language Processing (NLP) methods are used to gain cultural insights from consumer data, while clustering algorithms segment the market based on these factors. Predictive models are applied to understand consumer behavior patterns, and HBA is utilized to optimize key marketing parameters, including content personalization and ad placement. Compared to traditional marketing approaches, our data-driven methodology results in a 25% overall improvement in consumer interactions and campaign effectiveness. The proposed framework is validated through extensive experimentation, incorporating culturally tailored A/B testing, religion-related holidays' optimization, and personalized segmentation. These findings underscore the importance of integrating cultural insights into digital marketing strategies to enhance consumer engagement and optimize campaign performance.

关键词： Digital marketing strategies cultural factors consumer segmentation honey bee algorithm NLP campaign optimization Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Random Fuzzy Chance-constrained Programming Based on Adaptive Chaos Quantum honey bee algorithm and Robustness Analysis

引用

International Journal of Automation and computing 2010年第1期7卷 115-122页

作者： Han Xue Xun Li Hong-Xu Ma College of Electromechanical Engineering and Automation, National University of Defense Technology, Changsha 410073, PRC College of Electromechanical Engineering and Automation National University of Defense Technology Changsha PRC

This paper proposes an adaptive chaos quantum honey bee algorithm （CQHBA） for solving chance-constrained program- ming in random fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing a solution. Chaos optimization searches space around the selected best-so-far food source. In the marriage process, random interferential discrete quantum crossover is done between selected drones and the queen. Gaussian quantum mutation is used to keep the diversity of whole population. New methods of computing quantum rotation angles are designed based on grads. A proof of con- vergence for CQHBA is developed and a theoretical analysis of the computational overhead for the algorithm is presented. Numerical examples are presented to demonstrate its superiority in robustness and stability, efficiency of computational complexity, success rate, and accuracy of solution quality. CQHBA is manifested to be highly robust under various conditions and capable of handling most random fuzzy programmings with any parameter settings, variable initializations, system tolerance and confidence level, perturbations, and noises.

关键词： honey bee algorithm random fuzzy programming quantum computation chaos optimization robustness

来源：评论

学校读者我要写书评

暂无评论

honey bee algorithm-based efficient cluster formation and optimization scheme in mobile ad hoc networks

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2017年第6期13卷 1-1页

作者： Ahmad, Masood Ikram, Ataul Aziz Lela, Rabo Wahid, Ishtiaq Ulla, Riaz Iqra Univ Dept Comp & Technol Islamabad 44000 Pakistan Natl Univ Comp & Emerging Sci Sch Elect Engn Islamabad Pakistan Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Elect Sci & Technol China Dept Elect Engn Chengdu Peoples R China

In mobile ad hoc networks, topology changes very frequently due to node's mobility. Frequent change in topology increases traffic signaling that may arise energy and scalability issue. Cluster-based routing is the energy-efficient technique in mobile ad hoc networks to address the scalability issue and to minimize control messages. In this article, honey bee algorithm is used for dividing the mobile ad hoc network nodes into different clusters. The bees work to gather in groups to perform their activities. The proposed honey bee algorithm-based clustering forms clusters in an efficient manner with fewer resources such as energy and bandwidth utilization. A node is selected as cluster head based on node degree, neighbor's behavior, mobility direction, mobility speed, and remaining energy. Due to the efficient nature of bees and maximum parameter's consideration, the proposed technique inspired from the foraging behavior of honey bees gives efficient and stable cluster formation. The control message overhead is also avoided. The work is validated mathematically, and simulation has been performed for different scenarios. Simulation results are compared with existing clustering schemes. The simulation results show that the honey bee algorithm-based clustering technique used for clustering outperforms the existing schemes under consideration.

关键词： honey bee algorithm mobile ad hoc network routing cluster-based routing network optimization

来源：评论

学校读者我要写书评

暂无评论

A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第11期11卷 4347-4361页

作者： Ahmad, Masood Hameed, Abdul Ullah, Fasee Wahid, Ishtiaq Rehman, Saeed Ur Khattak, Hasan Ali Iqra Univ Dept Comp Sci Islamabad Pakistan Natl Univ Comp & Emerging Sci Dept Elect Engn Islamabad Pakistan Sarhad Univ Sci & Technol Dept Comp Sci & IT Peshawar Pakistan Comsat Univ Attoack Campus Attoack Pakistan Comsat Univ Islamabad Pakistan

In mobile adhoc networks for internet of things, the size of routing table can be reduced with the help of clustering structure. The dynamic nature of MANETs and its complexity make it a type of network with high topology changes. To reduce the topology maintenance overhead, the cluster based structure may be used. Hence, it is highly desirable to design an algorithm that adopts quickly to topology dynamics and form balanced and stable clusters. In this article, the formulation of clustering problem is carried out initially. Later, an algorithm on the basis of honey bee algorithm, genetic algorithm and tabu search (GBTC) for internet of things is proposed. In this algorithm, the individual (bee) represents a possbile clustering structure and its fitness is evaluated on the basis of its stability and load balancing. A method is presented by merging the properties of honey bee and genetic algorithms to help the population to cope with the topology dynamics and produce top quality solutions that are closely related to each other. The simulation results conducted for validation show that the proposed work forms balance and stable clusters. The simulation results are compared with algorithms that do not consider the dynamic optimization requirements. The GTBC outperform existing algorithms in terms of network lifetime and clustering overhead etc.

关键词： Internet of things Mobile ad-hoc networks Optimization honey bee algorithm Genetic algorithm Cluster

来源：评论

学校读者我要写书评

暂无评论

Optimized clustering in vehicular ad hoc networks based on honey bee and genetic algorithm for internet of things

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2020年第2期13卷 532-547页

作者： Ahmad, Masood Ikram, Ataul Aziz Wahid, Ishtiaq Ullah, Fasee Ahmad, Awais Khan, Fakhri Alam Abdul Wali Khan Univ Mandan Dept Comp Sci Mandan Pakistan Natl Univ Comp & Emerging Sci Dept Elect Engn Islamabad Pakistan Sarhad Univ Sci & Technol Dept Comp Sci & IT Peshawar Pakistan Bahria Univ Dept Comp Engn Islamabad Pakistan Inst Management Sci Peshawar Pakistan

In vehicular ad hoc network (VANET), the size of routing table can be reduced with the help of clustering architecture. The frequent changes in topology are the noteworthy characteristics of a VANET as its nature is dynamic. To manage the topology dynamics in VANET with less overhead, the concept of clustering can be used. Henceforth, an effective procedure that adjusts quickly to the topology changes should be designed. Firstly, the clustering problem (CP) in VANET is formulated into a dynamic optimization problem in this paper. Secondly, an optimization algorithm named Vehicular Genetic bee Clustering (VGBC) based on honey bee algorithm and properties of genetic algorithm solves the CP in VANETs is suggested. In VGBC, individuals (bees) represent a realistic clustering structure and its fitness is measured on the basis of load balancing and stability. A technique that merges the properties of genetic algorithm and honey bee algorithm is proposed. It helps the population to handle the topology changes and harvest high quality solutions. The simulation results piloted for justification demonstrate that the VGBC form steady and balanced clusters. The simulation results are matched with state of the art clustering schemes in VANET. The VGBC outperform existing schemes in terms of cluster count, cluster duration, re-affiliation rate, computational overhead, load balancing, VANET lifetime and clustering overhead.

关键词： VANETs Optimization honey bee algorithm Genetic algorithm Cluster

来源：评论

学校读者我要写书评

暂无评论

Load Balancing in tasks using honey bee Behavior algorithm in Cloud Computing 5

Load Balancing in tasks using Honey bee Behavior Algorithm i...

引用

5th International Conference on Wireless Networks and Embedded Systems (WECON)

作者： Kaur, Anureet Kaur, Bikrampal CGC Landran Informat Technol Morinda India CGC Landran Comp Sci & Engn Morinda India

ISBN: (纸本)9781509008933

Proper scheduling of tasks on cloud is a crucial optimization concern. Load balancing between deterrent dependent tasks on virtual machines (VMs) in cloud datacenters is a significant feature of task arrangement procedure in the cloud environment. In this paper, the new task scheduling model has been proposed, which utilizes the honey bee inspired algorithm for the load balancing which maximize the throughput of virtual machines in the cloud and optimize the execution time of assigned dependent tasks for the proper utilization of resources in the least possible cost. The proposed model balances the load between the jobs on VM's in a way that the overall waiting time of tasks in the queue is minimized. The proposed model is designed to calculate the CPU time in terms of earliest finish time (EFT). The load is calculated on the basis of resource usage percentage whereas the communication cost is evaluated by using process memory allocation, memory requirement, and data size, which is further used for final decision making by comparing the communication cost with the process cost. The experimental results have proven the effectiveness of the proposed model in comparison with the existing models.

关键词： Load balancing honey bee algorithm Execution time response time cost evaluation

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZING GENETIC PROCESS MINING WITH HEURISTIC AND honey bee APPROACHES FOR ENHANCED INITIAL POPULATION DESIGN AND NEIGHBORHOOD SEARCH

引用

International Journal of Innovative Computing, Information and Control 2025年第3期21卷 781-790页

作者： Effendi, Yutika Amelia Kim, Minsoo Robotics and Artificial Intelligence Engineering Faculty of Advanced Technology and Multidiscipline Airlangga University Kampus C UNAIR Jl. Dr. Ir. H. Soekarno Mulyorejo Jawa Timur Surabaya60115 Indonesia Department of Industrial and Data Engineering College of Engineering Pukyong National University 45 Yongso-ro Namgu Busan48513 Korea Republic of

Genetic process mining is a method used to identify, monitor, and improve processes by extracting insights from event logs within modern information systems. While traditional Genetic algorithms (GAs) can mine models with all structural constructs, except for duplicate tasks, and are resilient to noise, their significant computational time remains a major drawback. This study enhances the honey bee Genetic algorithm (HBGA) by incorporating the Heuristic Miner (HM)’s dependency measure, addressing challenges related to random population generation and excessive parameter tuning while accelerating the initial stages of evolution. By leveraging HM for initial population design and HBGA for neighborhood search, the proposed approach reduces the number of fitness function evaluations and computational time without compromising the accuracy of the final process model. Experimental results demonstrate that the proposed method outperforms traditional genetic process mining in terms of execution time and fitness function evaluations, while achieving superior fitness. Consequently, this process mining algorithm offers a more efficient and effective solution for discovering workflow models. © 2025, ICIC International. All rights reserved.

关键词： Dependency relation Genetic process mining Heuristic miner honey bee algorithm Process discovery Process enhancement Process mining

来源：评论

学校读者我要写书评

暂无评论

Efficient Distribution of Virtual Machines using honey bee Windowing in Cloud Computing

Efficient Distribution of Virtual Machines using Honey Bee W...

引用

IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Firos, A. Khanum, Seema Rajiv Gandhi Univ Dept Comp Sci & Engn Doimukh Arunachal Prade India

ISBN: (纸本)9781509064717

This paper presents an approach for developing virtual computing layer which is used by the cloud terminals during the virtual infrastructure downloading. The virtual computing layer is cable of image creation, maintenance and downloading on behalf of the cloud terminals. Using such virtual computing layer will considerably reduce the load maintained between the virtual computing client and the server. It also schedules the image downloading in the cloud infrastructure based on software image windows. The honey bee foraging algorithm performs the work in different phases while creation of virtual images. The forage bees will search for the suitable image portion in the neighborhood and the scout bees will bring the found portion from the location indicated by the forage bees.

关键词： Cloud Computing Distributed Virtual Environments honey bee algorithm Windowing

来源：评论

学校读者我要写书评

暂无评论

Network lifetime optimization and route selection strategy towards energy enrichment in wireless body area networks

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第3期17卷 1158-1168页

作者： Pradeep, R. Kavithaa, G. Sri Shanmugha Coll Engn & Technol Dept ECE Salem Tamilnadu India Govt Coll Engn Dept ECE Salem Tamilnadu India

Medical breakthroughs are currently being made to widen human beings' existence. For the vast majority of online medical care applications, Wireless Body Area Networks (WBANs) have emerged as an intriguing and important invention. Route loss and obstacles in offering essential information are significant criteria that drain power from the battery source and impair battery lifespan. By employing Optimal K-Means Clustering (OKMC), all body sensor nodes are positioned in tandem to construct cluster head selection. This research explores a novel metaheuristic method for strengthening network lifetime optimization and a sophisticated route selection strategy incorporating the Energy Enrichment Multi-Hop Routing (EEMR) protocol. The EEMR is intended to operate in a pair of phases. The Enhanced Flower bee Optimisation algorithm (EFBOA) is laid out as the initial phase, with the key objective of augmenting the network lifetime of the WBAN by laying down a network of clusters. The next phase employs Dynamic Local Hunting and Location Discarding (DLH-LD) to figure out the fastest route among all possible paths. The results of this analysis are validated and implemented in real-time using MATLAB and the Network Simulator. Various protocols, such as Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop Protocol (M-ATTEMPT), Even Energy Utilization Convention Routing (EECR), and Energy Mindful Posterior Routing (EMPR), are being compared to parameters such as path loss, throughput rate, energy consumption, cost estimation, and network lifetime. The outcomes revealed that, in comparison with conventional approaches, the suggested EEMR performs substantially better than existing routing protocols, spanning a range of performance indicators. Additionally, the research and simulation findings show that the suggested protocol is 30% more energy efficient than existing protocols, extending the life of the network. The numerical results exhibit an extens

关键词： WBANs Optimization techniques honey bee algorithm Clustering Routing protocol

来源：评论

学校读者我要写书评

暂无评论

MHDORA-LBA: Dynamic and Optimized Resource-Aware Load Balancing Approach for Resource Allocation

引用

SN Computer Science 2024年第6期5卷 795页

作者： Mishra, Rahul Gupta, Manish GLA University Mathura India

This paper discusses the LB approach as an optimization-based method for dynamic resource allocation and its impacts on stability and profit. Artificial bee Colony (ABC), which is a swarm intelligence, was founded based on the foraging behaviour of honey bees (HB) to optimise problems. A newly developed strategy based on this concept is the Differential Evolution Optimized (DEO) Dynamic Resource-Aware LB Method that draws from the HB. It operates by integrating two schemas, DRALBA and Hybrid Modified honey bee, via DE algorithm known as HyMHBDE. The resource allocation in this paper is done dynamically with the help of DRALBA. It presents a novel HB that employs the DEO strategy, borrowed from scout bees, to enhance the performance of the standard ABC in exploration. This method modifies the traditional ABC scout-bee phase by employing the crossover and mutation operators of the DEO algorithm. To authenticate the performance and efficiency of the MHDORA-LBA technique, an experimental examination and comparative study are performed. Findings are shown on both GoCJ and synthetic data, and comparisons are made to the DRALBA or DRABC-LB procedures to see how successful the suggested method is. Based on the simulation findings, it is clear that the suggested strategy maximises throughput and resource utilisation while improving LB stability via task allocation that decreases response time and makespan. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud system Differential evolution Dynamic resource aware load balancing honey bee algorithm Optimized resource allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：