International Journal of Software Science and Computational Intelligence最新文献

SEO vs. UX in Web Design 网页设计中的搜索引擎优化与用户体验

International Journal of Software Science and Computational Intelligence Pub Date : 2024-04-24 DOI: 10.4018/ijssci.342127

David Juárez-Varón, Manuel Ángel Juárez-Varón

引用次数: 0

Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation 在认知无线电网络中开发用于能源管理和资源分配的增强型 Chimp 优化算法 (OFCOA)

International Journal of Software Science and Computational Intelligence Pub Date : 2024-01-10 DOI: 10.4018/ijssci.335898

D. K. Saini, Anupama Mishra, Dhirendra Siddharth, Pooja Joshi, Ritika Bansal, Shavi Bansal, Kwok Tai Chui

{"title":"Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation","authors":"D. K. Saini, Anupama Mishra, Dhirendra Siddharth, Pooja Joshi, Ritika Bansal, Shavi Bansal, Kwok Tai Chui","doi":"10.4018/ijssci.335898","DOIUrl":"https://doi.org/10.4018/ijssci.335898","url":null,"abstract":"Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control resources and energy, this research offers an energy-efficient CRN opposition function-based chimpanzee optimisation algorithm (OFCOA) solution. Combining the opposition function (OF) with the chimpanzee optimisation technique is recommended. OF in COAs improves decision-making. Spectrum measurements in energy management provide energy-efficient CRN operation. The suggested technique was evaluated using channel occupancy, CRN data, and four major and secondary user scenarios. CPU power, network life, transmission rate, latency, flush, power consumption, and overhead are utilized to evaluate the proposed approach in MATLAB. The proposed method is compared to existing approaches like Particle Swarm Optimisation (PSO), Chimpanzee Optimisation Algorithm (COA), and Whale Optimisation Algorithm.","PeriodicalId":503141,"journal":{"name":"International Journal of Software Science and Computational Intelligence","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems 基于深度联合学习的新型模型，用于增强关键基础设施系统的隐私性

International Journal of Software Science and Computational Intelligence Pub Date : 2023-12-15 DOI: 10.4018/ijssci.334711

Akash Sharma, Sunil K. Singh, Anureet Chhabra, Sudhakar Kumar, Varsha Arya, M. Moslehpour

{"title":"A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems","authors":"Akash Sharma, Sunil K. Singh, Anureet Chhabra, Sudhakar Kumar, Varsha Arya, M. Moslehpour","doi":"10.4018/ijssci.334711","DOIUrl":"https://doi.org/10.4018/ijssci.334711","url":null,"abstract":"Deep learning (DL) can provide critical infrastructure operators with valuable insights and predictive capabilities to help them make more informed decisions, improving system's robustness. However, training DL models requires large amounts of data, which can be costly to store in a centralized manner. Storing large amounts of sensitive critical infrastructure data in the cloud can pose significant security risks. Federated learning (FL) allows several clients to share learning data and train ML models. Unlike centralized models, FL does not require the sharing of client data. A novel framework is presented to train a VGG16 based CNN global model without sharing the data and only updating the local models among clients using federated averaging. For experimentation, MNIST dataset is used. The framework achieves high accuracy and keep data private using FL in critical infrastructures. The benefits and challenges of FL along with security vulnerabilities and attacks have been discussed along with the defenses that can be used to mitigate these attacks.","PeriodicalId":503141,"journal":{"name":"International Journal of Software Science and Computational Intelligence","volume":"119 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139178436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Smart Helmet Framework Based on Visual-Inertial SLAM and Multi-Sensor Fusion to Improve Situational Awareness and Reduce Hazards in Mountaineering 基于视觉-惯性 SLAM 和多传感器融合的智能头盔框架，用于提高登山运动中的态势感知能力并减少危险

International Journal of Software Science and Computational Intelligence Pub Date : 2023-11-15 DOI: 10.4018/ijssci.333628

Charles Shi Tan

引用次数: 0