This work addresses a research gap in digital marketing by attempting to compare the effort in achieving the best organic search engine ranking with the effort in providing the best user experience in web navigation. The objective is to validate companies' efforts in the digital world, and the study is focused on the toy sector in Spain, specifically on the Google search engine, measuring the user experience in web browsing through neuromarketing biometrics. The top 30 results for each Google search were collected for the 638 keywords related to toys in Spain. Subsequently, the three best-positioned websites for the Google search results were determined, and their user experience was measured using neuromarketing biometrics, triangulated with qualitative research. This approach allows for contrasting brand authority in the digital world (visibility in a search) with the user experience in navigation (trust and ease of purchase decision-making). Results indicate that the best-positioned websites do not necessarily correspond to the best web navigation experiences.
{"title":"SEO vs. UX in Web Design","authors":"David Juárez-Varón, Manuel Ángel Juárez-Varón","doi":"10.4018/ijssci.342127","DOIUrl":"https://doi.org/10.4018/ijssci.342127","url":null,"abstract":"This work addresses a research gap in digital marketing by attempting to compare the effort in achieving the best organic search engine ranking with the effort in providing the best user experience in web navigation. The objective is to validate companies' efforts in the digital world, and the study is focused on the toy sector in Spain, specifically on the Google search engine, measuring the user experience in web browsing through neuromarketing biometrics. The top 30 results for each Google search were collected for the 638 keywords related to toys in Spain. Subsequently, the three best-positioned websites for the Google search results were determined, and their user experience was measured using neuromarketing biometrics, triangulated with qualitative research. This approach allows for contrasting brand authority in the digital world (visibility in a search) with the user experience in navigation (trust and ease of purchase decision-making). Results indicate that the best-positioned websites do not necessarily correspond to the best web navigation experiences.","PeriodicalId":503141,"journal":{"name":"International Journal of Software Science and Computational Intelligence","volume":"39 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662938","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}
D. K. Saini, Anupama Mishra, Dhirendra Siddharth, Pooja Joshi, Ritika Bansal, Shavi Bansal, Kwok Tai Chui
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.
传输时间和功率优化可提高二次网络能源效率（EE）。认知无线电网络中的最佳资源分配策略是增强黑猩猩优化算法（OFCOA），因为 EE 最大化问题是一个非线性分数编程问题。为了控制资源和能源，本研究提供了一种基于对立函数的高能效认知无线电网络黑猩猩优化算法（OFCOA）解决方案。建议将反对函数（OF）与黑猩猩优化技术相结合。COA 中的反对函数可改善决策。能源管理中的频谱测量可提供高能效的 CRN 运行。利用信道占用率、CRN 数据以及四个主要和次要用户场景对建议的技术进行了评估。在 MATLAB 中利用 CPU 功耗、网络寿命、传输速率、延迟、冲洗、功耗和开销对建议的方法进行了评估。提议的方法与粒子群优化（PSO）、黑猩猩优化算法（COA）和鲸鱼优化算法等现有方法进行了比较。
{"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}
Akash Sharma, Sunil K. Singh, Anureet Chhabra, Sudhakar Kumar, Varsha Arya, M. Moslehpour
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.
{"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}
Sensitivity to surrounding circumstances is essential for the safety of mountain scrambling. In this paper, the authors present a smart helmet prototype equipped with visual SLAM (simultaneous localization and mapping) and barometer multi-sensor fusion (MSF), IMU (inertial measurement unit), omnidirectional camera, and global navigation satellite system (GNSS). They equipped the helmet framework with SLAM to produce 3D semi-dense pointcloud environment maps, which are then discretized into grids. Then, the novel danger metrics they proposed were calculated for each grid based on surface normal analysis. The A* algorithm was applied to generate safe and reliable paths based on minimizing the danger score. This proposed helmet system demonstrated robust performance in mapping mountain environments and planning safe, efficient traversal paths for climbers navigating treacherous mountain landscapes.
{"title":"A Smart Helmet Framework Based on Visual-Inertial SLAM and Multi-Sensor Fusion to Improve Situational Awareness and Reduce Hazards in Mountaineering","authors":"Charles Shi Tan","doi":"10.4018/ijssci.333628","DOIUrl":"https://doi.org/10.4018/ijssci.333628","url":null,"abstract":"Sensitivity to surrounding circumstances is essential for the safety of mountain scrambling. In this paper, the authors present a smart helmet prototype equipped with visual SLAM (simultaneous localization and mapping) and barometer multi-sensor fusion (MSF), IMU (inertial measurement unit), omnidirectional camera, and global navigation satellite system (GNSS). They equipped the helmet framework with SLAM to produce 3D semi-dense pointcloud environment maps, which are then discretized into grids. Then, the novel danger metrics they proposed were calculated for each grid based on surface normal analysis. The A* algorithm was applied to generate safe and reliable paths based on minimizing the danger score. This proposed helmet system demonstrated robust performance in mapping mountain environments and planning safe, efficient traversal paths for climbers navigating treacherous mountain landscapes.","PeriodicalId":503141,"journal":{"name":"International Journal of Software Science and Computational Intelligence","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271575","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}
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