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Optimizing IoT-Driven Smart Cities with the Dynamic Leader Sibha Algorithm:A Novel Approach to Feature Selection and Hyperparameter Tuning 认领 引用
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作者 Safaa Zaman Marwa M.Eid +2 位作者 Ebrahim A.Mattar Doaa Sami Khafaga El-Sayed M.El-Kenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期899-958,共60页
The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this dat... The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this data is crucial for optimizing urban infrastructure,enhancing quality of life,and supporting sustainable development.However,smart city data presents significant challenges,including non-linear dependencies,noisy signals,and high dimensionality.To address these challenges,this study proposes the Dynamic Leader Sibha Algorithm(DLSA),a novel metaheuristic optimization technique inspired by the structured counting dynamics of the Sibha.The DLSA was applied to the Smart Cities Index dataset,leveraging copula functions to model complex,multivariate dependencies and enhance predictive accuracy.The baseline machine learning(ML)evaluation revealed that the ExtraTreesRegressor achieved the lowest mean squared error(MSE)of 0.007462409,highlighting its superior initial performance.Following feature selection using the binary Dynamic Leader Sibha Algorithm(bSiba),the average error was reduced to 0.373245769,significantly improving data quality and model efficiency.Subsequent ML evaluation after feature selection further reduced the MSE of the ExtraTreesRegressor to 0.00151927,reflecting the effectiveness of dimensionality reduction.Finally,hyperparameter optimization using the DLSA achieved a remarkable MSE of 1.32249×10−6 with the Siba+ExtraTreesRegressor combination,demonstrating the algorithm’s powerful optimization capabilities.These findings indicate that the DLSA framework can significantly enhance the predictive performance of IoT-driven smart city models,offering valuable insights for urban planners,policymakers,and technology developers seeking to build smarter,more resilient cities. 展开更多
关键词 Smart cities analytics Internet of Things(IoT) Dynamic Leader Sibha Algorithm(DLSA) feature selection and optimization high-dimensional data analysis
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森林优化特征选择算法的增强与扩展 认领 引用 被引量：10
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作者 刘兆赓 李占山 +2 位作者 王丽 王涛 于海鸿 《软件学报》 EI CSCD 北大核心 2020年第5期1511-1524,共14页
特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林... 特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林优化特征选择算法具有更好的分类性能及维度缩减能力.然而,初始化阶段的随机性、全局播种阶段的人为参数设定,影响了该算法的准确率和维度缩减能力;同时,算法本身存在着高维数据处理能力不足的本质缺陷.从信息增益率的角度给出了一种初始化策略,在全局播种阶段,借用模拟退火控温函数的思想自动生成参数,并结合维度缩减率给出了适应度函数;同时,针对形成的优质森林采取贪心算法,形成一种特征选择算法EFSFOA(enhanced feature selection using forest optimization algorithm).此外,在面对高维数据的处理时,采用集成特征选择的方案形成了一个适用于EFSFOA的集成特征选择框架,使其能够有效处理高维数据特征选择问题.通过设计对比实验,验证了EFSFOA与FSFOA相比在分类准确率和维度缩减率上均有明显的提高,高维数据处理能力更是提高到了100 000维.将EFSFOA与近年来提出的比较高效的基于演化计算的特征选择方法进行对比,EFSFOA仍具有很强的竞争力. 展开更多
关键词 enhanced feature selection using forest optimization algorithm(EFSFOA) 高维 特征选择 演化计算
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Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data 认领 引用 被引量：1
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作者 Musaed Alrashidi 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期371-387,共17页
The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these t... The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively. 展开更多
关键词 Big data harvesting mosque load forecast data preprocessing machine learning optimal features selection
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Accurate Machine Learning Predictions of Sci-Fi Film Performance 认领 引用
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作者 Amjed Al Fahoum Tahani A.Ghobon 《Journal of New Media》 2023年第1期1-22,共22页
A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive researc... A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry. 展开更多
关键词 Film success rate prediction optimized feature selection robust machine learning nearest neighbors’ algorithms
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