Objective The aim of this study was to investigate the relationship between peripheral plasma stem cell factor (SCF)/c-kit levels and the types of dipper and non-dipper hypertension in hypertensive patients.Methods Th...Objective The aim of this study was to investigate the relationship between peripheral plasma stem cell factor (SCF)/c-kit levels and the types of dipper and non-dipper hypertension in hypertensive patients.Methods This cross-sectional study included newly diagnosed hypertensive patients who underwent 24-hour ambulatory blood pressure monitor (ABPM) between January 2009 and 2012 in Jiangning city. Patients were divided into the dipper group and the non-dipper group according to ABPM measurements. The levels of SCF and its receptor c-kit, tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6) in peripheral blood were measured via enzyme-linked immunosorbent assays. The serum levels of glucose and lipid were examined as well. The levels of SCF/c-kit were compared between the dippers and the non-dippers; and their correlation with 24-hour mean systolic blood pressure (MSBP), 24-hour mean diastolic blood pressure (MDBP), TNF-αand IL-6 were investigated using linear regression analyses statistically.Results A total of 247 patients with newly diagnosed hypertension were recruited into the study, including 116 non-dippers and 131 dippers. The levels of peripheral plasma SCF were higher in non-dipper group (907.1±52.7 ng/L vs. 778.7±44.6 ng/L; t=2.837, P<0.01), and the levels of c-kit were higher in non-dipper group too (13.2±1.7 μg/L vs 9.57±1.4 μg/L; t=2.831, P<0.01). Linear regression analysis revealed that SCF/ckit levels were significantly positively correlated with MSBP, MDBP, plasma TNF-α, and IL-6 levels (all P<0.01).Conclusions Peripheral plasma SCF/c-kit levels are higher in patients with non-dipper hypertension than those with dipper one, and significantly correlate with 24-hour MSBP, 24-hour MDBP, serum TNF-α and IL-6 levels.展开更多
Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collectio...Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.展开更多
Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The go...Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.展开更多
The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy...The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).展开更多
The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an e...The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.展开更多
Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining ...Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining popularity as a way to improve solutions in a range of fields.Machine learning approaches are currently a big part of current research,and they’re likely to be huge in the future.The model utilized determines the accuracy of the prediction in large part.The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain.The basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)algorithm.To choose the best features from the dataset,the binary(bDTO)algorithm is exploited.The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically.In addition,two other ensembles are incorporated in the conducted experiments for comparison.These ensembles are average ensemble and K-nearest neighbors(KNN)-based ensemble.The comparison is performed in terms of eleven evaluation criteria.The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.展开更多
In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data tran...In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.展开更多
Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is ...Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).展开更多
Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solution...Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.展开更多
In this paper we introduce methods for approximating local standard time in the Northern Hemisphere using Polaris and the Big Dipper as well as alternative reference stars, and describe in detail how to construct a de...In this paper we introduce methods for approximating local standard time in the Northern Hemisphere using Polaris and the Big Dipper as well as alternative reference stars, and describe in detail how to construct a device we call a dipperclock to facilitate this process. An alternative method which does not require a dipperclock is also discussed. Ways of constructing dipperclocks which glow in the dark are presented. The accuracy of dipperclocks is examined, both theoretically and through field testing. A java program is provided for creating dipperclocks customized to a particular year-long time period and place to get improved accuracy. Basic astronomical definitions and justifications of the results are provided. We also discuss the use of dipperclocks to find longitude and latitude.展开更多
The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in ma...The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.展开更多
Dr.Edmund Dipper,a distinguished German doctor practicing in China during the tumultuous decades of the 1920s and 1930s,rose to near-legendary status during the Republican era.Yet,the historical trajectory leading to ...Dr.Edmund Dipper,a distinguished German doctor practicing in China during the tumultuous decades of the 1920s and 1930s,rose to near-legendary status during the Republican era.Yet,the historical trajectory leading to his acclaim remains shrouded in conflicting narratives.Dipper’s formative years demonstrate that his success in China stemmed from a multifaceted background encompassing education,religion,and military service.This was coupled with an unwavering commitment to excellence in medical practice and adaptability and resourcefulness in navigating cross-cultural settings.These factors allowed him to successfully avoid strife between church factions when managing the Faber Hospital.Furthermore,Dipper’s remarkable intercultural acumen enabled him to seize a crucial historical opportunity.By adeptly navigating the German Hospital through the repatriation of German citizens after China declared war on Germany in 1917,he not only preserved his own standing but also safeguarded the hospital’s key personnel.This strategic maneuvering laid the groundwork for his illustrious career trajectory.Dipper epitomizes a subset of Western doctors in China during this era,underscoring the imperative to further explore their nuanced historical contributions.展开更多
In order to improve the seeder’seed-filling ability of the dipper hill-drop precision direct rice seeder,and to meet the mechanization requirement of high speed operation,the self-designed seeder was taken as the obj...In order to improve the seeder’seed-filling ability of the dipper hill-drop precision direct rice seeder,and to meet the mechanization requirement of high speed operation,the self-designed seeder was taken as the objective to explore its seed-filling mechanism and the movement status of rice seed in seed box from the perspective of mechanics.The force models of seed-filling process by dipper were established,and the influential regularity of its rotation speed to compressive resistance of seed population was analyzed as well.The image processing Module-Clipping of discrete element simulation software EDEM was used in the virtual simulation analysis for the process of the seed filling into the dipper,and the velocity relation curve and the force changing curve between rotation speed and seeds were obtained.According to the virtual experiment,the composite filling force of seeds,i.e.the qualified rate on filled rice seed amounts was the largest when rotation speed was at 40 r/min.The performance test bed of seeder was used to verify the simulation results,in which the qualified rate on scooped rice seed amounts was taken as the index,and six rotation speeds of seed-filling dipper were also selected for analysis of seed-filling ability of the device.The results are as follows:with the increase of working speed,the qualified rate on filled rice seed amounts fluctuated with a trend of cosine curve,the largest value was 94.16%occurred when the rotation speed of seed-filling dipper was at 40 r/min.The variation trend of simulation value was approximately consistent with that of verification value.The study can provide a reference for the research and development of mechanical seeder.展开更多
目的探讨孕中期24 h动态血压监测(ABPM)早期预警子痫前期(PE)及不良妊娠转归的临床价值。方法随机选取2024年2月至2025年2月在南京医科大学第一附属医院孕中期产检首次发现诊间血压处于临界状态的单胎孕妇共81例,行24 h ABPM并追踪其妊...目的探讨孕中期24 h动态血压监测(ABPM)早期预警子痫前期(PE)及不良妊娠转归的临床价值。方法随机选取2024年2月至2025年2月在南京医科大学第一附属医院孕中期产检首次发现诊间血压处于临界状态的单胎孕妇共81例,行24 h ABPM并追踪其妊娠结局。根据是否并发PE分为正常组(49例)和PE组(32例),比较两组孕妇一般情况、24 h ABPM参数,应用logistic回归分析与PE预警相关的危险因素。同时,根据血压分型分为3组:杓型组(28例)、非杓型组(32例)和反杓型组(21例),分析昼夜血压波动与母儿转归的相关性。结果PE组首次发现血压处于临界状态时舒张压、随机尿蛋白阳性率均高于正常组(P<0.01),血压临界发现孕周显著早于正常组(P=0.001)。PE组24 h、白天、夜间平均收缩压和舒张压均显著高于正常组(P<0.01)。反杓型组和杓型组、非杓型组PE发生率分别为53.1%、12.5%和34.4%,三组间差异有统计学意义(χ^(2)=22.899,P<0.01),且反杓型组PE发生率高于非杓型组和杓型组(P<0.017)。多因素logistic回归分析显示,首次发现血压临界状态的孕周早、门诊当天舒张压高、晨峰血压高和非杓型/反杓型血压是PE发生的独立预警因素(P<0.05)。杓型组、反杓型组和非杓型组分娩孕周、剖宫产率、胎儿生长受限(FGR)发生率差异有统计学意义(P<0.05),反杓型组分娩孕周低于杓型组(P<0.017),剖宫产率、FGR发生率高于杓型组;趋势χ^(2)检验显示,随血压夜间较白昼下降程度降低(杓型→非杓型→反杓型),早产儿率呈显著升高趋势(linearχ^(2)=5.245,P=0.022)。结论孕中期血压临界状态孕妇中,动态血压参数显著升高且血压昼夜节律紊乱。血压临界状态时孕周、门诊当天舒张压、晨峰血压和血压分型是PE发生的重要预警因素,且血压昼夜节律异常与剖宫产率、FGR等不良母儿转归密切相关。孕中期24 h ABPM可提供PE的预警及提示妊娠不良转归。展开更多
China has high expectations for its domestically produced global positioning system When a catastrophic earthquake measuring 8.0 on the Richter scale rocked Wenchuan County in China’s
文摘Objective The aim of this study was to investigate the relationship between peripheral plasma stem cell factor (SCF)/c-kit levels and the types of dipper and non-dipper hypertension in hypertensive patients.Methods This cross-sectional study included newly diagnosed hypertensive patients who underwent 24-hour ambulatory blood pressure monitor (ABPM) between January 2009 and 2012 in Jiangning city. Patients were divided into the dipper group and the non-dipper group according to ABPM measurements. The levels of SCF and its receptor c-kit, tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6) in peripheral blood were measured via enzyme-linked immunosorbent assays. The serum levels of glucose and lipid were examined as well. The levels of SCF/c-kit were compared between the dippers and the non-dippers; and their correlation with 24-hour mean systolic blood pressure (MSBP), 24-hour mean diastolic blood pressure (MDBP), TNF-αand IL-6 were investigated using linear regression analyses statistically.Results A total of 247 patients with newly diagnosed hypertension were recruited into the study, including 116 non-dippers and 131 dippers. The levels of peripheral plasma SCF were higher in non-dipper group (907.1±52.7 ng/L vs. 778.7±44.6 ng/L; t=2.837, P<0.01), and the levels of c-kit were higher in non-dipper group too (13.2±1.7 μg/L vs 9.57±1.4 μg/L; t=2.831, P<0.01). Linear regression analysis revealed that SCF/ckit levels were significantly positively correlated with MSBP, MDBP, plasma TNF-α, and IL-6 levels (all P<0.01).Conclusions Peripheral plasma SCF/c-kit levels are higher in patients with non-dipper hypertension than those with dipper one, and significantly correlate with 24-hour MSBP, 24-hour MDBP, serum TNF-α and IL-6 levels.
文摘Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.
基金Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber (PNURSP2022R308), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
文摘Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.
文摘The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.
文摘Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining popularity as a way to improve solutions in a range of fields.Machine learning approaches are currently a big part of current research,and they’re likely to be huge in the future.The model utilized determines the accuracy of the prediction in large part.The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain.The basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)algorithm.To choose the best features from the dataset,the binary(bDTO)algorithm is exploited.The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically.In addition,two other ensembles are incorporated in the conducted experiments for comparison.These ensembles are average ensemble and K-nearest neighbors(KNN)-based ensemble.The comparison is performed in terms of eleven evaluation criteria.The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.
文摘In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.
文摘Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).
文摘Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.
文摘In this paper we introduce methods for approximating local standard time in the Northern Hemisphere using Polaris and the Big Dipper as well as alternative reference stars, and describe in detail how to construct a device we call a dipperclock to facilitate this process. An alternative method which does not require a dipperclock is also discussed. Ways of constructing dipperclocks which glow in the dark are presented. The accuracy of dipperclocks is examined, both theoretically and through field testing. A java program is provided for creating dipperclocks customized to a particular year-long time period and place to get improved accuracy. Basic astronomical definitions and justifications of the results are provided. We also discuss the use of dipperclocks to find longitude and latitude.
基金funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No (43-PRFA-P-52).
文摘The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.
文摘Dr.Edmund Dipper,a distinguished German doctor practicing in China during the tumultuous decades of the 1920s and 1930s,rose to near-legendary status during the Republican era.Yet,the historical trajectory leading to his acclaim remains shrouded in conflicting narratives.Dipper’s formative years demonstrate that his success in China stemmed from a multifaceted background encompassing education,religion,and military service.This was coupled with an unwavering commitment to excellence in medical practice and adaptability and resourcefulness in navigating cross-cultural settings.These factors allowed him to successfully avoid strife between church factions when managing the Faber Hospital.Furthermore,Dipper’s remarkable intercultural acumen enabled him to seize a crucial historical opportunity.By adeptly navigating the German Hospital through the repatriation of German citizens after China declared war on Germany in 1917,he not only preserved his own standing but also safeguarded the hospital’s key personnel.This strategic maneuvering laid the groundwork for his illustrious career trajectory.Dipper epitomizes a subset of Western doctors in China during this era,underscoring the imperative to further explore their nuanced historical contributions.
基金The authors thank the financial support provided by the National Industry System of Rice Technology of China(CARS-01-44),Heilongjiang Modern Industrial Technology Collaborative Innovation System.
文摘In order to improve the seeder’seed-filling ability of the dipper hill-drop precision direct rice seeder,and to meet the mechanization requirement of high speed operation,the self-designed seeder was taken as the objective to explore its seed-filling mechanism and the movement status of rice seed in seed box from the perspective of mechanics.The force models of seed-filling process by dipper were established,and the influential regularity of its rotation speed to compressive resistance of seed population was analyzed as well.The image processing Module-Clipping of discrete element simulation software EDEM was used in the virtual simulation analysis for the process of the seed filling into the dipper,and the velocity relation curve and the force changing curve between rotation speed and seeds were obtained.According to the virtual experiment,the composite filling force of seeds,i.e.the qualified rate on filled rice seed amounts was the largest when rotation speed was at 40 r/min.The performance test bed of seeder was used to verify the simulation results,in which the qualified rate on scooped rice seed amounts was taken as the index,and six rotation speeds of seed-filling dipper were also selected for analysis of seed-filling ability of the device.The results are as follows:with the increase of working speed,the qualified rate on filled rice seed amounts fluctuated with a trend of cosine curve,the largest value was 94.16%occurred when the rotation speed of seed-filling dipper was at 40 r/min.The variation trend of simulation value was approximately consistent with that of verification value.The study can provide a reference for the research and development of mechanical seeder.
文摘目的探讨孕中期24 h动态血压监测(ABPM)早期预警子痫前期(PE)及不良妊娠转归的临床价值。方法随机选取2024年2月至2025年2月在南京医科大学第一附属医院孕中期产检首次发现诊间血压处于临界状态的单胎孕妇共81例,行24 h ABPM并追踪其妊娠结局。根据是否并发PE分为正常组(49例)和PE组(32例),比较两组孕妇一般情况、24 h ABPM参数,应用logistic回归分析与PE预警相关的危险因素。同时,根据血压分型分为3组:杓型组(28例)、非杓型组(32例)和反杓型组(21例),分析昼夜血压波动与母儿转归的相关性。结果PE组首次发现血压处于临界状态时舒张压、随机尿蛋白阳性率均高于正常组(P<0.01),血压临界发现孕周显著早于正常组(P=0.001)。PE组24 h、白天、夜间平均收缩压和舒张压均显著高于正常组(P<0.01)。反杓型组和杓型组、非杓型组PE发生率分别为53.1%、12.5%和34.4%,三组间差异有统计学意义(χ^(2)=22.899,P<0.01),且反杓型组PE发生率高于非杓型组和杓型组(P<0.017)。多因素logistic回归分析显示,首次发现血压临界状态的孕周早、门诊当天舒张压高、晨峰血压高和非杓型/反杓型血压是PE发生的独立预警因素(P<0.05)。杓型组、反杓型组和非杓型组分娩孕周、剖宫产率、胎儿生长受限(FGR)发生率差异有统计学意义(P<0.05),反杓型组分娩孕周低于杓型组(P<0.017),剖宫产率、FGR发生率高于杓型组;趋势χ^(2)检验显示,随血压夜间较白昼下降程度降低(杓型→非杓型→反杓型),早产儿率呈显著升高趋势(linearχ^(2)=5.245,P=0.022)。结论孕中期血压临界状态孕妇中,动态血压参数显著升高且血压昼夜节律紊乱。血压临界状态时孕周、门诊当天舒张压、晨峰血压和血压分型是PE发生的重要预警因素,且血压昼夜节律异常与剖宫产率、FGR等不良母儿转归密切相关。孕中期24 h ABPM可提供PE的预警及提示妊娠不良转归。
文摘China has high expectations for its domestically produced global positioning system When a catastrophic earthquake measuring 8.0 on the Richter scale rocked Wenchuan County in China’s
