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Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection algorithm

NEURAL PROCESSING LETTERS 2023年第1期55卷 153-169页

作者： Li, Xiaohua Zhang, Jusheng Safara, Fatemeh Hunan Univ Arts & Sci Sch Phys Educ Changde 415000 Hunan Peoples R China South China Univ Technol Guangzhou 510641 Guangdong Peoples R China Shenzhen Yiqizu Technol Co Shenzhen 518000 Guangdong Peoples R China Islamic Azad Univ Islamshahr Branch Dept Comp Engn Islamshahr Iran

Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.

关键词： Diabetes diagnosis application Genetic algorithm Particle swarm optimization harmony search algorithm K-means Artificial intelligence Coronavirus disease pandemic

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A novel robust model for location-allocation of healthcare facilities considering pre-disaster and post-disaster characteristics

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SCIENTIA IRANICA 2023年第2期30卷 619-641页

作者： Alinaghian, M. Hejazi, S. R. Bajoul, N. Velni, K. Sadeghi Isfahan Univ Technol Dept Ind & Syst Engn Esfahan *** Iran Univ Tehran Fouman Fac Engn Dept Ind Engn Fouman Iran

Healthcare centers are one of the most important municipal facilities that are responsible for providing personal and social health. Locating medical centers is an important strategy to improve the performance of these facilities and reduce any delay in the relief process. During disasters, these centers play a critical role in reducing the risk of human casualties. In this regard, the current study aims to establish a new robust mathematical model to simultaneously locate and allocate healthcare facilities, including different levels of medical care services, considering the characteristics in normal and disaster situations. Further, to help victims and prevent overcrowding at medical centers during disasters, establishment of temporary and outpatient centers was suggested for emergency provision of basic services. In addition, the possibility of delivering the needed equipment and medical teams to these centers was incorporated in the proposed modeling. To solve the problem, two metaheuristic algorithms, harmony search algorithm and hybrid tabu search combined with variable neighborhood search algorithm, and a lower bound based on Lagrangian relaxation method were employed. To assess the performance of the proposed algorithms, sample problems were randomly generated. The results obtained from the exact solutions and lower bound from the Lagrangian relaxation method were compared with those from the meta-heuristic algorithms, confirming the good performance of the proposed algorithms. (c) 2023 Sharif University of Technology. All rights reserved.

关键词： Health management system Network reliability Hierarchical healthcare facility Location problem Robust optimization Variable neighborhood search algorithm harmony search algorithm

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Dynamic obstacle avoidance and trajectory planning of five-axis redundant industrial manipulator

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JOURNAL OF VIBROENGINEERING 2023年第4期25卷 778-791页

作者： Ma, Jian Changzhou Vocat Inst Mechatron Technol Sch Elect Engn Changzhou 213164 Peoples R China

Aiming at the problem that five axis redundant industrial manipulator dynamic obstacle avoidance and trajectory planning algorithm does not consider the minimum difference of each joint of the manipulator, which leads to low success rate of obstacle avoidance planning, slow convergence speed of path cost and long time of obstacle avoidance planning, a simulation study on dynamic obstacle avoidance trajectory planning of five axis redundant industrial manipulator is proposed. According to the D-H rule, the coordinate system of each link joint of the five axis redundant industrial manipulator is established, and the forward and inverse kinematics of the five axis redundant industrial manipulator is analyzed. AABB's hierarchical bounding box tree algorithm is used to detect the collision of five axis redundant industrial manipulator. This paper uses harmony search algorithm to plan the obstacle avoidance path of five axis redundant industrial manipulator, determines the objective function and constraints of the optimization problem, sets algorithm parameters, initializes harmony memory, creates new harmony, updates harmony memory, checks and searches the target state, achieves the maximum number of iterations, and realizes the dynamic obstacle avoidance and trajectory planning of five axis redundant industrial manipulator. The experimental results show that the path cost of the proposed algorithm converges faster, and can effectively improve the success rate of obstacle avoidance planning and shorten the time of obstacle avoidance planning.

关键词： five axis redundant industrial manipulator dynamic obstacle avoidance trajectory planning hierarchical bounding box tree algorithm harmony search algorithm

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The effects of internet of things and industrial revolution 4.0 on urban waste management: a case study of Isfahan city

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第2期21卷 1619-1636页

作者： Morsali, M. Kianfar, K. Univ Isfahan Fac Engn Esfahan *** Iran

The waste collection problem is one of the critical problems in today's world, and ignoring this issue or the existence of a fault in this system can cause huge costs and damages. The advanced countries in the world are trying to improve the efficiency of their waste collection system with modern methods to solve the challenges of this system. The application of Internet of Things (IoT) and RFID tags is an interesting field of research in urban waste management systems. This study develops three models for urban waste collecting. The ST model is a traditional and static method currently used in many cities. The DSA is a semi-modern model based on greedy algorithms in which RFID tags are installed on garbage bins. The DAIoT model is a modern system working with IoT equipment installed on trucks and waste bins. This model uses a combination of greedy algorithm and harmony search metaheuristics. The main purpose of this study is to schedule the waste collection system and vehicle routing to reduce trucks' gas emissions and empty garbage bins on time. The results on Isfahan city show that compared to the traditional ST model, the DSA model causes a 2% reduction in gas emissions and a 6.7% reduction in the number of required trucks and improves system performance in critical situations. The DAIoT model, as the best model, causes a 33.9% reduction in greenhouse gas emissions and a 60% reduction in the number of trucks compared to the traditional ST model.

关键词： Urban waste management Industrial revolution 4.0 Internet of things Routing Greedy algorithm harmony search algorithm

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Multiobjective Pump Scheduling Optimization Using harmony search algorithm (HSA) and Polyphonic HSA

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WATER RESOURCES MANAGEMENT 2013年第5期27卷 1249-1261页

作者： Kougias, Ioannis P. Theodossiou, Nicolaos P. Aristotle Univ Thessaloniki Div Hydraul & Environm Engn Dept Civil Engn Thessaloniki 54124 Greece

harmony search algorithm (HSA) is a metaheuristic method that has attracted the scientific interest since its first presentation in 2001. It is a music inspired method, imitating the music creation process in order to find optimal solutions in complicated problems. HSA's successful application on single - objective optimization problems has resulted to an increasing interest in the implementation of HSA towards multiobjective optimization. The authors have adjusted HSA in order to deal successfully with multi-criteria water management problems. This adjustment has resulted to the creation of Multiobjective - HSA (MO-HSA). In addition, they have designed the multiobjective variant Polyphonic-HSA (Poly-HSA), which is inspired by the independent development of different voices in music and borrows elements from Swarm Intelligence and the single-objective variant Global-Best HSA. In the first part of this paper, both methods are presented in detail. Moreover, the performance of the proposed algorithms is evaluated using standard multiobjective test - functions. ZDT and DTLZ multiobjective tests have been chosen and indicators such as Hypervolume, C - metric and diversity metric - Delta have been used to measure the convergence to the optimal front and the diversity of the solutions obtained by the proposed methods. In the second part, MO-HSA and Poly-HSA have been introduced towards the optimization of a pump scheduling problem. The objectives considered are water supply, pumping cost, electric power peak demand and pump maintenance cost. Both methods converged to non-dominated fronts and provided excellent results which are presented in 3d figures, indicating that these methods can be effectively used in multiobjective water management problems.

关键词： Water management Pump scheduling Multiobjective optimization Pareto dominance harmony search algorithm

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An improved version of Inverse Distance Weighting metamodel assisted harmony search algorithm for truss design optimization

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2013年第2期10卷 283-300页

作者： Gholipour, Y. Shahbazi, M. M. Behnia, A. Univ Tehran Coll Engn Engn Optimizat Res Grp Tehran Iran Univ Malaya Civil Engn Dept Struct & Mat Kuala Lumpur Malaysia

This paper focuses on a metamodel-based design optimization algorithm. The intention is to improve its computational cost and convergence rate. Metamodel-based optimization method introduced here, provides the necessary means to reduce the computational cost and convergence rate of the optimization through a surrogate. This algorithm is a combination of a high quality approximation technique called Inverse Distance Weighting and a meta-heuristic algorithm called harmony search. The outcome is then polished by a semi-tabu search algorithm. This algorithm adopts a filtering system and determines solution vectors where exact simulation should be applied. The performance of the algorithm is evaluated by standard truss design problems and there has been a significant decrease in the computational effort and improvement of convergence rate.

关键词： Surrogate-based optimization Metamodeling harmony search algorithm Inverse Distance Weighting model

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Simulation and optimization of Lar Dam reservoir storage under climate change conditions

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JOURNAL OF WATER AND CLIMATE CHANGE 2023年第12期14卷 4336-4355页

作者： Sadeghijou, Hediyeh Sarraf, Amirpouya Ahmadi, Hassan Islamic Azad Univ Civil Engn Dept Roudehen Branch Roudehen Iran

In this research, the impact of climate change in the next 15 years (2036-2022) in the (LarDam) area has been investigated. The results showed that in the case of climate change under scenarios RCP2.6, RCP4.5, RCP8.5, the maximum temperature and the minimum temperature have increased by5, 5.23, 6.2% and 3.5, 5.6, 5.17%, respectively, and the amount of precipitation increased by 8.55, 9.5, 13%, respectively. Also, the highest rainfall will be in 2031 and the lowest will be in 2036. Then, based on the intermediate state of the scenarios, i.e. RCP4.5 scenario, the amount of runoff was obtained and the reliability index was calculated according to the upstream runoff of Lar Dam and downstream needs for drinking, agriculture, and environment. The simulation was also performed in the WEAP model. The obtained reliability showed that the highest reliability was 86.60% of the agriculture needs in the WEAP model, and by using the optimization of a honey badger and harmonic search algorithms, it was found that the reliability is approximately 5.06 and 1.73% higher than the reliability of the simulation, respectively. Moreover, in comparison with the optimization algorithms, due to the smaller value of the objective function of the honey badger algorithm and the greater reliability of this algorithm in optimizing downstream needs, it can be concluded that the performance of this algorithm was better than the harmonic search algorithm. The honey badger algorithm has a faster calculation speed than the harmony search algorithm with less execution time.

关键词： climate change climate scenarios harmony search algorithm Honey Badger algorithm WEAP model

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Fake news detection using an ensemble learning model based on Self-Adaptive harmony search algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 159卷 113584-113584页

作者： Huang, Yin-Fu Chen, Po-Hong Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu Yunlin Taiwan

In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve higher accuracy in fake news detection, the optimized weights of the ensemble learning model are determined using the Self-Adaptive harmony search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy of 99.4%. Furthermore, we also investigate the cross-domain intractability issue and achieve the highest accuracy of 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intractability issue. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep learning Fake news Natural language processing harmony search algorithm

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Ground Motion Records Selection Based on Scalar Frequency-Content Parameters

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第2期47卷 1041-1057页

作者： Yaghmaei-Sabegh, Saman Jodat-Saeidabad, Amir Univ Tabriz Dept Civil Engn Tabriz Iran

Proper selection of ground motion records plays an important role in time history analysis of structural systems. Consistency of average response spectrum of a record set with the target spectrum is the only criterion, that normally is considered in code-based frameworks. However, a spectrum curve cannot represent all the characteristics of a seismic event. To improve the code-based ground motion selection method, the compatibility of three scalar frequency content intensity measures (Np, T-0, and T-avg) is integrated in the proposed selection method of this paper. The harmony search (HS) optimization algorithm is utilized to find the best ground motion set from a database and their corresponding scale factors. In order to investigate the accuracy and efficiency of the proposed method on seismic demand estimation of structures, four steel moment resisting frames are used in the analysis (4, 6, 9 and 12-story). It is concluded that combinations of T-0 and T-avg with the code-based method are effective for low-rise and mid-rise buildings, respectively, since these methods give reliable responses with less error dissipation.

关键词： Ground motion selection Frequency content Scalar intensity measure Optimization harmony search algorithm

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A novel hybrid algorithm combining hunting search with harmony search algorithm for training neural networks

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JOURNAL OF THE OPERATIONAL REsearch SOCIETY 2013年第5期64卷 748-761页

作者： Kulluk, S. Erciyes Univ TR-38039 Kayseri Turkey

Neural networks (NNs) are one of the most widely used techniques for pattern classification. Owing to the most common back-propagation training algorithm of NN being extremely computationally intensive and it having some drawbacks, such as converging into local minima, many meta-heuristic algorithms have been applied to training of NNs. This paper presents a novel hybrid algorithm which is the integration of harmony search (HS) and Hunting search (HuS) algorithms, called h_HS-HuS, in order to train Feed-Forward Neural Networks (FFNNs) for pattern classification. HS and HuS algorithms are recently proposed meta-heuristic algorithms inspired from the improvisation process of musicians and hunting of animals, respectively. harmony search builds up the main structure of the hybrid algorithm, and HuS forms the pitch adjustment phase of the HS algorithm. The performance proposed algorithm is compared to conventional and meta-heuristic algorithms. Empirical tests are carried out by training NNs on nine widely used classification benchmark problems. The experimental results show that the proposed hybrid harmony-hunting algorithm is highly capable of training NNs. Journal of the Operational Research Society (2013) 64, 748-761. doi:10.1057/jors.2012.79 Published online 22 August 2012

关键词： neural networks supervised training heuristics harmony search algorithm hunting search algorithm

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