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A New Discretization Approach of Bat and K-Means

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第1期12卷 510-516页

作者： Mohamed, Rozlini Samsudin, Noor Azah Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Pant Raja Johor Malaysia

Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

关键词： Classification discretization feature selection optimization algorithm bat algorithm

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Increasing the essential oil yield from Shorea roxburghii inflorescences using an eco-friendly solvent-free microwave extraction method for fragrance applications

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JOURNAL OF APPLIED RESEARCH ON MEDICINAL AND AROMATIC PLANTS 2021年 24卷

作者： Yingngam, Bancha Navabhatra, Abhiruj Brantner, Adelheid Ubon Ratchathani Univ Fac Pharmaceut Sci Dept Pharmaceut Chem & Technol Ubon Ratchathani 34190 Thailand Rangsit Univ Coll Pharm Dept Pharmacol Pathum Thani 12000 Thailand Karl Franzens Univ Graz Inst Pharmaceut Sci Dept Pharmacognosy Univ Pl 4-1 A-8010 Graz Austria

The short bloom period of Shorea roxburghii *** is a technical challenge in the development of a fast and efficient process for recovering essential oils. This study aimed to optimize the conditions for the isolation of essential oil and the determination of the resulting oil's possible bioactivities for fragrance applications. Solvent-free microwave extraction (SFME) coupled with response surface methodology (RSM) was applied to study essential oil from fresh inflorescences. The microwave power and irradiation time were chosen as crucial independent variables, while essential oil yield was the response variable. The physicochemical, antioxidant, olfactory, cytotoxic, and irritant properties of the resulting oil were evaluated. SFME yielded essential oil with a unique and pleasant scent, similar to that of fresh flowers. Increased microwave power and irradiation time increased the oil yield. The proportions of oxygenated compounds with high antioxidant properties were notably higher after SFME than after hydrodistillation. The optimal conditions for maximizing the oil yield were 800 W and 35 min. Among 51 volatile substances, germacrene B, kaur-16-ene, beta-caryophyllene, and (E)-linalool oxide (furanoid) were the oil's main components. Low toxicity to primary human dermal fibroblast cells was defined at an oil concentration < 50 mu g/mL. Moreover, the application of the extracted volatile oil to hen's egg-chorioallantoic membrane resulted in a low degree of irritation. This research establishes optimal extraction conditions for the isolation of essential oil with potential for fragrance applications.

关键词： Aromatic plants Essential oil Extraction conditions Fragrance ingredients optimization algorithm Response surface methodology

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Method of Cultivating College Students' Independent Learning Ability Based on Integration of Multiple algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-10页

作者： You, Ying Kaifeng Univ Coll Tourism Kaifeng 475004 Henan Peoples R China

INTRODUCTION: The research on the multi-mode fusion of college students' independent learning ability cultivation method is conducive to college students' change of learning mode and learning thinking, improvement of the utilization rate of educational resources, and the development of the academic environment as well as the reform of the educational concept. OBJECTIVES: Aiming at the problems of college students' current independent learning mode, such as the need for more in-depth research and the single study means. METHODS: A method for cultivating college students' autonomous learning ability through the integration of intelligent optimization algorithms and multiple modes has been proposed. Firstly, the practices of analyzing the current college students' autonomous learning mode and multiple learning modes are analyzed;then, using the butterfly optimization algorithm, a weight optimization method for the cultivation of college students' independent learning ability based on the fusion of multiple modes is proposed;finally, the validity and robustness of the proposed method are verified through experimental analysis. RESULTS: The results show that the proposed method has a high cultivation effect. CONCLUSION: Solves the problem of fusion of college students' independent learning ability cultivation modes.

关键词： college students' independent learning ability cultivation multiple modes integration blended learning model butterfly optimization algorithm

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Research on factor analysis and method for evaluating grouting effects using machine learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Li, Wenxin Chen, Juntao Zhu, Jun Ji, Xinbo Fu, Ziqun Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao Peoples R China Shandong Energy Xinwen Min Grp Suncun Coal Mine Tai An 271219 Peoples R China

The evaluation of grouting effects constitutes a critical aspect of grouting engineering. With the maturity of the grouting project, the workload and empirical characteristics of grouting effect evaluation are gradually revealed. In the context of the Qiuji coal mine's directional drilling and grouting to limestone aquifer reformation, this study thoroughly analyzes the influencing factors of grouting effects from geological and engineering perspectives, comparing these with various engineering indices associated with drilling and grouting. This led to the establishment of a "dual-process, multi-parameter, and multi-factor" system, employing correlation analysis to validate the selected indices' reasonableness and scientific merit. Utilizing the chosen indices, eight high-performing machine learning models and three parameter optimization algorithms were employed to develop a model for assessing the effectiveness of directional grouting in limestone aquifers. The model's efficacy was evaluated based on accuracy, recall, precision, and F-score metrics, followed by practical engineering validation. Results indicate that the "dual-process, multi-parameter, multi-factor" system elucidates the relationship between influencing factors and engineering parameters, demonstrating the intricacy of evaluating grouting effects. Analysis revealed that the correlation among the eight selected indicators-including the proportion of boreholes in the target rock strata, drilling length, leakage, water level, pressure of grouting, mass of slurry injected, permeability properties of limestone aquifers before being grouted, permeability properties of limestone aquifers after being grouted-is not substantial, underscoring their viability as independent indicators for grouting effect evaluation. Comparative analysis showed that the Adaboost machine learning model, optimized via a genetic algorithm, demonstrated superior performance and more accurate evaluation results. Engineering validat

关键词： Grouting effect evaluation Correlation analysis optimization algorithm Machine learning

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Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Wang, Haonan Wu, Danhong Luo, Jie Zhang, Junhui Southwest Med Univ Sch Publ Hlth 1Sect 1Xianglin Rd Luzhou 646000 Sichuan Peoples R China Chengdu Univ Informat Technol Dept Appl Math Chengdu 610225 Sichuan Peoples R China Univ Durham Dept Psychol Durham DH1 3LE England

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

关键词： COVID-19 Dynamic model GA-SEIR model Epidemic forecasting Socio-psychological factors optimization algorithm

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Optimized machine learning model for air quality index prediction in major cities in India

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Natarajan, Suresh Kumar Shanmurthy, Prakash Arockiam, Daniel Balusamy, Balamurugan Selvarajan, Shitharth Jain Sch Comp Sci & Engn Bengaluru Karnataka India Presidency Univ Sch Comp Sci & Engn & Informat Sci Bengaluru Karnataka India Amity Univ ASET CSE Gwalior Madhya Pradesh India Shiv Nadar Inst Eminence Delhi India Leeds Beckett Univ Sch Built Environm Engn & Comp Leeds LS1 3HE W Yorkshire England

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

关键词： Air pollution Air quality index Machine learning optimization algorithm Grey-wolf optimization Decision tree regression

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Efficient feature selection for histopathological image classification with improved multi-objective WOA

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Sharma, Ravi Sharma, Kapil Bala, Manju Delhi Technol Univ New Delhi 110042 India Univ Delhi Indraprastha Coll Women Civil Lines New Delhi 110054 India

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

关键词： Image classification Multi-objective grey wolf optimizer optimization algorithm Pre processing

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering optimization algorithm

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A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas

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ECOLOGICAL INFORMATICS 2021年 63卷 101300-101300页

作者： Hung Van Le Duc Anh Hoang Chuyen Trung Tran Phi Quoc Nguyen Van Hai Thi Tran Nhat Duc Hoang Amiri, Mahdis Thao Phuong Thi Ngo Ha Viet Nhu Thong Van Hoang Dieu Tien Bui Hanoi Univ Min & Geol Fac Informat Technol Hanoi Vietnam Hanoi Univ Min & Geol Dept Environm Sci Hanoi Vietnam Duy Tan Univ Inst Res & Dev P809-03 Quang Trung Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn P809-03 Quang Trung Da Nang 550000 Vietnam Gorgan Univ Agr Sci & Nat Resources Dept Watershed & Arid Zone Management Gorgan Golestan Iran Hanoi Univ Min & Geol Dept Geol Geotech Engn Hanoi Vietnam Univ Transport & Commun Informat Technol Fac Hanoi Vietnam Univ South Eastern Norway Dept Business & IT GIS Grp Gullbringvegen 36 N-3800 Bo I Telemark Norway

Wildfire is an environmental hazard that has both local and global effects, causing economic losses and various severe environmental problems. Due to the adverse effects of climate changes and anthropogenic activities, wildfire is anticipated more frequent and extreme;therefore, new and more efficient tools for forest fire prevention and control are essential. This study proposes a new deep neural computing approach for spatial prediction of wildfire in a tropical climate area. For this purpose, deep neural computing (Deep-NC) with a structure of 3 hidden layers was proposed. The Rectified Linear Unit (ReLU) activation function was adopted to infer wildfire dangers from the input factors. To search and optimize the weights of the model, Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), and Adadelta optimizers were employed. Also, this study has established a Geographic Information System (GIS) database for Gia Lai province (Vietnam) to train and verify the newly developed deep computing approach. The twelve ignition factors, namely, slope, aspect, elevation, curvature, land use, NVDI, NDWI, NDMI, temperature, wind speed, relative humidity, and rainfall, have been used to characterize the study area with respect to forest fire susceptibility. According to experimental results, the Adam optimized Deep-NC model delivered the highest predictive accuracy (AUC = 0.894, Kappa = 0.63). Accordingly, this model has been employed to establish a forest fire susceptibility map for Gia Lai province. The proposed Deep-NC model and the newly constructed forest fire susceptibility map can help local authorities in land use planning and hazard mitigation/prevention.

关键词： Forest fire Deep neural computing optimization algorithm Geographical information system Gia Lai Vietnam

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Effective task scheduling based on interactive autodidactic school algorithm for cloud computing

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AUTOMATIKA 2024年第1期65卷 159-166页

作者： Senthilkumar, G. Suvarnamukhi, B. Lekashri, S. Thaha, M. Mohammed Panimalar Engn Coll Dept Comp Sci & Engn Chennai India Neil Gogte Inst Technol Dept CSE Hyderabad India Kings Engn Coll Dept ECE Sriperumbudur India BS Abdur Rahman Crescent Inst Sci & Technol Dept Comp Sci & Engn Vandalur India Panimalar Engn Coll Dept Comp Sci & Engn Chennai 600123 India

The topic of load balanced task scheduling has emerged as a prominent and intricate area of study within the realm of Cloud computing. Swarm intelligence-based meta-heuristic algorithms are commonly considered more suitable for the purposes of Cloud scheduling and load balancing. These algorithms employ a combination of local and global search strategies in order to ascertain the ideal location. To achieve an optimal mapping strategy for task allocation to resources, it is imperative to find a suitable equilibrium between local and global search techniques, since this approach has demonstrated significant efficacy. This research introduces a new approach to task scheduling using the Autodidactic Interactive School optimization algorithm (IASOA). The objective of this method is to decrease the time required for job execution while also enhancing throughput. The assessment of the suggested methodology has been executed, and a comparative analysis has been performed with five established algorithms in relation to makespan and throughput. The tests were subsequently extended to encompass a comparative analysis of the suggested methodology alongside four other established meta-heuristic scheduling methodologies. The study of the simulated experimentation reveals that the proposed approach yielded noteworthy advantages in makespan and throughput, with improvements of up to 10% and 60% respectively.

关键词： Task scheduling Makespan cloud computing optimization algorithm

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