:::

詳目顯示

回上一頁
題名:運用基因演算法進行重點行銷之研究-以台灣某電子商務網站為例
作者:李崇智
作者(外文):LEE,CHUNG-CHIH
校院名稱:長榮大學
系所名稱:經營管理研究所
指導教授:劉春初
曾信超
學位類別:博士
出版日期:2022
主題關鍵詞:基因演算法倒傳遞類神經網路電子商務農業管理Genetic AlgorithmsBPNNE-commerceAgricultural Management
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:1
在台灣電子商務是重大的商業趨勢,尤其在新冠肺炎發生後,民眾生活習慣的改變,其更加速電子商務的盛行,本研究目的是以台南某農會的電子商務網站為研究標的以建構「電子商務重點行銷客戶評估模型」。
在模型中主要是使用基因演算法調整倒傳遞類神經網路的參數以提高模型預測民眾之偏好的能力,此有利於企業進行「重點行銷」與傳送符合消費者需求的廣告資訊(避免消費者將企業的農產品廣告當垃圾郵件),此模型的可對消費者精準行銷。
本論文的研究對象是台灣南部某農會所經營的電子商務網站的消費者,本論文共約收集2451筆消費者偏好問卷,其所收集的資料包含性別、年紀、教育程度、婚姻狀況、年收入、年支出、產品偏好等資訊,部分資訊需進行數字化編碼以利運作,本研究資料使用五等分交互驗證, 490筆數據紀錄當作測試資料,1961筆數據紀錄當訓練資料。
實驗結果顯示,本研究所設計之模型的預測能力高於ACO-BPNN、CSO-BPNN、PSO-BPNN、SA-BPNN等其他研究方法的預測能力,本研究之模型在不同參數下亦具有高度的穩定性;所以,本研究之模型可有效讓台灣南部某農會進行重點行銷。
關鍵字:基因演算法、倒傳遞類神經網路、電子商務、農業管理
E-commerce in Taiwan is a major business trend, especially after the occurrence of COVID-19. Changing in people’s living habits have accelerated the prevalence of e-commerce. The goal of this research is to build "Key Marketing Customer Evaluation Model". In the model, the genetic algorithm is mainly used to adjust the parameters of the back-pass neural network to improve the ability of the model to predict the preferences of the people. It is beneficial for companies to carry out "key marketing" and transmit advertising information that meets consumer needs (to avoid consumers treating the company's agricultural product advertisements as spam), this model can be used for precise marketing to consumers.
The research object of this paper is the consumers of an e-commerce website operated by a farmer's association in southern Taiwan. A total of 2,451 consumer preference questionnaires were collected. The collected data included gender, age, education level, marital status, annual income, annual Expenses, product preferences and other information, some of the information needs to be digitally encoded to facilitate operation. In this research data, 490 data records are used as test data, and 1961 data records are used as training data.
The experimental results show that the predictive ability of this research model is higher than that of ACO-BPNN, CSO-BPNN, PSO-BPNN, SA-BPNN and other research methods, and the model of this research also has a high degree of stability.
Keywords: Genetic Algorithms, BPNN, E-commerce, Agricultural Management.
中文文獻

101傳媒網站(民107)。主要國家零售業電子商務發展概況,https://www.101newsmedia.com/m/news/49109
王斌、潘文鋒(民94)。基於內容的垃圾郵件過濾技術綜述。中文資訊學報,19(5) ,3-12。
王泰裕、江蕙民(民97)。模糊支援向量機應用於多類別的文件分類。成大研發快訊,3(6)。
方文昌、汪志堅、 蘇永盛 (民92)。電子商務的研究主題分類: 以台灣地區的研究論文所進行之內容分析。Electronic Commerce Studies,1(1),3-24。
可華明、陳朝鎮、張新合、王金亮(民99)。基於 Matlab 的 GA-BPNN 遙感圖像分類。西南科技大學學報,25(3),55-59。
台南市政府農業局(民111)。https://agron.tainan.gov.tw/cp.aspx?n=1232.
李奕威 (民110)。考慮不同風向下以基因演算法與粒子群演算法進行離岸風場風機排列最佳化之研究。未出版之博士論文,中興大學生物產業機電工程學系,台中市。
李佳福、汪晨騏、蔡欣穆、李朝陽(民104)。使用機器學習辨識駕駛情境。International Journal of Science and Engineering,5(1), 181-188。
何雍慶、林美珠(民96)。電子商務顧客網路購物知覺價值因果關係之研究-以國內大專生為例,顧客滿意學刊,3(1),61-96。
邵泰璋、史天元(民99)。類神經網路於多光譜影像分類之應用。航測及遙測學刊,5(1),1-15。
吳昌霖(民93)。資料探勘於旅遊網站顧客關係管理之個案研究。未出版之碩士論文,臺灣師範大學運動與休閒管理研究所,台北市。
吳冠霆(民107)。運用機器學習技術於分類販賣仿冒品的網站。未出版之碩士論文,交通大學網路工程研究所,新竹市。
吳春慶(民95)。台灣地區水果銷售預測研究。未出版之碩士論文,虎尾科技大學工業工程與管理研究所,雲林縣。
呂奇傑、李天行、高人龍、黃敏菁 (民98)。支援向量機與支援向量迴歸於財務時間序列預測之應用。Journal of Data Analysis,4(2),35-56。
呂明山、李岱潁(民110)。應用 NSGA-II 和 TOPSIS 在生產多種零件下可重構製造系統最佳化的規劃。商管科技季刊,22(2),185-220。
花琨詠(民104)。基於基因演算法之電子商務商品自動分類。未出版之碩士論文,中正大學資訊工程學系,嘉義縣。
林逸塵(民91)。類神經網路應用於空氣品質預測之研究。未出版之碩士論文,國立中山大學環境工程研究所,高雄市。
林義隆、蔡淳娟(民108)。機器學習與海量資料在醫學教育之應用。台灣擬真醫學教育期刊,6(1),37-47。
林泓宏、蘇家鈜、張勝麟 (民110)。以支援向量機處理題型符號與文字特徵應用於微積分試題難度分類。測驗學刊,68(2),75-99。
林豐澤(民94)。演化式計算下篇: 基因演算法以及三種應用實例。 智慧科技與應用統計學報,3(1),29-56。
張豔秋、王蔚(民98)。利用遺傳演算法優化的支援向量機垃圾郵件分類。電腦應用,29(10),2755-2757。
張譽騰、張樹之、蔡明軒(民101)。以代理人及網路供餵技術支援非同步式網路互動及建構電子商務應用。電子商務學報,14(3),441-470。
曾光華(民110)。行銷管理概論6/e,前程文化出版社。
黃曉波(民109)。建立預測模型,應用決策樹找客群,達到精準行銷,今周刊,https://www.businesstoday.com.tw/article/category/80407/post/202005260006/,
孫思源、黃照貴、方建生、楊清雲、邱碧珍(民105)。電子商務。新月出版社。
陳伯駒(民107) 。使用機器學習技法預測消費者的購買行為: 以網站的點擊資料為例。未出版之碩士論文,臺灣大學經濟學研究所,台北市。
陳華偉、郭佩鈺、吳柏青、王世昌、廖郁婷(民108)。以田口實驗設計法探討聚乙二醇和糖醇加固法對泡水文物之最佳化處理條件,林產工業,38(4),261-273。
陳欽輝(民110)。應用機器學習演算法改善模型優化方法的研究-以 UCI 慢性腎臟疾病資料集為例。未出版之碩士論文,中興大學資訊管理學系所學位論文,台中市。
陳福山、 鍾書華 (民104)。運用門檻標準與 AdaBoost 分類機篩選工具建立新進人員評選模型。致理學報,(35),57-85。
黃智暉(民108)。以機器學習探討中國網貸平台存續之預測模式。未出版之博士論文,國立屏東大學商業自動化與管理學系研究所,屏東縣。
黃名義(民97)。電子商務對辦公室租金, 區位與空間需求影響之研究—以台北都會區為例。JOURNAL OF HOUSING,17(1)。
黃美華(民91)。台灣果樹產業結構調整現況。農政與農情,125。
黃正魁、邱亞琪、林育志 (民106)。台灣電子商務宣告對市場反應的再研究。Information Management,24(4),409-434。
許哲榮(民96)。應用影像分割法結合倒傳遞類神經網路於印刷電路板之光學檢驗。未出版之碩士論文,大同大學機械工程學系所學位論文,台北市。
馬泰成(民108)。掠奪性定價與電子商務。公平交易季刊,27,2 145-178。
梁鈺翎(民106)。M5【電子商務做?不做?】,https://si.secda.info/scu05154106/?p=923。
鄭泳淩、馬龍華、 錢積新 (民91)。SGA (Simplex-Genetic Algorithm): 一類求解 Minimax 問題的通用演算法,系統工程理論與實踐, 22(12), 33-38。
鄭守成(民104)。一個結合機器學習與統計方法的大數據分析技術用於半導體製程良率的改善。未出版之博士論文,國立高雄應用科技大學電機工程系博士班博士論文,高雄市。
楊正華、曾愛華、丁雷(民103)。基於語義的 SVM 電子商務推薦模型研究,現代電腦 (專業版),(7),3-6。
雷祖強、周天穎、萬絢、楊龍士、許晉嘉(民96)。空間特徵分類器支援向量機之研究。Journal of Photogrammetry and Remote Sensing,12(2),145-163。
雷祖強、吳仕傑、李哲源、曾國欣(民110)。以生物序列演算法進行 UAV 影像幾何校正控制點匹配新型模式之探索性研究。Journal of Photogrammetry and Remote Sensing,26(3),143-162。
溫志宏、林祝興、陳澤雄、許介彥、黃國軒(民89)。電子商務安全之研究。大葉學報,9(1),11-18。
蔡智勇、薛義誠 (民94)。應用倒傳遞類神經網路預測台灣勞動市場人力需求,中華管理學報,6(2),1-14。
蔡崇煌、翁紹仁、周駿安、吳信宏、洪偉展 (民106)。運用 C4. 5 決策樹分析失眠症狀。台灣公共衛生雜誌,36(5),449-460。
謝怡翔(民106)。利用深度學習演算法分析商品描述自動擴增電子商務產品階層。未出版之碩士論文,朝陽科技大學資訊工程系,台中市。
顏七笙、張延飛、汪國華(民96)。基於 SVM 的企業績效綜合評價,中國管理資訊化,10(10),42-44。
郝沛毅、歐仁彬、黃天受、楊盛琮(民107)。網路直播聊天室情緒探勘-使用模糊支持向量機,資訊管理學報,25(2),185-218。
李惠妍、吳宗正、溫敏杰(民105)。迴歸模式與類神經網路在台股指數期貨預測之研究, 經營管理論叢,2(1), 83-99。
王藍亭、李傳房(民98)。以類神經網路探討網頁視覺圖像複雜度偏好之研究,設計學報 (Journal of Design), 8(2)。
禤祈華(民110)。COVID-19 對馬來西亞電子商務生態系統之影響,臺灣經濟研究月刊,44(8),89-97。
Li, W. (民110)。探討機器學習與深度學習之差異,https://www.wpgdadatong.com/tw/blog/detail?BID=B0286。
熊貓辦公網站,民111,https://www.tukuppt.com/muban/baxeerpv.html。



英文文獻
Ahmed, A. N., Othman, F. B., Afan, H. A., Ibrahim, R. K., Fai, C. M., Hossain, M. S. & Elshafie, A. (2019). Machine learning methods for better water quality prediction. Journal of Hydrology, 578, 124084.
Ahmed, A. M., Rashid, T. A., & Saeed, S. A. M. (2021). Dynamic Cat Swarm Optimization algorithm for backboard wiring problem. Neural Computing and Applications, 33(20), 13981-13997.
An, J., He, G., Qin, F., Li, R., & Huang, Z. (2018). A new framework of global sensitivity analysis for the chemical kinetic model using PSO-BPNN. Computers & Chemical Engineering, 112, 154-164.
Archetti, C., Speranza, M. G., & Hertz, A. (2006). A tabu search algorithm for the split delivery vehicle routing problem. Transportation science, 40(1), 64-73.
Baldwin, R., & Tomiura, E. (2020). Thinking ahead about the trade impact of COVID-19. Economics in the Time of COVID-19, 59, 59-71.
Ceylan, H., & Bell, M. G. (2004). Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transportation Research Part B: Methodological, 38(4), 329-342.
Chang, C., Sun, X., Chen, D., & Wang, C. (2017). Application of back propagation neural network with simulated annealing algorithm in network intrusion detection systems. International Conference On Signal And Information Processing, Networking And Computers, 172-180.
Chen, X. Y., Chen, Z., & Zhao, Y. (2018). Numerical research on virtual reality of vibration characteristics of the motor based on GA-BPNN model. Neural Computing and Applications, 29(5), 1343-1355.
Chu, S. C., Tsai, P. W., & Pan, J. S. (2006). Cat swarm optimization. Pacific Rim international conference on artificial intelligence, 854-858.
Cui, L., Tao, Y., Deng, J., Liu, X., Xu, D., & Tang, G. (2021). BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification. Expert Systems with Applications, 175, 114842.
Dhini, A., Surjandari, I., Riefqi, M., & Puspasari, M. A. (2015). Forecasting analysis of consumer goods demand using neural networks and ARIMA. International Journal of Technology, 6, 872-880.
Donna, L., & Novak, H. T. P. (1997). A new marketing paradigm for electronic commerce. The information society, 13(1), 43-54.
Duan, Y., Ye, Y., & Liu, Z. (2019). Risk assessment for enterprise merger and acquisition via multiple classifier fusion. Discrete & Continuous Dynamical Systems-S, 12(4&5), 747.
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. IEEE international conference on neural networks, 4, 1942-1948.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
Fu, Z., & Mo, J. (2011). Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA–BPNN. The International Journal of Advanced Manufacturing Technology, 53(5-8), 473-483.
Ghose, D. K., Panda, S. S., & Swain, P. C. (2010). Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. Journal of Hydrology, 394(3-4), 296-304.
Gruszczynski, L. (2020). The COVID-19 pandemic and international trade: Temporary turbulence or paradigm shift?. European Journal of Risk Regulation, 11(2), 337-342.
Guttman, R. H., Moukas, A. G., & Maes, P. (1998). Agent-mediated electronic commerce: A survey. The Knowledge Engineering Review, 13(2), 147-159.
Haupt, S. E., Cowie, J., Linden, S., McCandless, T., Kosovic, B., & Alessandrini, S. (2018). Machine learning for applied weather prediction. In 2018 IEEE 14th international conference on e-science (e-Science), 276-277.
Hou, E. S., Ansari, N., & Ren, H. (1994). A genetic algorithm for multiprocessor scheduling. IEEE Transactions on Parallel and Distributed systems, 5(2), 113-120.
Hu, Y., Li, J., Hong, M., Ren, J., Lin, R., Liu, Y., Man, Y. (2019). Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process. Energy, 170, 1215-1227.
Huang, J., Cai, Y., & Xu, X. (2007). A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern recognition letters, 28(13), 1825-1844.
Huerta, E. B., Duval, B., & Hao, J. K. (2006, April). A hybrid GA/SVM approach for gene selection and classification of microarray data. In Workshops on applications of evolutionary computation (pp. 34-44). Springer, Berlin, Heidelberg.
Hu, Y., Li, J., Hong, M., Ren, J., Lin, R., Liu, Y., ... & Man, Y. (2019). Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process. Energy, 170, 1215-1227.
Hu, Y. C., Jiang, P., & Lee, P. C. (2019). Forecasting tourism demand by incorporating neural networks into Grey–Markov models. Journal of the Operational Research Society, 70(1), 12-20.
Hu, H., Zhang, J., & Li, T. (2021). A novel hybrid decompose-ensemble strategy with a VMD-BPNN approach for daily streamflow estimating. Water Resources Management, 35(15), 5119-5138.
Jain, N., Jhunthra, S., Garg, H., Gupta, V., Mohan, S., Ahmadian, A. & Ferrara, M. (2021). Prediction modelling of COVID using machine learning methods from B-cell dataset. Results in physics, 21, 103813.
Jethmalani, C. R., Simon, S. P., Sundareswaran, K., Nayak, P. S. R., & Padhy, N. P. (2016). Auxiliary hybrid PSO-BPNN-based transmission system loss estimation in generation scheduling. IEEE Transactions on Industrial Informatics, 13(4), 1692-1703.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: a manager's guide. Addison-Wesley Professional.
Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018). Bankruptcy prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2), 791-803.
Kovacova, M., Kliestik, T., Valaskova, K., Durana, P., & Juhaszova, Z. (2019). Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana, 10(4), 743-772.
Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European journal of operational research, 180(1), 1-28.
Lee, C. Y., & Ou, H. Y. (2021). Induction motor multiclass fault diagnosis based on mean impact value and PSO-BPNN. Symmetry, 13(1), 104.
Lee, W. I., Chen, C. W., Chen, K. H., Chen, T. H., & Liu, C. C. (2012). A comparative study on the forecast of fresh food sales using logistic regression, moving average and BPNN methods. Journal of Marine Science and Technology, 20(2), 4.
Li, X., Tong, Z., Guo, E., & Luo, X. (2017). Quantifying spatiotemporal dynamics of solar radiation over the northeast China based on ACO-BPNN Model and Intensity Analysis. Advances in Meteorology.
Lin, P., Ke, B., & You, J. (2010). Evolutionary multiple combination Logistic regression model—Applied to credit rating. Journal of Information Management, 17, 2.
Li, Y., & Chen, W. (2020). A comparative performance assessment of ensemble learning for credit scoring. Mathematics, 8(10), 1756.
Liu, Y., & Huang, L. (2020). Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks, 16(1), 1550147720903631.
McKibbin, W., & Fernando, R. (2020). 3 The economic impact of COVID-19. Economics in the Time of COVID-19, 45.
Moghaddam, M. A., & Kolahan, F. (2015). An optimised back propagation neural network approach and simulated annealing algorithm towards optimisation of EDM process parameters. International journal of Manufacturing research, 10(3), 215-236.
Ngai, E. W. T., & Wat, F. K. T. Wat, (2002). A literature review and classification of electronic commerce research, Information and Management, 39, 415-429.
Ning, L., & Yin-sheng, Y. (2013). Enterprise Performance Evaluation by Using Parallel DEA Model and SVM Classifier. Industrial Engineering Journal, 16(4), 56.
Ouenniche, J., Bouslah, K., Perez-Gladish, B., & Xu, B. (2021). A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction. Annals of Operations Research, 296(1), 495-512.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
Podhorská, I., Vrbka, J., Lazaroiu, G., & Kovacova, M. (2020). Innovations in financial management: recursive prediction model based on decision trees.
Qin, S., Liu, F., Wang, J., & Song, Y. (2015). Interval forecasts of a novelty hybrid model for wind speeds. Energy Reports, 1, 8-16.
Rai, H. M., & Chatterjee, K. (2018). A novel adaptive feature extraction for detection of cardiac arrhythmias using hybrid technique MRDWT & MPNN classifier from ECG big data. Big data research, 12, 13-22.
Rowland, Z., Kasych, A., & Suler, P. (2021). Prediction of financial distress: case of mining enterprises in Czech Republic. Ekonomicko-manazerske spektrum, 15(1), 1-14.
Sandbrook, C., Gómez-Baggethun, E., & Adams, W. M. (2020). Biodiversity conservation in a post-COVID-19 economy. Oryx, 1-7.
Shahraki, A., Abbasi, M., & Haugen, Ø. (2020). Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Engineering Applications of Artificial Intelligence, 94, 103770.
Stefanovič, P., Štrimaitis, R., & Kurasova, O. (2020). Prediction of flight time deviation for lithuanian airports using supervised machine learning model. Computational intelligence and neuroscience.
Sun, W., Ye, M., & Xu, Y. (2016). Study of carbon dioxide emissions prediction in Hebei province, China using a BPNN based on GA. Journal of Renewable and Sustainable Energy, 8(4), 043101.
Sun, X., Shi, Z., & Zhu, J. (2020). Multiobjective design optimization of an IPMSM for EVs based on fuzzy method and sequential taguchi method. IEEE Transactions on Industrial Electronics, 68(11), 10592-10600.
Vladimir, Z. (1996). Electronic commerce: structures and issues. International journal of electronic commerce, 1(1), 3-23.
Wang, G., Hao, J., Ma, J., & Huang, L. (2010). A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert systems with applications, 37(9), 6225-6232.
Wang, G., & Ma, J. (2012). A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine. Expert Systems with Applications, 39(5), 5325-5331.
Wang, K., Li, X., Gao, L., Li, P., & Gupta, S. M. (2021). A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem. Applied Soft Computing, 107, 107404.
Wen, Y., & Chen, Y. (2014). Modified Parallel Cat Swarm Optimization in SVM Modeling for Short-term Cooling Load Forecasting. JSW, 9(8), 2093-2104.
Wu, J. Y. (2010). Advanced simulated annealing-based BPNN for forecasting chaotic time series. 2010 IEEE International Conference on Electronics and Information Engineering, 1, 1-38.
Xiao, Z., Wang, M., & Xie, L. (2006). Personal credit evaluation of college student loan based on support vector machine. Journal of Tsinghua University, 46.
Xiao, Y., Cao, Y., Zhong, K. Q., Yin, L., & Deng, J. (2022). Optimized neural network to predict the experimental minimum period of coal spontaneous combustion. Environmental Science and Pollution Research, 1-13.
Yin, H. W., & Li, F. Z. (2015). Survey on spectral machine learning. Journal of frontiers of computer science and technology, 9(12), 1409-1419.
Yu, B., Zhao, H., Tian, J., Liu, C., Song, Z., Liu, Y., & Li, M. (2021). Modeling study of sandstone permeability under true triaxial stress based on backpropagation neural network, genetic programming, and multiple regression analysis. Journal of Natural Gas Science and Engineering, 86, 103742.
Yu, H., Tao, J., Qin, C., Xiao, D., Sun, H., & Liu, C. (2021). Rock mass type prediction for tunnel boring machine using a novel semi-supervised method. Measurement, 179, 109545.
Yu, L., Wang, S., & Lai, K. K. (2007). Hybridizing BPNN and Exponential Smoothing for foreign exchange rate prediction. Foreign-Exchange-Rate Forecasting With Artificial Neural Networks, 121-131.
Zeng, N., Qiu, H., Wang, Z., Liu, W., Zhang, H., & Li, Y. (2018). A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing, 320, 195-202.
Zhang, L., Sun, Z., Zhang, C., Dong, F., & Wei, P. (2018). Numerical investigation of the dynamic responses of long-span bridges with consideration of the random traffic flow based on the intelligent ACO-BPNN model. IEEE Access, 6, 28520-28529.
Zhu, X. (2021). A Face Recognition System Using ACO-BPNN Model for Optimizing the Teaching Management System. Computational Intelligence and Neuroscience.
Zhu, M., Liu, S., Xia, Z., Wang, G., Hu, Y., & Liu, Z. (2020). Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN. Agriculture, 10(8), 318.
Zelenkov, Y., Fedorova, E., & Chekrizov, D. (2017). Two-step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 88, 393-401.
Zhou, L., & Lai, K. K. (2017). AdaBoost models for corporate bankruptcy prediction with missing data. Computational Economics, 50(1), 69-94.
Zhou, X., Ma, H., Gu, J., Chen, H., & Deng, W. (2022). Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism. Engineering Applications of Artificial Intelligence, 114, 105139.
Zin, A. A. M., Saini, M., Mustafa, M. W., & Sultan, A. R. (2015). New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation. Neurocomputing, 168, 983-993.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top