ارائه مدل طبقه بندی هوشمند مبتنی بر شبکه عصب مصنوعی پرسپترون (MLP) و تحلیل سلسله مراتبی (AHP) در خدمات بازاریابی دیجیتال برای اولویت بندی ریسک نقدینگی و سرمایه گذاری
محورهای موضوعی :
دانش سرمایه‌گذاری
علیرضا عاشوری رودپشتی
1
,
هرمز مهرانی
2
,
کریم حمدی
3
1 - گروه مدیریت بازرگانی ،د انشکده مدیریت و اقتصاد،, واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ،تهران،ایران
2 - استادیار گروه مدیریت، موسسه آموزش عالی غزالی، قزوین، ایران (نویسنده مسئول)
3 - دانشیار مدیریت بازاریابی، گروه مدیریت بازرگانی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
تاریخ دریافت : 1399/09/26
تاریخ پذیرش : 1399/10/21
تاریخ انتشار : 1400/01/01
کلید واژه:
"خدمات بازاریابی دیجیتال ",
" هوش مصنوعی",
" شبکه عصبی پرسپترون (MLP)",
" AHP",
" ریسک نقدینگی",
چکیده مقاله :
مطالعه حاضر با استفاده از تکنیک‌های یادگیری ماشین و نظرکاوی کوشیده است تا بتواند مدل راهبردی خودکار به منظور طبقه‌بندی و کاوش نظرات ارائه شده در مورد خدماتی خاص که در این مورد در حوزه ی سرمایه گذاری بررسی شده است را با استفاده از بررسی نتایج به دست آمده در خدمات بازاریابی دیجیتال ارائه نماید. مدل مبتنی بر شبکه عصبی با شناسایی نظرات مرتبط، خصوصیات مختلف را در سطوح گوناگون ارزشیابی سنجیده و نظرات را بسته به کیفیت ارائه بصورت خودکار طبقه-بندی می‌نماید. بحران‌های مالی موجود در نظام‌های بانکی معمولاً ناشی از عدم توانایی در مدیریت ریسک‌های مالی و نقدینگی است که عاملی بر عدم شفافیت و توانایی در مدیریت سرمایه می‌باشد. بطوری‌که وجود چنین عدم‌قطعیت‌هایی سبب کاهش علاقه-مندی سرمایه‌گذاران در مشارکت‌های صنعتی و اجرایی گردیده است. این مقاله با هدف شناسایی عوامل موثر بر ریسک نقدینگی و همچنین ارائه مدلی هوشمند جهت پیش‌بینی و طبقه‌بندی عوامل ایجادکننده ریسک نقدینگی، شناسایی و اولویت‌بندی فاکتورهای درگیر آن پایه‌ریزی گردیده است. بدین منظور از روش سنجش هوشمند با بکارگیری شبکه عصبی پرسپترون (MLP) بهره گرفته شده که به عنوان یک رویکرد کاربردی هوش مصنوعی بشمار می‌آید. بدین منظور بررسی های لازم بر روی اطلاعات مالی و نقدینگی در شعب بانک ملت در شهر تهران (مشتمل بر ۳۶ شعبه) مورد توجه بوده و برای جامعه نمونه از مجموعه تصادفی خوشه‌ای از۳۷۴ نفر از مشتریان و سرمایه‌گذاران منتخب بهره گرفته شده است.
چکیده انگلیسی:
The present study, using machine learning and polling techniques, attempts to examine the automated strategic model in order to classify and explore the ideas presented about specific services that have been studied in this area in the field of investment. Provide results in digital marketing services. The neural network-based model, by identifying related opinions, measures different characteristics at different levels of evaluation and automatically categorizes opinions depending on the quality of the presentation. Financial crises in the banking system are usually due to the inability to manage financial risks and liquidity, which is a factor in the lack of transparency and ability to manage capital. Thus, the existence of such uncertainties has reduced the interest of investors in industrial and executive partnerships. This article has been established with the aim of identifying the factors affecting liquidity risk and also providing an intelligent model for predicting and classifying liquidity risk factors, identifying and prioritizing the factors involved. For this purpose, the method of intelligent measurement using perceptron neural network (MLP) has been used, which is considered as a practical approach to artificial intelligence. For this purpose, the necessary studies on financial information and liquidity in Bank Mellat branches in Tehran (consisting of 36 branches) have been considered and for the sample population, a random cluster set of 374 selected customers and investors has been used.
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