Fuzzy Cognitive Mapping Analysis to Recommend Machine Learning-Based Effort Estimation Technique for Web Applications

2020 ◽  
Vol 22 (4) ◽  
pp. 1212-1223 ◽  
Author(s):  
Prateek Pandey ◽  
Ratnesh Litoriya
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
Cognitive Mapping ◽  
Estimation Technique ◽  
Effort Estimation ◽  
Fuzzy Cognitive Mapping ◽  
Mapping Analysis

Applicability of Machine Learning Methods on Mobile App Effort Estimation: Validation and Performance Evaluation

2020 ◽  
Vol 30 (01) ◽  
pp. 23-41 ◽  
Author(s):  
Mamta Pandey ◽  
Ratnesh Litoriya ◽  
Prateek Pandey
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Web Applications ◽  
Fuzzy Inference ◽  
Mobile Apps ◽  
Mobile App ◽  
Effort Estimation ◽  
Operational Environment ◽  
Software Cost ◽  
Absolute Residual

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

Participatory fuzzy cognitive mapping analysis to evaluate the future of water in the Seyhan Basin

2013 ◽  
Vol 4 (2) ◽  
pp. 131-145 ◽  
Author(s):  
Erol H. Cakmak ◽  
Hasan Dudu ◽  
Ozan Eruygur ◽  
Metin Ger ◽  
Sema Onurlu ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Demand ◽  
Cognitive Mapping ◽  
Mapping Technique ◽  
Sustainable Water Management ◽  
Fuzzy Cognitive Mapping ◽  
Mapping Analysis ◽  
The Future ◽  
Near Future

Stress on the water resources of Turkey is expected to increase in the near future. This paper presents the results of a case study in one of the most important water basins in Turkey, the Seyhan Basin, where the future of the basin is estimated using a fuzzy cognitive mapping technique applied at a participatory meeting with the stakeholders. Participants envisioned that water supply, water demand and water use would decline in the future in response to the increasing impact of climate change. Improvements in sustainable water management, irrigation efficiency and the use of water-saving technologies will diminish the severity of scarcity that is expected to occur due to climate change.

Unveiling the dynamics of social credit determinants using fuzzy cognitive mapping and system dynamics

2021 ◽  
Author(s):  
Leandro F. Pereira ◽  
Ricardo J. C. Correia ◽  
Fernando A. F. Ferreira ◽  
Pedro F. Falcão ◽  
Renato L. Costa
Keyword(s):  
System Dynamics ◽  
Cognitive Mapping ◽  
Fuzzy Cognitive Mapping ◽  
Social Credit

Association mapping analysis of oil palm interspecific hybrid populations and predicting phenotypic values via machine learning algorithms

2021 ◽  
Author(s):  
Min‐Gyoung Shin ◽  
Maizura Ithnin ◽  
Wendy T. Vu ◽  
Katialisa Kamaruddin ◽  
Ting Ngoot Chin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Association Mapping ◽  
Oil Palm ◽  
Interspecific Hybrid ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mapping Analysis

scholarly journals Algorithm for detecting attacks on Web applications based on machine learning methods and attributes queries

2022 ◽  
Vol 2 (14) ◽  
pp. 26-34
Author(s):  
Nguyen Manh Thang ◽  
Tran Thi Luong
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Cloud Storage ◽  
Web Applications ◽  
Detection Algorithm ◽  
Learning Methods ◽  
Google Docs ◽  
Effective Work ◽  
Web Resources ◽  
Machine Learning Methods

Abstract—Almost developed applications tend to become as accessible as possible to the user on the Internet. Different applications often store their data in cyberspace for more effective work and entertainment, such as Google Docs, emails, cloud storage, maps, weather, news,... Attacks on Web resources most often occur at the application level, in the form of HTTP/HTTPS-requests to the site, where traditional firewalls have limited capabilities for analysis and detection attacks. To protect Web resources from attacks at the application level, there are special tools - Web Application Firewall (WAF). This article presents an anomaly detection algorithm, and how it works in the open-source web application firewall ModSecurity, which uses machine learning methods with 8 suggested features to detect attacks on web applications. Tóm tắt—Hầu hết các ứng dụng được phát triển có xu hướng trở nên dễ tiếp cận nhất có thể đối với người dùng qua Internet. Các ứng dụng khác nhau thường lưu trữ dữ liệu trên không gian mạng để làm việc và giải trí hiệu quả hơn, chẳng hạn như Google Docs, email, lưu trữ đám mây, bản đồ, thời tiết, tin tức,... Các cuộc tấn công vào tài nguyên Web thường xảy ra nhất ở tầng ứng dụng, dưới dạng các yêu cầu HTTP/HTTPS đến trang web, nơi tường lửa truyền thống có khả năng hạn chế trong việc phân tích và phát hiện các cuộc tấn công. Để bảo vệ tài nguyên Web khỏi các cuộc tấn công ở tầng ứng dụng, xuất hiện các công cụ đặc biệt - Tường lửa Ứng dụng Web (WAF). Bài viết này trình bày thuật toán phát hiện bất thường và cách thức hoạt động của tường lửa ứng dụng web mã nguồn mở ModSecurity khi sử dụng phương pháp học máy với 8 đặc trưng được đề xuất để phát hiện các cuộc tấn công vào các ứng dụng web.

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