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A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system

Ayo, Femi Emmanuel; Ogundele, Lukman Adebayo; Olakunle, … - In: Decision analytics journal 10 (2024), pp. 1-21

spam classification using a fuzzy inference system. Using a rule-based hybrid feature selection technique, we choose the … model for spam classification, and fuzzy logic is employed to categorize each spam class into its severity levels to …

Persistent link: https://www.econbiz.de/10014541613

An integrated machine learning and MARCOS method for supplier evaluation and selection

Abdulla, Ahmad; Baryannis, George; Badi, Ibrahim - In: Decision analytics journal 9 (2023), pp. 1-11

-based classification. Results show that the proposed model selects suppliers in line with human evaluators and performs similarly to other …

Persistent link: https://www.econbiz.de/10014532525

Enhancing human activity recognition using features reduction in IoT edge and Azure cloud

Wazwaz, Ayman A.; Amin, Khalid M.; Semari, Noura A.; … - In: Decision analytics journal 8 (2023), pp. 1-8

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These technologies have triggered huge traffic and delay by continuously transmitting telemetry data to the cloud. IoT edge choice made decision-making closer to the environment,...

Persistent link: https://www.econbiz.de/10014516982

A decision support system for classifying supplier selection criteria using machine learning and random forest approach

Ali, Md. Ramjan; Ashiquzzaman Nipu, Shah Md.; Khan, … - In: Decision analytics journal 7 (2023), pp. 1-14

Supplier selection is an important process in supply chain management that sets a foundation for a long-term partnership with suppliers that can greatly contribute to the success or failure of a business. This study aims to identify, validate and propose a comprehensive list of supplier...

Persistent link: https://www.econbiz.de/10014497347

A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients

Ghasemieh, Alireza; Lloyed, Alston; Bahrami, Parsa; … - In: Decision analytics journal 7 (2023), pp. 1-13

classification models. Experimental results show that the stacking model provides high accuracy, Recall, and F1 score compared to the …

Persistent link: https://www.econbiz.de/10014497367

A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews

Moreno-Garcia, Carlos Francisco; Jayne, Chrisina; … - In: Decision analytics journal 6 (2023), pp. 1-9

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior … machine learning and zero-shot classification methods for automated abstract screening for systematic reviews. Extensive …

Persistent link: https://www.econbiz.de/10014505380

Classification of intent in moderating online discussions : an empirical evaluation

Gehweiler, Christoph; Lobachev, Oleg - In: Decision analytics journal 10 (2024), pp. 1-15

. This research explores content classification through methods such as sentiment analysis, keyword extraction and topic …

Persistent link: https://www.econbiz.de/10014543863

A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection

Vishwakarma, Monika; Kesswani, Nishtha - In: Decision analytics journal 7 (2023), pp. 1-8

, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which …

Persistent link: https://www.econbiz.de/10014436765

A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks

Dey, Arun Kumar; Gupta, Govind P; Sahu, Satya Prakash - In: Decision analytics journal 7 (2023), pp. 1-16

feature selection and classification approaches to overcome these issues. First, a metaheuristic-based ensemble feature … ensemble learning-based classification techniques such as AdaBoost and Random Forest (RF) are employed separately to detect and …

Persistent link: https://www.econbiz.de/10014434087

A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Bansal, Malti; Goyal, Apoorva; Choudhary, Apoorva - In: Decision analytics journal 3 (2022), pp. 1-21

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...

Persistent link: https://www.econbiz.de/10013339256