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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

An outliers detection and elimination framework in classification task of data mining

Dash, Ch. Sanjeev Kumar; Behera, Ajit Kumar; Dehuri, … - In: Decision analytics journal 6 (2023), pp. 1-8

the classification task of data mining where prior outliers preprocessing is necessary. …

Persistent link: https://www.econbiz.de/10014505859

A Delphi fuzzy analytic hierarchy process framework for criteria classification and prioritization in food supply chains under uncertainty

Gupta, Srikant; Chatterjee, Prasenjit; Rastogi, Raghavendra - In: Decision analytics journal 7 (2023), pp. 1-14

Effectively managing food supply chains is critical for ensuring food security and safety. Identifying and prioritizing the criteria affecting food supply chain performance is imperative to manage uncertainty and avoid undesirable consequences in Supply Chain Management (SCM). This study aims to...

Persistent link: https://www.econbiz.de/10014434173

An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management

Islam, Md Azharul; Rashid, Shawkh Ibne; Hossain, Niamat … - In: Decision analytics journal 7 (2023), pp. 1-13

identify flood-affected areas with image classification. The proposed integrated approach can be used to deliver relief on a … classification. The Inception v3 and DenseNet architectures achieve 83% and 81% accuracy in our self-made flood level dataset …

Persistent link: https://www.econbiz.de/10014436566

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

An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity

Alam, Shafiq; Ayub, Muhammad Sohaib; Arora, Sakshi; … - In: Decision analytics journal 9 (2023), pp. 1-14

, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification …. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN …

Persistent link: https://www.econbiz.de/10014532502

A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images

Venugopal, Vipin; Raj, Navin Infant; Nath, Malaya Kumar; … - In: Decision analytics journal 8 (2023), pp. 1-11

images were used in this study. The output of the layered architecture is aggregated to perform the binary classification for … skin cancer. The performance of the trained models is investigated for multiclass and binary classification tasks. The …-based multiclass classification models. …

Persistent link: https://www.econbiz.de/10014516608

A cybersecurity risk quantification and classification framework for informed risk mitigation decisions

Zadeh, Amir Hassan; Lavine, Brandon; Zolbanin, Hamed; … - In: Decision analytics journal 9 (2023), pp. 1-11

assess their severity. This study proposes a cybersecurity risk quantification and classification framework, addressing a …

Persistent link: https://www.econbiz.de/10014532382

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 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