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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

future scope of ML algorithms and artificial intelligence in the coming times and their roles in automation and holistic … previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then … machine learning algorithms namely-K-Nearest Neighbor (KNN), Genetic Algorithm (GA), Support Vector Machine (SVM), Decision …

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

A review of optimization models and applications in robotic manufacturing systems : Industry 4.0 and beyond

Vaisi, Bahareh - In: Decision analytics journal 2 (2022)

metaheuristic algorithms are the most frequently used tools to solve the robotic application problems. The author hopes that this …

Persistent link: https://www.econbiz.de/10013163132

A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters

Sufi, Fahim - In: Decision analytics journal 5 (2022), pp. 1-12

languages, lack of sentiment analysis, regional restrictions, lack of end-to-end automation, and lack of Mobile App support. In …

Persistent link: https://www.econbiz.de/10014336730

An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning

Bilgram, Alexander; Jensen, Peter G.; Jørgensen, … - In: Decision analytics journal 5 (2022), pp. 1-11

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to...

Persistent link: https://www.econbiz.de/10014336925

DIDS : a Deep Neural Network based real-time Intrusion detection system for IoT

Vishwakarma, Monika; Kesswani, Nishtha - In: Decision analytics journal 5 (2022), pp. 1-9

The number of people using the Internet of Things (IoT) devices has exploded in recent years. The instantaneous development in deploying constrained devices in numerous areas makes them vulnerable to assaults due to limited resources. Advanced cryptography cannot be constructed in these modest...

Persistent link: https://www.econbiz.de/10014337042

An explainable deep-learning approach for job cycle time prediction

Wang, Yu-Cheng; Chen, Toly; Chiu, Min-Chi - In: Decision analytics journal 6 (2023), pp. 1-10

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable...

Persistent link: https://www.econbiz.de/10014337140

An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks

Momenitabar, Mohsen; Dehdari Ebrahimi, Zhila; … - In: Decision analytics journal 7 (2023), pp. 1-24

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable...

Persistent link: https://www.econbiz.de/10014497305

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 comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction

Hassan, Md. Mehedi; Hassan, Md. Mahedi; Yasmin, Farhana; … - In: Decision analytics journal 7 (2023), pp. 1-17

) algorithms have been applied in this study. We have achieved the maximum accuracy of 90.68% by RF compared with LASSO. Similarly …

Persistent link: https://www.econbiz.de/10014497358

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

Khanna, Varada Vivek; Chadaga, Krishnaraj; Sampathila, … - In: Decision analytics journal 7 (2023), pp. 1-14

heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors …

Persistent link: https://www.econbiz.de/10014497362