Artificial Neural Networks and Data Science

Bihl, Trevor J.; Young, William A.; Moyer, Adam; Frimel, Steven

doi:10.4018/978-1-7998-9220-5.ch052

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…Artificial Neural Networks (ANNs) are ML model types that act as the human brain to solve complex problems. ANNs are composed of interconnected nodes, or "neurons," which work together to process and interpret input data [36]. These neurons are organized into layers, with each layer performing a specific task in the overall process.…”

Section: Ann Modelmentioning

confidence: 99%

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Terlapu

2023

Preprint

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Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-term damage to the vocal cords. A new procedure to automatically detect alcohol drinkers using vowel vocalizations is an earlier and lower-cost method than other alcohol drinker-detecting models and equipment. The hidden parameters of vowel sounds (such as frequency, jitter, shimmer, harmonic ratio, etc.) are significant for recognizing individuals who drink or do not drink. In this research, we analyze 509 multiple vocalizations of the vowels (/a, /e, /i, /o, and /u) from 290 multiple records of 46 drinkers and 219 multiple records of 38 non-drinkers. The age group is 22 to 34 years. Apply the 10-fold cross-validation vowelized dataset on intelligent machine learning models and incremental hidden layer neurons of artificial neural networks (IHLN-ANNs) with backpropagation. The findings showed that experimental ML models such as Naïve Bayes (NB), Random Forest (RF), k-NN, SVM, and C4.5 (Tree) performed well. The RF model performed best, with 95.3% accuracy. We also applied the incremental hidden layer (HL) neurons BP-ANNs model (from 2 to 5). In this analysis, accuracy increased proportionally with the incremental neurons (2–5) in the HL of the ANN. At the moment of 5 neurons HL ANN, the model performed with a highly accurate 99.4% without an over-fit problem. It will implement smartphone apps for caution and alerts for alcohol consumers to avoid accidents. Voice analysis has been explored as a non-invasive and cost-effective means of identifying alcohol consumers.

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Section: Ann Modelmentioning

confidence: 99%

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Terlapu

2023

Preprint

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“…SVM employs hyperplanes to separate classes and maximize the margin, making it suitable for handling complex relationships and highdimensional data (Hussain 2019). MLP is an artificial neural network architecture comprising interconnected nodes that process input data and learn complex patterns (Bihl et al 2023). In parallel with the adoption of ML algorithms for predictive modeling, the integration of GUI models has become pivotal.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for persistent free radicals in biochar: dual prediction of contents and types using regression and classification models

Latif,

Chen,

Saleem

et al. 2024

Carbon Res.

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Persistent free radicals (PFRs) are emerging substances with diverse impacts in biochar applications, necessitating accurate prediction of their content and types prior to their optimal use and minimal adverse effects. This prediction task is challenging due to the nonlinearity and intricate variable relationships of biochar. Herein, we employed data-driven techniques to compile a dataset from peer-reviewed publications, aiming to systematically predict the PFRs by developing supervised machine learning models. Notably, extreme gradient boosting (XGBoost) model exhibited the best predictive performance for both regression and classification tasks in predicting the PFRs, achieving a test R2 value of 0.95 for PFR content prediction, along with an Area Under the Receiver Operating Curve (AUROC) of 0.92 for PFR type prediction, respectively. Based on XGBoost model, a graphical user interface (GUI) was developed to access PFRs predictions. Analysis of feature importance revealed that the biochar properties, such as metal/non-metal doping, pyrolysis temperature, carbon content, and specific surface area were identified as the four most significant factors influencing PFRs contents. Regarding the types of PFRs in biochar, specific surface area, pyrolysis temperature, carbon content, and feedstock were top-ranked influencing factors. These findings provide valuable guidance for accurately predicting both the contents and types of PFRs in biochar, and also hold significant potential for highly efficient utilization of biochar across various applications. Graphical Abstract

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“…So data mining methods must extract useful knowledge from data [4], [5]. Knowledge Data Discovery (KDD) is the series of iterative steps to discover beneficial, salient, and comprehend patterns from massive datasets [6]. Data mining is the crux of KDD, including techniques to understand and explore data, augmenting meticulous models, and finding crucial patterns in extracting knowledge [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Market Basket Analysis using Association Rules

Ghous,

Malik,

Rehman

2023

KJCIS

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Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.

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Artificial Neural Networks and Data Science

Cited by 4 publications

References 55 publications

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Machine learning for persistent free radicals in biochar: dual prediction of contents and types using regression and classification models

Deep Learning based Market Basket Analysis using Association Rules

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