Browsing by Author "Appiahene, Peter"

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    Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model
    (Elseviere, 2023-07) Opoku, Eric; Gyening, Rose-Mary Owusuaa Mensah; Appiah, Obed; Takyi, Kate; Appiahene, Peter; 0000-0002-8087-5207
    Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variations in visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been proposed to address these challenges with significant results, but there is still a need for improvement. In this paper, we propose a color and texture extraction technique for image representation, as well as a generalized, less complex Neural Network model, to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively. The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool for cocoa bean classification.
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    Combining Data Envelopment Analysis with Machine Learning Algorithms for Predictions
    (KNUST, 2020-09) Appiahene, Peter
    Comparative to other methods, DEA is an improved method to organize and analyze data. However, it is very difficult to use only DEA to predict the efficiency and performance of other or new Decision Making Units (DMU). The main objective of this study is to build a high accuracy machine learning predictive models for predicting the efficiencies of banks by combining DEA with Machine Learning algorithm. The study built four Machine Learning Models namely; DEA-DT, DEA-RF, DEA-NN and DEA-LR to predict the efficiencies of banks. The study used 33% of the total bank branches in Ghana, largely in the nine regions. A two-stage DEA was used to determine the efficiencies of all bank branches and these banks were grouped based on a proposed algorithm, Bank Classification Algorithm (BC Algorithm). In building the predictive models, 70% of the banks dataset were used to train and validate the models. The developed models were used to predict the efficiencies of the other 30% banks. A 10-fold Cross-Validation was applied to check the performance of all predicting models on each case dataset. All experiments were executed within a simulation environment and conducted in R studio using R programming language. Standardized Machine Learning evaluation metrics were used to compare the models. The results suggested a very good performance of all the machine learning models proposed by the study. However, a comparison among them clearly indicated a much better performance by the DEA-RF for predicting banks’ efficiency in collecting deposit and DEA-DT for predicting banks’ efficiency in investing deposits. This study has demonstrated that combing two models improve the performance, predictions and classification accuracies suggested by previous studies. In conclusion, the study proposed the usage of the proposed BC Algorithm for classifying banks based on their efficiencies in deposit stage and investment stage.
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    Deep learning based capsule networks for breast cancer classification using ultrasound images
    (SyncSci Publisher, 2024-08) Afrifa, Stephen; Varadarajan, Vijayakumar; Zhang, Tao; Appiahene, Peter; Gyamf, Daniel; Gyening, Rose-Mary Owusuaa Mensah; Mensah, Jacob; Berchie, Samuel Opoku; 0000-0002-8087-5207
    Abstract: Purposes: Breast cancer (BC) is a disease in which the breast cells multiply uncon trolled. Breast cancer is one of the most often diagnosed malignancies in women worldwide. Early identification of breast cancer is critical for limiting the impact on affected people’s health conditions. The influence of technology and artificial intelligence approaches (AI) in the health industry is tremendous as technology advances. Deep learning (DL) techniques are used in this study to classify breast lumps. Materials and Methods: The study makes use of two distinct breast ultrasound images (BUSI) with binary and multiclass classification. To assist the models in understanding the data, the datasets are exposed to numerous preprocessing and hyperparameter approaches. With data imbalance being a key difficulty in health analysis, due to the likelihood of not having a condition exceeding that of having the disease, this study applies a cutoff stage to impact the decision threshold in the datasets data augmentation procedures. The capsule neural network (CapsNet), Gabor capsule network (GCN), and convolutional neural network (CNN) are the DL models used to train the various datasets. Results: The findings showed that the CapsNet earned the maximum accuracy value of 93.62% while training the multiclass data, while the GCN achieved the highest model accuracy of 97.08% when training the binary data. The models were also evaluated using a variety of performance assessment parameters, which yielded consistent results across all datasets. Conclusion: The study provides a non-invasive approach to detect breast cancer; and enables stakeholders, medical practitioners, and health research enthusiasts a fresh view into the analysis of breast cancer detection with DL techniques to make educated judgements.
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    Developing a DSS for a university with satellite campuses using data warehouse. Case study at University of Education, Wineba.
    (2016-10-24) Appiahene, Peter
    Data warehouse (DW) is an important contemporary issue for many organizations and is relatively a new field in the realm of information technology...
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    Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks
    (Advances in Fuzzy Systems, 2020) Appiahene, Peter; Missah, Yaw Marfo; Najim, Ussiph; 0000-0002-6098-4537; 0000-0002-2926-681X; 0000-0002-6973-7495
    %e financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence,efficiency and performance analysis in the banking industry has become a hot issue. %is is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. %is paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). %e results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. %e results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. %e DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. %e study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.