IEEE

Music genres are helpful means for recommending songs of preferences by containing characteristics related to instruments, musical rhythms and harmonic structure and melodies of the song. Song listeners often face difficulty in finding desired tracks due to the vast volume of available music data. So, in this context, machine learning-based approaches can contribute in developing sophisticated method that can classify music genres and eventually building recommendation systems for online streaming services. In this paper, we propose an integrated framework that considers musical features from both time and frequency domain and after necessary preprocessing stages, incorporates into a boosting model for classification. We incorporate CatBoost as an ensemble learning model due to the obvious benefits of increased speed, reduced overfitting and the ability to assign greater weights to certain samples, and minimal variance sampling. We evaluated our proposed framework on a Bangla music dataset and discovered some noteworthy results that support the effectiveness of our proposed integrated model. A remarkable characteristic of such an integrated machine learning model, which is a significant contribution to the Bangla Music Industry in the era of Industrial Revolution 4.0, is its ability to analyze information from multidimensional data in a self-optimized approach with strong decision-making abilities.

Tic-Tac-Toe, also known as Noughts and Crosses, is a widely popular game among people of all ages. In recent times, due to the rapid development of Artificial Intelligence (AI) based algorithms, AI in Games has become an interesting topic for research in both academia and industry. Due to the complicated yet competent nature of AI algorithms, the design and implementation of such AI-driven approaches in games are challenging and time intensive. In this regard, we propose a supervised Machine Learning (ML)-based approach that contributes in designing an innovative and less complex Tic-Tac-Toc expert system. Integrating AI and ML in the solution process will lead the concerned community toward a more lightweight and computationally efficient systems for playing games. In this study, we propose a novel algorithmic solution by combining an ensemble-based boosting approach and rule-based inference to build a probabilistic expert system that strategically chooses the best optimal move for next possible state of the game. A benchmark dataset containing 255,168 unique game states of Tic Tac Toe was utilized at training stage. The proposed strategy is able to successfully settle a draw against never-loosing MiniMax algorithm in 18 standard test cases.

PolyCystic Ovary Syndrome (PCOS) is one of the most common causes of female infertility, affecting a large number of women of reproductive age, even continuing far beyond the childbearing years. This hormonal disorder may further lead to the risk of other long-term complications. Considering the powerful recognition abilities of the probabilistic nature of ensemble-based gradient boosting algorithms, particularly in the field of the medical domain, we propose the use of Extreme Gradient Boosting, XGBoost, for early detection of PCOS. To strongly support an effective classification performance, we have resampled our data using a combination of SMOTE(Synthetic Minority Oversampling Techniques) & ENN (Edited Nearest Neighbour), to solve class imbalance and data outliers issues. Also, by exploiting popular statistical correlation methods, ANOVA Test Chi-Square Test, we have identified 23 most significant metabolic and clinical parameters that best classify PCOS conditions. Finally, we experimented with our model on a benchmark dataset collected from Kaggle to justify the effectiveness of our proposed findings where the Extreme Gradient Boosting classifier outperformed all other classifiers with a 10 Fold Cross-validation score of 96.03 % all over, along with a 98% Recall in the detection of patients not having PCOS, which outperforms all the existing recent methods where the numerical data-driven diagnosis of PCOS have been studied on this particular dataset.

Breast cancer has been identified as one of the most common invasive cancers and the second leading cause of cancer death among women. The survival rates have, however, improved dramatically in recent years, thanks to the advances in the screening and treatment process, hugely depending on how early the disease was detected. Along with the physicians, this had also initiated researchers all over the globe to dedicate themselves to extensive research to produce automated diagnosis strategies for breast cancer. Realizing the extraordinary potential of machine learning-based models in the biomedical domain, a large number of diagnosis methods have been proposed in this direction. In our study, we propose a hybrid unique machine learning framework that integrates individual prediction probabilities from 3 machine learning (Logistic Regression, Support Vector Machine, and K Nearest Neighbors) classifiers, then enhances the performance of these 3 classifiers through hybridization, stacking a gradient boosting algorithm over the combination of these classifiers which ultimately results in a 10 Fold Cross-Validation Score of 98.4%, Recall of 100% and Precision of 97.3%. Besides, to handle the class imbalance problem we have incorporated SMOTE(Synthetic Minority Oversampling Technique) for minority classes and also Robust Scaling for normalization to deal with outliers in the dataset. In our proposed hybrid solution, we successfully adopted the breast cancer domain in every stage of our framework, starting from data pre-processing, feature extraction, and finally classification. Our framework outperformed some recent state of the art studies in the breast cancer domain.