Polycystic Ovary Syndrome (PCOS) is the most familiar endocrine illness in women of reproductive age. Many Bangladeshi women suffer from PCOS disease in their older age. The aim of our research is to identify effective vision-based medical image analysis techniques and evaluate hybrid models for the accurate detection of PCOS. We introduced two novel hybrid models combining convolutional and transformer-based approaches. The training and testing data were organized into two categories: “infected” (PCOS-positive) and “noninfected” (healthy ovaries). In the initial stage, our first hybrid model, ’DenConST’ (integrating DenseNet121, Swin Transformer, and ConvNeXt), achieved 85.69% accuracy. The final optimized model, ’DenConREST’ (incorporating Swin Transformer, ConvNeXt, DenseNet121, ResNet18, and EfficientNetV2), demonstrated superior performance with 98.23% accuracy. Among all evaluated models, Den-ConREST showed the best performance. This research highlights an efficient solution for PCOS detection from ultrasound images, significantly improving diagnostic accuracy while reducing detection errors.

The classification of online buyers' purchasing intentions is of paramount importance, especially in the context of the period of the COVID-19/post-COVID-19 pandemic, as it carries significant implications for the business industry. However, effectively managing the diverse ever-changing intentions of individual Internet customers remains a challenging task. This study aims to improve the classification techniques used to classify different sorts of online buyers' purchasing intents in Bangladesh. A comprehensive analysis of different classification algorithms reveals that the Random Forest algorithm outperformed other methods, achieving exceptional accuracy rates of 99.9% in training and 89.7% in testing. Conversely, the Gaussian Naive Bayes algorithm demonstrated comparatively lower accuracy, with training testing accuracies of 80% and 79%, respectively. This study contributes not only to a better understanding of online buyers' purchase intentions in Bangladesh but also provides valuable insights into the business industry. Moreover, our work highlights the potential for future investigations in recognizing Bangla numerals throug gestures to enhance the accuracy of categorizing online buyers' intended purchases. This research serves as a stepping stone for further advancements in classifying and understanding online buyers' purchase intentions, ultimately fostering more accurate decision-making in the realm of E-commerce in Bangladesh.

The newspaper is often regarded as one of the most effective means of acquiring news and remaining informed about both local and global occurrences. Technological improvements have contributed to the comparable popularity of online newspapers and traditional magazines. The datasets used in this analysis were obtained from the online newspaper sections of two well-known and respected English and Bengali daily newspapers in Bangladesh, “The Daily Star” and “Prothom Alo”. This study examines several aspects of the analysis, organizing them into distinct categories. Similarly, online newspapers are using Google Analytics to monitor both overall views and unique page views. Consequently, this industry has been the focal point of various scholarly publications. In this study, different clustering techniques were used, and it was observed that in both newspapers, Spectral Clustering performed better than the other clustering techniques. Spectral Clustering obtained $\mathbf{8 9 \%}$ and $\mathbf{8 3 \%}$ Silhouette Scores for the Daily Star and Prothom Alo respectively. Achieving this goal will allow newspapers to obtain significant insights on market trends and customer behavior, as well as increase revenues and attract more readers.

Polycystic Ovary Syndrome (PCOS) is the most familiar endocrine illness in women of reproductive age. Many Bangladeshi women suffer from PCOS disease in their older age. The aim of our research is to identify effective vision-based medical image analysis techniques and evaluate hybrid models for the accurate detection of PCOS. We introduced two novel hybrid models combining convolutional and transformer-based approaches. The training and testing data were organized into two categories: "infected" (PCOS-positive) and "noninfected" (healthy ovaries). In the initial stage, our first hybrid model, 'DenConST' (integrating DenseNet121, Swin Transformer, and ConvNeXt), achieved 85.69% accuracy. The final optimized model, 'DenConREST' (incorporating Swin Transformer, ConvNeXt, DenseNet121, ResNet18, and EfficientNetV2), demonstrated superior performance with 98.23% accuracy. Among all evaluated models, DenConREST showed the best performance. This research highlights an efficient solution for PCOS detection from ultrasound images, significantly improving diagnostic accuracy while reducing detection errors.

Bangla music is enrich in its own music cultures. Now a days music genre classification is very significant because of the exponential increase in available music, both in digital and physical formats. It is necessary to index them accordingly to facilitate improved retrieval. Automatically classifying Bangla music by genre is essential for efficiently locating specific pieces within a vast and diverse music library. Prevailing methods for genre classification predominantly employ conventional machine learning or deep learning approaches. This work introduces a novel music dataset comprising ten distinct genres of Bangla music. For the task of audio classification, we utilize a recurrent neural network (RNN) architecture. Specifically, a Long Short-Term Memory (LSTM) network is implemented to train the model and perform the classification. Feature extraction represents a foundational stage in audio data processing. This study utilizes Mel-Frequency Cepstral Coefficients (MFCCs) to transform raw audio waveforms into a compact and representative set of features. The proposed framework facilitates music genre classification by leveraging these extracted features. Experimental results demonstrate a classification accuracy of 78%, indicating the system's strong potential to enhance and streamline the organization of Bangla music genres.