Automatic Recognition of Diseased Cotton Plant and Leaves (ARDCPL) using Deep Learning (DL) carries a greater significance in agricultural research. The cotton plant and leaves are severely infected by a disease named Bacterial Blight-affected by bacterium, Xanthomonas axonopodis pv. Malvacearum and a new rolling leaf disease affected by an unorthodox leaf roll dwarf virus. Existing research in ARDCPL requires various complicated image preprocessing, feature extraction approaches and cannot ensure higher accuracy in their detection rates. This work suggests a Deep Convolutional Neural Network (CNN) based DCPLD-CNN model that achieves a higher accuracy by leveraging the DL models ability to extract features from images automatically. Due to the enormous success of numerous pre-trained architectures regarding several image classification task, this study also explores eight CNN based pre-trained architectures: DenseNet121, NasNetLarge, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and Xception models by Fine-Tuning them using Transfer Learning (TL) to recognize diseased cotton plant and leaves. This study utilizes those pre-trained architectures by adding extra dense layers in the last layers of those models. Several Image Data Augmentation (IDA) methods were used to expand the training data to increase the model's generalization capability and reduce overfitting. The proposed DCPLD-CNN model achieves an accuracy of 98.77% in recognizing disease in cotton plant and leaves. The customized DenseNet121 model achieved the highest accuracy of 98.60% amongst all the pre-trained architectures. The proposed method's feasibility and practicality were exhibited by several simulated experimental results for this classification task.

Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.

Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth that records for its number of people. A balanced participation ratio of both males and females in the industry by ensuring skilled and technical education for all provides a stable economic development in a country. Population and Gender impact on GDP prices in Bangladesh were investigated in this study. To address the effect of gender factors in GDP prices, we considered the following parameters: year, combined population, male population, and female population. Based on these parameters, the global domestic product-current prices of Bangladesh were analyzed. For the predictive analysis, we have used various machine learning algorithms to make prediction and visualization of the predicted output. A quantitative analysis was also performed to examine the correlation among different gender factors with the growth of GDP. Based on analysis and study results, we can say that the machine learning approach could be applied efficiently in numerous applications of GDP forecasting.