Aamir Mazhar Abbas

’HOMESWEET’ is a deep learning model created with the YOLO object detection algorithm that has been trained to detect certain human physical states that could result in road accidents and deaths. The model achieved an accuracy score of 83% and a Precision rate of over 90% but had a fairly modest Recall rate of just over 70%.
The model can be deployed in various other applications as it was able to detect not only facial cues but other micro-expressions and gesticulations that lead to the various states; in particular, in this research, it was created to detect fatigue, drowsiness and lack of total concentration while driving.

Numerous studies and research work has been undertaken in the area of creating predictive models for studying Bank Churn. In these studies, the end goal was to create a high accuracy predictive model; while this is commendable, this research focuses on creating an architecture for a predictive model by aggregating the power of various predictive models. The architecture and model proposed in this paper achieved an accuracy of 91% in the test data (35% of the original data set), and an AUC of 96% - confirming the generalized nature of the model. Also, various feature extrapolation techniques were introduced which provide valuable insights to the banking sector.

It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust , molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model’s functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease.

The academic efficiency and knowledge retention of students can be improved by practicing active recall testing and implementing spaced repetition techniques. The process of trying to recall information previously learned with the aim of increasing the chance of committing the information to long-term memory is called active recall. Spaced repetition is a technique which can help students to memorize and learn information by outspreading reviews of the topics over larger range of time revising the same topic multiple times in a single session.
A qualitative method has been followed in this paper which takes a grounded theory approach while evaluating literature on different memory models, memory creation and retrieval processes. Based on the literature review, an algorithm has been proposed with the aim of improving learning effectiveness by leveraging spaced repetition techniques.