Journal of Computers and Intelligent Systems

Optimizing Crop Yield Forecasts Using Quantum Machine Learning Techniques with High-Dimensional Soil and Weather Data

Jamshaid Basit, Hira Arshad, Amna Bibi — Fri, 25 Oct 2024 00:00:00 +0000

This paper focuses solely on the possibility of applying quantum machine learning methods to increase crop yield prediction accuracy based on multi-feature soil and climate data. The main goal is to increase the efficiency of crop yield prediction models, which are critical for increasing a nation's production and food ratio. Complexity also throws off supervised analytical methods, and nonlinearity grew as the agricultural industry expanded its fields. These fields now encompass a wider range of interconnected elements, including soil type and nutrient content, their relationship to soil water content, air temperature, rainfall, and other factors. In this research, we use quantum computing to solve the problem of handling high-order data more proficiently than the same problems formulated in classical computers. In this paper, we developed and incorporated QSVM and QNN into conventional machine learning models to learn from large and highly complex datasets containing multiple years' worth of regional and temporal information on soil and weather. We believe these models can reveal patterns that QSVM and QNN are better equipped to detect due to their scalability and ability to compute over large datasets. As a result, the quantum-enhanced models outperform the conventional methods in terms of predictive power, demonstrating superior MSE values and robustness values. Specifically, the integration of quantum techniques enhanced the generalization ability because of the highly nonlinear relationship between the variables. These results suggest that QML could significantly improve crop yield estimates, as its predictions are more accurate and directly applicable to agricultural practices and policies. This study would expand the literature on the application of quantum computing in agriculture because it is an emerging field that holds potential for addressing various challenges in food production. In the domain of crop yield prediction, we are laying down the foundations for less vulnerable farming structures that are able to meet the future climate conditions and the growing global food requirements. Thus, the study calls for more research on potential quantum-based solutions in other essential use cases in agriculture.

Comparative Analysis of Deep Learning Architectures for Customer Churn Prediction in the Banking Sector

Jamshaid Basit, Azmeena Sheikh, Naeem Umer, Maria Syed — Fri, 25 Oct 2024 00:00:00 +0000

Customers’ churn is a critical problem for the banking industry since the prediction of customers’ attrition impacts the business’s key outcomes and decision-making. As such, the research aim of this study is to assess the performance of different deep learning networks in the context of customer churn rate estimation with banking datasets. This study specifically compares feedforward neural networks, long short-term memory networks, convolutional neural networks, and multi-layer perceptrons. We train and test the models on a data set that includes customer details such as credit score, age, balance, and tenure. Hence, we measure each model’s ability depending on factors like accuracy, precision, recall, F1 score, and the ROC-AUC. However, for the evaluation of the models, we use various visualizations, including confusion matrix, receiving operating characteristic curve, precision-recall curve, and learning curves. The results show that all models can achieve comparable performance, but there are some models with specific edges. For example, long short-term memory networks, a type of RNN, excel at modeling sequential relationships in the data, whereas convolutional neural networks craft intricate structures within the data input. This work's main ideas include examining the characteristics and potential of various deep learning designs in the context of customer churn prediction while comparing the architectures. The primary goal of this study is to analyze and identify the most effective deep learning model for customer churn prediction, as well as provide recommendations for banks to improve customer retention. Thus, the results emphasize the importance of selecting an adequate model based on the data's characteristics and prediction goals. It is in this vein that the study proposes to advance the understanding of the deep learning models above with a view to informing banking institutions on how best to address customer churn problems.

Adaptive YOLO-V8 for Low-Earth Orbit Debris Detection Improving Detection Accuracy with Dynamic Training

Jamshaid Basit — Fri, 25 Oct 2024 00:00:00 +0000

It is crucial to identify methods to detect debris due to a new tendency that is emerging in LEO orbits, which is threatening the functionality of satellites and interplanetary missions. This research solves this problem using an improved YOLO-V8 model that enables the detection of space debris with higher precision while using adaptive dynamic learning approaches. It was critical for our model to be able to identify and categorize as many kinds of objects as possible, and our database currently contains 11 object classes, including space debris and satellites. To specifically detect small and moving objects, we utilized the YOLO-V8 model, tailoring the train options to the unique object detections in this class. For the training of our model, we used a large quantity of data in addition to the images from these 11 classes and used SGD as our optimizer with the learning rate of 0.25 and individual weight decay parameters. Additionally, we utilized blur and grayscale transforms to enhance the model through data augmentation. By comparing the obtained results, we can observe enhanced detection accuracy in each class separately, as well as a general boost in prediction and recall. Due to the cross-entropy function's flexibility, the model was able to perform well on various object sizes and speeds in an orbital context, making detection consistent. A lot of fine tuning was required in the training parameters in order to get the desired or even better results devoid of false positive detection. This paper describes how YOLO-V8 with adaptive training achieved outstanding results for object and debris detection in low Earth orbit (LEO) to improve space usage safety and define better approaches to space debris management.