International Journal of Computer Science and Engineering Archives (IJCSEAR)

Personalized Mobile App Experience: Using Contextual Information to Enhance User Interactions

Haider Imam Rizvi , Rizwan Ullah Bangash — Tue, 31 Dec 2024 00:00:00 +0000

The explosive development of mobile applications has led to the necessity of increased personalization and context awareness of the user experiences. Contextual background is crucial in the formation of these experiences in terms of location, time and user preferences as well. The paper will also research how contextual data can be used to personalize interaction and how it is achieved in mobile applications by providing a particular example of improving the level of engagement and satisfaction of people using the mobile application. The study tries to understand how machine learning models, especially decision trees and clustering algorithms are used in contextual information processing and analysis. By addressing the behavior patterns of users, across the different types of apps such as social media, fitness, and e-commerce, the research will illustrate that personalized functionalities (customized content, notifications, and recommendations) of the app may greatly enhance peoples interaction. The findings show that applications that make use of contextual information beat those that do not in activation and retention of users. The implications of these findings are wider in the context of mobile apps design and development since it has been shown that the implementation of context-aware features can significantly improve user experience and lead to a successful app.

The Impact of Temporal Context on Mobile App Usage: A Linear Regression Approach

Hassan Mehmood Raja — Tue, 31 Dec 2024 00:00:00 +0000

The recent increased popularity of the use of mobile apps has led to aggressive study of what makes user engagement and interaction. Temporal is one of the major areas that hasn Bow been well studied in terms of time of the day, day of the week or seasonal differences affecting the use of mobile applications. In this study, the hypothesis is to analyse how the context of time plays a part in predicting usage of mobile apps by using linear regression models. The main aim is to discover temporal variables, which can be considered an important aspect and to determine its connection with the frequency of use, the duration of the session, as well as the type of application. The authors utilize a dataset containing records on user activities in one of the most popular mobile apps over a six-months period to create a linear regression model. Findings suggest that hour of the day and weekday are other key factors impacting user engagement and that there are definite surges in the evening and weekends time. The results indicate that by profiling user experiences based on such temporal patterns developers of apps can optimize notifications and delivery of content to users. The study can be one addition to the rising number of studies on mobile app analytics and can also serve as a guide to investigators and other applications developers who may consider using temporal context to enhance user retention.

Evaluating the Role of Location-Based Context in Predicting Mobile App Usage Behavior

Laraib Atta — Tue, 31 Dec 2024 00:00:00 +0000

The behavior of mobile apps usage is also needed as it can assist a developer to raise the involvement of users to their mobile app and polish mobile experience. One of the key information that determines a user behavior is the location-based context and this may be used to indicate the user input made with applications depending on the place that he/she is in. In this case, the study answers the question concerning the predictive capacity of location-based context when it comes to mobile applications, or, in other words, how sites of information (such as those portrayed by the coordinates of the global positioning system, distance to the landmarks, and time) can assist in the enhancement of the app use predictions. Previous research studies have shown the significance of the contextual information on the prediction of the user behavior (Zhao et al., 2019; Huang et al., 2020). Random Forest and Support Vector Machines (SVM) are the machine learning algorithms we perform to analyze the application usage patterns on the basis of location data within our works. The predictions on basis of user behavior only instead of running model at location-based context have also been suggested as the quality of prediction accuracy will be much better. The study suggested is significant since the availability of literature on context-aware computing presents a lot, particularly on the effectiveness of the location-based features in predicting app use. The implications of such an approach are huge to the producers of the apps, since this can extend the tendency towards personal user experience and tailor-made advertising initiatives.

A Comprehensive Study on Deep Learning Techniques for Enhancing Predictive Performance in Healthcare Diagnostics

Salman Esa — Tue, 31 Dec 2024 00:00:00 +0000

The use of deep learning to implement diagnostics in the field of healthcare became one of the most critical fields of research, and may be used in predictive care with groundbreaking prospects. The given research is associated with a question on making more effective the mechanisms of the human conditions diagnosing in terms of introducing deep learning algorithms. We would like to mention, as a continuation of the existing approaches, the combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyse patient data and medical images. We seize the publicly available medical data so as to quantify the efficiency of various deep learning models, and we are focused on such metrics as accuracy, precision and recall. The results can suggest that there have been tremendously significant changes in the predictive behaves in cases where the CNN has been applied to radiology images, especially in identification of cancerous and non-cancerous tissues. The following outcome of our work highlights the necessity to introduce deep learning to the field of healthcare diagnostics, so among the possible avenues of the research, one may examine employing hybrid models and utilizing transfer learning strategies to increase precision. The given paper is the addition to the literature already overflowing with the articles on the possibilities of AI and healthcare and makes the knowledge about how these models may be used to assist in the early diagnosis and pre-treatment plans.

Predicting Mobile Application Usage Patterns Using Linear Regression: A Data-Driven Approach

Waseem Talha — Tue, 31 Dec 2024 00:00:00 +0000

One central aspect on the usage patterns of mobile applications was on the prediction aspect that has become important since mobile applications have become central to individuals in the current world with digital caricatures. This is the correct forecast of how people are going to use mobile applications and it will assist the App developers and marketers a long way to improve their user touch and programming policies. In the current paper, the linear regression approach to forecasting the usage trend of mobile applications is utilized based on the data-driven technique. The existing study builds on the study by Smith et al. (2018) who examined the user behavior (in the mobile environment) and certain user-based product aspects, such as the frequency/period/and demographics of the user to which a predictive model will be applied. Using an actual interactions data with mobile application, the performance of the model is quantified in some of common measures of regression, which include mean squared error (MSE) and R-squared (R 2 ). The results confirm that GRs are adequate to capture meaningful usage patterns but there could be certain usage patterns, which may require more intelligent regression models. The article identifies the strengths and shortcomings of the linear regression to forecast the mobile utilization and sets the implications of any further research on the same topic. The recommendation regarding future is that advanced machine learning algorithms would generate improved results of prediction and could be connected to more contextual data sources.