International Journal Of Mathematics And Computer Research

Virtual Mouse with Integrated Chatbot

2024-04-04T07:24:01+00:00

Notably, the research addresses challenges in achieving precision and user adaptability within Virtual Gesture Systems. Employing a systematic methodology, the study utilizes cutting-edge hardware and software to capture and analyze data related to hand motion. Additionally, this research integrates a voice assistant-based chatbot with limited functions, expanding the scope of interaction possibilities. The results offer a comprehensive evaluation of the combined Virtual Gesture System and voice-assisted chatbot. This evaluation includes a comparative analysis of accuracy, efficiency, and user satisfaction against existing systems in the field.

The ensuing discussion interprets the findings, addressing initial research questions and outlining implications for future development. The integration of a voice assistant-based chatbot enhances the versatility of the system, opening avenues for streamlined and user-friendly interactions. Despite inherent limitations, this research significantly contributes to the understanding of Virtual Gesture Systems, presenting a novel approach to human-computer interaction across diverse domains.

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

2024-04-04T10:25:35+00:00

An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.

Availability Evaluation of Warm Standby System with Fault Detection Delay and General Repair Times

2024-04-05T10:15:16+00:00

This study analyzed the availability of a warm standby system that works with fault detection delay and general repair times. The time-to-detection delay is also considered as exponentially distributed. The detection state is used to detect the faults in the failed unit. The steady state availability of the system is obtained by using supplementary variable technique. Three types of repair time distributions are compared to find the best one.

On Alpha and Gamma Gourava Indices

2024-04-11T05:27:18+00:00

In this paper, we introduce the alpha Gourava and gamma Gourava indices of a graph. Also we compute the alpha and gamma Gourava indices of some standard graphs, armchair polyhex nanotubes and zigzag polyhex nanotubes.

Characterization of PSU (3, q) by its order and one special conjugacy class size

2024-04-19T07:43:07+00:00

Abstract.

Suppose that G be a finite group, and let N (G)

be the set of conjugacy class sizes of G. By Thompson’s conjecture, if H is a finite non abelian simple group, G is a finite group with a trivial center, and N (G) = N (H), then H and G are isomorphic. Chen et al. contributed interestingly to Thompsons conjecture under a weak condition. In this article, we investigate validity of Thompsons conjecture under a weak condition for the projective special unitary groups. This work implies that Thompsons conjecture holds for the PSU (3, q), where q is prime power.

Analyzing Retrieval Method Using Classification and Clustering Process of Remote Sensor Satellite Images

2024-04-29T11:24:41+00:00

Today, remote sensors and satellite images are utilized across enormous regions. As visual tools, they are very important in cities, agriculture, disaster relief, and tracking systems. Pattern recognition, classification, and clustering are examples of machine learning techniques that can be used to acquire and retrieve the images. This is because they can accurately categorize and classify the images into clusters, which will help search engines discover the image that was given as a question. In our study, we look at Naïve Bayes, SVM Linear and Non-Linear, and Random Forest as classification methods, and Agglomerative Hierarchy and BIRCH as clustering methods. In this study, the fast recovery method and high accuracy rate of image retrieval by classifiers from machine learning were compared with clustering techniques from transfer learning. Using the UC Merced dataset, this research demonstrates that the accuracy classifier as SVM linear is 64.6%, SVM non-linear is 77.7%, random forest is 66.8%, and naïve bayes is 60.3%, and clustering of Agglomerative is 96.5% and BIRCH is 92.8%. The work that was mentioned is one of a kind because the clustering methods work better than the classification methods when they are used. It's unsupervised learning whose scores are above 90% for all classes.

Gain Profitable Insights by Focusing on Moving Averages for Intelligent Trading Solutions

2024-04-29T11:24:53+00:00

It is more important than ever to find intelligent trading strategies as financial markets get more complex. This paper offers a thorough investigation into the use of moving averages in Python programming for the creation of intelligent trading algorithms. This study explores the complex relationship between data analysis and profitable decision-making, providing a sophisticated viewpoint on the use of moving averages in financial planning. An overview of the Python programming language as a flexible tool for algorithmic trading and quantitative analysis opens the investigation. The discussion then turns to the use of moving averages, a key instrument in technical analysis, in the context of trading strategies. We highlight Python's flexibility by showcasing how moving averages can be easily incorporated into the trading process to improve decision support.

To sum up, this paper provides an invaluable resource for traders, scholars, and algorithmic enthusiasts who want to learn more about the relationship between moving averages and Python programming. The framework that is being presented not only broadens the range of tools available for algorithmic trading, but it also emphasises how important data-driven decision-making is in turning information into profits.