Dr. Jayanti Goyal is a Head and Associate professor in the department of computer science Kanoria PG Girls College Jaipur. She is distinguished academic professional with over 18 years of teaching and research experience. Dr. Goyal has made significant contributions to academia, publishing over 35 research articles in leading journals, conference proceedings, and books, including IEEE Transactions, Springer and Scopus-indexed journals. She has also authored more than 15 books and book chapters and holds four published and granted patents, showcasing her innovative spirit and interdisciplinary expertise. Her research interests span Artificial Intelligence, Machine Learning, Software Engineering, General Management, Human Resources, Entrepreneurship and Skill Development. She is passionate about fostering interdisciplinary programs and has actively contributed to curriculum development as a Board of Studies (BoS) member at the University of Rajasthan.
Professionally, Dr. Goyal is associated with esteemed organizations such as the Computer Society of India (CSI), Rajasthan University Women's Association (RUWA), Cyber Crime Awareness Society, and INSPIRA and GAP. She is also serves on the editorial boards and technical program committees of renowned conferences, including IEEE, Springer, ACM, and AMA. Her outstanding contributions have earned her numerous accolades, including the Outstanding Contribution in Research Award, Best Paper award, Global Teacher Award, Best Teacher Award, and Outstanding Academician Award. Dr. Goyal’s unwavering commitment to education, research and the holistic development of her students exemplifies her dedication to academic excellence and innovation.
Software Engineering Artificial intelligence Machine Learning Entrepreneurship Skill Development Education technology Cloud Computing General Management
Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..
The term cloud computing possesses the critical aspect to enhance the network by leveraging the available resources in an effective manner. It has been widely stated that the usage of enhanced IT infrastructure support in realising the goals of the stakeholders in an easier aspect. Cloud computing is a shared pool of operations that is growing in popularity due to its low cost, high efficiency, and high output. Along with its many advantages, cloud computing presents a considerably more difficult scenario in terms of data privacy, intellectual property rights, authenticated access, data security, and so on. Cloud computing technology is becoming ever more challenging in today's society as a result of these challenges. This paper aims to evaluate the security issues in cloud services and implementation of advanced technology to prevent these challenges. In this context, mixed method has been considered (primary quantitative and secondary qualitative) to gather relevant and factual information.
This paper emphasizes on the challenges faced by women entrepreneurs and their prospects. As they are the emerging human resource in the 21st century to overcome the economic confronts in global perspective, the emergence of women entrepreneur and their contribution to the national economy is quite visible in India. Women have become aware of their existence, their rights & their work situations. Though women entrepreneurship and the formation of women business networks is growing rapidly, still there are a number of challenges like External finance and sex discrimination, fierce competition , the negative international outlook ,cash flow etc. Promoting entrepreneurship for women will require an even greater reversal of traditional attitudes than the mere creation of jobs for women would. They are flourishing as corporate officers, designers, decorators, exporters, publishers, manufacturers and still exploring new areas of economic participation. Women entrepreneurs should make a success of their organization and help for economic progress of their countries.
Cloud computing (CC) is rising rapidly; an expansive number of clients are pulled in towards cloud administrations for more fulfillments. Distributed computing is most recent developing innovation for expansive scale dispersed processing and parallel registering. CC gives vast pool of shared assets, program bundle, data, stockpile and a broad variety of uses according to client requests at any example of time. Adjusting the heap has turned out to be all the more intriguing examination zone in this field. Better load adjusting calculation in cloud framework builds the execution and assets use by progressively dispersing work stack among different hubs in the framework. Virtual machine (VM) is an execution unit that goes about as an establishment for distributed computing innovation. Bumble bee conduct propelled stack adjusting enhances the general throughput of handling and need construct adjusting centers with respect to decreasing the measure of time an errand needs to look out for a line of the VM.
Software fault prediction is a critical task in software engineering that aims to identify and prevent faults in software code before they occur.Machine learning algorithms have been shown to be effective in this area, providing accurate and timely predictions of software faults.In this research paper, we examine the effectiveness of different machine learning algorithms for software fault prediction using publicly available datasets.We compare the performance of four popular machine learning algorithms, namely support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN), and Nave Bayes (NB), using various metrics such as accuracy, precision, recall, and F1-score.We also perform feature selection to identify the most relevant features for each algorithm.In conclusion, our research highlights the effectiveness of machine learning algorithms for software fault prediction and provides insights into the most suitable algorithm for specific datasets.By leveraging the power of machine learning algorithms, software developers can effectively predict and prevent software faults.These findings provide a reference point that can be used to evaluate the effectiveness and advancements of any novel approaches in software defect prediction.
I am inviting researchers, academicians, and scholars who are interested in collaborative research in the areas of Artificial Intelligence,…