International Journal of Modern Research <p><strong>International Journal of Modern Research (IJMORE) is interdisciplinary open access and peer-reviewed journal, focusing on theoretical, practical, and scientific aspects in all research areas. It is an international scientific journal that aims to promote research in all the research fields like Engineering, Science, Technology, Education, Management, Medical Sciences, Dental Sciences, Agricultural Sciences, Social sciences, Health Care, Arts &amp; Humanities, and many more. IJMORE International Editorial/Reviewer Board representing many well known Colleges/Institutions, Universities, and Organizations in the USA, UAE, UK, Canada, Australia, China, India, Russia, and many more.</strong></p> <p>Authors are invited to submit papers via the online portal. Articles must be original and should not have been published previously or under consideration for publication to any other conference or journal. The team of IJMORE advises you, do not to submit the same article to multiple journals simultaneously.</p> en-US (Dr. Gaurav Dhiman) (Dr. Amandeep Kaur) Tue, 01 Feb 2022 05:21:03 +0000 OJS 60 Self-aware Execution Environment Model (SAE2) for the Performance Improvement of Multicore Systems <p>Multicore systems are known for operating in a dynamic execution environment. Various conventional approaches/efforts carried out so far towards achieving the dynamism have gradually become obsolete. And there is a dire need to find out and integrate novel paradigms, research theories (such as self-awareness) in Multicore systems to find possible performance optimization alternatives. Self-awareness is one of the important principles of autonomic computing and has been shown a remarkable hope in building dynamic systems. Taking the inspiration from such systems, in this paper a “Self-aware Application Execution Environment” (SAE2) has been proposed. The aim of the SAE2 model is to explore and find out the impact of “self-awareness” in the performance of Multicore systems. The “SAE2 model” is driven through the “autonomic computing principles” and exploits parameter tuning and tradeoff attributes to leverage the Multicore system's potential. The proposed model has a feedback-based mechanism, where an application could interact with the system and signal for various performance issues (at run time) and get the inputs to get adopted as per the system resources availability. A novel “application cooperative behaviour” has been introduced to address various performance issues of Multicore systems.</p> Surendra Kumar Shukla, Vishan Kumar Gupta, Kireet Joshi, Ankit Gupta, Mukesh Kumar Singh Copyright (c) 2022 International Journal of Modern Research Sun, 10 Apr 2022 00:00:00 +0000 Crime Tracking System and People’s Safety in India using Machine Learning Approaches <p>The era of digitization and computers has already arrived. The enormous coverage of cyberspace has changed the way of overall major and minor aspects of the life of doing and looking over things. Most of our processes aims and future planning are now partially or fully dependent on technology. Thus, being new to this technology-driven and dominated environment, we are forced to cling upon machine learning that will guide us to improve our processes and to adapt to this new culture. To provide the optimal suggestion to the citizen to select the best residential location as well as for the police department to tackle crime through the dataset.</p> Vishan Kumar Gupta, Surendra Kumar Shukla, Anupriya, Ramesh Singh Rawat Copyright (c) 2022 International Journal of Modern Research Tue, 01 Feb 2022 00:00:00 +0000 Breast Cancer Image Classification using Transfer Learning and Convolutional Neural Network <p>Breast cancer is the world's second most frequent cancer among women. In 2012, new cancer cases made up 12% of all new cases, with female malignancies accounting for 25% of all cancer diagnoses. These cells can be detected by an x-ray or a bump on the body. To be cancerous, a tumour must have cells that have invaded or spread to other areas of the body. Develop an algorithm that can determine whether or not a patient has breast cancer based on biopsy photos. To protect human life, the algorithm must be extremely precise. In this study, a database of breast cancer photos is used for analysis, and the categorization is done using a deep learning approach. The deep learning model is applied by implementing a Convolutional Neural Network with transfer learning. The accuracy has achieved more than 96%, which is better than other states of the art algorithms.</p> Tripti Sharma, Rajit Nair, S. Gomathi Copyright (c) 2022 International Journal of Modern Research Sat, 09 Apr 2022 00:00:00 +0000