EDITOR

ABSTRACT

PUNE RESEARCH DISCOVERY 

AN INTERNATIONAL JOURNAL OF ADVANCED STUDIES

( ISSN  2455 - 9202 ONLINE ) (JIF 3.96) 

 (VOL 9, ISSUE 1)   (FEB to APR 2024) 

ADITI KUMAR

ABSTRACT

The purpose of this paper is to discuss Gender, as an issue in children's literature and how gender roles stereotype men and women in society, also an attempt to solve the problem of gender bias in the domain of children's literature ensuring equality between men and women concerning children's books. Books that convey the message of gender bias or sexism may have damaging effects on both boys and girls. For instance, Stereotypical representations might engrain females with the idea of selecting more traditional areas of employment. As childhood is defined as the most impressionable age, it should be taken care of what type of content is being exposed to the child. Sexism is based on the idea that women are less efficient than men and functions to oppress women in society. It should be the sole aim of the writer to offer content that talks about equality and a patriarchy-free community giving an image of an  Egalitarian society.

Keywords: Gender bias, Stereotype, Sexism, Patriarchy, Egalitarian society.

SUDHIR J. JOSHI; DR. MAGAN P. GHATULE & DR. SONAL SINGLA

ABSTRACT

Image segmentation, a long-standing area of ​​research, is central to image processing because it provides meaningful insight into image content. This is essential for understanding and analyzing images, which is a major challenge for computer vision. Digital media images are common in various fields and require efficient processing techniques. This paper presents a new digital media image segmentation algorithm that contributes to the continuous development of image processing. In addition, it explores the role of image enhancements, particularly in applications such as medical imaging and monitoring. The study compares Self-Adaptive Mapping (SAM) algorithms with existing methods and highlights their effectiveness in improving image quality. In addition, it presents a general approach to image segmentation and object detection by adapting algorithms to different environmental conditions to achieve optimal performance. The adaptation process involves reinforcement learning techniques that improve both image segmentation and object recognition results. This paper presents image segmentation using k-means clustering and SAM-based image segmentation. It focuses on a comparative study of both methods, their advantages, and their applications.

Keywords: Image segmentation, image enhancement, self-adaptive mapping (SAM), clustering, MATLAB.

DR. MAGAN P. GHATULE, DR. KAILASH ASERI, VARSHA GHATULE SUDHIR J. JOSHI

ABSTRACT

Safety and security are paramount in today's society, increasing the need for effective antitheft measures. This paper presents an Antitheft Strategy (ATS) based on the Internet of Things (IoT), which focuses on improving visual theft image notification systems using IoT technology and advanced image processing. The ATS system aims to improve the accuracy of theft detection and reduce risk. It covers design, implementation, and evaluation and demonstrates the potential of IoT-based solutions to strengthen security measures. Safety and security considerations require effective antitheft measures. In this regard, the paper proposes ATS using Raspberry Pi (RP) and required image enhancing for real-time theft detection and notification. The system uses the camera and PIR (passive infrared) sensor, which detects movement. Additionally, a cost-effective intrusion detection system notifies owners and neighbors regardless of unauthorized activity, thus improving security. The need for security frameworks cannot be overstated.

In this paper we offer a security investigation system capable of conducting intrusion investigation and notifying owners via a mobile application. Our system autonomously detects motion and sends instant notifications and images of intruders. This study introduces an economical intrusion detection system employing human detection algorithms and IoT-based applications for remote monitoring and notification via three mediums: text message, alarm/lighting, and theft image notifications or transformations on the web. The researcher concentrated on enhancing image quality for effective detection, with detailed analysis to be conducted by the antitheft agency or security personnel. Traditional surveillance systems lack scalability and require continuous monitoring. However, an IoT-based theft detection system using Raspberry Pi (RP) includes real-time image capturing and its processing to detect thieves by movement. Images are transmitted over the Internet for online viewing and stored for later review. The system improves the accuracy of theft detection and reduces risks ultimately by means of image processing using Matlab IP tools. Additionally, in this research study, open-source platforms like Python and PHP are also utilized to complement the building blocks of the ATS-IoT system, which is applied in various places like offices, homes, and ATM shops for security.

Keywords: Matlab, Motion Detection, Image Processing, Burglar Detection, Android Notification, Buzzer, Alarm.