Entrepreneurs contribute to economic development regarding innovation, job creation and income generation. It is also effectively recognized as a fundamental part of economic development and vital in lifting countries from poverty. Women entrepreneurs organize and manage an organization, particularly a business. Women’s entrepreneurship has consistently expanded worldwide during the 20th and 21st centuries. Bangladesh will have a big number of young women and productive workers after the COVID-19 epidemic, allowing them to engage in the Small and Medium Enterprises (SME) sector. Women entrepreneurs can help in the elimination of hunger, the reduction of inequities, and the improvement of children’s nutrition, health, and school attendance. Also, it has been acknowledged as an important source of economic growth since it creates new jobs and provides society with various solutions to managerial, organizational, and business challenges. Therefore, the purpose of this research was to determine the impact of technological factors, parental support factors, municipal factors, knowledge and skills factors, psychological factors, and financial factors on women’s engagement in innovative business development in the context of Bangladesh’s SME sector in the post COVID-19 pandemic. This study included both statistical and discussion of the data. Data was collected from over 300 real Bangladeshi ladies using an online purposive sampling approach. To test hypotheses, the data were evaluated using descriptive statistics, collinearity statistics, and other regression analyses. As per study’s results, most variables, including administrative, scientific, and familial cooperation, knowledge and skills, psychological, and financial component, has both advantages statistically impressive relationship with women’s involvement in the context of the SME area in Bangladesh in the post COVID-19 pandemic. In the post COVID-19 pandemic, everyone from every sector will use the findings to encourage women’s participation in entrepreneurial activities in the context of the SME division in Bangladesh.

Background Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. The optimum classification technique helps make the proper choice and delivers the best therapy. Despite several significant efforts and encouraging discoveries in this subject, precise segmentation and classification remain challenging tasks. Method In this study, we proposed a new method for the exact segmentation and classification of brain tumors from MR images. Initially, the tumor image is pre-processed and segmented by using the Threshold function for removing image noises. To minimize complexity and enhance performance used Discrete wavelet transformation (DWT) for getting the accurate in MR Images. Principal component analysis (PCA) are used to condense the feature vector dimensions of magnetic resonance images.Finally, for differentiate between benign and malignant tumor types, the Classification stage employs a pre-trained Support Vector Machine with several kernels, also known as a kernel support vector machine (KSVM). Result The efficacy of the suggested approach is also compared to that of other existing frameworks for segmentation and classification. Results demonstrated that developed approach is effective and quick, where as we obtained excellent accuracy and recognized the brain MR Images as normal and pathological tissues.

Shadow detection is a fundamental challenge in the field of computer vision. It requires the network to understand the global semantics and local details of the image. All existing methods depend on the aggregation of the features of a multi-stage pre-trained convolution neural network but in comparison to high-level capabilities, low-level capabilities provide less to the detection performance. Using low-level features not only increases the complex difficulty of the network but also reduces the time efficiency. In this article, we propose a new shadow detector, which only uses high-level features and explores the complementary information between adjacent feature layers. Experiments show that the technique in this paper can accurately detect shadows and perform well compared with the most advanced methods. The detailed experiments performed in three public shadow detection datasets SUB, UCF, and ISTD demonstrate that the suggested method is efficient and stable.

The most flexible and reliable technological system is Wi-Fi, which is made possible by a wireless connection that transmits data using radio frequencies. Wi-Fi networks, however, encounter numerous issues related to power supply, availability, efficiency, and security as a result of the various access points. While relational waves describe the medical device, Wi-Fi radios produce radio waves that are very dangerous for patients. This document offers line-of-sight communication between the transmitter and receiver using LED technology. Li-Fi technology is a method that transmits audio data using LED light, which is faster and more efficient than Wi-Fi. Since it is practically ubiquitous, light can be used for communication as well. A cutting-edge technology called optical communication includes a subset called light fidelity. By sending out visible light, the Li-Fi device enables wireless intranet communication. This paper is an in-depth study and analysis of Light Fidelity (Li-Fi), a novel technology that transmits data at high speeds over a wide spectrum by using light as a medium of transmission. The research fields that are pertinent to Li-Fi networks are thoroughly analyzed and categorized in this paper: high speed data transmission, receiving, sharing, broadcasting through light in free space optical communication system by Li-Fi technology. In this paper, we followed some methods and developed a unique method to develop this study: VLC, OOK, a Lambertian discharge mechanism, LOS, NLOS, or a CMOS optical receiver. The proposed model tested transmits and receives audio, video, and other data, which is very high-rated and near the 2 GB/s range.