A market which provides an innovative way to allow customers to interact with each other called Customer-to-customer (C2C) market. In C2C communications, online communities play an important role in decision making to buy a product. This investigation develops a research model for online communities of Facebook commerce (F-Commerce) in Bangladesh region, which is based on Information Adoption Model (IAM). This study exhibits a model to influences of C2C communication on Bangladeshi consumers' purchase decision in the online communities of F-Commerce. The proposed model used the Partial Least Squares (PLS) technique to test 120 effective survey data. This survey data has been taken from the Bangladesh Facebook users and strongly involved in product buy-sell at F-Commerce. The analyzed results show that Argument Quality (AQ), Source Credibility (SC) and Tie Strength (TS) positively influence Purchase Decision (PD) through Product Usefulness Evaluation (PUE). In addition, Tie Strength exhibits difference effect on Product Usefulness Evaluation between the contexts of consumers communicating with virtual consumers relationships. Theoretical and executive implications are discussed for constructing our proposed model.

Automated attendance management system will reduce complexity by eliminating plenty of manual processes involved in attendance system and calculating hours attended. This paper presents a simple technique of taking student attendance in the form of an Internet of Things (IoT) based system that records the attendance using fingerprint-based system and stores them securely in a database. We use NodeMCUV3, RFID Module and Fingerprint sensor module in our system. The fingerprint module is responsible for authentication of the students. RFID Module is used to scan the RFID tag and sends data to the central server. By using this information, the system will generate an attendance report which can be accessed for further use.

Health-related issues and occurrences with regard to a particular population are the subject of an epidemiology study. This paper presents the results of a retrospective epidemiological investigation on 15922 hospitalized hand trauma patients from Central China between 2011 and 2020. Gender, age, onset season, injury mechanism, injury environment, injury location, and clinical characteristics are among the characteristics of the data gathered. The study is using computational analysis to draw inferences from the case studies collected in the databases of the hospitals. The types and characteristics of occupational injuries at home and outdoor are compared and analyzed. The purpose of the study is to present the findings from recent case studies for future reference and to recommend useful roles for the industrial sector in the care of patients with hand trauma in order to lower occupational harm. The injuries of preschool children are also analyzed. The incidence rate of hand injuries in infants has been increasing year by year which is directly related to the inefficient growth of children in rural areas. The data are collected from hospitals, then the data analytical tools are applied to draw conclusions. The suggested model is intelligently learned through the application of computational techniques, which are also used to suggest treatments to trauma victims. According to this study, males are more likely than females to sustain hand trauma; occupational injuries are more common than living injuries; males between the ages of 20 and 50 are at an increased risk of suffering an occupational injury. This study showed that the proportion of hand trauma in preschool children was higher (12.27%), and the 2-3-year-old group was the main injury target of preschool children (45.70%). The accidental injuries of newborns and young children can be reduced by government assistance, social support, and tighter monitoring.

Marriage is a momentous event in anyone's life. It is not just an ordinary relationship. This partnership is supported legally by a civil contract between a man and a woman. According to Islamic values, the Muslim community keeps records of their marriage contract called Nikah-Nama. Currently, the Bangladesh government records the Nikah-Nama in a classic logbook. It is 2022, and this sector has not seen significant upgradation for decades. This system is highly inefficient, prone to impairment, and has fraud loopholes. Corrupt citizens use these loopholes to cheat life partners, cross the marriage limits and conduct an enormous number of offenses. This paper proposes an approach to revolutionize the entire marriage recording system of Bangladesh. It describes step-by-step procedures and the better way to implement a digital Muslim marriage data preservation system. Bleeding-edge technologies are prioritized in this work by keeping web 3.0 in mind to bring innovation to this segment. This paper reflects the minimal approach to the proper digitalization of this issue. The whole idea of this concept is highly scalable. This prototype implementation is ready for any community, group, religion, or Government with an affordable technical infrastructure. The demonstration version is developed according to the conventional marriage rules and guidelines of Bangladesh. Nevertheless, others can also adopt this software ecosystem with minor or further modifications.

Breast cancer is a prevalent and potentially lifethreatening disease that affects millions of individuals worldwide. Early detection plays a crucial role in improving patient outcomes and increasing the chances of survival. In recent years, machine learning (ML) techniques have gained significant attention in the field of breast cancer detection and diagnosis due to their ability to analyze large and complex datasets, extract meaningful patterns, and facilitate accurate classification. This research focuses on leveraging ML algorithms and models to enhance breast cancer detection and provide more reliable diagnostic results in the real world. Two datasets from Kaggle have been used in this study and Decision tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Classifier (KNN) etc. are applied to identify potential breast cancer cases. On the first dataset, A, the test's accuracy using Logistic Regression, SVM, and Grid SearchCV was 95.614%, however in dataset B, the accuracy of Logistic Regression and Decision Tree increased to 99.270%. The accuracy of Boosting Decision Tree was 99.270% when compared to other algorithms. To defend the performances, various ensemble models are used. To assign the optimal parameters to each classifier, a hyper-parameter tweaking method is used. The experimental study examined the findings of recent studies and discovered that LRBO performed best, with the highest level of accuracy for predicting breast cancer being 95.614%.