Anemia is a common condition among adults, particularly in children and pregnant women. Anemia is defined as a lack of healthy red blood cells or hemoglobin. Early identification of anemia is critical for excellent health and well-being, which contributes to the sustainable development goals (SDGs), notably SDG 3. The intrusive way to detecting anemia has several hurdles, including anxiety and cost, which impedes health development. With the advent of technology, it is critical to create non-invasive techniques to diagnose anemia that can minimize costs while also improving detection efficacy. A distinct non-invasive technique is developed in this study employing machine learning (ML) models. This study's dataset contains 4260 observations of non-anemic (0) and anemic (1) children. To train the dataset, six (6) different ML models were employed: k-Nearest Neighbor (KNN), decision tree (DT), logistic regression (LR), nave bayes (NB), random forest (RF), and kernel-support vector machine (KSVM). The DT and RF models obtained the highest accuracy of 99.92%, followed by the KNN at 98.98%. The ML models used in this study produced substantial results. The models also received high marks on performance evaluation metrics such as accuracy, recall, F1-score, and Area Under the Curve-Receiver Operating Characteristics (AUC-ROC). When compared to the other ML models, the DT and RF had the best precision (1.000), recall (0.9987), F1-score (0.9994), and AUC-ROC (0.9994) ratings. According to the findings, ML models are crucial in the detection of anemia using a non-invasive technique, which is critical for health facilities to boost efficiency and quality healthcare. Various machine learning models were used in this study to detect anemia in children using palm images. Finally, the findings confirm earlier studies on the effectiveness of ML algorithms as a non-invasive means of detecting iron deficiency anemia.

In the modern era, when globalization is widespread, the intellectual potential of the population has become one of the factors of socio-economic and innovative progress. The integration of Azerbaijan into the civilized world and the provision of socio-economic development in the country depend more on the development of science and education, the level of development of new scientific knowledge, techniques and technologies, etc. Today, the importance of the formation and capitalization of intellectual potential is assessed as a factor influencing competitiveness at various levels of the economy. At the modern stage of the development of the information-knowledge economy society, the assessment of intellectual potential plays an important role in increasing the efficiency of the national economy. In the article, the existing methodical approaches to the evaluation of the intellectual potential of higher education and scientific-research institutions are comparatively analyzed and summarized. Indicators that allow the assessment of intellectual potential in the field of education and science are presented in the form of a table. Based on these indicators, the assessment of intellectual potential was carried out for the first time with one of the approaches considered, and the results were presented. This will support making optimal decisions for the development of intellectual potential.

This paper investigates teaching methods that leverage programming techniques to strengthen the understanding of partial ordering relations. Partial orders are vital in diverse domains, such as mathematics and economics. A comprehensive teaching framework is presented in this paper, incorporating standard programming languages to instruct partial order relations effectively. The approach integrates theoretical concepts, practical illustrations, and interactive programming exercises to enhance students' comprehension and application of partial order relations. Furthermore, the evaluation of teaching effectiveness and potential implications for computer science and mathematics education are discussed.

This research paper investigates the attitudes and behaviors of Sri Lankan internet users toward passwords and password managers. The study addresses the security flaws and malpractices associated with passwords and aims to identify effective password management solutions. Two surveys were conducted, one focusing on user attitudes and strategies related to passwords, and the other evaluating user experiences with decentralized offline password managers. The findings reveal that a significant portion of the participants employed complex password-creation strategies and utilized various methods for storing and reusing passwords. Male participants and individuals in the 20-29 age group were predominant in the study. Surprisingly, only a minority of participants had received training in password creation and management. The analysis also indicated that participants without training tended to create easily breakable pass-words, while those with training opted for more complex and stronger passwords. In terms of password management methods, participants without training relied on manual note-taking or memorization, while those with training pre-ferred secure password managers. Furthermore, the study found a higher prevalence of password reuse among partici-pants who used manual password creation methods compared to those who used password generators. The research underscores the need for improved password management practices and increased awareness among Sri Lankan internet users. The findings introduce novel insights into the existing knowledge of password management and lay the groundwork for developing targeted interventions and strategies to enhance security in the Sri Lankan online landscape.

Management of forest area land is a concern for community cultural values, aspirations, and involvement of local forest area communities in managing forest land with the existence of Community Based Forest Management(CBFM). In an effort to make it easier to collect data, currently, the management of forest land management data at Perum Perhutani KPH (KPH is the smallest unit of the forest management system at the site level) Tenggarong, especially in the Community Based Forest Management (CBFM) section, is still done manually by writing in a ledger. With the aim that Perum Perhutani KPH(is the smallest unit of the forest management system at the site level) Tenggarong, especially the Community Based Forest Management (CBFM) section, can use web-based application technology to be able to collect data more efficiently in terms of time and cost and print reports. The methodology of making this research uses the Waterfall methodology which consists of needs analysis, system design, program code writing, system testing, and application implementation and maintenance. The programming language used is PHP with a database using MySQL. This research develop a web-based forest area land management information system, that can facilitate and assist in recording and reporting “Pesanggem” (forest cultivator) data carried out by Community Based Forest Management(CBFM) expert staff at Perum Perhutani KPH (which is the smallest unit of the forest management system at the site level) Tenggarong.