Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.

When it comes to tracking goods from manufacturers to consumers, barcodes are useful instruments for monitoring and certifying their legitimacy. Because of its capacity to self-validate all generated codes, the EAN-13 is the most extensively used barcode for processed consumable products. Further investigations into the construction of the EAN-13 barcode revealed that several additional components were required to make the EAN-13 more credible for consumer use. Country code, production code, product code, and check digit make up the present EAN-13 format. Meanwhile, the type of product and the number of times it has been manufactured are key components that must be included in the EAN-13 barcode structure for barcode analysts to interpret. The study presented the GHBS-13, an upgraded barcode structure that captured the two new components, namely the product type and production count. The paper proposed a universal method called Tabiri Check Digit (TCD) as a mathematical means of easily computing the check digit of the two barcodes. The formula was validated using EAN-13 and GHBS-13 barcodes, and the results were correct. The study also established a central point platform for customers to use to validate processed consumable products they buy in Ghana

Government policies face challenges whenever it is implemented or proposed. The ordinary Ghanaian always feels the down side of government policies. This paper ponders on the government of Ghana proposed electronic levy on mobile money transactions which was announced in the 2022 budget on November 17, 2021. Using the concept of sentiment analysis and Twitter data, we have carefully studied this government policy from the perspective of the ordinary citizen. Aside from conducting a non-bias examination, a full data analysis has also been performed on the data to further expound the reasons associated with the discontentment among people in the country. The results showed that data on sentiments are enormous on social media (twitter) and it serves as means for people to share their views. Our analysis depicted that phase five recorded the highest number of data, thus, 18,423 data with 24.43%, 59.29%, and 16.28% been positive, neutral and negative respectively. In our results, phase one recorded the least amount of data with 8.93%, 89.29%, 1.78% been positive, neutral and negative respectively in a total of 1,400. In the full data analysis on 38,771 tweets, 25.50%, 59.02%, and 15.48% were positive, neutral, and negative respectively.

The field of Artificial Intelligence (AI) in recent years, has proven critical in almost every sector of the economy. Numerous applications have been developed based on the concepts and techniques provided by this field. Every field of great potential continues to draw attention and evolve. As a result, well-defined terminology also plays a key role in the development and evolution of these important fields as AI is no exception. The PRISMA Flow Diagram is used in scanning the retrieved articles from the Scopus database. A total number of 347 articles were obtained as search results based on the research query of which 21 articles provided 34 definitions. A generic definition for Artificial Intelligence is proposed.