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

Optical cables are enormous transmission media which carries high-speed data across transatlantic, intercontinental, international boundaries and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though optical time-domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning (ML) approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci-kit learn library was used to predict the actual location of a fault in an underground optical network. The MSE and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.

Optical cables are enormous transmission media which carries high-speed data across transatlantic, intercontinental, international boundaries and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though optical time-domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning (ML) approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression technique was used to predict the actual location of a fault. Single-layer perceptron and sigmoid activation function were applied in the predictive model to obtain good accuracy value.