Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

The aim of this study was to critically appraise and synthesize the best available evidence on the effectiveness of interventions suitable for delivery by nurses, designed to enhance cardiac patients' adherence to their prescribed medications.

The present study reports Morchella conica Pers.1818, which belongs to the family, Morchellaceae as a new record of Iraqi macromycota based on the morphological and molecular methods. During their short and often sporadic fruiting season, this fungal species was found in mixed forest unburned areas in Branan ranges (Suliamaniya Province, Northeast Iraq). Currently, M. conica is the second Morchella species reported from Iraq. The current study aimed to introduce this new record, which is poorly studied in the Middle East. M. conica is morphologically described and phylogenetically confirmed. The relationship between this species and other species within the genus was studied using the nrDNA ITS sequences from different species and divers