Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death.

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources.

Human beings live in a world that is vulnerable to disasters and huge security threats. The common occurrence of global natural disasters and security threats call for major attention by the government of nations and the International community. Man continues to live under perpetual fear of the unknown because of these events. Notably, the human being is at the receiving end of these events either as a victim or at times as a perpetrator. Locating and rescuing victims or perpetrators of these events can be effectively achieved using smart digital devices that are configured with their unique identities and this will aid in achieving sustainable peace and stability in society. Equally, the global proliferation of digital devices is always on the rise; hardly today is there anyone on the planet earth without a form of one digital device or the other. This study is exploring the use of smart mobile digital devices, geospatial, cloud, communication, and technologies in mitigating the impact of natural and security threats with the significance of promoting peaceful human coexistence in the society. A Google App for finding a device, clock-timer app, an Internet speed check app and an online weather app were used for the experiments conducted for the proof of concept of the model. A test of model robustness was evaluated using response time and visibility. The model consistently showed good attributes of robustness in its behavior. Furthermore, the following parameters: latency, upload speed, download speed, air quality index, visibility, humidity, wind gust and temperature were observed against the response time in the experiments. A single sample (one-tail) t-test for the response time with a mean population of 0.9 gave a t-test value of 1. The value of p is 0.162791 and the result is not significant at p<.05. This indicates that there was no significant difference in the response time of the model irrespective of the time of the day, weather conditions, and the communication parameters. The result showed that the response time of the conceptual model was fast and consistent with the experiments indicating that the model can be leveraged, to address the problem identified in an affordable manner. The study is highly significant due to the fact that exploring the technologies that support location-based systems has greater potential in addressing the problem occasioned by natural disasters and security threats to humans. Future consideration is focused on enhancing the model by scaling up the biometrics features of the victim or perpetrator for speedy location.

Malaria remains a serious public health problem in many developing countries, particularly in Sub-Saharan Africa. Early detection and treatment of malaria are crucial in the fight against malaria in order to reduce morbidity and mortality, especially in the endemic regions. We set out to develop a simple, accurate, and efficient innovative diagnostic tool for malaria parasite identification that uses automated image processing to provide shorter diagnosis times while improving accuracy, efficiency, and standardization. Our primary goal in this study is to collect, curate, annotate and achieve blood smear images containing Plasmodium species for effective malaria diagnosis using Artificial Intelligent based system. The study curated 881 blood smear images which are categorized as positive and negative images.

Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the electronic identification and categorization of photographs of cassava leaves. For feature extraction and classification, the study used databases of cassava images and a deep convolutional neural network model. The methodology of this study retrained the models' current weights for visual geometry group (VGG-16), VGG-19, SqueezeNet, and MobileNet. Accuracy, loss, model complexity, and training time were all taken into consideration when evaluating how well the final layer of CNN models performed when trained on the new cassava image datasets.

This paper is set out to evaluate the performance of feature extraction techniques that can determine ethnicity of an individual using fingerprint biometric technique and deep learning approach. Hence, fingerprint images of one thousand and fifty-four (1054) persons of three different ethnic groups (Yoruba, Igbo and Middle-Belt) in Nigeria were captured. Kernel Principal Component Analysis (K-PCA) and Kernel Linear Discriminant Analysis (KLDA) were used independently for feature extraction while Convolutional Neural Network (CNN) was used for supervised learning of the features and classification. The results showed that out of sixty (60) individual fingerprints tested, eight (8) were classified as Yoruba, forty-eight (48) as Igbo and four (4) as Hausa. The Recognition Accuracy for K-PCA was 93.97% and KLDA was 97.26%. For Average Recognition time, K-PCA used 9.98seconds while KLDA used 10.02seconds. The memory space utilized by K-PCA was 94.57KB while KLDA utilized 52.17KB. T-Test paired sample statistics was carried out on the result obtained; the outcome presented reveal that KLDA outperformed the K-PCA technique in terms of Recognition Accuracy. The relationship between the average recognition time () and threshold value () was found to be polynomial of order four (4) with a high correlation coefficient for KPCA and polynomial of order three (3) with a high correlation coefficient for KLDA. In terms of computation time analysis, KLDA is computationally more expensive than KPCA by reason of processing speed.