Agriculture is essential to a flourishing economy. Although soil is essential for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy soil cannot be overstated, as a lack of nutrients can significantly lower crop yield. Smart soil prediction and digital soil mapping offer accurate data on soil nutrient distribution needed for precision agriculture. Machine learning techniques are now driving intelligent soil prediction systems. This article provides a comprehensive analysis of the use of machine learning in predicting soil qualities. The components and qualities of soil, the prediction of soil parameters, the existing soil dataset, the soil map, the effect of soil nutrients on crop growth, as well as the soil information system, are the key subjects under inquiry. Smart agriculture, as exemplified by this study, can improve food quality and productivity.

This study identified high-risk locations (hotspots) using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013–2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. The accident concentration analysis was conducted using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I statistics (spatial autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and network spatial weight matrix approaches of the Getis–Ord Gi* statistic were used in the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95–99%. However, hotspots for 2015 and 2016 have a low significance level and the pattern is random. The spatial autocorrelation analysis of the overall accident locations and the Moran’s I statistic showed that the distribution of the accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the northbound and southbound directions of the Abaji–Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features.

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there has been a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review of the relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified, and 77 met the final inclusion criteria. The key elements in the included papers are presented and conclusions are drawn from them. Results: The findings show that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for the choice of loss function in the deep learning models are presented. The limitations and lessons learnt are discussed, and some key recommendations are made. Conclusion: The discoveries and the conclusions of this work are organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

A signature-based system (SBS) is a common approach for intrusion detection and the most preferable by researchers. In spite of the popularity of SBS, it cannot detect new attacks on the network compared to anomaly-based systems (ABS). The most challenging problem of SBS is keeping an up-to-date database of known attack signatures and the setting of a suitable threshold level for intrusion detection. In this article, a network intrusion detection system based on combinatorial algorithm (CA-NIDS) is proposed. The CA-NIDS uses additional databases to enable the SBS to act as an ABS for the purpose of detecting new attacks and to speed up network traffic during traffic analysis by the combinatorial algorithm. A suitable threshold of 12 was also set based on the study of past works to lower the false positive rate. The CA-NIDS was evaluated with similar online schemes and result shows a small false-positive rate of 3% and a better accuracy of 96.5% compared with related online algorithms.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics. The majority of the farmers relied on perception and other non-scientific approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil tests and digital applications, respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizers to improve soil fertility. Although 4.8% of the farmers indicated that they had digital applications on their mobile phones, only 2.9% claimed to have used these. More than half (56.4%) of the farmers stated that a lack of awareness of the existence of digital applications and internet-enabled telephones were the reasons they have not been able to use digital applications. The majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital applications which could provide more information, especially on soil fertility, if introduced. More extensive services focusing on older, less literate farmers and farmers who hitherto did not belong to any farmers’ association are advocated for in order to encourage the use of digital applications and soil fertility management and conservation practices.

Purpose: The need for maximum cognition from massive amounts of data is becoming explosive. Different visualization mechanisms are being introduced to achieve this aim. This paper aims to consider the state of organizations as regards being abreast with the recent visualization technologies.Design/methodology/approach: In this paper, the authors look critically at the system adoption life cycle as it applied to visualization system. Also, the two forms of visualization; static and dynamic are looked at, considering the least applied and why. An evaluation of the visualization system was implemented in order to discern if it provides any benefit to organizations.Findings: The study showed that the RT‐DANGO tool when compared to result from Excel exposed some spikes at some points when visualized dynamically which are not observed using usual static graph as it exposed some unseen events. The mean time taken to finish analysis of 4,000 concurrent‐related data were 168 s using RT‐DANGO and 121 s with the MS‐Excel. The difference was significant to p<0.05 (Z=−2.040). This was considered a reasonable tradeoff for accuracy, clarity and completeness of information.Originality/value: The paper shows the factors that are responsible for visualization's usage in organizations. The paper will thus serve as an eye opener to critical issues affecting the visualization industry to the benefit of all stakeholders. Some references are made to the challenges faced in Nigeria.