This paper proposes a new mathematical model for estimating the cost of explicit Agile software development risk management with its Impact Benefit s (savings/profits). This is necessitated by the fact that despite the increase in the need for managing risks explicitly in medium-to-large scale agile software development projects presently, there are no known ways to estimate explicit risk management costs/benefits. With the proposed model, explicit risk management procedures alongside with risk management estimation techniques is made known to Stakeholders who will be able to make the right decisions on risk management costs and its impacts as well as when to utilise implicit or explicit risk management. The proposed system proves to be feasible and dependable and is evidently capable of enhancing the agile methods for use for all sizes of software projects while still maintaining the swiftness of the agile process.

The application of expert system is increasingly becoming an area of interest. Since reliability is the key matter for computer technology, fuzzy-logic-expert-system allows knowledge to be simplified by avoiding the knowledge-engineer to forestall the imaginable circumstances. This paper designed an enhanced system for position tracking, motion prediction and face recognition using fuzzy logic. Earlier research indicates that Fuzzy-Logic when applied can track the movement of an entity contrary to a dim environment, in a comparatively low improvement and run-time-costs. This enhanced system, detects and tracks objects using exclusive recognition arrangements against a dim background. This is a vital step to tracking-down the fuzzy-inputs for the motion-prediction. Fuzzy-Inference-System describes the use of correlation to get the midpoints mass of the entities in the image captured via a camera.

The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse feature generation and adaptive multi-layered recurrent prediction for the detection and subsequent mitigation of zero-day threats. The new model has been applied to the Kyoto benchmark datasets for intrusion detection systems, and compared to an existing system, that uses a multi-layer protection and a rule-based ranking (RBK) approach to detect a zero-day attack likelihood. Experiments were performed using the dataset, and simulation results show that the Deep-RL-MCB-PR technique when measured with the classification accuracy metrics, produced about 67.77%. The dataset was further magnified, and the result of classification accuracy showed about 75.84%. These results account for a better error response when compared to the RBK technique.

Artificial Intelligence (AI) is becoming pervasive. It is also an exciting field because it is making our lives much better, by doing most of the work for us. For example, driving our cars, medical jobs, accounting jobs, all sorts of jobs.

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.