To address the challenge of requiring extensive data and computational resources in intelligent control game systems, this paper proposes a triangular game relationship model on a plane, involving dynamic player A, dynamic player B, and the fixed-point base camp b of player B. The model, which is typically used in real game scenarios where only small-scale effective data can be obtained to support the operation of intelligent game control systems, eliminates the necessary need for complete information in the adversarial deterrence game. In this triangular game relationship model, the coordinate position relationships between player A, player B, and player B's base camp b provide the constraint conditions for optimizing confrontation positions. Subsequently, an objective function for player A's optimal game position is constructed, and the local optimal confrontation position is derived and verified by incorporating the optimization constraints.

Qualitative models help policymakers understand land-use changes by examining the processes involved and predicting future patterns. This study aims to use dynamic synthesis methodology to establish cause-and-effect relationships. The study, conducted in the Mt. Elgon region encompassing Mbale, Manafwa, Bududa, and Namisindwa districts, utilized questionnaires and interviews to gather data. Findings suggest that short-term solutions to reduce wood fuel consumption include raising awareness, promoting mindset change, and providing subsidies for alternative energy sources. Long-term strategies involve advocating for tree planting, strengthening land use policies, and ensuring strict implementation. While stakeholder engagement does not automatically guarantee high-quality decision analysis, it is crucial for promoting natural resource protection and fostering a sense of ownership over the issue.

One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.

The economic boom over the recent past and the quest to further develop has made several nation states the business hubs in their regions. Along with the investments, there has been growth in the number of property sales. Social media has become convenient platform of choice for advertising property sales after the introduction of Web 2.0. This article utilizes social media platforms like Facebook to scrape data from user groups advertising properties and then using data mining techniques and approaches to determine true valuation of properties. This methodology is based on set attributes in the urban areas by looking at the property sales of the recent past within the same area. This enables investors interested in these properties and provides a fair idea of price of properties based on the key attributes associated with the respective property.

During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, the authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of nine factors have been considered which are classified into risk and preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of clinical and epidemiological factors with respect to COVID-19 using a fuzzy approach.

This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.

By the second decade of the 21st century, there has been a multi-faceted technological development in the field of networked control system (NCS). This progression in NCS has not only revealed its significant applications in various areas but has also unveiled various difficulties associated with it that hampered the operations of networked control system. Network-induced delays are issues that promote many other issues like packet dropout and brevity in bandwidth utilization. In this research article, network-induced delay has been curtailed by using the harmony between Smith predictor and Markov approach. The error estimation of the Smith predictor controller used for the simulation is carried out through a Markov approach which allows the control of the system to operate smoothly by optimizing the control signal. To implement the proposed method, the authors have simulated a third order system in Matlab/Simulink software.