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This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.

As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.

Fading and obstacles constitute major threats to effective quality of service (QoS) delivery in wireless local area network (WLAN) environments. In this contribution, we investigate the signal quality of indoor and outdoor WLANs over a defined coverage area. We present experimental analysis of case studies that will be useful for further research and validate the system’s performance in practice. Using an optimized form of the pathloss models, a simulation of the system is carried out over short and extended coverage. Simulation results show that signal quality could be effectively managed to improve the system’s performance for both indoor and outdoor environments in the presence of fading and other environmental factors.

An attempt has been made in this paper to access the control layer of the General Packet Radio Service (GPRS) network base station (BS), using throughput optimization. We focus on the effect of packet transmissions on the throughput of forwardlink (downlink) power balancing, with specific interest on the relative throughput of the four GPRS coding schemes for a realistic GPRS network operating in Nigeria. We derive a model capable of improving the channel quality and simulate the throughput and choice of optimum coding scheme over diverse packet sizes using the MATrix LABoratory toolkit. Simulation results show that the existing system requires effective network optimization to improve the system’s throughput performance.

In this paper, the signal propagation characteristics of urban environments are predicted by modeling important propagation parameters of the Code Division Multiple Access (CDMA) network for macrocells. The MOPEM propagation model has been selected as a model of choice due to its robustness in handling urban parameters. The model is simulated with some modifications using empirical data from the Visafone CDMA gathered from the Nigerian Telecommunications Limited (NITEL), Uyo, Nigeria and data from the field. From the simulation, we observe that propagation model parameters such as orientation angel, street width, building height, among others, has great influence on the system performance of CDMA wireless networks. We hope that with this research, systems designers could approach the installation of radio frequency equipment with some degree of confidence that the transmission link will effectively work, especially in urban areas.

An accurate procedure for evaluating the strong correlation and conflicting goals of coverage, capacity and Quality of Service (QoS) is required for efficient planning of 3G radio networks. In this paper, we explore and implement an agent-based simulation methodology which allows for the fine-tuning of the input model parameters to study and evaluate the performance of Code Division Multiple Access (CDMA) systems. The results obtained are represented graphically to show scenarios for both uplink and downlink limited CDMA. The analysis is extended to demonstrate how Tower Mounted Amplifiers (TMA) can be used to benefit the uplink performance of a 1× EV-D0 data system.