http://localhost/xmlui/handle/123456789/5458 Publications by students of COPAS 2026-04-07T00:40:40Z http://localhost/xmlui/handle/123456789/6919 Hidden Markov Model for Cardholder Purchasing Pattern Prediction Okoth, Jeremiah Otieno, Abstract—This study utilizes the Hidden Markov Model to predict cardholder purchasing patterns by monitoring card transaction trends and profiling cardholders based on dominant transactional motivations across four merchant sectors, i.e., service centers, social joints, restaurants, and health facilities. The research addresses shortfalls with existing studies which often disregard credit, prepaid, and debit card transactions outside online transaction channels, primarily focusing only on credit card fraud detection. This research also addresses the challenges of existing prediction algorithms such as support vector machine, decision tree, and naïve Bayes classifiers. The research presents a three-phased Hidden Markov Model implementation starting with initialization, de-coding, and evaluation all executed through a Python script and further validated through a 2-fold cross validation technique. The study uses an experimental design to systematically investigate cardholder transactional patterns, exposing training and validation data to varied initial and transition state probabilities to optimize prediction outcomes. The results are evaluated through three key metrics, i.e., accuracy, precision, and recall measures, achieving optimal performance of 100% for both accuracy and precision rates, with a 99% on recall rate, thereby outperforming existing predictive algorithms like support vector machine, decision tree, and Naïve Bayes classifiers. This study proves the Hidden Markov Model’s effectiveness in dynamically modeling cardholder behaviors within merchant categories, offering a full understanding of the real motivations behind card transactions. The implication of this research encompasses enhancing merchant growth strategies by empowering card acquirers and issuers with a better approach to optimize their operations and marketing synergies based on a clear understanding of cardholder transactional patterns. Further, the research significantly contributes to consumer behavior analysis and predictive modeling within the card payments ecosystem. Keywords—Hidden Markov Model; cardholder transaction patterns; merchant categories; predictive algorithms MSc Research Publication 2026-03-18T00:00:00Z http://localhost/xmlui/handle/123456789/6911 Waveform based speech coding using nonlinear predictive techniques: a systematic review Gebremichael, Sheferaw Kibret Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effec tive waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial f ilters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models PhD Research Publication 2026-03-05T00:00:00Z http://localhost/xmlui/handle/123456789/6910 Interactive Multimedia Association-Adaptive Differential Pulse Code Modulation Codec With Gated Recurrent Unit Predictor Gebremichael, Sheferaw Kibret Speech coding is important for effective storage and transmission of audio signals. However, current Interactive Multimedia Association Adaptive Differential Pulse Code Modulation (IMA-ADPCM) speech coding techniques that use a fixed predictor have an impact on the encoding of dynamic and non-stationary speech signals. The limitation of the fixed predictor in IMA-ADPCM speech coding is the motivation for this study. Our goal is to improve the fixed predictor by integrating a GRU predictor that can adapt to and make better predictions of dynamic speech signals. We evaluated the performance of the IMA-ADPCMencodingbaselineandtheGRUpredictorembeddedwiththeIMA-ADPCMcodecalgorithm. The proposed pre-trained GRU predictor based encoding system outperformed the maximum Signal-to Noise Ratio (SNR) (43.2 dB and MOS scores 3.8 to 4.3) of 5.0, and our results demonstrated considerable improvements in audio quality. The main contribution of this study is the development of a GRU Predictor that integrates IMA-ADPCM coding algorithms according to the IMA-ADPCM output speech sample and the actual PCM speech sample dataset required. By integrating the GRU predictor model in accordance with these data samples, the newly designed algorithm significantly improved the quality of the IMA-ADPCM speech codec PhD Research Publication 2026-03-05T00:00:00Z http://localhost/xmlui/handle/123456789/6809 Phytochemical characterization, antimicrobial and antioxidant activities of Terminalia catappa methanol and aqueous extracts Wangui, Clement Mwangi Abstract Background A study carried out by World Health Organization revealed that around 80% of individuals globally depends on herbal forms of medication with 40% of pharmaceutical products being sourced from medicinal plants. The study objective was to evaluate the phytochemicals composition, in vitro antimicrobial and antioxidant proper- ties of the leaves of Terminalia catappa L. aqueous and methanolic extracts. Methods Antimicrobial activity was analyzed by disk diffusion, the minimum inhibitory concentration in-vitro assays with ciprofloxacin as the standard for antibacterial assay while nystatin for antifungal assay. Ferric reducing antioxi- dant power and 2,2-diphenyl-1-picryl-hydrazyl-hydrate assays were used for the evaluation of antioxidant proper- ties of the crude extracts while the groups responsible for this activity identified using Fourier transform infrared spectrophotometer. Results The study found that the leaves of Terminalia catappa contained alkaloids, tannins, steroids, cardiac glyco- sides, flavonoids, phenols, saponins, and coumarins, but terpenoids were absent. Presence of functional groups asso- ciated with this class of compounds such as OH vibrational frequencies were observed in IR spectrum of the crude extracts. Methanolic extract from Terminalia catappa exhibited greater antibacterial properties against Pseudomonas aeruginosa, Escherichia coli and Staphylococcus aureus, whereas aqueous extract displayed greater antibacterial activity against Bacillus subtilis for all concentrations tested. The amount of the sample that scavenged 50 percent of DPPH (IC50) was found to be 8.723, 13.42 and 13.04 μg/mL for L-ascorbic acid, Terminalia catappa L. methanolic and aqueous extracts respectively. The antimicrobial and antioxidant activities varied with the extract concentration and solvent used in extractions. Conclusion Terminalia catappa L. leaves are prospective for use as a source of therapeutic agents that could lead to the advancement of new antimicrobial and antioxidant product 2025-10-28T00:00:00Z