Yaw Osei Adjei

Yaw Osei Adjei

Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most published benchmarks report within-country performance that overstates true generalisability. This paper evaluates whether geospatial foundation model embeddings, specifically Prithvi-EO-1.0-100M and ViT-Base, outperform traditional Sentinel-2 spectral features under a Leave-One-Country-Out cross-validation scheme on 6,404 maize field observations from five African countries. The results show a clear generalisability gap: within-country random cross-validation yields moderate R^2 values, but all feature sets perform poorly under cross-country testing, with universally negative R^2. Frozen Prithvi-EO embeddings provide no meaningful advantage over engineered spectral features for cross-country prediction in this setting. The paper argues that the main limitation is a shift in yield distribution between countries rather than representation quality and releases a reproducible negative benchmark for future work.

Yaw Osei Adjei, Davis Opoku, Ephraim Abotsi et al.

Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion system trained on 2,068,901 records from 404 monitoring locations in 29 African countries (OpenAQ, 2017-2022), combining LightGBM with leakage-resistant spatial cross-validation and conformal prediction to quantify predictions and their geographic applicability limits. Under 5-fold location-grouped spatial cross-validation, LightGBM achieves RMSE = 30.83 +/- 5.07 ug/m3, MAE = 14.54 +/- 1.66 ug/m3, R2 = 0.134 +/- 0.023, and macro F1 = 0.336 +/- 0.018. This R2 is substantially below random-split benchmarks (>0.90) but reflects true geographic generalisation difficulty rather than model failure. Split conformal prediction targeting 90% marginal coverage reveals severe East Africa degradation (actual PICP = 65.3% vs. nominal 90%), consistent with medium-strength covariate shift (humidity KS = 0.2237, sat_pblh KS = 0.2558). We operationalise these findings through regional reliability flags (High/Medium/Low/Unreliable) and a monitor prioritisation score directing infrastructure expansion toward highest-burden unmonitored populations, directly supporting Africa's green industrial transition and SDGs 3.9, 7.1.2, 9, 11.6.2, and 13.

Yaw Osei Adjei, Frederick Ayivor, Davis Opoku

Business Email Compromise (BEC) is a sophisticated social engineering threat that manipulates organizational hierarchies, leading to significant financial damage. According to the 2024 FBI Internet Crime Report, BEC accounts for over $2.9 billion in annual losses, presenting a massive economic asymmetry: the financial cost of a False Negative (fraud loss) exceeds the operational cost of a False Positive (manual review) by a ratio of approximately 5,480:1. This paper contrasts two detection paradigms: a Forensic Psycholinguistic Stream (CatBoost), which analyzes linguistic cues like urgency and authority with high interpretability, and a Semantic Stream (DistilBERT), which utilizes deep learning for contextual understanding. We evaluated both streams on a hybrid dataset (N=7,990) containing human-legitimate and AI-synthesized adversarial fraud. Benchmarked on Tesla T4 infrastructure, DistilBERT achieved near-perfect detection on synthetic threats (AUC >0.99, F1 =0.998) with acceptable real-time latency (7.4 ms). CatBoost achieved competitive detection (AUC =0.991, F1 =0.949) at 8.4x lower latency (0.8 ms) with negligible resource consumption. We conclude that while DistilBERT offers maximum accuracy for GPU-equipped organizations, CatBoost provides a viable, cost-effective alternative for edge deployments. Both approaches demonstrate a theoretical ROI exceeding 99.9% when optimized via cost-sensitive learning.