Journal of Applied Artificial Intelligence

Next-Generation Artificial Intelligence for Sustainable and Intelligent Food Production Systems: A Review

2026-04-12T21:19:39+00:00

Artificial Intelligence has become an indispensable force in changing the public's perception of every field, turning sustainability into climate friendliness, with smart food production being one of the largest casualties of this transformation. In a world facing mounting environmental challenges and ever-increasing pressures to maintain the global food supply, artificial intelligence demonstrates characteristics which are especially beneficial for improving production efficiency, reducing waste and improving quality at each stage of an agricultural or food value chain. The article makes a systematic analysis of new developments in artificial intelligence applied to food systems, covering both resource optimization and waste reduction at the supply chain level; predictive analytics for livestock management and precision agriculture; as well as high-sensitivity food manufacturing processes. The paper shows that the integrated power of advanced technologies such as robotics, computer vision, deep learning (DL), machine learning (ML), and the Internet of Things (IoT) promotes smart and sustainable manufacturing systems. A lot of quantitative data suggest that artificial intelligence on the one hand can achieve monitoring today's crops, early discovery of diseases and pests in them, intelligent irrigation management and safety standards for food production. However, there are still significant obstacles that prevent widespread adoption of these technologies: shortages in infrastructure, weaknesses in managing data like that from IoT devices (which are usually generated on your premises), economic constraints linked with further investment and ethical issues associated with deploying algorithms in such sensitive environments.

Exploration of Human Design with Genetic Algorithms as Artistic Medium for Color Images

2026-03-09T22:33:35+00:00

Genetic Algorithms (GAs), a subclass of evolutionary algorithms, seek to apply the concept of natural selection to promote the optimization and furtherance of attributes/features designated by the user. GAs generate a population of chromosomes represented as value strings, score each chromosome with a fitness function on a defined set of criteria, and mutate future generations depending on the scores ascribed to each chromosome. In this paper, each chromosome is a bitstring representing one canvased artwork. Artworks are scored with a variety of design fundamentals and user preference. The artworks are then evolved through thousands of generations and the final art piece is computationally drawn for analysis. GAs have applications in various domains such as hyperparameter tuning, mathematical optimization, reinforcement learning, and black box scenarios. Neural networks are favored presently in image generation due to their pattern recognition and ability to produce new content; however, in cases where a user is seeking to implement their own vision through careful algorithmic refinement, genetic algorithms find a place in visual computing. This paper shows that GAs can act as a medium for artistic expression.

A Predictive Model for Early Asthma Detection

2026-01-29T19:24:56+00:00

Asthma is a respiratory disease that affects millions of people and has become one of the major causes of death worldwide. Early predictions of asthma can help health workers take the necessary precautions to prevent further complications. The traditional ways of asthma prediction are no longer effective because they are prone to error and time-consuming. Studies have employed sophisticated techniques, such as machine learning and deep learning, for asthma prediction, yielding promising results. However, previous research failed to consider datasets from different demographics, limiting the models to a particular population. Also, previous research has failed to perform a thorough comparative analysis between ensemble, machine learning, and deep learning models. This research addresses these gaps by developing a comparative multi-source predictive framework using four different algorithms, including Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron, and a hybrid Stacking Ensemble model (SVM + RF + MLP), using datasets collected from Federal Teaching Hospital Lokoja, Specialist Hospital Lokoja, and Kaggle data. The dataset undergoes the process of an 80/20 stratified train/test split, removing of low variance using a threshold of 0.01. The training set was balanced using SMOTETomek. The three models (RF, SVM, MLP) were tuned using GridSearchCV hyperparameter optimization (with 5-fold cross-validation) before combining the best-performing models in a stacking ensemble (SVM + RF + MLPF) with a LogisticRegression meta-learner, leveraging kernel-based, tree-based, and neural network models for improved predictive performance. The models' performances were compared using Accuracy, Recall, F1-Score, and AUC-ROC. The hybrid stacking ensemble model achieved the highest AUC-ROC (0.9910) while maintaining 99.23% accuracy and an F1-score of 0.9920. RF and the ensemble models identified the most important predictors, variables that distinguish between asthmatic and non-asthmatic patients. The study demonstrates that integrating heterogeneous datasets improves predictive robustness and provides a strong foundation for real-world asthma detection systems.

Infrastructure-Mediated Multilateralism

2026-01-16T17:59:24+00:00

International AI governance confronts a paradox: normative consensus on principles like transparency, fairness, and accountability has never been stronger, yet this consensus has failed to prevent ethics washing, capability concentration, or harmful deployments. This paper argues that the problem lies not in insufficient agreement but in absent infrastructure for translating principles into verifiable practice. We propose the Multilateral AI Commons for Peace and Sustainability (MACPS), a five-layer technical architecture addressing three governance deficits: coordination failures across fragmented regulatory regimes; capability asymmetries between Global North and South; and credibility gaps enabling performative compliance. Drawing on Huayan Buddhist philosophy's concept of 'perfect interfusion'—mutual constitution without loss of distinctiveness—we theorise infrastructure-mediated multilateralism: cooperation emerging from shared technical systems that generate material interdependence rather than relying on normative alignment alone. Engaging critically with scholarship on the politics of infrastructure, we argue that technical systems' inevitable embedding of values presents an opportunity for intentional design that encodes equity commitments as architectural constraints. The architecture comprises semantic ontology for standards interoperability, federated compute resources with equity-weighted allocation, privacy-preserving data trusts, mechanism-level evaluation benchmarks, and machine-readable implementation guides. This design paper demonstrates that shared infrastructure can achieve governance objectives that normative frameworks alone cannot, offering a path forward for multilateral cooperation amid geopolitical fragmentation.

End-to-End Multimodal Emotion Recognition with Deep Temporal and Cross-Modal Feature Integration

2025-12-31T12:51:44+00:00

Abstract: Emotion recognition has advanced significantly with the adoption of deep learning methods, yet reliable inference of affective states remains challenging under real-world conditions characterized by noise, occlusion, and expressive ambiguity. These limitations are particularly evident in unimodal systems that rely on a single source of affective information. To address this challenge, this study proposes a novel end-to-end multimodal framework for temporal emotion recognition that jointly models facial and vocal cues within a unified deep learning architecture. The proposed framework integrates deep residual networks for spatial and spectral feature encoding with bidirectional long short-term memory networks for sequence-level temporal modeling. Audio signals are represented using Mel-Frequency Cepstral Coefficients, while facial information is extracted from video frames, with both modalities processed using a shared ResNet-50 backbone to ensure consistent high-level representations. To enhance multimodal interaction, the framework incorporates attention mechanisms that refine modality-specific temporal features and explicitly align audio and visual representations prior to classification. The model is evaluated on the RAVDESS and CREMA-D benchmark datasets using strict subject-disjoint cross-validation to ensure unbiased assessment of generalization performance. Experimental results demonstrate that the proposed approach achieves classification accuracies of 91.22 percent on RAVDESS and 87.32 percent on CREMA-D, outperforming recent multimodal methods evaluated under comparable conditions. Confusion-matrix-based analyses further indicate improved discrimination among emotionally overlapping categories. These results demonstrate that jointly modeling deep spatial representations, temporal dynamics, and adaptive cross-modal interaction yields robust emotion recognition under realistic variability. The proposed framework provides a transparent and extensible foundation for future research in multimodal affective computing and emotion-aware intelligent systems.

A Hybrid CNN-BiLSTM Framework with Attention-Based Explainability for Interpretable Fake News Detection

2025-11-23T19:34:46+00:00

The rapid spread of fake news on social media has far-reaching implications, affecting various aspects of national life, including political stability, governance, economic systems, public health, and education. These platforms not only facilitate information exchange but also shape global public opinion. Effective fake news detection on social media is therefore essential to protect democratic processes, maintain public trust in institutions, prevent election manipulation, mitigate social polarization and violence, reduce the spread of health misinformation during crises (such as pandemics), curb financial fraud, and preserve the integrity of public discourse. Despite the ongoing efforts in combating fake news, most existing solutions remain limited in scope and interpretability. This study evaluates deep learning (DL) models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and a hybrid DL model (CNN-BiLSTM with attention mechanism) to improve both robustness and prediction interpretability. The proposed model (CNN-BiLSTM+Attn) was evaluated on a Nigerian social media news dataset (FN_data, 126,974 records after deduplication and preprocessing) across five independent training runs with different random seeds (42 - 46), yielding a mean accuracy of 82.86% ± 0.22% and mean F1-score of 77.12% ± 0.52% as the best performing model, with statistically significant improvement over the other models (p < 0.05). The proposed model was further validated on a standard benchmark dataset (The ISOT fake news dataset), achieving mean accuracy of 99.33% ± 0.10% and mean F1-score of 99.39% ± 0.09% over ten independent runs (seeds 42 - 51). This improvement on ISOT confirms the robustness of the proposed model. Attention visualizations provide token-level explainability, highlighting the model’s focus on deceptive cues. This work provides a transparent