Malach Obisa Amonga | Searcharxiv

arXiv2026

A literature review of recent advances in software design and architecture

Software architecture has evolved considerably in response to the increasing complexity of modern software systems, particularly those based on cloud computing, microservices, artificial intelligence (AI), and distributed computing environments. This literature review synthesizes recent studies published between 2024 and 2025 to examine emerging trends, challenges, and future directions in software design and architecture. The review adopts a thematic synthesis approach to analyse contemporary research across five major areas: architectural modelling and representation, software quality attributes and self-adaptive architectures, architectural evolution and complexity management, artificial intelligence-assisted architectural decision-making, and existing research gaps. The findings indicate that modern software architecture extends beyond traditional structural design to support continuous architectural governance, stakeholder communication, runtime observability, resilience, and intelligent decision support throughout the software lifecycle. Furthermore, the reviewed studies demonstrate that multiple architectural views, continuous monitoring, domain-driven decomposition, and AI-assisted design techniques contribute significantly to improving scalability, maintainability, adaptability, and long-term software sustainability. Despite these advances, several research gaps remain, including limited empirical validation of proposed approaches, insufficient integration of security and privacy into architectural decision-making, inadequate exploration of emerging paradigms such as edge and serverless computing, and the absence of standardized frameworks for trustworthy AI-assisted architecture.

cs.CC↗

arXiv2025

Evaluation of deep learning architectures for wildlife object detection: A comparative study of ResNet and Inception

Wildlife object detection plays a vital role in biodiversity conservation, ecological monitoring, and habitat protection. However, this task is often challenged by environmental variability, visual similarities among species, and intra-class diversity. This study investigates the effectiveness of two individual deep learning architectures ResNet-101 and Inception v3 for wildlife object detection under such complex conditions. The models were trained and evaluated on a wildlife image dataset using a standardized preprocessing approach, which included resizing images to a maximum dimension of 800 pixels, converting them to RGB format, and transforming them into PyTorch tensors. A ratio of 70:30 training and validation split was used for model development. The ResNet-101 model achieved a classification accuracy of 94% and a mean Average Precision (mAP) of 0.91, showing strong performance in extracting deep hierarchical features. The Inception v3 model performed slightly better, attaining a classification accuracy of 95% and a mAP of 0.92, attributed to its efficient multi-scale feature extraction through parallel convolutions. Despite the strong results, both models exhibited challenges when detecting species with similar visual characteristics or those captured under poor lighting and occlusion. Nonetheless, the findings confirm that both ResNet-101 and Inception v3 are effective models for wildlife object detection tasks and provide a reliable foundation for conservation-focused computer vision applications.

cs.CV↗