International Journal of AI and Machine Learning Innovations in Electronics and Communication Technology

Generative Artificial Intelligence for Visual Applications: Architectures, Applications, and Challenges

Priyanka Dinesh Patil, Sai Takawale, Prasad Bhosle — Mon, 19 Jan 2026 00:00:00 +0000

Generative artificial intelligence (generative AI) represents one of the most transformative advances in modern computing, especially in the domain of visual applications. Its ability to generate, reconstruct, and enhance visual content has redefined the boundaries of creativity, automation, and perception. This study presents a systematic review of existing studies on generative AI in visual domains, focusing on three dominant architectures—Generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. The methodology involves a structured literature search across major databases, screening studies using defined inclusion criteria, and synthesizing results into thematic insights. The review identifies core technical principles, major datasets, evaluation metrics, and application areas such as image synthesis, video generation, and 3D content creation. Moreover, it discusses ongoing challenges related to reproducibility, computational cost, interpretability, and ethical considerations, including bias and misinformation. The paper concludes with emerging trends such as controllable generation, multimodal fusion, and sustainability-oriented generative modeling, aiming to guide future research toward responsible and transparent visual AI.

Explainable AI in Legal Text Analysis: A Comprehensive Review of Legal Threat Assessment Approaches

Archana Kale, Sanika Bhawale — Wed, 22 Apr 2026 00:00:00 +0000

The volume of legal documents continues to expand rapidly, and their language is becoming increasingly intricate, making manual review both time-intensive and vulnerable to individual bias and inconsistency. As a result, recent research has increasingly focused on the use of machine learning techniques to predict threat levels and evaluate legal risks directly from textual data. Although such models have demonstrated promising predictive performance, a major limitation remains the lack of transparency in their decision-making processes. In legal practice, where accountability and justification are essential, predictions without clear explanations are difficult to accept and apply. This review examines studies published between 2018 and 2025 addressing automated legal document analysis, with particular emphasis on methods that combine accurate threat prediction with explainability. The reviewed works cover a broad range of approaches, including natural language processing, deep learning, transformer-based architectures, graph-based models, large language models, and contract analysis techniques. Explainable artificial intelligence methods such as LIME, SHAP, and attention-based visualization are examined for their role in making model predictions transparent and legally meaningful. Recurring design patterns observed across the reviewed systems are synthesized into a conceptual framework of six sequential stages covering input, text processing, feature encoding, threat classification, explainability, and output. The analysis identifies dataset scarcity, inconsistent evaluation practices, and limited generalization across legal domains as the primary barriers to practical deployment. The review concludes that explainability is a foundational requirement rather than an optional feature for any AI system intended to support legal decision-making.

Artificial Intelligence-driven Optimization of Electronic Device Performance and Energy Efficiency

Melissa Haynes-Smith, Kanbiro Orkaido Deyganto, Edward Lambert — Mon, 27 Apr 2026 00:00:00 +0000

Artificial intelligence (AI) is transforming the energy efficiency landscape of modern electronic systems by enabling intelligent optimization across algorithms, hardware architectures, and mixed‑signal interfaces. Through predictive workload management, adaptive task scheduling, and reinforcement‑learning‑based control policies, AI reduces redundant computation and data movement, which are the two dominant contributors to power consumption in mobile, IoT, and embedded platforms. Complementary algorithmic advances, including model compression and lightweight neural architectures, further minimize resource usage while preserving performance. At the hardware level, innovations such as edge AI accelerators, memory‑centric and compute‑in‑memory architectures, and adaptive memristor‑based sensing pipelines improve performance per watt by tightly integrating computation with storage and signal acquisition. System‑level intelligence additionally enables coordinated energy governance in distributed electronics, supporting efficient load balancing, renewable‑energy interaction, and improved responsiveness in smart‑grid and cyber‑physical environments. Despite these advances, critical challenges persist, including reproducible energy‑measurement standards, privacy‑preserving telemetry for device analytics, robust verification of mixed‑signal AI circuits, and the absence of unified cross‑layer co‑design methodologies. Addressing these gaps is essential for the development of next‑generation electronic systems capable of delivering high‑performance intelligence while ensuring sustainable, energy‑responsible operation.