explainable ai

Microbiome and Metabolomics in Obesity: Advances in Understanding and Interventions Across the Lifespan

ABSTRACT Obesity arises from intertwined and reciprocal diet‐microbiome‐host pathways that reshape energy balance, insulin sensitivity, and inflammation. This review synthesizes mechanistic links between microbial functions and metabolic control, charts lifestyle‐related lifecourse dynamics from birth to older age, examines how GLP‐1‐based therapies may perturb gut ecology and metabolite output and surveys AI/ML frameworks for multi‐omics integration. Plant‐based, fiber‐rich dietary patterns generally enrich saccharolytic guilds, boost SCFAs production, and modulate bile acid signaling, whereas Westernized patterns favor bile‐tolerant, amino acid‐fermenting consortia and proinflammatory metabolites. Preclinical data suggest that incretin‐based therapies remodel the microbiome‐metabolome axis, but human causal mediation remains unproven and observed changes may partly reflect weight loss or metabolic improvement. Function‐centered metrics outperform phylum‐level ratios for translation. Harmonized longitudinal cohorts and explainable ML‐derived microbial and metabolomic signatures are now pivotal to identify responder subtypes and actionable microbe‐metabolite targets, enabling precision nutrition alongside pharmacotherapy across the lifespan.

Intelligent Martial Arts Coaching Framework Using Artificial Intelligence for Real-Time Action Detection and Performance Feedback

Karate training relies on subjective manual evaluation of intricate motions, hindering scalable, consistent coaching. This paper proposes MMAF-Net, a multimodal AI framework for real-time karate action recognition and automated performance feedback. Its three-branch deep learning architecture integrates visual, pose-estimation and inertial sensor streams to extract complementary motion features, fused via a temporal attention module to classify 23 karate action types accurately. Trained and validated on MS-KARD (2.8 million video frames and 5.6 million sensor readings from dual cameras and three IMUs), the model generates explainable coaching tips through rule-based modules referencing prediction confidence, posture bias and motion stability. Tests yield 96.3% accuracy and 95.1% F1-score, outperforming benchmarks like KarateNet. With only 24 ms inference latency, this real-time system suits interactive martial arts training scenarios.

A Study to Analyze the Role of Artificial Intelligence in Enhancing Customer Experience in Digital Marketing

Customer experience has become a key differentiator for organisations operating in increasingly digital markets, and Artificial Intelligence (AI) is now widely used to make digital marketing faster, more personalised and more responsive. This study examines the role of AI in enhancing customer experience in digital marketing, with special reference to SSR Compressor Service, an industrial air-compressor sales and service organisation based in Chennai. The study adopts a quantitative, descriptive and analytical cross-sectional research design. Primary data were collected from 31 respondents through a structured questionnaire covering four composite constructs — AI accessibility and use (QTotal), AI-enabled personalisation and interaction (ATotal), responsiveness and convenience (CTotal), and overall customer experience (ETotal) — and analysed using descriptive statistics, Pearson correlation, multiple linear regression, and Cronbach’s alpha reliability testing. All four composite measures were positively and significantly correlated with one another (r = .615 to .892, p < .001). A multiple regression of the three predictor composites on overall customer experience was statistically significant, F(3,27) = 28.626, p < .001, explaining 76.1 percent of the variance (R² = .761, Adjusted R² = .734). CTotal (Beta = .640, p < .001) and ATotal (Beta = .565, p = .022) emerged as significant unique predictors of customer experience, while QTotal did not retain a significant unique effect once the overlapping variance among the predictors was accounted for. Reliability testing confirmed good internal consistency across all scales (alpha = .730 to .869). The findings suggest that AI can meaningfully enhance digital customer experience when it is implemented as a supporting technology built around personalisation, convenience and responsiveness rather than as a replacement for human interaction, and the study offers practical recommendations for SSR Compressor Service and similar industrial businesses. Keywords: Artificial Intelligence; Digital Marketing; Customer Experience; Personalization; Chatbots; Customer Engagement; Trust.

AI-Driven Risk Management Optimization for Economic Resilience and Crisis Recovery

This chapter looks at how AI techniques including ML, DL, NLP and RL can be used in many fields, such as finance, operations, cybersecurity, and healthcare. It shows how AI makes predictive analytics better, speeds up decision-making, and makes dynamic response systems better while also cutting down on human mistakes and bias. It also talks about ethical and governance issues like accountability, privacy, and openness. The chapter ends by stressing how important it is to have explainable and sustainable AI systems in order to build risk management frameworks that are optimised, data-driven, and robust. AI has become a game-changer for improving how businesses find, evaluate, and reduce risks. The business world is getting more complicated, so we need smart risk management tools that can deal with the unexpected.

Voice-Based Screening of Depression and Anxiety Using Machine Learning and Deep Learning: A Scoping Review of Methods and Clinical Readiness

Despite the high global prevalence of depressive and anxiety disorders, access to early clinical assessment remains limited for many. Although this field has grown rapidly, existing reviews have focused primarily on technical performance, with limited systematic attention to whether current models meet the prerequisites for clinical implementation. This scoping review mapped methodological approaches across this domain and evaluated clinical readiness using five predefined indicators: sample size adequacy, external validation, prospective data collection, real-world evaluation, and model explainability. Searches of PubMed, Web of Science, and IEEE Xplore (March 2026) identified 2463 records; 34 studies (37 dataset evaluations) were included. The majority of studies (91%) were published from 2022 onwards. Depression was the primary target in 91% of studies, while only one study addressed anxiety. Hand-crafted acoustic features were the most frequent (57%), while classical machine learning was the most common model type (32%). External validation was conducted in only 32% of evaluations and real-world testing in 8%. Clinical readiness was classified as Low in 24%, Moderate in 65%, and High in 11% of evaluations. No evaluation met all five indicators simultaneously. These findings apply primarily to voice-based depression screening; 36 of 37 evaluations targeted depression, and the evidence base for anxiety disorders is limited to a single evaluation, precluding comparable characterisation for that condition. The principal challenges to clinical implementation are insufficient external validation, reliance on laboratory conditions, narrow linguistic coverage, and inconsistent metric reporting. The framework applied in this scoping review provides a replicable structure for assessing the clinical validity of AI-driven psychiatric screening tools.

Predicting Craniospinal Surgery in Pediatric Achondroplasia: Benchmarking Statistical Inference and Explainable AI Under Rare-Disease Constraints

How to Build a Controlled AI Assistant for Quantum Computing

An architectural approach in which free-form dialogue is turned into a controlled computational workflow: the lifecycle stage determines the AI's goal, the context available to it, and the set of tools it is allowed to use right now. The user describes a task in ordinary language, the assistant translates it into a structured scenario, helps gather the missing information, guides it through validation, runs it on a simulation-first basis, and explains the result. The LLM does not own the computational process: experiment state is stored separately from the dialogue, the AI tools available depend on the current lifecycle stage, and any run goes through independent validation and an execution policy.

How to Build a Controlled AI Assistant for Quantum Computing

An architectural approach in which free-form dialogue is turned into a controlled computational workflow: the lifecycle stage determines the AI's goal, the context available to it, and the set of tools it is allowed to use right now. The user describes a task in ordinary language, the assistant translates it into a structured scenario, helps gather the missing information, guides it through validation, runs it on a simulation-first basis, and explains the result. The LLM does not own the computational process: experiment state is stored separately from the dialogue, the AI tools available depend on the current lifecycle stage, and any run goes through independent validation and an execution policy.

Artificial Intelligence in Smart Photovoltaic Systems for High-Efficiency Energy Conversion: A Systematic Review

Artificial intelligence (AI) is transforming photovoltaic (PV) systems from passive generators into self-forecasting, self-diagnosing, and self-optimizing energy assets. This review synthesizes 170 Scopus-indexed documents published between 2020 and 2026 across six technical domains: solar resource and PV power forecasting; intelligent monitoring, fault diagnosis, and cybersecurity; AI-driven control and design optimization; smart grid integration and real-time energy management; AI-assisted PV materials, devices, and manufacturing; and cross-sectoral smart PV applications. Quantitative synthesis shows that hybrid deep learning forecasters reduce root mean square error by 31.9–43.9% relative to persistence and single-model baselines. Attention-based architectures achieve mean absolute percentage errors of up to 5%. Machine learning classifiers achieve fault detection accuracies of 92.3–99.4% with protection response times below 100 ms, enabling predictive maintenance at the fleet scale. Reinforcement learning energy management lowers electricity costs by 20–55%, reduces peak demand by 13–31.5%, and raises PV self-sufficiency from 71.5% to 89.7%. AI-optimized thermal and material interventions deliver efficiency gains of up to 22.2%, and indoor perovskite devices exceed 40% conversion efficiency. Reported performance metrics are derived from heterogeneous datasets, horizons, and baselines; they are therefore synthesized as indicative ranges rather than directly comparable benchmarks. Persistent barriers include data scarcity, model opacity, cybersecurity vulnerabilities, edge deployment constraints, and energy justice concerns. These barriers are mapped to research directions in explainable AI, federated and transfer learning, digital twins, and blockchain-enabled energy markets. A roadmap to 2040 consolidates the findings and charts the transition toward high-efficiency, resilient, and sustainable solar energy conversion.

Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems

This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artificial intelligence–enabled AIoT interactive systems. As digital licensing, NFT copyright management, royalty distribution, and cross-chain content circulation become increasingly embedded in smart media ecosystems, existing contract auditing approaches remain limited by unimodal analysis, weak explainability, black-box decision processes, and insufficient cross-platform generalization. To address these challenges, the proposed framework integrates cross-modal data fusion, graph neural networks, and evolutionary expert knowledge fusion to model smart contract code, abstract syntax trees, control-flow graphs, intercontract transactions, cross-chain records, licensing metadata, NFT copyright attributes, and multimodal IoT interaction logs as unified graph representations. By incorporating causal priors and differentiable rules, the framework supports transparent risk reasoning, verifiable explanations, and trustworthy decision support. The study contributes to explainable blockchain security, multimodal AI governance, and intelligent media systems by enabling more robust detection of copyright misuse, unauthorized licensing, abnormal content distribution, and cross-chain transaction risks.

Trustworthy Reinforcement Learning for AI-Driven Urban Decision-Making: Sustainable Dynamic Pricing and Resource Optimization for Smart City Operations

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management.

Optimization of Machine Learning–Based Recommendation Systems on E-Commerce Platforms

This study aims to analyze optimization strategies for machine learning–based recommendation systems in e-commerce environments, identify commonly applied algorithms, and examine emerging opportunities and implementation challenges. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 framework. Literature was collected from six major academic databases, covering publications from 2020 to 2025. From an initial pool of 286 records, 10 studies met the eligibility criteria and were included in the final analysis. The findings indicate that deep learning, hybrid recommendation models, sequential recommendation approaches, and large language model–based systems significantly enhance recommendation accuracy and personalization. User behavior analytics emerged as a critical factor in adaptive recommendation systems, while conversational AI and multimodal technologies represent promising future directions. Despite these advancements, issues related to scalability, explainability, fairness, and privacy remain significant challenges requiring further research and optimization.