An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production by Pramod N. Belkhode, Shrikant M. Awatade, Chander Prakash, Sagar D. Shelare, Deepali Marghade, Sameer Sheshrao Gajghate, Muhamad M. Noor, Milon Selvam Dennison

“…Physics-Informed Neural Networks (PINNs) successfully predict reaction pathways and intermediate states, minimizing synthesis errors by 25%. Variational Autoencoders (VAEs) generate novel material configurations, improving photocatalytic efficiency by up to 15%. …”
Get full text

Editorial by Christian Gütl

“…These contributions, together with the generous support of the KOALA initiative, maintain the quality of our journal.In the sixth regular issue, I am very pleased to present the following 4 accepted articles: Michele Berti, Matheus Camilo da Silva, Sebastiano Saccani, and Sylvio Barbon from Italy focus their research on synthetic data generation as an alternative to traditional data anonymization based on variational autoencoders to generate high-quality synthetic tabular datasets.Roger Vieira and Kleinner Farias from Brazil introduce in their research CognIDE, a tool-supported methodology that aims to seamlessly integrate psychophysiological data linked to cognitive indicators into VS Code by offering actionable contextual cues alongside dynamic source code.Pedro Henrique Dias Valle and Elisa Yumi Nakagawa from Brazil discuss in their research a catalog of the main interoperability architectural solutions for addressing the four levels of interoperability - namely technical, syntactic, semantic, and organizational – for solving interoperability issues in software systems by analyzing 65 studies from the scientific literature.Ji Woong Yoo, Kyoung Jun Lee and Arum Park from the Republic of Korea explore the potential of deep learning techniques - Long Short-Term Memory (LSTM) algorithm and Word2Vec model – for cleansing malicious comments from users, and enhancing the ethical nature of AI systems.Enjoy Reading!…”
Get full text

Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery by Priyanka Nair, Devesh Kumar Srivastava, Roheet Bhatnagar

“…To this end, we propose Multi-Modal Deep Embedded Clustering (MM-DEC) approach, an innovative unsupervised learning framework that integrates Convolutional Autoencoders(CAEs), Variational Autoencoders (VAEs), and Gray Level Co-occurrence Matrix (GLCM) based texture extraction that is able to exploit the spatial, spectral, and texture features of mineral in consideration We demonstrate the MM-DEC potential to identify hematite prospects in the mineralized Kiriburu area of Jharkhand, India using EO-1 Hyperion hyperspectral data. …”
Get full text

Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. …”
Get full text

Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams by ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

“…Using field-measured data from hydraulic supports during pressurized moving in a fully mechanized ultra-thin coal seam mining face, key influencing factors of residual support force—including support number, support force before pressurized moving, pushing cylinder inlet pressure, and pushing cylinder stroke variation speed—were identified through visualization and correlation analysis. …”
Get full text

Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management by Geeta Sandeep Nadella, Santosh Reddy Addula, Akhila Reddy Yadulla, Guna Sekhar Sajja, Mohan Meesala, Mohan Harish Maturi, Karthik Meduri, Hari Gonaygunta

“…By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. …”
Get full text

Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments by Nithesh Naik, Neha Surendranath, Sai Annamaiah Basava Raju, Chennaiah Madduri, Nagaraju Dasari, Vinod Kumar Shukla, Vathsala Patil

“…This paper proposes a novel trust-aware hybrid framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Variational Autoencoders (VAE) to analyze spatial, temporal, and latent characteristics of physiological signals. …”
Get full text

Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System by Homari Matsumoto, Atomu Nakamura, Shun Nishide

“…Future directions include using more advanced models such as Variational Autoencoders and diffusion models, and enhancing consistency in long-term sequences. …”
Get full text

Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. …”
Get full text

A Robust Deep Learning Framework for Mitigating Label Noise With Dual Selective Attention by Hasnain Hyder, Gulsher Baloch, Amreen Batool, Yong-Woon Kim, Yung-Cheol Byun

“…To further address mislabeling, the Latent Space Variation using Supervised Autoencoder (AQUAVS) technique was also applied to remove mislabeled data and assess model recovery. …”
Get full text

Emerging role of generative AI in renewable energy forecasting and system optimization by Erdiwansyah, Rizalman Mamat, Syafrizal, Mohd Fairusham Ghazali, Firdaus Basrawi, S.M. Rosdi

“…Generative Artificial Intelligence (Gen-AI), including architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, offers new capabilities to overcome data sparsity, nonlinearity, and uncertainty in renewable-dominant systems. …”
Get full text

Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…Special attention is given to de novo drug design using generative adversarial networks (GANs) and variational autoencoders (VAEs), as well as AI-driven high-throughput virtual screening that reduces computational costs while improving hit identification. …”
Get full text

The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…A comprehensive comparison of six algorithms (linear regression, support vector regression, random forest, XGBoost, CatBoost, and K-nearest neighbors) is conducted, assessing their performance under a combined strategy of data augmentation (noise injection, generative adversarial networks, Gaussian mixture model, variational autoencoders) and transfer learning (random, clustering, and hierarchical parameter transfer). …”
Get full text

Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Molecular generation technology employs deep learning techniques for automatically generating new molecular structures, often based on generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These technologies can learn the rules of molecular generation from existing corrosion inhibitor molecule data and generate new molecules with specific properties. …”
Get full text

Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Deep learning architectures, including convolutional and recurrent neural networks, generative adversarial networks, and variational autoencoders, proved instrumental in multiepitope vaccine design and adaptive clinical trial simulations. …”
Get full text

Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development by Yuan‐Tao Liu, Le‐Le Zhang, Zi‐Ying Jiang, Xian‐Shu Tian, Peng‐Lin Li, Pei‐Huang Wu, Wen‐Ting Du, Bo‐Yu Yuan, Chu Xie, Guo‐Long Bu, Lan‐Yi Zhong, Yan‐Lin Yang, Ting Li, Mu‐Sheng Zeng, Cong Sun

“…Recent advances in AI—particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models—have introduced data‐driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. …”
Get full text

Recent advances in deep learning for protein-protein interaction: a review by Jiafu Cui, Siqi Yang, Litai Yi, Qilemuge Xi, Dezhi Yang, Yongchun Zuo

“…We evaluate core architectures (GNNs, CNNs, RNNs) and pioneering approaches—attention-driven Transformers, multi-task frameworks, multimodal integration of sequence and structural data, transfer learning via BERT and ESM, and autoencoders for interaction characterization. Moreover, we examined enhanced algorithms for dealing with data imbalances, variations, and high-dimensional feature sparsity, as well as industry challenges (including shifting protein interactions, interactions with non-model organisms, and rare or unannotated protein interactions), and offered perspectives on the future of the field. …”
Get full text

MosaicFormer: A Novel Approach to Remote Sensing Spatiotemporal Data Fusion for Lake Water Monitors by Dongxue Zheng, Aifeng Lv

“…A case study on lake water monitoring reveals that our method captures daily variations in the surface area of Hala Lake, providing accurate and robust results. …”
Get full text

Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…The proposed method utilizes deep learning models to maximize signal processing and suppress peak-power variations, while ensuring signal integrity. The simulations result prove that the proposed method attains a PAPR reduction of about 4 dB and 3.8 dB for 256-QAM and 2.2 dB and 1.6 dB for 64-QAM under a Rayleigh and Rician channel at a Complementary Cumulative Distribution Function (CCDF) of 10-5, better than conventional PAPR reduction methods. …”
Get full text

Optimizing Inotropic Infusion With Cluster Specific AI Decision Models and Digital Twins by Vidya S Nair, G. D. Heshan Niranga, Aryalakshmi C.S, Dipu T. Sathyapalan, Thushara Madathil, Rahul Krishnan Pathinarupothi

“…Conventional feedback controllers, including fuzzy logic controllers (FLC), struggle to adapt to complex BP variations due to fixed algorithms and intracohort variability in drug responses. …”
Get full text