Designing an immersive interactive environment for IIoT-enhanced vertical centrifugal casting by Dhaval Anadkat, Amit Sata, Minal Shukla, Slaheddine Jarboui, Divya Mobarsa

“…In this process, metal is poured in to the rotating mold to produce cylindrical components. The present work focuses on development of young and emerging talent to build their skills in traditional processes like casting through experimental learning and develop the learning environment for the young and curios minds. …”
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Emerging Technologies Driving Zero Trust Maturity Across Industries by Hrishikesh Joshi

“…The research investigates how artificial intelligence, machine learning, blockchain, quantum computing, and cloud/edge technologies are reshaping the implementation and efficacy of Zero Trust architectures. …”
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Optimizing Blockchain Querying: A Comprehensive Review of Techniques, Challenges, and Future Directions by Emmanuel Acheampong Asiamah, Eliel Keelson, Andrew Selasi Agbemenu, Eric Tutu Tchao, Theresa-Samuelle Adjaidoo, Griffith Selorm Klogo

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Augmented reality and robotics in education: A systematic literature review by Christina Pasalidou, Chris Lytridis, Avgoustos Tsinakos, Nikolaos Fachantidis

“…Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. …”
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Service Function Chain Migration: A Survey by Zhiping Zhang, Changda Wang

“…Existing approaches have demonstrated promising results in both passive and active migration strategies, leveraging techniques such as reinforcement learning for dynamic scheduling and digital twins for resource prediction. …”
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Prospective Teachers’ Conceptions of Area by Betsy McNeal, Sayonita Ghosh Hajra, Michael Battista, Ayse Ozturk

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Artificial Sensing: AI-Driven Electronic Nose for Real-Time Gas Leak Detection and Food Spoilage Monitoring by Lubna Aziz, Hassan Adil, Raheel Sarwar

“…The system integrates multi-channel MQ-series gas sensors with a Seeed Studio Wio Terminal, leveraging near-sensor computing and machine learning for accurate detection. The development followed a structured, multi-phase approach: (1) gathering requirements from industry experts and individuals with olfactory impairments; (2) designing and integrating hardware and software components; (3) implementing machine learning models (Support Vector Machine, Random Forest, Artificial Neural Network) in Python and hardware interfacing in C++; (4) conducting rigorous testing across unit, integration, and real-world scenarios; and (5) deploying trained neural networks on the Edge Impulse platform for real-time inference. …”
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The Intrinsic Flattening of Galaxy Disks by Jeremy Favaro, Stéphane Courteau, Sébastien Comerón, Connor Stone

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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…The robustness of the model is further enhanced by the edge augmentation technique. The edge augmentation technique in the context of graph has introduced a significant degree of generalization in the detection capabilities of the model as analyzed on apple, potato, and sugarcane leaves. …”
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Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations by Santhakumar Jayakumar, Sathish Kannan, Poongavanam Ganeshkumar, U. Mohammed Iqbal

“…The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. …”
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FPGA Hardware Acceleration of AI Models for Real-Time Breast Cancer Classification by Ayoub Mhaouch, Wafa Gtifa, Mohsen Machhout

“…However, the high computational demands and latency of deep learning models in medical imaging present significant challenges, especially in resource-constrained environments. …”
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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

“…This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. …”
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Application and prospect of artificial intelligence in empowering the operation and managment of oil and gas pipelines by Qi LIAO, Chunying LIU, Jian DU, Hao LAN, Yongtu LIANG, Haoran ZHANG

“…Examining the research hotspots and phased evolution of cutting-edge AI technologies in the operation of oil and gas pipelines over the past 20 years is critical to delineate the key issues of AI application in this field at present and outline future research directions. …”
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An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3 by Arnav Bhagwat, Soham Dutta, Debdeep Saha, Maddikara Jaya Bharata Reddy

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STL-ELM: A computationally efficient hybrid approach for predicting high volatility stock market by Temitope Olubanjo Kehinde, Oluyinka J. Adedokun, Morenikeji Kabirat Kareem, Joseph Akpan, Oludolapo A. Olanrewaju

“…With faster runtimes and minimal memory usage, STL-ELM is tailored for real-time trading applications and high-frequency financial forecasting, offering institutional investors, traders, and financial analysts a competitive edge in volatile markets. The hybrid nature of STL-ELM, which combines STL’s multiscale decomposition with ELM’s rapid learning, enhances its adaptability to various financial domains, including stocks, commodities, foreign exchange, and cryptocurrencies, by efficiently capturing domain-specific volatility patterns. …”
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Latent Graph Attention for Spatial Context in Light-Weight Networks: Multi-Domain Applications in Visual Perception Tasks by Ayush Singh, Yash Bhambhu, Himanshu Buckchash, Deepak K. Gupta, Dilip K. Prasad

“…Moreover, existing approaches are limited to only learning the pairwise semantic relation between any two points in the image. …”
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Studies on Underwater Image Processing Using Artificial Intelligence Technologies by Sugunapriya A, Markkandan S

“…The findings of this survey suggest promising directions for future research, particularly in the development of more sophisticated deep learning models that can further improve image quality and contribute to the underwater exploration and monitoring system.…”
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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment by Ponugoti Kalpana, Sunitha Tappari, L. Smitha, Dasari Madhavi, K. Naresh, Maddala Vijayalakshmi

“…This evokes a need for designing intelligent systems to eradicate data breaches and privacy problems. This research presents a groundbreaking framework that uniquely combines privacy-preserving optimized deep learning to effectively diagnose cardiac troubles utilizing edge and fog computing devices. …”
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A Methodological and Structural Review of Parkinson’s Disease Detection Across Diverse Data Modalities by Abu Saleh Musa Miah, Taro Suzuki, Jungpil Shin

“…By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.…”
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An adaptive dual distillation framework for efficient remaining useful life prediction by Xiang Cheng, Jun Kit Chaw, Shafrida Sahrani, Mei Choo Ang, Saraswathy Shamini Gunasekaran, Moamin A. Mahmoud, Halimah Badioze Zaman, Yanfeng Zhao, Fuchen Ren

“…Abstract Predicting the Remaining Useful Life (RUL) of industrial equipment is essential for proactive maintenance and health assessment, particularly under the computational constraints of edge devices. While deep learning methods, such as Long Short-Term Memory (LSTM) networks, excel at modeling complex time series, their high computational cost often restricts real-time deployment. …”
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