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Topic Review

Digital Image Authentication for Security and Validation Enhancement

Digital face approaches possess currently received awesome attention because of their huge wide variety of digital audio, and visual programs. Digitized snapshots are progressively more communicated using an un-relaxed medium together with cyberspace. Consequently, defence, clinical, medical, and exceptional supervised photographs are essentially blanketed towards trying to employ it; such controls ought to damage such choices constructed totally based on those pictures.

365
15 Dec 2023

Topic Review

Unmanned Aerial Vehicle Search Target Recognition Techniques

The traditional method of finding missing people involves deploying fixed cameras in some hotspots to capture images and using humans to identify targets from these images. However, in this approach, high costs are incurred in deploying sufficient cameras in order to avoid blind spots, and a great deal of time and human effort is wasted in identifying possible targets. Further, most AI-based search systems focus on how to improve the human body recognition model, without considering how to speed up the search in order to shorten the search time and improve search efficiency. As the technology of the unmanned aerial vehicle (UAV) has seen significant progress, a number of applications have been proposed for it due to its unique characteristics, such as higher mobility and more flexible integration with different equipment, such as sensors and cameras, etc.

354
29 Jan 2024

Topic Review

New Efficient Hybrid Technique for Human Action Recognition

This research paper presents a hybrid 2D Conv-RBM & LSTM model for efficient human action recognition. Achieving 97.3% accuracy with optimized frame selection, it surpasses traditional 2D RBM and 3D CNN techniques. Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. To address these limitations, we propose a novel model combining a two-dimensional convolutional restricted Boltzmann machine (2D Conv-RBM) with a long short-term memory (LSTM) network. The 2D Conv-RBM efficiently extracts spatial features such as edges, textures, and motion patterns while preserving spatial relationships and reducing parameters via weight sharing. These features are subsequently processed by the LSTM to capture temporal dependencies across frames, enabling effective recognition of both short- and long-term action patterns. Additionally, a smart frame selection mechanism minimizes frame redundancy, significantly lowering computational costs without compromising accuracy. Evaluation on the KTH, UCF Sports, and HMDB51 datasets demonstrated superior performance, achieving accuracies of 97.3%, 94.8%, and 81.5%, respectively. Compared to traditional approaches like 2D RBM and 3D CNN, our method offers notable improvements in both accuracy and computational efficiency, presenting a scalable solution for real-time applications in surveillance, video security, and sports analytics.

335
13 Feb 2025

Topic Review

Electric Technocracy—Reinventing Democracy through Technology

Electric Technocracy is a Post-National Governance Model for the AI Age. It refers to a proposed governance architecture that replaces the nation-state system with a post-national, digitally coordinated, and automation-supported order. It is characterized by Direct Digital Democracy (DDD), an advisory Artificial Superintelligence (ASI), and an economy centered on machine taxation, Universal Basic Income (UBI), and post-scarcity sustainability. Humanity remains the sole sovereign decision-maker; ASI performs analytical, predictive, and administrative roles but holds no autonomous political authority. The model envisions a planetary administration based on transparency, ecological integration, and technological abundance, aimed at eliminating structural scarcity and war.

85
29 Dec 2025

Topic Review

TrinityOne Logic

The integration of abstract conceptual frameworks, such as TrinityOne, into the operational and theoretical underpinnings of Artificial Intelligence (AI) represents a significant frontier in contemporary AI research. This endeavor extends beyond mere computational efficiency, delving into the fundamental nature of intelligence, knowledge, and existence within artificial systems. The complexity of this challenge necessitates a robust architectural choice for the AI's core.

24
19 Jan 2026

Topic Review

Cordelia-11 is a representative example of harmonic convergence between mathematics, psychology, psychiatry, sociology, and Lagrangian dynamics, demonstrating how cognitive, emotional, and social phenomena can be modeled within a unified variational framework.

23
19 Jan 2026

Biography

Javed Iqbal Bangash

Dr. Javed Iqbal Bangash is an accomplished academic and researcher, currently serving as Assistant Professor and HEC-Approved PhD Supervisor at the Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan. He holds a PhD in Computer Science from University of Technology Malaysia (2015), where he was awarded the Best Student Award and a Certificat

11
27 Nov 2025

Biography

Dr. Nedal Ababneh is an Associate Professor at the University of Khorfakkan (UKF), Sharjah, UAE, where his academic work focuses on the intersection of security, intelligent systems, and distributed computing. He previously served as Acting Director of Abu Dhabi Polytechnic (ADPoly) and as Head of the Information Security Engineering Technology Department, positions in which he led large-scale aca

3
06 Feb 2026

Topic Review

Artificial Intelligence in Food Science

Artificial intelligence (AI) has begun to demonstrate considerable promise in food science, enabling new ways to analyze complex data, accelerate discovery, and support decision-making across research and industry. However, many of AI’s most transformative opportunities in food systems remain only partially explored. This entry provides an overview of this area and a practical guide for food scientists interested in building AI models that align with the unique characteristics of food systems. It introduces a three-pillar framework—high-quality datasets, tailored algorithms, and impactful applications—that highlights emerging opportunities for advancing AI-driven research and innovation in food science.

2
08 Feb 2026

Topic Review

Five Key Initiatives for AI in Food Science

Artificial intelligence (AI) has demonstrated growing potential to advance food science by supporting data-driven research, prediction, and decision-making across nutrition, safety, flavor, and sustainability. While AI applications in food systems are expanding, their broader impact depends on how effectively they are integrated with domain knowledge, evaluated, and supported by robust data infrastructures. This entry outlines five forward-looking initiatives proposed to guide the responsible and impactful development of AI in food science, highlighting key opportunities to align computational advances with the complexity of real-world food systems.

1
08 Feb 2026

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