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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.
  • 378
  • 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.
  • 375
  • 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.
  • 354
  • 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.
  • 284
  • 29 Dec 2025
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.
  • 116
  • 09 Feb 2026
Topic Review
AI in Private Equity Due Diligence
Artificial intelligence (AI) is increasingly applied within private equity (PE) due diligence processes to automate document analysis, enhance risk identification, and improve decision velocity. This entry examines the principal AI methodologies deployed in PE workflows, including natural language processing for financial document extraction, retrieval-augmented generation for institutional knowledge synthesis, and machine learning-based risk scoring models. Adoption challenges related to data governance, model explainability, and human-in-the-loop design are discussed alongside observed performance outcomes across deal screening, IC memo preparation, and portfolio monitoring functions.
  • 71
  • 01 Apr 2026
Topic Review
Cordelia-11
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.
  • 61
  • 19 Jan 2026
Topic Review Peer Reviewed
Deepfakes and Synthetic Media: Generation, Detection, and Governance
Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability.
  • 61
  • 05 Aug 2026
Biography
Nedal Ababneh
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
  • 51
  • 06 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.
  • 45
  • 09 Feb 2026
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.
  • 44
  • 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
  • 33
  • 27 Nov 2025
Topic Review
High-Throughput Screening vs. Deep Generative Inverse Design
The discovery of advanced materials is fundamentally transitioning from brute-force, database-dependent computational screening to targeted generative inverse design. High-Throughput Screening (HTS), powered by Density Functional Theory (DFT), provides a forward-mapping approach that remains constrained by the limits of known structural libraries. Conversely, deep generative models utilize artificial intelligence to navigate continuous chemical spaces via backward-mapping. This topic review explores the distinct mechanics of both paradigms, examining foundational chemical space representations alongside advanced deep learning architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion models. Furthermore, it critically addresses the inherent computational bottlenecks of HTS and the ongoing challenge of material synthesizability in generative AI, charting the future trajectory of autonomous materials discovery.
  • 27
  • 18 Jun 2026
Topic Review Peer Reviewed
Artificial Intelligence and Machine Learning for Road Traffic Congestion Prediction and Forecasting: A Systematic Review of Methods, Validation, Explainability, and Reproducibility
Artificial intelligence (AI) and machine learning (ML) are increasingly applied to road traffic congestion prediction, but heterogeneous outcomes, models, horizons, and evaluation practices limit comparability. The objective of this study was to synthesize methods, applications, validation, explainability, and reproducibility in AI/ML-based road traffic congestion prediction and forecasting. Following PRISMA 2020, Scopus, Web of Science Core Collection, and IEEE Xplore were searched through 5 July 2026 for English-language journal articles and full conference papers published from 2000 to 2026. Two external reviewers independently screened 734 unique records and assessed the retrieved full texts, while the author resolved disagreements against the predefined eligibility criteria. Study characteristics, prediction tasks, congestion indicators, model families, metrics, explainability, validation, and data/code availability were synthesized descriptively and narratively. Of 1131 records identified, 397 duplicates were removed and 734 records were screened. Full-text retrieval was sought for 339 reports; 195 could not be retrieved, 144 were assessed for eligibility, and 129 were included. Congestion level was the main prediction task, while traffic flow and speed were the most frequent indicators. Heterogeneity and the absence of verified numerical performance values precluded meta-analysis or model ranking. Explainability was limited, and external validation, transferability, and reproducibility were insufficiently documented. Progress requires standardized outcomes, transparent validation, reproducible workflows, explainable models, and independent testing across networks and cities.
  • 22
  • 30 Sep 2026
Topic Review Peer Reviewed
Artificial Intelligence in Business Research: A Synthesis of Accounting, Finance, and Management
Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI.
  • 20
  • 17 Sep 2026
Topic Review
Generative Adversarial Networks
A generative adversarial network (GAN) is a class of generative model in machine learning composed of two neural networks trained simultaneously within a minimax two-player game framework [1]. The two networks are a generator, which maps samples drawn from a latent prior distribution to synthetic data samples, and a discriminator, which distinguishes between real samples drawn from the training distribution and fake samples produced by the generator [1]. During training, the generator minimizes a value function by producing samples that the discriminator classifies as real, while the discriminator maximizes the same function by correctly classifying real and fake samples; the equilibrium occurs when the generator’s distribution matches the data distribution and the discriminator’s output is one-half everywhere [1]. Under an appropriate choice of divergence, the training objective corresponds to minimization of the Jensen–Shannon divergence between the empirical data distribution and the learned model distribution [2]. GANs differ from other generative models—such as variational autoencoders and autoregressive models—in that they do not explicitly model the data density, instead learning an implicit distribution through adversarial training, and they are characterized by the joint optimization of two parametric networks without an explicit likelihood [3].
  • 17
  • 16 Sep 2026
Topic Review Peer Reviewed
AI-Powered Computer Vision Industrial Quality Inspection Systems: A Practice Review
Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.
  • 16
  • 17 Sep 2026
Topic Review
Autonomous Systems
Autonomous systems are technical systems equipped with integrated sensing, state estimation, reasoning, decision-making and actuation subsystems that can perceive external environmental states, generate context-appropriate goal-oriented decisions and execute corresponding physical actions without continuous real-time human operator command intervention [1]. Autonomy operates within predefined functional and environmental boundaries set by high-level human mission objectives; it does not imply full independence from human-set top-level goals, nor self-generated mission purposes outside originally defined operational constraints [2]. Different real-world implementations exhibit varying degrees of autonomy, ranging from conditional partial autonomy under human supervision to high-level full autonomy in structured environments. The core conceptual characteristic lies in the closed-loop perception-decision-action mechanism, distinguishing it from open-loop purely automated equipment that executes fixed pre-programmed sequences without environmental perception and adaptive feedback [3].
  • 9
  • 20 Sep 2026
Topic Review
Shapley Additive Explanation
Shapley Additive Explanations (SHAP) is a unified framework for interpreting the predictions of machine-learning models by assigning each input feature a real-valued importance (a SHAP value) for a specific prediction [1]. SHAP values are derived from the Shapley value concept of cooperative game theory, in which the contribution of a player is the average marginal contribution of that player over all possible coalitions of other players [2]. In SHAP, each feature is treated as a player and the model prediction as the game payout, so that the explanation of a particular prediction is decomposed into a baseline value plus the sum of the feature-attribution values [1]. The framework unifies a family of additive feature-attribution methods—including LIME, DeepLIFT, and tree-interception methods—under a common set of desired properties: local accuracy, missingness, and consistency [1]. SHAP distinguishes itself by providing a theoretically unique and consistent attribution under these properties, and by supporting both local explanations of individual predictions and global summaries of feature importance [3].
  • 7
  • 17 Sep 2026
Topic Review
Multi-Agent Systems
Multi-agent systems are computational systems composed of multiple interacting intelligent agents—autonomous software entities that perceive their environment through sensors and act upon it through actuators—operating within a shared environment to collectively solve problems that are beyond the capacity of any single agent. Each agent is characterized by autonomy, social ability, reactivity to environmental change, and proactivity toward goals, and agents may be homogeneous or heterogeneous in their capabilities, knowledge, and objectives [1]. The system-level behavior emerges from local interactions governed by communication protocols, negotiation strategies, and coordination mechanisms, rather than from centralized control. Key structural dimensions include the degree of agent autonomy, the topology of the interaction network, the distribution of information, and the conflict or cooperation relationships among agent goals. Agents may cooperate to achieve joint objectives, compete through auction or bidding mechanisms, or negotiate through argumentation protocols, and system properties such as global coherence, stability, and scalability emerge from these local interaction rules [2]. The theoretical foundation distinguishes multi-agent systems from distributed systems in that agents are intentionally autonomous and goal-directed, rather than merely executing distributed functions under central orchestration, and from expert systems in that problem-solving is inherently social and interactive [3].
  • 7
  • 28 Sep 2026
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