Richard Adusei^1,2,*, Gaddafi Abdul-Salaam²

Journal on Artificial Intelligence, Vol.8, pp. 425-472, 2026, DOI:10.32604/jai.2026.085269 - 14 September 2026

Abstract Fine-grained visual categorization (FGVC) presents a class of recognition problems in which the discriminative signal is spatially concentrated, visually subtle, and easily destroyed by the preprocessing and augmentation strategies that serve coarse recognition well. Where standard image classification requires a model to distinguish birds from cars, FGVC requires it to distinguish one bird species from another, a task that demands localization, feature-space shaping, and representation learning to operate in close coordination. This survey synthesizes thirty recent works spanning 2021 to 2026, organizing them under five interlocking themes, namely discriminative region discovery, metric learning and loss… More >

Ashwin Sankaran^*

Journal on Artificial Intelligence, Vol.8, pp. 403-423, 2026, DOI:10.32604/jai.2026.080854 - 29 July 2026

Abstract Human Motion Kinetics Recognition (HMKR) plays a pivotal role in advancing intelligent monitoring systems for public safety, machine interaction, and elderly-care technologies. Despite the increasing availability of affordable Inertial Measurement Units (IMUs), achieving accurate full-body motion analysis remains challenging due to sensor noise and variability across real-world environments. Addressing these limitations requires robust processing strategies capable of handling complex inertial time-series data. This research introduces an optimized HMKR system that leverages a comprehensive pipeline focusing on inertial sensor time-series data. In this research, we introduce an HMK-based recognition system that primarily focuses on six-degree-of-freedom (6-DoF) More >

Rahul Raj^*

Journal on Artificial Intelligence, Vol.8, pp. 359-375, 2026, DOI:10.32604/jai.2026.083202 - 22 July 2026

Abstract Background: Unemployment Insurance (UI) fraud represents one of the most costly threats to social benefit integrity, with the U.S. DOL/ETA estimating improper payments exceeding $45 billion between 2020 and 2023. Existing detection systems fail to model the temporal evolution of claiming behavior or the relational topology connecting fraudulent actors across employer-claimant networks. This study introduces FRAUD-LENS, a hybrid deep learning framework delivering interpretable and scalable fraud detection for large-scale federal UI systems. Methods: FRAUD-LENS integrates three coordinated architectural modules: a Bidirectional Long Short-Term Memory (BiLSTM) network encoding temporal claim behavior sequences, a Graph Attention Network… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Anitya Kumar Gupta^*, Pankaj Vaidya, Anurag Rana

Journal on Artificial Intelligence, Vol.8, pp. 377-402, 2026, DOI:10.32604/jai.2026.074171 - 22 July 2026

Abstract Sentiment analysis aims at determining the stance or point of view of a topic, author, or speaker about a certain topic, document, or event. The given paper proposes a hybrid quantum-classical model of sentiment classification, which is known as Complex-Valued Quantum-Enhanced Recurrent Neural Network (CQRNN). The model combines Quantum Long Short-Term Memory (QLSTM) and Quantum Gated Recurrent Units (QGRU) with the use of Variational Quantum Circuits (VQCs) and complex-valued embeddings designed at the level of semantic information, both in the real and imaginary domains. Experiments on benchmark sentiment datasets show that CQRNN can be fine-tuned More >

Ping Zhang¹, Chungang Li², Jian Song¹, Jianjun Tan¹, Han Tian¹, Le Chen³, Yongkun Song^3,*

Journal on Artificial Intelligence, Vol.8, pp. 323-334, 2026, DOI:10.32604/jai.2026.080463 - 02 July 2026

Abstract Accurate segmentation of coal pile point clouds is essential for automated inventory management and safety supervision in industrial coal sheds. With the widespread adoption of LiDAR technology, massive point cloud data are available, yet traditional clustering algorithms often fail to distinguish physically adhering coal piles in high-density storage scenarios, resulting in severe under-segmentation. To address this challenge, this paper proposes an automated point cloud segmentation framework tailored for complex industrial environments. The proposed framework consists of three core stages: data preprocessing, ground segmentation, and instance segmentation. First, relative elevation mapping and selective statistical filtering are… More >

Mohammadreza Nehzati^*

Journal on Artificial Intelligence, Vol.8, pp. 299-322, 2026, DOI:10.32604/jai.2026.082982 - 16 June 2026

Abstract Pedestrian detection in surveillance environments remains fundamentally challenging due to three coupled phenomena: severe occlusion, extreme scale variation, and low-resolution imagery. While contemporary detectors achieve high frame rates on standard benchmarks, they exhibit systematic failures under partial visibility where geometric consistency becomes the primary discriminative signal. This paper introduces a hybrid probabilistic framework that integrates Conditional Random Fields (CRF) with fractal geometry regularization to enforce scale invariant shape priors in deep pedestrian detectors. The core mathematical insight is that human silhouettes exhibit self-similarity across scales—a property precisely characterized by fractal dimension, self-similarity coefficients, and non-dimensionality… More >

Akash Garg^*

Journal on Artificial Intelligence, Vol.8, pp. 273-298, 2026, DOI:10.32604/jai.2026.079401 - 12 May 2026

Abstract As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to… More >

Jianping Wu¹, Nian Li^2,*, Libin Dong³, Ping Zhang⁴

Journal on Artificial Intelligence, Vol.8, pp. 259-271, 2026, DOI:10.32604/jai.2026.075753 - 12 May 2026

Abstract This paper presents a static fusion framework that enhances object detection by integrating camera and LiDAR-based detection results. The proposed method focuses on associating 2D candidate bounding boxes from a camera detector with 3D candidate boxes from a LiDAR detector using an Intersection over Union (IoU)-based matching approach. To enhance the quality of 2D detection, we refine the baseline Cascade R-CNN detector by incorporating a dual self-attention mechanism into both the backbone and the region proposal network (RPN), resulting in the DA-Cascade R-CNN. This enhancement strengthens the network’s ability to detect small or distant objects More >

Harri Renney^1,*, Maxim Nethercott¹, Nathan Renney², Peter Hayes¹

Journal on Artificial Intelligence, Vol.8, pp. 231-257, 2026, DOI:10.32604/jai.2026.078487 - 17 April 2026

Abstract This paper provides systemisation on the emerging design patterns and paradigms for Large Language Model (LLM)-enabled multi-agent systems (MAS), evaluating their practical utility across various domains, bridging academic research and industry practice. We define key architectural components, including agent orchestration, communication mechanisms, and control-flow strategies, and demonstrate how these enable rapid development of modular, domain-adaptive solutions. Three real-world case studies are tested in controlled, containerised pilots in telecommunications security, national heritage asset management, and utilities customer service automation. Initial empirical results show that, for these case studies, prototypes were delivered within two weeks and pilot-ready More >