Open Access

ARTICLE

Miah A. Robinson, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*
Journal on Artificial Intelligence, Vol.7, pp. 615-632, 2025, DOI:10.32604/jai.2025.073535 - 01 December 2025
Abstract Ensuring the safety of Artificial Intelligence (AI) is essential for providing dependable services, especially in various sectors such as the military, education, healthcare, and automotive industries. A highly effective method to boost the precision and performance of an AI agent involves multi-configuration training, followed by thorough evaluation in a specific setting to gauge performance outcomes. This research thoroughly investigates the design of three AI agents, each configured with a different number of hidden units. The first agent is equipped with 128 hidden units, the second with 256, and the third with 512, all utilizing the… More >

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54

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ARTICLE

Tesfaye Adisu Tarekegn^1,*, Taye Girma Debelee^1,2
Journal on Artificial Intelligence, Vol.7, pp. 585-613, 2025, DOI:10.32604/jai.2025.067333 - 01 December 2025
Abstract The identification of defect types and their reduction values is the most crucial step in coffee grading. In Ethiopia, the current coffee defect investigation techniques rely on manual screening, which requires substantial human resources, time-consuming, and prone to errors. Recently, the deep learning driven object detection has shown promising results in coffee defect identification and grading tasks. In this study, we propose KN-YOLOv8, a modified You Only Look Once version-8 (YOLOv8) model optimized for real-time detection of coffee bean defects. This lightweight network incorporates effective feature fusion techniques to accurately detect and locate defects, even… More >

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132

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30

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ARTICLE

Chirag Devendrakumar Parikh^*
Journal on Artificial Intelligence, Vol.7, pp. 571-584, 2025, DOI:10.32604/jai.2025.073765 - 01 December 2025
Abstract With the increasing complexity and scale of hyperscale data centers, the requirement for intelligent, real-time power delivery has never been more critical to ensure uptime, energy efficiency, and sustainability. Those techniques are typically static, reactive (since CPU and workload scaling is applied to performance events that occur after a request has been submitted, and is thus can be classified as a reactive response.), and require manual operation, and cannot cope with the dynamic nature of the workloads, the distributed architectures as well as the non-uniform energy sources in today’s data centers. In this paper, we… More >

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106

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35

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ARTICLE

Yu Tao¹, Tian Yin², Yang Jie^1,*
Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025
Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

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124

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35

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ARTICLE

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*
Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025
Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

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929

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463

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ARTICLE

Meile Li¹, Yinuo Jiang^2,*
Journal on Artificial Intelligence, Vol.7, pp. 509-516, 2025, DOI:10.32604/jai.2025.073792 - 28 November 2025
Abstract This paper conducts topic mining and analysis of research literature in the domestic smart library field based on the BERTopic model, aiming to reveal its topic development context and evolution trends. Journal literature in the smart library field collected by CNKI (China National Knowledge Infrastructure) from 2015 to 2024 was analyzed using the BERTopic model and dynamic topic modeling for topic mining and evolution trend analysis. The study found that the domestic smart library field involves multiple core topics, identifying a diversified topic structure centered around “data”, “user”, “5g”, etc. The research results provide data More >

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81

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37

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ARTICLE

Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*
Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025
Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

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263

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115

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ARTICLE

Ayaz Hussain¹, Asad Hayat², Muhammad Hasnain^1,*
Journal on Artificial Intelligence, Vol.7, pp. 485-498, 2025, DOI:10.32604/jai.2025.071933 - 17 November 2025
Abstract Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the… More >

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262

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99

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ARTICLE

Qinzhen Fang^1,2, Dongliang Peng^1,2, Lu Zeng^1,2,*, Zixuan Jiang^1,2
Journal on Artificial Intelligence, Vol.7, pp. 469-484, 2025, DOI:10.32604/jai.2025.073016 - 06 November 2025
Abstract To address the issues of small target miss detection, false positives in complex scenarios, and insufficient real-time performance in maglev train foreign object intrusion detection, this paper proposes a multi-module fusion improvement algorithm, YOLO11-FADA (Fusion of Augmented Features and Dynamic Attention), based on YOLO11. The model achieves collaborative optimization through three key modules: The Local Feature Augmentation Module (LFAM) enhances small target features and mitigates feature loss during down-sampling through multi-scale feature parallel extraction and attention fusion. The Dynamically Tuned Self-Attention (DTSA) module introduces learnable parameters to adjust attention weights dynamically, and, in combination with More >

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391

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117

Open Access

ARTICLE

Amit Prakash Singh¹, Kajal Kaul^1,*, Anuradha Chug¹, Ravinder Kumar², Veerubommu Shanmugam²
Journal on Artificial Intelligence, Vol.7, pp. 451-468, 2025, DOI:10.32604/jai.2025.070773 - 06 November 2025
（This article belongs to the Special Issue: Advances in Artificial Intelligence for Engineering and Sciences)
Abstract In agricultural farms in India where the staple diet for most of the households is potato, plant leaf diseases, namely Potato Early Blight (PEB) and Potato Late Blight (PLB), are quite common. The class label Plant Healthy (PH) is also used. If these diseases are not identified early, they can cause massive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation. This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy… More >

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273

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118

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ARTICLE

Qiang Peng^1,2, Jia-Yu Yang¹, Xu-Yu Xiang^1,*
Journal on Artificial Intelligence, Vol.7, pp. 437-450, 2025, DOI:10.32604/jai.2025.072332 - 20 October 2025
（This article belongs to the Special Issue: Advances in Artificial Intelligence for Engineering and Sciences)
Abstract Camellia oleifera is one of the four largest oil tree species in the world, and also an important economic crop in China, which has overwhelming economic benefits. However, Camellia oleifera is invaded by various diseases during its growth process, which leads to yield reduction and profit damage. To address this problem and ensure the healthy growth of Camellia oleifera, the purpose of this study is to apply the lightweight network to the identification and detection of camellia oleifolia leaf disease. The attention mechanism was combined for highlighting the local features and improve the attention of the model to the More >

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348

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156

Open Access

ARTICLE

Muhammad Adeen Jamal¹, Bilal Khan^2,*, Sameed Ur Rehman¹, Wahab Khan¹
Journal on Artificial Intelligence, Vol.7, pp. 417-435, 2025, DOI:10.32604/jai.2025.070762 - 20 October 2025
Abstract The development of artificial intelligence (AI) has sparked the invention of chatbots, which are intelligent conversational agents. These chatbots have the potential to completely transform how people interact while enhancing user experience. This study explores the building along with its execution of a chatbot for customer service support at a university reception using recurrent neural networks (RNNs). To increase user requests, the accuracy of the information, and overall satisfaction with the service, it evaluates machine learning models including RNN, XLNet, and Bidirectional Encoder Representations from Transformers (BERT). In this research project, data were gathered from… More >

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288

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155

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ARTICLE

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel
Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025
Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… More >

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319

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142

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ARTICLE

Yan Zhou, Hengyang Wu^*
Journal on Artificial Intelligence, Vol.7, pp. 381-396, 2025, DOI:10.32604/jai.2025.071674 - 20 October 2025
Abstract Crack Detection is crucial for ensuring the safety and durability of buildings. With the advancement of deep learning, crack detection has increasingly adopted convolutional neural network (CNN)-based approaches, achieving remarkable progress. However, current deep learning methods frequently encounter issues such as high computational complexity, inadequate real-time performance, and low accuracy. This paper proposes a novel model to improve the performance of concrete crack detection. Firstly, the You Only Look Once (YOLOv11) backbone replaces the original Real-Time Detection Transformer (RTDETR) backbone, reducing computational complexity and model size. Additionally, the Dynamic Snake Convolution (DSConv) has been introduced More >

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296

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153

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ARTICLE

Fan Chen^*, Xian Bao, Jianlin Liu, Man Wang, Qiang Zhang
Journal on Artificial Intelligence, Vol.7, pp. 365-380, 2025, DOI:10.32604/jai.2025.069903 - 06 October 2025
Abstract The Thyristor-Controlled Series Compensator (TCSC) presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow. However, inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation, resulting in non-coherent phenomena of line blocking. In response to this challenge, this paper introduces a novel two-stage site selection method for TCSC, emphasizing the enhancement of coherence in addressing line-blocking issues. Through rigorous non-coherent verification, this method mitigates the risk of line congestion deterioration due to TCSC faults.… More >

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462

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336

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REVIEW

Eleena Sarah Mathew^*
Journal on Artificial Intelligence, Vol.7, pp. 347-363, 2025, DOI:10.32604/jai.2025.069841 - 06 October 2025
Abstract This review paper explores advanced methods to prompt Large Language Models (LLMs) into generating objectionable or unintended behaviors through adversarial prompt injection attacks. We examine a series of novel projects like HOUYI, Robustly Aligned LLM (RA-LLM), StruQ, and Virtual Prompt Injection that compel LLMs to produce affirmative responses to harmful queries. Several new benchmarks, such as PromptBench, AdvBench, AttackEval, INJECAGENT, and RobustnessSuite, have been created to evaluate the performance and resilience of LLMs against these adversarial attacks. Results show significant success rates in misleading models like Vicuna-7B, LLaMA-2-7B-Chat, GPT-3.5, and GPT-4. The review highlights limitations… More >

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1227

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620

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REVIEW

Charles Munyao^*, John Ndia
Journal on Artificial Intelligence, Vol.7, pp. 329-346, 2025, DOI:10.32604/jai.2025.069226 - 22 September 2025
Abstract The natural language processing (NLP) domain has witnessed significant advancements with the emergence of transformer-based models, which have reshaped the text understanding and generation landscape. While their capabilities are well recognized, there remains a limited systematic synthesis of how these models perform across tasks, scale efficiently, adapt to domains, and address ethical challenges. Therefore, the aim of this paper was to analyze the performance of transformer-based models across various NLP tasks, their scalability, domain adaptation, and the ethical implications of such models. This meta-analysis paper synthesizes findings from 25 peer-reviewed studies on NLP transformer-based models,… More >

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3009

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2004

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REVIEW

Ragava Raja R^1,2,*, Girish Khanna R³
Journal on Artificial Intelligence, Vol.7, pp. 301-327, 2025, DOI:10.32604/jai.2025.069693 - 22 September 2025
Abstract Packaging materials are indispensable in modern industries but also significantly contribute to environmental degradation, resource consumption, and waste generation. This systematic review critically assesses the integration of artificial intelligence (AI), life cycle sustainability assessment (LCSA) following ISO 14040 standards, and circularity mapping to overcome sustainability barriers in packaging. The study identifies environmental, economic, and social hotspots across the life cycle stages of packaging materials by examining real-world case studies such as Coca-Cola’s adoption of recycled PET bottles and Unilever’s commitment to 100% recyclable plastic. AI technologies highlight transformative tools for optimising resource allocation, enhancing waste… More >

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2105

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ARTICLE

Meenakshi S. Patil^1,*, Rajesh B. Ghongade², Hemant B. Dhonde³
Journal on Artificial Intelligence, Vol.7, pp. 289-300, 2025, DOI:10.32604/jai.2025.069500 - 12 September 2025
Abstract This study seeks to establish a novel, semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes, significantly contributing to the dependability of concrete quality evaluations. The study utilizes image processing and machine learning (ML) methods, namely object detection models such as YOLOv8 and Convolutional Neural Networks (CNNs), to evaluate images of concrete cubes. These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions. Preliminary results indicate a good performance in the classification of concrete More >

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5335

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2563

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ARTICLE

Geoffry Mutiso^*, John Ndia
Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025
（This article belongs to the Special Issue: Advances in Artificial Intelligence for Engineering and Sciences)
Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

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7662

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7118

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ARTICLE

Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹
Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025
Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >

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1007

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484

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REVIEW

Michael Juma Ayuma¹, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka^2,*
Journal on Artificial Intelligence, Vol.7, pp. 205-254, 2025, DOI:10.32604/jai.2025.067361 - 19 August 2025
Abstract The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges, particularly in securing token-based communication protocols used for authentication and authorization. This survey systematically reviews the vulnerabilities in token transmission within IoT environments, focusing on various sophisticated attack vectors such as replay attacks, token hijacking, man-in-the-middle (MITM) attacks, token injection, and eavesdropping among others. These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth, JSON Web Tokens (JWT), and bearer tokens, which are widely used in IoT ecosystems for managing device interactions and access control. The impact of such attacks… More >

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1693

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ARTICLE

Saurabh Sanjay Singh^*, Rahul Joshi, Deepak Gupta
Journal on Artificial Intelligence, Vol.7, pp. 177-203, 2025, DOI:10.32604/jai.2025.065078 - 30 June 2025
Abstract This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem. In this problem, each job has a fixed sequence of operations to be performed on parallel machines, and each operation can be assigned to any capable machine. The problem statement aims to schedule every job in a way that minimizes the total energy consumption of the job shop. The paper’s primary objective is to develop a reinforcement learning-based scheduling framework using the Advantage Actor-Critic algorithm to generate energy-efficient schedules that are computationally fast… More >

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1029

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677

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ARTICLE

Mohammed Moawad Alenazi^*
Journal on Artificial Intelligence, Vol.7, pp. 143-176, 2025, DOI:10.32604/jai.2025.066067 - 27 June 2025
Abstract This paper presents a novel machine learning (ML) enhanced energy management framework for residential microgrids. It dynamically integrates solar photovoltaics (PV), wind turbines, lithium-ion battery energy storage systems (BESS), and bidirectional electric vehicle (EV) charging. The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting, a Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning agent for optimal BESS scheduling, and federated learning for EV charging prediction—ensuring both privacy and efficiency. Simulated in a high-fidelity MATLAB/Simulink environment, the system achieves 98.7% solar/wind forecast accuracy and 98.2% Maximum Power Point… More >

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874

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535

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ARTICLE

Muhammad Naeem^*, Abdul Majid
Journal on Artificial Intelligence, Vol.7, pp. 115-141, 2025, DOI:10.32604/jai.2025.062446 - 30 May 2025
Abstract Brain tumor identification is a challenging task in neuro-oncology. The brain’s complex anatomy makes it a crucial part of the central nervous system. Accurate tumor classification is crucial for clinical diagnosis and treatment planning. This research presents a significant advancement in the multi-classification of brain tumors. This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d, a Convolutional Neural Network (CNN) with a multi-head self-attention (MHSA) mechanism. This combination harnesses the strengths of the feature extraction, feature representation by CNN, and long-range dependencies by MHSA. Magnetic Resonance Imaging (MRI) datasets were employed to check… More >

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984

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642

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ARTICLE

Ilyas Haouam^*
Journal on Artificial Intelligence, Vol.7, pp. 85-114, 2025, DOI:10.32604/jai.2025.064145 - 27 May 2025
Abstract This study investigates the integration of Artificial Intelligence (AI) technologies—particularly natural language processing and machine learning—into qualitative research (QR) workflows. Our research demonstrates that AI can streamline data collection, coding, theme identification, and visualization, significantly improving both speed and accuracy compared to traditional manual methods. Notably, our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency, accuracy, and usability across various QR tasks. By presenting and discussing studies on some AI & generative AI models, we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its… More >

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1264

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ARTICLE

Muhammad Izhar^1,*, Khadija Parwez², Saman Iftikhar³, Adeel Ahmad⁴, Shaikhan Bawazeer³, Saima Abdullah⁴
Journal on Artificial Intelligence, Vol.7, pp. 55-83, 2025, DOI:10.32604/jai.2025.062479 - 25 April 2025
Abstract The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a… More >

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1287

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869

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ARTICLE

Qingpeng Wei^1,#, Wenai Song^1,#, Lizhen Fu¹, Yi Lei², Qing Wang^2,*
Journal on Artificial Intelligence, Vol.7, pp. 39-53, 2025, DOI:10.32604/jai.2025.063188 - 23 April 2025
Abstract Pneumoconiosis, a prevalent occupational lung disease characterized by fibrosis and impaired lung function, necessitates early and accurate diagnosis to prevent further progression and ensure timely clinical intervention. This study investigates the potential application of the Vision Transformer (ViT) deep learning model for automated pneumoconiosis classification using digital radiography (DR) images. We utilized digital X-ray images from 934 suspected pneumoconiosis patients. A U-Net model was applied for lung segmentation, followed by Canny edge detection to divide the lungs into six anatomical regions. The segmented images were augmented and used to train the ViT model. Model component… More >

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1131

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761

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ARTICLE

Zheng Zhang, Hengyang Wu^*, Na Wang
Journal on Artificial Intelligence, Vol.7, pp. 17-37, 2025, DOI:10.32604/jai.2025.059607 - 19 March 2025
Abstract This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. Addressing challenges such as the complexity of medical terminology, the difficulty of constructing medical knowledge graphs, and the scarcity of medical data, the method retrieves structured knowledge from clinical cases via external knowledge graphs. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model’s understanding and reasoning capabilities for the task. We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results More >

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1112

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1040

Open Access

ARTICLE

Guodong Yuan^1,*, Jin Xie²
Journal on Artificial Intelligence, Vol.7, pp. 1-16, 2025, DOI:10.32604/jai.2025.061437 - 14 March 2025
Abstract This paper addresses the problem of access efficiency in multi-robot systems to the monitoring area. A distributed algorithm for multi-robot continuous monitoring, based on the uncertainty of target points, is used to minimize the uncertainty and instantaneous idle time of all target points in the task domain, while maintaining a certain access frequency to the entire task domain at regular time intervals. During monitoring, the robot uses shared information to evaluate the cumulative uncertainty and idle time of the target points, and combines the update list collected from adjacent target points with a utility function More >

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1336

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795