International Journal of Intelligent Systems Technologies and Applications (7 papers in press)

Regular Issues

• Spatio-Temporal Hierarchical Network for Human Pose Recognition Using Millimetre-Wave Radar  
  by Gang Yang, Ya Gao, Jintao Shi 
  Abstract: To enhance the accuracy and real-time performance of human pose recognition using millimeter-wave radar, an end-to-end spatio-temporal hierarchical feature fusion network (STHNet) is proposed. Multi-scale feature extraction is conducted through cascaded spatial and temporal convolutions, where a temporal convolution module as an independent unit links spatial representations with long-term dependencies, improving extraction of short-term motion cues and long-range semantics from sparse radar sequences. An adaptive feature selection mechanism is introduced, where learnable weights dynamically emphasize key spatio-temporal features, improving the discrimination of subtle motion variations. The end-to-end training strategy removes handcrafted feature design and enables a direct mapping from raw radar signals to pose categories. Experimental results show that STHNet achieves 99.5% overall recognition accuracy across six daily postures and a 100% recall rate for fall detection. Moreover, the average inference latency is 25.61 ms, with 95% of samples processed within 35.433 ms, demonstrating satisfactory real-time performance for practical deployment.
  Keywords: millimeter-wave radar; human pose recognition; end to end; deep learning; spatio-temporal features.
  DOI: 10.1504/IJISTA.2026.10079881
   
  
• Explainable AI-Driven Face Morphing and Demorphing Using Face Recognition Techniques Based on FSGAN  
  by M.K.Mohamed Faizal, S. Geetha, A. Barveen 
  Abstract: Face morphing attacks are a threat to security systems that use faces to identify people. This is because people can combine faces from individuals to gain unauthorised access. This study proposes a method for face morphing and demorphing using face swapping generative adversarial network (FSGAN). Facial images are encoded using a pre-trained ArcFace model. These images are created by combining features from two identities is done at a 70:30 ratio. The result is a realistic hybrid face. It was tested on the CelebA dataset. The results are as good in accuracy of morphing, morph detection and identity recovery rate. 3.12% is considered to be the false acceptance rate of this system and also considered to have a better performance than other technologies like StarGAN and DeepFakeGAN. As a whole, this proposed system increases the transparency and helps in automated biometric verification system that paves way for morphed image generation, identity recovery and identity detection by showing the improved potential in real-time applications by providing greater security.
  Keywords: Biometric Security; Face Recognition; Face Morphing; Morph Detection; Demorphing; ArcFace; FSGAN; SHAP; Grad-CAM XGBoost; Explainable AI.
  DOI: 10.1504/IJISTA.2026.10079886
   
  
• Quantum Inspired Siamese Convolutional Forward Harmonic Net with THEuCaLoss function for Percentage of Nutrient Deficiency Estimation and Fertilizer Recommendation using cotton plants leaf images  
  by Swapnil S. Ayane, Mukesh Tiwari 
  Abstract: In this work, we introduce a novel model, the Tanimoto Harmonic Euclidean Cauchy Loss Net (THEuCaLossNet), for accurate identification of nutrient deficiencies in cotton plants. The process begins with image pre-processing using a Non-Local Means (NLM) filter, followed by segmentation through region-based Online Selective Examination (ROSE). Image augmentation techniques such as cropping, resizing, and Grid Mask enhance model performance. Feature extraction is conducted using Locally Adaptive Regression Kernel (LARK) and EfficientNet. These features are passed into the Quantum Siamese Convolutional Forward Harmonic Network (QuSCFHNet), combining Quantum-Inspired Convolutional Neural Networks (CNNs) and Siamese CNNs. The network is trained using Cauchy-Schwarz Divergence, Euclidean loss, and harmonic functions. Based on the results, organic fertilizer recommendations are provided. The model achieves 92.66% accuracy, 92.74% True Positive Rate (TPR), and 92.43% True Negative Rate (TNR), proving its effectiveness.
  Keywords: Non-Local Means; Region-based Online Selective Examination; Locally Adaptive Regression Kernel; Quantum-Inspired Convolutional Neural Networks; Siamese Convolutional Neural Networks.
  DOI: 10.1504/IJISTA.2026.10080503
   
  
• Implementation of a Voice Pathology Detection and Severity Analysis System for Parkinsons Disease using Convolutional Neural Networks  
  by Anitha Sankaran, Shaik Fardeen Hussain, Vinoth B, Dhanvantraj M, Lakshmi Sutha Kumar 
  Abstract: A simple yet effective CNN model is proposed in this paper for Parkinsons disease detection. The proposed Convolutional Neural Network (CNN) is fed with Mel Frequency Cepstral Coefficients (MFCC) for detecting Parkinsons disease from speech signals and gives an accuracy of 86.67%. The DisVoice framework is used to extract the phonation and prosodic characteristics from speech signals. The MFCCs, which are an articulation parameter, along with the phonation and prosody features, provide a high-dimensional feature vector comprising of 171 features. The SelectKBest feature selection method is employed to optimize the model performance and identify the most significant features for predicting the severity of Parkinsons disease. The selected significant features are fed to the three different machine learning algorithms for determining the severity analysis of Parkinsons disease, of which the XGBoost algorithm performs the best with a recognition accuracy of 83.33%
  Keywords: Parkinson’s Disease; PC GITA; Mel Frequency Cepstral Coeffecients; CNN; XGBoost.
  
  
• Machine Learning-Driven Replica Placement for Cost-Efficient Cloud-Edge Storage Systems  
  by Zeng Sai-feng 
  Abstract: Cloud-edge computing architectures are pivotal for low-latency, cost-efficient data services, yet optimizing replica placement faces challenges from heterogeneous resources and dynamic workloads. Traditional methods lack adaptability and cross-tier coordination, leading to suboptimal performance. We propose DMCC-ML, a hybrid algorithm integrating LSTM-based demand forecasting, GCN-guided load balancing, and RL with distributed greedy heuristics, preserving DMCCs 2-approximation guarantee. It enables proactive adaptation to spatial-temporal demand patterns while balancing local and global objectives. Experiments show DMCC-ML achieves 85-92% storage utilization, reduces average access latency by 35-50%, and improves load balancing compared to existing approaches. It also minimizes replica redundancy and remote accesses, outperforming state-of-the-art strategies across varying workload intensities.
  Keywords: cloud-edge computing; replica placement; cost optimization; machine learning.
  DOI: 10.1504/IJISTA.2026.10080811
   
  
• A Review of Redundant Force Control Approaches in Electro-Hydraulic Load Simulation Systems  
  by Chao Jiang, Bingwei Gao, Jiajian Jiang, Yuxun Huang, Bo Cui, Junqi Song 
  Abstract: Electro-hydraulic load simulator is a semi-physical simulation device used to simulate loads on equipment, such as aircraft, under laboratory conditions. It generates redundant forces due to its mechanical structure when it is used, which will affect the simulation performance of the equipment. Firstly, the mechanism of redundant force is studied, and the influence of various factors on redundant force within the system is examined. Then, the common compensation methods are summarized and introduced, including structural compensation method and control compensation method. Finally, based on the objective advantages and disadvantages of the existing technology, the future development trend of redundant force compensation are discussed.
  Keywords: Load Simulator; Redundant Force; Force Compensation; Motion Disturbance.
  DOI: 10.1504/IJISTA.2027.10080940
   
  
• In-Situ Digital Twins for Sustainable Building Operations: A Systematic Review of Energy, Comfort, and Maintenance Synergies  
  by Hamza Benyezza, Aya Sayed, Faycal Bensaali, Mounir Bouhedda 
  Abstract: In-situ digital twin (ISDT) has emerged as a transformative tool for optimising building operations (BO), yet a comprehensive synthesis of its applications, challenges, and future potential is lacking. This systematic literature review (SLR) analyses 95 peer-reviewed studies from 2020 to May 2025 to fill this gap. Four key themes are identified: energy management and optimisation, real-time monitoring and control, predictive maintenance, and occupant comfort. Building on these thematic findings, a detailed review of the 95 studies reveals that AI/ML-driven ISDT is the predominant approach, comprising 70% of cases. Nevertheless, critical gaps and practical limitations persist: only 12% of the corpus addresses data privacy risks, model drift leads to an annual accuracy loss of approximately 8%12%, and scalability remains limited, with 68% of the studies focusing on single-building deployments. Based on these findings, we propose a roadmap for future research, emphasising open-source frameworks, regulatory sandboxes, and longitudinal validation. This work advances SDG 11 by bridging the divide between ISDT innovation and real-world deployment in the built environment
  Keywords: In-situ Digital Twin; building operations; energy management; predictive maintenance; occupant comfort.
  DOI: 10.1504/IJISTA.2027.10081198