IEEEP New Horizons Journal

Fifty Years of the IEEEP Journal: A Historical Review and Editorial Reflection (1972–2022)

Dr. Sajid Iqbal — 2026-06-24

MS Mathematical Modeling of Cyber-Physical Attacks on Water Tank Systems Using Sinusoidal and Stochastic Disturbance Signals

Misbah Kanwal — 2026-06-24

Cyber-Physical Systems (CPS) play a vital role in managing real-world infrastructure, including water distribution systems. These systems bring together physical processes and computational control, but this integration also makes them easy to harm to cyber-attacks that can produce serious physical consequences. In this work, I developed a mathematical model to simulate how such an attack could disrupt a water tank system. The attack is represented using a combination of sine and cosine signals along with random noise to reflect both planned and unpredictable interference. By applying the fourth-order Runge-Kutta method, the model shows how these intrusions can destabilize the water level over time. The results highlight key weaknesses in CPS and emphasize the importance of using mathematical simulations to better understand and secure these systems

Multifunctional IoT-Based Numerical Relay for Power System Protection

Rehan Liaqat — 2026-06-24

Conventional relays such as electromechanical and solid-state relays, exhibit inadequate speed and flexibility, preventing their applicability in existing smart grid infrastructures. Their constrained adaptability and maintenance requirements reduce their efficiency in fluctuating power contexts. Realization of smart distribution networks demands cost-effective, adaptive, and remotely controlled protective systems. This study presents the design and development of a multifunctional numerical relay developed/working on the concept of IoT. The proposed relay module offers accurate fault detection and real-time monitoring. The Arduino UNO microcontroller serves as the central part of the relay, which takes voltage and current signals from a sensor and, based upon post-processed signals, detects the system status and sends the alert command to the circuit breaker. The built-in board is presented as a multifunctional numerical relay that can detect various faults, such as overcurrent, under/over voltage, under/over frequency, under impedance, and transmission short circuit fault. For testing, an LCD is interfaced with the relay to display electrical parameters and the system’s status. For remote monitoring, a cloud-based logging mechanism is implemented. The proposed relay module is validated for various faulty conditions in the laboratory environment, and accurate responses are observed. The proposed relay can be used for the protection of evolving smart grid faults with SCADA or building management system platforms. It can serve as a cost-effective power system protection solution to safeguard electrical circuits, and sensitive power electronic devices.

Hybrid Speech Feature Learning Ensembled with Dual Stage Hierarchical Boosting for Classification of Parkinson’s Disease

Zeeshan Hameed — 2026-06-24

As a long-term and progressive neurodegenerative disease, Parkinson’s disease (PD) is challenging to diagnose at premature stages. Building machine learning models based on speech data for classifying PD objects has proven to be an effective approach. However, PD speech data has highly correlated features, including noise and redundancy, which affect the performance of machine learning models. Although dimensionality reduction algorithms (feature selection and feature extraction) can solve these issues to some extent, it has their limitations. Feature selection algorithms select a subset of the original feature space, thereby losing substantial information by eliminating the features. Feature extraction methods transform the features in a new feature space without reducing the number of features; However, the algorithms suffer from the problem of high aliasing and instability while transforming the high-dimensional data. To address these issues, we propose the HBD-SFDSB Model that leverages hybrid speech feature learning and dual-stage hierarchical Boosting approaches for Accurate PD diagnosis. The main goals of HBD-SFDSB are as follows: (1) constructing a deep chain-based sample space (DCS) by an iterative extraction method. (2) developing a dual-stage feature learning by introducing a hybrid feature learning technique. (3) Performing the dual-stage feature learning recurrently employing the AdaBoost technique to acquire high-quality speech features that significantly enhance the classification accuracy of the machine learning model. The performance of the HBD-SFDSB was evaluated using a self-collected and two publicly available speech datasets. The results show that HBD-SFDSB improves classification accuracy up to 38.71\% compared to available feature reduction and deep learning algorithms. Consequently, HBD-SFDSB may provide valuable insights for future research in this field.

Deep Learning-Based NER for Digitizing Unani Medicines Text

shantal khalid — 2026-06-24

Traditional medical systems such as Unani Medicine (UM) encompass centuries of therapeutic wisdom derived from Greco-Arabic traditions and enhanced by South Asian influences. Nevertheless, much of the Unani literature is still in non-digitized forms and various classical languages, making it difficult to integrate with contemporary healthcare technologies. This research introduces a named entity recognition (ner) machine learning preprocessing tool method to digitize and extract structured data from Unani medicinal texts systematically. Utilizing the reference work Classification among Unani Drugs with English and Scientific Names” by Ahmed and Nizami, a specialized dataset was created through OCR-based text extraction, followed by thorough preprocessing and manual annotation. We developed and assessed a BiLSTM-CRF model employing furtherness is pre-trained GloVe embeddings to discern and group four main entity types: Unani drug names, English names, scientific (Latin) names, and temperament attributes. An accuracy of 94.0% and an F1-score of 92.7%, indicating strong performance despite challenges related to OCR noise and multilingual diversity. In a comparative analysis, it can be observed that our approach competes favorably with other NER models published in the recent past in the non-biomedical and biomedical domain. The results point to the possibility of deep learning-assisted NER as an effective tool of digitalizing classical Unani texts enabling the development of structured databases to be used in expert systems, knowledge graphs, and AI-driven drug discovery. The research contributes to the maintenance of cultural heritage and helps to combine the traditional medicine with the data-driven healthcare system. Future studies will test what the combination of transformer-based models can do to nest an NER strategy, and extend the multilingual corpora with a view to further increasing recognition accuracy and domain-adaptability.

Influence of Yarn Linear Density and Weave Interlacement on the Dynamic Response of Woven Temperature Sensors

Shehroze Ali Baig — 2026-06-24

The integration of conductive yarns into woven fabrics offers a promising route to flexible, durable, and scalable temperature sensors. In this study, silver-coated yarns with varying ply levels (1-ply, 2-ply, and 4-ply) were systematically incorporated into plain and twill woven structures to investigate the influence of yarn linear density and weave interlacement on sensor performance. The developed sensors were evaluated for resistance–temperature response within 30–60 °C using a digital multimeter and controlled oven setup. Results indicate that plain weave structures demonstrated higher sensitivity (?R/R up to 5.1%) but moderate linearity, while twill weave structures provided lower sensitivity (?R/R as low as 1.01%) but superior linearity (R² up to 0.9966). Increasing ply levels reduced sensitivity but improved repeatability and stability. These findings highlight a trade-off between sensitivity and stability, offering design guidelines for tailoring textile-based temperature sensors to specific applications. The outcomes support future development of smart and sustainable textile composites for healthcare, structural health monitoring, and industrial applications.