Sani SAMINU

Scopus Publications

A Multi-Branch BiLSTM with Multi-Head Self-Attention for Suspicious Sound Recognition
Shehu Mohammed Yusuf, Hamza Saidu, Sani Saleh Saminu
Journal of Computing Theories and Applications, 2026
Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.
A Lightweight Maize Leaf Disease Recognition Using PCA Compressed MobileNetV2 Features and RBF-SVM
Mustapha Abubakar, Yusuf Ibrahim, Ore-Ofe Ajayi, Sani Saleh Saminu
Journal of Computing Theories and Applications, 2026
The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.
Dielectric Characterization of Breast Cancer Cells using SplitRectangular Ring Resonator Sensor
Adamu Halilu Jabire, Sani Saminu, Muhammed Jajere Adamu, Abubakar Saddiq Mohammed, Sha'awanatu Aminu, Abubakar Muhammad Sadiq
Buletin Ilmiah Sarjana Teknik Elektro, 2025
Exploring a universal method to enhance the performance of metamaterials by quantifying the impact of gap capacitance is an intriguing topic for many researchers. However, achieving this through conventional methods is extremely challenging. In this paper, we present a microwave sensor designed to characterize cancerous cells based on their electrical properties. The proposed design features a split rectangular ring resonator placed on a flame-retardant four (FR-4) substrate. The sensor aims to achieve high sensitivity and quality factors through the unique characteristics of the metamaterial structure in the GHz frequency range. Through simulations and experimental measurements, we demonstrate the sensor's effective capabilities in detecting cancer. The high sensitivity for both simulation and measurement, is estimated at 10 %. The simulations and validation confirm that this biosensor exhibits significant frequency shifts and high sensitivity. Our proposed configurations highlight the microwave sensor's potential for detecting six different breast cancer cell types: HSS-2, HS578-T_nm, MCF-2, MCF-10A_nm, T-47D, and T-47D_nm. Based on the existing literatures, the sensitivity of the proposed sensor is determined to be greater.
Development of Metamaterial-Based Biosensor for Biomedical Applications
Nigerian Journal of Technological Development, 2025
Detection of Multi-Class Epileptic Intracranial EEG Signals Based on Advanced Hybrid Time-Frequency and Machine Learning Technique
Sani Saminu
Jordan Journal of Electrical Engineering, 2025
Epilepsy is one of the chronic brain disorders that affects the quality of life and well-being of millions of people around the globe. It is characterized by excessive electrical activity of the brain’s cells that usually leads to recurrent seizures. Accurate, efficient, and robust techniques suitable for recent Internet of Medical Things (IoMT) devices to detect, classify, and diagnose epileptic seizures in a challenging multi-classification scenario and noisy environment are of paramount importance. Electroencephalograph (EEG) signals recorded even from the surface of the brain suffer from contaminated artifacts and noise from various sources, such as from EOG and EMG for eye-blinks and muscle artifacts, respectively. This work aims to address the challenges of multi-class classification and automatic seizure detection in intracranial EEG signals by developing a detection system suitable for real-world clinical settings. To achieve this, this work uses an effective feature extraction technique and efficient seizure detection methods based on a recent big data resource, along with advancements in deep machine learning techniques, to propose and develop robust hybrid models that combine conventional machine learning techniques and deep learning architectures to increase the performance of epileptic detection systems to levels that are close to acceptable for real-world applications. Firstly, a robust computationally efficient technique that characterizes different types of seizures with high precision and low latency of its onset was proposed. The system relies on an effective and low in complexity feature extraction approach based on the proposed advanced time-frequency Fourier Basel series Expansion based Flexible Time-Frequency Analytic Wavelet Transform (FBSE-FTFAWT) that extracts notable features associated with EEG seizure signals in a time-effective manner. Secondly, two noise robustness seizure detection techniques were developed to address the research question: can the hidden patterns in artifact-induced epileptic EEG data be identified and characterized? Stacked Auto Encoder based Support Vector Machine (SAE-SVM) and Deep Belief Network based Support Vector Machine (DBN-SVM) as hybrid classifiers are proposed with a novel feature extraction to classify various seizure and non-seizure class combinations. The proposed optimized SVM classifier, FBSE-FTFAWT /SAE-SVM, shows better detection accuracy, sensitivity, specificity, precision, and F1-score of 99.7%, 99.6%, 99.6%, 99.7%, and 99.6%, respectively, over the other two proposed models and the state-of-the-art methods in the literature.
Optimization of Grid-Connected PV Systems: Balancing Economics and Environmental Sustainability in Nigeria
Habib Muhammad Usman, Nirma Kumari Sharma, Deepak Kumar Joshi, Baba Isah Sani, Muhammad Mahmud, Sani Saminu, Abdulbasid Bashir Yero, Rabiu Sharif Auwal
Buletin Ilmiah Sarjana Teknik Elektro, 2024
Nigeria faces the dual challenge of harmful industrial emissions contributing to climate change and unreliable power supply, demanding urgent attention. This study focuses on optimizing a grid-connected photovoltaic (PV) system at the Department of Electrical Engineering, Ahmadu Bello University Zaria, Kaduna, Nigeria, with the goal of achieving economic and environmental sustainability. The study utilizes HOMER, a widely used optimization tool for renewable energy systems, to design and evaluate three distinct energy scenarios. The first scenario relies solely on grid power, resulting in high annual costs of $2,838, significant environmental degradation, and zero renewable energy contribution. The second scenario integrates solar PV with grid power, reducing grid dependency but only partially addressing cost and environmental concerns, with an annual energy cost of $2,714 and 1,867 kWh generated from solar PV. The third scenario demonstrates the most favourable outcomes, combining high solar PV generation with economic benefits. The system produces 29,684 kWh annually, selling $521 worth of surplus energy back to the grid, resulting in a net yearly energy cost of $1,043. The initial installation cost is expected to be recovered within two years, offering potential savings of $20,000 over the system's 20-year lifespan. These findings show the viability of solar PV systems as a solution to Nigeria's energy challenges, underscoring the importance of balancing economic and environmental factors in energy system design. The study provides valuable insights for institutions and similar contexts looking to transition to more sustainable energy systems.
Analysis of Impact Attenuation in 3D Printed Hip Protectors: A Support Vector Regression Approach
S.A. Yahaya, I.O. Muniru, S. Saminu, M.O. Ibitoye, T.M. Ajibola, L.J. Jilantikiri, Z.M. Ripin, M.I.Z. Ridzwan
Nigerian Journal of Technological Development, 2024
This study examined the impact attenuation of 3D printed hip protectors using thermoplastic polyurethanes with different shore hardness values for preventing osteoporotic hip fractures. Rigorous testing at various energy levels were carried out to determine the protector’s abilities to attenuate impact. Subsequently, the prediction of impact attenuation capacity based on key design parameters was achieved from developed Support Vector Regression (SVR) model generated from the data of the impact attenuation capabilities. The key design parameters were shell thickness, infill density and shore hardness. The results demonstrated a significant correlation between the impact attenuation ability and the infill density of the hip protectors with R2 of 91% for the training set and 99% for the test set. The generated RMSE values are 0.0012 and 0.0208, respectively. Remarkably, the SVR model exhibited excellent agreement with the experimental test results, affirming the efficacy of SVR in the design of hip protectors to enhance protective performance and cut the cost of experimentation.
EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE
SANI SAMINU, GUIZHI XU, ZHANG SHUAI, ISSELMOU ABD EL KADER, ADAMU HALILU JABIRE, YUSUF KOLA AHMED, IBRAHIM ABDULLAHI KARAYE, ISAH SALIM AHMAD
Journal of Mechanics in Medicine and Biology, 2024
Objective: Most studies in epileptic seizure detection and classification focused on classifying different types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain’s is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as effective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of [Formula: see text] and [Formula: see text] features were used to obtain a joint instantaneous frequency and amplitude components using a sub-band alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT_SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a significant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using [Formula: see text]-rhythms in channels 1 and 2. Significance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient’s brain to increase the success of the surgery and reduce computational cost.
Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier
, Sani Saminu, , , Guizhi Xu, , , Shuai Zhang, , , Isselmou Ab El Kader, , , Hajara Abdulkarim Aliyu, , Adamu Halilu Jabire, , Yusuf Kola Ahmed, , Isah Salim Ahmad, , and
Electrica, 2023
Epilepsy is a disease with various forms. However, limited dataset has confined classification studies of epilepsy into binary classes only. This study sort to achieve multiclassification of epileptic seizures through a robust feature extraction technique by comprehensively analyzing various advanced feature parameters from different domains, such as energy and entropy. The values of these parameters were computed from the coefficients of dilation wavelet transform (DWT) and modified DWT, known as dual-tree complex wavelet transform decomposition. The model was evaluated from the features of each of the parameters. The hybrid features were divided into three experiments to extract the meaningful features as follows: 1). features from combined energy features were extracted; 2). features from combined entropy features were also extracted; and 3). features from combined parameters as hybrid features were extracted. Finally, the model was developed based on the extracted features to perform a multi-classification of seven types of seizures using an optimized support vector machine (SVM) classifier. A recently released temple university hospital corpus dataset consisting of long-time seizure recordings of various seizures was employed to evaluate our proposed model. The proposed optimized SVM classifier with the hybrid features performed better than other experimented models with the value of accuracy, sensitivity, specificity, precision, and F1-score of 96.9%, 96.8%, 93.4%, 95.6%, and 96.2%, respectively. The developed model was also compared with some recent works in literature that employed the same dataset and found that our model outperformed all the compared studies. Cite this article as: S. Saminu, et al. "Multi-classification of electroencephalogram epileptic seizures based on robust hybrid feature extraction technique and optimized support vector machine classifier,". Electrica, 23(3), 438-448, 2023.
Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review
Sani Saminu, Guizhi Xu, Shuai Zhang, Isselmou Ab El Kader, Hajara Abdulkarim Aliyu, Adamu Halilu Jabire, Yusuf Kola Ahmed, Mohammed Jajere Adamu
Artificial Intelligence and Applications, 2023
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment. Received: 5 July 2022 | Revised: 10 August 2022 | Accepted: 27 August 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
A crossed-polarized four port MIMO antenna for UWB communication
Adamu Halilu Jabire, Salisu Sani, Sani Saminu, Mohammed Jajere Adamu, Mousa I. Hussein
Heliyon, 2023
Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies
Mohammed Jajere Adamu, Li Qiang, Charles Okanda Nyatega, Ayesha Younis, Halima Bello Kawuwa, Adamu Halilu Jabire, Sani Saminu
Frontiers in Psychiatry, 2023
Design and Development of a Hybrid Eye and Mobile Controlled Wheelchair Prototype using Haar cascade Classifier: A Proof of Concept
Yusuf Kola Ahmed, Nasir Ayuba Danmusa, Taofik Ahmed Suleiman, Kafilat Atinuke Salahudeen, Sani Saminu, Abdul Rasak Zubair, Abdulwasiu Bolakale Adelodun
Lecture Notes on Data Engineering and Communications Technologies, 2023
Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals
Sani Saminu, Guizhi Xu, Zhang Shuai, Isselmou Abd El Kader, Adamu Halilu Jabire, Yusuf Kola Ahmed, Ibrahim Abdullahi Karaye, Isah Salim Ahmad
Applied Sciences Switzerland, 2022
Performance Analysis of Turbo Codes for 5G Massive Machine-Type Communication(mMTC)
Mohammed Jajere Adamu, Li Qiang, Rabiu Sale Zakariyya, Adamu Halilu Jabire, Halima Bello Kawuwa, Sani Saminu
2021 IEEE 23rd International Conference on High Performance Computing and Communications 7th International Conference on Data Science and Systems 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor Cloud and Big Data Systems and Applications Hpcc Dss Smartcity Dependsys 2021, 2022
Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images
Isselmou Abd El Kader, Guizhi Xu, Zhang Shuai, Sani Saminu
Current Medical Imaging, 2021
Brain tumor detection and classification on mr images by a deep wavelet auto‐encoder model
Isselmou Abd El Kader, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad, Souha Kamhi
Diagnostics, 2021
A recent investigation on detection and classification of epileptic seizure techniques using eeg signal
Sani Saminu, Guizhi Xu, Zhang Shuai, Isselmou Abd El Kader, Adamu Halilu Jabire, Yusuf Kola Ahmed, Ibrahim Abdullahi Karaye, Isah Salim Ahmad
Brain Sciences, 2021
Differential deep convolutional neural network model for brain tumor classification
Isselmou Abd El Kader, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad
Brain Sciences, 2021
Metamaterial based design of compact UWB/MIMO monopoles antenna with characteristic mode analysis
Adamu Halilu Jabire, Adnan Ghaffar, Xue Jun Li, Anas Abdu, Sani Saminu, Mohammad Alibakhshikenari, Francisco Falcone, Ernesto Limiti
Applied Sciences Switzerland, 2021
Convolutional neural networks model for emotion recognition using EEG signal
Isah Salim Ahmad, Zhang Shuai, Wang Lingyue, Sani Saminu, Abd El Kader Isselmou, Zilian Cai, Imran Javaid, Souha Kamhi, Ummay Kulsum
International Journal of Circuits Systems and Signal Processing, 2021
A Novel Computer Aided Detection System for Detection of Focal and Non-Focal EEG Signals using Optimized Deep Neural Network
S. Saminu, G. Xu, S. Zhang, A. E. K. Isselmou, A. H. Jabire, Y. K. Ahmed, H. A. Aliyu, M. J. Adamu, A. Y. Iliyasu, F. A. Umar
2021 IEEE Signal Processing in Medicine and Biology Symposium Spmb 2021 Proceedings, 2021
Design of a Compact UWB/MIMO Antenna with High Isolation and Gain
Adamu Halilu Jabire, Adnan Ghaffar, Xue Jun Li, Anas Abdu, Sani Saminu, Abubakar Muhammad Sadiq, Adamu Mohammed Jajere
Proceedings of 2020 IEEE Workshop on Microwave Theory and Techniques in Wireless Communications Mttw 2020, 2020
Brain tumor identification by convolution neural network with fuzzy c-mean model using mr brain images
Abd El Kader Isselmou, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad
International Journal of Circuits Systems and Signal Processing, 2020
Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique
Sani Saminu, Guizhi Xu, Shuai Zhang, Abd El Kader Isselmou, Rabiu S. Zakariyya, Adamu Halilu Jabire
2019 15th International Conference on Electronics Computer and Computation Icecco 2019, 2019
Deep learning algorithm for brain tumor detection and analysis using MR brain images
Abd El Kader Isselmou, Guizhi Xu, Shuai Zhang, Sani Saminu, Imran Javaid
ACM International Conference Proceeding Series, 2019
Analysis of cardiac beats using higher order spectra
Ibrahim Abdullahi Karaye, Sani Saminu, Nalan Ozkurt
IEEE International Conference on Adaptive Science and Technology Icast, 2015
Wavelet feature extraction for ECG beat classification
Sani Saminu, Nalan Ozkurt, Ibrahim Abdullahi Karaye
IEEE International Conference on Adaptive Science and Technology Icast, 2015

RECENT SCHOLAR PUBLICATIONS

Optimization of Photovoltaic System Performance and Power Electronics Efficiency Under Varying Environmental Conditions Through Implementation of Effective Incremental …
HM Usman, J Haruna, HA Aliyu, SH Sulaiman, S Saminu, S Muhammad
BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE 9 (4A), 29-45 , 2025
2025
Detection of Multi-Class Epileptic Intracranial EEG Signals Based on Advanced Hybrid Time-Frequency and Machine Learning Techniques
Sani Saminu, Adamu Halilu Jabire, Habib Muhammad Usman, Mohammed Jajere ...
Jordan journal of electrical engineering 11 (3), 440-469 , 2025
2025
Stigmasterol Isolation and Anticonvulsant Activity of Methanol Extract from the Aerial Parts of Indigofera Stenophylla (FABACEAE)
A. Sanusi, H.S. Hassan, S. Sani, L.A. Akinpelu, H. Muhammad, Y.M. Ado, R.B ...
Bulletin of Pharmaceutical Sciences, Assiut University 48 (2), 1065-1077 , 2025
2025
TECHNO-ECONOMIC FEASIBILITY AND SENSITIVITY ANALYSIS OF OPTIMAL DESIGN OF HYBRID GRID-CONNECTED MICROGRID CONFIGURATION FOR SUSTAINABLE AND RELIABLE ENERGY
HM Usman, NK Sharma, DK Joshi, A Kaushik, S Kumhar, S Saminu, ...
Journal of Engineering Studies and Research 30 (4), 56-75 , 2025
2025
Stabilizing environmental conditions for improved biogas generation: A comparative analysis of above-ground and underground plastic digester in fed-batch systems
H Usman Muhammad, M Sulaiman, M Sulaiman Yahaya, S Saminu
Iranica Journal of Energy & Environment 16 (2), 227-239 , 2025
2025
Citations: 5
Development of Metamaterial-Based Biosensor for Biomedical Applications
AH Jabire, S Saminu, AA Owoade, DA Ariyoosu, AM Sadiq, MJ Adamu, ...
Nigerian Journal of Technological Development 22 (1), 115-123 , 2025
2025
Dielectric Characterization of Breast Cancer Cells using Split-Rectangular Ring Resonator Sensor
AH Jabire, S Saminu, MJ Adamu, AS Mohammed, S Aminu, AM Sadiq
Buletin Ilmiah Sarjana Teknik Elektro 7 (1), 42-55 , 2025
2025
Development of a low-cost and accessible hand tremor rehabilitation game for unhealthy patients
S Saminu, SA Yahaya, AO Adewale, IO Muniru, TM Ajibola, MO Ibitoye, ...
Journal of Engineering and Technology (JET) 15 (2), 1-18 , 2024
2024
Citations: 1
Role of Next Generation Sequencing in Biomolecular Sciences: A Review
Y Danjuma, TT Samuel, GJ Joseph, WC Austin-Amadi, JWK Jonah, ...
BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE 8 (4A), 110-123 , 2024
2024
Development of an integrated smart monitoring system for enhanced peritoneal dialysis care
S Saminu, A Iliyas, SA Yahaya, IO Muniru, TM Ajibola, MO Ibitoye, ...
Kathmandu University Journal of Science Engineering and Technology 18 (2) , 2024
2024
Techno-Economic Optimization and Sensitivity Analysis of a Hybrid Grid-Connected Microgrid System for Sustainable Energy
HM Usman, NK Sharma, DK Joshi, A Kaushik, S Kumhar, S Saminu, ...
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 10 (4), 704-722 , 2024
2024
Citations: 4
Enhancing Photovoltaic Efficiency through Engine Oil Coatings: A Comparative Analysis of New, Partially Used, and Degraded Oils
HM Usman, NK Sharma, S Saminu, AB Yero, AS Yahya, FI Muhammad
The Islamic University Journal of Applied Sciences 6 (2), 134-154 , 2024
2024
Citations: 1
Analysis of Impact Attenuation in 3D Printed Hip Protectors: A Support Vector Regression Approach
SA Yahaya, IO Muniru, S Saminu, MO Ibitoye, TM Ajibola, LJ Jilantikiri, ...
Nigerian Journal of Technological Development 21 (3) , 2024
2024
Citations: 1
Optimization of grid-connected PV systems: Balancing economics and environmental sustainability in Nigeria
HM Usman, NK Sharma, DK Joshi, BI Sani, M Mahmud, S Saminu, ...
Buletin Ilmiah Sarjana Teknik Elektro 6 (3), 237-253 , 2024
2024
Citations: 3
Optimization of Grid-connected Pv Systems: Balancing Economics and Environmental Sustainability
HM Usman, BI Sani, M Mahmud, S Saminu
2024
Citations: 2
Innovative Optimization of Microgrid Configuration for Sustainable, Reliable and Economical Energy
HM Usman, NK Sharma, DK Joshi, A Kaushik, S Saminu
2024
Achieving Quality Education Through AI: Predicting Engineering Student Retention at the University of Ilorin
I. O. Muniru, S. A. Yahaya, S. Saminu
2nd Faculty of Engineering and Technology International Conference (FETICON … , 2024
2024
Design Modification and Performance Evaluation of Solar PV System at AutoCAD Laboratory
H. M. Usman, M. S. Yahaya, S. Saminu, M. Muhammad, S. Ibrahim, B. I. Sani, M ...
2nd Faculty of Engineering and Technology International Conference (FETICON … , 2024
2024
PERFORMANCE INVESTIGATION OF A BOOST CONVERTER DRIVING A SEPARATELY EXCITED DC MOTOR USING RC FILTER
HM Usman, GA Olarinoye, S Saminu, MS Yahaya, M Muhammad
Journal of Engineering and Technology (JET) 15 (1) , 2024
2024
Recent trends and future prospects in electric vehicle technologies: A comprehensive review
HM Usman, NK Sharma, DK Joshi, A Kaushik, S Saminu
Kathmandu University Journal of Science, Engineering, and Technology 18 (1 … , 2024
2024
Citations: 15

Sani SAMINU

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS