Dr. Sulaiman Syed Mohamed

@alliance.edu.in

Professor CSE/Alliance College of Engineering and Design
Alliance University

Dr. Sulaiman Syed Mohamed


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https://researchid.co/smsulai
Dr. Sulaiman Syed Mohamed is an experienced educator in the field of Computers & Information Technology, having taught for approximately 26 years to students hailing from various parts of India and abroad. He has adeptly managed students from diverse cultural backgrounds and with varying language differences. He possesses a keen interest in addressing challenges that have a positive impact on humanity.

EDUCATION

B.E., M.S.,Ph.D.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Information Systems
23

Scopus Publications

338

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • EfficientNet-B0 Based Multi-Class Kidney Abnormality Classification from CT Images Using Transfer Learning
    Ramya S S, Prathiba A, Krishnakumar B, Sulaiman Syed Mohamed
    Proceedings of the 12th International Conference on Biosignals Images and Instrumentation Icbsii 2026, 2026
    CT scans while evaluating manually consumes a lot of time that motivates the necessity for computer assisted diagnosis systems which are reliable. Kidney abnormalities such as cysts, stone and tumor are diagnosed in advance by the use of computerized analysis of CT images. A deep learningbased multi-class classification framework for the detection of kidney abnormality is presented by this work with the help of CT images. The proposed lightweight, convolutional neural network architecture can be called as EfficientNet-B0 used along with transfer learning to categorize CT images into four groups as normal kidney, cyst, stone, and tumor. Metrics such as accuracy, precision, recall, and F1-score were used for performance assessment. The model is worked with a publicly available CT Kidney dataset with an overall accuracy of 84.02% which shown a strong performance for the classes of cyst and tumor and at the same time providing importance to the challenges of class imbalance and visual resemblance present in stone classification. From the results, the practical applicability of EfficientNet-based models for the multi class classification of kidney abnormalities is discovered.
  • Deep Learning Models for Enhanced RUL Prediction in Turbofan Jet Engines
    J. Judeson Antony Kovilpillai, Sulaiman Syed Mohamed, Pragya, Mahmood Hussain Mir, Tinka Singh, Uday Kumar Singh
    Lecture Notes in Networks and Systems, 2025
  • Enhancing Building Safety Through Machine Learning-Based Smoke Detection
    Sulaiman Syed Mohamed, Judeson Antony Kovilpillai J, Pragya, Mahmood Hussain Mir, Golda Brunet R, Tawseef Ahmad Mir
    2025 5th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2025, 2025
    Smoke detection in surveillance systems plays a crucial role in ensuring the safety and security of various environments, including buildings, forests, and industrial sites. In this paper, a comprehensive analysis of machine learning algorithms applied to smoke detection is presented. The proposed technique is validated using the Smoke Detection Dataset from Kaggle. The dataset comprises a diverse collection of images captured in different scenarios, including both smoke and non-smoke instances. The performance of various machine learning classifiers was evaluated. This included ensemble methods Random Forest (RF), Gradient Boosting (GB), AdaBoost (AB), linear models Logistic Regression (LR), kernel methods Support Vector Machine (SVM), decision-based methods Decision Tree (DT), and nearest neighbor methods K-Nearest Neighbors (KNN). Their precision, recall, AUC-ROC score, and Intersection over Union (IoU) are measured to assess their effectiveness in smoke detection. The results highlight that classifiers such as RF, DT, KNN, GB and AdaBoost achieve outstanding performance, with perfect scores in multiple metrics.
  • Deep learning based Crop Monitoring for effective Agricultural-IoT Management
    Mahmood Hussain Mir, Judeson Antony Kovilpillai J, Sulaiman Syed Mohamed, Pragya, Tawseef Ahmad Mir, Banibrata Paul
    Procedia Computer Science, 2025
    Achieving sustainable crop production to meet the increasing demand for food depends on the efficient management of agricultural greenhouses. Conventional methods of greenhouse management typically depend on human observation and intervention, resulting in a lack of scalability, efficiency, and precision. In this research, a deep learning technique for managing agricultural greenhouses is proposed by utilizing IoT sensor data obtained from a smart greenhouse. The proposed method aims to enhance crop monitoring and control, hence improving the overall efficiency of greenhouse operations. The utilization of several deep learning models with various activation functions, such as ELU, Swish, ReLU, SELU, and Mish, enables the ongoing surveillance and independent regulation of greenhouse conditions throughout the day. The proposed methodology can be used to enable real-time tracking and autonomous control, leading to resource optimization, increased productivity, and reduced manual intervention. Empirical validation demonstrates the efficacy of utilizing deep learning and IoT in agriculture to improve crop management and sustainability.
  • Deep Learning and Explainable AI Based Blast Wave Pressure Prediction in IoV Applications
    Mahmood Hussain Mir, Judeson Antony, Sulaiman Syed Mohamed, Soumi Dhar, Pragya, Danish Fayaz
    Procedia Computer Science, 2025
    Predicting blast wave pressure is crucial for enhancing safety and response strategies in Internet of Vehicles (IoV) applications, particularly in the context of urban environments and high-risk areas. This paper presents a novel deep learning model designed for predicting blast frequencies in BLEVE (Boiling Liquid Expanding Vapor Explosion) scenarios. The article evaluates several activation functions, including ReLU, Mish, ELU, Silu, and Leaky ReLU, to determine their effectiveness in improving model accuracy. The results indicate that the Mish activation function achieves slightly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores on the test dataset, with Train RMSE at 0.075 and Test RMSE at 0.099. The Silu activation function also demonstrates strong performance, yielding a Train RMSE of 0.074 and Test RMSE of 0.095, alongside high R 2 scores, indicating a good fit to the data. The paper further explores the interpretability of the model’s predictions using the LIME framework, revealing insights into how sensor positions and vapor heights influence blast frequency predictions. This research explores the potential of deep learning techniques in enhancing safety measures and predictive capabilities in hazardous scenarios, contributing valuable insights to the field of risk assessment and management in IoV applications.
  • A Transfer Learning Approach for Enhancing Road Surface Classification in Intelligent Driving Systems
    J Judeson Antony Kovilpillai, S Jayanthy, Sulaiman Syed Mohamed, Mahmood Hussain Mir, Pragya, Soumi Dhar
    IEEE International Conference on Signal Processing and Advance Research in Computing Sparc 2024, 2024
    Road surface classification is very critical in the domain of intelligent driving systems as it ensures enhanced safety and comfort in autonomous vehicles. In this paper, a transfer learning approach is proposed for effectively classifying the road surface with enhanced accuracy and real-time performance mitigating the constraints of traditional image processing techniques A deep learning model is developed and trained in this research using an open-source dataset, encompassing 72,400 images capturing distinct road surface characteristics present across varying terrains. This research adopts pre-trained models like Mobilenet, VGG-19 and Resnet101 as feature extractors to optimize and improve the classification performance of the deep learning model significantly. The proposed methodology is evaluated using different key performance metrics such as loss, accuracy, average rate of improvement and change in accuracy, and total training time. The proposed deep learning technique achieved an accuracy of $\\mathbf{9 8. 7 7 \\%}$, signifying its critical importance for real-world applications and practical deployment.
  • Advancing Water Quality Monitoring in Smart Cities using Machine learning Techniques
    Judeson Antony Kovilpillai J, Sulaiman S M, Mahmood Hussain Mir, Jayanthy S, Pragya Pragya, Rajkumar N
    2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
    In smart cities and urban environment, monitoring of water quality is very essential for environmental sustainability, resource optimization, cost management and streamlining the treatment process. In this research, an extensive dataset containing various contaminants such as aluminum, ammonia, arsenic, and others is utilized to effectively classify the safety level of water. Different machine learning and ensemble learning classifiers including LightGBM, XGBoost, CatBoost, Bagging, Gradient Boosting, Random Forest, Decision Tree, AdaBoost, MLP, and Extra Trees were implemented to conduct an empirical experimentation. Various performance metrics like Accuracy, Precision, Recall, F-1 Score, F-2 Score, Sensitivity, Specificity and AUC-ROC were used to evaluate the machine learning techniques utilized in this research. Experimental results indicate that the LightGBM ensemble technique outperformed other models with the accuracy of 97.13% and AUC-ROC of 98.99%, due to its optimized gradient boosting and effective processing of categorical features. This research contributes to the algorithmic approach for developing decision support tools for improving sustainable smart city water management techniques.
  • Data-Driven Concrete Quality Optimization in Industry 4.0: Predictive Compressive Strength Modeling Through Machine Learning and Ensemble Approaches
    Judeson Antony Kovilpillai J, Sulaiman Syed Mohamed, Pragya, Jayanthy S, Viji C, Rajkumar N
    2024 IEEE International Conference on Information Technology Electronics and Intelligent Communication Systems Iciteics 2024, 2024
    As numerous manufacturing enterprises are progressing towards Industry 4.0, advanced predictive models are required to optimize contemporary construction practices. The precise prediction of the compressive strength of cement is crucial, as it is an integral material for any constructional unit. This research paper explores numerous advanced machine learning and ensemble learning techniques for effective concrete strength prediction, enabling proactive quality control measures in an Industry 4.0 based environment. The research utilizes an open-source dataset and employs advanced machine learning techniques to interpret and learn intricate relationships among input features, such as cement quantity, blast furnace slag content, fly ash ratios, water weight, superplasticizer usage, and coarse and fine aggregate proportions, as well as curing age for predictive modeling. Experimental results validate the Histogram-Based Gradient Boosting model as an optimal technique for effectivey forecasting the compressive strength of cement in Newtons per square millimeter (MPa), with a cross-validation R2 Score of 0.922. The findings of this research work contributes to the increasing demand for accurate and scalable predictive models within the quality control unit of an Industry 4.0 based manufacturing firm.
  • Predicting Steel Fatigue Using Machine Learning Techniques for sustainable Infrastructure Monitoring
    Sulaiman Syed Mohamed, J Judeson Antony Kovilpillai, R Golda Brunet, Pragya, Mahmood Hussain Mir, Tawseef Ahmad Mir
    IEEE International Conference on Signal Processing and Advance Research in Computing Sparc 2024, 2024
    Ensuring the structural integrity of steel infrastructure is crucial for sustainable development. Fatigue damage, a major concern in steel structures, can lead to catastrophic failures. This paper investigates the application of machine learning (ML) techniques for predicting steel fatigue life. The work compares the performance of eight regression models using a publicly available dataset MatNavi from the National Institute for Material Science (NIMS). The model performance is evaluated using three metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared Score. The results show that Gradient Boosting achieves the lowest error metrics, demonstrating superior performance in predicting fatigue life. Random Forest also exhibits high accuracy, suggesting its potential as an alternative method. While Huber Regressor shows higher error values, its potential for handling outliers might be valuable in specific scenarios. This study highlights the effectiveness of machine learning, particularly ensemble methods, for predicting steel fatigue and emphasizes their potential in enhancing sustainable infrastructure management.
  • Predictive Modelling for Effective Energy Consumption in Industry 4.0 using machine learning techniques
    Pragya Pragya, Judeson Antony Kovilpillai J, Mahmood Hussain Mir, Tawseef Ahmad Mir, Goutham E., Sulaiman Syed Mohamed
    2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
    Optimizing energy consumption has become an important challenge for the steel industry as it moves towards Industry 4.0. This paper develops a predictive modelling framework that employs machine learning methods to improve the energy efficiency of steel manufacturing processes. Our focus is on energy consumption patterns in relation to industry 4.0, and this highlights the need for accurate prediction models to guide decision making processes. Therefore, our research presents the following steps; data pre-processing, feature engineering and appropriate model selection for steel-specific purposes only. Comprising of regression and classification algorithms such as support vector machines, random forests and neural networks we prove that our method can be used to make accurate predictions on different operational scenarios for the energy needs of users. The integrated approach consisting of real-time data streams with sensor networks supporting adaptive modelling is investigated here in relation to dynamic production environments. Finally, we outline how forecasting models such as those developed can be used as foundation blocks in achieving sustainable energy practices in Industry 4.0 transition by employing machine learning facilitated proactive energy management strategies by stakeholders with a view to optimize resource utilization, minimize environmental impact, and enhance competitiveness in the evolving industrial landscape.
  • Enhancing street light fault detection in Smart Cities using Machine Learning and Deep Neural Network Approaches
    Mahmood Hussain Mir, Judeson Antony Kovilpillai J, Sulaiman Syed Mohamed, Pragya, Golda Brunet R, Tawseef Ahmad Mir
    2024 International Conference on Electrical Electronics and Computing Technologies Iceect 2024, 2024
  • Battery Range Estimation in Electric Vehicles Using Machine Learning and Deep Learning Techniques
    Sulaiman S. M., Judeson Antony Kovilpillai J, Mahmood Hussain Mir, Golda Brunet R, Pragya, Soumi
    2024 IEEE International Conference on Information Technology Electronics and Intelligent Communication Systems Iciteics 2024, 2024
  • A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines
    S.M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj, K.V. Shihabudheen
    Computers and Electrical Engineering, 2022
  • Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia
    Sheik Mohammed S., Femin Titus, Sudhakar Babu Thanikanti, Sulaiman S. M., Sanchari Deb, Nallapaneni Manoj Kumar
    Sustainability Switzerland, 2022
  • Study on deep learning: Applications and research trends
    Kian Yang Lee
    Journal of Advanced Research in Dynamical and Control Systems, 2020
  • Smart Meter Data Analytics for Load Prediction using Extreme Learning Machines and Artificial Neural Networks
    S. M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj, S. Sheik Mohammed, K. V. Shihabudheen
    2019 International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development Incces 2019, 2019
  • Smart Meter Data Analysis Issues: A Data Analytics Perspective
    S. M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj
    IEEE International Conference on Intelligent Techniques in Control Optimization and Signal Processing Incos 2019, 2019
  • Smart meter data analysis using big data tools
    S. M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj
    Journal of Computational and Theoretical Nanoscience, 2019
  • Big data analytics of smart meter data using Adaptive Neuro Fuzzy Inference System (ANFIS)
    S. M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj
    Proceedings of IEEE International Conference on Emerging Technological Trends in Computing Communications and Electrical Engineering Icett 2016, 2017
  • Artificial neural network based day ahead load forecasting using Smart Meter data
    S.M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj
    2016 Biennial International Conference on Power and Energy Systems Towards Sustainable Energy Pestse 2016, 2016
  • Impact of Redhat IPv6 router on heterogeneous host connections
    Sulaiman Syed Mohamed, M. I. Buhari, Akbar Badhusha
    International Journal of Communication Systems, 2007
  • Performance comparison of packet transmission over IPv6 network on different platforms
    S.S. Mohamed, M.S. Buhari, H. Saleem
    IEE Proceedings Communications, 2006
  • Evaluation of IPv6 and comparative study with different operating systems
    S.S. Mohamed, A.Y.M. Abusin, D. Chieng
    Proceedings 3rd International Conference on Information Technology and Applications Icita 2005, 2005

RECENT SCHOLAR PUBLICATIONS

  • Enhancing Building Safety Through Machine Learning-Based Smoke Detection
    SS Mohamed, MH Mir, TA Mir
    2025 Fifth International Conference on Advances in Electrical, Computing … , 2025
    2025
    Citations: 1
  • Deep learning based Crop Monitoring for effective Agricultural-IoT Management
    MH Mir, SS Mohamed, TA Mir, B Paul
    Procedia Computer Science 258, 332-341 , 2025
    2025
    Citations: 10
  • Deep learning and explainable AI based blast wave pressure prediction in IoV applications
    MH Mir, J Antony, SS Mohamed, S Dhar, D Fayaz
    Procedia Computer Science 252, 270-278 , 2025
    2025
    Citations: 6
  • Predicting steel fatigue using machine learning techniques for sustainable infrastructure monitoring
    SS Mohamed, JJA Kovilpillai, RG Brunet, MH Mir, TA Mir
    2024 International Conference on Signal Processing and Advance Research in … , 2024
    2024
    Citations: 2
  • A transfer learning approach for enhancing road surface classification in intelligent driving systems
    JJA Kovilpillai, S Jayanthy, SS Mohamed, MH Mir, S Dhar
    2024 International Conference on Signal Processing and Advance Research in … , 2024
    2024
    Citations: 1
  • Advancing Water Quality Monitoring in Smart Cities using Machine learning Techniques
    JA Kovilpillai J, S S M, MH Mir, J S, Pragya, R N
    2024 Asia Pacific Conference on Innovation in Technology (APCIT) , 2024
    2024
    Citations: 1
  • Predictive Modelling for Effective Energy Consumption in Industry 4.0 using machine learning techniques
    Pragya, JA Kovilpillai J, MH Mir, TA Mir, G E., SS Mohamed
    2024 Asia Pacific Conference on Innovation in Technology (APCIT) , 2024
    2024
    Citations: 2
  • Enhancing street light fault detection in Smart Cities using Machine Learning and Deep Neural Network Approaches
    MH Mir, SS Mohamed, TA Mir
    2024 International Conference on Electrical Electronics and Computing … , 2024
    2024
    Citations: 2
  • Data-Driven Concrete Quality Optimization in Industry 4.0: Predictive Compressive Strength Modeling Through Machine Learning and Ensemble Approaches
    JA Kovilpillai J, SS Mohamed, Pragya, J S, V C, R N
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
  • Battery Range Estimation in Electric Vehicles Using Machine Learning and Deep Learning Techniques
    SS Mohamed., JA Kovilpillai J, MH Mir, G Brunet R, Pragya, Soumi
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
    Citations: 6
  • Deep Learning Models for Enhanced RUL Prediction in Turbofan Jet Engines
    J Judeson Antony Kovilpillai, SS Mohamed, Pragya, MH Mir, T Singh, ...
    International Conference on Emerging Trends and Technologies on Intelligent … , 2024
    2024
  • A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines
    SM Sulaiman, PA Jeyanthy, D Devaraj, KV Shihabudheen
    Computers & Electrical Engineering 98, 107663 , 2022
    2022
    Citations: 68
  • Charge scheduling optimization of plug-in electric vehicle in a PV powered grid-connected charging station based on day-ahead solar energy forecasting in Australia
    F Titus, SB Thanikanti, S Deb, NM Kumar
    Sustainability 14 (6), 3498 , 2022
    2022
    Citations: 88
  • Smart meter data analytics for load prediction using extreme learning machines and artificial neural networks
    SM Sulaiman, PA Jeyanthy, D Devaraj, SS Mohammed, ...
    2019 IEEE international conference on clean energy and energy efficient … , 2019
    2019
    Citations: 11
  • Smart meter data analysis using big data tools
    SM Sulaiman, P Aruna Jeyanthy, D Devaraj
    Journal of Computational and Theoretical Nanoscience 16 (8), 3629-3636 , 2019
    2019
    Citations: 6
  • a Combinatorial Approach to Decide Initial Root Value for the Solution of Non-Linear System of Equations
    G Ramaiyan, SM Sulaiman, R Irene Hepzibah
    Journal of Physics: Conference Series 1228 (1), 012063 , 2019
    2019
    Citations: 1
  • Smart meter data analysis issues: a data analytics perspective
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2019 IEEE International Conference on Intelligent Techniques in Control … , 2019
    2019
    Citations: 10
  • Big data analytics of smart meter data using Adaptive Neuro Fuzzy Inference System (ANFIS)
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2016 International Conference on Emerging Technological Trends (ICETT), 1-5 , 2016
    2016
    Citations: 13
  • Artificial neural network based day ahead load forecasting using Smart Meter data
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2016 Biennial International Conference on Power and Energy Systems: Towards … , 2016
    2016
    Citations: 48
  • Predicting pressure ulcer risk: a study of the predictive validity of the Braden scale at different health care settings
    S Mohamed
    Cairo University. Faculty of Nursing , 2013
    2013
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Charge scheduling optimization of plug-in electric vehicle in a PV powered grid-connected charging station based on day-ahead solar energy forecasting in Australia
    F Titus, SB Thanikanti, S Deb, NM Kumar
    Sustainability 14 (6), 3498 , 2022
    2022
    Citations: 88
  • A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines
    SM Sulaiman, PA Jeyanthy, D Devaraj, KV Shihabudheen
    Computers & Electrical Engineering 98, 107663 , 2022
    2022
    Citations: 68
  • Artificial neural network based day ahead load forecasting using Smart Meter data
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2016 Biennial International Conference on Power and Energy Systems: Towards … , 2016
    2016
    Citations: 48
  • Evaluation of IPv6 and comparative study with different operating systems
    SM Sulaiman, AYM Abusin, D Chieng
    Third International Conference on Information Technology and Applications … , 2005
    2005
    Citations: 29
  • Performance comparison of packet transmission over IPv6 network on different platforms
    SM Sulaiman, MS Buhari, H Saleem
    IEE Proceedings-Communications 153 (3), 425-433 , 2006
    2006
    Citations: 28
  • Big data analytics of smart meter data using Adaptive Neuro Fuzzy Inference System (ANFIS)
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2016 International Conference on Emerging Technological Trends (ICETT), 1-5 , 2016
    2016
    Citations: 13
  • Smart meter data analytics for load prediction using extreme learning machines and artificial neural networks
    SM Sulaiman, PA Jeyanthy, D Devaraj, SS Mohammed, ...
    2019 IEEE international conference on clean energy and energy efficient … , 2019
    2019
    Citations: 11
  • Deep learning based Crop Monitoring for effective Agricultural-IoT Management
    MH Mir, SS Mohamed, TA Mir, B Paul
    Procedia Computer Science 258, 332-341 , 2025
    2025
    Citations: 10
  • Smart meter data analysis issues: a data analytics perspective
    SM Sulaiman, PA Jeyanthy, D Devaraj
    2019 IEEE International Conference on Intelligent Techniques in Control … , 2019
    2019
    Citations: 10
  • Deep learning and explainable AI based blast wave pressure prediction in IoV applications
    MH Mir, J Antony, SS Mohamed, S Dhar, D Fayaz
    Procedia Computer Science 252, 270-278 , 2025
    2025
    Citations: 6
  • Battery Range Estimation in Electric Vehicles Using Machine Learning and Deep Learning Techniques
    SS Mohamed., JA Kovilpillai J, MH Mir, G Brunet R, Pragya, Soumi
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
    Citations: 6
  • Smart meter data analysis using big data tools
    SM Sulaiman, P Aruna Jeyanthy, D Devaraj
    Journal of Computational and Theoretical Nanoscience 16 (8), 3629-3636 , 2019
    2019
    Citations: 6
  • Predicting pressure ulcer risk: a study of the predictive validity of the Braden scale at different health care settings
    S Mohamed
    Cairo University. Faculty of Nursing , 2013
    2013
    Citations: 4
  • Predicting steel fatigue using machine learning techniques for sustainable infrastructure monitoring
    SS Mohamed, JJA Kovilpillai, RG Brunet, MH Mir, TA Mir
    2024 International Conference on Signal Processing and Advance Research in … , 2024
    2024
    Citations: 2
  • Predictive Modelling for Effective Energy Consumption in Industry 4.0 using machine learning techniques
    Pragya, JA Kovilpillai J, MH Mir, TA Mir, G E., SS Mohamed
    2024 Asia Pacific Conference on Innovation in Technology (APCIT) , 2024
    2024
    Citations: 2
  • Enhancing street light fault detection in Smart Cities using Machine Learning and Deep Neural Network Approaches
    MH Mir, SS Mohamed, TA Mir
    2024 International Conference on Electrical Electronics and Computing … , 2024
    2024
    Citations: 2
  • Enhancing Building Safety Through Machine Learning-Based Smoke Detection
    SS Mohamed, MH Mir, TA Mir
    2025 Fifth International Conference on Advances in Electrical, Computing … , 2025
    2025
    Citations: 1
  • A transfer learning approach for enhancing road surface classification in intelligent driving systems
    JJA Kovilpillai, S Jayanthy, SS Mohamed, MH Mir, S Dhar
    2024 International Conference on Signal Processing and Advance Research in … , 2024
    2024
    Citations: 1
  • Advancing Water Quality Monitoring in Smart Cities using Machine learning Techniques
    JA Kovilpillai J, S S M, MH Mir, J S, Pragya, R N
    2024 Asia Pacific Conference on Innovation in Technology (APCIT) , 2024
    2024
    Citations: 1
  • a Combinatorial Approach to Decide Initial Root Value for the Solution of Non-Linear System of Equations
    G Ramaiyan, SM Sulaiman, R Irene Hepzibah
    Journal of Physics: Conference Series 1228 (1), 012063 , 2019
    2019
    Citations: 1