Dr. Mohammed Saleh Al Ansari

@uob.edu.bh

Associate Professor, College of Engineering, Department of Chemical Engineering
University of Bahrain



                 

https://researchid.co/malansariuob
10

Scopus Publications

89

Scholar Citations

2

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • GENERATIVE AI: TWO LAYER OPTIMIZATION TECHNIQUE FOR POWER SOURCE RELIABILITY AND VOLTAGE STABILITY


  • Experimental Insights and ANN-Based Surface Roughness Prediction through analysis of Machined Surface Quality of Al2024/SiCp Composites
    Mohammed Saleh Al Ansari, A. Krishnakumari, M. Saravanan, Chappeli Sai Kiran, Seeniappan Kaliappan, and Ramya Maranan
    EDP Sciences
    This present research deals with optimizing machining parameters and surface quality improvement of Al2024/SiCp composites which are important materials used in the aerospace industry. The optimal quartet of factors was investigated to achieve the best outcomes using Taguchi design approach and includes cutting speed of 105 m/min, feed rate of 0.15 mm/rev, and depth of cut of 0.35 mm with a minimal level of roughness of 0.9 μm. An ANN model has been trained and validated, and a high level of predictive accuracy with an overall accuracy of 100% after 195 epochs has been achieved. The results indicated that systematic experimentation and the application of advanced modeling approaches, including the beneficial configuration of parameters and validated ANN model, can help to achieve a superior surface quality meeting the requirements of the aerospace industry. As a result, manufacturers can benefit from the proposed solutions to optimize their production practices, enhance the performance of components, and contribute to the field of aerospace engineering.

  • Estimation of Machining Performance in Wire EDM of Aluminum Silicon Nitride Composite an Experimental Analysis and ANN Modeling
    Mohammed Saleh Al Ansari, Seeniappan Kaliappan, G. Bharath Reddy, M. Muthukannan, Ramya Maranan, and Parthasarathi Mishra
    EDP Sciences
    The primary objective of the current research is to optimize machining performance in Al 7010 alloyreinforced with silicon nitride nanoparticles. This has been accomplished through a combination ofexperimental analysis and predictive modeling methodologies. Initially, composite materials were createdusing stir casting, and varied percentages of silicon nitride were incorporated into the material to supplementits mechanical properties. Wire Electrical Discharge Machining was performed using different parameters suchas Pulse On Time , Pulse Off Time , and Current , and a range of these parameters was defined according tolevels . Material Removal Rate and Surface Roughness were chosen as the machining responses and indicatedhigh sensitivity to variations in chosen parameters. Each response was thoroughly investigated and detectedusing these responses before establishing the optimized levels. Taguchi design of experiments and signal-tonoiseratio were two common techniques used to investigate parameter interactions, and they were also used todetermine the optimum combinations for both the parameters for optimizing MRR and minimizing SR.Moreover, an Artificial Neural Network (ANN) model was also established to foresee the response readingswith great precision and predict the parameter effect to enhance further predictive modeling capabilities inmachining. The present research optimization results indicated that the maximum MRR is obtained at Pulse OnTime , Pulse Off Time , and Current levels, while the minimum SR is obtained at Pulse On Time , Pulse OffTime , and Current levels. These findings provide promising avenues of research in the field of aerospace,indicating the possibility of machining components with superior machinability and mechanical strength.Furthermore, the predicting ability of an ANN model helps in obtaining the insights to engineers to optimizetheir process by gaining information about performance and material response.

  • Optimizing Milling Parameters for Al7075/ nano SiC/TiC Hybrid Metal Matrix Composites using Taguchi Analysis and ANN Prediction
    Mohammed Saleh Al Ansari, S. Kaliappan, G. Mrudula, Prashant B. Dehankar, Ramya Maranan, and Putti Venkata Siva Teja
    EDP Sciences
    This research deals with the optimization of milling parameters for Al7075/nano SiC/TiC hybrid metal matrix composites by Taguchi approach an Artificial Neural Network. Experimental trials conducted in accordance with Taguchi L9 orthogonal array design conveyed that the optimum combination to minimize surface roughness is with a cutting speed of 100 m/min, feed 0.1 mm/tooth, and depth of cut as 1 mm. The results revealed that the surface roughness was significantly decreased under the optimal conditions and the values were in the range of 0.85 μm. Further, an ANN model was developed to predict the surface roughness based on the inputs. It is found that it showed excellent prediction, and the overall accuracy was 99.48% after 195 epochs. Therefore, system validation using experimental results showed that the ANN can be relied upon to forecast the surface roughness values. Thus, the combination of the experimental validation and ANN modeling studies provided valuable information for the optimization of machining parameters, which helped manufacturers to improve the surface quality and performance of the product in Al7075/nano SiC/TiC hybrid metal matrix composites .

  • Enhancing Mechanical Properties of Composites with Plasma-Treated Linear Low-Density Propylene Matrix, SiC Nanoparticles, and Carbon Fiber Filler
    Mohammed Saleh Al Ansari, S. Kaliappan, G. Vanya Sree, Pranav Kumar Prabhakar, Ramya Maranan, and Pawan Devidas Meshram
    EDP Sciences
    In this research, the optimization of composite materials for improving their mechanical properties is investigated. It is achieved by applying different compositions of the PTLLDPE matrix, SiC nanoparticles, and carbon fibre filler. For this purpose, six composite samples are prepared using different compositions of PTLLDPE from 40% to 60%, SiC nanoparticles from 0% to 3%, and carbon fibre filler from 10% to 20%, which are mechanically tested . Results show that tensile strength increases with increasing PTLLDPE contents, Sample 6 having the highest value of 62 MPa. As the SiC nanoparticles contents increase, the flexural strength and impact resistance increases, Sample 4 having the highest flexural strength at 75 MPa and impact resistance at 200 J/m2. The hardness increases with increasing carbon fiber fillers, Sample 6 having the highest hardness value at 88 shore D. This is important in the synthesis and the optimization of composite formulations, helping various industries in in their choice and application of the composites.

  • Optimizing Aluminum Metal Matrix Composites with SiC Nanoparticles using Taguchi-ANN Approach for Enhanced Mechanical Performance
    Mohammed Saleh Al Ansari, K.M.B. Karthikeyan, Seeniappan Kaliappan, S. Yogeswari, Ramya Maranan, and Pawan Devidas Meshram
    EDP Sciences
    The current research explores the optimization of Silicon Carbide particle-reinforced aluminum metal matrix composites to improve mechanical properties. An integrated method based on Taguchi Design of Experiment and Artificial Neural Network has been adopted, with the novel approach to explore the optimal combination of parameters. The obtained best set includes the minimum load of 30 N, the minimum speed of 100 rpm, and the larger composition of 9% SiC particle. The designed L9 orthogonal experimental plan was used to conduct the experiments, and the findings explicitly indicated the significant impacts on the reduction of specific wear rate and friction force . Furthermore, the Artificial Neural Network trained through the backpropagation algorithm estimated all the percentages correctly to the ideal combination, equivalent to 100% in predicting the target responses. Moreover, the confirmation experience has validated the optimal combination, as it approaches specific wear rate of 0.0019, and friction force was 10.5. These results highlight the role of the integrated research approach for assessing the optimal parameters of aluminum MMCs to the required mechanical properties. Consequently, the current study highlights the importance of experimental plan integration and predictive modeling for optimizing materials, and it applies to various engineering fields where wear resistance and friction performance are critical.

  • Optimizing Surface Roughness in Turning of Al7072 with Nano particles of Carbon Metal Matrix Composite using Taguchi Analysis and ANN Prediction
    Mohammed Saleh Al Ansari, Seeniappan Kaliappan, P. Bhargavi, Shital P. Dehankar, T. Mothilal, and Ramya Maranan
    EDP Sciences
    This research centers on optimizing the machining process of Al7072 alloy reinforced with carbon nanoparticles. While surface roughness is the primary research focus, it is one of the most critical parameters in the manufacturing of aerospace components. According to the Taguchi design of experiments tool, the structured experimental framework has been used to learn the precise consequences of Cutting speed (Cs) , Feed rate (Fr), and Depth of Cut (DoC) on surface roughness outcomes. Using cutting-edge algorithms, particularly the Artificial Neural Network, significantly increases these predictive abilities. It hence forecasts the surface roughness achieved with various machining outcomes. According to the initial results, the surface roughness response is extremely dependent on the machining outcomes. The signal-to-noise ratio conducted the statistical analysis to discover the best parameter equation that would allow for the best surface quality and machining economy. Furthermore, the ANN-based model has been created, demonstrating a high level of accuracy in providing feed response. This might be used to optimize the machining process. The results recommend improving the accessibility of machining and increasing aerospace equipment’s quality of service. Thus, the process presented in this research might improve the public’s communication with respect to machining and machining economics.

  • ENHANCING WATER PURIFICATION EFFICIENCY THROUGH MACHINE LEARNING-DRIVEN MXENE FUNCTIONALIZATION


  • Python-Powered remote sensing data
    Aamir Raza, Sheraz Maqbool, Muhammad Safdar, Hasnain Ali, Ikram Ullah, Ali Akbar, Avery Williams, Mohammed Saleh Al Ansari, Mubashir Ahmed, Awn Abbas,et al.
    IGI Global
    Remote sensing is a crucial technique in environmental and spatial investigations, and Python is a popular programming language for analyzing this data. This chapter provides a comprehensive guide to using Python for remote sensing data analysis, covering various data types, attributes, and practical implementations. It introduces Python and its data processing libraries, discusses preprocessing operations like data conversion and import, geometric rectification, and radiometric correction, and covers image enhancement techniques like edge detection, contrast enhancement, and filtering. It also covers image analysis techniques like band mathematics, indices, classification, and segmentation. The chapter also covers exporting data and generating visualization maps and charts. Python's application in remote sensing data analysis is illustrated through case studies.

  • Implementation of a neuro-fuzzy- based classifier for the detection of types 1 and 2 diabetes
    Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
    Wiley

  • An intelligent IoT-based healthcare system using fuzzy neural networks
    Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
    Wiley

  • Sustainable agriculture and the SDGs: A convergence approach
    Muhammad Asim, Aamir Raza, Muhammad Safdar, Mian Muhammad Ahmed, Amman Khokhar, Mohd Aarif, Mohammed Saleh Al Ansari, Jaffar Sattar, and Ishtiaq Uz Zaman Chowdhury
    IGI Global
    This chapter explores the connection between sustainable agriculture and the Sustainable Development Goals (SDGs). It discusses various practices like conservation agriculture, organic farming, agroforestry, and precision agriculture, and how they contribute to various SDGs. It focuses on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 15 (Biodiversity Preservation), and SDG 1 and 8 (Rural Development). The chapter also discusses barriers to widespread adoption, including economic, technological, and sociocultural factors. It uses case studies to illustrate successful models and offers policy recommendations, emphasizing national policies aligning with sustainable agriculture, fostering international cooperation, and investing in education and capacity building. The chapter provides valuable insights for policymakers, researchers, and practitioners in agriculture, sustainability, and development.

  • OPTIMIZING WATER DESALINATION: A NOVEL FUSION OF EXTREME LEARNING MACHINE AND GAME THEORY FOR ENHANCED PH PREDICTION - UNVEILING REVOLUTIONARY INSIGHTS


  • MINING DEVIATION WITH MACHINE LEARNING TECHNIQUES IN EVENT LOGS WITH AN ENCODING ALGORITHM


  • DYNAMIC SLICING OPTIMIZATION IN 5G NETWORKS USING A RECURSIVE LSTM MECHANISM WITH GREY WOLF OPTIMIZATION


  • Automated plant disease detection: A convergence of agriculture and technology
    Aamir Raza, Muhammad Safdar, Hasnain Ali, Mudassir Iftikhar, Qandeel Ishfaqe, Mohammed Saleh Al Ansari, Peng Wang, and Ali Shahroze Khan
    IGI Global
    This chapter explores automated plant disease detection, a transformative approach in agriculture and technology. It discusses the types and causes of plant diseases, their economic and environmental consequences, and the need for early and accurate detection. The chapter details various types of automated disease detection systems, including image-based, sensor-based, and hybrid systems. It also covers the design and implementation aspects of automated plant disease detection, from data collection to model deployment. The chapter highlights the diverse applications of automated disease detection in agriculture, including crop disease, weed, pest, nutrient deficiency, and abiotic stress detection. It addresses challenges and opportunities in adopting these systems, including data quality, costs, scalability, and usability.

  • Applications of sensors in precision agriculture for a sustainable future
    Muhammad Fawaz Saleem, Ali Raza, Rehan Mehmood Sabir, Muhammad Safdar, Muhammad Faheem, and Mohammed Saleh Al Ansari
    IGI Global
    The advent of precision agriculture has revolutionized the agricultural sector, emphasizing the utilization of data-driven strategies for decision-making and the optimization of resources. Sensors, encompassing soil, crop, weather, and drone sensors, offer real-time data to facilitate informed decision-making and enhance agricultural outcomes. These sensors facilitate the optimization of irrigation and fertilization and the timely identification of soil-related problems. In addition, they contribute to the surveillance of plant health, the detection of weed infestations, and the monitoring of meteorological conditions. The gathering and management of data play a crucial role in precision agriculture. The advantages encompass decreased utilization of resources, heightened agricultural productivity, a diminished ecological footprint, and better economic viability. Nevertheless, persistent obstacles like technological problems, concerns around data security, and the imperative for advancements in artificial intelligence and machine learning persist.

  • Precision agriculture and unmanned aerial vehicles (UAVs)
    Rehan Mehmood Sabir, Abid Sarwar, Muhammad Safdar, and Mohammed Saleh Al Ansari
    IGI Global
    This chapter examines the correlation between precision agriculture (PA) and unmanned aerial vehicles (UAVs), emphasizing their pivotal significance in contemporary agricultural practices. This chapter delves into the historical origins of public administration (PA), tracing its progress over time and examining the introduction of unmanned aerial vehicles (UAVs) within this field. The text provides a study of different types of unmanned aerial vehicles (UAVs), examining their distinct qualities, advantages, and diverse range of applications. These applications encompass crop monitoring, soil analysis, irrigation management, and livestock tracking. The chapter also discusses many challenges, including regulatory compliance, data security, and technical limits. Additionally, the chapter emphasizes the practical implementation of unmanned aerial vehicles (UAVs) in both extensive and small-scale agricultural practices, as well as potential advancements and emerging patterns in this field.

  • Optical sensing for precision agriculture
    Muhammad Talal, Aamir Raza, Muhammad Safdar, Mohammed Saleh Al Ansari, Syed Kashif Ali, and Jaffar Sattar
    IGI Global
    Optical sensing technologies have revolutionized agriculture by enabling precision farming practices that optimize resource use and enhance crop productivity. This chapter provides an overview of optical sensing, its definition, historical development, fundamental principles, various sensing technologies, and applications. Optical sensing plays a crucial role in monitoring crop health, soil properties, water quality, weeds, and pests, and predicting yields. However, it faces challenges like environmental factors, calibration, and integration issues. The chapter emphasizes the continued significance of optical sensing in sustainable agriculture and its potential role in shaping future farming practices. As technology develops and becomes more affordable, optical sensing is poised to play an increasingly important role in precision agriculture.

  • EXPLORING THE DYNAMICS OF EDUCATIONAL FEEDBACK NETWORKS WITH GRAPH THEORY AND LSTM-BASED MODELING FOR ENHANCED LEARNING ANALYTICS AND FEEDBACK MECHANISMS


  • Adaptive Neuro-Fuzzy Inference System for Cognitive Load Assessment in Brain Machine Interfaces
    M. Siva Mala, Mohd Mushtaq Karche, Arfanda Anugrah Siregar, Mohammed Saleh Al Ansari, Vuda Sreenivasa Rao, and I Infant Raj
    IEEE
    An Adaptive Neuro-Fuzzy Inference System (ANFIS) for assessing cognitive load in Brain-Machine Interfaces (BMIs) is suggested to be developed and evaluated in this work. EEG characteristics and task-related metrics are only two examples of the pertinent input variables that the ANFIS model uses to forecast and evaluate cognitive load levels. In order to deal with the inherent imprecision and uncertainty in cognitive load data, fuzzy learning is utilised to convert discrete input values into fuzzy sets through the use of predefined membership functions. To represent the complex interactions between input variables and cognitive load, rule activation and inference mimic the cognitive process of integrating data from several rules. The resulting fuzzy output is then defuzzed using techniques like centroid or mean of maxima to yield an understandable and straightforward measure of cognitive burden. The efficacy of the ANFIS model in offering a comprehensive and precise assessment of cognitive load levels in BMIs is demonstrated. The ANFIS model has potential as a reliable and flexible method for evaluating cognitive stress in the context of brain-machine interfaces. The study shows superior performance over an existing KNN method with an accuracy of ${9 0 \\%}$, precision of 88%, recall of 82%, and sensitivity of 85%.

  • A Hybrid CNN- GRU Approach with Transfer Learning for Advanced Waste Classification in Support of Environmental Sustainability
    Nalla Siva Kumar, Tripti Sahu, Mohammed Saleh Al Ansari, Shamim Ahmad Khan, J.P. Swagatha, and I Infant Raj
    IEEE
    Waste classification remains pivotal to environmental sustainability along with proper waste management. Traditional approaches such as CNNs and LSTM networks prove to be inadequate in properly capturing the spatial and temporal correlations in waste images. To cover for this, the study puts forward a new waste classification strategy that leverages CNNs, GRUs and transfer learning for increased classification performance. It is worth mentioning that proposed approach relies on CNNs for the spatial feature extraction, GRUs for temporal sequence learning and transfer learning for utilizing pre-trained models for both feature extraction and sequential learning. The proposed framework is developed in Python and tested on the waste classification dataset with accuracy of 97% which is superior to the traditional CNN (89%) and LSTM ($\\mathbf{9 2 \\%}$). This result underlines the capability of applying the transfer learning of convolution neural network (CNN-GRU) that has the capability to develop an effective framework for waste classification efficiently. The research finds that use of such techniques enhances development of AI based solutions towards efficient waste management and environment protection.

  • Hybrid MLP-GRU Federated Learning Framework for Industrial Predictive Maintenance
    K. Praveena, M. Misba, Chamandeep Kaur, Mohammed Saleh Al Ansari, Veera Ankalu. Vuyyuru, and S Muthuperumal
    IEEE
    Assuring the dependability and effectiveness of industrial gear, cutting downtime, and lowering maintenance costs all depend on predictive maintenance. We provide a hybrid MLP-GRU model-based Federated Learning-Enabled Advanced Predictive Maintenance Framework in this work for defect prediction and detection in industrial machinery. By using federated learning approaches, the framework is made to effectively utilize the combined intelligence of dispersed datasets while maintaining data security and privacy. Three distinct datasets, representing various types of equipment and failure scenarios, are integrated into the framework: the IMS Bearing Dataset, C-MAPSS Dataset, and Pump Sensor Dataset. The records are carefully combined into a training dataset by means of integration and preprocessing, which makes it easier to create a hybrid MLP-GRU model that can recognize intricate temporal correlations and fault patterns. The model may learn from a variety of sources without centralizing sensitive data thanks to the Federated Learning architecture, which facilitates collaborative model training across dispersed data subsets. Across dispersed datasets, the optimization layer effectively updates model parameters while decreasing loss functions by utilizing sophisticated optimization methods. The adapted framework's efficacy in defect detection and prognosis tasks across a range of industrial machinery types and fault circumstances has been demonstrated through training and assessment. All things considered, the suggested framework is a major step forward in industrial machinery predictive maintenance, providing a scalable, accurate, and privacy-preserving approach for proactive failure identification and prediction. Its use of hybrid MLP-GRU model architecture and federated learning approaches shows promising outcomes of 0.94% accuracy in practical industrial applications.

  • Innovations in Media C: Federated Learning and BiLSTM Integration for Image and Video Analysis
    A Suresh Kumar, A Balavivekanandhan, Mohammed Saleh Al Ansari, P N V Syamala Rao M, D Gouse Peera, and S Muthuperumal
    IEEE
    In the ever-evolving landscape of media, the demand for efficient and robust analysis of images and videos has intensified. Traditional methods often struggle to keep pace with the scale and complexity of media data. In response, this study introduces a novel approach that integrates Federated Learning (FL) and Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance the analysis of images and videos in media applications. Federated Learning, a decentralized machine learning technique, enables collaborative model training across multiple edge devices without centralized data aggregation, thus addressing privacy concerns and data silo issues inherent in traditional approaches. By leveraging FL, The proposed framework facilitates the aggregation of insights from diverse sources while preserving data privacy. Furthermore, the integration of BiLSTM networks offers enhanced temporal modeling capabilities, allowing for the extraction of contextual information from sequential data such as video frames.Through experimentation on diverse media datasets, including images and videos, demonstrate the effectiveness of approach in tasks such as object recognition, scene understanding, and action recognition. The results showcase significant improvements in accuracy and efficiency compared to baseline methods, highlighting the potential of Federated Learning and BiLSTM integration for advancing image and video analysis in media applications.Overall, This study contributes to the ongoing efforts to innovate media analysis techniques by harnessing the power of decentralized learning and advanced sequential modeling, paving the way for more intelligent and privacy-preserving media analysis systems. This method achieves an accuracy of 97.5% and has been implemented in Python.

  • Advancing Surveillance Systems: Leveraging Sparse Auto Encoder for Enhanced Anomaly Detection in Image Data Security
    Ravindra Changala, Praveen Kumar Yadaw, Mansoor Farooq, Mohammed Saleh Al Ansari, Veera Ankalu Vuyyuru, and S Muthuperumal
    IEEE
    In the realm of surveillance systems, ensuring robust anomaly detection capabilities is crucial for safeguarding against potential security breaches or hazardous incidents. This work uses the Sparse Autoencoder architecture to provide a unique method for improving anomaly identification in surveillance imagine information. The methodology begins with the collection of a comprehensive dataset, termed DCSASS, comprising videos from security cameras capturing a diverse range of abnormal and typical actions across various categories. Each video is meticulously labeled as normal or abnormal based on its content, facilitating the differentiation between routine operations and potential security threats. Individual frames extracted from the videos serve as image samples for subsequent model training and evaluation in anomaly detection tasks. In deep learning-driven anomaly identification networks, the preprocessing stage uses Min-Max Normalization to normalize pixel values, improving model stability and efficacy. Subsequently, the Sparse Autoencoder architecture, consisting of encoder and decoder components, is utilized for anomaly detection. The encoder, designed using convolutional neural network (CNN) architecture, extracts meaningful features from input images while inducing sparsity in learned representations through techniques like L1 regularization. Experimentation with various hyperparameters and architectural choices optimizes performance, with the decoder symmetrically mirroring the encoder's architecture to ensure accurate reconstruction of input images. The proposed approach outperforms existing methods such as CNN, RF and SVM, achieving 99% accuracy, making it 2.3% superior to other methods. Implemented in Python, our methodology demonstrates its efficacy in effectively capturing and reconstructing meaningful representations of input images, thereby enhancing anomaly detection capabilities in surveillance image data for improved security and safety measures.

RECENT SCHOLAR PUBLICATIONS

  • Structural Engineering Optimization Techniques for Earthquake-Resilient Buildings
    P Endla, A Nainwal, MS Al Ansari, MN Alemayehu, NA Upadhye
    Structural Engineering Optimization Techniques for Earthquake-Resilient 2024

  • Leveraging Multi-Task Learning and Uncertainty Estimation for Accurate Sales and Profit Forecasting
    P Madhuri, D Karthik Raj, MS Al Ansari, DP BS, YA Mergiaw, R Monisha
    Available at SSRN 5083805 2024

  • Mechanical and Aerospace Engineering: New Frontiers in Optimization
    V Sakravarthy N, P KH, PK Yekula, B Kumar, MS Al Ansari
    Pradeep and KH, Preethi and Yekula, Prasanna Kumar and Kumar, Bhupendra and 2024

  • A Study Analyzing the Major Determinants of Implementing Internet of Things (IoT) Tools in Delivering Better Healthcare Services Using Regression Analysis
    C Kaur, MS Al Ansari, N Rana, B Haralayya, Y Rajkumari, KC Gayathri
    Advanced Technologies for Realizing Sustainable Development Goals: 5G, AI 2024

  • A Hybrid CNN-GRU Approach with Transfer Learning for Advanced Waste Classification in Support of Environmental Sustainability
    NS Kumar, T Sahu, MS Al Ansari, SA Khan, JP Swagatha, II Raj
    2024 International Conference on Intelligent Systems and Advanced 2024

  • Adaptive Neuro-Fuzzy Inference System for Cognitive Load Assessment in Brain Machine Interfaces
    MS Mala, MM Karche, AA Siregar, MS Al Ansari, VS Rao, II Raj
    2024 International Conference on Intelligent Systems and Advanced 2024

  • Generative AI: Two layer optimization technique for power source reliability and voltage stability
    DS Gupta, R KOLIKIPOGU, VS PITTALA, S SIVAKUMAR, RB PITTALA, ...
    Journal of Theoretical and Applied Information Technology 102 (15) 2024

  • Enhancing water purification efficiency through machine learning-driven mxene functionalization
    A KOUR, V Vidyasagar, ML Suresh, YA BAKER, RM EL-EBIARY, ...
    Journal of Theoretical and Applied Information Technology 102 (14), 5500-5524 2024

  • Advancing Surveillance Systems: Leveraging Sparse Auto Encoder for Enhanced Anomaly Detection in Image Data Security
    R Changala, PK Yadaw, M Farooq, MS Al Ansari, VA Vuyyuru, ...
    2024 International Conference on Data Science and Network Security (ICDSNS), 1-6 2024

  • Innovations in Media C: Federated Learning and BiLSTM Integration for Image and Video Analysis
    AS Kumar, A Balavivekanandhan, MS Al Ansari, DG Peera, ...
    2024 Third International Conference on Electrical, Electronics, Information 2024

  • Hybrid MLP-GRU Federated Learning Framework for Industrial Predictive Maintenance
    K Praveena, M Misba, C Kaur, MS Al Ansari, VA Vuyyuru, ...
    2024 Third International Conference on Electrical, Electronics, Information 2024

  • MAYFLY OPTIMIZATION WITH DEEP LEARNING ASSISTED GLAUCOMA DIAGNOSIS ON RETINAL FUNDUS IMAGES
    DRS HEMALATHA, CR KOMALA, DRKM ADAVALA, SFA KHADRI, ...
    Journal of Theoretical and Applied Information Technology 102 (14) 2024

  • Implementation of a Neuro‐Fuzzy‐Based Classifier for the Detection of Types 1 and 2 Diabetes
    C Kaur, MS Al Ansari, VK Dwivedi, D Suganthi
    Advances in Fuzzy‐Based Internet of Medical Things (IoMT), 163-178 2024

  • An Intelligent IoT‐Based Healthcare System Using Fuzzy Neural Networks
    C Kaur, MS Al Ansari, VK Dwivedi, D Suganthi
    Advances in Fuzzy‐Based Internet of Medical Things (IoMT), 121-133 2024

  • Dynamic Fault Diagnosis in Wind Turbines: A GNN-LSTM Approach
    GVR Babu, SB Porlekar, HM Ali, VA Vuyyuru, MS Al Ansari, II Raj
    2024 IEEE 3rd International Conference on Electrical Power and Energy 2024

  • Impact of absorber surface coatings on the thermal performance of non-concentrating solar thermal collectors: An overview
    M Sethi, A Bodhe, A Chauhan, MS Umaralievich, KI Nematovich, ...
    Materials Today: Proceedings 2024

  • A comprehensive review of the influence of nanomaterials on the thermal performance of a solar thermal collectors
    T Sood, MS Al Ansari, TL Kishore, LM Rao, J Menaria, MR Mallu, D Tyagi, ...
    Materials Today: Proceedings 2024

  • Cross Scoop Fractal Antenna Design with Notch at 15 Degree for Emerging Applications at 5.2 GHz
    R Saravanakumar, R Thommandru, EK Kumar, MS Al Ansari, PS Manage, ...
    2024 International Conference on Recent Advances in Electrical, Electronics 2024

  • Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture
    MK Raja, M Shermatova, MS Al Ansari, S Abdufattokhov, VS Rao, II Raj
    2024 International Conference on Cognitive Robotics and Intelligent Systems 2024

  • Ant Colony Optimization for Network Customization in Cognitive Human-Computer Communication
    SA Khan, SS Pande, MS Al Ansari, AH Siddiqui, B Abdurasul
    2024 International Conference on Cognitive Robotics and Intelligent Systems 2024

MOST CITED SCHOLAR PUBLICATIONS

  • Leaf disease identification and classification using optimized deep learning
    YM Abd Algani, OJM Caro, LMR Bravo, C Kaur, MS Al Ansari, BK Bala
    Measurement: Sensors 25, 100643 2023
    Citations: 145

  • Chronic kidney disease prediction using machine learning
    C Kaur, MS Kumar, A Anjum, MB Binda, MR Mallu, MS Al Ansari
    Journal of Advances in Information Technology 14 (2), 384-391 2023
    Citations: 57

  • Removed: Machine learning in health condition check-up: An approach using Breiman's random forest algorithm
    YM Abd Algani, M Ritonga, BK Bala, MS Al Ansari, M Badr, AI Taloba
    Measurement: Sensors 23, 100406 2022
    Citations: 50

  • Improving solid waste management in gulf co-operation council states: Developing integrated plans to achieve reduction in greenhouse gases
    MS Al Ansari
    Modern Applied Science 6 (2), 60 2012
    Citations: 41

  • Municipal solid waste management systems in the Kingdom of Bahrain
    MSA Ansari, M Saleh
    International Journal of Water Resources and Environmental Engineering 4 (5 2012
    Citations: 41

  • Implementation of cloud based IoT technology in manufacturing industry for smart control of manufacturing process
    SI Khan, C Kaur, MS Al Ansari, I Muda, RFC Borda, BK Bala
    International Journal on Interactive Design and Manufacturing (IJIDeM), 1-13 2023
    Citations: 38

  • Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model
    S Rajesh, YM Abd Algani, MS Al Ansari, B Balachander, R Raj, I Muda, ...
    Measurement: Sensors 22, 100384 2022
    Citations: 33

  • Biological fouling and control at Ras Abu Jarbur RO plant-a new approach
    SR Ahmed, MS Alansari, T Kannari
    Desalination 74, 69-84 1989
    Citations: 23

  • & Bala, BK (2022). Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning
    KB Naidu, BR Prasad, SM Hassen, C Kaur, MS Al Ansari, R Vinod
    Measurement: Sensors 24, 100545
    Citations: 23

  • Synthesis of Mn-doped ZnO nanoparticles and their application in the transesterification of castor oil
    A Zahid, Z Mukhtar, MA Qamar, S Shahid, SK Ali, M Shariq, HJ Alathlawi, ...
    Catalysts 13 (1), 105 2023
    Citations: 22

  • Open and closed R&D processes: Internal versus external knowledge
    MSA Ansari
    European Journal of Sustainable Development 2 (1), 1-1 2013
    Citations: 16

  • The water demand management in the Kingdom of Bahrain
    MSA Ansari
    Journal of Engineering and Advanced Technology 2 (5), 544-554 2013
    Citations: 15

  • A review of optimal designs in relation to supply chains and sustainable chemical processes
    MS Al Ansari
    Modern Applied Science 6 (12), 74 2012
    Citations: 14

  • Thermal and effective assessment of solar thermal air collector with roughened absorber surface: an analytical examination
    R Kumar, M Sethi, V Goel, MK Ramis, M AlSubih, S Islam, MS Al Ansari, ...
    International Journal of Low-Carbon Technologies 19, 1112-1123 2024
    Citations: 13

  • Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques
    A Upadhya, MS Thakur, MS Al Ansari, MA Malik, AA Alahmadi, ...
    Materials 15 (24), 8944 2022
    Citations: 9

  • Performance investigation of a Scheffler solar cooking system combined with Stirling engine
    Q Alkhalaf, ARS Suri, SS Chandel, S Thapa, MS Al Ansari
    Materials Today: Proceedings 2023
    Citations: 8

  • Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm
    AG Zainal
    (IJACSA) International Journal of Advanced Computer Science and Applications 2022
    Citations: 8

  • Concentrating solar power to be used in seawater desalination within the Gulf Cooperation Council
    MS Al Ansari
    Energy and Environment Research 3 (1), 10 2013
    Citations: 8

  • Generative AI: Two layer optimization technique for power source reliability and voltage stability
    DS Gupta, R KOLIKIPOGU, VS PITTALA, S SIVAKUMAR, RB PITTALA, ...
    Journal of Theoretical and Applied Information Technology 102 (15) 2024
    Citations: 7

  • ManikandanRengarajan,“Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning” International Journal of Advanced
    K Deeba, OR Devi, MS Al Ansari, BP Reddy, HT Manohara, ...
     2023
    Citations: 7