Publications

  • Cloud Computing Visualization for Resources Allocation in Distribution Systems
    Chinmaya Dash, Mohammed Saleh Al Ansari, Chamandeep Kaur, Yousef A. Baker El-Ebiary, Yousef Methkal Abd Algani, and B. Kiran Bala

    AIP Publishing

  • Artificial Intelligence and Machine Learning Driven Framework for Investigating the Usefulness of Physiological Indicators for User Trust in Forecasting Decision Making
    Sanjeev Kumar Thalari, Afsana Anjum, Mohammed Saleh Al Ansari, R. Manikandan, S. Prema, S. Anuradha, and A. Balakumar

    AIP Publishing

  • Implementation of cloud based IoT technology in manufacturing industry for smart control of manufacturing process
    Sohail Imran Khan, Chamandeep Kaur, Mohammed Saleh Al Ansari, Iskandar Muda, Ricardo Fernando Cosio Borda, and B. Kiran Bala

    Springer Science and Business Media LLC

  • A study analyzing the major determinants of implementing Internet of Things (IoT) tools in delivering better healthcare services using regression analysis
    Chamandeep Kaur, Mohammed Saleh Al Ansari, Nisha Rana, Bhadrappa Haralayya, Yaisna Rajkumari, and K. C. Gayathri

    BENTHAM SCIENCE PUBLISHERS
    The new advancements in healthcare systems are influenced majorly by the adoption of the Internet of Things (IoT). This is especially important in light of the present state of affairs in the healthcare, social welfare, and energy sectors. By understanding the interconnected problems such as energy efficiency and sustainable development, it may be possible to enhance the well-being of both humans and the environment. The incorporation of sensors and other intelligent devices is crucial to the accomplishment of the aims of sustainable development. In todays rise in population, there is a key area in which the latest scientific developments really need to be put into practice: public health. For the sake of the well-being of future generations, it is essential toconduct research on the ways in which the SDGs have an impact on the uses of sensors and the Internet of Things in human environments. Peoples lives are being influenced by applications of technology, sensor networks, intelligent systems, and the Internet of Things (IoT), all of which are having a positive impact on the environmental sustainability and energy efficiency of the world.The digitization and application of intelligent systems and the Internet of Things devices are carried out in blocks of analysis, organized in a variety of disciplines, in urbanized settings, and in human-inhabited communities; nonetheless, they all have a similar center of gravity, which is the trilogy: human, technology, and sustainability. The management of effective and healthy resources, enhanced governance, and programs that encourage the adoption of new technological solutions are all necessary for sustainable development in better healthcare services. The study is focused on the major determinants of implementing Internet of Things (IoT) tools in delivering better healthcare services.

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

  • 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 .

  • 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 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.

  • 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.

  • 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.

  • 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

  • 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.

  • 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.

  • 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.

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

  • Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture
    M. Karthick Raja, Makhbuba Shermatova, Mohammed Saleh Al Ansari, Shokhjakhon Abdufattokhov, Vuda Sreenivasa Rao, and I. Infant Raj

    IEEE
    The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.

RECENT SCHOLAR PUBLICATIONS

  • Revolutionizing Communication Assessment with the GCN-LSTM-A Automation Method
    RN Pawar, MS Al Ansari, M Pavithra, M Rajeswari, LK Yadav
    2025 3rd International Conference on Data Science and Information System 2025

  • Cloud computing visualization for resources allocation in distribution systems
    C Dash, MSA Ansari, C Kaur, YAB El-Ebiary, YMA Algani, BK Bala
    AIP Conference Proceedings 3137 (1), 020038 2025

  • Artificial intelligence and machine learning driven framework for investigating the usefulness of physiological indicators for user trust in forecasting decision making
    SK Thalari, A Anjum, MSA Ansari, R Manikandan, S Prema, S Anuradha, …
    AIP Conference Proceedings 3137 (1), 020035 2025

  • 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) 19 (2 2025

  • AI-Driven Solutions for Water Quality Assessment
    MH Mahmood, Z Nishtar, U Basharat, RM Sabir, MS Al Ansari, M Munir, …
    AI and Ecological Change for Sustainable Development, 101-124 2025

  • Utilizing Hybrid Deep Reinforcement Learning and Transformer Networks for Predictive Maintenance in Product Safety Engineering
    D Nimma, M Kannaiyan, D Gupta, M Roy, MS Al Ansari, A Balakumar
    2024 International Conference on Artificial Intelligence and Quantum 2024

  • Development of Scalable Deep Learning Frameworks Utilizing Advanced Hybrid Models like Transformer-CNN for Distributed Computing Environments
    D Nimma, S Sandhiya, D Gupta, D Nagalavi, MS Al Ansari, II Raj
    2024 IEEE 2nd International Conference on Innovations in High Speed 2024

  • Enhancing Kitchen Waste Composting with Black Soldier Fly Larvae: Integrating Life Cycle Assessment and CNN-GRU Models
    GA Zeeshan, MS Al Ansari, S Pokhriyal, RM Chowdary, VS Rao, II Raj
    2024 IEEE 2nd International Conference on Innovations in High Speed 2024

  • 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

  • Enhancing Engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks
    PL Mamidi, D Nimma, D Venkateswarlu, MS Al Ansari
    2024 IEEE 1st International Conference on Green Industrial Electronics and 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
    S Gupta, R Kolikipogu, VS Pittala, S Sivakumar, RB Pittala, DMS Al Ansari
    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

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: 207

  • 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: 76

  • Machine learning based corporate climate change disclosure in integrating institutional and resource approach based on computational intelligence method
    BR Supreeth, MS Al Ansari, NH Jasim, PC Swain, D Srinivasan, S Pundir
    2022 5th International Conference on Contemporary Computing and Informatics 2022
    Citations: 59

  • 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) 19 (2 2025
    Citations: 45

  • 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: 45

  • 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: 43

  • 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: 40

  • Generative AI: Two layer optimization technique for power source reliability and voltage stability
    S Gupta, R Kolikipogu, VS Pittala, S Sivakumar, RB Pittala, DMS Al Ansari
    Journal of Theoretical and Applied Information Technology 102 (15) 2024
    Citations: 36

  • 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: 31

  • Optimizing Network Security and Performance Through the Integration of Hybrid GAN-RNN Models in SDN-based Access Control and Traffic Engineering.
    G Khekare, K Pavan Kumar, KN Prasanthi, SR Godla, V Rachapudi, …
    International Journal of Advanced Computer Science & Applications 14 (12) 2023
    Citations: 29

  • 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
    Citations: 28

  • 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: 24

  • 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: 17

  • The water demand management in the Kingdom of Bahrain
    MSA Ansari
    Journal of Engineering and Advanced Technology 2 (5), 544-554 2013
    Citations: 17

  • 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: 16

  • Open and closed R&D processes: Internal versus external knowledge
    MSA Ansari
    European Journal of Sustainable Development 2 (1), 1-1 2013
    Citations: 14

  • Applications of sensors in precision agriculture for a sustainable future
    MF Saleem, A Raza, RM Sabir, M Safdar, M Faheem, MS Al Ansari, …
    Agriculture and Aquaculture Applications of Biosensors and Bioelectronics 2024
    Citations: 12

  • & 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: 12

  • 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: 10

  • 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: 10