Dr. Mohammed Saleh Al Ansari
@uob.edu.bh
Associate Professor, College of Engineering, Department of Chemical Engineering
University of Bahrain
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley
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.
Raj Kumar, Muneesh Sethi, Varun Goel, M K Ramis, Majed AlSubih, Saiful Islam, Mohammed Saleh Al Ansari, Daeho Lee, and Anteneh WogassoWodajo
Oxford University Press (OUP)
Abstract The current work analyses the thermal (ηth) and effective efficiency (${\\eta}_{\\mathrm{eff}}$) of a solar thermal air collector (STAC) that has an arc-shaped dimple as a roughness geometry on the absorber plate. Nusselt number (Nu) and friction factor (ff) were computed for roughness geometry during the testing, which was done on STAC. Additionally, for different roughness values, the correlations for Nu and ff were developed and further used in this study. The temperature rise parameter and a parametric design are used to assess these efficiencies. The influence of design variables on STAC performance is analyzed using a numerical model based on thermal and effective evaluations. During the investigation, parameters such as relative roughness height (e/Dh) varied from 0.021 to 0.036, relative roughness pitch (p/e) from 10 to 20, arc angle (α) from 45 to 60°, temperature rise parameter from 0.003 to 0.02 and Reynolds number (Re) from 3000 to 48 000 at a constant solar intensity (I = 1000 W/m2). The ηth and ${\\eta}_{\\mathrm{eff}}$ are observed to be 85% and 78%, respectively, at the optimum values of roughness parameters, i.e. e/Dh = 0.036, p/e = 10, and α = 60°. The curves have been plotted between each of the roughness parameters and Re in order to evaluate the best ηth and ${\\eta}_{\\mathrm{eff}}$ . The research emphasizes the usefulness of MATLAB for STAC analysis and optimization, roughness parameters of the suggested collector design, by integrating simulation and experimental data.
Kambala Vijaya Kumar, Y Dileep Kumar, Sanjiv Rao Godla, Mohammed Saleh Al Ansari, Yousef A.Baker El-Ebiary, and Elangovan Muniyandy
The Science and Information Organization
— Forecasting water quality is critical to environmental management because it facilitates quick decision-making and resource allocation. On the opposite hand, current methods are not always able to produce reliable forecasts, which is often due to challenges in parameter optimization for complex models. This research presents a novel approach to enhance the forecasting accuracy of water quality by optimizing neuro-fuzzy models using Tunicate Swarm Optimisation (TSO). The introduction highlights the limitations of current techniques as well as the necessity for precise estimates of water quality. One of the drawbacks is that neuro-fuzzy models are not well-modelled, which makes it harder for them to identify the minute patterns in data on water quality. The suggested approach is unique in that it applies TSO, an optimization algorithm inspired by nature that emulates tunicates' behaviour, to the neuro-fuzzy models' parameter optimization process. The highly complex parameter space is effectively navigated by TSO's swarm intelligence, which strikes a balance between exploration and exploitation to improve model performance. To optimize model parameters, the process comprises three steps: creating an objective function, defining the neuro-fuzzy model, and seamlessly integrating TSO. By mimicking the motions of tunicates as they look for the best conditions in the marine environment, TSO constantly optimizes the variables. Experiments demonstrate that the proposed strategy is more effective than traditional optimization techniques in forecasting water quality. As seen by the optimised neuro-fuzzy model's increased prediction accuracy and several dataset validations, Tunicate Swarm Optimisation has potential for reliable environmental forecasting. This work presents a potential path for improved environmental decision-making systems by offering an optimisation strategy inspired by nature that overcomes the limitations of existing methods and enhances water quality forecasting tools
Andip Babanrao Shrote, K Kiran Kumar, Chamandeep Kaur, Mohammed Saleh Al Ansari, Pallavi Singh, Bramah Hazela, and Madhu G C
European Alliance for Innovation n.o.
The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.
Sasikala P, Sushil Dohare, Mohammed Saleh Al Ansari, Janjhyam Venkata Naga Ramesh, Yousef A.Baker El-Ebiary, and E. Thenmozhi
The Science and Information Organization
Hussein Tuama Hazim, Chamandeep Kaur, Sambhrant Srivastava, Iskandar Muda, Harish Chander Anandaram, and Mohammed Saleh Al Ansari
AIP Publishing
N. Nagabhooshanam, N. Bala sundara ganapathy, C. Ravindra Murthy, Al Ansari Mohammed Saleh, and Ricardo Fernando CosioBorda
Elsevier BV
Yousef Methkal Abd Algani, Mahyudin Ritonga, B. Kiran Bala, Mohammed Saleh Al Ansari, Malek Badr, and Ahmed I. Taloba
Elsevier BV
Yousef Methkal Abd Algani, Orlando Juan Marquez Caro, Liz Maribel Robladillo Bravo, Chamandeep Kaur, Mohammed Saleh Al Ansari, and B. Kiran Bala
Elsevier BV
Vinay Kumar Nassa, Manish Gupta, Mohammed Saleh Al Ansari, Ramesh R, Hayder Al-Chilibi, and Malik Bader Alazzam
IEEE
Using Cisco Packet Tracer as a modelling tool, this research study investigates the manner in which sensor-based networks and the Internet of Everything (IoE) might function together. It describes a thorough technique for creating network topologies, setting up devices, establishing connectivity, monitoring data, putting security measures in place, diagnosing issues, as well as assessing outcomes. When assessing the behavior and difficulties of sensor-based networks and IoE, a controlled realistic environment is intended. This study bridges the theoretical and practical implementation gaps by employing Packet Tracer, providing insightful information about the actual uses of these game-changing technologies. Future scopes will include upcoming technologies like 6G and edge computing as well as broaden simulations to accommodate more complicated situations.
Lipsa Das, Akanksha Singh, Shazia Ali, Harish Chowdhary, Mohammed Saleh Al Ansari, Ankesh Kumar, and Ajay Rana
IEEE
Players may now receive top-notch gaming experiences from cloud gaming, no matter where they are or when they play. According to this paradigm, powerful servers housed in data centers run complex game software, and the resulting game scenes are transmitted in real time to players via the internet. Players interact with these games via little apps that are installed on various devices. Ever since the late 2000s, cloud gaming has attracted significant interest from the academic and industrial communities, driven by the increasing accessibility of high-speed networks and the pervasiveness of cloud computing. This manuscript undertakes a comprehensive investigation of current research in the field of cloud gaming, covering a wide range of topics including platforms for cloud gaming, optimization strategies, and commercial cloud gaming services. The most recent developments in this rapidly developing subject will be introduced to readers, who will also acquire insights into the state of cloud gaming research today.
Manish Kaushik, P. Amrutha, Anita Gehlot, Shikha Kuchhal, Mohammed Saleh Al Ansari, and V Malathy
IEEE
Energy efficiency optimization while preserving dependable data transport is crucial in the world of Wireless Sensor Networks (WSNs). In order to thoroughly assess energy-efficient protocols, and data aggregation approaches, including node power control strategies, this research uses MATLAB-based simulations. In an effort to find a balance, the study examines the trade-offs between energy saving and data delivery dependability. Based on the findings, WSN lifespans can potentially be greatly extended by fine-tuning procedures, which is advantageous for applications like industrial automation and environmental monitoring. In order to further improve WSN sustainability, the study also identifies areas for further investigation, such as dynamic adaptability including the incorporation of renewable energy sources.
Ramesh Dugyala, Sandeep Kumar Singh, Mohammed Saleh Al Ansari, C. Gunasundari, Kilaru Aswini, and G. Sandhya
IEEE
Artificial intelligence (AI) is recognized as a valuable tool in various healthcare uses for diagnosing and therapeutic decision-making. Due to the tremendous rise in accessible data and processing capacity, machine learning (ML) models have performed well or greater than doctors in numerous activities. The AI platform needs to be transparent, resilient, and interpretable to adhere to the principles of trusted AI. Present ML systems are alluded to as black boxes because of the absence of understanding of the mechanics related to the decision-making procedures. As a result, before ML can be implemented into ordinary healthcare processes, its transparency and interpretability must be understood. To address this issue, this study presents a method for understanding the transparency and interpretability of ML suggestion systems. We particularly adapt the suggested technique to a chronic condition that is frequent in seniors: heart disease. The suggested approach illustrates the fundamental cause for these suggestions and increases patient trust and interpretability of ML models by assessing the influence of various patient features on the suggestions.
Eshwararao Boddepalli, Gujar Anantkumar Jotiram, Thilagham K T, Mohammed Saleh Al Ansari, Anil Baburao Desai, and K. K. Bajaj
IEEE
This study analyses wear and tear characteristics of EN 24 alloy better suited for optimum proportions of nanosilicon carbide (Nano SiC) particles. The study employed an intensive Taguchi layout of experiments to take a look at the influence of three crucial elements, particularly the friction coefficient (Fc) and the specific wear rate (Ks),. The three variables are the Nano SiC composition, the load, and the rotating speed of the disc. The results of the take a look at show that an increase in the Nano SiC concentration greatly reduces the specific, underlining the crucial function that Nano SiC composition performs in improving put on resistance. The test in addition reveals that the sliding distance and implemented force are the number one component that effect friction behaviour. These insights are particularly useful, notably for engineering projects wherever the management of friction and the development of wear resistance are vital. This study applies Artificial Neural Networks (ANN) to enhance our understanding and predicting skills by simulating the complicated interconnections among input factors and answers. The artificial neural network (ANN) model properly estimates an ordinary performance of 85.229 %. This prediction achievement improves the usefulness of the observe by supplying a sturdy foundation for increasing the performance and durability of material in actual-world circumstances.
E. Manigandan, Mohammed Saleh Al Ansari, Praveena Nuthakki, Bhuvneshwari S, G Kala Priyadharshini, and Muruganantham Ponnusamy
IEEE
The suggested system uses face detection and identification algorithms to automate the attendance marking and management processes. Face recognition refers to the process of identifying a person by their distinctive facial traits. At the moment, face recognition software is the most rapidly developing field in IT. This suggested system seeks to replace manual attendance tracking with an automated system that uses facial recognition technology to keep track of which students are present in the classroom at any given time. The primary goal of this endeavor is to create an automatic, user-friendly system for recording and managing attendance. The suggested method involves analyzing data, selecting features to employ, and assessing the model's efficiency. The suggested method employs Gaussian blur, segmentation, and scaling for preprocessing. PCA and LDA are used for feature selection and extraction, respectively. The LSTM-KNN hybrid method is used for model training. When compared to LSTM and KNN, two existing approaches, the proposed methodology performs exceptionally well.
Murali Karri, Prasenjit Yashwant Fulzele, Mohammed Saleh Al Ansari, K. M. Devendraiah, B. Umamaheswari, and Dharmendra Singh
IEEE
There are several options for controlling the temperature and humidity within buildings like schools, offices, hospitals, houses, and even grocery stores. Many different theories of control have been suggested and investigated during the past few decades. The most effective method of controlling the temperature in a wide range of building types remains a matter of debate. A straightforward method of regulation is needed to create a comfortable interior environment while reducing energy consumption. The proposed method includes preprocessing, feature selection, and model training. Normalization and standardization procedures are employed in the preprocessing stage. When data is normalized, the maximum and minimum values are swapped out. In the realm of machine learning, standardization is a common practice for preprocessing data. Correlation based feature selection is utilized in the process of feature selection. CBLS TM-AE is used to train the models after the features have been collected. When compared to widely used algorithms like LSTM and AE, the proposed technique comes out on top. The probability of success when employing this method is 95.24%.
Ganesh Khekare, K. Pavan Kumar, Kundeti Naga Prasanthi, Sanjiv Rao Godla, Venubabu Rachapudi, Mohammed Saleh Al Ansari, and Yousef A. Baker El-Ebiary
The Science and Information Organization
— By offering flexible and adaptable infrastructures Software-Defined Networking (SDN) has emerged as a disruptive technology that has completely changed network provisioning and administration. By seamlessly integrating Hybrid Generative Adversarial Network-Recurrent Neural Network (GAN-RNN) modeling into the foundation of SDN-based traffic engineering and accessibility control methods, this work presents a novel and comprehensive method to improve network efficiency and security. The proposed Hybrid GAN-RNN models address two important aspects of network management: traffic optimization and access control. They combine the benefits of Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs). Traditional traffic engineering techniques frequently find it difficult to quickly adjust to situations that are changing quickly within today's dynamic networking environments. The models' capacity to generate synthetic traffic patterns that nearly perfectly replicate the complexity of real network traffic demonstrates the power of GANs. Network administrators can now allocate resources and routing methods more dynamically, as well as in responding to real-time network inconsistencies, due to this state-of-the-art technology. The technique known as Hybrid GAN-RNN addresses the enduring problem of network security. With their reputation for continuous learning and by utilizing Python software, recurrent neural networks (RNNs) are at the forefront of developing flexible management of access rules. With an incredible 99.4% accuracy rate, the "Proposed GAN-RNN" approach outperforms the other approaches. A comprehensive evaluation of network traffic and new safety risks allow for the immediate modification of these policies. This work is interesting because it combines hybrid GAN-RNN algorithms to strengthen security protocols with adaptive access control while also optimizing network efficiency through realistic traffic modeling.
Deeba K, O. Rama Devi, Mohammed Saleh Al Ansari, Bhargavi Peddi Reddy, Manohara H T, Yousef A. Baker El-Ebiary, and Manikandan Rengarajan
The Science and Information Organization
— The optimization of crop yield projections has arisen as a major problem in modern agriculture, due to the increasing demand for food supply and the necessity for effective resource management. Precision and scalability are hampered by the limits associated with conventional agricultural production prediction techniques, which mostly rely on observations and simple data sources. While methods like random forest (RF) and K-nearest neighbors (KNN) are widely used, their reliance on personal assessments and insufficient knowledge of crop attributes typically results in less accurate forecasts and makes them unsuitable for agricultural precision. The suggested method combines deep learning, spectral unmixing, and hyperspectral imaging methods to overcome these obstacles. With the use of hyperspectral imaging, which records a vast array of data that is not visible to the human eye, crop attributes may be thoroughly examined and can identify the unique spectral fingerprints of different agricultural constituents by using spectral unmixing approaches, which makes it easier to evaluate the health and growth phases of the crop. Then, using this augmented spectral data, deep learning algorithms create a solid, data-driven basis for precise crop production prediction. MATLAB has been used in the suggested workflow. The combination of deep learning, spectrum unmixing, and hyperspectral imaging provides a comprehensive, cutting-edge approach that goes beyond the constraints of conventional techniques were implemented in python. Some of the algorithms that were examined, this one with integration has the lowest Root Mean Square Error (RMSE) of 0.15 and Mean Absolute Error (MAE) of 0.14, demonstrating higher prediction accuracy above other current models. This novel method represents a substantial breakthrough in precision agriculture while also improving crop production prediction.