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
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
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.
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.
A Satchidanandam, R. Mohammed Saleh Al Ansari, A L Sreenivasulu, Vuda Sreenivasa Rao, Sanjiv Rao Godla, and Chamandeep Kaur
The Science and Information Organization
— The goal of artistic style translation is to combine an image's substance with an equivalent image's spirit of innovation. Current approaches are unable to consistently capture complex stylistic elements and maintain uniform stylization over semantic segments, which results in artefacts. Also suggest a novel approach which blends subjective loss algorithms using deep networks of neurons with segmentation using semantics to address these issues. By guaranteeing contextually-aware design distribution together with information preservation, the combination improves general aesthetic correctness during the styling transmission process. With this technique, perceptive components are extracted using both the subject matter and the style photos using previously trained deep neural systems. These components combine to provide perceptive loss coefficients, which are subsequently included into the design of a Generative Adversarial Network (GAN). For offering the representation a better grasp of the meaning contained in any given image, an automatic segmenting module is subsequently implemented. This historical data directs the style transferring process, producing an additional precise and sophisticated transition. The outcomes of our experiments confirm the efficacy of this method and demonstrate improved visual accuracy over earlier approaches. The use of semantic segmentation and loss of perceptual information algorithms together provide a significant 95.6% improvement in visual accuracy. This method effectively overcomes the drawbacks of earlier approaches, providing precise and trustworthy transference of style and constituting a noteworthy advancement in the field of imaginative style transfer. The final output graphics further demonstrate the importance of the recommended approach by deftly integrating decorative elements into functionally significant places.
Moresh Mukhedkar, Chamandeep Kaur, Divvela Srinivasa Rao, Shweta Bandhekar, Mohammed Saleh Al Ansari, Maganti Syamala, and Yousef A.Baker El-Ebiary
The Science and Information Organization
— Reliable classification of Land Use and Land Cover (LULC) using satellite images is essential for disaster management, environmental monitoring, and urban planning. This paper introduces a unique method that combines a Convolutional Neural Network (CNN) with Human Group-based Particle Swarm Optimization (HPSO) and Ant Colony Optimization (ACO) algorithms to improve the accuracy of LULC classification. The suggested hybrid HPSO-ACO-CNN architecture effectively solves the issues with feature selection, parameter optimization
A. Leela Sravanthi, Sameh Al-Ashmawy, Chamandeep Kaur, Mohammed Saleh Al Ansari, K. Aanandha Saravanan, and Veera Ankalu. Vuyyuru
The Science and Information Organization
— Diabetes is a major health issue that affects people all over the world. Accurate early diagnosis is essential to enabling adequate therapy and prevention actions. Through the use of electronic health records and recent advancements in data analytics, there is growing interest in merging multimodal medical data to increase the precision of diabetes prediction. In order to improve the accuracy of diabetes prediction, this study presents a novel hybrid optimisation strategy that seamlessly combines machine learning techniques. In order to merge many models in a way that maximises efficiency while enhancing prediction accuracy, the study employs a collaborative learning technique. This study makes use of two separate diabetes database datasets from Pima Indians. A feature selection process is used to streamline error-free classification. A third method known as Binary Grey Wolf-based Crow Search Optimisation (BGW-CSO)
Bhargavi Peddi Reddy, K Rangaswamy, Doradla Bharadwaja, Mani Mohan Dupaty, Partha Sarkar, and Mohammed Saleh Al Ansari
The Science and Information Organization
Chandra Mouli Venkata Srinivas, Hajiyeva Rena, AR. Arunarani, ST Naitik, Mohammed Saleh Al Ansari, and Ammar Younas
IEEE
One of the most difficult problems in the technological sector is the versatility of cloud computing, which can offer a flexible and responsive infrastructure in the realm of information technology. A significant problem in cloud computing that is addressed in recent research is workflow scheduling. With the quick advancement of cloud computing, scheduling the intricate scientific process on the cloud has grown to be an incredibly difficult task. It has been identified that one of the key challenges to optimizing the performance of cloud computing is scheduling workflow tasks so that it is processed by the most effective cloud network resources. Meta-heuristic optimization algorithms are frequently utilized to find a solution to effective task scheduling because of the intricacy of the problem and the extent of the search space. This research suggests a unique Improved Red Deer Algorithm (IRDA) to shorten execution time subject to a set budget. The performance of the proposed algorithm is evaluated against conventional optimization algorithms by applying it to scientific workflows including Montage and Epigenomics. The experimental results reveal that the proposed algorithm works better than other compared methods in reducing workflow task execution time and cost.
AR. Arunarani, S. Selvanayaki, Mohammed Saleh Al Ansari, Md. Abul Ala Walid, Nagamalleswari Devireddy, and Murugesan Mohana Keerthi
IEEE
Crop yield forecasting has been thoroughly studied recently and has become more important in preserving the safety of food. The majority of studies have concentrated on obtaining fixed spatial data from remote sensing images. Crop development, however, is a very complicated attribute influenced by a variety of factors. This work is intended to concurrently use spatial and temporal information from multimodal remotely sensed images in order to fully investigate these diverse characteristics. With the goal to make use of their supportive nature, Spatio temporal Convolutional Neural Networks (STCNN), is proposed as a revolutionary deep learning architecture for agricultural production prediction. To recognize the combined spatial-temporal representation, the STCNN specifically combines a spatial training segment and a temporal dependent segment into the convolutional network. The innovative spatial training segment begins by extracting enough spatial information from the multimodal spatial images. Then, in order to determine the temporal connection from the lengthy time-series images, the temporal dependent segment is concatenated on top of the spatial training segment. Both common machine learning techniques and cutting-edge deep learning techniques are compared to the outcomes of the proposed method. The experimental findings show that the STCNN can offer superior prediction performance of 0.64 RMSE and 12.03 MAPE values compared to the rival methods with minimized errors.
Andino Maseleno Rao Godla, D. Kavitha, Koudegai Ashok, Mohammed Saleh Al Ansari, Nimmati Satheesh, and R. Vijaya Kumar Reddy
The Science and Information Organization
— Medical image fusion plays a vital role in enhancing the quality and accuracy of diagnostic procedures by integrating complementary information from multiple imaging modalities. In this study, we propose an ensemble learning approach for multi-modal medical image fusion utilizing deep convolutional neural networks (DCNNs) to predict brain tumour. The proposed method aims to exploit the inherent characteristics of different modalities and leverage the power of CNNs for improved fusion results. The Generative Adversarial Network (GAN) strengthens the input images. The ensemble learning framework comprises two main stages. Firstly, a set of DCNN models is trained independently on the respective input modalities, extracting high-level features that capture modality-specific information. Each DCNN model is fine-tuned to optimize its performance for fusion. Secondly, a fusion module is designed to aggregate the individual modality features and generate a fused image. The fusion module employs a weighted averaging technique to assign appropriate weights to the features based on their relevance and significance. The fused image obtained through this process exhibits enhanced spatial details and improved overall quality compared to the individual modalities. On a diversified dataset made up of multi-modal medical images, thorough tests are carried out to assess the efficacy of the suggested approach. The fusion images exhibit improved visual quality, enhanced feature representation, and better preservation of diagnostic information. The BRATS 2018 dataset, which contains Multi-Modal MRI images and patients’ healthcare information were used. The proposed method also demonstrates robustness across different medical imaging modalities, highlighting its versatility and potential for widespread adoption in clinical practice.