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Saeed Rubaiee
Department of Mechanical and Materials Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia

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Journal article
Published: 05 August 2021 in Journal of Materials Research and Technology
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Owing to the extreme heat generated during Inconel 718 machining, the application of a minimum quantity lubrication (MQL) strategy is restricted to mild cutting conditions. By incorporating vegetable-based cutting oils reinforced by nanoparticles as possible additives, the effectiveness of MQL can be improved in high-speed machining. In this study, hybrid nano-green oils were developed by combining graphene nanoparticles in various volume concentrations with sunflower oil. Subsequently, dispersion stability, thermal conductivity, viscosity, and wetting angle of nano-green oils were measured. An MQL device is used to disperse the smallest amount of nano-green oils throughout the machining area. Later, the experimentally optimized graphene-based green oil is used for milling experiments. Furthermore, hard machining experiments were conducted with cutting speed of 80 m/min, feed rate of 0.2 mm/rev, and depth of cut of 0.5 mm under four different lubricating mediums: dry, flooded, sunflower oil, and 0.7% graphene reinforced sunflower oil. Comparative results show that 0.7% graphene reinforced sunflower oil performs better and reduces surface roughness by 49%, cutting force by 25%, cutting temperature by 31%, and tool wear by 20% as compared to dry machining environment. Finally, elemental analysis of cutting insert reports that adhesion is the major wear mechanism in all mediums.

ACS Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; Murat Sarikaya. Influence of graphene reinforced sunflower oil on thermo-physical, tribological and machining characteristics of inconel 718. Journal of Materials Research and Technology 2021, 15, 135 -150.

AMA Style

Mohd Danish, Munish Kumar Gupta, Saeed Rubaiee, Anas Ahmed, Murat Sarikaya. Influence of graphene reinforced sunflower oil on thermo-physical, tribological and machining characteristics of inconel 718. Journal of Materials Research and Technology. 2021; 15 ():135-150.

Chicago/Turabian Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; Murat Sarikaya. 2021. "Influence of graphene reinforced sunflower oil on thermo-physical, tribological and machining characteristics of inconel 718." Journal of Materials Research and Technology 15, no. : 135-150.

Journal article
Published: 27 July 2021 in Chemosphere
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The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Both models were tested and validated, which showed promising results. In addition, a detailed optimization study was conducted to investigate the optimum thermal conductivity and viscosity by varying temperature and NF weight per cent. Four case studies were explored using different objective functions based on NF application in various industries. The first case study aimed to maximize thermal conductivity (0.15985 W/m oC) while minimizing viscosity (0.03501 Pa.s) obtained at 57.86 °C and 0.85 NF wt%. The goal of the second case study was to minimize thermal conductivity (0.13949 W/m °C) and viscosity (0.02526 Pa.s) obtained at 55.88 °C and 0.15 NF wt%. The third case study targeted maximizing thermal conductivity (0.15797 W/m °C) and viscosity (0.07611 Pa.s), and the optimum temperature and NF wt% were 30.64 °C and 0.0.85 respectively. The last case study explored the minimum thermal conductivity (0.13735) and maximum viscosity (0.05263 Pa.s) obtained at 30.64 °C and 0.15 NF wt%.

ACS Style

Khuram Maqsood; Abulhassan Ali; Suhaib Umer Ilyas; Sahil Garg; Mohd Danish; Aymn Abdulrahman; Saeed Rubaiee; Mustafa Alsaady; Abdulkader S. Hanbazazah; Abdullah Bin Mahfouz; Syahrir Ridha; Muhammad Mubashir; Hooi Ren Lim; Kuan Shiong Khoo; Pau Loke Show. Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids. Chemosphere 2021, 286, 131690 .

AMA Style

Khuram Maqsood, Abulhassan Ali, Suhaib Umer Ilyas, Sahil Garg, Mohd Danish, Aymn Abdulrahman, Saeed Rubaiee, Mustafa Alsaady, Abdulkader S. Hanbazazah, Abdullah Bin Mahfouz, Syahrir Ridha, Muhammad Mubashir, Hooi Ren Lim, Kuan Shiong Khoo, Pau Loke Show. Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids. Chemosphere. 2021; 286 ():131690.

Chicago/Turabian Style

Khuram Maqsood; Abulhassan Ali; Suhaib Umer Ilyas; Sahil Garg; Mohd Danish; Aymn Abdulrahman; Saeed Rubaiee; Mustafa Alsaady; Abdulkader S. Hanbazazah; Abdullah Bin Mahfouz; Syahrir Ridha; Muhammad Mubashir; Hooi Ren Lim; Kuan Shiong Khoo; Pau Loke Show. 2021. "Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids." Chemosphere 286, no. : 131690.

Focus
Published: 12 July 2021 in Soft Computing
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The development of the Internet of Things (IoT) widened its definition to incorporate aquatic conditions. The submarine sensor structures and intelligent underwater linked devices have been built into the IoT environment as the Internet of Underwater Things. Energy-sensitive and accurate data collection is carried out using extremely secure communications in underwater sensor networks that face major drawbacks like timing and place-dependent connectivity. Therefore in this paper, Optimized energy planning based intelligent data analytics has been proposed to offer a programming system for distributing intelligent data analytics underwater with high energy efficiency. IDA implements two stages: the first stage is to overcome a drawback caused by secret and expose terminals by a possibility-based disputing method. The second stage investigates the possibilities for slight specificity recovery by adding a space focused on transmitter and receiver. OEP is used to capture data through an activity that uses intelligent data focused on self-learning to identify highly secure and effective route directions across communication gaps in a sensor network. By balancing data traffic loading in a vast network, the OEP transport system minimizes greater energy usage and delay issues. In a controversial approach, IDA resolves a limitation of confidentiality and reveals terminals based on choice. OEP collects data via smart self-learning information to track safety and productive paths through connectivity holes in a sensor network. The experimental findings illustrate the improved results have been built in terms of the high packet distribution rate of 97.11% and low latency, and less energy consumption.

ACS Style

Rajakumar Arul; Roobaea Alroobaea; Seifeddine Mechti; Saeed Rubaiee; Murad Andejany; Usman Tariq; Saman Iftikhar. Intelligent data analytics in energy optimization for the internet of underwater things. Soft Computing 2021, 25, 12507 -12519.

AMA Style

Rajakumar Arul, Roobaea Alroobaea, Seifeddine Mechti, Saeed Rubaiee, Murad Andejany, Usman Tariq, Saman Iftikhar. Intelligent data analytics in energy optimization for the internet of underwater things. Soft Computing. 2021; 25 (18):12507-12519.

Chicago/Turabian Style

Rajakumar Arul; Roobaea Alroobaea; Seifeddine Mechti; Saeed Rubaiee; Murad Andejany; Usman Tariq; Saman Iftikhar. 2021. "Intelligent data analytics in energy optimization for the internet of underwater things." Soft Computing 25, no. 18: 12507-12519.

Journal article
Published: 08 July 2021 in Tribology International
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The poor thermal conductivity of Inconel 718 leads to higher cutting temperatures and, as a consequence, rapid tool degradation is a common phenomenon. As a result, a hybrid lubri-cooling environment for turning Inconel 718 alloys is proposed, incorporating the theory of cryogenic cooling and minimum quantity lubrication (Cryo-MQL). For improved lubri-cooling effect, Cryo-MQL integrates the application of a minimum quantity of vegetable oil and liquid nitrogen from two distinct nozzles in the cutting zone. Surface roughness, cutting temperature, tool wear, chip morphology, and micro-structure of the machined surface were evaluated for different lubri-cooling mediums: dry, MQL, Cryogenic, and Cryo-MQL. In comparison to a dry medium, the Cryo-MQL environment decreases surface roughness, cutting temperature, and tool wear by 60.6%, 37%, and 19.5%, respectively. Adhesion and abrasion were patented to be common tool wear types, as per SEM micro-graphs. Eventually, in the Cryo-MQL environment, a spike in micro-hardness value has been reported. However, during processing with Cryo-MQL, the grain structure of the working material is found to be smaller as compared to other mediums.

ACS Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; Mehmet Erdi Korkmaz. Influence of hybrid Cryo-MQL lubri-cooling strategy on the machining and tribological characteristics of Inconel 718. Tribology International 2021, 163, 107178 .

AMA Style

Mohd Danish, Munish Kumar Gupta, Saeed Rubaiee, Anas Ahmed, Mehmet Erdi Korkmaz. Influence of hybrid Cryo-MQL lubri-cooling strategy on the machining and tribological characteristics of Inconel 718. Tribology International. 2021; 163 ():107178.

Chicago/Turabian Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; Mehmet Erdi Korkmaz. 2021. "Influence of hybrid Cryo-MQL lubri-cooling strategy on the machining and tribological characteristics of Inconel 718." Tribology International 163, no. : 107178.

Review
Published: 28 June 2021 in Materials
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Together, 316L steel, magnesium-alloy, Ni-Ti, titanium-alloy, and cobalt-alloy are commonly employed biomaterials for biomedical applications due to their excellent mechanical characteristics and resistance to corrosion, even though at times they can be incompatible with the body. This is attributed to their poor biofunction, whereby they tend to release contaminants from their attenuated surfaces. Coating of the surface is therefore required to mitigate the release of contaminants. The coating of biomaterials can be achieved through either physical or chemical deposition techniques. However, a newly developed manufacturing process, known as powder mixed-electro discharge machining (PM-EDM), is enabling these biomaterials to be concurrently machined and coated. Thermoelectrical processes allow the migration and removal of the materials from the machined surface caused by melting and chemical reactions during the machining. Hydroxyapatite powder (HAp), yielding Ca, P, and O, is widely used to form biocompatible coatings. The HAp added-EDM process has been reported to significantly improve the coating properties, corrosion, and wear resistance, and biofunctions of biomaterials. This article extensively explores the current development of bio-coatings and the wear and corrosion characteristics of biomaterials through the HAp mixed-EDM process, including the importance of these for biomaterial performance. This review presents a comparative analysis of machined surface properties using the existing deposition methods and the EDM technique employing HAp. The dominance of the process factors over the performance is discussed thoroughly. This study also discusses challenges and areas for future research.

ACS Style

Al- Amin; Ahmad Abdul-Rani; Mohd Danish; Saeed Rubaiee; Abdullah Mahfouz; Harvey Thompson; Sadaqat Ali; Deepak Unune; Mohd Sulaiman. Investigation of Coatings, Corrosion and Wear Characteristics of Machined Biomaterials through Hydroxyapatite Mixed-EDM Process: A Review. Materials 2021, 14, 3597 .

AMA Style

Al- Amin, Ahmad Abdul-Rani, Mohd Danish, Saeed Rubaiee, Abdullah Mahfouz, Harvey Thompson, Sadaqat Ali, Deepak Unune, Mohd Sulaiman. Investigation of Coatings, Corrosion and Wear Characteristics of Machined Biomaterials through Hydroxyapatite Mixed-EDM Process: A Review. Materials. 2021; 14 (13):3597.

Chicago/Turabian Style

Al- Amin; Ahmad Abdul-Rani; Mohd Danish; Saeed Rubaiee; Abdullah Mahfouz; Harvey Thompson; Sadaqat Ali; Deepak Unune; Mohd Sulaiman. 2021. "Investigation of Coatings, Corrosion and Wear Characteristics of Machined Biomaterials through Hydroxyapatite Mixed-EDM Process: A Review." Materials 14, no. 13: 3597.

Journal article
Published: 25 June 2021 in Materials
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Magnesium alloys are widely used in numerous engineering applications owing to their superior structural characteristics. However, the machining of magnesium alloy is challenging because of its poor machinability characteristics. Therefore, this paper investigates the machining of magnesium alloys under different sustainable cooling conditions. The machining was performed by varying cutting velocity, feed rate, and depth of cut under dry and cryogenic cooling conditions. The primary focus of the paper is to develop a predictive model for surface roughness under different machining environments. The models developed were found to be in excellent agreement with experimental results, with only 0.3 to 1.6% error. Multi-objective optimization were also performed so that the best surface finish together with high material removal rate could be achieved. Furthermore, the various parameters of surface integrity (i.e., surface roughness, micro-hardness, micro-structures, crystallite size, and lattice strain) were also investigated.

ACS Style

Mohd Danish; Saeed Rubaiee; Hassan Ijaz. Predictive Modelling and Multi-Objective Optimization of Surface Integrity Parameters in Sustainable Machining Processes of Magnesium Alloy. Materials 2021, 14, 3547 .

AMA Style

Mohd Danish, Saeed Rubaiee, Hassan Ijaz. Predictive Modelling and Multi-Objective Optimization of Surface Integrity Parameters in Sustainable Machining Processes of Magnesium Alloy. Materials. 2021; 14 (13):3547.

Chicago/Turabian Style

Mohd Danish; Saeed Rubaiee; Hassan Ijaz. 2021. "Predictive Modelling and Multi-Objective Optimization of Surface Integrity Parameters in Sustainable Machining Processes of Magnesium Alloy." Materials 14, no. 13: 3547.

Journal article
Published: 22 June 2021 in Applied Sciences
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This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.

ACS Style

Sami Bourouis; Roobaea Alroobaea; Saeed Rubaiee; Murad Andejany; Nizar Bouguila. Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications. Applied Sciences 2021, 11, 5798 .

AMA Style

Sami Bourouis, Roobaea Alroobaea, Saeed Rubaiee, Murad Andejany, Nizar Bouguila. Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications. Applied Sciences. 2021; 11 (13):5798.

Chicago/Turabian Style

Sami Bourouis; Roobaea Alroobaea; Saeed Rubaiee; Murad Andejany; Nizar Bouguila. 2021. "Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications." Applied Sciences 11, no. 13: 5798.

Original paper
Published: 19 June 2021 in Personal and Ubiquitous Computing
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The Internet of Medical Things (IoMT) is a kind of associated smart-medical device infrastructure with applications, health services, and systems. These medical devices and applications are linked via the Internet to healthcare systems. The privacy and security for patient data, scalability, and data accessibility are the most complex IoT challenges (particularly in IoMT) and need to be considered. Blockchain can disrupt the current modes of patient data access, exchange, accumulation, control, and contribution. Hence, in this study, blockchain-assisted secure data management framework (BSDMF) has been suggested for health information based on the Internet of Medical Things to securely exchange patient data and enhance scalability and data accessibility healthcare environment. The proposed BSDMF provides secure data management between personal servers and implantable medical devices and between cloud servers and personal servers. The IoMT-based security framework utilizes blockchain to guarantee data transmission security and data management between linked nodes. The experimental results show that the suggested BSDMF method achieves a high accuracy ratio of 97.2%, a precision ratio of 97.9%, an average trust value of 98.3%, and less response time of 11.2%, and a latency ratio of 15.6% when compared to other popular methods.

ACS Style

Asad Abbas; Roobaea Alroobaea; Moez Krichen; Saeed Rubaiee; S. Vimal; Fahad M. Almansour. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal and Ubiquitous Computing 2021, 1 -14.

AMA Style

Asad Abbas, Roobaea Alroobaea, Moez Krichen, Saeed Rubaiee, S. Vimal, Fahad M. Almansour. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal and Ubiquitous Computing. 2021; ():1-14.

Chicago/Turabian Style

Asad Abbas; Roobaea Alroobaea; Moez Krichen; Saeed Rubaiee; S. Vimal; Fahad M. Almansour. 2021. "Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things." Personal and Ubiquitous Computing , no. : 1-14.

Journal article
Published: 11 May 2021 in Sensors
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A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.

ACS Style

Mujeeb Rehman; Arslan Shafique; Sohail Khalid; Maha Driss; Saeed Rubaiee. Future Forecasting of COVID-19: A Supervised Learning Approach. Sensors 2021, 21, 3322 .

AMA Style

Mujeeb Rehman, Arslan Shafique, Sohail Khalid, Maha Driss, Saeed Rubaiee. Future Forecasting of COVID-19: A Supervised Learning Approach. Sensors. 2021; 21 (10):3322.

Chicago/Turabian Style

Mujeeb Rehman; Arslan Shafique; Sohail Khalid; Maha Driss; Saeed Rubaiee. 2021. "Future Forecasting of COVID-19: A Supervised Learning Approach." Sensors 21, no. 10: 3322.

Journal article
Published: 10 May 2021 in IEEE Access
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Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for proportional positive vectors modeling and unsupervised learning. However, little attention has been devoted to investigate these generative models within discriminative classifiers. Our aim here is to reveal the structure of non-Gaussian data by generating new probabilistic SVM kernels from inverted-Beta Liouville mixture models. The inverted Beta-Liouville has a more general covariance structure and a smaller number of parameters than the inverted Dirichlet and generalized inverted Dirichlet, respectively, which makes it more practical and useful. A principled Bayesian learning algorithm is developed to accurately estimate the model’s parameters. To cope with the problem of selecting the optimal number of components, we further propose a nonparametric Bayesian learning algorithm based on an extended infinite mixture model which may have better modelling and clustering capabilities than the finite model for some applications. Finally, the resulting generative model is exploited to build several efficient probabilistic SVM kernels in order to enhance the expected clustering and modeling performance. Through a number of experimental evaluations involving visual scenes classification, text categorization and texture images discrimination, we prove the merits of the proposed work.

ACS Style

Sami Bourouis; Roobaea Alroobaea; Saeed Rubaiee; Murad Andejany; Fahad M. Almansour; Nizar Bouguila. Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models. IEEE Access 2021, 9, 71170 -71183.

AMA Style

Sami Bourouis, Roobaea Alroobaea, Saeed Rubaiee, Murad Andejany, Fahad M. Almansour, Nizar Bouguila. Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models. IEEE Access. 2021; 9 (99):71170-71183.

Chicago/Turabian Style

Sami Bourouis; Roobaea Alroobaea; Saeed Rubaiee; Murad Andejany; Fahad M. Almansour; Nizar Bouguila. 2021. "Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models." IEEE Access 9, no. 99: 71170-71183.

Original article
Published: 22 April 2021 in The International Journal of Advanced Manufacturing Technology
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Carbon fiber–reinforced polymers (CFRPs) are vulnerable to damage locally through machining operations due to an applied cutting force and high-temperature generation. Though traditional lubri-cooling medium will reduce the heat-generated damage of CFRPs, the use of synthetic fluids, however, significantly affects the environment and public health equally. Therefore, this paper aims to explore the milling performance of CFRPs in sustainable lubri-cooling mediums, i.e., dry, minimal lubrication (MQL), cryogenic-liquid nitrogen (N2liquid), and carbon dioxide (CO2ice). Furthermore, the correct choice of process parameters and lubri-cooling environments influences the cutting mechanism in any metal cutting operations. Accordingly, response surface methodology (RSM) is used to create a relationship between responses to machining inputs. In addition, two evolutionary techniques named Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) were used to execute parametric optimization. Eventually, the performances of the adopted approaches are compared in this manuscript, which reveals the prediction made by NSGA-II and PSO is quite consistent with the experimental results. However, in view of convergence characteristics and computational time, the PSO is shown to surpass the NSGA-II approach. Moreover, empirical cumulative distribution–based data analysis indicates that all responses are good compatible with the function of the normal distribution. At the 95% confidence level, the established ANOVA models were found to be substantial. Furthermore, lubri-cooling medium was the most critical factor affecting the response parameters with a 49% contribution in minimizing Fr, 46% contribution in decreasing VB, 38.89% contribution in Ra, and 50.21% contribution in reducing the T value.

ACS Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; A. Mahfouz; Muhammad Jamil. Machinability investigations on CFRP composites: a comparison between sustainable cooling conditions. The International Journal of Advanced Manufacturing Technology 2021, 114, 3201 -3216.

AMA Style

Mohd Danish, Munish Kumar Gupta, Saeed Rubaiee, Anas Ahmed, A. Mahfouz, Muhammad Jamil. Machinability investigations on CFRP composites: a comparison between sustainable cooling conditions. The International Journal of Advanced Manufacturing Technology. 2021; 114 (11-12):3201-3216.

Chicago/Turabian Style

Mohd Danish; Munish Kumar Gupta; Saeed Rubaiee; Anas Ahmed; A. Mahfouz; Muhammad Jamil. 2021. "Machinability investigations on CFRP composites: a comparison between sustainable cooling conditions." The International Journal of Advanced Manufacturing Technology 114, no. 11-12: 3201-3216.

Journal article
Published: 12 April 2021 in International Journal of Environmental Research and Public Health
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Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient’s critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient’s classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

ACS Style

Hamid Mukhtar; Saeed Rubaiee; Moez Krichen; Roobaea Alroobaea. An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors. International Journal of Environmental Research and Public Health 2021, 18, 4022 .

AMA Style

Hamid Mukhtar, Saeed Rubaiee, Moez Krichen, Roobaea Alroobaea. An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors. International Journal of Environmental Research and Public Health. 2021; 18 (8):4022.

Chicago/Turabian Style

Hamid Mukhtar; Saeed Rubaiee; Moez Krichen; Roobaea Alroobaea. 2021. "An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors." International Journal of Environmental Research and Public Health 18, no. 8: 4022.

Research article
Published: 25 February 2021 in Journal of Healthcare Engineering
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Pneumonia is a fatal disease responsible for almost one in five child deaths worldwide. Many developing countries have high mortality rates due to pneumonia because of the unavailability of proper and timely diagnostic measures. Using machine learning-based diagnosis methods can help to detect the disease early and in less time and cost. In this study, we proposed a novel method to determine the presence of pneumonia and identify its type (bacterial or viral) through analyzing chest radiographs. We performed a three-class classification based on features containing diverse information of the samples. After using an augmentation technique to balance the dataset’s sample sizes, we extracted the chest X-ray images’ statistical features, as well as global features by employing a deep learning architecture. We then combined both sets of features and performed the final classification using the RandomForest classifier. A feature selection method was also incorporated to identify the features with the highest relevance. We tested the proposed method on a widely used (but relabeled) chest radiograph dataset to evaluate its performance. The proposed model can classify the dataset’s samples with an 86.30% classification accuracy and 86.03% F-score, which assert the model’s efficacy and reliability. However, results show that the classifier struggles while distinguishing between viral and bacterial pneumonia samples. Implementing this method will provide a fast and automatic way to detect pneumonia in a patient and identify its type.

ACS Style

Mehedi Masud; Anupam Kumar Bairagi; Abdullah-Al Nahid; Niloy Sikder; Saeed Rubaiee; Anas Ahmed; Divya Anand. A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm. Journal of Healthcare Engineering 2021, 2021, 1 -11.

AMA Style

Mehedi Masud, Anupam Kumar Bairagi, Abdullah-Al Nahid, Niloy Sikder, Saeed Rubaiee, Anas Ahmed, Divya Anand. A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm. Journal of Healthcare Engineering. 2021; 2021 ():1-11.

Chicago/Turabian Style

Mehedi Masud; Anupam Kumar Bairagi; Abdullah-Al Nahid; Niloy Sikder; Saeed Rubaiee; Anas Ahmed; Divya Anand. 2021. "A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm." Journal of Healthcare Engineering 2021, no. : 1-11.

Journal article
Published: 01 January 2021 in International Journal of Operational Research
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In this study, a parallel replacement problem with retrofitting (PRP-R) model is proposed to determine the trade-off between retrofitting and replacing an asset. The primary objective is to identify the replacement, maintenance and retrofitting schedule that optimise purchasing new assets, operation and maintenance (O&M) cost, and retrofitting cost under budget and production constraints resulting in a mixed-integer linear programming formulation. This model is applied to a case study of energy industry involving wind turbines (WTs). Results show that due to a lower O&M cost, retrofitting is less costly than keeping the WTs. In addition, the effects of key parameters such as O&M cost, retrofitting cost, budget allocated for retrofitting and governmental subsidy on the optimal replacement policy on total cost are studied. This research contributes a model that can be used to determine if WT retrofitting is economically justified and provides a rigorous analytical framework for optimising the decision-making process over the wind farm life cycle.

ACS Style

Suna Cinar; Saeed Rubaiee; Mehmet Bayram Yildirim. Asset management strategies for wind turbines: keeping or retrofitting existing wind turbines. International Journal of Operational Research 2021, 40, 318 .

AMA Style

Suna Cinar, Saeed Rubaiee, Mehmet Bayram Yildirim. Asset management strategies for wind turbines: keeping or retrofitting existing wind turbines. International Journal of Operational Research. 2021; 40 (3):318.

Chicago/Turabian Style

Suna Cinar; Saeed Rubaiee; Mehmet Bayram Yildirim. 2021. "Asset management strategies for wind turbines: keeping or retrofitting existing wind turbines." International Journal of Operational Research 40, no. 3: 318.

Review article
Published: 27 December 2020 in Journal of Manufacturing Processes
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A well-acknowledged role of cutting fluids in any cutting operation has made them inevitable to utilize regarding the provision of adequate cooling and lubrication. Mineral-based cutting fluids are common practice in the industry; however, they are not suitable for our ecology and health. Therefore, there is a need to implement sustainable cooling/lubrication system that helps the environment and improves the machinability of light weight alloys. This review is presenting the machining and sustainability characteristics of minimum quantity lubrication (MQL), nanofluids-MQL, Ranque-Hilsch vortex tube MQL (RHVT + MQL), cryogenic-MQL as alternative to flood cooling applications in the cutting of light-weight materials. It can be stated that MQL advancements can offer clear guidelines to implement hybrid cooling techniques to improve heat transfer, lubrication, and sustainable implementations.

ACS Style

Murat Sarikaya; Munish Kumar Gupta; Italo Tomaz; Mohd. Danish; Mozammel Mia; Saeed Rubaiee; Mohd Jamil; Danil Yu Pimenov; Navneet Khanna. Cooling techniques to improve the machinability and sustainability of light-weight alloys: A state-of-the-art review. Journal of Manufacturing Processes 2020, 62, 179 -201.

AMA Style

Murat Sarikaya, Munish Kumar Gupta, Italo Tomaz, Mohd. Danish, Mozammel Mia, Saeed Rubaiee, Mohd Jamil, Danil Yu Pimenov, Navneet Khanna. Cooling techniques to improve the machinability and sustainability of light-weight alloys: A state-of-the-art review. Journal of Manufacturing Processes. 2020; 62 ():179-201.

Chicago/Turabian Style

Murat Sarikaya; Munish Kumar Gupta; Italo Tomaz; Mohd. Danish; Mozammel Mia; Saeed Rubaiee; Mohd Jamil; Danil Yu Pimenov; Navneet Khanna. 2020. "Cooling techniques to improve the machinability and sustainability of light-weight alloys: A state-of-the-art review." Journal of Manufacturing Processes 62, no. : 179-201.

Journal article
Published: 17 December 2020 in The Journal of Academic Librarianship
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Rapid use of internet-based applications like mobile library applications (MLA) are depicting the modern era of digital students and literature broadly discussed the initial adoption of MLA among students. However, there is a need to investigate the continuance use intention of applications to overcome an acceptance-discontinuance phenomenon. Therefore, this research was performed for the empirical support toward continued usage of MLA by integrating an extended expectation confirmation model (EECM), technology acceptance model (TAM), media affinity theory, and service quality. This study worked on the focus of uncovering the factors which were creating hindrance in long term use of MLA. It was conducted with the self-controlled cross-sectional survey-based study. An overall 307 surveys were collected to verify the proposed theoretical model with structural equation modelling (SEM) technique. Finding of the study inferred that service quality, confirmation, MLA affinity, perceived usefulness, satisfaction and perceived ease of use are explaining the direct or indirect strong influence on continuous use of MLA. Current research empirically assessed to expose the deep intuition toward users' continuous usage intention of MLA. Outcomes will oblige as a controller for operative choices in development and resource distribution toward confirming the accomplishment of the mobile library application's mission and vision.

ACS Style

Hamaad Rafique; Roobaea Alroobaea; Bilal Ahmed Munawar; Moez Krichen; Saeed Rubaiee; Ali Kashif Bashir. Do digital students show an inclination toward continuous use of academic library applications? A case study. The Journal of Academic Librarianship 2020, 47, 102298 .

AMA Style

Hamaad Rafique, Roobaea Alroobaea, Bilal Ahmed Munawar, Moez Krichen, Saeed Rubaiee, Ali Kashif Bashir. Do digital students show an inclination toward continuous use of academic library applications? A case study. The Journal of Academic Librarianship. 2020; 47 (2):102298.

Chicago/Turabian Style

Hamaad Rafique; Roobaea Alroobaea; Bilal Ahmed Munawar; Moez Krichen; Saeed Rubaiee; Ali Kashif Bashir. 2020. "Do digital students show an inclination toward continuous use of academic library applications? A case study." The Journal of Academic Librarianship 47, no. 2: 102298.

Journal article
Published: 04 December 2020 in Mechanics & Industry
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This research work presents a numerical study of the orthogonal cutting process employing a finite element approach to optimize dry machining of aluminium alloy 2024. The main objective of the research work is to perform three-dimensional finite element simulations for a better understanding of temperature distribution and residual stresses development in the workpiece and tool regions along depth of cut direction. While, two-dimensional models don't predict true picture of aforesaid parameters along cutting depth due to material's out of plane flow and deformation. In the present study, effects of tool rake angles (7°, 14°, 21°) and cutting speeds (200, 400, 800 m/min) upon variations in chip geometry at various sections along workpiece width (depth of cut) have been discussed at large. Furthermore, cutting forces and tool-workpiece temperature profiles are also in depth analysed. The findings will lead the manufacturers to better decide post machining processes like heat treatment, deburring, surface treatments, etc. The results showed that a combination of a rake angle of 14° at cutting velocity of 800 m/min produces serrated chip segments with relatively moderate cutting forces in comparison to other parametric combinations. The efficacy of the presented finite element model is verified by comparing the numerically obtained results with experimental ones.

ACS Style

Hassan Ijaz; Mohd Danish; Muhammad Asad; Saeed Rubaiee. A three-dimensional finite element-approach to investigate the optimum cutting parameters in machining AA2024. Mechanics & Industry 2020, 21, 615 .

AMA Style

Hassan Ijaz, Mohd Danish, Muhammad Asad, Saeed Rubaiee. A three-dimensional finite element-approach to investigate the optimum cutting parameters in machining AA2024. Mechanics & Industry. 2020; 21 (6):615.

Chicago/Turabian Style

Hassan Ijaz; Mohd Danish; Muhammad Asad; Saeed Rubaiee. 2020. "A three-dimensional finite element-approach to investigate the optimum cutting parameters in machining AA2024." Mechanics & Industry 21, no. 6: 615.

Journal article
Published: 01 November 2020 in Symmetry
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In the digital multimedia era, digital forensics is becoming an emerging area of research thanks to the large amount of image and video files generated. Ensuring the integrity of such media is of great importance in many situations. This task has become more complex, especially with the progress of symmetrical and asymmetrical network structures which make their authenticity difficult. Consequently, it is absolutely imperative to discover all possible modes of manipulation through the development of new forensics detector tools. Although many solutions have been developed, tamper-detection performance is far from reliable and it leaves this problem widely open for further investigation. In particular, many types of multimedia fraud are difficult to detect because some evidences are not exploited. For example, the symmetry and asymmetry inconsistencies related to visual feature properties are potential when applied at multiple scales and locations. We explore here this topic and propose an understandable soft taxonomy and a deep overview of the latest research concerning multimedia forgery detection. Then, an in-depth discussion and future directions for further investigation are provided. This work offers an opportunity for researchers to understand the current active field and to help them develop and evaluate their own image/video forensics approaches.

ACS Style

Sami Bourouis; Roobaea Alroobaea; Abdullah Alharbi; Murad Andejany; Saeed Rubaiee. Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis. Symmetry 2020, 12, 1811 .

AMA Style

Sami Bourouis, Roobaea Alroobaea, Abdullah Alharbi, Murad Andejany, Saeed Rubaiee. Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis. Symmetry. 2020; 12 (11):1811.

Chicago/Turabian Style

Sami Bourouis; Roobaea Alroobaea; Abdullah Alharbi; Murad Andejany; Saeed Rubaiee. 2020. "Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis." Symmetry 12, no. 11: 1811.

Article
Published: 08 October 2020 in Multimedia Tools and Applications
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This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm’s performance. For model’s parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. We demonstrate the effectiveness of the developed segmentation method through several experiments. Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking.

ACS Style

Sami Bourouis; Ines Channoufi; Roobaea AlRoobaea; Saeed Rubaiee; Murad Andejany; Nizar Bouguila. Color object segmentation and tracking using flexible statistical model and level-set. Multimedia Tools and Applications 2020, 80, 5809 -5831.

AMA Style

Sami Bourouis, Ines Channoufi, Roobaea AlRoobaea, Saeed Rubaiee, Murad Andejany, Nizar Bouguila. Color object segmentation and tracking using flexible statistical model and level-set. Multimedia Tools and Applications. 2020; 80 (4):5809-5831.

Chicago/Turabian Style

Sami Bourouis; Ines Channoufi; Roobaea AlRoobaea; Saeed Rubaiee; Murad Andejany; Nizar Bouguila. 2020. "Color object segmentation and tracking using flexible statistical model and level-set." Multimedia Tools and Applications 80, no. 4: 5809-5831.

Journal article
Published: 30 September 2020 in IEEE Access
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Wind speed interval prediction is gaining importance in optimal planning and operation of power systems. However, the unpredictable characteristics of wind energy makes quality forecasting an arduous task. In this paper, we propose a novel hybrid model for wind speed interval prediction using an autoencoder and a bidirectional long short term memory neural network. The autoencoder initially extracts important unseen features from the wind speed data. The artificially generated features are utilized as input to the bidirectional long short term memory neural network to generate the prediction intervals. We also demonstrate that for time series prediction tasks, feature extraction through autoencoder is more effective than making deep residual networks. In our experiments which involve eight cases distributed among two wind fields, the proposed method is able to generate narrow prediction intervals with high prediction interval coverage and achieve an improvement of 39% in coverage width criterion over the traditional models.

ACS Style

Adnan Saeed; Chaoshun Li; Mohd Danish; Saeed Rubaiee; Geng Tang; Zhenghao Gan; Anas Ahmed. Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction. IEEE Access 2020, 8, 182283 -182294.

AMA Style

Adnan Saeed, Chaoshun Li, Mohd Danish, Saeed Rubaiee, Geng Tang, Zhenghao Gan, Anas Ahmed. Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction. IEEE Access. 2020; 8 (99):182283-182294.

Chicago/Turabian Style

Adnan Saeed; Chaoshun Li; Mohd Danish; Saeed Rubaiee; Geng Tang; Zhenghao Gan; Anas Ahmed. 2020. "Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction." IEEE Access 8, no. 99: 182283-182294.