Rabia Emhamed Al Mamlook
Verified @wmich.edu
Department of Industrial and Manufacturing Engineering
Al Zawiya University
Ph.D. graduate Industrial engineering &Engineering Management, and data scientist with strong skills in Statistics, and algorithms to big data sets related industries and health care engineering. Skilled in machine learning, data models, data mining, statistics process quality control, pre-processing, and visualization data to solve challenging business and industrial problems. Demonstrates experience with programming languages (e.g., R, SAS, Minitab, and Python).
EDUCATION
Ph.D. Industrial Engineering and Engineering Management from WMU, USA.
Master’s in Applied Statistics & Biostatistics from WMU, USA.
Master’s in Engineering Management from the University of Tripoli, Libya.
RESEARCH INTERESTS
Smart Manufacturing, Machine learning, Data Mining and Healthcare Management ,Data models, Statistics Process Quality Control, and Deep Learning
Scopus Publications
Scopus Publications
Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ghassan Samara, Ahmad Nasayreh, Saja Smadi, Khalid M. O. Nahar, Mohammad Aljaidi, Essam Al-Daoud, Mohammad Gharaibeh, and Laith Abualigah
Springer Science and Business Media LLC
Odey Alshboul, Rabia Emhamed Al Mamlook, Ali Shehadeh, and Tahir Munir
Elsevier BV
Ahmad Nasayreh, Rabia Emhamed Al Mamlook, Ghassan Samara, Hasan Gharaibeh, Mohammad Aljaidi, Dalia Alzu'bi, Essam Al-Daoud, and Laith Abualigah
Association for Computing Machinery (ACM)
In the realm of ChatGPT's language capabilities, exploring Arabic Sentiment Analysis emerges as a crucial research focus. This study centers on ChatGPT, a popular machine learning model engaging in dialogues with users, garnering attention for its exceptional performance and widespread impact, particularly in the Arab world. The objective is to assess people's opinions about ChatGPT, categorizing them as positive or negative. Despite abundant research in English, there is a notable gap in Arabic studies. We assembled a dataset from X (formerly known as Twitter), comprising 2,247 tweets, classified by Arabic language specialists. Employing various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB), we implemented hyperparameter optimization techniques such as Bayesian optimization, Grid Search, and random search to select the best hyperparameters that contribute to achieving the best performance. Through training and testing, performance enhancements were observed with optimization algorithms. SVM exhibited superior performance, achieving 90% accuracy, 88% precision, 95% recall, and 91% F1 score with Grid Search. These findings contribute valuable insights into ChatGPT's impact in the Arab world, offering a comprehensive understanding of sentiment analysis through machine learning methodologies.
Arz Y. Qwam Alden, Ahmed N. Uwayed, and Rabia Al Mamlook
AIP Publishing
Abeer Aljohani, Nawaf Alharbe, Rabia Emhamed Al Mamlook, and Mashael M. Khayyat
Elsevier BV
Khalid M. O. Nahar, Izzat Alsmadi, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Hasan Gharaibeh, Ali Saeed Almuflih, and Fahad Alasim
MDPI AG
Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.
Rabia Emhamed Al Mamlook, Lee J. Wells, and Robert Sawyer
Elsevier BV
Ameera S. Jaradat, Rabia Emhamed Al Mamlook, Naif Almakayeel, Nawaf Alharbe, Ali Saeed Almuflih, Ahmad Nasayreh, Hasan Gharaibeh, Mohammad Gharaibeh, Ali Gharaibeh, and Hanin Bzizi
MDPI AG
The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.
Ameera S. Jaradat, Ahmad Nasayreh, Qais Al-Na'amneh, Hasan Gharaibeh, and Rabia Emhamed Al Mamlook
IEEE
Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work deployed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively.
Rabia Emhamed Al Mamlook, Mohammad Zahrawi, Hasan Gharaibeh, Ahmad Nasayreh, and Sujeet Shresth
IEEE
Dubai's growing population and public transportation have led to an increase in vehicular traffic and associated challenges. To tackle these issues, there is a rising interest in using machine learning (ML)techniques to improve the city's traffic control system. This study aims to explore the potential of ML in enhancing traffic management and creating a sustainable urban environment. A novel approach to traffic management in Dubai that combines AI and ML algorithms has been proposed, with the potential to significantly improve traffic flow and safety. A simulation study on a smart traffic control system that utilizes YOLO algorithms for real-time vehicle detection and counting is presented. The system optimizes traffic light timings and balances traffic load among different roads to reduce congestion and improve traffic flow. The simulation results demonstrate that the system is highly effective in adapting to changing traffic conditions and reducing congestion. The study concludes that the use of ML algorithms such as YOLO has the potential to revolutionize traffic management in urban areas, leading to a more efficient and sustainable transportation system. Further research and development in this area could bring significant benefits to both motorists and the environment.
Rabia Al Mamlook, Sujeet Shresth, Tasnim Gharaibeh, Ali Saeed Almuflih, Wassnaa Al-Mawee, and Hanin Bzizi
IEEE
Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80% compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features.
Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Hasan Gharaibeh, and Sujeet Shrestha
IEEE
Glioblastoma multiforme (GBM) is a highly ma-lignant type of brain cancer with a bleak prognosis. This study aimed to apply machine learning methods to classify GBM samples from the Cancer Genome Atlas (TCGA) dataset. Several supervised learning algorithms, including Support Vector Machine, Ad boost, Neural Network, and Decision Tree, were employed in the analysis. Our findings indicate that the Decision Tree algorithm was the most effective for this classification task, achieving an impressive 99% accuracy. Our study provides evidence that machine learning can accurately classify GBM samples in large-scale genomic datasets, enabling a deeper understanding of the genomic characteristics of this cancer. This study emphasizes the potential of machine learning approaches for improved cancer diagnosis and treatment through the analysis of large-scale genomic datasets.
Reyadh Alluhaibi, Nawaf Alharbe, Abeer Aljohani, and Rabia Emhmed Al Mamlook
MDPI AG
Ambient Assisted Living (AAL) is a medical surveillance system comprised of connected devices, healthcare sensor systems, wireless communications, computer hardware, and software implementations. AAL could be used for an extensive variety of purposes, comprising preventing, healing, as well as improving the health and wellness of elderly individuals. AAL intends to ensure the wellbeing of elderly persons while also spanning the number of years seniors can remain independent in their preferred surroundings. It also decreases the quantity of family caregivers by giving patients control over their health situations. To avert huge costs as well as possible adverse effects on standard of living, classifiers must be used to distinguish between adopters as well as nonadopters of such innovations. With the development of numerous classification algorithms, selecting the best classifier became a vital and challenging step in technology acceptance. Decision makers must consider several criteria from different domains when selecting the best classifier. Furthermore, it is critical to define the best multicriteria decision-making strategy for modelling technology acceptance. Considering the foregoing, this research reports the incorporation of the multicriteria decision-making (MCDM) method which is founded on the fuzzy method for order of preference by similarity to ideal solution (TOPSIS) to identify the top classifier for continuing toward supporting AAL implementation research. The results indicate that the classification algorithm KNN is the preferred technique among the collection of different classification algorithms for the ambient assisted living system.
Abdulla I. M. Almadi, Rabia Emhamed Al Mamlook, Irfan Ullah, Odey Alshboul, Nishantha Bandara, and Ali Shehadeh
Informa UK Limited
Odey Alshboul, Ali Shehadeh, Maha Al-Kasasbeh, Rabia Emhamed Al Mamlook, Neda Halalsheh, and Muna Alkasasbeh
Emerald
PurposeHeavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.Design/methodology/approachBased on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.FindingsThe developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.
Arshad Jamal, Muhammad Ijaz, Meshal Almosageah, Hassan M. Al-Ahmadi, Muhammad Zahid, Irfan Ullah, and Rabia Emhamed Al Mamlook
MDPI AG
Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections.
Irfan Ullah, Kai Liu, Toshiyuki Yamamoto, Rabia Emhamed Al Mamlook, and Arshad Jamal
SAGE Publications
The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.
M.M. Samy, Rabia Emhamed Almamlook, Heba I. Elkhouly, and Shimaa Barakat
Elsevier BV
Odey Alshboul, Ali Shehadeh, Ghassan Almasabha, Rabia Emhamed Al Mamlook, and Ali Saeed Almuflih
MDPI AG
As a fundamental feature of green building cost forecasting, external support is crucial. However, minimal research efforts have been directed to developing practical models for determining the impact of external public and private support on green construction projects’ costs. To fill the gap, the current research aims to develop a mathematical model to explore the balance of supply and demand under deflationary conditions for external green construction support and the accompanying spending adjustment processes. The most current datasets from 3578 green projects across Northern America were collected, pre-processed, analyzed, post-processed, and evaluated via cutting-edge machine learning (ML) techniques to retrieve the deep parameters affecting the green construction cost prediction process. According to the findings, public and private investments in green construction are projected to decrease the cost of green buildings. Furthermore, the impact of public and private investment on green construction cost reduction during deflationary periods is more significant than its influence during inflation. As a result, decision-makers may utilize the suggested model to monitor and evaluate the yearly optimal external investment in green building construction.
Neda Halalsheh, Odey Alshboul, Ali Shehadeh, Rabia Emhamed Al Mamlook, Amani Al-Othman, Muhammad Tawalbeh, Ali Saeed Almuflih, and Charalambos Papelis
MDPI AG
This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite [Se(IV)] removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite’s ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of Se (IV), and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, XGBoost, LightGBM, and CatBoost models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The CatBoost model provided the best fit for a breakthrough prediction with a coefficient of determination R2 equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model’s accurateness. A feature importance assessment indicated that Se (IV) initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed Se (IV) sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation.
Odey Alshboul, Ghassan Almasabha, Ali Shehadeh, Rabia Emhamed Al Mamlook, Ali Saeed Almuflih, and Naif Almakayeel
MDPI AG
The influence of concrete mix properties on the shear strength of slender structured concrete beams without stirrups (SRCB-WS) is a widespread point of contention. Over the past six decades, the shear strength of SRCB-WS has been studied extensively in both experimental and theoretical contexts. The most recent version of the ACI 318-19 building code requirements updated the shear strength equation for SRCB-WS by factoring in the macroeconomic factors and the contribution of the longitudinal steel structural ratio. However, the updated equation still does not consider the effect of the shear span ratio (a/d) and the yield stress of longitudinal steel rebars (Fy). Therefore, this study investigates the importance of the most significant potential variables on the shear strength of SRCB-WS to help develop a gene expression-based model to estimate the shear strength of SRCB-WS. A database of 784 specimens was used from the literature for training and testing the proposed gene expression algorithm for forecasting the shear strength of SRCB-WS. The collected datasets are comprehensive, wherein all considered concrete properties were considered over the previous 68 years. The performance of the suggested algorithm versus the ACI 318-19 equation was statistically evaluated using various measures, such as root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. The evaluation results revealed the superior performance of the proposed model over the current ACI 318-19 equation. In addition, the proposed model is more comprehensive and considers additional variables, including the effect of the shear span ratio and the yield stress of longitudinal steel rebars. The developed model reflects the power of employing gene expression algorithms to design reinforced concrete elements with high accuracy.
Odey Alshboul, Ali Shehadeh, Rabia Emhamed Al Mamlook, Ghassan Almasabha, Ali Saeed Almuflih, and Saleh Y. Alghamdi
MDPI AG
Highway construction projects are important for financial and social development in the United States. Such types of construction are usually accompanied by construction delay, causing liquidated damages (LDs) as a contractual provision are vital in construction agreements. Accurate quantification of LDs is essential for contract parties to avoid legal disputes and unfair provisions due to the lack of appropriate documentation. This paper effort sought to develop an ensemble machine learning technique (EMLT) that combines algorithms of the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), k-Nearest Neighbor (kNN), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), and Decision Tree (DT) for the prediction of LDs in highway construction projects. Key attributes are identified and examined to predict the interrelated correlations among the influential features to develop accurate forecast models to assess the impact of each delay factor. Various machine-learning-based models were developed, where the different modeling outputs were analyzed and compared. Four performance matrices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) were used to assess and evaluate the accuracy of the implemented machine learning (ML) algorithms. The prediction outputs implied that the developed EMLT model has shown better performance compared to other ML-based models, where it has the highest accuracy of 0.997, compared to the DT, kNN, CatBoost, XGBoost, LightGBM, and ANN with an accuracy of 0.989, 0.988, 0.986, 0.975, 0.873, and 0.689, respectively. Thus, the findings of this research designate that the EMLT model can be used as an effective administrative decision adding tool for forecasting the LDs. As a result, this paper emphasizes ML’s potential to aid in the advancement of computerization as a comprehensible subject of investigation within highway building projects.
Ali Saeed Almuflih, Khushi Popat, Viral V. Kapdia, Mohamed Rafik Noor Mohamed Qureshi, Naif Almakayeel, and Rabia Emhamed Al Mamlook
MDPI AG
Across the globe, wireless devices with Internet facilities such as smartphones and tablets have become essential assets for communication and entertainment alike for everyday life for millions of people, which increases the network traffic and the demand for low-latency communication networks. The fourth-generation (4G)/long-term evolution (LTE)/ fifth-generation (5G) communication technology offers higher bandwidth and low latency services, but resource utilization and resiliency cannot be achieved, as transmission control protocol (TCP) is the most common choice for most of the state-of-art applications for the transport layer. An extension of TCP—multipath TCP (MPTCP)—offers higher bandwidth, resiliency, and stable connectivity by offering bandwidth aggregation and smooth handover among multiple paths. However, MPTCP uses multiple disjointed paths for communication to offer multiple benefits. A breach in the security of one of the paths may have a negative effect on the overall performance, fault-tolerance, robustness, and quality of service (QoS). In this paper, the research focuses on how MPTCP options such as MP_CAPABLE, ADD_ADDR, etc., can be used to exploit the vulnerabilities to launch various attacks such as session hijacking, traffic diversion, etc., to compromise the availability, confidentiality, and integrity of the data and network. The probable security solutions for securing MPTCP connections are analyzed, and the secure key exchange model for MPTCP (SKEXMTCP) based on identity-based encryption (IBE) is proposed and implemented. The parameters exchanged during the initial handshake are encrypted using IBE to prevent off-path attacks by removing the requirement for key exchange before communication establishment by allowing the use of arbitrary strings as a public key for encryption. The experiments were performed with IBE and an elliptic curve cryptosystem (ECC), which show that IBE performs better, as it does not need to generate keys while applying encryption. The experimental evaluation of SKEXMTCP in terms of security and performance is carried out and compared with existing solutions.
Abdulla I. M. Almadi, Rabia Emhamed Al Mamlook, Yahya Almarhabi, Irfan Ullah, Arshad Jamal, and Nishantha Bandara
MDPI AG
The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.