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    A Data-Driven Approach to MBTI Personality Classification: Insights from Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dael, Fares A.; Maidanova, Symbat; Shayea, Ibraheem; Abitova, Gulnara; Seraly, Aigul
    The Myers-Briggs Type Indicator (MBTI) is one of the most widely recognized psychological tools for categorizing personality types, often used in various professional and personal development contexts. This study presents a data-driven approach to MBTI personality classification using a range of machine learning models. By leveraging a dataset comprising user responses and linguistic patterns, we aim to predict the MBTI personality types with greater accuracy and reliability. Various models, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, were evaluated to determine their effectiveness in classifying the 16 MBTI types. Our findings reveal that machine learning models can significantly enhance the predictive accuracy of MBTI classification compared to traditional methods. The Random Forest model, in particular, demonstrated superior performance, achieving an accuracy of [insert specific accuracy here] across the dataset. We also explore the importance of feature selection and data preprocessing in improving model outcomes, highlighting key features that contribute to personality type prediction. The results of this study suggest that a data-driven approach, combined with machine learning techniques, provides a promising avenue for more nuanced and accurate MBTI personality assessments. This approach not only enhances our understanding of personality prediction but also offers practical implications for applications in psychology, human resources, and personal development. © 2024 IEEE.
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    AirWave: Enhancing UAV connectivity in cellular networks through an integrated simulation framework
    (Institute of Electrical and Electronics Engineers Inc., 2024) Zor, Emirhan; Taskiran, Yusuf; Dael, Fares Abdulhafidh Derhem; Shayea, Ibraheem; Rzayeva, Leila; Syzdykova, Zuleikha
    This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-depth investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications. © 2024 IEEE.
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    Comparative effectiveness of classification algorithms in predicting diabetes
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dael, Fares Abdulhafidh Derhem ; Mareyev, D.; Shayea, Ibraheem; Kulniyazova Korlan, S.; Abitova, Gulnara
    Diabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs. © 2024 IEEE.
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    Comparative study of K-NN algorithm for transportation mode detection using mobile phone sensor data
    (American Institute of Physics, 2024) Erkil, Hasan; Aktl, Ilknur; Dael, Fares Abdulhafidh Derhem ; Shayea, Ibraheem; El-Saleh, Ayman A.
    This paper examines the use of mobile phone sensor data to identify transportation mode detection using the K-nearest Neighbor algorithm. The model tries to recognize the walking, still walking, Bus, Train, and Car transportation modes. One of the normalization methods, such as the Min-Max normalization or the Z-Score Normalization, is implemented to pre-process the data. It uses four distance methods such as Euclidian, Manhattan, Chebyshev, and Minkowski as distance calculation mechanisms. Based on the highest accuracy result, the model is selected. The analysis also concluded that the optimal model has the highest accuracy, which has validated the results achieved through extensive normalization methods and the choosing of the most appropriate distance functions. As a result, the outcomes of the research have further indicated the importance of selecting the appropriate normalization techniques and distance functions on the accuracy of the model used in transportation detection. Additionally, the results have also provided critical additional knowledge in the development and formation of Intelligent Transportation Systems (ITS). © 2024 Author(s).
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    Data-driven strategies for improving railway ticket demand forecasting accuracy
    (Institute of Electrical and Electronics Engineers Inc., 2024) Boltaikhanova, Tomiris; Dael, Fares Abdulhafidh Derhem ; Shayea, Ibraheem; Leila, Rzayeva
    The accurate prediction of railway ticket demand is vital for effective operational planning and resource management in the transportation sector. This study investigates various time series analysis techniques, including ARIMA, SARIMAX, and neural networks such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to forecast railway ticket demand. Utilizing an extensive dataset of ticket sales spanning several years, we trained and validated these models, evaluating their performance through key metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Demand patterns were represented using Origin-Destination (OD) matrices, where the CNN model was employed to predict the entire OD matrix, while the other models focused on individual OD pairs. The findings reveal that the CNN model outperforms ARIMA, SARIMAX, and LSTM in terms of prediction accuracy, offering a more reliable approach for forecasting demand in railway networks. This study underscores the importance of data-driven strategies in enhancing the precision of demand forecasting, thereby contributing to more informed decision-making and optimized railway operations. © 2024 IEEE.
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    Detecting Questions in Online Communities: A Machine Learning Approach
    (Institute of Electrical and Electronics Engineers Inc., 2024) Omarova, Dilnaz; Dael, Fares A.; Shayea, Ibraheem; Abitova, Gulnara; Sailaukhanov, Eldos
    The proliferation of online forums and communities has greatly facilitated knowledge sharing and user support but has also introduced the significant challenge of managing redundant and semantically similar questions. Traditional keyword-based methods have proven inadequate in addressing this issue due to the inherent complexities of natural language, where the same idea can be expressed in numerous ways. This study investigates the use of advanced machine learning algorithms - Logistic Regression, Random Forest, and Gradient Boosting (XGBoost) - to detect semantically similar questions. By employing the Quora Question Pairs dataset, the performance of these models is evaluated using metrics such as accuracy, precision, recall, and F1-score. This research not only provides a comparative analysis of these machine learning models but also suggests a framework for improving information retrieval and user experience in online forums. The study highlights the potential for future integration of deep learning models and advanced semantic understanding techniques to further enhance the detection of semantically similar questions. © 2024 IEEE.
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    Machine learning approaches to forecasting car prices in the secondary market
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dael, Fares Abdulhafidh Derhem ; Talipov, Daulet; Shayea, Ibraheem; Kamshat, Asmaganbetova
    This study investigates the use of machine learning techniques to predict car prices in the secondary market. Utilizing a comprehensive dataset of used car listings from the United Kingdom, we applied advanced machine learning models, including Random Forest and Neural Networks, to understand the factors influencing car prices and to develop accurate predictive models. Our analysis identified engine size and registration year as key determinants of car prices. The Neural Network model provided highly accurate predictions, closely matching actual prices in the majority of cases. Visual representations of feature importance and prediction errors further elucidate the model's effectiveness. This research demonstrates that machine learning can significantly enhance the accuracy of price predictions in the used car market, offering valuable insights for consumers, dealers, and policymakers. By leveraging these predictive models, stakeholders can make more informed decisions, optimize pricing strategies, and better understand market dynamics. © 2024 IEEE.
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    Performance evaluation of routing protocols in vehicular Ad-Hoc Networks for highway scenarios
    (Institute of Electrical and Electronics Engineers Inc., 2024) Dael, Fares Abdulhafidh Derhem; Amran, Mohammed A.; Shayea, Ibraheem; Rzayeva, Leila
    This study assesses the efficacy of three routing protocols, namely Ad-hoc on-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Direct Sequence Distance Vector (DSDV), in Vehicular Ad-Hoc Networks (VANETs) utilizing the Network Simulator-2 (NS-2). The research evaluates the impact of different speeds and node densities on network efficiency in highway settings that simulate real-world scenarios. This assessment focuses on throughput, packet delivery ratio (PDR), end-to-end delay, and normalized routing overhead. The simulation findings demonstrate that DSR outperforms in terms of PDR, throughput, and routing overhead as the node density increases. On the other hand, DSDV performs exceptionally well in offering greater packet delivery ratios and throughput, with reduced delays, as mobility increases. These findings emphasize the need to select the appropriate routing protocol for VANETs, considering specific deployment factors such as node density and mobility. The study not only enhances comprehension of the strengths and weaknesses of AODV, DSR, and DSDV in VANETs but also guides network designers and researchers to optimize protocol performance for enhanced network stability and efficiency. Potential areas for future research include investigating the implementation of hybrid protocols to optimize the performance of VANETs. © 2024 IEEE.