Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique

Rahul Sharma; Shiv Shakti Shrivastava; Aditi Sharma

doi:https://doi.org/10.54216/JISIoT.100203

International Journal of Neutrosophic Science | 2023

On Radical of Neutrosophic Primary Submodule

In this paper, we introduce and study the concept of neutrosophic submodules and neutrosophic primary submodule with the help of the definition of a radical submodule, and we also study the properties of these submodules. Furthermore, homomorphic image and preimage of neutrosophic primary submodule are investigated.

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M. Vasuki mail -

P. Senthil Kumar mail -

Said Broumi mail -

N. Rajesh mail

link https://doi.org/10.54216/IJNS.220303

Volume & Issue

Vol. Volume 22 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2023

The Symbolic Plithogenic Complex Numbers

In this paper, we presented symbolic plithogenic complex numbers, and studied the arithmetic operations: addition, subtraction, multiplication and division. Also, we defined the conjugate, inverted and the absolute value of symbolic plithogenic complex numbers, the theories related to the conjugate of symbolic plithogenic complex numbers are proved.

groups

Yaser Ahmad Alhasan mail -

Raja Abdullah Abdulfatah mail

link https://doi.org/10.54216/IJNS.220211

Volume & Issue

Vol. Volume 22 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Enhancing Mushroom Detection Using One-Dimensional Convolutional Neural Networks

The classification of mushrooms as either deadly or edible stays a important challenge due to their similar appearances, which can lead to fatal poisonings. The primary difficulty lies in identifying complex patterns in mushroom appearances, such as cap shape, color, and gill structure, which complicate accurate classification. Traditional approaches and even some machine learning (ML) models fail to capture these subtle but important distinctions, leading to misclassifications. To address this issue, this paper proposed a One-Dimensional Convolutional Neural Network (1D-CNN) approach aimed at improving the accurate of mushroom classification. By effectively recognizing complex patterns in the mushroom data set, the proposed approach greatly improves classification accuracy. The model performance evaluated utilizing Precision, Accuracy, Recall, and F1-Score that achieved high scores of 100% across all metrics. These results highlight the strength of deep learning (DL) method, specifically 1D-CNNs, in recognizing with learning complex data patterns. This shows a clear advancement over traditional ML methods and ensemble techniques, establishing the 1D-CNN as a highly reliable tool for mushroom classification that can help reduce mushroom poisoning incidents.

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Jabbar Abed Eleiwy mail -

Mustafa Muslih Shwaysh mail -

Ahmed Mubdir Kadhim mail -

Ahmed Adil Nafea mail -

Aythem Khairi Kareem mail -

Mustafa Nadhim Owaid mail

link https://doi.org/10.54216/FPA.200201

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2023

Algorithm for possibility interval-valued neutrosophic soft decision-making based on distance measures settings

Soft set(SS) is one of the soft computing techniques that plays an important role in addressing the hiddenness and uncertainty associated with uncertain data. In other hand the idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this high-performance IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given distance measures (DM) between two PIVNSSs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

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Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Noor Kareem Assi Halaf mail -

Mona Aladil mail -

Maha M. Rasheed mail

link https://doi.org/10.54216/IJNS.220304

Volume & Issue

Vol. Volume 22 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2023

Algorithm for decision-making based on similarity measures of possibility interval-valued neutrosophic soft setting settings

hand the idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this highperformance IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given similarity measure (SM) between two PIVNSSs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

groups

Yousef Al-Qudah mail -

Faisal Al-Sharqi mail

link https://doi.org/10.54216/IJNS.220305

Volume & Issue

Vol. Volume 22 / Iss. Issue 3

Details open_in_new

Fusion: Practice and Applications | 2023

Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies

The Industrial Internet of Things (IIoT) has ushered in a new era of connectivity and intelligence in industrial settings. At the heart of this transformative landscape lies Fog Computing, a distributed computing paradigm that brings processing power and intelligence closer to the edge of industrial networks. This paper provides a comprehensive survey of Fog Computing's pivotal role in IIoT, elucidating its significance, challenges, emerging trends, and strategies for successful implementation. We delve into the challenges that industrial environments present for Fog Computing, encompassing issues such as scalability, cybersecurity, data management, and interoperability. Strategies for mitigating these challenges are explored, ranging from efficient resource management to robust cybersecurity measures. Furthermore, we investigate recent developments and innovations in Fog Computing, including the integration of Edge AI, 5G networks, and hybrid cloud-fog architectures, shaping the landscape of IIoT. Promising research areas and opportunities are identified, with a focus on optimizing edge AI, secure data sharing, and sustainable Fog Computing practices.

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Andres Leon Yacelga mail -

Nelson B. Arevalo mail -

Luis Albarracin Zambrano mail

link https://doi.org/10.54216/FPA.130208

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Internet of Things based Predictive Crop Yield Analysis: A Distributed Approach

The intersection of IoT technology and machine learning has ushered in a new era of precision agriculture, offering innovative solutions to the pressing challenges of food security and environmental sustainability. This paper presents a comprehensive study on the integration of IoT sensors and machine learning techniques for crop yield prediction, with a focus on the ten most consumed crops worldwide. Leveraging a wealth of historical data encompassing environmental variables, pest conditions, and crop-specific attributes collected by IoT sensors, we develop and rigorously evaluate a predictive model employing gradient-boosting regressors. Our findings reveal that the proposed model excels in capturing the intricate relationships between IoT sensor data and crop yield predictions, outperforming established ML regressors in a series of comprehensive experimental comparisons. These results underscore the potential of data-driven decision-making in agriculture, equipping farmers and policymakers with tools to optimize resource allocation, risk management, and sustainable farming practices. In the context of a growing global population and changing climate, the insights from this research hold significant promise for transforming precision agriculture and enhancing global food production.

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Fausto Vizcaino Naranjo mail -

Fredy Canizares Galarza mail -

Edmundo Jalon Arias mail

link https://doi.org/10.54216/FPA.130209

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Predictive Energy Management in Internet of Things: Optimization of Smart Buildings for Energy Efficiency

As energy efficiency and sustainability become paramount in the face of growing urbanization and environmental concerns, predictive energy management in smart buildings has emerged as a promising avenue for mitigating energy consumption and optimizing resource utilization. In this paper, we investigate the application of advanced machine learning techniques, particularly a multi-layer Long Short-Term Memory (LSTM) model, within the framework of the Internet of Things (IoT), to predict and manage energy consumption. We rigorously evaluate our approach against a suite of machine learning baselines, including Linear Regression, Random Forest, Support Vector Machine, and Gradient Boosting, utilizing a comprehensive dataset encompassing power consumption data from smart home appliances and associated weather variables.  Our experimental results demonstrate the superior predictive capabilities of the LSTM model, showcasing its ability to outperform traditional machine learning baselines across various metrics, including Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings underscore the potential of deep learning models in capturing intricate temporal dependencies within energy consumption data, contributing to improved energy efficiency, cost savings, and environmental sustainability in smart building environments. The integration of predictive energy management models into IoT-enabled smart buildings holds the promise of a more intelligent and sustainable future in urban development and resource management.

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Dionisio Ponce Ruiz mail -

Rita Azucena D. Vasquez mail -

Bolivar Villalta Jadan mail

link https://doi.org/10.54216/JISIoT.100201

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Distributed Facial Recognition Facial Recognition in Visual Internet of Things (VIoT) An Intelligent Approach

In the rapidly evolving landscape of the Visual Internet of Things (VIoT), this paper presents a pioneering approach to distributed facial expression recognition—an intelligent system that holds transformative potential for security, human-computer interaction, and personalized services. Our journey unfolds with the development of the Light Vision Transformer (LVT) model, specifically engineered to operate on the resource-constrained edges of the VIoT network. Differentially private federated training ensures both the model's prowess and the preservation of user privacy. Through meticulous experimental evaluations, we validate the effectiveness and efficiency of our approach, shedding light on its scalability and ethical implications. This work is more than a technical endeavor; it symbolizes a commitment to responsible AI, balancing innovation with the preservation of individual rights. Our findings resonate beyond facial expression recognition, serving as a beacon for the VIoT community to explore the dynamic interplay between distributed computing, edge intelligence, and ethical considerations. As we stride towards a more connected and responsive world, this research paves the way for continued exploration, propelling VIoT technology towards a future that is both intelligent and ethically attuned.

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Luis Freire Lescano mail -

Marcos Lalama Flores mail -

Maria Pico Pico mail

link https://doi.org/10.54216/JISIoT.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique

Data analysis is an essential component of decision support in various industries that includes industrial and educational institutions. This research proposes Data Mining (DM) techniques to improve the efficiency of higher education (HE) institutions. DM has a substantial impact on different higher education activities including student performances, management of student’s life cycle, selection of courses, monitoring of retention rate, grants & funds management by using technique’s such as clustering, decision trees (DT), and association. Educational Data Mining (EDM) is an interdisciplinary study topic that focuses on getting DM to the fields of education by leveraging methods from (ML) statistics, (DM), and (DA) to get important insights from educational sets of data. EDM is critical in transforming raw data into useful information, allowing for a greater knowledge of students and their academic settings, as well as promoting better teacher assistance and ESD (Educational System Decisions). The study's goal is to provide a complete overview of EDM (Educational Data Mining), highlighting its various applications and benefits in the context of higher education.

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Rahul Sharma mail -

Shiv Shakti Shrivastava mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new