Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning

Faris H.; Mahmoud; Basant; Karim; Ahmed Mohamed

doi:https://doi.org/10.54216/JAIM.070102

International Journal of Neutrosophic Science | 2024

Investigating the Impact of Artificial Intelligence on Digital Marketing Tactics Strategies Using Neutrosophic Set

The burgeoning proliferation of Artificial Intelligence (AI) technologies has engendered a transformative shift in various industries, and digital marketing is not an exception to this trend. The thrust of this paper is to explore, analyze, and conceptualize the multi-dimensional impact of AI on digital marketing strategies using Neutrosophic set. By employing statistical mechanics and stochastic models, we aim to delineate the underlying mechanisms that facilitate the operational synergy between AI algorithms and marketing frameworks in the light of Neutrosophic analysis. We invoke the concept of AI-Enabled Marketing Efficiency (AIME), which we define as AIME =(ROI{AI} - ROI{Traditional} )/(Time{AI}  ) , to assess the quantitative aspects of this interaction. Our empirical findings suggest that AI integration could enhance marketing campaign effectiveness by approximately 27% (p < 0.05) while reducing human-led execution time by 33%. We further discuss the ethical implications of AI-driven decision-making in digital marketing, such as the potential for reinforcing societal biases and the abuse of personal data. Artificial Intelligence has been an area of extensive research and development, permeating through diverse sectors including healthcare, finance, and now more prevalently, digital marketing. While the application of AI in digital marketing is not a nascent concept, the nuanced interplay between the two remains largely underexplored. We leverage neutrosophic set theory as a powerful analytical tool to investigate the transformative effects of Artificial Intelligence on various digital marketing tactics and strategies.

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Astanakulov Olim Tashtemirovich mail -

Muhammad Eid Balbaa mail -

Foziljonov Ibrohimjon mail -

Nilufar Batirova mail

link https://doi.org/10.54216/IJNS.230315

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

q-rung square root interval-valued neutrosophic sets with respect to aggregated operators using multiple attribute decision making

This paper introduces the concept of multiple attribute decision making (MADM) using q-rung square root interval valued neutrosophic sets (q-rung SRIVNS). The interval valued neutrosophic set (IVNS) and the q-rung square root neutrosophic set (q-rung SRNS) deals with the q-rung SRIVNS. The purpose of this article is to provide an analysis of several aggregating operations. In this article, we discuss a novel idea for the q-rung square root interval valued neutrosophic weighted averaging (q-rung SRIVNWA), q-rung ortho square root interval valued neutrosophic weighted geometric (q-rung SRIVNWG), generalized q-rung SRIVN weighted averaging (q-rung GSRIVNWA) and generalized q-rung SRIVN weighted geometric (q-rung GSRIVNWG). Using Euclidean distances and Hamming distances is illustrated with examples. These sets will be subjected to various algebraic operations in this communication. By doing this, models will be more accurate and will be closed to an integer q. The four most important factors for courier services in India are reliability, turnaround time, payment options, and tracking capabilities. Expert judgments and criteria will determine the most appropriate options. Furthermore, several proposed and current models are compared to demonstrate their reliability and utility. A fascinating and intriguing conclusion can be drawn from the study.

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C. Sivakumar mail -

Mowafaq Omar Al-Qadri mail -

Abdallah shihadeh mail -

Ahmed Atallah Alsaraireh mail -

Abdallah Al-Husban mail -

P. Maragatha Meenakshi mail -

N. Rajesh mail -

M. Palanikumar mail

link https://doi.org/10.54216/IJNS.230314

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

International Journal of BIM and Engineering Science | 2023

Impact of BIM Implementation in Engineering Projects in Syria

Engineering projects are often exposed to problems during their design and implementation, due to the lack of availability and accuracy of sufficient information, the failure to set an accurate timetable and budget, and the lack of cooperation and coordination between team members. Therefore, a technology was found to help solve these problems, which is building information modeling. It is one of the most important developments in the field of engineering in general, and the second generation of model design tools is the result of decades of research and development. Through it, one or more virtual models were created to support the design process through all its stages, and it is a simulation of the reality of the real project. In our research, we will discuss the most important problems that engineers face when working on a project, the importance of applying this technology and the benefits it has on the project and its parties, a simplified explanation of it, its dimensions, the stages that the project goes through, and the most important problems that we face when applying it. Building quantities will be studied in the traditional way and compared with quantities calculated through building information modeling technology.

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Lina Alshibly mail -

Mohammad shaban mail

link https://doi.org/10.54216/IJBES.070204

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Single Valued Neutrosophic Sets for Analysis Opinions of Customer in Waste Management

This study presents an analysis of consumer opinions on waste medicine management. The study explores consumers' concerns, preferences, and suggestions regarding correctly disposing unused or expired medications. The analysis shows the key points that emerged from consumer opinions, including environmental impact, public health and safety, accessibility and affordability, education and awareness, pharmaceutical industry responsibility, convenience and ease of disposal, privacy and confidentiality, community engagement, alternatives to disposal, extended producer responsibility, international collaboration, technology solutions, environmental stewardship, and government regulation and support. This study shows the importance of understanding consumer perspectives in developing effective waste medicine management strategies prioritizing environmental sustainability, public health, and consumer satisfaction. We used the multi-criteria decision-making (MCDM) methodology to deal with these criteria. We gathered 15 criteria concerned with waste medicine management. We used the DEMATEL method to show the criteria weights and relationships between criteria. The DEMATEL method is integrated with the single-valued neutrosophic set to deal with uncertain data. The results show the environmental impact has the most significant weight.

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Mona Gharib mail -

Ahmed E. Fakhry mail -

Ahmed M. Ali mail -

Ahmed Abdelhafeez mail -

Hussam Elbehiery mail

link https://doi.org/10.54216/IJNS.230316

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

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Ravindra Raman Cholla mail -

J. Anitha Josephine mail -

Priya N. mail -

C. Anuradha mail -

R. Kavitha mail

link https://doi.org/10.54216/JISIoT.110208

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems

Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches.

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R. Rajkumar mail -

Dınesh Valluru mail -

Siva Satya Sreedhar P. mail -

N. Ramshankar mail -

Sujatha S. mail -

Somasundaram R. mail -

M. Sudha mail -

S. Navaneethan mail

link https://doi.org/10.54216/JISIoT.110209

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Analyzing Social Media Data to Understand Long-Term Crisis Management Challenges of COVID-19

In the past three years, social media has had a significant impact on our lives, including crisis management. The COVID-19 pandemic highlighted the importance of accurate information and exposed the spread of false information. This paper specifically examines the COVID-19 crisis and analyzes relevant literature to provide insights for national authorities and organizations. Utilizing social media data for crisis management poses challenges due to its unstructured nature. To overcome this, the paper proposes a comprehensive method that addresses all aspects of long-term crisis management. This method relies on labeled and structured information for accurate sentiment analysis and classification. An automated approach is presented to annotate and classify tweet texts, reducing manual labeling and improving classifier accuracy. The framework involves generating topics using Latent Dirichlet Allocation (LDA) and ranking them with a new algorithm for data annotation. The labeled text is transformed into feature representation using Bert embeddings, which can be utilized in deep learning models for categorizing textual data. The primary aim of this paper is to offer valuable insights and resources to researchers studying crisis management through social media literature, with a specific focus on high-accuracy sentiment analysis.

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Ali S. Abed Al Sailawi mail -

Mohammad Reza Kangavari mail

link https://doi.org/10.54216/FPA.140219

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Integrated Neutrosophic methodology and Machine Learning Models for Cybersecurity Risk Assessment: An exploratory study

  Information technology security, or Cybersecurity, guards against hostile cyberattacks on computers, mobile devices, servers, electronic systems, and networks. Cybersecurity risks have been a significant concern for any vital digital infrastructure in recent years, and different online cyberattacks are also becoming a significant problem for society. Consequently, it's critical to adopt technology created to provide cybersecurity. However, one should consider the associated hazards while selecting among Cybersecurity systems. We have developed a multi-criteria decision-making (MCDM) approach based on a single-valued neutrosophic set (SVNS). This allows specialists more latitude in assessing the criteria and alternatives using language and overcoming uncertain information. The VIKOR is an MCDM methodology used to rank the other options. The VIKOR method is integrated with the neutrosophic set. There are 18 criteria, and 10 alternatives are used in this study. The sensitivity analysis and comparative analysis are conducted in this study. The sensitivity analysis results show the alternatives' rank is stable under different cases. The comparative analysis compares the suggested method with other MCDM methods. The comparative analysis shows the suggested method was effective compared with other MCDM methods. Machine learning methods predict the type of attack in Cybersecurity. This study uses Three machine learning methods: decision tree, random forest, and support vector machine.

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Ali Alqazzaz mail

link https://doi.org/10.54216/IJNS.230317

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

Fusion: Practice and Applications | 2024

Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT

The rapid adoption of the Internet of Things throughout healthcare and smart city construction has led to a rise in networked devices and security issues. This work suggests new techniques to improve IoT safety and maximise computing resources. We develop a complete security architecture integrating lightweight cryptography, blockchain, machine learning anomaly detection, and federated learning. We did so because we know that traditional security measures are inadequate for the Internet of Things. The lightweight cryptographic algorithm (LCA) provides efficient encryption and decryption, making it ideal for low-resource Internet of Things devices. Twenty processes comprise the LCA design. These operations include key generation, data encryption, digital signatures, and integrity checking. These procedures secure IoT data transfers. ADML detects anomalies in encrypted Internet of Things data using machine learning. This approach may identify security issues better. To keep up with data trends, this method extracts features, trains models, and updates them. Blockchain-based data integrity (BDI) is the third element. Blockchain ensures that Internet of Things data is reliable and full. BDI developed an immutable ledger solution to increase IoT data security and dependability. This data integrity system generates blocks, hashes, confirms blocks, and updates the blockchain. Fourth, FLIoT (Federated Learning for the Internet of Things) emphasises data privacy and collaborative model training across IoT devices. Foundation for the Internet of Things (FIoT) protocols and standards aim to increase IoT devices' collective intelligence while safeguarding users' privacy. It includes local model training, model aggregation, and the latest global model distribution. Our work also uses Secure Multi-party Computation (SMC) to analyse data more thoroughly and continuously, addressing online transaction cybersecurity issues. The framework outperforms the current state of the art in memory use, energy consumption, anomaly detection accuracy and precision, and encryption and decryption time. The "Hybrid Fusion Framework" combines lightweight cryptographic algorithms with federated learning, machine learning, blockchain technology, and other similar technologies to provide an effective, adaptable, and affordable IoT security solution.

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Abhishek Kumar mail -

Samta Jain Goyal mail -

Sumit Kumar mail -

Hitesh Kumar Sharma mail

link https://doi.org/10.54216/FPA.140220

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning

This paper deals with a pivotal part of educational data analytics, aiming to increase the accuracy and interpretability of student performance prediction models. The cornerstone of our method is the innovative application of binary waterwheel plant algorithm bWWPA in the feature selection. As we can see, an essential part of any model is the predicted values, which correctly define all the characteristics of this model. Practically, we begin with solid data pre-processing, which incorporates data cleaning and missing values, duplicate removal, and data transformation in order to get model input as optimally as possible. Preceding the application of bWWPA, we employ an ensemble of regression machine learning models. Set up a baseline for predictive capability, getting initial outcomes with an average Mean Squared Error (MSE) of 0.064. The following feature selection phase proceeds, showing the algorithm. Ability to recognize important elements and, as a result, improve model effectiveness and explain power. The comparative analyses after feature selection point to refined gains in the model, and the performance is reporting a lower MSE of 0.032 with the refined models. These findings, methodologically, add to student performance prediction. Accordingly, it emphasizes the decisive status of feature selection in improving models. The paper's significance extends to teachers, institutions, and researchers, giving insights into more precise and relevant student success-supporting interventions.

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Faris H. Rizk mail -

Mahmoud Elshabrawy mail -

Basant Sameh mail -

Karim Mohamed mail -

Ahmed Mohamed Zaki mail

link https://doi.org/10.54216/JAIM.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new