Efficient Spam Email Detection Model based on Dynamic Embedding with Deep Learning Classification

Salam; Zahraa Ch.; Hasanen; Fahad Ghalib

doi:https://doi.org/10.54216/JISIoT.170205

International Journal of BIM and Engineering Science | 2025

AHMAD (PBUH) Model: A Lean Transformational Framework for Organizational Change – Insights from the Leadership of Prophet Muhammad (PBUH)

The dynamic business world of today has introduced a necessity of efficient models of organizational change that are adaptive and ethical in nature. Organizations have been challenged by the necessity of innovative models of change management based on the ethical leadership dimension and culture awareness. The aim of this study is to examine the AHMAD Model as a change model for organizational transformation, drawing on the leadership behavior of Prophet Muhammad (PBUH). It would like to explore how applicable the model is in contemporary organizational contexts and if it can bring together ethical leadership and effective change management practices. Comparative analysis of AHMAD Model earlier Islamic scholarship and recent organizational transformation theories by Kotter's 8-Step Change Model, Lewin's Change Theory, and Agile methodologies will be employed. Adaptability, holism, motivation, accountability, and discipline are the five key pillars of the AHMAD Model. The acronym is "AHMAD" as pronounced by the followers of the Holy Prophet Muhammad (PBUH); it encourages ethical leadership and further provides participative decision-making, reactiveness as three important ingredients of successful change projects and effective communication. The AHMAD Model can serve as a template for organizations that strive to embark on changing initiatives founded on high moral and people-centered principles. Driven by such values, these organizations will be capable of triggering a process that humanizes the workplace and creates a teamwork-based work environment and more plural. This paper fills an important gap in literature by connecting religious-influenced leadership frameworks with classical organizational expectations. This paper offers a new paradigm of strategic leadership based on the Prophet's practices where ethics supersede modern management. The model gives an organization a change management process that is methodical in approach but moral in nature. Future studies can be done on how AHMAD Model can be implemented in different cultures and the impact of that on organizational performance. Similarly, research on long-term effects of the implementation of this model on organizational culture and employee morale would be useful.

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Ahmed Fahim Elgendi mail -

Ghada Moukhtar Elgendi mail -

Nael Zabel mail

link https://doi.org/10.54216/IJBES.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2025

A Reconsideration of the Mathematical Frameworks for Fuzzy and Neutrosophic Supply Chain Management (FSCM and NSCM)

Numerous frameworks have been developed to address uncertainty in various domains. Among the most prominent are Fuzzy Sets,26 Rough Sets,15 Intuitionistic Fuzzy Sets,4 Hesitant Fuzzy Sets,23 Neutrosophic Sets,3 as well as other emerging theories that continue to be actively explored. Supply Chain Management (SCM) involves planning, coordinating, and optimizing the flow of goods, information, and finances across the entire supply network.9, 16 In this paper, we introduce rigorous Mathematical Frameworks for Fuzzy Supply Chain Management (FSCM) and Neutrosophic Supply Chain Management (NSCM). We hope that these formulations will foster further advances in both supply chain optimization and the development of Fuzzy Set and Neutrosophic Set-based models.

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Takaaki Fujita mail

link https://doi.org/10.54216/JNFS.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

Hessian matrix for testing the convexity and concavity of the objective function in nonlinear programming and neutrosophic nonlinear programming problems

Mathematical examples rely on constructing mathematical models consisting of an objective function and constraints. These models may be linear, nonlinear, or otherwise. The objective function is either a maximization function or a minimization function for a given quantity. Nonlinear programming constitutes an important and fundamental part of operations research and is more comprehensive than linear programming. Therefore, researchers have focused on presenting studies that help find the optimal solution to these problems. Most of these studies have focused on the importance of knowing the type of objective function—whether it is convex or concave—because this knowledge helps determine the type of maximum value we obtain when studying a nonlinear programming problem. The Hessian matrix was used for this purpose. In this research, we will present the most important concepts that can be used when determining the type of maximum value for a nonlinear programming problem, as mentioned in some classic references. We will then reformulate them using the concepts of neutrosophic logic.

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Maissam Jdid mail

link https://doi.org/10.54216/PAMDA.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Assessing Readiness for mHealth Adoption in Coronary Artery Disease Management: Iraq Case Study

Chronic diseases (CDs) have become as significant as communicable diseases due to their rising mortality rates and long-term effects. Coronary artery disease (CAD), one of the most common NCDs, is increasingly concerning due to its impact on both death rates and overall health. Managing CAD typically requires professional care and lifestyle changes, which may be inaccessible to some patients due to financial constraints or difficulty in modifying their habits. However, remote health solutions, like mobile applications, could help CAD patients improve their condition and lower risks. In Iraq, the willingness of CAD patients to use mHealth apps has not been explored. This study examines existing mHealth readiness models and incorporates additional factors that consider the needs of CAD patients and the Iraqi context. This will be achieved by adapting a questionnaire based on expert feedback and distributing it to CAD patients in Iraq.

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Iman Kadhim Ajlan mail -

Ahmad Fadhil Yusof mail -

Fahad Taha AL-Dhief mail -

Nurhizam Saif mail -

Ali Hashim Abbas mail

link https://doi.org/10.54216/FPA.200202

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Detection of Fake News on Twitter Using a Novel Data-Mining Algorithm

Social media has supplanted conventional media as one of the most important venues for information exchange. Because of the internet's accessibility and simplicity, news on community media tends to spread quicker and simpler than a conventional news source. Still, not all of the information shared on ‘social media’ is true and/or comes from untrustworthy sources. Fake news may readily be manufactured and disseminated throughout ‘social media’, and this counterfeit news has the potential to mislead or misinform readers. Though several physical fact-inspection websites have been built to determine if the news is reliable, they cannot keep up with the amount of rapidly circulated internet information, particularly on social media. Twitter, being one of the most well-known continuing news sources, also happens to be one of the most dominating news disseminating media. Topic models facilitate the detection of the most relevant vocabulary and concept within a text corpus. This paper proposes a model for recognizing fake news messages from twitter posts using a novel data-mining algorithm. Here initially the twitter dataset is collected preprocessing is done by using word embedding. ‘Term Frequency Inverse Document Frequency ‘(TF-IDF)’ and Latent Semantic Analysis (LSA) do feature extraction. Feature selection is based on the Adaptive Whale Optimized Wrapper (AWOW) method. We proposed Fine-tuned Weighted Probabilistic Bayesian Neural Network (FWP-BNN) for the classification of the normal and the fake news. The proposed method is compared with existing approaches and the metrics are evaluated. The efficacy of the suggested technique in recognizing fake tweets is shown by test findings on a large miscellaneous events dataset.

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Dena Kadhim Muhsen mail -

Azhar F. Al-zubidi mail -

Gheed Tawfeeq Waleed mail

link https://doi.org/10.54216/FPA.200203

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Smart System to Enhance Medical Examinations Data Analysis for University Students

Mobile health (mHealth) applications have revolutionized the healthcare sector by providing innovative solutions for patient monitoring, health tracking, and medical consultation. These applications leverage the widespread use of smartphones to deliver health services that are accessible, affordable, and efficient. Research indicates that mHealth technologies significantly improve healthcare service delivery processes, enhancing patient outcomes and healthcare management. Furthermore, the functionality of mobile apps in health interventions has been systematically reviewed, showing positive impacts on user engagement and behavior change. This study explores the development and implementation of a medical screening application for incoming university students using an Android platform. The application is designed to perform basic health check-ups, including monitoring and assessing general health status, and providing recommendations for further medical consultation if necessary. The application includes several modules: blood test analysis, vision test, hearing test, and speech test. By leveraging advancements in mobile health (mHealth) technologies and artificial intelligence, the application offers a cost-effective and scalable solution for university health services. This paper highlights the potential benefits, challenges, and future implications of deploying mobile health screening applications in educational institutions.

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W. K. ElSaid mail -

Basma E. Seif mail -

Ahmed Abd El-Badie Abd Allah Kamel mail

link https://doi.org/10.54216/JISIoT.170201

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Cloud-Enabled Assistive Robotics System for Secure and Interoperable Internet of Medical Things Ubiquitous

The current landscape of assistive robotics in digital healthcare faces significant challenges, particularly in ubiquitous environments. Existing systems need the necessary infrastructure to monitor and process data, hindering their effectiveness. Moreover, the arrangement and management of IoMT (Internet of Medical Things) data across various nodes present a new challenge, further complicating the deployment of assistive digital healthcare solutions. We propose a novel Assistive Robotics-Based Digital Healthcare System within a Ubiquitous IoMT Cloud network to address these challenges. This system supports various medical care applications, including digital wheelchair location tracking, artificial limbs, and remote surgical operations across different hospitals. Our contributions are as follows: We introduce the ARDTS (Assistive Robot Digital Healthcare Task Scheduling) algorithm to efficiently process data across multiple nodes; ensuring secure data handling based on the systems security protocols. We implement a convolutional neural network for data standardization, converting non-linear data into a linear form to predict relevant features accurately. We develop a socket-enabled cross-platform system to enhance interoperability for seamless data sharing and processing.

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Ahmed Ali Alhammad mail -

Israa Badr Al-Mashhadani mail -

Marwa K. Farhan mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/JISIoT.170202

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Countermeasure to Black Hole Attack in MANET Wireless Network Security

Establishing basic network connectivity by mobile devices depends on wireless communication during infrastructure downtime. Nodes within these networks use routing protocols to send data packets between one another until the packets reach their endpoint. The protocols have security weaknesses that permit harmful nodes to stage assaults on the network. Network disruption occurs through the Black Hole Attack, which blocks all data packets from getting to their destinations by intercepting them during their transmission. Security systems that detect intruders executing these attacks protect against the security challenge. A simulated wireless ad-hoc network scenario is the basis for assessing how well response systems fight against the Black Hole attack. In this paper, the Anti-Black Hole Ad hoc On-Demand Distance Vector (ABAODV) is the proposed solution to combat the Black Hole attack effects. During the experiments, ABAODV's modified AODV version and standard AODV protocol underwent performance measurements through throughput, Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), and Normalized Routing Load (NRL) while operating in Black Hole attack environments and without such attacks. Through its NS-2 implementation, ABAODV achieved 99% effectiveness in combating the Black Hole attack. The entire simulation was conducted on a Linux platform, including mobility generation, analysis, results presentation, and NS-2 simulation.

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Bahaa Kareem Mohammed mail -

Hayder Najm mail -

Mohammed Salih Mahdi mail -

Riyadh Rahef Nuiaa Alogaili mail -

Waleed Khaled mail

link https://doi.org/10.54216/JISIoT.170203

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Comparative Analysis of Machine Learning Models for Predictive Healthcare in Chronic Disease Management

This study investigates the application of AI-powered predictive analytics in chronic disease management, focusing on the most effective machine learning models for predicting patient risk and optimizing healthcare interventions, like Random Forest, Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting were evaluated using a dataset of 10,000 patient records. The models were assessed based on their accuracy, interpretability, and clinical relevance. Gradient Boosting attained the highest predictive accuracy, with an AUC of 0.89. Random Forest followed closely with an AUC of 0.85, offering a good balance of accuracy and interpretability. Linear Regression, with an AUC of 0.75, demonstrated the trade-offs between simplicity and model performance, while SVM and KNN performed with AUCs of 0.82 and 0.78, respectively, with SVM being robust but facing scalability challenges and KNN being less practical for large datasets. These AI models improve patient outcomes, decrease healthcare costs, and optimize healthcare delivery.

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Dena Kadhim Muhsen mail -

Bushra Fuaad Khmas‎‎ mail -

Amjed Abbas Ahmed mail -

Ahmed T. Sadiq mail

link https://doi.org/10.54216/JISIoT.170204

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Efficient Spam Email Detection Model based on Dynamic Embedding with Deep Learning Classification

One of the major concerns when transitioning emails is the potential influx of unsolicited and unwanted spam emails. These unwanted emails can clog inboxes, causing recipients to overlook important messages and opportunities. To ensure security and avoid the destructive and dangerous effect of these spam emails, machine learning and deep learning methods have been conducted to design spam detection models. In this work, a combination of embedding models and multi-layer artificial neural networks as deep learning classification models is utilized in order to introduce an approach to spam detection. The proposed classifier leverages the Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, applied to the Enron-Spam dataset, offering a noteworthy technique for considerable spam detection. Experimental results demonstrate that the proposed spam detection model achieved a 99% recall rate for detecting spam emails. Notably, this model is a step forward in generality and improving the efficiency of spam detection. It presents a good attempt at presenting a solution for detecting spam emails and fake text within communication environments.

groups

Salam Al-augby mail -

Zahraa Ch. Oleiwi mail -

Hasanen Alyasiri mail -

Fahad Ghalib Abdulkadhim mail

link https://doi.org/10.54216/JISIoT.170205

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new