AI-Powered Election Insights: Predicting the 2024 Trump vs. Kamala Election Showdown with Machine Learning

Yazan; Mohammad; Malik; Aseel

doi:https://doi.org/10.54216/JISIoT.150116

Fusion: Practice and Applications | 2025

An Approach to Develop a Model to Detect the Phosphorus and Potassium Deficiency in Paddy Crop on Agriculture Farm Using DIP & ML

Excessive use of fertilizers harms the environment and disrupts plant habitats, while also raising costs for farmers. Proper timing and amounts of nutrients are crucial for plant health and environmental balance. The greenness of rice leaves indicates their chlorophyll and nutrient levels. Agronomy studies show rice plants need 10 nutrients, including primary ones like Nitrogen (N), Phosphorus (P), and Potassium (K), and secondary ones like Iron (Fe), Manganese (Mn), Copper (Cu), Zinc (Zn), Boron (B), Molybdenum (Mo), and Chlorine (Cl). Leaf nitrogen concentration (LNC) is highly correlated with chlorophyll content. There are several tools on LEAF+ to measure it, such as leaf color (LCC), SPAD, chlorophyll or nitrogen. Since these tools are cost-effective and not available to all farmers, LCC offers farmers the ability to estimate plant nitrogen needs in real-time for efficient fertilizer use and increased rice yield. Notable innovation in agriculture is the Leaf Color Chart (LCC), developed by Japanese experts. It measures chlorophyll levels in rice plants and aids in nitrogen management without harming the plant. Today, LCC is used globally to improve production efficiency and optimize nitrogen application rates. The remaining 2 major nutrients potassium and phosphorus can also be measured by experimentally expanding the available database of LCC, as has been done in the two models developed in this research paper.

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Mohammad Arif Ali Usmani mail -

Ausaf Ahmad mail

link https://doi.org/10.54216/FPA.180114

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

A Comprehensive Review of Arabic and English Sentiment Analysis in BBC and SANAD News

News agencies connect global events to local communities. It plays a pivotal role in influencing public opinion. Thus, the necessity arises to recognize news article’s sentiment. The purpose of this paper is to analyze sentiment for English and Arabic news articles in terms of positivity, negativity, or neutrality. Analyzing the articles of Arabic and English news can be challenging from the perspective of morphology. In this paper, we introduce 4 Machine Learning methods, including Logistic Regression (LR), k Nearest Neighbors (KNN), Random Forests (RF) and Naive Bayes (NB), with the TF-IDF as the feature extraction. The study was validated using 2 data sets (BBC, SANAD Arabic news), and two learning models (Hold out and 10-fold cross-validation). The evaluation was based on; Accuracy (ACC), Precision (PREC), Recall (REC), F1-score (F1), and The Matthews Correlation Coefficient (MCC) where it shows an outstanding performance for ML on a 10-fold strategy. The experiments provided in the paper indicated that the proposed ML models achieved the best results.

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Hassan Al-Sukhni mail -

Qusay Bsoul mail -

Sharaf Alzoubi mail -

Fadi yassin Salem Al jawazneh mail -

Dalia Ehab Abdelaziz mail -

Hisham Mohamed Gamel mail -

Diaa Salama AbdElminaam mail

link https://doi.org/10.54216/FPA.180115

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

New Keys for Cloud Resource Provisioning Optimization Method in Multi-Tier Style

Resource provisioning is regarded as a crucial technology in the cloud-computing environment. Nonetheless, the primary challenge associated with the cloud involves ensuring resource availability while improving throughput, balancing loads, and optimizing execution time. There are two types of provisioning methodologies in a cloud environment: single-tier and multi-tier. This paper presents a novel method that combines hybrid metaheuristic optimization techniques, specifically Ant Colony Optimization (ACO) and Firefly Algorithm (FA), referred to as ACOF. This study presented an implementation of dynamic resource provisioning in a multi-tier cloud architecture. The results obtained from the proposed method demonstrate an enhancement in resource provisioning compared to other studies. Indeed, the ACOF algorithm demonstrates a reduced execution time for resource provisioning compared to alternative algorithms. Furthermore, ACOF algorithms have the potential to decrease implementation time by up to 13.2% in comparison to the execution time of alternative methods.

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Omer K. Jasim Mohammad mail

link https://doi.org/10.54216/JISIoT.150115

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

Intelligent Enhancement of Biometric Verification Using Deep Learning Technology

Biometric verification has grown into critical to privacy across areas such as finance and safe accessing services. The present study addresses the utilization of techniques for deep learning, namely convolutional neural networks (CNNs), to boost both the precision and dependability of biometric authentication. Researchers explore the effectiveness of these algorithms on collections containing genuine and forged banknote photos, taking into account information collecting obstacles such as operator condition changes and ambient conditions. The novelty shows an incredible proficiency in classification of 100%, with clarity, recall, and F1-scores of 1.00 across the two categories, demonstrating that the representation is excellent at discerning amongst legitimate and replica materials. Further, researchers investigate the effects of different design variables on efficiency and precision. This investigation provides important insights into merging deep learning with biometric data, laying the basis for future safe authorization developments.

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Maha A. Al-Bayati mail

link https://doi.org/10.54216/FPA.180116

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Enhanced Entity Recognition of Islamic Hadiths based-on Hybrid LSTM and AraBERT Model

This paper focuses on the training, evaluation and development of named entity recognition (NER) models designed for Islamic hadiths in Arabic Utilizing the Hadith Noor dataset, the study uses the BIO (Basic, In, Out) tagging scheme to classify words or tokens in NER tasks and the segmentation of the text into individual tokens. The right-skewed distribution revealed by examining the lengths of the Islamic hadiths revealed a right-skewed distribution, indicating that shorter texts are more common. Texts less than 100 words were most prevalent, followed by texts between 100 and 200 words, while texts longer than 200 words were rare. The dataset identifies eight types of entities, such as common names among narrators and locations. The study by training the three models AraBERT, LSTM and the hybrid model AraBERT-LSTM on Arabic text processing respectively, the hybrid model showed a performance, efficiency and accuracy of 0.981, outperforming the rest of the models, confirming its worth and reliability in NER tasks for natural language in Arabic, especially Islamic hadiths, which opens the way for exploring further investigations for future research in natural language processing.

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Wessam Lahmod Nados mail -

Behrooz Minaei Bidgoli mail -

Sayyed Sauleh Eetemadi mail -

Mohammad Ebrahim Shenasa mail -

Seyyed Ali Hosseini mail

link https://doi.org/10.54216/FPA.180117

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Efficient Deployment Approach in WSNs Using Heuristic Technique

Several researchers have paid attention to designing deployment algorithms in WSNs. In fact, there are many different ways to deploy sensors in sensors' fields. Selecting one of them mainly is based on the application for which WSN design. However, two main factors should be considered when designing a deployment approach in WSN: coverage and connectivity. In this paper, we present a genetic algorithm (GA) to enhance the sensor deployment in WSNs while concurrently improving the coverage and connectivity rate. The most popular deployment approach is to deploy sensor nodes randomly in the field. Although this approach is simple and easy, it may not achieve good results. In the proposed GA algorithm, the metaheuristic algorithm is used to deploy sensors. Simulations demonstrate that GA achieves a good deployment result compared to other research papers by ensuring maximum network coverage and connectivity rate by achieving efficient coverage and connectivity.

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Noor Ali Abbas mail -

Muhammed Abaid Mahdi mail -

Mahdi Abed Salman mail

link https://doi.org/10.54216/JCIM.150224

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Algebraic structures such as distributive, associativity and boundedness properties via tangent neutrosophic set acting generalized weighted averaging and geometric

A novel technique to produce complicated tangent trigonometric (ζ,∂,e) neutrosophic sets is presented in this study. Complex tangent trigonometric (ζ,∂,e) neutrosophic weighted averaging, geometric, generalized weighted averaging, and generalized weighted geometric will all be discussed in this article. We calculated the weighted average and geometric using an aggregating model. The following algebraic methods will be used to further study several sets having significant properties.

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Aiyared Iampan mail -

Murugan Palanikumar mail -

T. T. Raman mail

link https://doi.org/10.54216/IJNS.250417

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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Fusion: Practice and Applications | 2025

Enhanced Clustering Techniques for Marine Ad-Hoc Networks Using Dynamic PFCM

Ship Ad Hoc Networks (SANETs) are an integral part of modern maritime communication and shipping, characterized by dynamic topology and heavy traffic. Accurate node localization in SANETs is of great importance to ensure effective communication, security, and operational decisions. Traditional clustering algorithms, such as Fuzzy C-Means (FCM) and Possibilistic Fuzzy C-Means (PFCM), struggle with the dynamic and collaborative nature of SANETs, being sensitive to noise, outliers, and node distribution of rapidly changing. In this paper, a new clustering algorithm, the Dynamic Weighted Gradient-Based Possibilistic using Fuzzy C-Means (DWGB-PFCM), is specially designed to address the limitations of traditional methods in dynamic SANETs. The DWGB-PFCM contains dynamic weighted distances, flexible membership and uniqueness functions, and enhanced objective functions to improve robustness, adaptability, and efficiency of the cluster. Detailed data processing from the National Buoy Data Center (NDBC) combines spatial environmental parameters such as wind speed, atmospheric pressure, and wave characteristics to simulate real-world ocean challenges. Experimental results show that DWGB-PFCM outperforms traditional methods and separation measurements, with PFCM improving by 15.8%, decreasing by 22.2% in separation entropy, and decreasing by 32.1% in RMSE. In addition, DWGB-PFCM achieves a 15.0% improvement in computational efficiency over FCM. This research lays the foundation for further innovations in clustering algorithms designed for dynamic environments.

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Ghufran Abdulameer mail -

Yossra H. Ali mail

link https://doi.org/10.54216/FPA.180118

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion Data Framework for Enhanced Outlier Detection Integrating Statistical and Machine Learning Techniques for Retail Analytics

This paper aims at presenting an overview of the most popular outlier detection methods that can be used in the retail sector to solve such important problems as fraud, inventory issues, and untypical customer behavior. The techniques discussed in this paper include the conventional statistical methods such as Z-score, Mahalanobis Distance, and Elliptic Envelope and the advanced machine learning methods such as Local Outlier Factor (LOF), Isolation Forest, and DBSCAN. Each method is discussed in detail and the advantages and disadvantages of each are evaluated in relation to different retail scenarios. The primary contribution of this study is the new approach to use Artificial Neural Networks (ANN) for tuning contamination parameters in the Elliptic Envelope model, which makes the anomaly detection more accurate and efficient. Furthermore, the study also depicts the application of min-max scaling for normalizing the features where it helps in reducing the effect of outliers and thus improves the model performance. The results show that the integration of the statistical and machine learning methods is very useful for the real-time detection of anomalies particularly in the ever-changing environment of the retail industry. This research presents a practical insight and new methodological approaches that may be useful for researchers and practitioners who develop outlier detection systems. The outcomes of this study have the potential of enhancing data fusion quality, workflow, and decision-making in the context of retailing.

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Botirjon Karimov mail -

Murodjon Sultanov mail -

Jasurbek Nematullaev mail

link https://doi.org/10.54216/FPA.180119

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

AI-Powered Election Insights: Predicting the 2024 Trump vs. Kamala Election Showdown with Machine Learning

The United States presidential elections receive a substantial attention not only from American voters, but also from news agencies, politicians, and international governments due to the local and global impact of the outcome. Therefore, different parties strive to predict the election’s results ahead of time, and opinion polls remain the predominant prediction method despite their bias and flaws. Online political communication has immensely evolved in recent years, especially on social media websites like Reddit, which has become a key platform in political discourse offering a valuable resource for studying public opinions on key issues. This study aims to utilize advanced machine learning methods to predict the outcome of the upcoming 2024 U.S. presidential election with a focus on the two primary candidates, former President Trump and Vice President Harris. Employing deep learning techniques to analyze more than 25 thousand online posts on Reddit, the results indicate that on the national level, Harris has more favorable sentiment in comparison to Trump among online users. However, analyzing the data associated with the battleground states, our model predicts that Trump has an edge over Harris, which may result in Trump winning the majority of the electoral votes in these states. This study underscores the importance of integrating social media data with machine learning capabilities for enhanced data-driven forecasts in upcoming elections and major public events.

groups

Yazan Alnsour mail -

Mohammad Alsharo mail -

Malik AL-Essa mail -

Aseel Smerat mail

link https://doi.org/10.54216/JISIoT.150116

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new