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

Hassan; Qusay; Sharaf; Fadi yassin Salem Al; Dalia Ehab; Hisham Mohamed; Diaa Salama

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

Fusion: Practice and Applications | 2025

The Imperative Necessity of Erbil-Koya Highway Stretch

Human civilization encompasses all that humans have created, both materially and morally, within a specific time and place. Thus, building highway extensions represents a significant addition to the material aspects of civilization. Highways are a crucial component of human development, affecting societies in social, economic, environmental, urban, and cultural ways. Connecting Erbil with Koya via a highway is expected to affect the populations of both cities and their surrounding areas. This paper examines the role of highways in societal development, with a particular focus on Koya. We have demonstrated the importance of highway design through mathematical models using modern speed parameters, fuzzy logic, and control methods. Additionally, we proposed a method for managing highway speeds through radar and remote sensing technologies. The paper highlights the inevitable societal progress resulting from the Koya-Erbil highway connection.

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Abdulqader Othman Hamadameen mail

link https://doi.org/10.54216/FPA.180107

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Real-Time Electric Vehicle Battery SOC Estimation Using Advanced Optimization Filtering Techniques

Improving the Extended Kalman Filter's (EKF) State of Charge (SOC) prediction for EV battery packs is the primary goal of this section. Optimised batteries management procedures rely on SOC estimate that is both accurate and reliable. The EKF is a popular tool for estimating nonlinear states, but how well it works relies heavily on which noise coefficient matrices are used (Q and R). Experimental testing and other conventional approaches of calibrating these matrix systems are extremely costly and time-consuming. In order to tackle this, the section delves into the integration of four state-of-the-art metaheuristic optimisation methods: GA, PSO, SFO, and HHO. By minimising the mean square error (MSE) among the real and expected SOC, these techniques optimise the Q and R matrices. When looking at preciseness, converging speed, and resilience, SFO-EKF comes out on top in both static and dynamic comparisons. By greatly improving the reliability of SOC estimations, the numerical results show that SFO-EKF obtains the lowest MSE & RMSE. This study advances electric car batteries by providing a realistic scheme for combining optimisation methods with EKF to offer highly effective and exact SOC estimates. When as opposed to TR-EKF, GA-EKF, PSO-EKF, and HHO-EKF, the SFO-EKF approach shows the best accuracy, with an improvement of over 94%. This is a result of the suggested model's exceptional efficiency in SOC estimates.

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Hari Prasad Bhupathi mail -

Srikiran Chinta mail -

Vijayalaxmi Biradar mail -

Sanjay Kumar Suman mail

link https://doi.org/10.54216/FPA.180108

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Efficient Data Processing Techniques for Structured Data Analysis Using Stream Pipeline Parallelism

 This research illustrates how dynamic task balancing and data sharing may improve distributed data processing. The technology handles parallel processing system difficulties with huge datasets by minimizing resource utilization, time complexity, and output. We modify the workload on the fly after splitting to ensure that all processing units receive equal work. One last optimization phase optimizes job distribution to maximize system efficiency. We test the solution for latency, speed, scalability, resource utilization, fault tolerance, and synchronization overhead. Results reveal that the new strategy outperforms existing ones in every regard. It features the lowest latency, quickest production, and highest growth potential. The approach handles mistakes well, divides data effectively, and syncs everything at a cheap cost. These properties make it ideal for real-time data processing and fast-growing applications. Future study will concentrate on flexible splitting strategies, fault tolerance mechanisms, and predictive analytics machine learning models. These modifications will improve real-time data handling.

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Sampath Kini K. mail -

D. K. Sreekantha mail

link https://doi.org/10.54216/FPA.180109

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Sentiment Analysis on Amazon Reviews of Mobile Phones using Machine Learning

The world is witnessing a boom in the digital age. Digital shops have literally landed into our homes. Almost any required product can now be purchased online via websites or mobile apps without having to step out. Due to online shopping, many customers rely on online reviews from other customers before making a purchase. Customer reviews are gaining more and more importance as they play a probably vital role in the sale and purchase of a product. Customer reviews also provide firsthand feedback coming directly from the customers themselves; this can benefit even the sellers in improving future sales. Analyzing the reviews can provide probable causes for failure or success of a product. Henceforth, the current paper presents the sentiment analysis of the reviews to better understand the feelings expressed by the customers. The very popular and widely used mobile phones were chosen as the product and Amazon was chosen as the digital seller for the current study. Initially, this work began with data preprocessing. Followed by data preprocessing, Bow and n-grams word embedding have been used to represent the clean reviews in vector representation, and then the features were derived. Finally, the performance of supervised machine learning classifiers such as Decision Tree, Naive Bayes, Random Forest, and SVM was empirically evaluated through accuracy, recall, f1-score, and precision. The results of empirical evaluation revealed that the Random Forest Classifier shows best performance with 97.48% accuracy.

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Shweta Singhal mail -

Huda Lafta Majeed mail -

Hassan Muayad Ibrahim mail -

Nishtha Jatana mail -

Charu Gupta mail -

Agam Kumar mail -

Bharti Suri mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/FPA.180110

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

A Review of Online Signature Recognition system

Biometrics has reached an important place in the field of authentication for both financial transactions and document verification. Signatures can be broadly classified into online and offline types, depending on how they are acquired. Captured through devices like tablets and digital pens, online signatures contain rich features concerning position, velocity, and acceleration; hence, they offer a better resistance to forgery compared to offline, more traditionally taken signatures. The review summarized the current research in online signature verification systems. There are methodologies and techniques deployed for feature extraction, data pre-processing, and classification. The main stages reviewed within the verification process are about data acquisition, including the use of several publicly available databases like DEEPSIGN, SVC2004 and MCYT-100. Wavelet transforms and Fourier analysis are discussed as a number of methods employed for feature extraction, showing good results about signature dynamics. This review follows the SLR approach for analysing and synthesizing relevant studies published between 2017 and 2024. This review uses PRISMA guidelines for the selection of studies, hence making the results methodologically rigorous and unbiased. The paper identifies commonly used algorithms, including CNN, RNN, and DTW, and examines popular signature databases by outlining their characteristics and relevance to system performance. The insights from this review will help in pointing towards the future ahead in online signature verification systems through emphasizing deep learning-based techniques along with realistic challenges.

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Ibtisam Ghazi Nsaif mail -

Sharifah Mumtazah Syed Ahmad mail -

Syamsiah Bt. Mashohor mail -

Marsyita Bt. Hanafi mail

link https://doi.org/10.54216/FPA.180111

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Optimized Machine Learning Framework for SMS Spam Detection and Classification:A Comparative Evaluation

This paper presents an optimized framework for detecting SMS spam using advanced machine learning algorithms and natural language processing (NLP) techniques. Two datasets, the Filtering Mobile Phone Spam Dataset and the SMS Spam Collection Dataset, were utilized to evaluate the performance of various classifiers, including Multinomial Naive Bayes, K-Nearest Neighbors, Support Vector Classifier, Decision Trees, and AdaBoost. The methodology encompasses comprehensive data preprocessing steps, such as tokenization, stopword removal, and text normalization, followed by feature extraction using TF-IDF and Bag-of-Words models. The classifiers’ performances were evaluated using accuracy, precision, recall, and F1-score, alongside cross-validation techniques. Results indicate that Support Vector Classifier and AdaBoost consistently achieved superior accuracy in distinguishing between spam and ham messages. The study underscores the importance of data preprocessing and model optimization in enhancing spam detection accuracy, offering valuable insights for improving SMS filtering systems in cybersecurity applications.

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Firas Zawaideh mail -

Qusay Bsoul mail -

Ala Alzoubi mail -

Nardine T. Botros mail -

Moaz T. Fawzy mail -

Diaa Salama AbdElminaam mail -

Nour Mostafa mail

link https://doi.org/10.54216/FPA.180112

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A novel Q-neutrosophic soft under interval matrix setting and its applications

Decision-making theory serves as an effective framework to guide decision-makers in solving problems. One notable application of this theory is in the medical field, where it aids doctors in analyzing patient data to determine whether a patient is infected. To enhance this theory with more adaptable mathematical methods, we propose an expanded approach based on previously introduced matrixes of Q-neutrosophic soft under an Interval-valued setting (IV-Q-NSM). This represents a new finding of existing mathematical tools to address the two-dimensional uncertainty prevalent in various life domains. This work explores several algebraic properties and matrix operations associated with IV-Q-NSM. Subsequently, we introduce a new methodology for decision-making (DM) in medical diagnosis selection problems. This approach aims to provide a more flexible and comprehensive framework for evaluating complex medical data and improving diagnostic accuracy.

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Ayman Hazaymeh mail -

Yousef Al-Qudah mail -

Faisal Al-Sharqi mail -

Anwar Bataihah mail

link https://doi.org/10.54216/IJNS.250413

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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Fusion: Practice and Applications | 2025

Heart Failure Early Prediction Using Machine And Deep Learning Algorithm

In this article, we use machine learning approaches to give a thorough investigation into the prediction of cardiac illnesses and strokes. The Stroke Prediction Dataset and the Heart Failure Prediction Dataset are the two datasets that we use. Our objective is to maximize accuracy and minimize Mean Absolute Error (MAE) and Mean Squared Error (MSE) in order to enhance predictive performance. We use a variety of machine learning methods, such as Random Forests, Naive Bayes, Decision Trees, and k-Nearest Neighbors (KNN). We also use Artificial Neural Networks (ANN) and Multi-Layer Perceptrons (MLP) as deep learning models. We use oversampling approaches to rectify the imbalance in classes. For hyperparameter tweaking, we also use Grid Search and k-Fold Cross Validation. Our goal is to deliver valuable insights into early detection and preventive measures through comprehensive testing and assessment for prevention of strokes and heart diseases.

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Lamis F. Al-Qora’n mail -

Qusay Bsoul mail -

Firas Zawaideh mail -

Ala Alzoubi mail -

Silvyras Sayed mail -

Raghad W. Bsoul mail -

Diaa Salama AbdElminaam mail -

Nour Mostafa mail

link https://doi.org/10.54216/FPA.180113

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

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

Details open_in_new

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

Details open_in_new