Journal of Al-Qadisiyah for Computer Science and Mathematics
How to publish in Al-Qadisiyah Journal of Computer Science and Mathematics The Journal of Al-Qadisiyah for Computer Science and Mathematics (JQCM) is a scientific journal published by the College of Computer Science and Information Technology at the University of Al-Qadisiyah. It has been published since 2009, initially two issues a year, and since 2019, four issues a year. To date, the journal has published 15 issues. The articles published in Volume 5 and above have the submission and acceptance dates indicated, whereas the articles published in Volume 10 and higher have the online availability date indicated. ISSN 2521-3504 (Online), ISSN 2074-0204 (Print)
From Pixels to Sentence: A Comprehensive Study of Transformers-Based Models for Image Captioning
The task of image captioning, which involves generating descriptive textual content from visual input, is a pivotal challenge in multimodal learning. This research delves into the advancements in image captioning facilitated by Transformer-based models, comparing their performance, architectures, and innovations across various tasks. Traditional models, such as CNNs paired with RNNs, were initially used to extract visual features and generate corresponding captions. However, the introduction of Transformer architectures has significantly enhanced the performance of image captioning systems, allowing for more coherent, context-aware, and grammatically correct captions. This paper explores the evolution of Transformer-based models, with a particular focus on the Encoder-Decoder, Vision-Language Fusion, and End-to-End Transformers models. By analyzing state-of-the-art architectures such as ViT, GPT, BLIP, and CoCa, the study demonstrates how these models address long-range dependencies, utilize self-attention mechanisms, and seamlessly integrate vision and language for improved caption generation. Furthermore, the paper evaluates the strengths, challenges, and limitations of these approaches, including issues related to computational complexity, dataset biases, and caption diversity. Ultimately, this study presents a comprehensive comparison of these models, offering insights into future research directions in the field of image captioning.
Copyright (c) 2026 Haider Jaber Samawi, Ayad Rodhan Abbas http://creativecommons.org/licenses/by-nc-nd/4.0
Intelligent Learning Techniques for Epileptic Seizure Prediction Using EEG Signals: A Comprehensive Review
Epileptic seizure prediction facilitates proactive rather than reactive medical treatment, thereby promoting patient safety and improved living conditions. One of the most prominent tools employed for epileptic seizure prediction purposes is electroencephalography, considering its temporal resolution and ability to detect brain activity. This paper offers an organized and tightly focused review on new intelligent learning methods applied for epileptic seizure prediction via EEG. This review discusses publicly and privately accessible datasets, preprocessing and segmentation, feature spaces, machine learning and deep neural networks, evaluation schemes, and postprocessing. It points out major bottlenecks preventing more effective implementation, including an overdependence on a few common datasets, preprocessing conventions, cross-validation on different patients, and poor interpretation and implementation strategies. This paper proposes future developments on more reliable seizure predictors.
Copyright (c) 2026 Zahraa Hadi Kazem Al-janabi, Mohammad Khalaf Rahim Al-juaifari http://creativecommons.org/licenses/by-nc-nd/4.0
Robust Brain Strokes Diagnosis in the CT Images into Several Categories Using Deep Machine Learning Models
Brain stroke represents the main contributor to both disability and mortality in the world. The precise and immediate detection of this disease is crucial to save life of patients and enable effective intervention. A deep learning framework is developed in this research to detect and diagnosis brain stroke into three main categories: normal, ischemic and hemorrhagic. Four pre-trained deep learning models are leveraged in this study including VGG19, CNN, EfficientNetB0 and ResNeXt-50_32x4d. An augmentation and pre-processing techniques are utilized in this framework to reduce class imbalance and unify wide imaging data, hence improving model generalization. Evaluation on the a well curated dataset reveals perfect performance especially precision and recall metrics with scores of (99%) among different stroke types. ResNeXt-50_32x4d model demonstrates the best performance due its strong and robust architecture. Next is the EfficientNetB0 delivers also good performance for its efficient architecture and a smaller number of parameters. Overall, the evaluation analysis results of diagnosing brain stroke types refer to the superiority of the proposed approach in comparison with the baseline deep learning methods. This study also overcomes challenges such as false negatives in the diagnosis of early ischemic cases and false positives arising from anatomical variations, suggesting solutions that integrate several AI models. To sum up, this framework demonstrates encouraging pathway to enhance diagnosis of brain strokes by efficient, automated and interpretable AI tools.
Copyright (c) 2026 Ali J. Abboud, Maather Alshaibi, Saad Albawi http://creativecommons.org/licenses/by-nc-nd/4.0
Applying Collaborative Filtering in E-Marketing Systems
Collaborative filtering is very important in e-marketing systems since it enables users to search through the large product lists and become better buyers. This paper will look into the application of machine learning algorithms for instance K-Nearest Neighbors (KNN) and Decision Tree Classifier in formulating a collaborative filtering for supermarket products like, food, dairy, canned goods, juices and detergents. Three data files were employed; a supermarket products file of five Categories each having ten products, a supermarket manager’s offers file of the same Categories with each set with four products, and a file of past user’s purchases. The methodology employed involves using a user product matrix to capture purchasing behavior, using KNN algorithms to predict user preferences using the distance of Euclidean, also, a decision tree to set up the decision-making rules for products recommendations implemented. The number of the previous user’s purchases is shown in a histogram. The paper compares the KNN algorithm with the decision tree algorithm on recommendation accuracy, precision, recall, F1-score and execution time. The KNN algorithm yielded the best accuracy (0.9651), precision (0.8233), recall (0.8430), F1 score (0.8293) and execution time (0.896). The decision tree algorithm yielded lower accuracy (0.8392), precision (0.2094), recall (0.2111), F1 score (0.2072), and execution time (3.686).
Copyright (c) 2026 Kasem Maher Ahmed, Omar Muayad Abdullah http://creativecommons.org/licenses/by-nc-nd/4.0
Designing a Modular Framework for Processing and Enhancing Scanned Documents Using Advanced Denoising Algorithms
Scanned documents are often flawed such as background noise, skew and uneven illuminations which negatively affect reading and text recognition. The given study presents a two-step processing model that can serve to improve the quality of grayscale and color-scanned documents because of the use of combined denoising and deskewing methods. Three denoising methods were tested under various noise levels: DRUNet, DnCNN, and Total Variation (TV). To get the best results in restoring image quality, we used pre-trained models for the deep learning algorithms (DRUNet and DnCNN) through the DeepInv library. This allowed us to use powerful, ready-to-use features to clean the documents effectively. Their performance was then measured using standard quality scores and visual checks. The results showed that DRUNet produced the best and more reproducible performance, which was able to suppress noise and preserve fine structural and textual fidelity. In addition, preprocessing step of Otsu thresholding and minimum bounding rectangle estimation was also applied to automatically correct document skew to enhance text alignment and readability. Python and Gradio were used as the implementation language of the system to offer an interactive, transparent, and reproducible platform. In general, the suggested framework would significantly increase the clarity, alignment, and the overall quality of scanned documents and make them more reliable to use in OCR and digital archiving purposes.
Copyright (c) 2026 Zahraa Ali Mohamed Nather, Hasan Maher Ahmed http://creativecommons.org/licenses/by-nc-nd/4.0
An Analytical and Comparative Study of Modern Intelligent Models for Skin Cancer Diagnosis
Early diagnosis of skin cancer especially melanoma is a significant issue because of the visual similarities of lesion types, intra class variability, and drawbacks of the manual diagnosis. Over the past years, there is a wide range of artificial intelligence (AI) methods suggested, but the results reported were quite different because of the variations in datasets, preprocessing pipelines, model architectures, and evaluation protocols. This is a systematic review article of 29 peer-reviewed studies published 2015–2025 that were identified in major scientific databases and divided into three key categories, namely traditional machine learning (ML), deep learning (DL), and transfer learning (TL) frameworks. Comparatively, the studies are assessed by aspects of characteristics of the dataset, feature representation strategies, model complexity, diagnostic performance and clinical applicability. The discussion has shown that deep and transfer learning models tend to perform better than traditional techniques of ML in terms of classification accuracy, yet various issues on the class imbalance, data heterogeneity, the ability to perform generalization as well as efficiency of computation still exist. Another critical research gap that has been found in the literature is the lack of a standardized analytical framework to compare the ML, DL, and TL models under standardized assessment procedures taking into account the real-life limitations in clinical deployment. This paper is a comparative perspective on the current intelligent skin cancer diagnostic systems in a structured way and it identifies the future research directions of stronger, scalable and clinically sound AI-based solutions.
Copyright (c) 2026 Zahraa Saad Abdul Wahid, Zahraa Ch. Oleiwi, Rasha Falah kadhem http://creativecommons.org/licenses/by-nc-nd/4.0
A Comprehensive Survey on using Segmentation and Density Peaks Clustering (DPC) for Healthcare Data Streams
Healthcare systems have recently undergone a significant digital transformation, driven by the rapid growth of the Internet of Medical Things (IoMT) and smart sensing technologies. The sensing technologies generate continuous, high-speed stream of medical information that require real-time analysis and processing. Healthcare data streams are evolving over time. Recently, medical data segmentation and clustering are considered one of the most important techniques used to enhance IoMT reliability, scalability and to support the online medical decisions. Furthermore, these techniques employ bandwidth optimization by reducing the overhead and transmission delay. To date, several surveys have also been proposed in the literature. However, current challenges such as real-time processing and dynamic maintaining of wide variety of medical data streams, which raise the question of developing intelligent and adaptive analytical systems for use in the medical field. Therefore, we conduct a comprehensive survey on the recent advancements in the segmentation and clustering methods for healthcare data streams. This survey examines healthcare data streams, employing clustering and segmentation techniques to improve diagnostic accuracy and enable early disease prediction.
Copyright (c) 2026 Ghadeer Qasim Al-jaberi, Ahmed Al-Shammari http://creativecommons.org/licenses/by-nc-nd/4.0
Anomaly Based Network Intrusion Detection Using Autoencoders
Network intrusion detection helps to prevent cyber-attacks on the current networks. Classical signature-based approaches cannot identify new attacks, which drives application of the anomaly-based ones. This paper suggests a composite framework of anomaly detection, which combines autoencoder, latent-space Isolation Forest, and weighted ensemble, which is specifically implemented to the NSL-KDD dataset. The autoencoder is only trained on mainstream traffic to be able to grasp the distribution of benign traffic, and anomaly scoring is used by calculating reconstruction errors. Latent-space representations are additionally analyzed using an Isolation Forest to increase the distinction between aberrant designs. The best weighting program is adjusted according to a validation set and the ultimate threshold is selected based on Youdens J statistic to adjust the false positive and true positive. As the experimental findings demonstrate, the proposed ensemble method provides the accuracy of 95.53 percent, the macro F1-score (0.9551), and the ROC-AUC (0.9867), which is remarkably better in detecting the objects than the solo-model autoencoder methodologies. The paper verifies that deep representation learning coupled with ensemble-based scoring is effective in terms of network intrusion detection.
Copyright (c) 2026 Ali Hussein Alsaroah http://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Software Requirements Classification Using AI-Based Text Processing Techniques
The categorization of software requirements as functional (FR) and non-functional requirements (NFR) is an important problem in software engineering that is widely performed using manual analysis by domain experts. The process is tedious, prone to errors, and lacks consistency from one project to another and from one organization to the next. New approaches are needed to address this challenge, we present such an approach, a new hybrid approach, which combines pre-trained transformer models to automate the requirements classification process, and machine learning techniques. We propose a novel methodology that utilizes BERT (Bidirectional Encoder Representations from Transformers) for contextual feature extraction, supplemented with multi-head attention mechanisms and bidirectional LSTM layers for capturing sequential dependencies in requirements text. We combine this deep learning architecture using ensemble classifiers (Support Vector Machines and Random Forest) with a weighted voting mechanism. We perform an experimental validation using two of the most common datasets (PROMISE with 625 requirements and PURE with 969 requirements), showing that our approach can achieve 94.2% accuracy and 94.1% F1 score for binary classification and an average of 89.7 F1 score across six categories (Security, Performance, Usability, Reliability, Portability, and Maintainability) for the multi-class NFR categorization task. Statistical significance tests further confirm that our hybrid model substantially outperforms each of the state-the-art approaches, with absolute gain ranging from 2.1% to 14.4% respective to different evaluation criteria. This directly addresses some of the main challenges in automated requirements engineering, and hence a unique, pragmatic approach towards large-scale software development processes.
Copyright (c) 2026 Maryam Jawad Kadhim, Hasanain Hazim Azeez http://creativecommons.org/licenses/by-nc-nd/4.0
Formal Verification Approach for IoT Conflict Resolution by Priority
Internet of Things (IoT)-based solutions, especially automation systems, are essential for controlling networked devices and creating adaptive and intelligent environments. End-user-developed IoT services/apps interact and share simultaneous access to devices depending on their preferences, thereby increasing safety, security, and correctness issues. System failures or reduced performance might result from the traditional reactive approach to dispute resolution, which identifies and fixes problems after they have already happened. To tackle important issues, IoT-based applications require special tools to check and analyse their design, helping to find problems and conflicts in the instructions and interactions of IoT applications. This paper employs the Event-B formal method to introduce a novel approach for detecting and resolution conflicts in IoT systems by using priority. To systematically identify and resolve any conflicts, formal models of the IoT system are developed and refined using abstraction and refinement techniques. The objective of this paper is to promote the use of formal Event-B modelling and verification during the initial stages of the development of an IoT system to expedite the detection and fixing of problems. The proposed model addresses several types of conflicts, such as resource, functional, priority, and policy conflicts. The Rodin platform automates verification, ensuring that all declared invariants and attributes are preserved during system operation. As an example of our method in action, we will take a look at an ECG IoT system. The model accurately detects possible conflicts in these situations. We verify our method using the Rodin model checking tool, proof obligations and the ProB animator.
Copyright (c) 2026 Zinah Hussein Toman http://creativecommons.org/licenses/by-nc-nd/4.0
Graph-Based Community Detection in Hepatitis C Patient Data Using KNN Graph Construction and the Louvain Algorithm
Analyzing medical data to identify clinically relevant patient groups remains a complex task, particularly for hepatitis C virus patients, as traditional clustering methods often struggle to capture heterogeneous patient relationships and may rely on specific geometric assumptions or be constrained by the number of groups to be found. The proposed framework for detecting patient communities combines local similarity representation and global structural analysis. This framework builds a network of patient similarity using the K-Nearest Neighbors (KNN) algorithm, followed by the detection of patient communities through the Louvain algorithm. The proposed method is implemented on two real hepatitis C virus (HCV) datasets: the Egyptian HCV dataset and the HCV (UCI) dataset. The results of the experiments show effective detection of coherent and stable patient communities. The highest modularity values reach 0.740 and 0.805 for the Egyptian and UCI datasets respectively, at a neighborhood value of K=3. In addition, in this context, low distortion values indicate strong cohesion within the detected communities. Overall, the results confirm that graph-based patient community discovery provides a powerful alternative to traditional clustering techniques for medical data analysis. The proposed framework enables reliable and stable discovery of clinically relevant and interpretable latent patient subgroups.
Copyright (c) 2026 Bashair Mohammed Obaid, Hayder K. Fatlawi http://creativecommons.org/licenses/by-nc-nd/4.0
An LSTM –based approach for predicting the next word in Arabic language
Next word prediction, or language modeling, is a crucial task in natural language processing (NLP) that streamlines the typing process by recommending the subsequent word, hence saving time during conversations. The method of next word prediction is intricate, as the machine must anticipate the user's thoughts. A recurrent neural network (RNN), specifically Long Short-Term Memory (LSTM), comprehends previous text and forecasts subsequent words, aiding the user in sentence construction. This study seeks to employ RNN, specifically LSTM, to forecast the subsequent word in one of the most intricate languages with limited resources (Arabic). The results indicated an accuracy of 97.49% and loss of 0.5155%, suggesting that it is sufficiently effective for predicting subsequent Arabic words.
Copyright (c) 2026 Heba Adnan Raheem http://creativecommons.org/licenses/by-nc-nd/4.0
A Survey on Incremental Learning Techniques for Streaming data
The rapid growth of data stream applications, such as Internet of Things (IoT) systems, smart environments, and real-time analytics, has intensified the need for learning models capable of adapting to continuously evolving data distributions. The traditional techniques of batch learning presuppose fixed data and as such they find it hard to sustain performance when concept drift occurs where the characteristics of data evolve slowly, suddenly or repeatedly with time. Incremental learning has become one of the most important solutions to this issue because it allows models to keep on updating themselves with new incoming data and retain the information previously learned and does not force them to retrain at a high cost. This survey provides an in-depth overview of incremental learning approaches that are used in the streaming data setting with concept drift. We critically review supervised, unsupervised and semi-supervised machine learning methods and deep learning and hybrid methods. The review studies the fundamental adaptation techniques, such as sliding windows, replay, prototype-based learning, methods of detecting drift, expansion of dynamic architecture, and stability-plasticity balancing. To each category we comment on the underlying mechanisms, strength, weakness, computational efficiency and their ability to adapt to various drift conditions. Overall, 38 more recent studies are critically analyzed and compared in the wide range of application areas, data sets, and indicators of assessment. Some of the challenges that are highlighted in this survey include catastrophic forgetting, scalability, interpretability, and resistance to complex and recurring concept drift. Lastly, we recognize the important research gaps and establish future plans on how to develop coherent, scalable and explainable incremental learning models of actual streaming data systems in the world.
Copyright (c) 2026 Akram Asdkhan Mussa, Ali Saeed Alfoudi, Ali Hakem Asaeedi http://creativecommons.org/licenses/by-nc-nd/4.0
ECG Images-based Cardiovascular Disease Classification utilizing a Deep Learning Model
Early and accurate cardiovascular diseases detection is indispensable in specifying efficient treatment and prohibiting life-threatening complications. Traditional detection schemes that depend on manual interpretation of electrocardiograms (ECGs) are generally subject to inter-observer variability and are time-consuming. In this paper, a deep learning model is proposed for classifying cardiovascular diseases relying on two benchmark publicly available datasets of “twelve-lead ECG images”. In order to improve signal diversity, distinct pre-processes and augmentation are adapted on these datasets of cardiac patients. The proposed deep learning model of Convolutional Neural Network (CNN) encompasses distinct blocks of layers (Extensible and detachable convolutions) that are intended to possess morphological patterns and wider spatial dependencies in ECG images. Furthermore, multiple layers of batch normalization and dropouts were employed for stabilizing training and achieving generalization. Experimental results revealed a superior classification accuracy for the proposed system with augmentation utilizing the two datasets, outperforming existing related systems. These results demonstrated the capability of the proposed system to assist cardiologists in early diagnosis and preventive treatment.
Copyright (c) 2026 Abdulrahman Hamid Mahmood, Taha Mohammed Hasan http://creativecommons.org/licenses/by-nc-nd/4.0
Examination of relative efficacy in computational search techniques
In communication and transaction systems, searching is a crucial procedure, and there are several techniques available to maximize data retrieval. These include the well-known techniques for effective searching in sorted datasets: Hash Search, Ternary Search, Exponential Search, and Interpolation Search. By putting these four algorithms into practice in Python and evaluating their search effectiveness on artificial datasets made up of sorted arrays of distinct random integers with values ranging from 1 to , where n varies from 1,000 to 1,000,000 items, this study evaluates the performance of these algorithms. calculated average search durations for existing elements across 100 queries using a pseudo-process technique with timed code execution. Because they are deterministic exact-match searches, all algorithms achieve accuracy, precision, and recall, and performance data include average search time (in seconds). According to the results, Hash Search performs best with near-constant times of roughly microseconds, followed by Interpolation Search at 1-2 microseconds, and Ternary and Exponential Searches, which increase with dataset size and vary from 1-4 microseconds. Though newer deep learning techniques, such as learned indexes, may predict data distributions and offer speedups and 10–100x size reductions in specific database contexts, these classic strategies are still effective for broad use.
Copyright (c) 2026 Arif Hasan Abd Ali http://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid Context -Knowledge Representation Model for Arabic Next Word Prediction
One of the controversial topics that has been raised in recent decades. Several approaches involving deep learning and machine learning was implemented to investing in the field of next word prediction in multiple language. In Arabic contexts, this topic is still challenges and in its early stages which need more investigation researches. this field suffers from lakes of robust model that designed specifically for predicted next Arabic word and high-quality dataset that used to develop this domain. This paper proposed a parallel hybrid model (AraBERT- knowledge graph) that augmented the pre-trained AraBERT model with knowledge graph to significantly enhanced next word prediction in Arabic corpus. The constructed of the proposed model involved integrating the AraBERT’s contextual vectors with entities of embedding of knowledge graph (KG). The SANAD dataset (195k articles) was used train this model. The model achieved an accuracy of 90% and an F1-score of 91% which outperformed several of fine adaption baseline models including AraGPT-2 (84.8) and AraBERT (82.6%). The significant improvement in results indicates that the combining of contextual and knowledge based has a promising direction for advancing several of Arabic language application including Arabic text prediction, understanding, and auto-generation, and other NLP application.
Copyright (c) 2026 Noralhuda N. Alabid http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Analysis of Optimization Algorithms on the Travelling Salesman Problem: Insights from TSPLIB Benchmarking
This study submits a comprehensive comparative analysis of three advanced optimization algorithms applied to the classic Traveling Salesman Problem (TSP), a cornerstone of combinatorial optimization. The chosen algorithms are the Ant Colony Optimization, Particle Swarm Optimization, and Gray Wolf Optimization, were evaluated on nine standard cases from the TSPLIB95 library: att48, berlin52, st70, eil76, brg180, pa561, gr666, pr1002, and pr2392, reflecting varying problem sizes and complexities. Results, such as random variance, best path length, relative error, and mean ± standard deviation, were obtained after each algorithm was executed in 30 independent runs. The findings provide empirical insights into the strengths, limitations, and scalability of each algorithm across different problem sizes. It is worth noting that the ACO and PSO algorithms demonstrate a superior balance between solution accuracy and robustness, making them promising candidates for solving large-scale combinatorial problems. They also highlight the importance of statistical validation and analysis of variance in comparative optimization studies, and provide valuable insights into the suitability of algorithms across various TSP metrics.
Copyright (c) 2026 Bayadir Abbas Himyari http://creativecommons.org/licenses/by-nc-nd/4.0
Sentiment-Aware Fuzzy Clustering Model for X Social Media Behavior Analysis
Social media sites such as Twitter (X) changed into high-velocity observatories of collective mood with millions of short, informal utterances recording public reactions to events, products, policies, and cultural moments in near real time. Mining the stream for actionable insight calls for approaches that respect two notoriously hard to handle properties of social text: (i) ambiguity — in reality, most posts display mixed or low-intensity affect rather than a single discrete label; and (ii) contextual drift — lexical and topical signals co-evolve with communities and time. Many standard sentiment pipelines requiring each message to be labeled as a single discrete class (positive/negative/neutral) fail to provide adequate behavioral insights for crisis monitoring or policy assessment. We close this gap by presenting a Sentiment-Aware Fuzzy Clustering model that models sentiment as a continuous signal and community mood as overlapping regions instead of disjoint boxes. We assign a polarity score to each post and then discretize the space using Fuzzy C-Means (FCM) to assign partial memberships to a number of emotional groups. This uncertainty-aware representation is more reflective of actual online behavior (i.e. a post could be strongly positive but still exhibit features of the neutral discourse) and serves as a principled basis for downstream interpretation in population scale.
Copyright (c) 2026 Ammar Saood Aziz http://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Objective Dynamic Workflow Scheduling for Energy-Efficient and Cost-Effective Cloud Computing
Scheduling scientific workflows in cloud environments under competing objectives—makespan, cost, and energy—remains a challenging multi-objective optimization problem. While hybrid metaheuristics have been explored, they often suffer from random initialization, premature convergence, and static parameter settings. To address these limitations, we propose a novel dynamic scheduling framework that integrates NSGA-II and an enhanced Firefly Algorithm. The method begins with a Pareto-based, non-dominated initial population generated by NSGA-II, ensuring high diversity and quality. This population is then refined using a Firefly Algorithm with adaptive randomness and a stagnation-aware local search mechanism. The model is evaluated on Montage, CyberShake, and Epigenomics workflows using CloudSim with Amazon EC2-like VMs. Results demonstrate statistically significant improvements over state-of-the-art schedulers: up to 31.2% reduction in makespan, 28.7% in cost, and 17.4% in energy consumption, while preserving solution diversity. This work advances the state of the art by synergistically combining evolutionary guidance, swarm intelligence, and domain-aware refinement for sustainable cloud workflow orchestration.
Copyright (c) 2026 Suha Mubdir Farhood http://creativecommons.org/licenses/by-nc-nd/4.0
Improving the Reliability and Accuracy of Image Captioning Systems Using Ensemble of FC, Softmax, and LSTM Deep Decoders
In this work, a deep system for automatic image description is presented, which aims to produce fluent, meaningful, and structurally coherent sentences for input images. The proposed architecture is based on an encoder-decoder framework, in which high-level image features are first extracted by an Inception-v3 deep convolutional network and then fed as a compressed image representation to an LSTM-based language decoder to produce a word-by-word sentence. On this basic structure, a voting-based ensemble learning framework is designed, in which three deep paths, including a fully connected (FC) network, a Softmax linear model, and a sequence-oriented LSTM decoder, are trained independently, and the word probability vectors at the output level are combined with a maximum voting mechanism. The evaluation is performed on the standard Flickr8k database and using BLEU-1 to BLEU-4, METEOR, and ROUGE-L metrics. The results show that the best single LSTM model achieves values ​​of 0.64, 0.39, 0.23, and 0.16 for BLEU-1 to BLEU-4, and 0.22 and 0.50 for METEOR and ROUGE-L, respectively, while the Ensemble model improves the values ​​to 0.74, 0.50, 0.35, and 0.22 for BLEU-1 to BLEU-4, 0.475 for METEOR, and 0.55 for ROUGE-L; such that the relative improvements in BLEU-3 and BLEU-4 are 54% and 41%, respectively. The paired t-test also shows that the difference in Ensemble performance with single models is significant at the 95% confidence level, and compared to the existing methods on Flickr8k, competitive results are obtained and, in some measures, superior.
Copyright (c) 2026 Ghadeer Abdulrasool Mohammed, Raidah S. Khudeyer, Maytham Alabbas http://creativecommons.org/licenses/by-nc-nd/4.0
Neuromorphic Federated Learning Framework for Real-Time DDoS Attack Detection in Distributed Networks
Distributed Denial of Service (DDoS) attacks pose a serious threat to network infrastructure, necessitating real-time detection mechanisms that can operate within distributed environments without compromising data privacy. This paper presents a novel neuromorphic federated learning system that combines bio-inspired spiking neural networks (SNNs) with a federated learning architecture for self-ambient DDoS detection within distributed networks. Our approach employs stateful Leaky Integrate-and-Fire (LIF) neurons with sufficient membrane potential dynamics, Poisson rate encoding for handling temporal information, and network-centric communication protocols. The architecture was evaluated on the CIC-DDoS2019 dataset on five federated nodes with uniform data distribution. Experimental findings reveal 96.64% accuracy, 99.83% precision, 93.44% recall, and 99.65% ROC-AUC score. The system demonstrates real-time performance with an average latency of 0.797ms, a P95 latency of 0.859ms, and a throughput of 1,254.67 samples/second, meeting the critical 100ms requirement for real-time intrusion detection. The federated architecture accommodates collaborative learning without centralising sensitive network data, with an overall communication overhead of 108.13 MB over 20 rounds of training. Our neuromorphic solution offers a promising solution to energy-efficient, privacy-preserved DDoS detection in modern distributed network environments.
Copyright (c) 2026 Rawaa Amer Mansoor Al-karkh http://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Security Model for Data Protection Using Behavioral User Authentication
Credential compromise is one of the most widespread security threats, allowing adversaries to bypass traditional authentication measures and impersonate legitimate users. Traditional intrusion detection systems are often based on network-level or macro-behavioral indicators, which can be easily spoofed by an attacker, thus compromising the effectiveness of those mechanisms. This study presents an improved adaptive intrusion detection system to authenticate user behavior based on micro-digital behavioral profiling. It involves the use of timing of keystrokes, micro-mouse, navigation in the application, and interaction rhythm signatures. The proposed system uses a hybrid model consisting of Long Short-Term Memory (LSTM) sequence prediction and an Autoencoder reconstruction network to learn both structural and temporal variation of user behavior. Also, an adaptive learning module (implemented by a replay buffer and a drift-detection mechanism based on Kullback-Leibler divergence) to continually recalibrate the model when authentic user behavior varies. Experimental testing on a controlled set of 42 subjects in multiple sessions shows that the proposed model can achieve 94.8 0.91 F1-score and 0.05 false-positive rate, which outperforms the use of individual models; adaptive learning brings this number down by half in the case of drift. The comparison analysis proves the superiority of the proposed system in the areas of anomaly detection, stability, and real-time performance, which demonstrates the viability of micro-behavior analytics as a high-resolution security layer that can be used as a persistent authentication and identity-based threat detector.
Copyright (c) 2026 Sura Abed Sarab Hussien, Mustafa S. Ibrahim Alsumaidaie, Nada Hussein M. Ali, Ayat Z. Al-Zouri http://creativecommons.org/licenses/by-nc-nd/4.0
Design of a 5G MIMO-OFDM System Using Artificial Neural Networks Under Realistic Channel Impairments
This paper attempts to design and compare an equalizer for a 5G 2×2 MIMO-OFDM system employing a data-driven ANN equalizer for realistic wireless channel impairments. The focus here is whether a data-driven equalizer approach can attain better or a comparable equalizer performance to classic linear techniques: Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), MMSE-Successive Interference Cancellation MMSE-SIC, Decision Feedback Equalizer (DFE) but taking into account both accuracy and complexity. Build a 5G MIMO-OFDM simulator in Python for the physical 3GPP TDL-C and TDL-E and do add hurts like Doppler spread and existent inaccuracies like carrier frequency offset CFO phase noise and outdated or imperfect channel state information CSI. Propose a deep fully connected ANN and train using supervised learning on pairs of OFDMs exchanged using Monte Carlo simulating the wireless channel from the previous step. Vast range of SNR/Doppler/CFO/phase noise. Use the ANN as equalizer. For evaluation use BER/EVM/NMSE/and FLOPs and inference time per OFDM frame. Simulation results show that the ANN equalizer is much better than ZF and DFE. And matches / slightly better than MMSE and MMSE-SIC across varying SNR/Doppler/CFO/and phase noise – even in more severe settings with TDL-E with imperfect CSI. Finally, the ANN once trained has better latency for inference and FLOPs/value performance than classic equalizer results. Concluding that ANN based is a good avenue with a 5G MIMO-OFDM reception in mind.
Copyright (c) 2026 Zahraa Luay Fouad http://creativecommons.org/licenses/by-nc-nd/4.0
A Review of Techniques for Muscle Fatigue Analysis and the Associated Noise Challenges
To reduce the risk of impression muscle fatigue in the medical field, sports, and rehabilitation of disorders and it is muscles are a critical neuromuscular phenomenon. Electromyography (EMG) is the most important Bio signal used to detect muscle fatigue. Many studies over the past few years have been conducted to address the challenge of muscle fatigue (detection, recognition, and prediction). This study presents a review of various approaches to build models, and evaluation metrics, and applications for each structure begins with exploring artificial intelligence (AI) methods such as machine learning (ML) and deep learning (DL), as well as hybrid model showing the way of data acquisition (sensors types, techniques, preprocessing, models…) specially noise affected of data collection of each type of power spectrum. Furthermore, this review compares fatigue detection, recognition, and prediction approaches, highlighting their performance, strengths, and limitations. Finally, a discussion of various aspects of bio-signal-based muscle fatigue, with specific applications and descriptions, and analyses of the datasets used in muscle fatigue to address the most trending issues and challenges in applying upper limb muscle fatigue. The synthesis presented here aims to guide future developments toward robust, interpretable, and real-time neuromuscular fatigue monitoring systems.
Copyright (c) 2026 ‪Noor Bahaa Jaber Aldalal ‬‏, Mohammad Khalaf Rahim Al-juaifari http://creativecommons.org/licenses/by-nc-nd/4.0
Vision-Based UAV Detection Methods Using Deep Learning: A Review
Unmanned Aerial Vehicles (UAVs), or drones are increasingly used for civilian and military purposes. But their misuse raises serious concerns related to privacy, safety and security making them double-edged weapons. Consequently, there is an urgent need for effective UAV detection systems to mitigate threats posed by unauthorized UAV operations over restricted territories. With rapid advances in deep learning and computer vision, vision-based UAV detection systems have achieved notable progress. However, the existing reviews often lack systematic algorithmic analysis and clear summarization of trends and limitations. Therefore, this review aims to consolidate and summarize recent vision-based UAV detection methods using deep learning, focusing on convolutional neural network (CNN)-based models and to provide actionable directions for future research. Firstly, this study presents the evolution of UAV detection, key challenges and the pros and cons of the technologies used. Next it presents a summary of the recent advances in UAV detection methods utilizing one- or two-stage detectors only; the literature shows a strong dominance of YOLO-based architectures due to their favorable accuracy–speed trade-off and suitability for real-time deployment. It further summarizes commonly reported evaluation metrics (e.g., precision, recall and F1-score). Finally, it systematically reviews public UAV datasets and their characteristics highlighting persistent dataset limitations, including limited diversity in altitude, weather, illumination and environment which contributes to a comprehensive understanding of their characteristics and applicability.
Copyright (c) 2026 Firas Mohammed Kadhum, Abbas Abdulazeez Abdulhameed, Jameelah Harbi Saud http://creativecommons.org/licenses/by-nc-nd/4.0
A Performance Comparison of Microsoft Excel and Python for Tabular Data Analysis
Microsoft Excel is commonly treated as a simple spreadsheet program that is mainly applied to do simple calculations, and Python has become a common solution to complex data analysis based on the programming language. In this work, a technical performance analysis of Microsoft Excel and Python (Pandas) is provided as an attempt to assess their compatibility with basic data analysis functions. The comparison is done which consists of loading datasets, cleaning data, calculating features, aggregating and searching them. The main performance parameter used is execution time and measurements are taken at every processing phase so as to provide a detailed and equitable evaluation. To ensure a robust assessment, the methodology utilized two distinct transactional datasets: a medium-scale over 500000 records and a large-scale dataset over one million records. A key contribution of this work is the implementation of a search loop algorithm as a stress test, where execution time was measured for three specific scenarios: searching for a record at the beginning, at the end, and for a non-existent value. According to the experimental findings, Excel can efficiently handle all of the analyzed tasks and its execution time is as approximate Python as both systems are able to complete the tasks in an approximate time interval. Despite Python having more accuracy in procedures and measurements of time, more automation, and higher reproducibility due to code-based workflows, Excel has competitive analytical capabilities in interactive processing of data. These results underscore the idea that Microsoft Excel can no longer be viewed as an exclusively computational device, and instead of that, it can be viewed as a technically competent data analysis platform capable of performing similarly to Python on large-scaled analytical workloads.
Copyright (c) 2026 Basma Mustafa M. H http://creativecommons.org/licenses/by-nc-nd/4.0
Jordan Bi- (Homomorphism, Left (Right) α-Centralizer) Mappings on a Prime Rings
In this paper, we focus on the study of some classes of bi-derivation. In addition, there are new concepts that have been introduced, including functions bi- (homomorphism, α-centralizer) and Jordan bi- (homomorphism, α-centralizer) on the prime ring R × R, particularly with reference to the elementary rings' permutation structure L = R × R. The concepts of bi- (homomorphism, left (right) α-centralizer), Jordan bi- (homomorphism, left (right) α-centralizer), and triple bi- (homomorphism, left(right) α-centralizer) within the prime ring L are also introduced. Furthermore, we investigate an important condition that states if the ring is 2-torsion free ring, then any Jordan bi- (homomorphism, left α-centralizer) on L is necessarily a Jordan triple bi- (homomorphism, left α-centralizer).
Copyright (c) 2026 Mohammed Baqeer Khadhim, Auday Hekmat Mahmood http://creativecommons.org/licenses/by-nc-nd/4.0
Adjacent Line Graphs: Combinatorial Bounds and Structural Properties
The adjacent line graph of a graph G is defined as the graph whose vertices correspond to the edges of . Two vertices e and f are adjacent in if and only if the distance between their corresponding edges in G is at most one. Formally, for edges e and f, the adjacency condition is: is adjacent to in ⇔ ≤ 1 where is the minimum distance between any pair of their endpoints in G. This paper establishes new tight upper bounds for the independence number and chromatic number for several fundamental classes of graphs, including paths , cycles ​, and complete bipartite graphs . We present rigorous theoretical proofs for these bounds, correct previous inaccuracies in edge-count formulas, and derive important structural properties, including results on the Hamiltonicity of adjacent line graphs. The findings contribute to a deeper understanding of the combinatorial structure of and its applications in network interference modeling, channel assignment, and coding theory.
Copyright (c) 2026 Karrar Khudhair Obayes, Ghadeer Khudhair Obayes http://creativecommons.org/licenses/by-nc-nd/4.0
Topological Approximation Spaces Via Finite Groups
In this paper introduced, investigates the relation between finite group and topological space.We presented a new operator topology approximation space via finite groups and new class of approximations using the equivalence relation and interior, closure sets. We reached some theories and results related to the new approximation sets and Our research extends to the measure of membership function in topological approximation space.
Copyright (c) 2026 Zahraa Mohsin Shadahan Gharbawee, Yaser Tolooei http://creativecommons.org/licenses/by-nc-nd/4.0
Advances in Fixed Point Theory for Extended b-Metric Spaces Results and Applications
This study presents fixed point theorems in extended b-metric spaces using bounded compactness, Ʈ orbital compactness, and asymptotic regularity. We show that Kannan type mappings admit unique fixed points under these weakened topological conditions, without employing auxiliary control functions. The results unify and extend classical theorems, providing applicable tools for nonlinear analysis in variable-scale spaces. Illustrative examples demonstrate the validity and scope of the theory
Copyright (c) 2026 Kadhim Mohammed Saeed, Qusuay Alqifiary http://creativecommons.org/licenses/by-nc-nd/4.0
Numerical Solution of Laplace and Poisson Equations Using Finite Difference Grid Methods
The Laplace and Poisson equations form the basis for an important area of computational physics and engineering, with massive applications in electrostatics, heat conduction, and fluid flow. Classical Finite Difference Methods (FDM) are very efficient on structured grids, but suffer from low accuracy and flexibly geometric on the irregular domain. In this paper, we propose a unified high-order finite-volume method (FDM) framework that unifies Lagrange interpolation for arbitrary-order discretization without loss of generality and neural-network-assisted coefficient prediction that overcomes the mesh distortion limits. Dirichlet and Neumann boundary problems are used to thoroughly verify the method in both 2D and 3D benchmarks. Maximum error is reduced by two orders of magnitude at equal grid resolutions, with fourth-order convergence results shown. A neural-network component facilitates the ability to operate robustly, quasi-free of a mesh, and with accuracy, on very severely perturbed grids. In addition, due to the much lower degrees of freedom for equivalent level of accuracy, the high-order schemes achieve a 16-fold reduction in computational cost. Our work reconciles traditional simplicity of classical FDM with modern demands for geometric flexibility and automation, and provides a robust and efficient solver End2End with potential applications to arbitrary real-world elliptic PDEs found in the science and engineering applications.
Copyright (c) 2026 Hessa Sallal Rabeea http://creativecommons.org/licenses/by-nc-nd/4.0
A Numerical Solution of Mixed-Order Fredholm Integro-Differential Equations by Using Trapezoidal, Two-Block, and Modified Two-Block Methods
The purpose of this article is to use the trapezoidal rule, two-point block schemes, and a modified two-point block approach to numerically solve classical and fractional-order (Mixed-orders in the Caputo sense) Fredholm integro-differential equations with variable coefficients under limit conditions (IFDEs-CF). Three new algorithms are proposed to find approximate solutions to these equations, firstly transforming them into systems of linear algebraic equations using the trapezoidal method aided with finite difference approximation, then applying two-block techniques with the predictor value obtained, and finally modifying the results by modification technique to obtain correcting values for these equations at each fixed point. This approach is computationally attractive, and illustrative examples with usage explanations are provided. Additionally, we provide specific examples to showcase the accuracy of the method, and we employ the least-squares error methodology to minimize error terms within the given domain. Finally, the most common application suggested for the numerical approaches is developed in the Python program.
Copyright (c) 2026 Kurdistan H. Hama, Shazad S. Ahmed http://creativecommons.org/licenses/by-nc-nd/4.0
Coefficient Inequalities for a Novel Class of m-Fold Symmetric Bi-Univalent Functions Related to λ-Pseudo-Starlike and λ-Pseudo-Convex Functions
This article addresses the problem of coefficient bounds for certain subclasses of normalized holomorphic and m-fold symmetric bi-univalent functions associated with λ-pseudo-starlike and λ-pseudo-convex structures in the open unit disk Ữ. Upper estimates for the initial Taylor–Maclaurin coefficients are derived for two newly considered families. Several previously known results follow as particular instances of the obtained estimates.
Copyright (c) 2026 Shahad Kareem Atiyah, Abbas Kareem Wanas, Fethiye Muge Sakar http://creativecommons.org/licenses/by-nc-nd/4.0
New subclasses for bi-univalent functions in relation to Sălăgean integro-differential operator
In this present work, two novel classes for bi-univalent functions are introduced using the recently developed Salagean integro-differential operator. Estimates for the two initial coefficients of Taylor-Maclaurin series are also provided. Furthermore, the well known Fekete-Szego functional is investigated and bounds for each of the initial coefficients for the functions belonging to this classes are obtained.
Copyright (c) 2026 Maryam S. Maje, Mustafa I. Hameed http://creativecommons.org/licenses/by-nc-nd/4.0
A Mathematical Modeling and Comparative Analysis of PSO and GWO for Efficient Cloud Job Scheduling
Cloud computing has become a basic part of modern IT services. Job scheduling remains a key technical issue because it influences performance, cost, and resource use. Traditional static and heuristic schedulers often struggle with the dynamic, heterogeneous, and multi-objective nature of current cloud environments. This has encouraged the use of metaheuristic methods. This study presents a systematic empirical comparison of two algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO)) for cloud job scheduling. Both algorithms were implemented in a Cloud Sim simulation environment. We used Planet Lab-style workload traces and EC2-like heterogeneous VM catalogs to support repeatable experiments. The evaluation considered Make span, Flowtime, Total Cost, and Average CPU Utilization in a baseline setup (512 tasks, 8 VMs). In the other tests, the number of tasks was increased to 2,000. Under these conditions, the PSO results were more acceptable. Compared with GWO, mean completion time was reduced by about 39.11%. Average CPU utilization increased by 16.21%. Total cost showed a modest decrease of 4.11%. PSO generally was able to produce schedules within fewer iterations. On the other hand, GWO showed a different pattern. When larger populations or longer runtimes were available, it appeared more suitable for deeper exploration. Overall, the study provides a reproducible methodology, a documented data pipeline, and a set of empirical benchmarks.
Copyright (c) 2026 Dunia Ameen Abd Al-sahib, Zainb Hassan Radhy, Dhuha Taima Al-Dawoodi http://creativecommons.org/licenses/by-nc-nd/4.0
An Adaptive Coordinate-Stretched Spectral Collocation–QLM Scheme for Singularly Perturbed Boundary-Layer Problems
An adaptive coordinate-stretched spectral collocation algorithm with the quasilinearization technique is developed in this paper for the numerical solution of singularly perturbed boundary value problems. The method uses a stretching parameter in the computational domain, determined adaptively by a residual-based spectral criterion, thereby obviating the need for manual control over boundary layers. The approach is tested on a benchmark set of problems, including linear and nonlinear boundary-layer models. The scheme's accuracy is close to machine precision for the well-posed test cases. It remains stable and high-order accurate for strong singular perturbation regimes when sharp boundary layers are resolved using a modest degree of polynomials. Comparisons with the classical spectral collocation and fixed-mapped strategies are also presented, which show that the adaptive procedure both achieves node clustering results as good as those methods and yields competitive performance without the need for any specific ad hoc parameter tuning. Numerical results indicate the possibility of a simple and reliable alternative method for solving nonlinear boundary-layer problems.
Copyright (c) 2026 Ali Jabbar Ouda Al-Nasrawi http://creativecommons.org/licenses/by-nc-nd/4.0
A Critical Analytical Review of Hybrid Grey Models and Their Applications in Forecasting Non-Stationary Time Series
Even so, forecasting non-stationary time series remains challenging due to the unavailability of data, uncertainties, and highly time-variable patterns. Due to the unique advantages of grey system theory, such as the low-data requirement, hybrid grey models have gained more and more extensive attention as hybrid models, which are realized by merging grey system theory with other intelligent methods, including fuzzy logic, artificial neural networks, met heuristic optimization, and deep learning, and have displayed significant flexibility and accuracy. This study provides a systematic and critical review of hybrid grey models, encompassing their structural framework, integration strategies, and applications in various forecasting domains. The nested, parallel, and serial hybridization approaches are then categorized, and the trade-offs between model sophistication and predictive performance are discussed. The significant drawbacks, including interpretability issues, the risks of over fitting, and the lack of standard benchmarking protocols, are also highlighted. Ultimately, the paper outlines potential research paths, including explainable AI, probabilistic reasoning, and automated mechanisms for model selection. The provided insights serve as a valuable reference for researchers and practitioners seeking to design hybrid grey forecasting models that are effective in uncertain environments.
Copyright (c) 2026 Ali Abdul Karim Kazem Naji http://creativecommons.org/licenses/by-nc-nd/4.0
Certain geometric properties define on meromorphic via Bernardi integral operator on complex Hardy space
The aim goal of this paper to study the bernardi integral operator on a new sub class of analytic meromorphic function of Hilbert space and more specifically on the hardy space , and we obtain the coefficient estimates for the new subclass with certain geometric properties as growth and distortion also discussed radii of starlikeness and convexity functions in the end the convexity of linear combination with the class is discussed.
Copyright (c) 2026 Omar j. mohammed, Abdul Rahman S. juma http://creativecommons.org/licenses/by-nc-nd/4.0
Comparison between numerical solutions and analytical solutions of the fractional logistic biological equation in its linear form
Microsoft Excel is commonly treated as a simple spreadsheet program that is mainly applied This study compares the analytical and numerical solutions of linear fractional logistic equation qualitatively. Findings differ from both numerical of fractional solution biological logistic equation and analytical solution of first order linear fractional logistic both have same behavior during ascent process but do present some difference.
Copyright (c) 2026 Ahmed Salam Razzaq http://creativecommons.org/licenses/by-nc-nd/4.0
An Advanced Generalized Fixed Point Theorem for Solving Ordinary Differential Equations
This study examines the use of the generalized fixed point (FP) theorems in handling ordinary differential equations (ODEs), and specifically in the framework of fundamental principles and applications. Their major findings such as Banach's Contraction Principle and Schauder's Theorem are used to prove the existence and uniqueness of solutions in diverse mathematical systems. The paper generalizes fixed point theories to include more general classes of contraction mappings and boundary value problems, thus providing new methods of analysis to solve complicated ODEs. Through a critical analysis of metric spaces and iterative procedures, the study presents some of the historical and theoretical breakthroughs and puts into perspective practical applications in the engineering, physics, and computational mathematics sectors, among others. The major contribution of the work is the formulation of generalized contraction principles, that allow extending the applicability of FP theorems to non-traditional spaces and open up new methods of solving differential equations.
Copyright (c) 2026 Mohammed Amer Atiyah, Hassan Dawwas Kashash, Faiz Chaseb Khuder http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Review of Ranking Functions in Fuzzy Number
Fuzzy numbers represent vague numerical quantities, making them difficult to organize according to their magnitude. For making critical decisions, scheduling these numbers is essential. Because fuzzy numbers are often in a state of uncertainty, alternatives are calculated, and comparing them essentially involves choosing between alternatives. Previous studies abound with methods and methodologies addressing fuzzy number (FN) comparison. Many different, simple, and consistent methods are used in research and studies for FN comparison. The purpose of this procedure review is to understand the concepts of different ordering functions for triangular and trapezoidal fuzzy numbers, as presented in previous studies and research. This research will present the results applied to triangular and quaternary fuzzy numbers and their various ordering functions, and compare them.
Copyright (c) 2026 Rasha Jalal Mitlif http://creativecommons.org/licenses/by-nc-nd/4.0
Constructing Regression Model Using Penalty Methods to Process High-Dimensional Data for a Factorial Experiment
Research addressed high-dimensional data in a 2³ factorial experiment model using a completely randomized design. The columns of matrix (X) represented the effects of factor levels and their interactions, along with the overall arithmetic mean, while the rows represented the number of experimental observations. The high-dimensional data problem has been addressed by using several methods, comparing them according to various criteria. Penalty methods (Bridge, LASSO, ALASSO, SCAD) were employed to select significant factors in the model. Through simulation and application of statistical criteria, the performance of these methods was compared, with results Lasso and Adaptive Lasso show poor performance in most cases, with high MSE and MAD values. CAD generally falls in between, offering better performance compared to Lasso and Adaptive Lasso, but not as good as Bridge.
Copyright (c) 2026 Mahmood M Taher http://creativecommons.org/licenses/by-nc-nd/4.0
The Penalized Trimmed Least Squares Method for Robust Variable Selection in Multiple Linear Regression
The large number of independent variables often causes problems in the accuracy of the multiple linear regression model. This has motivated researchers to find or select the best model using methods such as forward selection, backward elimination, and stepwise regression. However, these methods have become time-consuming and ineffective when dealing with high-dimensional data. Therefore, statistical literature has proposed the Lasso method and its variants to address these issues. Nevertheless, these methods are sensitive to outliers, which has led to the emergence of several robust studies—particularly focusing on robustifying penalized methods for high-dimensional data, in order to achieve effective variable selection and robust parameter estimation. Among these methods is the Trimmed Penalized Least Squares (RPLTS), which aims to make the Lasso approach more robust against outliers appearing in the dependent variable or in the residuals. However, this method remains sensitive to the presence of leverage points. Accordingly, this research aims to weight the RPLTS method to select the best subset of variables by reducing the influence of leverage points and improving the model’s accuracy. Simulations and real data were used to evaluate the efficiency of the proposed method and compare it with the previous method. The comparison was based on several statistical criteria such as variable selection accuracy, sensitivity to influential variables, specificity of non-influential variables, and mean squared error (MSE) of the model. The method that achieves the highest accuracy, sensitivity, and specificity rates, along with the lowest MSE, is considered superior. The analysis of both real and simulated data demonstrated that the proposed method outperforms the previous one in terms of robustness and efficiency.
Copyright (c) 2026 Hassan S. Uraibi, Hassan Ali Abis http://creativecommons.org/licenses/by-nc-nd/4.0