Iraqi Journal for Computers and Informatics
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An Automated Tool for Streamlining Software Engineering: Information Extraction and Decision
Full text linkIn the always-evolving and dynamic field of software development, good decision-making is absolutely critical. Developers have to regularly decide how best to apply features, optimize performance and debug issues. This process could be much improved by extracting actionable insights from software code. The presented work explores the tools and metrics available to enable developers to make data-driven decisions, therefore enhancing the development efficiency as well as code quality. Also, it introduces a new automated tool called CodeLens which analyzes software code, extract lines of code (LOC), documentation quality, complexity, and other key criteria. Through a consolidated view of such metrics, the tool helps developers evaluate code fit, spot possible bottlenecks, and prioritize optimization or refactoring efforts. Furthermore, the tool\u27s support of Java and Python languages guarantees general applicability, hence fitting for many software projects
Assist Blind People in Road Crossing by Integrating a Smart System into Wearable Devices and Vehicle Applications
Full text linkThe ability to navigate safely is a critical issue for people with visual impairments, especially in urban cities where traffic flow is a constant danger. While traditional tools such as white canes and guide dogs provide some assistance, they are often limited in detecting fast-moving hazards (such as a vehicle) and lack integration with modern technologies. The goal of this research paper is to create and test a prototype of a smart system based on the Internet of Things (IoT) that will help blind individuals become more aware of their surroundings and be able to get around on their own. There are two primary pieces to the system: a smartwatch that one can wear and a mobile web-based application. The smartwatch is built on the NodeMCU ESP8266 module and has a GPS module, a buzzer, and an LED indication. All of them are built into a comfortable design that can be worn on the wrist. The system keeps an eye on where the blind person is and gets alerts through a real-time Firebase database. The web program runs on the driver\u27s phone, tracks the car\u27s location, figures out how close it is to the blind user, and sends warnings when the predicted arrival time drops below two minutes. We did field experiments at different speeds (10 km/h, 20 km/h, and 50 km/h) across a distance of 2 kilometers. The results demonstrated a high response accuracy exceeding 99.6%, stable GPS accuracy ranging between 96% and 98%, and an average alert response time of less than 1.5 seconds. These results confirm the system\u27s ability to provide immediate alerts and improve the safety of the blind. By combining real-time cloud services, geolocation, and alerts, this prototype provides an effective system that enhances the safety and mobility of blind people in urban environments. The system\u27s performance indicators support its effectiveness in providing two-way interactive alerts and proactive responses to protect the user
Post-Quantum Cryptographic Techniques for Future-Proofing-Blockchain-Based Personal Data Sharing
Full text linkBlockchain has become a critical enabler of secure data sharing in domains such as healthcare, finance, and digital identity. However, its reliance on classical cryptographic schemes (e.g., RSA, ECDSA, SHA-256) makes current systems vulnerable to emerging quantum computing attacks, raising risks to data confidentiality, integrity, and long-term trust. This paper addresses this challenge by proposing a modular hybrid framework that integrates post-quantum cryptographic (PQC) techniques into blockchain-based personal data sharing. The framework combines lattice-based encryption for protecting off-chain data, hash-based signatures for smart contract authentication, and quantum-safe zero-knowledge proofs and trusted execution environments (TEEs) for privacy-preserving verification and secure key management. To ground this design, we conducted a systematic literature review of 35 studies published between 2018 and 2025, analyzing security, scalability, interoperability, regulatory alignment, and user autonomy. Findings reveal that only 5 out of 35 studies (14%) explicitly addressed quantum threats, with over 80% focusing on theoretical resilience without testing implementation constraints. Furthermore, 90% of proposals neglected smart contract compatibility, and only 8% (3/35) incorporated TEEs, underscoring implementation barriers in contract execution, secure key management, and performance integration. Prototype evaluation demonstrated that the framework sustained 1,500 TPS on Hyperledger Fabric, achieved a 75% reduction in storage bloat using IPFS, and supported GDPR-aligned workflows with 99.98% audit log completion and 95% successful erasure requests. Privacy was further strengthened through zk-STARK proofs, which reduced unauthorized access by 40%, while TEEs improved key management efficiency by ~28%. Although PQC introduced 5–12 seconds of latency, consent revocation was processed in under 2.1 seconds, highlighting both the feasibility and trade-offs of practical post-quantum deployment. This work demonstrates a clear pathway toward quantum-resilient blockchain infrastructures that safeguard personal data, comply with regulatory standards, and maintain user trust in the quantum era
Reducing Bias in Classification using Fairness Stacking Meta-Learning
No full textThe predictive validity of machine learning models depends on the training data. In some cases, training data contains historical, social, or demographic inequalities, which leads algorithms to reproduce unfair results. This paper proposes a fairness-constrained stacking meta-learning approach for reducing bias in classification by aggregating a set of classifiers through a constrained ensemble learning scheme. A set of base classifiers, including Decision Tree, Naive Bayes, Support Vector Machine (SVM), and LightGBM, are trained and evaluated on the Adult Census Income dataset using both predictive and fairness metrics. The final meta-model is constructed as an aggregation of only the fair-performing models, while models failing to meet the fairness threshold are excluded. Learned weights are then optimized to maximize the F1-score while maintaining fairness constraints. Experimental results demonstrate that the proposed method achieves predictive performance (Accuracy = 0.91, F1-score = 0.82) while substantially reducing disparity between demographic groups (EOD = 0.03 for sex and 0.04 for race). These findings indicate that fairness-aware stacking ensembles can provide a solution for mitigating algorithmic bias through an aggregation framework that balances accuracy and fairness
User Authentication Based on Mouse Dynamics Using an Efficient-Net Model
Full text linkAs digital threats become increasingly sophisticated, user authentication has become vitally important in cybersecurity. Traditional authentication methods such as passwords are under increasing assault from a range of attacks. Behavioral biometrics, such as mouse dynamics, have the potential to address these attacks in a way that is largely passive and continuous. In this paper, we present a new solution that rests on mouse dynamics behavior together with a lightweight deep learning model inspired by EfficientNet, specifically designed for Behavioral Assessment of Numerical Data (BAND). The SapiMouse dataset, consisting of mouse tracking data from 120 actual users, is harnessed. By applying preprocessing techniques such as Quantile Transformation and Min-Max Encoding, along with encoding, the raw data were prepared for model training. The modified EfficientNet model retains its computational efficiency while also being tailored to work with numerical input. Its structure uses compact convolutions along with compound scaling to capture time-series mouse data discriminative features, lowering the processing burden while maintaining accuracy. Moreover, to stabilize training and enhance generalization, dropout and batch normalization layers were added, ensuring robustness to overfitting, even when using data generated by a model. CGAN’s capacity for class sample synthesis was harnessed towards improving recognition of unused user profiles, resulting in a total of 240 unique classes (120 real + 120 synthetic). The model reached an accuracy of 99.24% for classification and a macro-averaged F1-score of 0.991 on the testing set. An inference time of only 0.2331 seconds per sample, alongside a cumulative training duration of 158.25 seconds, suggests real-time applicability. These findings support the promise of repurposing advanced deep learning models for behavioral biometrics, providing affordable, scalable, and efficient user verification for sensitive security contexts
An Extractive Text News Summarization: A Hybrid Optimization with Ensemble Learning Approach
Full text linkAutomatic Text Summarization is a crucial feature for managing the ever-increasing volume of textual data. However, existing methods often struggle with feature identification for sentence importance, which leads to a lack of maintained narrative coherence and accuracy. In this proposed approach, the summarization process leverages the Chi-square Binary Cuckoo Search (Chi-BCS) method for feature selection, this optimizes text features enhance the summary content and utilizes insights from classification to ensure summaries are contextually relevant and concise. Feature selection aims to improve the performance of machine learning models by reducing the dimensionality of the input data and removing irrelevant or redundant features. Classification, on the other hand, contributes to better summarization by distilling lengthy or redundant content into key points, thereby enhancing both efficiency and accuracy. The proposed approach implements a model that leverages advanced Natural Language Processing and machine learning techniques for effective extractive summarization on both BBC and CNN/DailyMail datasets. Key features extracted from the text include Named Entity Recognition, Cue phrases, TF-IDF, Sentence position, sentiment analysis, etc. Various algorithms are employed to improve classification performance, such as Decision Trees, Support Vector Classifier, Gradient Boosting, Random Forest, K-Nearest Neighbors, and Logistic Regression. Among all the methods evaluated, the Random Forest and Ensemble Hard Voting approach achieved the highest F-score of 96.26 and 0.9322 respectively on the BBC and CNN/DailyMail dataset. In the text summary evaluation, the ensemble method also delivered exceptional results, with ROUGE-2 and ROUGE-L F1 scores reaching 0.799 and 0.818, respectively on BBC. While our ensemble model achieved to high score on ROUGE1 and ROUGE 2 reaching 0.275, 0.5017, respectively on CNN/DailyMail when compared with state of art highlighting the model\u27s strong performance. These findings demonstrate that the proposed model is highly effective for both the classification and summarization of large-scale textual data
Hybrid LSTM–Seq2Seq Models: Improved Patient Interaction for Healthcare Chatbots
Full text linkList of Contributors (قائمة المساهمين):
Name: Saba A. A. AliE-mail: [email protected]: AuthorPrimary Contact: Yes
Name: Prof. Dr. Mohammed HadiE-mail: [email protected]: Co-AuthorPrimary Contact: No
Name: Dr. Mustafa MusaE-mail: [email protected]: Co-AuthorPrimary Contact: NoHealthcare chatbots play a critical role in improving communication between patients and healthcare providers by offering accurate and timely responses. A novel approach is proposed, which leverages a deep learning model that combines long short-term memory (LSTM) neural networks and a sequence-to-sequence (Seq2Seq) architecture to enhance text prediction accuracy in medical dialogue systems. The model leverages the capability of LSTM to capture long dependencies in sequential data alongside the contextual encoding of Seq2Seq, which improves predictive quality in dialogue responses. The encoder–decoder architecture, which utilizes tokenization and padding to standardize input sequences, contributes to the improvement in data processing. The validation accuracy of the model is 0.9766, with a loss of 0.0184. Specifically, the precision is 0.9961, the recall is 0.9981, and the F1 score is 0.9971. The capability of the model for sequence prediction is attributed to its robustness. Other methods of evaluation employing measures such as the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and normalized root mean square error demonstrate that the model is superior to other machine learning algorithms utilizing linear regression and GP regression. Employing callback functions during training ensures the best-fit model is saved, which makes the method viable in different tasks described in the job descriptions
An Enhanced and Adaptive Algorithm for Secure Encryption of Data using Advanced Encryption
Full text linkBecause of new multimedia types and advances in technology, protecting data has become extremely valuable. Because of the changes happening around us, cryptography continues to protect information that is crucial for security. Security methods and systems are continually being improved, but we should continue to review data protection methods while data travel through different services. One of the reasons data systems are secure today is because of encryption which makes plaintext into ciphertext. It examines the strengths and weaknesses of various cryptographic algorithms using what has been written by other authors. This paper reviews the use of cryptography in several literatures to make data security better nowadays. Data is protected using encryption during online and other transfers, yet the information may be obtained from an unauthorized attacker who is persistent enough. Two or more approaches to security should be combined, according to what is highlighted in the article. AES encryption and decryption can be improved by adding other algorithms such as the replacement algorithm. AES is used in this study to safeguard a message before it is presented. In 1998, Rijmen and Daemen designed the AES algorithm and named it Rijndael. Many people have started using it because its security is strong and it is not easily broken using brute force. The technique is applied on the plaintext of the AES algorithm. Thanks to this approach, breaking the encryption is extremely difficult, as you need the right key, though AES itself remains a simple system. The performances of both Advanced Encryption Standard and its modified option were tested after they were put into practice. There was an avalanche of 54.69% with the new version of Advanced Encryption Standard compared to the 50.78% observed in the standard AES. The strong encryptio
A Systematic Review of Federated Learning: Emerging Techniques, Challenges, and Research Directions
Full text linkFederated Learning FL is a rapidly evolving machine learning paradigm that enables collaborative model training across decentralized data sources while preserving data privacy. Since its inception in 2016, FL has emerged as a transformative approach in domains such as healthcare, IoT, and edge computing, where data sensitivity and regulatory constraints limit centralized processing. This systematic review consolidates findings from 50 high-quality studies selected from over 250 papers to present a comprehensive synthesis of FL methodologies, core aggregation techniques, privacy-preserving mechanisms, security threats, domain-specific applications, and emerging trends. We categorize challenges into communication overhead, heterogeneity (device, data, model), fairness, trust, and evaluation inconsistencies. We highlight research gaps, notably in standardized evaluation, incentive mechanisms, and deployment scalability. By reviewing recent advances such as vertical federated learning, federated unlearning, and blockchain-based incentives, this paper offers a roadmap for future research and identifies open questions vital for widespread FL adoption
Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework
Full text linkAcute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. The design method employed deep neural networks to recognize the features of the cells in question and then distinguish them as either leukemic or healthy cell types. The proposed laboratory method, with the use of the curated datasets of annotated pathological images of normal lymphoid progenitor cells, aimed to tackle the challenges related to the lack of curated datasets with annotated images of these cells. These challenges are believed to be linked to imprecise and time-consuming leukemia diagnosis and cure process. The simulated results confirmed the efficiency of the suggested technique, where CAE showed a correlation coefficient of 0.987 for lymphoblastic cells and CNN had an accuracy rate of 99.92% in ALL diagnosis. Such data demonstrated the capability of deep-based methodologies to fight leukemia