An Automated Tool for Streamlining Software Engineering: Information Extraction and Decision

In 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

Image-Based Malware Detection Using Deep CNN Models

Malware or malicious software represents one of the most remarkable threats to cybersecurity, as it compromises the integrity, confidentiality, and availability of computer systems and networks. Traditional malware detection methodologies frequently prove inadequate in identifying innovative and sophisticated malware variants. Deep learning (DL) presents a promising strategy for malware detection by utilizing advanced algorithms that are capable of discerning intricate patterns from extensive datasets. This study presents a model based on deep learning with Convolutional Neural Network (CNN) for malware classification. This research utilized the Malimg dataset, which includes 9,339 malware samples from 25 distinct families. The approach requires resizing malware images to a resolution of 64 x 64 pixels and normalizing these images for model training. The selection of a 64 × 64 size frame reduces network complexity while speeding up training without sacrificing important information. The architecture of the proposed CNN primarily consists of more than one convolutional layer, max-pooling, dropout to mitigate overfitting problem, fully connected layers for achieving better classification results. The proposed model established an impressive accuracy of 96%. For model evaluation, the following measures of accuracy were used: precision, recall, F1-score, and accuracy. This research shows that CNN-based methods can have a high level of effectiveness in detecting obfuscated malware

IoT intrusion detection system based on machine learning and deep learning

The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments

User Authentication Based on Mouse Dynamics Using an Efficient-Net Model

As 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

Securing DNA Profiles Using AES Cryptography: New Approach to Encrypted Biometric Authentication in EHR Systems

Electronic Health Records EHRs have revolutionized healthcare by storing patient data in a digital format, increasing accessibility and efficiency. However, the flaws of traditional authentication methods necessitate the development of advanced security solutions. This study presents a novel methodology integrating AES-256 encryption with DNA-based steganography to enhance biometric verification in Electronic Health Records EHRs. The approach involves extracting Short Tandem Repeats STRs and Single Nucleotide Polymorphisms SNPs from DNA profiles, encoding the genetic data into binary format, and securing it with AES-256 encryption to ensure high confidentiality. Encrypted DNA profiles are embedded in MRI images by using the Discrete Cosine Transform DCT, which ensures the concealed data remains both imperceptible and secure against unauthorized alterations, and during the authentication phase, and the encrypted genetic information is extracted, decrypted, and matched with reference samples for verification, the experimental results indicate that the system significantly improves both biometric security and medical data protection, with average processing time of approximately 320 milliseconds, and as it exhibits strong resistance to tampering, achieving has a 99.8% success rate in preventing unauthorized modifications. The embedding method maintains a high level of image quality, reflected by a Peak Signal-to-Noise Ratio PSNR of around 47 dB, confirming that the diagnostic utility of MRI images remains unaffected. This approach effectively combines biometric security with medical data safeguarding, providing a dependable and scalable solution for patient authentication in electronic health record EHR systems

Post-Quantum Cryptographic Techniques for Future-Proofing-Blockchain-Based Personal Data Sharing

Blockchain 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

Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach

Land Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq\u27s Land Use and Land Cover Monitoring with Remote Sensing Data in the Period 2019–2023. This paper performed land use/land cover LULC type classification and time series analysis using Sentinel-2 satellite imagery for the years 2019 and 2023 to identify changes over time. Remote sensing data is used in this paper to address the challenge of detecting land cover change in Iraq through SVM classification. This goal aims to develop a fundamental method of mapping and monitoring these changes, encouraging sustainable land use practices, and achieving the United Nations Sustainable Development Goals. Land cover classes were categorized into five main types: Water, Barren, Building, Vegetation, and Rangeland. The study showed a marked increase in urbanization, and most of this occurring in previously bare soils at the edges of cities. This urbanization was primarily driven by population growth and economic development. What is beneficial for the environment can also be beneficial for us as people humanity as these findings have major implications for urban planning, green space management, and sustainable city development. It seems that there was no change to the existing barren land and buildings, which increased by 8% and 11% respectively, as noted from the data up to October 2023. However, vegetation coverage decreased by 27%, indicating a significant loss of green area. The water category was also up 9%. Results showed satisfactory accuracy assessment (OA: 93.11%) from applying a Support Vector Machine SVM for the LULC classification. The study lays the foundation for ongoing monitoring of LULC changes in Iraq

Cross-sectional time series models for identifying the most important factors of e-government growth in Arab countries

The goal of this study was to determine the key elements driving the expansion of e-government in Arab nations. One of the econometric models—the cross-sectional time series model (panel)—was employed to accomplish the intended outcome. The following techniques were used to estimate the models: fixed effects model, random effects model, and linear panel data regression analysis. The results revealed that e-government is negatively affected by political stability and the absence of terrorism and violence (PV), positively affected by government effectiveness (GE), positively affected by regulatory quality (RQ), and positively affected by the rule of law (RL)

Optimized Security for Blockchain Edge-Fog Systems Performance Analysis and Optimization Strategies

The trends of resource consumption and optimization mechanisms for blockchain-enabled security in edge-fog computing environments. While blockchain provides robust security for fog networks in a decentralized fashion, its demand for resources creates tremendous challenge in resource-constrained settings. Through in-depth examination of a Practical Byzantine Fault Tolerance PBFT-based blockchain deployment across 50 edge devices and 10 fog nodes. The study reveals the most critical resource bottlenecks and proposes an adaptive resource management framework that maximizes the tradeoff between security requirements and operational efficiency dynamically. The proposed work shows that data-type-based optimization and intelligent workload distribution can reduce CPU utilization by 27%, memory by 22%, and network bandwidth by 38% without sacrificing security assurance. The introduction of a novel dynamic resource allocation algorithm that adjusts consensus participation and cryptographic strength to current system conditions, demonstrating that security-performance trade-offs can be optimally resolved through context-sensitive optimization. These advancements are a move towards resource-constrained security architectures for edge-fog computing, enabling the broader applicability of blockchain security in resource-poor IoT environments

An Enhanced and Adaptive Algorithm for Secure Encryption of Data using Advanced Encryption

Because 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