is a peer-reviewed publication dedicated to cybersecurity, system design, and emerging technologies. It focuses on the challenges of the digital and quantum age, publishing research on quantum-safe cryptography, cyber threat detection, secure communication, privacy-preserving technologies, system vulnerability analysis, emerging technology security, incident response & forensics, and human factors in cybersecurity. The journal publishes two issues annually, promoting global collaboration and advancing research in these critical areas.
Quantum-Resistant Digital Image Encryption: A Practical Permutation–Diffusion Framework for Securing Visual Content in the Post-Quantum Era
The migration toward quantum computing threatens the cryptographic primitives that secure digital imagery. Shor’s algorithm dismantles the integer-factorization and discrete-logarithm assumptions underlying RSA and ECC, while Grover’s algorithm erodes the effective strength of symmetric ciphers and hash functions. Digital images, owing to their bulk volume, strong inter-pixel correlation, and high redundancy, demand encryption schemes that differ from those designed for text. This paper proposes a Quantum-Resistant Image Encryption (QRIE) framework that combines a chaotic Logistic–Sine permutation stage with a diffusion stage whose keystream is generated from SHAKE256—an extendable-output function of the SHA-3 family and whose session key is established by a NIST-standardized module-lattice key-encapsulation mechanism (ML-KEM, FIPS 203). The plaintext-dependent seeding binds every keystream to the image content, providing resistance to chosen-plaintext and differential attacks. The framework was implemented and evaluated on the standard 512×512 Cameraman benchmark. Measured results show an information entropy of 7.9992 bits/pixel, adjacent-pixel correlation coefficients reduced from above 0.97 to below 0.02, NPCR of 99.62%, UACI of 33.53%, and a cipher-image chi-square of 272.66 (below the 0.05 critical value of 293.25), confirming statistical uniformity. A single-bit change in the key fails to recover any visual information, demonstrating high key sensitivity. The results are consistent with state-of-the-art chaos-based ciphers while additionally inheriting post-quantum key-establishment and keystream generation.
Copyright (c) 2026 Mustafa Shubber , Armin Rashno (Author) https://creativecommons.org/licenses/by/4.0/
AI as a Cognitive Shortcut: Examining the RelationshipBetween AI Dependency, Students’ Understanding, and Critical Thinking in Higher Education
This research papers Studying the impact of artificial intelligence as a pithiness knowledge tool in higher education this research focuses on students exploring although the references of this study the dependency of AI in the academic field, is still limited by Deeping understanding and syllabus. Research cautions on the risk of cognitive dependency. To study this phenomenon a structured questionnaire was adopted as a quantitative approach and was distributed to students of higher education..190 responses were collected to describe the statically analysis to test AI usage, dependency and the Expected educational results The findings revealed that, although there were many tools available to use within AI, students could only understand to a very limited extent. In addition the reliance on AI tools is taken to mean the short cuts rather than Deeping learning and individual thinking. Should display the concept that AI is a shallow knowledge and is centered on the gap between the widely adopted and fruitful learning result.
Copyright (c) 2026 Suha Al-Dulaimi (Author) https://creativecommons.org/licenses/by/4.0/
A Multi-Plane Detection Framework for Quantum-Enabled Cryptographic Attacks: Operationalizing the Detection of Harvest-Now-Decrypt-Later Activity and Attacks on Post-Quantum Cryptography
The advent of cryptographically relevant quantum computers (CRQCs) poses a fundamental threat to public-key cryptography securing modern digital communications. The most immediate danger is not direct decryption but the harvest-now-decrypt-later (HNDL) attack, wherein adversaries passively collect and archive encrypted traffic today for retrospective decryption once a CRQC becomes operational. This threat is structurally difficult to counter because the collection phase generates no host- or account-level signals detectable by conventional intrusion-detection systems.Simultaneously, migration to NIST-standardized post-quantum cryptography (PQC) specifically ML-KEM (FIPS 203), ML-DSA (FIPS 204), and SLH-DSA (FIPS 205) introduces new vulnerabilities, including chosen-ciphertext side-channel attacks, fault-injection attacks, and protocol downgrade attacks arising during hybrid deployment periods.Existing security tooling addresses cryptographic inventory, encrypted-traffic anomaly detection, and PQC readiness as isolated problems, leaving a critical operational gap. To address this, we propose QCAD (Quantum Cryptographic Attack Detection), a unified multi-plane detection framework integrating four complementary detection planes: (A) flow-metadata behavioral analytics targeting HNDL collection indicators; (B) host- and protocol-level telemetry for PQC implementation flaws and downgrade attacks; (C) continuous crypto-agility monitoring with per-asset Mosca-inequality risk scoring; and (D) quantum-vulnerable deception canaries. These planes feed a unified correlation layer that maps observations to MITRE ATT&CK techniques and produces risk-ranked alerts.Evaluation against synthetic HNDL and PQC-attack benchmarks demonstrates that cross-plane fusion achieves a recall of 0.97 at a 5% false-positive rate (F1 = 0.90, ROC-AUC = 0.99), substantially outperforming any individual plane, confirming that no single observable captures all attack families.A key boundary condition remains: HNDL detection is inherently a probabilistic behavioral-inference problem, and interception occurring on transit infrastructure beyond the defender's perimeter may remain undetectable regardless of sensor deployment.
Copyright (c) 2026 K G Kharade, K.Vengatesan (Author); Hayder Kareem Algabri (Translator) https://creativecommons.org/licenses/by/4.0/
Assessing Digital Government Systems through AI-Powered Techniques
Digital government represents the implementation of information and communication technologies (ICT) in provision of public services, governance process optimization and transparent interaction with citizens. However, despite rapid progress in the field, the comprehensive assessment of digital government maturity and quality is rather challenging due to complex nature of related measures. Existing frameworks for evaluation of digital government—from EGDI of UN to DESI of EU—are based on complex indexation across many metrics, thus rendering comparisons difficult and impeding specific recommendations for improvements. This paper proposes a new AI-based approach to digital government assessment and benchmarking based on use of Kohonen Self-Organizing Map (SOM) neural network and focused on the generation of comprehensible cluster maps of digital government performance at national and sub-national level. Data set comprising information over ten-year period was gathered from government repositories and supplemented by public survey with five major dimensions addressed: Data, Technology, Service, People, and Governance. Preprocessing of the data included an elaborate preprocessing pipeline involving the use of imputation, elimination of outliers, data normalization, and principal component analysis for dimensionality reduction before modeling could be undertaken. The results from this process showed four groups of performance, each with built-in outlier detection. Validation of results was achieved using ten-fold cross-validation. The technique proved more effective and accurate than conventional techniques like K-means and DBSCAN and multilayer perceptrons, as indicated by a Davies-Bouldin index value of 0.43 and an accuracy of 93.2%. These results suggest that unsupervised deep learning can be effectively employed as a tool by policymakers and digital government administrators.
Copyright (c) 2026 Zaid Ibrahim Rasool (Author) https://creativecommons.org/licenses/by/4.0/
A Robust and Efficient Attention-CNN-BiGRU Framework for Sentiment Analysis in Noisy Social Media Environments
The proliferation of social media platforms, particularly X, has revolutionized public communication but poses challenges for sentiment analysis due to informal language characterized by abbreviations, slang, emojis, and creative spellings. This study presents an Attention-Enhanced Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU) model, a hybrid architecture designed for both accuracy and computational efficiency. It integrates CNNs for local feature extraction, BiGRUs for capturing sequential dependencies, and an attention mechanism to prioritize sentiment-critical components. Evaluated on Sentiment140 (1.6 million tweets, binary classification) and SemEval-2017 Task 4 (65,000 tweets, three-class classification), the model demonstrates statistically significant accuracy improvements (up to 1.7 percentage points) compared to baseline models, including BERT Base, while requiring substantially fewer parameters and offering potential for faster inference. The model achieves 86.3% accuracy on Sentiment140 and 77.2% on SemEval-2017 Task 4, effectively handling linguistic variability. Its comparatively lightweight design makes it suitable for real-time sentiment analysis scenarios where computational resources may be constrained. This framework offers a robust and scalable solution for analyzing sentiment in evolving online communication, with applications in market research, public opinion monitoring, and crisis management.
Copyright (c) 2026 Rahman Abid (Author) https://creativecommons.org/licenses/by/4.0/
Cyber-Physical System Security in the Age of Quantum Computing: Identifying Threats and Developing Mitigation Strategies
Cyber-Physical Systems (CPS) integrate heterogeneous hardware and software components across multi-layered architectures, enabling real-time interaction between computational elements and physical environments. The rapid evolution of quantum computing introduces a paradigm shift in the threat landscape for CPS security, as quantum algorithms — particularly Shor's algorithm — threaten to compromise widely deployed public-key cryptographic schemes such as RSA and ECC. This paper provides a structured analysis of quantum-induced threats to CPS and evaluates candidate mitigation frameworks, including Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD). We present a taxonomy of attack scenarios organised by CPS layer, a comparative analysis of quantum-resilient defence models, and a conceptual risk-projection model illustrating how cryptographic breach probabilities may evolve as quantum hardware matures between 2025 and 2050. NOTE: All tabular data in this paper are based on conceptual simulations derived from theoretical models and existing literature; they are intended as illustrative benchmarks rather than empirically validated results. Our findings indicate that migration toward hybrid PQC architectures, combined with hardware-level security measures and regulatory modernisation, is the most pragmatic near-term strategy for CPS operators facing the quantum transition.
Copyright (c) 2026 Hawraa Nori (Author) https://creativecommons.org/licenses/by/4.0/
Digital Transformation and Its Role in Enhancing Organizational Resilience
This research aims to provide an in-depth and comprehensive review of the role of digital transformation in enhancing organizational resilience within contemporary dynamic environments characterized by high levels of uncertainty and constant change. The research is based on a critical and systematic analysis of recent literature in the field of IT management and digital transformation, which addresses this concept as a strategic tool for reshaping organizational capabilities and achieving institutional sustainability.The results of the analysis indicate that digital transformation is no longer limited to adopting new technologies, but has become a comprehensive structural and cultural transformation encompassing the redesign of organizational processes, the development of business models, and the enhancement of data-driven decision-making. It also contributes to building dynamic capabilities that enable organizations to adapt to environmental changes and respond quickly to crises, thereby enhancing business continuity and achieving a sustainable competitive advantage.The research also highlights the importance of organizational resilience as a crucial factor in organizational survival, especially in light of global crises such as the COVID-19 pandemic, where digital organizations demonstrated a greater capacity for adaptation through the adoption of remote work, improved resource management, and the reduction of operational risks. However, organizations face numerous challenges, most notably weak digital infrastructure, a shortage of skilled personnel, and resistance to cultural change.The research concludes that there is a research gap characterized by a lack of applied studies and integrated models linking digital transformation and organizational resilience, particularly in developing environments.
Copyright (c) 2026 Mustafa Adnan Madloul Al-Bakri (Author) https://creativecommons.org/licenses/by/4.0/
IT Management and Managerial Mindset Transformation: A Critical Subject Review
Purpose: This subject review investigates how Information Technology (IT) Management contributes to reshaping the managerial mindset within contemporary organizations, with particular attention to decision-making behavior, cognitive adaptation, and the mediating role of human factors between technology adoption and organizational performance.Design and Approach: A descriptive-analytical literature review was conducted on peer-reviewed publications retrieved from Scopus, Web of Science, Science Direct, and Google Scholar, covering the period from 2020 to 2025, with a small number of seminal earlier works retained where conceptually indispensable. Thirty-five sources were retained for in-depth analysis after applying inclusion and exclusion criteria, including topical relevance, methodological transparency, and publication in indexed journals or recognized academic presses.Findings: The review indicates that IT Management has shifted from a supportive operational function to a strategic cognitive enabler. Its influence on organizational performance is rarely direct; rather, it is mediated by managerial cognition, organizational culture, and digital readiness. Emerging technologies artificial intelligence, cloud computing, big-data analytics, and cybersecurity frameworks amplify this mediating effect but also introduce technostress, algorithmic bias, and ethical concerns.Originality: The study proposes an integrated conceptual framework that positions managerial mindset as a cognitive intermediary between IT capabilities and performance outcomes, with explicit attention to developing-country contexts that remain underrepresented in mainstream IS literature.
Copyright (c) 2026 Noor Thamer, Mustafa Adnan Madloul Al-Bakri (Author) https://creativecommons.org/licenses/by/4.0/
A Robust Intelligent Approach for Phishing URL Detection Using Hybrid Machine Learning
Phishing attacks are still the main and most severe vulnerability to cyber security through deceptive URLs for sensitive user information. Therefore, this paper proposes a framework based on deep learning techniques for phishing and legitimate URLs as an alternative to the currently employed blacklist-based detection approaches that have proven rather limited. The architecture includes one custom Convolutional Neural Network (CNN) and three Transfer Learning Architectures, ResNet50, InceptionV3, and VGG16 using representations of features based on URLs. All models under consideration shall be trained with Adam Optimizer and Binary Cross-Entropy loss function so that a fair comparison can be made under unified experimental set-up conditions. The set is broken down as 70% training, 10% validation, and 20% testing. Experimental results provide clear evidence that transfer learning models perform much better than the baseline CNN. The InceptionV3 model posted a validation accuracy of 100% leading the pack, followed by VGG16 at 98.96%. ResNet50 could muster only 97.92%. The proposed CNN model has also achieved quite competitive performance with a validation accuracy above 97%. Therefore, these results are explicit in confirming the effectiveness, robustness, and generalization capability of deep learning architectures toward the problem of phishing URL detection. This proposed framework will go a long way in ensuring web security is beefed up as it creates a barrier against all evolving cyber threats.
Copyright (c) 2025 Hind Abdulkareem Abdalrazaq (Author) https://creativecommons.org/licenses/by/4.0/
Quantum-Resilient System Architectures: Designing Secure Infrastructure for Next-Generation Computing
There's a lot of hope that, with quantum computing, the future can look much brighter and be focused on solving a lot of complex issues we face in modern society. The power to give and manipulate information with quantum systems can be game-changing for fields such as secure data transfer, unmanageable optimization, drug discovery in medical applications, and certain facets of artificial intelligence. Algorithms over the discouraging search spaces would be able to break existing encryption, and significant progress has been achieved in algorithmic attacks on both secret key (cryptographic hashes and message authentication codes) and public key systems. A thorough survey is required so that the state-of-the-art quantum resilient theoretical and experimental methods and models may be understood from academia as well as from industry, and new directions which need to be promoted are suggested. It is important to investigate the possible security threats, challenges, impact and risk factors introduced by the integration of quantum computing in application systems, middleware, quantum system core components networks and cloud services critical infrastructures standards and regulation.
Copyright (c) 2025 Uqba bn Nafaa Mohammed (Author) https://creativecommons.org/licenses/by/4.0/
Mobile App for Purchasing Tickets: Setram Tickets Ouargla, Algeria
This project presents the design and implementation of "Setram Go", a hybrid mobile application forpurchasing public transport tickets and managing subscriptions in Ouargla, Algeria. During theconception phase, several UML diagrams were used — including use case diagrams, class diagrams,and activity diagrams — to model the system architecture, user interactions, and core functionalities.The application aims to solve problems related to manual ticketing, the absence of electronicpayments, and limited accessibility. Developed using React Native (frontend), Express.js (backend),and MongoDB, the app supports electronic wallet recharge, QR code validation, bilingual interface(Arabic/English), and real-time station mapping. Functional testing confirmed the application's corecapabilities, with future work planned to integrate real payment gateways, offline functionality, and improved security.
Copyright (c) 2025 AZZAOUI Nadjet, Bessedike Zakaria, Touhami Mohammed Lazhar, Osmani Youcef, Koudia Souhil (Author) https://creativecommons.org/licenses/by/4.0/
Comparison between HTTP and HTTPS based on Software engineering Concepts
Hypertext Transfer Protocol Secure (https) has many advantages in terms of performance and security; most importantly, it achieves higher quality standards than Hypertext Transfer Protocol (http). All browsers strongly advise users to trust websites that use HTTPS because it is the only way to protect themselves from a number of risks, attacks, in this paper, the most important characteristics of HTTPS versus HTTP will discussed, and Tested based on software engineering concepts and selected test tool in comparison with Software Quality Factors.
Copyright (c) 2025 Ahmed Saleem Abbas , Noor Thamer Mahmood, Rusul Khalil Hussein (Author) https://creativecommons.org/licenses/by/4.0/
Developing A Lightweight Homomorphic Encryption Technique for Secure Data Transmission
This paper proposes a lightweight homomorphic encryption (HE) technique designed for secure data transmission in resource-constrained environments such as IoT, sensor networks, and edge computing platforms. The method integrates a modified AES structure—in which the number of rounds and key-expansion operations are optimized—with RSA’s multiplicative homomorphism to support limited encrypted computation while minimizing computational overhead. The modified AES reduces complexity in the Mix Columns and Key Expansion stages without compromising cryptographic soundness. Experiments conducted on a 32-bit ARM-based emulator (100 MHz) and Arduino-class microcontroller demonstrate reduced encryption time, lower energy consumption, and improved throughput compared with standard AES, RSA, and AES+RSA hybrids. Preliminary comparisons with lightweight HE baselines (LWE-based and CKKS variants) indicate promising efficiency advantages for constrained hardware. Security analysis confirms that modifications do not weaken resistance against known cryptanalytic attacks. The resulting framework is suitable for healthcare monitoring, smart grids, industrial IoT, and privacy-preserving cloud analytics.
Copyright (c) 2025 Sumaira Bashir, Amit Sharma (Author) https://creativecommons.org/licenses/by/4.0/
Quantum Machine Learning for Cyber Threat Detection: Enhancing Security in the Quantum Era
The cat-and-mouse game is endless. On the one hand, the security industry is ceaselessly countering security technologies to immunize against perceptible threats. On the other hand, adversaries are always attempting to devise cutting-edge exfiltration schemes for potential return on investment. Ever since the Federal Bureau of Investigation (FBI) released the Flash report that hinges on raw indicators to unveil LICAT backdoor on Joomla websites running PHP, adversaries tweaked their infection vectors to avoid detectable host-based artifacts. Economic and geopolitical aspects also spurred the entrenchment of Advanced Persistent Threats (APTs). Major players in cyber industry are investing manifold resources into protracted espionage campaigns by concentrating on high-value targets. While research in QC is progressing, there are also challenges in harnessing the technology’s full capabilities. Much like conventional computing requiring some understanding about a computer’s architecture, a fundamental understanding of quantum mechanics is needed to comprehend QC . To explore how advances in quantum may be applied in cybersecurity tasks, developments in this technology are reviewed. Quantum computing can help to deal with cybersecurity tasks by enhancing problem complexity due to its advantages in performing complex tasks effectively. Cybersecurity tasks may be substantially complex, requiring understanding non-local correlations and collecting a large quantity of data points in different fidelities and complexities.
Copyright (c) 2025 Mohammed abdulhamza Noor (Author) https://creativecommons.org/licenses/by/4.0/
Securing IoT Devices against Quantum Threats:Developing a Framework for Future-Proof Encryption
Quantum Computers are designed to be sufficiently large to be able to efficiently solve specific problems that would take an impractical amount of time to solve using classical computers. The most widely discussed application of quantum computing technology is more powerful algorithms, which efficiently solves all instances of integer factorization—a number theoretic problem that is the foundation of the most of the existing public-key encryption schemes. This means that all public-key cryptographic systems commonly used on the Internet for security will become as insecure as an open communication channel, easy to eavesdrop on or to tamper with. Moreover, traffic passes through numerous fixed, mobile and personal sensors connected to the Internet. This extremely large amount of data becomes the “air” of IoT and only a small part of it will be automatically analysed for a specific purpose. Nevertheless, an important part of the immense amount of data exchanged across the network is sensitive. Presently, these data communication and storage are protected by encryption, but that means of protection will be lost to a future quantum adversary. Because of these urgent necessities, products implementing quantum-resistant security solutions can be built and put into operation in a “post-quantum world”. This must be done now because cryptography research is currently inventing and standardizing new cryptographic algorithms. Once the new algorithms are widely and publicly agreed upon, systems with old encryption will remain vulnerable. This topic is motivated by the above.
Copyright (c) 2025 Noor Thamer Mahmood (Author) https://creativecommons.org/licenses/by/4.0/
Post-Quantum Digital Forensics: Investigating Cybercrime in a Quantum-Compromised Landscape
Although quantum computing poses a number of opportunities, it also represents an emergent threat to conventional cryptographic practices, particularly to digital forensics in cybercrime prosecution. While post-quantum cryptanalysis is still in its infancy, early stages of quantum supremacy are being achieved. As feature sets of quantum computing develop, substantial emerging technologies are expected to be available in the next five years, including Shor’s and Grover’s algorithms. While the former can be used for the fast factoring of large integers, this implies that public-key cryptographic systems will be efficiently calculable, facilitating the detection of digital communication channels. Simultaneously, this allows encrypted data storage to be broken, undermining claims protection. Meta data, for example, phone numbers dialed, contact lists or text length, is vulnerable through Grover’s algorithm, which provides a quadratic acceleration of brute force attacks.
Copyright (c) 2025 Sarmad Jawad, Huda Kadhum Ayoob, Shahad Fahim Aljanabi, Ali Salah Mahdi Alobaidy (Author) https://creativecommons.org/licenses/by/4.0/
Human-Centered Cybersecurity: Examining Ethical and Societal Considerations of Quantum Technologies
In the past decade, quantum technologies have made remarkable progress. Quantum technologies are able to collect and utilize quantum phenomena to facilitate the accomplishment of tasks deemed infeasible by conventional means. Developments of quantum computers and cryptographic protocols have drawn extensive attention of both academia and industry. On one hand, quantum computation facilitates the rapid progress of various tasks/computing. The prime examples are the algorithms for simulating quantum systems, quantum cryptography, and solving the integer factorization and discrete logarithm problems through an algorithm. These advances have important and far-reaching discoveries: the first physical quantum computer was introduced in 2011; quantum cryptography has been used in widely-deployed devices; the first quantum-resistant algorithms were standardized in 2017. Vendors have recently launched their cloud-accessible quantum computers. This is expected to stimulate the rapid development of the quantum industry and its derivative technologies. However, the advantage in the rise of quantum technologies comes concomitant with various new challenges as well. Intractable problems overcome by the conventional methods become vulnerable to their quantum counterparts. New vulnerabilities have been spotted prior to the advent of quantum computers – more sophisticated attacks are underway, and organizations may indeed be breached already but remain unaware of. Furthermore, almost all advanced security technologies rely on the tacit assumption that the computational task required by the adversary is computationally harder than the security-related one. Therefore, the backgrounds of cyber security and quantum technologies call for a thorough rethinking of their avoidances. Under such circumstance, the scientific community should openly discuss the ethical and societal considerations of quantum technologies – what responsible researchers and developers should do to protect the world from their prospective vulnerabilities and harms. This comprehensive survey intends to explore a discussion of these emerging issues.
Copyright (c) 2025 Wafaa Sallal Abbood, Ali Kahtan Lelo, Nisreen Saad Hadi, Mohammed Abdulhamza Noor (Author) https://creativecommons.org/licenses/by/4.0/
A Comparative Study of Post-Quantum Cryptographic Algorithms Assessing Security and Performance Trade-offs
Lattice-based cryptography is encryption that uses lattice issues as the basis of a conjecture of the complexity of the worst-case problem. This complexity is considered hard in the world of classical and quantum providing for a primitives design of lattice-based cryptography algorithms. The NIST held a contest to find the best algorithm to implement. This paper compares three finalists of that contest by analyzing the theoretical basis of the lattice problems they hold, with the security of the worst case of the attack through some algorithms, lattices with underlying problems, construction of cryptographic algorithms, and comparative analysis of three finalists of the lattice schemes of the NIST competition. Comparison includes the safety and robustness of the lattice problem used the cryptographic algorithm that is built and finally there will be an analysis in a computational time context to discuss the efficiency algorithm created. Two advantages and disadvantages of each the algorithm finalist are implemented and also described.
Copyright (c) 2025 Mohammed Hasan Al-Dulaimi, Suha Kamal Al-Dulaimi (Author) https://creativecommons.org/licenses/by/4.0/
A Hybrid Deep Learning Approach for Arabic Speech Emotion Recognition Using Acoustic and Spectrogram Features
Arabic Speech Emotion Recognition (ASER) presents unique challenges due to linguistic diversity, phonetic complexity, and the limited availability of labeled datasets. This work presents a hybrid deep learning model that integrates a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN) to effectively classify emotions from Arabic speech data, using the KEDAS dataset. The model is designed to process two complementary sets of features: acoustic features, such as low-level descriptors, as well as Mel-spectrogram features that capture time-frequency information. Experimental results demonstrate that the hybrid architecture effectively leverages the strengths of both feature types, achieving a high accuracy of 98% in emotion recognition.
Copyright (c) 2025 Dalal Djeridi, Bachir Said, Mourad Belhadj (Author) https://creativecommons.org/licenses/by/4.0/
Enhancing Lithology Prediction through Preprocessing Techniques and Machine Learning Models
Lithology classification through well log interpretation is a fundamental task in reservoir characterization, enabling accurate delineation of subsurface formations and assessment of hydrocarbon potential. However, measurements are rarely full, and missing data intervals are prevalent due to operational difficulties or logging device failure. Thus, imputation of missing data from down-hole well logs is a prevalent issue in subsurface processes. Our work a strong emphasis on the preprocessing phase and data imputation, acknowledging that missing data in well logging is a prevalent problem that can have a major impact on classification results. Our work is part of the FORCE2020 Lithology Classification Competition. Our method underlines how important extensive data preprocessing is for improving model performance, including regression-based imputation, normalization, and class balancing by SMOTE. Traditional models like Random Forest and XGBoost were able to produce reliable results in the challenging FORCE2020 Lithology Classification. By leveraging multiple models, we aim to enhance the accuracy and robustness of our predictions, addressing the challenges posed by missing data and ensuring a more reliable classification process. We show that the Random Forest model obtains the greatest accuracy of 95% using the FORCE 2020 dataset from 118 wells in the Norwegian Sea. This study emphasizes how crucial thorough data imputation and preprocessing techniques are to raising the precision and dependability of lithology classification.
Copyright (c) 2025 Cherif Mesroua, Ibrahim Lahouel, Basma Hamrouni, Khadra Bouanane, Faiza Zidouni, Wafa Kafi (Author) https://creativecommons.org/licenses/by/4.0/
Enhancement of GPS Signals Acquisition in non-Homogeneous Environment using the OS-CFAR Techniques with Optimization Technique
In this paper, we propose to use metaheuristic optimization techniques to improve the adaptive acquisition of the global navigation satellite system (GNSS) in both homogeneous and non-homogeneous environments, The main objective of this work is to optimize the thresholding of the Constant False Alarm Rate (OS-CFAR) in Rayleigh fading channels we compare the result with the base detector CA-CFAR . In GNSS acquisition, the pilot and data blocks may have different thresholds. Therefore, the optimization will focus on two scaling factors (T and k). Two fusion rules have been used here "AND" and "OR". Due to their performance in different optimization problems, metaheuristics have been chosen as the tool to solve this type of problem. The simulation results show that the optimized thresholds have a significant impact on the performance of the acquisition system.
Copyright (c) 2025 El bahdja OURFELLA, Sabra BENKRINAH (Author) https://creativecommons.org/licenses/by/4.0/
Transforming Algerian Industry Through Intelligence: Academic Insights on AI And Digital Transition
This article explores the transformative potential of Artificial Intelligence (AI) in reshaping Algeria’s industrial landscape within the broader framework of digital transition. Focusing on key sectors such as energy, agriculture, and manufacturing, the study examines how AI technologies ranging from machine learning and computer vision to natural language processing are being leveraged to enhance efficiency, safety, and decision-making across industrial operations. Through a detailed case study of the national energy leader, Sonatrach, the research highlights measurable improvements including reduced equipment downtime, more accurate resource forecasting, and accelerated data analysis. The article also discusses the alignment of these technological advancements with Algeria’s national digital strategy, while critically addressing persistent challenges such as data infrastructure, workforce readiness, and sectoral scalability. Ultimately, this study offers academic and policy-driven insights into how AI can serve as a catalyst for sustainable industrial modernization in Algeria .
Copyright (c) 2025 Abdelkrim BENALI, Somia BENALI (Author) https://creativecommons.org/licenses/by/4.0/
Quantum Cryptanalysis: Evaluating the Impact of Shor’s and Grover’s Algorithms on Modern Encryption Standards
Motivated by Shor’s algorithm and its attractive features, the milestone results in quantum cryptanalysis were the discovery of quantum differential and linear cryptanalysis. In quantum cryptanalysis, quantum distinguishers imitate the classical setting. Details differ; however, as the distinguisher in the quantum setting can take maximum coset superposition of all possible subsets of plaintext pairs. Using quantum measurements, the winning gap of the game is exploited so that a distinguisher can still guess the secret key perfectly with a constant probability. Symmetric primitives also suffer reduced ideal security in the quantum world, but this security reduction is less drastic than for most asymmetric primitives. Stream ciphers, block ciphers, and cryptographic hash functions are primitives that are believed to resist quantum attacks. Analogs to Shor’s algorithm may say how hard it is to build a quantum computer, but devising quantum algorithms to outperform the best classical attacks for widely-used cryptographic primitives is probably outside the reach of current even if exponentially growing noisy intermediate-scale quantum (NISQ) computers.
Copyright (c) 2025 Mithal Hadi Jebur, Sumar Mohamed Khaleel (Author) https://creativecommons.org/licenses/by/4.0/
The Role of Deep Learning in Enhancing Solar Panel Efficiency: A Review of Models and Metrics
The rising desire to get long-term power has stepped up the utilisation of solar photovoltaic (PV) systems all over the world. Nonetheless, keeping the solar panels in optimal performance is a major concern because of the changeable environmental conditions, wear and tear of the equipment and breakdown of operations. Deep learning (DL) has become an efficient solution of offering sophistication in predicting solar power generation, fault detection, and optimization of the performance. The review will go in depth over several DL methodologies, such as, CNN, LSTM, Transformer-based models, Reinforcement Learning, GANs, and hybrid architectures of CNN-LSTM. The implementation of each model, performance metrics of MAE, RMSE, R2, and MAPE as well as weak points and advantages of each model, are provided in their relation to increasing PV efficiency. Moreover, the paper covers real-life data, real-life implementations, and comparative performance of these models in addressing critical issues in the solar energy systems. Nonetheless, despite the significant progress, there are still a number of challenges that need to be addressed, among which are the data sparsity, inability to generalize the models to different areas of interest, computational limitations, and inability to interpret the models. To resolve these problems, future directions outlined by the present paper include the creation of lightweight models of edge-AI, the use of transfer learning to achieve regional adaptability, the possibility of Explainable AI (XAI), and the combination of deep learning and IoT and digital twin technology. I would say that the given comprehensive review is an excellent source of information that can be used by researchers, engineers, and policymakers who want to use deep learning to make solar panels as efficient as possible to deliver more resilient, reliable, and sustainable energy delivery.
Copyright (c) 2025 Amjed Abbas Ahmed, Haneen Siraj Ibrahim, Ihtiram Raza Khan, Rabiu Aliyu Abdulkadir, Abubakar Amoo Akinlawon, Munzali Alhassan (Author) https://creativecommons.org/licenses/by/4.0/
Exploring the Techniques and Challenges of Privacy-Preserving Data Sharing in Quantum-Enabled Networks
Privacy-preserving data-sharing mechanisms traditionally rely on data anonymization techniques, which aim to dissociate individual records from their corresponding data subjects in order to transform sensitive information into a form that minimizes the risk of re-identification. In parallel, trust-broker models have emerged as an additional layer of protection, whereby authorized data providers disclose controlled contact information that enables potential data users to request access under predefined conditions determined by the data owner. In contemporary data-sharing environments, cryptography remains a fundamental pillar for ensuring confidentiality and integrity during data exchange. Cryptographic protection typically involves two operations: an encryption process, in which the sender transforms plaintext into ciphertext that is unintelligible to unauthorized entities, and a decryption process, in which only the intended recipient restores the ciphertext to its original form. Both operations rely on a cryptographic algorithm paired with one or more secret keys. Symmetric-key cryptography employs a single shared key for both encryption and decryption. In contrast, asymmetric cryptography uses a mathematically linked pair of keys, where one key is used for encryption and the other for decryption. When the encryption key is publicly available while the corresponding decryption key remains private, only the legitimate recipient holding the private key is capable of recovering the plaintext. This cryptographic structure forms the basis for securing data confidentiality against unauthorized access in both classical and quantum-aware communication systems.
Copyright (c) 2025 Zaid Ibrahim Rasool, Nisreen Saad Hadi (Author) https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence in the Era of Educational Big data: A Systematic Review
In this paper, a systematic review of the latest research on the deployment of AI in educational Big data analytics is provided including its applications, challenges and future research possibilities. The databases IEEE Xplore, ScienceDirect, SpringerLink, Scopus, and Web of Science were systematically searched to find peer-reviewed journal articles written by Published by Elsevier B.V.8676,during 2020–2025. Fifty studies are included in the qualitative synthesis according to pre-designed inclusion/exclusion criteria. The results highlight four key themes for the application of AI: predictive analytics for predicting academic performance, personalised and adaptive learning systems, learning analytics for supporting teacher and institution decision making and institutional decision support using educational data. Challenges, including technical limitations, institutional readiness, and ethical considerations in privacy, bias, and transparency are also discussed.In addition, this analysis points to gaps in literature and paves ways for researchers to extend the current frontiers of research, including the design of interpretable AI models, multi-language tools, and privacy-preserving approaches. The findings offer important implications for researchers, educators, and policymakers striving to harness AI to design better and fairer educational systems.
Copyright (c) 2025 Ali Hussein Khalaf AL-Sammarraie (Author) https://creativecommons.org/licenses/by/4.0/
Leveraging deep Learning for Efficient Intrusion Detection in IoT Networks
Internet of Things (IoT) technology is experiencing rapid development and increasing use in a variety of applications, making it a potential target for cyber-attacks. Machine learning and deep neural network techniques are an effective way to address these challenges and improve IoT security. This research aims to design a deep learning techniques for intrusion detection in an Internet of Things environment with limited resources. The research focuses on improving the efficiency and effectiveness of current model using artificial intelligence and LSTM algorithms, ensuring reliable and effective security in the IoT environment. The proposed model is evaluated using a realistic data set, Canadian Institute for Cybersecurity Internet of Things 2023 Dataset (CICIoT2023) devices, and using performance metrics, namely Accuracy, Precision, F1 Score, and Recall. The results show its compatibility and effectiveness in a real environment, with 99.1% accuracy recorded. This paper is considered an important contribution to the field of IoT security and provides an effective methodology for developing advanced security solutions in the IoT environment that enhance traffic analysis, identify abnormal behavior, and take the necessary measures.
Copyright (c) 2025 Noor Thamer, Suhad Hatem Jihad, Sumar Mohamed Khaleel, Ahmed Saleem Abbas (Author) https://creativecommons.org/licenses/by/4.0/
Emerging Trends and AI-Driven Defense Mechanisms in Cybersecurity: A Novel Framework for Threat Prediction and Prevention
Rapid digital ecosystem growth has made cybersecurity a major issue nowadays. As gadgets, cloud platforms, and critical infrastructures become more interconnected, fraudsters may exploit weaknesses with unparalleled sophistication. Advanced threats including ransomware, deepfake-driven phishing, supply-chain breaches, and AI-powered assaults are beyond firewalls and intrusion detection systems. This paper presents a hybrid cybersecurity system that uses AI, blockchain, and Zero Trust to anticipate, prevent, and mitigate intrusions in real time. Our system uses machine learning to identify anomalies and decentralized, blockchain-based trust management to safeguard data and authentication. A proactive strategy improves detection accuracy, decreases false positives, and builds resistance to emerging threats. Trials utilizing benchmark intrusion detection datasets show that the framework outperforms standard systems. Its use in high-risk industries including banking, healthcare, and industrial IoT is shown by the results. For a safer digital future, our study develops adaptable, intelligent, and scalable cyber protection methods.
Copyright (c) 2025 Muna A. Radhi, Majd S. Ahmed, Ethar Abdul Wahhab Hachim, Zeyad Farooq Lutfi (Author) https://creativecommons.org/licenses/by/4.0/
Mitigating Cybersecurity Threats in Autonomous Vehicles: A Real-World Case Study on Protecting V2X Communication Against Remote Hacking Attempts
As of now, maintaining the security and dependability of V2X communication remains an open issue due to the complexity and vulnerability of wireless networking technologies for Autonomous Vehicles (AVs). This study is conducted to address the research gap. The initial research activities are to routinely test the security vulnerability of the communication technologies in AVs, which encompass vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and vehicle-to-device (V2D) communication. Industry tools and software licenses are used to perform these experimental tests. The results reveal that malicious cyber-attacks targeting V2X communication are successful. Building on the results, a model-based SDLC and a set of guidelines are proposed to securely develop software for AVs 2. Participants of the project are also trained in these newly developed methodologies to enhance the development security of the AVs in the future.
Copyright (c) 2024 Yasmin Makki Mohialden, Nadia Mahmood Hussien, Samira Abdul Kader Hussain (Author) https://creativecommons.org/licenses/by/4.0/
AI-Driven Optimization of Cloud Resource Allocation for Personalized Medical Imaging in Hospitals: A Case Study from a Major Medical Center
This study is to introduce a cloud-based, AI-driven architecture that can address the above-mentioned challenges properly and demonstrate its feasibility through real-scale implementations and practical testing in a major traditional hospital in Asia that typically operates a variety of diagnostic imaging services. A typical scenario is that vital organ medical images need immediate attention and response from any physician and hospital. CT brain imaging and PET-CT, Gallium imaging of the whole body, glucose, and myocardial ischemia will be implemented and tested in this project. These diagnostic imaging devices usually generate cross-sectional images of patients and typically produce an average of 500 - 600 high-resolution images each. In the local hospital, the human eye ballot reading and deep learning mean image classifications take a considerable amount of time to present the results. This project will take advantage of the cloud, AI, and web-based technology to rapidly assist physicians in providing better medical services. The research objectives of this project are summarized as follows: (1) Gaining experience and lessons by deploying AI technology on a real hospital scale. AI/HA medical imaging services operated in the cloud can be made available for other hospitals to use.
Copyright (c) 2024 Mohammed Mukhtar Magaji, Usman Aliyu Magaji (Author) https://creativecommons.org/licenses/by/4.0/