Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs...

Face detection in low-light conditions remains challenging due to underexposure, noise, and unstable contrast, which significantly degrade the performance of convolutional-based models. Conventional enhancement techniques, such as Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Low-Light Image Enhancement (LIME), often improve brightness but introduce visual artifacts and lack robustness for face detection. This study proposes Low-light Unified Multi-stage Enhancement Network (LUMEN), a lightweight multi-stage image enhancement pipeline designed as a preprocessing module to improve RetinaFace-based face detection under low-light conditions. LUMEN integrates Multiscale Retinex with Color Restoration, adaptive gamma correction, CLAHE-based local contrast enhancement, controlled image fusion, and Non-Local Means denoising to jointly stabilize illumination, preserve texture, and maintain visual naturalness. The method is evaluated on a low-light subset of the Human Faces Object Detection Dataset using RetinaFace. Detection performance is assessed using detection rate and confidence score, while visual quality is evaluated using no-reference metrics such as the Natural Image Quality Evaluator (NIQE) and the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Experimental results show that LUMEN achieves a face detection rate of 91% with a high confidence score (0.9545), rep-resenting an improvement of 33 percentage points over raw low-light images (58%), while maintaining detection performance comparable to CLAHE, which ranks as the second-best method, LUMEN delivers superior perceptual quality, evidenced by the lowest BRISQUE score, as well as more stable visual appearance with reduced noise amplification and fewer contrast artifacts in low-light facial regions. LUMEN also achieves the lowest BRISQUE score, indicating superior texture preservation and perceptual stability. Ablation studies confirm that Retinex and CLAHE are the most critical components for detection robustness, while gamma correction, fusion, and denoising mainly contribute to visual naturalness. These results demonstrate that LUMEN provides an effective and practical preprocessing solution for low-light face detection.

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education...

Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics.

This study investigates how Artificial Intelligence (AI)-based Tutoring Systems can be used to provide secondary school students with personalized learning feedback and to enhance their academic performance. The two main empirical objectives were to assess individualized formative assessment in classroom settings and to examine teachers’ and students’ perceptions of AI adoption. A mixed-methods quasi-experimental design was employed, integrating quantitative pre-/post-tests, interviews, and focus groups across six schools (n = 600). This approach enabled triangulation between measurable learning outcomes and contextual perception data for robust validation. Quantitative data were analyzed using Python (t-tests, ANCOVA), while thematic coding in NVivo was applied to qualitative data. Expert review, pilot testing, and Cronbach’s α (>0.80) were used to validate the instruments and ensure reliability, including pre-/post-tests and engagement scales. Findings revealed that students who received AI-based interventions achieved significantly higher academic performance (Cohen’s d = 1.05) and engagement (d = 0.72) compared with control groups. Teachers with AI exposure reported greater preparedness (mean = 3.4) and fewer perceived barriers. The study provides empirical evidence on the pedagogical viability of AI tutoring in under-resourced contexts, contributing to self-regulated and socio-technical learning theories. It also recommends enhanced systemic teacher education, ethical leadership, and structural support to foster equitable adoption of AI in education. The findings carry strong implications for policy development and educational innovation in promoting data-driven, inclusive learning within Nigeria’s secondary education system

Sentiment analysis is a critical task in natural language processing (NLP) that classifies text into sentiment categories, such as positive, negative, or neutral. This task is particularly challenging for languages like Persian due to the complexity of their linguistic structure and the scarcity of high-quality labeled datasets. Previous studies on Persian sentiment analysis have largely relied on TF-IDF representations or deep learning models, often overlooking handcrafted statistical and stylistic features that capture subtle textual patterns. This limitation reduces their effectiveness, especially when dealing with informal or noisy text data. Experiments in this study were conducted on a dataset of Persian product reviews from Digikala.com, labeled according to user ratings to indicate positive, negative, or neutral sentiment. In this paper, we propose a novel approach to Persian text sentiment analysis by combining statistical and stylistic (surface-level) features with traditional text-based features such as Term Frequency–Inverse Document Frequency (TF-IDF). Unlike prior works that rely solely on TF-IDF or deep learning representations, our method integrates stylistic and statistical cues to capture expressive nuances in informal Persian text. Additionally, the Support Vector Machine (SVM) classifier is optimized using RandomizedSearchCV to enhance performance. The proposed system utilizes both statistical and textual features to improve classification accuracy. We compare its performance with four baseline models, i.e., Naïve Bayes, Logistic Regression, Random Forest, and Decision Tree, that rely solely on TF-IDF features. The experimental results demonstrate that the proposed approach outperforms the baseline models in terms of accuracy, F1-score, recall, and precision. Specifically, the proposed system achieved the highest accuracy (0.8354), significantly improving negative sentiment detection while maintaining strong performance in positive sentiment classification.

Bone fractures are among the most common global health problems and require rapid and accurate diagnosis to prevent complications and reduce healthcare costs. Traditional diagnostic methods rely on the manual examination of X-ray images by experienced radiologists, a process that is time-consuming and prone to human error, especially in emergency departments with high patient volumes. This study proposes a lightweight hybrid approach that integrates deep learning–based feature extraction with classical machine learning algorithms for automatic bone fracture detection. The dataset used comprises 4,840 fractured and 4,623 non-fractured bone X-ray images obtained from the Kaggle platform, in which data augmentation was previously applied by the dataset developer. A compact convolutional neural network architecture, MobileNetV2, was employed exclusively for deep feature extraction. The extracted feature vectors were then used to train and evaluate five ensemble learning algorithms: Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and Bagging Classifier. Model performance was assessed using 10-fold cross-validation based on accuracy, precision, recall (sensitivity), and F-score metrics. Experimental results show that the Extra Trees algorithm achieved the highest performance with 99.14% accuracy, 99.22% precision, 99.03% recall, and 99.12% F-score, while also yielding the shortest training time of 199 seconds. Random Forest followed with an accuracy of 98.38%. The superior performance and computational efficiency of the Extra Trees algorithm demonstrate its suitability for real-time clinical applications. These findings confirm that combining deep feature extraction with ensemble learning can effectively support automated diagnostic systems for bone fracture detection, enabling healthcare professionals to make faster and more reliable decisions.

Analisis sentimen merupakan salah satu pendekatan penting dalam pemrosesan bahasa alami yang digunakan untuk memahami opini konsumen melalui data ulasan produk di platform digital. Tantangan utama dalam analisis ini adalah memilih algoritma klasifikasi yang paling efektif untuk memproses data teks dalam jumlah besar dan kompleks. Penelitian ini bertujuan membandingkan kinerja empat algoritma klasifikasi populer, yaitu Naive Bayes, Support Vector Machine (SVM), Regresi Logistik, dan Decision Tree, dalam mengklasifikasikan sentimen ulasan produk berbasis fitur TF-IDF. Data yang digunakan berjumlah 830 ulasan produk yang dikumpulkan melalui teknik web scraping dari salah satu platform e-commerce. Seluruh data dipra-pemroses dengan tahapan pembersihan, tokenisasi, stopwords removal, dan stemming, kemudian diubah menjadi vektor numerik menggunakan metode TF-IDF. Selanjutnya, data dibagi menjadi data latih dan data uji dengan proporsi 80:20 dan diuji menggunakan keempat algoritma klasifikasi. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Support Vector Machine (SVM) memiliki performa terbaik dengan akurasi sebesar 94%, diikuti oleh Regresi Logistik (92%), Naive Bayes (91%), dan Decision Tree (76%). Nilai precision, recall, dan F1-score pada SVM juga unggul dibandingkan algoritma lain. Temuan ini merekomendasikan SVM sebagai algoritma yang paling efektif untuk klasifikasi sentimen pada ulasan produk berbasis teks. Penelitian ini dapat menjadi referensi bagi pengembangan sistem analisis sentimen yang lebih optimal di masa mendatang.
 
Kata Kunci - Analisis sentimen, Naive Bayes, Support Vector Machine (SVM), Regresi Logistik, Decision Tree.

This study focuses on the design and implementation of an electrical system for a bread dough proofer tool, specifically optimized for donut dough fermentation, utilizing a microcontroller with Proportional-Integral-Derivative (PID) control. The proofing process is crucial for dough quality, and maintaining precise temperature and humidity conditions is essential for consistent results. The PID control algorithm continuously calculates the error between desired setpoints and measured process variables, adjusting actuator outputs to minimize this error. The PID parameters were tuned using a trial-and-error method to achieve optimal response speed and stability, with values of , , and . Experimental results demonstrate the system’s effectiveness in stabilizing the proofing environment. The temperature was maintained at 40.10  with a minimal overshoot of 0.30  and a rise time of 10.25 minutes. Humidity was regulated within the range of 80-90% RH for 38.4 minutes, with a rise time of 40.41 minutes. The system exhibited robust disturbance rejection, quickly recovering from external perturbations. Compared to manual proofing, the automated system reduced proofing time by approximately 25% and significantly improved donut dough quality, evidenced by enhanced volume expansion and uniform texture.
 
Keywords - Bread Dough Proofer, PID Control, Microcontroller, Temperature and Humidity Control, Automated Proofing System