Arpad Kiss, GreenEyes Artificial Intelligence Services, LLC, Lewes, Delaware, USA
This research report provides a comprehensive analysis of Compact Composite Descriptors (CCDs) as a highly ef icient alternative to deep learning embeddings for Content-Based Image Retrieval (CBIR) in resource-constrained environments. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) of er superior semantic performance, their computational overhead and storage requirements—often exceeding 8KB per image—limit their applicability in Edge AI and IoT scenarios. In contrast, engineered descriptors such as the Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), and Joint Composite Descriptor (JCD) utilize fuzzy inference systems to encode visual features into ultra-compact vectors ranging from 54 to 72 bytes. The study explores the algorithmic foundations of these descriptors, their implementation within the LIRE (Lucene Image Retrieval) framework, and benchmarks demonstrating their competitive retrieval accuracy against MPEG-7 standards. Finally, the report highlights the strategic utility of CCDs for privacy-preserving, low-bandwidth visual search on edge devices, proposing hybrid architectures that leverage the speed of fuzzy composites with the semantic power of neural re-ranking.
Computer Vision, Cloud Computing, Embedded Systems, Content-based Image Retrieval Systems
Awatef Balobaid and R.Y. Aburasain, Jazan University, KSA
This research suggests a new technique to detect and categorize student performance that will assistschools in improving outcomes. A regression-based technique estimates student performance, and aclassification model classifies students by performance. It begins with a regression model that predictsstudent performance. It then utilizes gradient descent to refine the model over and over again to generatebetter predictions. The model is then cross-validated and retrained on the complete set of data to make itmore accurate and helpful in different circumstances. The system organizes students by predictedperformance using the regression model. To increase classification accuracy, further optimization isutilized to determine the appropriate option limit for splitting performance groups. We assess themethod's efficiency in terms of accuracy, response time, scalability, and resource utilization. The findingsdemonstrate the new procedure is superior to the old ones. This strategy is robust, versatile, and cost-effective for educational organizations since it can generate correct predictions 95% of the time, reactmore rapidly, utilize resources economically, and be employed on a big scale. It helps instructors knowhow their pupils are doing so they may intervene early and make better decisions to support them. Data-based analysis can enhance educational results by utilizing the system's power and ability to adapt to newdata
Adaptability, Classification, Data-driven, Educational institutions, Optimization, Performance prediction,Regression, Resource utilization, Scalability, Student outcomes
Jean-Marie Kabasele Tenday, University ND Kasayi(UKA), Belgium
Traditional threat modelling techniques often focus on theoretical or system-specific threats without grounding them in empirical adversarial behaviour. Conversely, frameworks such as MITRE ATT&CK provide rich, intelligence-based taxonomies of real-world attacker tactics, techniques, and procedures (TTPs), but are rarely integrated into early software design phases. This paper proposes a methodology for linking misuse cases—UML-based representations of malicious system interactions—with MITRE ATT&CK techniques, enabling traceability between system-level threats and empirically observed attacks. The proposed framework enhances the relevance, completeness, and operational value of misuse case–based threat modelling. A structured mapping template and example implementation demonstrate how software architects can enrich their security design processes using ATT&CK-informed misuse cases.
Misuse case, Mitre ATT&CK, Threat Analysis, Threat Modelling, Cybersecurity, Secure Design.
