A high-security hybrid image encryption framework, LexiCAL, is introduced, focusing on nonlinear adaptability, strong diffusion, and proven statistical randomness. Extensive testing on eight USC-SIPI benchmark RGB images shows strong encryption distortion with an average MSEEnc of 7775.82878 and a low PSNREnc of 10·25 dB, confirming effective visual concealment. Perfect reconstruction is achieved with an average MSEDec of 0.00125 and PSNRDec of 118.17309 dB, along with a near-perfect structural similarity (SSIMOrg-Dec 0.9998). Resistance to differential attacks reaches a mean NPCR of 99.6533
As food delivery platforms are rapidly growing, so are the needs to use personalized food recommendation systems that will make the experience of the user more enjoyable. Rising food delivery platforms wishing to boost the user experience and user engagement have put extreme demand for personalized food recommendation systems. This paper proposes the architectures, implementation and comparative study of four food recommendation models: Content-based Filtering, Singular Value Decomposition (SVD++) Collaborative Filtering, Time-Season-Aware-Meal-Temporal and Item-Feature User Preference Cycle(Time-Season-Aware-Meal-TIFU-CYCLE) and a Context-Aware Hybrid Model. On the other hand, Content-Based approach utilize Term Frequency–Inverse Document Frequency (TF-IDF) vectorization to encode item features including food type, product name and outlet features separately, calculates a similarity score based on content between User items that interacted with and the potential items for recommendation. The Collaborative Filtering model uses SVD + + to model latent user preferences based on past user–item interactions. A Time-Season-Aware-Meal-TIFU-CYCLE Model is implemented to account for food reorder cycles, meal-time preferences, seasonal preferences and recency. Content-Based and Collaborative signals are not sufficient to overcome the limitations of each approach, so the Context-Aware Hybrid Model uses both types of signals and contextual parameters such as meal time, season, location etc. The models were tested with Precision, Recall, F1-Score, Hit Rate, NDCG and MAP. The Context-Aware Hybrid Model achieved optimal output with 51.16
Online Social Networks (OSNs) have become very important communication and digital identity platforms in the last few years. But compromised user accounts have become an increasing menace that endangers privacy, data integrity, and network trust. Existing compromised account detection methods primarily rely on static profile attributes, graph-based relationships, or content analysis, which often fail to capture subtle behavioral deviations exhibited by compromised legitimate accounts. This limitation motivates the need for a behavior-centric detection framework capable of identifying anomalous user activities using dynamic behavioral and security-related features. In this paper, a machine learning-based behavioral analysis framework is presented to promptly recognize compromised accounts. The approach does not consider contents or graph structures but rather looks for inconsistency in behaviors such as irregular posting patterns, abrupt changes in activity, and a change of devices or credentials. The proposed framework integrates behavior-driven feature engineering, class imbalance handling using SMOTE-based resampling, and comparative evaluation of multiple interpretable supervised machine learning classifiers to detect compromised accounts based on behavioral anomalies rather than content or network topology. A number of features were gathered from users’ interactions, profile activities, and session behavior, then the dataset was processed and multiple traditional supervised machine learning classifiers were evaluated and compared to identify the most effective model for compromised account detection. The framework was evaluated using a behavior-oriented benchmark dataset curated from publicly available social network repositories (SNAP, UCI Machine Learning Repository, and NetworkRepository), representing realistic Facebook-like user behavioral patterns. Due to the imbalance in the distribution of classes, resampling was carried out to solve the problem. Among the evaluated models, the best-performing classifier achieved an accuracy of 88.0
Quantum teleportation and superdense coding are two different protocols that were developed to transmit a limited number of qubits and two classical bits, respectively. Therefore, they are inefficient for transmitting messages containing a massive number of individual qubits or classical bits. Thus, it is necessary to scale up these two protocols in larger systems to enable them to transmit massive messages. To achieve this goal, it is essential to integrate each of these two protocols within architectures that contain a quantum main memory that functions as a buffer capable of storing both quantum and classical data and managing a massive amount of data efficiently during transmission operations. Q1RAM is a cutting-edge quantum main memory and the fastest quantum main memory available, as it has a time complexity of O(1) for accessing memory locations. In this paper, two architectural schematics are proposed based on integrating Q1RAM as a core component with the quantum teleportation and superdense coding protocols. The first architectural scheme is developed to scale up the quantum teleportation protocol, making it efficient for transmitting massive messages of separable individual qubits. The second architectural schema is proposed, consisting of two Q1RAM chips, two quantum processing units, two of the classical RAMs, and a quantum gateway. This proposed schema is developed to scale up the superdense coding protocol so that it can transmit massive classical messages containing large amounts of data. The experimental simulation results validate the architectural schematics for scaling up the quantum teleportation and the superdense coding protocols for large-scale quantum communications.
We study a recently published dataset of 396 real-world cloud architectures on AWS from a wide range of industries. We focus on those architectures that contain HPC, edge or serverless components and investigate how they are built, which services they use, the types of storage used, quantify architectural complexity, assess the use of machine learning services, and distill insights regarding how cloud applications in these three domains are being built in the cloud continuum. Our data and analysis can be valuable for researchers and practitioners seeking to build and optimize cloud architectures and to help guide new research.
