Authors - Harshala Shingne, Shwetambari Borade, Dhanashree Hadsul, Aditya D. Nandgirwar, Pranali Pawar, Rupali Vairagade Abstract - Increasing proliferation of networked systems have compounded the necessity to seek effective and non-invasive intrusion detection solutions. The conventional intrusion detecting systems (IDS) are mainly centralized into data aggregation scheme that introduces essential constraints pertaining to data exposure, scalability, and robustness of the system itself. Partially in reaction to this, this paper presents a privacy conscious federated intrusion detection design that allows collinear model training by many network participants in the absence of exchanging raw data. This framework exploits federated learning to create a global intrusion detecting model by continually aggregating local-trained updates, thus retaining the data locality and ownership. In order to achieve high privacy assurances, there are inbuilt secure aggregation mechanisms and perturbation-based mechanisms that accomplish this by avoiding the leakage of sensitive information during model sharing. Additionally, an adaptive-aggregating strategy is proposed that can effectively manipulate non identically distributed data of the participants, as well as improving the generalization process of the global model. Extensive testing on test sets of benchmark intrusion detection has shown that the proposed framework has very high detection rates and much less privacy risk and communication overhead than the traditional centralized techniques. The findings confirm that the framework has the capability of offering a scalable, secure and efficient intrusion detection solution in distributed networks.
Authors - Shashank Mallesh, Anithadevi M D, Srinidhi G A, Chandana Sreenivas Abstract - Hospital surge capacity management remains a critical challenge in healthcare systems, particularly during pandemic events. This research presents a novel Adaptive Multi-Objective Capacity Management (AMCM) framework that integrates Long Short-Term Memory (LSTM) networks with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize bed allocation, staffing schedules, and equipment distribution. The framework simultaneously minimizes patient wait times, operational costs, and resource wastage while maximizing bed utilization efficiency. Comprehensive evaluation against state-of-the-art algorithms including Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and traditional Mixed Integer Linear Programming (MILP) demonstrates superior performance across all metrics. The proposed method achieves 94.7% bed utilization accuracy, reduces emergency department wait times by 42.3%, and decreases surge-related costs by 38.9% compared to conventional approaches. Validated on real-world COVID-19 hospital data spanning 18 months across five major health systems, the AMCM framework provides healthcare administrators with an intelligent decision support system for proactive capacity planning and dynamic resource allocation.
Authors - Nwagu Chima Ajanwachuku, Onyemaobi Bethram Chibuzo, Nwafor Franca Amaka, Divine Nnodim Oluchi Abstract - Tertiary institutions across the world are now adopting artificial intelligence-based academic detection systems to aid in detecting different forms of academic misconduct. For these detection systems, there is more focus on technical performance metrics such as detection accuracy, precision and recall and little focus on whether these systems validly measure the complex construct of academic misconduct. This study aims to examine how AI-based academic integrity detection systems operationalise, measure, and validate academic misconduct in higher education, focusing on construct operationalisation, measurement accuracy, and construct validity, through a systematic literature review. We conducted a systematic review and retrieved articles from ACM Digital Library, IEEE Xplore, Web of Science, and Google Scholar. Of 793 articles, 56 were selected using the PRISMA framework, and the findings were synthesised narratively. The 56 studies focused on plagiarism detection, AI-generated text detection, authorship verification, behavioural monitoring, biometric authentication, and multimodal detection systems. Across all the studies we considered, detection systems mainly measured observable digital signals. 55 of 56 studies showed evidence of construct misalignment between the measured signal and the claimed misconduct construct. Most studies we considered treated similarity as plagiarism, AI-generated probability as dishonesty, and behavioural anomalies as cheating, even though these signals could not capture intent, differentiate between acceptable collaboration and collusion, or account for disclosure practices or alignment with institutional policy. Also, we observed that most studies validated detection systems using technical metrics such as accuracy, precision, recall, and F1 score, and just a few directly addressed construct validity, bias, or robustness.
Authors - Dang Trung Thanh, Nguyen Huynh Anh Tuyet Abstract - The objective of this project is to apply GPS devices in combination with GIS software to build image interpretation keys for spatial data management. The research content includes: collecting satellite imagery data and documents; conducting field surveys and collecting GPS coordinates for 149 sample points; and building image interpretation keys for geographical objects. The main research method combines GPS field surveys, remote sensing image interpretation, and the application of GIS software such as QGIS and Google My Maps to process, analyze, and manage spatial data. In addition, the project utilizes methods of document collection, statistics, and comparison to ensure the accuracy and scientific validity of the research results. The project results: A set of image interpretation keys was developed for several key geographical objects in the study area, including: water bodies (27 samples), transportation (33 samples), agricultural land (27 samples), residential construction land (28 samples), and vacant land (34 samples). The research has contributed to demonstrating the effective application of GPS combined with GIS and remote sensing in surveying, mapping, and managing geographic information on current land use. The research and development direction is: Integrating artificial intelligence (AI) and machine learning (Deep Learning) into the image interpretation process
Authors - Dang Trung Thanh, Nguyen Huynh Anh Tuyet Abstract - This study evaluates the implementation of the 2024 Land Law regarding land-use conversion in Thuan An Ward, Ho Chi Minh City, Vietnam. The research aims to assess the changes introduced by the new legal framework and examine its practical impacts on land-use conversion procedures at the local level. The study employed a combination of document analysis and a questionnaire survey of 100 respondents, including local government officials and citizens. The collected data were analyzed using descriptive statistics and comparative methods. The results indicate that the 2024 Land Law has improved the landuse conversion process by simplifying administrative procedures, reducing processing time, and increasing transparency. Survey findings show that most respondents considered the new regulations easy to understand, the processing time efficient, and the procedural costs reasonable. Overall public satisfaction increased from 65% under the 2013 Land Law to 85% under the 2024 Land Law. Nevertheless, several challenges remain, including incomplete digital land databases, limited public understanding of some legal provisions, and issues related to the implementation of market-oriented land pricing. These findings provide practical evidence for improving land-use conversion management and support the effective implementation of the 2024 Land Law at the local level.
Authors - Rebecca Hufkie, Dane Brown Abstract - Protein structure determines function, yet experimental determination methods remain costly and slow. This study presents an optimised transformer-based system for predicting protein sidechain angles and reconstructing 3D structures directly from sequence data. Trained on the SidechainNet CASP12 dataset comprising 25,044 proteins, the systematically refined model achieves 0.244 radians RMSE for angle prediction and 1.413 ĚŠA RMSD for structural accuracy, representing a 70% improvement over the baseline. Incorporating backbone angles, secondary structure, and evolutionary information reduces RMSD from 1.861 ĚŠA to 1.413 ĚŠA compared to sequence-only inputs. On challenging CASP12 free-modelling targets, the system scores 84 to 86 GDC. This performance is competitive with leading methods while maintaining computational efficiency through single-sequence prediction without multiple sequence alignment generation. Results indicate that specific architectural choices, including deeper networks, GELU activation, and increased embedding dimensions, combined with robust dropout and weight decay, enable highly accurate structure prediction from limited training data. This demonstrates that carefully constrained models can capture complex biological folding patterns efficiently without massive computational overhead.
