Authors - Marcos Paulo Jeronimo Francisco, Carlos Hideo Arima, Napoleao Verardi Galegale, Joshua Onome Imoniana Abstract - The increasing complexity of digital systems and the growing sophistication of cyber threats have intensified the need for proactive security assessment methods. Threat Modeling is a structured practice for identifying potential vulnerabilities, attack paths and mitigation strategies during the software development lifecycle. However, its manual application is often time-consuming, subjective and dependent on scarce cybersecurity expertise. In this context, Large Language Models (LLMs) may support security teams by generating threat hypotheses, classifying risks and recommending controls. This study evaluates the effectiveness of three LLM-based tools — ChatGPT, Gemini and Manus — in cybersecurity threat modeling for a real-world backend information system. A controlled computational experiment was conducted using standardized prompts applied to the three models, with three independent executions per prompt. The evaluation considered five dimensions: threat identification coverage, technical depth of analysis, quality of risk classification, assertiveness of control recommendations and result consistency. To consolidate the comparison, a Final Effectiveness Metric (FEM) was proposed. The results show different performance profiles among the evaluated models. Manus achieved the highest FEM score, with stronger threat coverage, technical depth, control recommendations and consistency. ChatGPT presented intermediate performance, with structured and detailed analyses, while Gemini showed lower threat coverage, but satisfactory technical reasoning in specific tasks. The findings also indicate that LLMs can enhance threat modeling activities by expanding analytical capacity and supporting DevSecOps practices. Nevertheless, their outputs require human validation, especially regarding risk classification, framework alignment and false positive analysis.
Authors - Jeanne Roux Ngo Bilong, Python Ndekou Tandong Paul, Bakary Kone, Dethie Dione, Ibrahima Toure, Boris Sourou ZANNOU, Hamidou Dathe, Mamadou Diarra, Olga Ngangmo Kengni, Mamadou Thiam, Cheikh Amed Diloma Gabriel Traore Abstract - Breast cancer is a non-communicable disease that causes thousands of deaths each year worldwide. Early detection of breast cancer in women is a public health priority in developing countries. Based on data collected from patients’ breasts, machine learning models can help predict the risk of developing breast cancer.We used three machine learning algorithms (SVM, decision tree, and random forest) for predicting the risk of developing breast cancer, taking into account the physiological factors of breasts. Data collected from 568 patients was used to train the machine learning algorithms. An evaluation of the performance of the three algorithms showed that the random forest algorithm had the highest F1 score, which led to the selection of this algorithm for creating a computer application to diagnose breast cancer risk. The results of this algorithm show 97% accuracy with 94% recall in predicting high breast cancer risk and an F1 score of 96%. The result obtained indicates the model’s excellent ability to correctly predict high cancer risk across the entire risk probability spectrum. The good performance of the model using the Random Forest algorithm could be useful as first-level medical support for breast cancer screening. The developed model is capable of providing the probability of the risk of developing breast cancer for each female patient.
Authors - Sergey Kubinski, Emil Hadzhikolev Abstract - This paper presents a Smart Fitness Assistant system for generating personalized workout and dietary recommendations using machine learning and domain-informed physiological feature engineering. The proposed approach incorporates indicators such as Basal Metabolic Rate, Total Daily Energy Expenditure, and target caloric intake derived from user data. The recommendation task is formulated as a multi-class classification problem for exercise and diet planning. Decision Tree, Multilayer Perceptron, and Random Forest models are evaluated using both baseline and enriched feature sets. Experimental results demonstrate that the inclusion of physiological features improves predictive performance, with Random Forest achieving the highest accuracy. The developed system is implemented within a modular software architecture that supports user interaction, recommendation generation, data management, and progress tracking.
Authors - Madhulika Gajjala Abstract - The fast-paced development in digital transformation strategies has considerably transformed human resource management systems towards cloudbased, smart and efficient workforces management tools. As current HR systems face various problems like data repository fragmentation, poor connectivity options, slow synchronization processes, scalability issues, and lack of advanced workforce intelligence, the current study aims to develop an AFDHRP framework, which would comprise a CSIL, AOIE, WIMM, and ADHOU components. In addition, this research proposes the implementation of Cloud-Integrated HR Data Synchronization and Interoperability (CHDSI) algorithm and Adaptive Workforce Intelligence and HR Optimization (AWIHO) algorithm into an innovative solution. A comparison was made with the two state-of-the-art models, Deep-Hill and Deep Learning-Based ERP Optimization System, on several performance metrics including human resource integration accuracy, interoperability efficiency, synchronization reliability, workforce intelligence metric, decision support capability, cloud scalability, API ecosystem performance, and FutureReady HR Platform Effectiveness Metric. According to the results, the AFDHRP performed excellently scoring an accuracy of 99.5%, 99.2% for interoperability efficiency, 99.4% for synchronization reliability, and 99.8% for overall platform effectiveness.
Authors - Jose Hernandez Cortaza, Arturo Corona Ferreira, Pablo Payro Campos Abstract - The research adopts a qualitative approach through a descriptive-explanatory case study, supported by source triangulation, integrating direct observation and expert judgment in the requirements engineering process, with the purpose of identifying critical points in the traditional digital document certification workflow at a Mexican public higher education institution. Based on this approach, a digital transformation model was designed alongside a system architecture aimed at guaranteeing the integrity, decentralization, and verifiability of digital documents, integrating Blockchain, IPFS, and smart contracts, which enabled a multilayer verification scheme. The results demonstrate that the proposed system allows multilayer verification, prevents document duplication, and strengthens transparency and trust in academic issuance and certification processes. In terms of performance, the system presents an estimated Gas per transaction of 327,423, with an average processing time of 17.94ms, during which the entire document issuance and certification process is fully executed. The main contribution of this work is the proposal of a replicable digital transformation model that combines emerging technologies (Blockchain, IPFS, and smart contracts) with a qualitative organizational analysis, providing a practical framework for the secure management of academic documents in public higher education institutions, significantly contributing to the improvement of document management. This model can be adopted and adapted by institutions seeking to strengthen the integrity and interoperability of digital issuance and certification processes.
Authors - Shayma W. Nourildean, Yousra Abd Mohammed, Nahida Naji Kadhim Abstract - The growth of the Internet of Things (IoT) equipment has changed many industrial and social applications, but it has made the IoT network vulnerable to a wide range of malicious activities by increasing the number of potential attack points. Traditional IDS has a problem with scalability, adaptability and accuracy when facing new and complex cyber threats. In this study, a strong ensemble machine learning framework that integrates Decision Tree (DT), Random Forest (RF), and XGBoost through confidence-based soft voting, had been validated across individual datasets. The proposed system (DTXG-RF) uses different forces and weaknesses of these algorithms to improve the accuracy of the detection and reduce the chances of causing it a false alarm. Two Benchmark IoT data sets, CIC-IoT2023and IoTID20, were used. These data sets showed a wide range of scenarios in the real IoT attack. To reduce overfitting risk and data leakage, strict train–test separation, stratified splitting, and pipeline-based preprocessing were enforced, and additional cross-validation experiments were conducted to verify model stability across folds and datasets. Assessment results showed that DTXG-RF ensemble voting model consistently improves traditional machine learning models such as DT, XGBOOST, KN, logistic regression, Nave Bayes and Catboost. The model accuracy of CIC -IoT-2023 and IOTID20 were 94.03% and 99.99%, respectively, with AUCs of 0.95025 and 0.9994. These results indicated that the ensemble IDS was lightweight and could achieve high detection accuracy with low latency and memory overheads, which is also suitable for low-latency IoT edge deployment.
Authors: Manuela Moreno-Arcila, Luz Marcela Restrepo-Tamayo, Gloria Piedad Gasca-Hurtado Abstract. Productivity in software development teams is a multidimensional phenomenon that cannot be reduced to delivery metrics or technical activity indicators. The social and human factors that influence collective performance are consistently overlooked in the measurement frameworks used in practice. The lack of a tool that integrates social, human, and process dimensions with execution results in a structured way prevents a holistic and actionable measurement of software teams' productive capacity. The lack of a tool that systematically integrates social, human, and process dimensions with execution results prevents a holistic and actionable assessment of software teams' productivity. The PCI was designed following the Design Science Research (DSR) paradigm, through a process consisting of two interconnected components: 1) the design and content validation of the Team Capacity Measurement Instrument (TCMI), through expert judgment and calculation of the Content Validity Coefficient (CVC), and 2) the conceptual construction of the index, including the definition of socio-technical dimensions, standardization of variables, conceptual weighting, aggregation, and interpretation using bands and alerts by dimension. The PCI generates a standardized composite score on a [0–100] scale using a weighted aggregation formula with theoretically justified weights, accompanied by a classification scheme that divides the system’s health into four bands and a mechanism for generating actionable alerts by dimension.
