Authors - Rabab A. Rasool, Muthana H. Hamd Abstract - This paper introduces a novel unimodal biometric system based on a derivative angle-based feature. Unlike traditional edge-based features, this approach leverages a one-to-one correlation between the angle and its corresponding edge, enabling robust and reliable recognition for a single-source trait biometric system. This unique property allows for performance comparable to multimodal systems, which typically rely on multi-source traits fusion techniques for improved accuracy. To evaluate the effectiveness of angle-based features, an extensive experiments is conducted on three diverse facial datasets (AR, FEI, and CK+) using 150 subjects. The facial features are extracted using seven distinct methods at varying levels, ensuring a comprehensive and fair comparison between edge and angle-based approaches. Recognition accuracy was assessed using various metrics, including False Acceptance Rate (FAR), False Rejection Rate (FRR), and three Error Distance Measures (EDMs): Euclidean, Manhattan, and Cosine distances. Results demonstrate that angle-based features achieve highly competitive performance compared to their edge-based counterparts. Across 210 recognition processes, edge-based features achieved an overall accuracy of 79%, while angle-based features recorded a closely comparable 73%. These findings highlight the potential of angle-based features as a promising approach for developing robust and reliable unimodal biometric systems.
Authors - Zakaria Kinda, Sadouanouan Malo Abstract - In Burkina Faso, farmers and plant pathologists remain with important challenges in treating cotton diseases. The development of natural language algorithms has advantages to the implementation of a tool for suggesting treatments for cotton diseases and the classification of cotton diseases using meteorological data. In order to propose a tool for suggesting appropriate treatments for diseases using large language models (LLM), this paper uses meteorological data collected by the National Agency of Meteorology of Burkina Faso (ANAM-BF) to 2014 and 2023 in addition to knowledge bases on cotton diseases. In this study, we compare the Llama2 model with the RAG system to the BERT model for classification and proposed of cotton disease treatments. The result of our approach obtains a 95.4% classification precision for cotton diseases with weather data. Users may interact with the tools to generate treatments for cotton diseases with the use of a console for Llama2 with RAG and a chatbot for BERT. The performance of Llama2 model with RAG to generate appropriate responses to cotton diseases in Burkina Faso was evaluated by comparing it with GPT.
Authors - Adarsh Varshney, Karthick Seshadri, Viswa Kiran Andraju Abstract - The Knowledge-Infused Policy Gradient with Upper Confidence Bound (KIPGUCB) strategy addresses contextual multi-armed bandit problems by balancing exploration and exploitation. This study evaluates the performance of a KIPGUCB-based agent in the partially observable environment of StarCraft II. Unlike traditional deep reinforcement learning models that rely on low-level atomic actions, our approach enhances decision-making by employing higher-level tactical strategies. A tactic manager dynamically selects optimal tactics based on game state and reward signals which improves resource management and structured tasks such as unit training. The agent’s performance is compared with a StarCraft II Grandmaster, a novice human player and DeepMind’s baseline RL agent across five mini-games. Experimental results show that the KIPGUCB-based agent outperforms the baseline model in resource focused and structured tasks but struggles in combat-oriented scenarios requiring adaptive responses.
Authors - Rajyashree H, Govindarajan J Abstract - The FMCW radar sensor technology has demonstrated remarkable effectiveness in various applications, both civilian and military, due to its affordability and efficiency in a wide range of operational conditions. Nonetheless, this technology faces challenges related to mutual interference during object detection, which can negatively impact its performance. Advanced signal processing techniques, combined with cutting-edge software tools, can help mitigate this issue. This study embarks on the innovative journey of developing a module within NS-3 a distinguished network simulator, that encompasses all essential components of FMCW radar, including transmission, reception, and processing units for the very first time. The prevailing challenges experienced by the researchers in asserting their protocols in real-time have motivated us to design and develop the radar module in NS-3 that fosters to validate the protocols for radars in heterogenous environment in an emulated setup. Two unique mitigation algorithms—Ti’sInterference Mitigation, and Frequency Hopping via Unslotted Aloha—have been meticulously implemented and assessed. This undertaking lays a firm groundwork for the enhancement of the radar module, facilitating better performance in intricate setups.
Authors - Vipin Bansal, Manisha Malhotra Abstract - Artificial Intelligence (AI) is transforming industries such as automotive, healthcare, insurance, and manufacturing through computer vision and Convolutional Neural Networks (CNNs) for image analysis. In medical imaging, AI enhances the interpretation of MRI, X-rays, and CT scans, reducing human error and improving diagnostic efficiency. Early detection of Diabetic Retinopathy (DR), a severe diabetes complication, is crucial to preventing vision loss. Traditionally, ophthalmologists manually analyzed retinal images to detect abnormalities like fluid leaks or lesions. AI now enables more precise and efficient analysis. This paper presents ViT-MADv2, an improved method for detecting DR-related abnormalities using Vision Transformer (ViT) generative models. As an extension of previous research [1], ViT-MADv2 enhances the base image generation module by incorporating diverse training data with variations in contrast, color, and lesion size. It also refines the similarity evaluator module to improve analysis. The model leverages a novel approach to compare embeddings from original and generated images, identifying DR-specific patterns. Experimental results demonstrate a 2% accuracy gain, reaching 96.5%, with improved sensitivity—a crucial factor in healthcare. These advancements strengthen AI-driven diagnostics, enhancing clinical confidence. Source code: https://github.com/vipinbansal1/vitmadv2.
Authors - Nuno J. P. Rodrigues, Tania S. S. Azevedo Abstract - The literature review on licensing and technology commercialization delves into the critical role that Intellectual Property (IP) management and effective licensing strategies play in the commercialization of technological innovations. Intellectual property, particularly patents, provides a competitive edge for firms, enabling them to appropriate financial returns from their innovations. Licensing emerges as a vital method for both leveraging technologies developed within organizations and universities and generating revenue streams. The review emphasizes the importance of robust IP management, which allows firms to secure their innovations and strategically negotiate licensing agreements to expand their market reach. The studies analyzed highlight the complex dynamics involved in technology transfer, with a particular focus on how universities play a pivotal role in transferring innovations to the market. University-based licensing officers often act as intermediaries between academic inventors and the commercial sector, facilitating the commercialization process. However, challenges such as bias in decision-making and the difficulties in securing timely licensing agreements, particularly in emerging fields like nanotechnology, are discussed. Additionally, the review explores how partnerships between companies and universities, through strategic alliances and well-structured contracts, can mitigate risks and enhance the successful commercialization of new technologies. Overall, this review synthesizes key insights into how companies and academic institutions can optimize their technology commercialization efforts through efficient IP management, strategic licensing, and collaborative partnerships.
Authors - VanVinh Le, HongGiang Nguyen Abstract - This study aims to use the 2025 Times Higher Education (THE) Asia University Rankings as a frame of reference to consider what drives and dictates university rankings in the ASEAN region. Their analysis looks at six major indicators – Teaching, Research, Citations, Industry Income, International Outlook and Rank.. Comparative analysis confirmed that while Vietnam universities are purely citation-dependent in their quest to enhance higher ranked tables, some universities of our Southeast Asian neighbors at least start from a more balanced and diverse evaluation. The study deployed three deep learning models BiLSTM, BiGRU, and a Hybrid BiGRU-BiLSTM to predict university rankings. Among all the models, the Hybrid model performed the best with the lowest RMSE (3.72) and MAE (2.61), which indicated the highest predictive accuracy and stability. The analysis additionally showed the power of higher citation scores to boost rankings, with the greatest gains accruing to institutions with the lowest ranking. Feature importance analysis indicated that collaboration between industry and research is very important for ASEAN universities. Quality of teaching was still a main focus in Vietnam universities. The results highlight the role of research impact and international collaboration to go up in ranking. The analysis cautioned that universities and colleges looking to secure their position on the world stage needed to prioritize the volume of research produced, degree of international engagement, and depth of industry collaboration. The findings guaranteed the suitability of deep learning models as a robust methodological toolbox for ranking prediction and data-driven strategic improvement of university performance.
Authors - HongGiang Nguyen, VanVinh Le, TrungKien Tran Abstract - This paper is an attempt to evaluate ASEAN universities’ research impact and more specifically that of Vietnam by analyzing research-oriented key performance indicators that drive rankings. According to data, these universities have large gaps between the average reputations for academia and employers, faculty-student ratios, and research output. The experiment's three models named LSTM, GRU, and Hybrid predicted rankings on citation performance. The results revealed that the Hybrid model produces the highest accuracy, reiterating that the citation per paper variable tends to most strongly drive the positive correlation between citations per paper and university ranking. In addition, the study also indicated that Hue University cited 175% more research opened up to gain below a rank of 55. This point emphasized that research output is a major driver of rankings and institutional prestige. At the same time, the study offered several recommendations for universities, particularly those looking to increase their rankings, including focusing on high-impact publications, developing global research partnerships, and investing in faculty development.
