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.
Authors - Jawad Mahmood, Muhammad Adil Raja, John Loane, Fergal McCaffery Abstract - This research focuses on developing a testbed for Artificial Intelligence (AI) enabled Unmanned Aerial Vehicles (UAVs), particularly addressing the challenges posed by delayed rewards in Reinforcement Learning (RL). The Multi-UAV testbed integrates a realistic flight simulator with a Flight Dynamics Model (FDM), creating a versatile environment for testing and training RL algorithms. Two primary models were implemented: the Advantage Actor-Critic (A2C) model controls the target UAV, while the Asynchronous Advantage Actor-Critic (A3C) model governs the tracking UAV, leveraging asynchronous updates for efficient exploration and faster learning. A significant obstacle in reinforcement learning is the issue of delayed rewards, where feedback for an agent’s actions is not immediately available, potentially leading to unstable learning and reduced performance. This project addresses this challenge by integrating the Intrinsic Curiosity Module (ICM) with the A3C model. The ICM generates intrinsic rewards encouraging the A3C-controlled tracking UAV to explore new states, even in the absence of external rewards. This approach mitigates the effects of delayed rewards, allowing the tracking UAV to maintain effective pursuit of the target UAV under dynamic conditions.
Authors - Ufuk Sanver, Mustafa Cem Kasapbasi Abstract - Psoriasis, a chronic autoimmune skin condition, is an abnormal proliferation of skin cells along with scaling and inflammation. Proper detection and identification of psoriasis early on are critical to avoid inappropriate treatment and management. Traditional methods of diagnosis heavily rely on prolonged and subjective to physician experience of clinical presentation and histopathological study. Deep learning algorithms have proved highly successful for medical image analysis in the past few years and offer computerized, accurate, and quick diagnosis features. Here the use of convolutional neural networks (CNNs) is explored for classifying and detecting psoriasis from dermatology images. In the current research a pre-trained deep learning models that are ResNet50, MobileNetV2 and EfficientNet-B0 with SGDM, and DenseNet201 with ADAM optimization techniques have been employed. In this paper a curated dataset of psoriasis skin images affected with psoriasis was curated, in order to discriminate psoriatic lesions. To increase the accuracy dataset is augmented with various im-age processing techniques. In the training The experimental results demonstrate that the developed model is very accurate, sensitive, and specific. Our work suggests that deep learning models might be employed as valuable diagnostic aids, reducing errors in diagnosis and improved patient outcomes. Future studies will focus on enhancing model explain ability and increasing datasets with varied skin types and disease severities.
Authors - Diego Perez-Lopez, Rodolfo Bojorque, Jorge Duenas-Lerin, Fernando Ortega Abstract - Recommender systems play a crucial role in personalized content delivery, with collaborative filtering (CF) being a widely used approach. However, traditional CF methods often struggle to fully capture complex user-item interactions. In this study, we propose neural stacking models that integrate multiple CF techniques to enhance predictive accuracy. Experimental results show that, among baseline matrix factorization (MF) models, Biased MF and BNMF achieve the best Mean Absolute Error (MAE), demonstrating their effectiveness in modeling user-item relationships. Nonetheless, the proposed neural stacking models outperform these approaches by dynamically weighting CF models based on contextual factors. Comparisons with deep learning-based CF models (GMF, MLP, and NeuMF) confirm that neural stacking provides a more personalized and adaptive recommendation strategy. Future research will focus on optimizing model architectures, incorporating additional contextual information, and evaluating scalability for large-scale applications.
Authors - Ujjwal MK, Sunil Parameswaran, V. Guna Chowdary, Varun Bharadwaj, Suresh Jamadagni Abstract - In today’s fast-paced world, mental wellness is as essential as physical health. EmoHealth is a new mobile application designed to help individuals harmonize their mind and body. By combining emotion recognition with health analytics, EmoHealth delivers a real-time, holistic snapshot of a person’s mental and physical health. Using Apple technologies like HealthKit and CoreML, EmoHealth continuously tracks your heart rate, breathing patterns, and vocal activity through your Apple Watch and iPhone. This advanced approach detects even subtle shifts in emotional and physical health, sending timely alerts that promote self-care and help prevent stress-related issues from escalating. The EmoHealth app empowers users to stay mindful of their mental health while providing essential information to assist with stress management and emotional well-being. It keeps users updated on physical and emotional health metrics, fostering greater self-awareness and offering a new path to well-being. With on-device data processing, EmoHealth ensures both convenience and peace of mind, bridging gaps in traditional healthcare and enhancing proactive mental health support. This innovative approach highlights the potential of technology to deliver early, personalized feedback that can make a meaningful difference in people’s lives.
Authors - Motsotua Confidence Hlatshwayo,Kayode Oyetade,Tranos Zuva Abstract - In today’s rapidly evolving digital landscape, the ethical adoption of Artificial Intelligence (AI) has become a critical concern for organizations seeking to innovate responsibly. This study explores the integration of AI within South African Small and Medium Enterprises (SMEs), emphasizing the ethical challenges and strategic considerations associated with adoption in resource-constrained environments. While AI offers transformative potential for enhancing innovation, efficiency, and competitiveness, its deployment raises significant ethical risks, including algorithmic bias, data privacy violations, and a lack of transparency and accountability. Grounded in the Technology-Organization-Environment (TOE) framework and Ethical Decision-Making (EDM) theory, this research adopts a literature review methodology to examine the drivers, barriers, and ethical dimensions of AI adoption in SMEs. The study reveals that ethical awareness, organizational readiness, and external regulatory support are crucial to enabling responsible innovation. It highlights that SMEs with stronger ethical intent and governance practices are better positioned to adopt AI in ways that promote trust, fairness, and long-term sustainability despite resource limitations. This study contributes a context-sensitive conceptual framework that integrates structural and ethical considerations, offering practical guidance for policymakers, business leaders, and technology developers. The findings underscore the importance of aligning digital transformation with ethical imperatives to ensure inclusive and equitable AI adoption in developing economies.
Authors - Elrasheed Ismail Mohommoud Zayid, Abdulmalik A. Humayed, Yagoub Abbker Adam Abstract - This study aims to test machine learning (ML) classification analysis across a rich network traffic dataset sample to figure out the patterns of Saudi cyberspace and alert the community for cybersecurity risks. The network topology used for generating a sample dataset was a kind of heterogeneous simultaneous photonic multiprocessor exchange bus architecture (SOME-Bus). First, the dirty and noisy dataset was cleaned using essential cleaning procedures. Dimensions of 22 characteristics and 1048575 datapoints were considered for the model/data evaluation procedures. Second, the top-ranked learning model candidates were nominated by using the LazyPredict technique. Third, the Saudi cyber domain's terra-Byte payload traffic is shaped and visualized using a potent supervised computing algorithm. Powerful performance measure criteria were employed to calculate the model’s final accuracy and error rates. The findings conclude that Saudi was in the ninth rank with respect to cybercrime source; and, decision tree (DT) was the highest-performing algorithm with respect to destination address.
Authors - Vindiya Wickramathunga, Sapna Kumarapathirage Abstract - Accurate cinnamon plantation disease detection is crucial for planters, especially those with limited expertise. Traditional manual inspection methods are time-consuming and subjective, risking delays or incorrect diagnoses. This paper proposes an automated detection system using a Convolutional Neural Network (CNN) integrated with Explainable Artificial Intelligence (XAI) techniques. A publicly available dataset comprising three disease classes and one healthy class was used for training. The CNN achieved 75–80% accuracy with a 0.78 F1-score. Visual explanations via HiRes-CAM provided higher resolution compared to GradCAM++, enhancing user trust. The system demonstrated fast inference times (1–2 seconds), supporting real-time field deployment.
Authors - Leila BENOUNISSA, Djilali BENABOU, Soumia TABETI Abstract - The mobile phone is the most widely used mass communication object in the world. It gives its user the feeling of being everywhere at the same time and at all times (Jauréguiberry, 2005). Calls, ringtones and button clicks can be a source of undesirable phenomena for others and disrupt social interactions in public spaces (Palen, Salzman and Youngs, 2001). This is how several studies have attempted to understand the attitudes of mobile phone users and their preferences in different social settings. The objective of this contribution is to understand how users perceive the impact of the mobile phone use in different public spaces and if there are differences in the use compared to the characteristics of the users. Keywords: Mobile Phone, Social Settings, Attitudes, Algeria.
