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.
