Authors - Syed Muhammad Raza Abidi, David C. Henshall,Gabriel-Miro Muntean Abstract - The weak electromagnetic signals originating from the brain’s neu-ronal activities can be assessed by electroencephalography (EEG) and magne-toencephalography (MEG). Due to the continuous time series data of EEG and low amplitude and nonstationary characteristics, it is difficult to achieve a con-sistent and satisfactory diagnosis outcome. It is hard to use these signals to identify and describe neuronal activation in the brain and adequate knowledge of signal processing, statistics, and numerical techniques are required. This paper introduces an innovative hybrid approach using machine learning, i.e., Modality Integration for Neuro Signals to Enhance Accuracy (MINE-Acc) which com-bines EEG and MEG data to increase brain activity prediction accuracy. This approach leverages the complementary strengths of both modalities to improve the accuracy and robustness of prediction. We employed the machine learning pipeline and used a Logistic Regression (LR) classifier in the research, performed a 5-fold cross-validation on sample dataset given by MNE-Python and by com-bining modalities together the findings provide a prediction accuracy of 99.8%. Traditional methods, such as functional magnetic resonance imaging (fMRI) and Positron Emission Tomography (PET) etc. are available to use these signals to characterize normal and pathological brain activity but there remain difficulties with integration and interpretation. They have high spatial resolution but lack real-time capabilities. The study determines the improved prediction accuracy of the activity participants engage with based on combined analysis of EEG and MEG data. We used the MNE-Python, a software package to test this novel approach.
Authors - Ahmed Noorim, Raina Nusrat Jahan, Md. Sabbir Al Ahsan, Sourav Adhikary, Md. Jamil Uddin Abstract - Early detection of Diabetic Retinopathy (DR) remains essential due to its status as a leading cause of vision loss along with severe complications. Ever increasing worldwide diabetes situation makes it necessary to develop an automated diagnosis system for detecting DR at an early stage precisely. The research proposes a weighted average ensemble deep learning architecture to perform two stages of DR diagnosis along with severity classification from fundus images. The first stage identifies cases of the presence of DR by applying a binary classifier followed by a multiclass classifier in the second stage to evaluate severity levels. This model is trained and evaluated on a merged dataset which amalgamates APTOS 2019, MESSIDOR 2, and IDRiD with three different preprocessing to boost its generalized application capabilities. DenseNet121, EfficientNetB1 and Xception models complement each other for optimal feature extraction and classification task thus utilized in the development of ensemble model. Outperforming several state-of-the-art models, 97% recall with 92% accuracy was achieved in determining the existence of DR, while severity classification reaches 93% accuracy and 94% recall. The research shows promising assistance for ophthalmologists in becoming an essential diagnostic screening instrument for the early detection of DR in resource limited areas.
Authors - Dario Galic, Dejan Stosovic, Elvir Cajic, Anita Katic Abstract - This paper explores numerical methods for solving partial differential equations (PDEs) using the method of nets. The focus is on hyperbolic equations, such as the wave equation, and the application of net methods in solving problems with boundary conditions. The process of solving these equations using computational tools is illustrated, and the accuracy of the results is analyzed. The iterative Gauss-Seidel method is applied to solve systems of algebraic equations generated by the net method.
Authors - Kamil Samara, Syed Rizwan Abstract - Mental health challenges, particularly anxiety and stress, are prevalent among students due to academic pressures, social expectations, and personal struggles. Traditional mental health support systems often fail to provide timely interventions, leading to severe consequences such as depression or suicidal ideation. This study presents an Early Detection and Support System for Student Mental Health, integrating machine learning models to proactively assess and predict student anxiety levels. The system utilizes data from activity logs, and survey responses to classify students into different anxiety categories and provide personalized support recommendations. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, were employed to optimize prediction accuracy. The results demonstrate that Logistic Regression achieved the highest accuracy (89.1%) in predicting stress levels, while the Random Forest model performed best in stress reduction prediction. The system's predictive capabilities extend beyond anxiety detection, enabling multi-feature mental health analysis, including depression, self-esteem, and stress levels. By integrating an automated alert mechanism and real-time monitoring, this framework offers a proactive solution for universities to support student mental well-being.
Authors - Alaya Parven Alo, Md Ruhul Amin, Md Imran Kabir Joy, Kazi Rezwana Alam, Shahriar Sultan Ramit, Md. Sadekur Rahman Abstract - Cervical cancer is a leading cause of cancerrelated deaths among women, and early detection is crucial for improving patient prognosis. Traditional diagnostic methods, while effective, are often timeconsuming and prone to subjectivity. This paper explores the use of deep learning techniques for automating cervical cancer diagnosis, employing five distinct models MobileNetV2, VGG19, Xception, ConvNeXtBase, and InceptionV3 along with a tuned version of MobileNetV2. A secondary dataset with five classes of cervical cell images were utilized to build the models and the performance of each model was evaluated with precision, recall and F1score. The tuned MobileNetV2 model achieved the highest accuracy and robustness in classification.TunedMobileNetV2 provided an accuracy of 0.99, with precision and recall values of 0.99.To address the "blackbox" nature of deep learning models, Explainable AI (XAI) techniques were incorporated, including LIME and Saliency Maps, to improve model interpretability. The use of XAI in the tuned MobileNetV2 model enhances transparency, allowing for visual interpretation of model predictions. Findings of the research suggests that deep learning, coupled with XAI, offers a promising and more explainable approach to cervical cancer diagnosis, advancing both accuracy and interpretability in automated clinical decisionmaking.
Authors - Katleho Seatlolo, Khutso Lebea Abstract - This paper investigates the potential of Retrieval Augmented Generation (RAG) technology to enhance the effectiveness of Intrusion Detection Systems (IDS) in the energy sector. By leveraging vast amounts of historical data, real-time threat intelligence, and advanced natural language processing techniques, RAG can significantly improve IDS capabilities in detecting and responding to cyber threats. The paper addresses the limitations of traditional IDS, such as their reliance on predefined signatures and vulnerability databases. It explores how RAG can overcome these limitations by analysing network traffic patterns, identifying anomalies, and correlating them with known attack vectors. The paper discusses the potential benefits of RAG in terms of improved threat detection accuracy, reduced false positives, and enhanced response times. Case studies and research findings are presented to support the argument. Challenges and considerations related to data quality, privacy, and ethical implications are also addressed. The conclusion emphasises the importance of RAG technology in safeguarding smart grids from evolving cyber threats and highlights potential future directions for research and development. The paper aims to explore the potential of RAG technology to enhance IDS in smart grids and contribute to Sustainable Development Goal 9: Industry, Innovation, and Infrastructure. It describes the limitations of traditional IDS, the benefits of RAG technology, and the potential applications of RAG in the energy sector.
Authors - Aditi Choudhary, Aditya Gupta, Pulkit Jain, Nikunj Agarwal, Mukund Wagh Abstract - We present a novel programming-by-example (PBE) approach that synthesizes natural and human-readable code by integrating higher-order functions with standard and third- party libraries in Haskell. This technique leverages refinement types to efficiently prune the search space, ensuring scalability while preserving soundness. Using Liquid Haskell, we extend support for complex data structures, enabling the synthesis of reusable and idiomatic code. Our evaluation demonstrates the tool’s versatility across lists, trees, maps, and domain-specific languages, including musical scores. The results highlight that our method generates concise, interpretable programs, bridging the gap between formal verification and practical usability in functional programming. Index Terms—Programming-by-Example (PBE), Haskell, Higher-Order Functions, Refinement Types, Liquid Haskell, Code Synthesis, Functional Programming, Search Space Pruning, Domain-Specific Languages (DSLs), Formal Verification, Scalability, Reusable Code, Idiomatic Programming.
Authors - Wanda Syauqi Mikola, Tiurida Lily Anita Abstract - This Research looks at how Customer’s Intentions to Buy from Fast-Food Restaurants are influenced by Online Reviews, Brand Images, and Menu Visuals. Understanding the factors that influence Customer’s Purchase Decisions is of business interest due to the impact of technology and the evolution of services. The objective is to empirically ascertain how these three factors increase Customer’s Intentions to Buy for sustainability in The Restaurant Industry. Structural Equation Modeling-Partial Least Squares (SEM-PLS), A Quantitative Method, is used in this study to examine the connection between variables. Data was gathered by randomly selecting 200 Customer’s Fast-Food Restaurants in The Jakarta Region. The Results indicate that while Online Reviews do not significantly affect Customer’s Intentions to Buy, Brand Images and Menu Visuals do. This implies that when it comes to Fast-Food Restaurants, Customers are more swayed by The Reputation of A Well-Known of Brand Images and Menu Visuals than by Online Reviews. For Restaurant Businesses looking to improve their Brand Images and optimize Their Menu Visuals to draw in and keep Customers, This Research offers Insightful Information.
