Authors - Swasti Shinde, Ishita Rajarshi, Shravani Mote, Abhilasha Gandhi, Megha Dhotay Abstract - The use of artificial intelligence (AI), especially deep learning, has brought major changes in the study of histopathology images for cancer diagnosis for the doctors. This review compares the latest deep learning methods used to identify whether the tissues are Benign. (noncancerous) or malignant (cancerous). This paper discusses different technologies used for the study of histopathology images. Convolutional neural network (CNNs) is effective in capturing small local details in the images. Several studies report that Transformers (ViTs) can outperform CNNs in complex classification tasks [18, 22]. Along with the discussion about newer hybrid models and large foundation models, the paper specifies the strength of combining the strength of both. Although these models are developed focusing on achieving high accuracy on good data sets, there are few challenges in their practical use, such as i. Models often fail to generalize data from different hospitals due to domain shift [1, 28] ii. Model interpretability remains a significant challenge in clinical development [34]. iii. There is a lack of proper methods to measure the uncertainty in the decisions [40,41]. This paper highlights the research gaps in real clinical use and focuses on the need to develop models that are robust, interpretable, and suitable for practical healthcare applications.
Authors - Ioana Chan Mow, Fiafaitupe Lafaele, Sarai Faleupolu-Tevita, Vensel Chan, Soonalote Eti, Fiti Tolai Abstract - The need to move to online proctored exams urged the National University of Samoa (NUS) to trial and evaluate a variety of online proctoring systems to ensure the offering of safe and secure exams online. The aim of the 4-phase research was to answer the following question: “What are some feasible options for online proctoring systems (OPSs) for offering online exams for NUS?” This paper is based on the last phase of this 4-phase study conducted at NUS to evaluate the feasibility of two proctoring systems, Integrity Advocate and Proctorio, for online exams, particularly during lockdown. Specifically, the objectives were to i) trial and evaluate the suitability of each OPS as well as the type of exam mode (two options: in the laboratory or from home) using a diffusion of innovation framework and, from the evaluation, recommend a suitable OPS for NUS. Both between-subjects and within-subjects analyses revealed highly positive responses for both OPS and exam mode across the 5 variables of the diffusion of innovation model of relative advantage, compatibility, ease of use, observability, and trialability. Most of the findings did not show any differences by OPS type, exam mode, or gender, as most responses across the 5 variables of diffusion of innovation were highly positive and very similar, indicating positive and high rates of adoption of the two OPS. An in-depth investigation into the features of the two OPS also revealed that Proctorio had a wider scope of features than Integrity Advocate.
Authors - Syammas Pinasthika Syarbini, Irmawan Rahyadi, Muhammad Aras, La Mani Abstract - The need to move to online proctored exams urged the National University of Samoa (NUS) to trial and evaluate a variety of online proctoring systems to ensure the offering of safe and secure exams online. The aim of the 4-phase research was to answer the following question: “What are some feasible options for online proctoring systems (OPSs) for offering online exams for NUS?” This paper is based on the last phase of this 4-phase study conducted at NUS to evaluate the feasibility of two proctoring systems, Integrity Advocate and Proctorio, for online exams, particularly during lockdown. Specifically, the objectives were to i) trial and evaluate the suitability of each OPS as well as the type of exam mode (two options: in the laboratory or from home) using a diffusion of innovation framework and, from the evaluation, recommend a suitable OPS for NUS. Both between-subjects and within-subjects analyses revealed highly positive responses for both OPS and exam mode across the 5 variables of the diffusion of innovation model of relative advantage, compatibility, ease of use, observability, and trialability. Most of the findings did not show any differences by OPS type, exam mode, or gender, as most responses across the 5 variables of diffusion of innovation were highly positive and very similar, indicating positive and high rates of adoption of the two OPS. An in-depth investigation into the features of the two OPS also revealed that Proctorio had a wider scope of features than Integrity Advocate.
Authors - Maulikkumar Pandya Abstract - Skin lesion segmentation is essential for computer-aided dermatological diagnosis, but reliable pixel-level annotations are costly and require experts. To reduce dependence on manual labeling, pseudolabeling combined with foundation models such as the Segment Anything Model (SAM) has been explored; however, most pipelines rely on a single pseudo-label per image, which can introduce boundary bias when pseudo-labels are noisy. In this paper, we compare two U-Net training pipelines built on pseudo-labels generated using U²-Net and SAM. The first pipeline follows a single pseudo-label inheritance strategy as a strong annotation-free baseline. The second pipeline synthesizes multi-style pseudo-labels (tight/moderate/loose) and applies agreement-based learning to supervise only high-confidence consensus regions while suppressing uncertain boundary pixels. No ground-truth masks are used during training; manual annotations, when available, are used only for offline evaluation. Experiments on ISIC 2018 under a pseudo-reference protocol show improved boundary behavior (higher Boundary F-score) and more coherent contours, especially in ambiguous border regions.
Authors - Satyendra Sharma, Pradeep Laxkar Abstract - Reconstructing polyphonic musical sequences represents a significant challenge in computational music analysis. This study presents a method based on empirical entropy and the analysis of multi-voice bigrams to identify and re-construct missing notes in polyphonic sequences. The approach combines statistical modeling of transitions between simultaneous voices in a musical piece, represented as tuples duration:interval|duration:interval|... depending on the number of voices, with techniques for generating and ranking possible segments according to probability and entropy. Results show that considering multi-voice bigrams effectively captures the polyphonic structure and improves the accuracy of missing note prediction. This work opens new perspectives for the application of probabilistic models to polyphonic music and AI-assisted music generation.
Authors - Paponsun Eakkapun, Sulak Sumitsawan, Chukiat Chaiboonsri Abstract - Cloud cover, shadows, haze, illumination variation, and atmospheric noise severely degrade the reliability of satellite image change detection. This paper proposes an atmosphere-aware, physics-driven preprocessing framework that performs cloud, shadow, haze, and illumination compensation before change analysis, without relying on convolutional or transformer-based networks. Two multi-temporal satellite images are processed through unified cloud and shadow handling, haze correction, illumination normalization, and residual atmospheric noise suppression, followed by a spectrally invariant change detector with structural consistency validation. The system also generates semantic multi-class change maps and geo-contextual text explanations to enhance interpretability. Experiments on diverse multi-temporal datasets demonstrate a change detection accuracy of 98.9% with high precision and recall, significantly outperforming conventional and deep learning baselines
Authors - C. R. Patil, Arundhati Sarvadnya, Diksha Shejwal, Sakshi Nehe, Sobiya Shaikh Abstract - The rapid expansion of the Internet, together with the pervasive diffusion of mobile technologies, has fundamentally reshaped contemporary socio-economic activities, positioning e-commerce as a core pillar of the digital economy. In response to increasing competitive pressures and the growing demand for personalized consumer experiences, enterprises have progressively adopted advanced analytical technologies, among which machine learning has emerged as a key strategic instrument. This study develops and empirically evaluates a machine learning–based product recommendation framework that integrates historical transaction data with sentiment information extracted from user-generated reviews. Data were collected from multiple e-commerce platforms and assessed using widely adopted evaluation metrics, including Accuracy, Recall, and F1-score. The experimental findings demonstrate that the XGBoost algorithm consistently outperforms alternative models, exhibiting superior capability in identifying latent consumer preferences and behavioral patterns. Overall, the results provide robust empirical evidence supporting the effectiveness of the proposed approach and underscore its practical potential for enhancing personalization quality and improving recommendation performance in large-scale e-commerce environments.
Authors - Upendra Pratap Singh, Akshay Anand Abstract - The rapid proliferation of Internet of Things (IoT) systems has led to the widespread adoption of artificial intelligence for autonomous sensing, prediction, and decision-making across critical application domains. While these AIdriven IoT systems achieve high operational efficiency, their increasing reliance on complex and opaque models raises serious concerns regarding transparency, trust, accountability, and regulatory compliance. These concerns are particularly acute in distributed IoT environments, where decisions are made across heterogeneous devices under resource constraints. Existing explainable artificial intelligence (XAI) approaches largely focus on centralized or standalone machine learning models and fail to address the unique challenges of IoT systems, including deployment heterogeneity, dynamic data distributions, privacy requirements, and real-time decision-making. As a result, explanations are often disconnected from system behavior, lack consistency across layers, and provide limited support for trust assessment and human oversight. This paper presents a comprehensive survey of explainable AI techniques for trustworthy IoT systems and introduces a deployment-aware reference architecture that integrates explainability, trust evaluation, privacy preservation, and human-in-the-loop feedback across edge, fog, and cloud intelligence layers. The architecture emphasizes localized explanation generation, context-aware refinement, explanation validation, and multi-metric trust assessment, enabling explanations to evolve alongside system behavior. By explicitly coupling explanation quality with trust monitoring and adaptive feedback, the proposed framework bridges the gap between predictive performance and operational trustworthiness in distributed IoT environments. The survey highlights key research trends, identifies critical gaps in current methodologies, and outlines future directions for scalable, reliable, and human-centered explainable IoT systems. By positioning explainability as a core system property rather than a post-hoc add-on, this work provides a foundation for designing AI-enabled IoT systems that are transparent, accountable, and trustworthy by design.
Authors - Sreebala V S, Arun Kumar V N, Agna.S. Nath Abstract - The Commercial Territory Design Problem (CTDP) plays an important role in sales and marketing management. The problem focuses on partitioning some basic units into territories to optimize compactness while ensuring workload balance and connectivity constraints. Due to the NP-hard property of the problem, exact approaches often have limitations in scalability across large datasets. This study proposes a combination of the classical ALNS algorithm framework and an ActorCritic Deep Reinforcement Learning architecture to deal with the large CTDP instances. Our proposed algorithm can automatically select destroy and repair operators, and dynamically fine-tune hyperparameters such as destruction level and acceptance criteria based on the actual state of the search process. Experimental results on benchmark instances with various sizes show that our algorithm not only achieves superior quality solutions compared to traditional ALNS but also surpasses exact solutions in terms of convergence speed within the same runtime limit. It can achieve high-quality solutions within a reasonable execution time and has the potential for real-world applications.
Authors - Tri Wiyana, Roberto Tomahuw Abstract - Mental health disorders are among the major global health problems, and early diagnosis is the key for effective management. Conventional methods are based on self-reported or clinical scales, for which intervention comes late. In this paper, we propose a multimodal AI framework for the detection of early mental health detection from typing and voice behaviors. We extract BERT-based linguistic embeddings of text transcripts and spectral features of the speech signals from the audio data using the DAIC-WOZ dataset for capturing verbal cues. These features are then combined by machine learning algorithms to classify depression. The proposed framework prioritizes non invasive, privacyconscious detection with explainability techniques used to foster clinical confidence. We further present experimental results to show that the multimodal fusion also provides classification gain over unimodal baselines. This study demonstrates the capability of AI-based, real-time methods for proactive mental health monitoring and provides a stepping stone towards healthcare deployment.
