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.
Authors - Shylaja P, Jayasudha J S Abstract - The Question Answering system (QA) is one of the popular and widely used ap-plications of NLP. It is an information retrieval system that attempts to find the correct answer for a question based on the given paragraph text. Transformers have been widely used for QA tasks, due to their contextual embedding, attention mechanism, and transfer learning for specialized tasks. Transformer-based models can be easily fine-tuned with large datasets. Such models provide state-of-the-art performance over large datasets for question-answering tasks. The proposed approach compares performance of transformer based model over a small sized dataset. We incorporated an answer formation layer along with transformers to comprehend contextual, syntactical, and semantic information from small-sized datasets. We developed a set of rules according to question categories to generate semantically and syntactically coherent option sentences based on the questions. These rules transformed option phrases into contextually appropriate sentences. We evaluated SBERT transformer models namely all-mpnet-base-v2, all-MiniLM-L6-v2, all-distilroberta-v1 over answer formatted data and it showed in-crease in accuracy. Answer formation rules over noun phrases of small-sized datasets can help transformers to learn contextual knowledge about the options in the QA sample, Addition of answer formation stage on samples of SciQ data resulted in a rise in accuracy from 86 % to 92 % when using all-MiniLM-L6-v2 model.
Authors - Asritha Paruchuri, Gudivada Krishna Prakash, Mulla Junaid Rahman, Lambu Damarukanath, Guttikonda Prashanti Abstract - The sharing of images in decentralized settings needs high assurances of secrecy, integrity and controlled access. The fast development of cloud-based services and online communication tools have multiplied the communication of sensitive images, and the traditional centralized storage and single-layer security systems are susceptible to cyber-attacks, unauthorized access, and data leakage. The presented paper outlines a safe and decentralized image-sharing system based on Advanced Encryption Standard (AES), the Secret Sharing scheme by Shamir, blockchain authentication, and decentralized storage with the Interplanetary File System (IPFS). First, the input image is encrypted with the help of AES to provide high cryptographic confidentiality. The ciphertext image is further split into shares with secret sharing scheme that avoids unauthorized disclosure and only allows the reconstruction of the encrypted image when the necessary number of valid shares is received. The encrypted shares that are generated are stored in a decentralized way using IPFS, which is highly available, fault tolerant, and does not have a single point of failure. Decentralized access control, participant authentication and integrity verification that is tamper-resistant are enforced using blockchain technology. In the reconstruction process, the encrypted image is reconstructed with the help of Lagrange interpolation and then decrypted with the help of AES, which guarantees safe and lossless recovery of the original im-age. The suggested framework offers multi-layer security, increases confidentiality and data integrity, removes centralized vulnerabilities, and is highly resistant to unauthorized access and data-alteration.
Authors - Mach Thai Loc, Nguyen Hong Phuc, Huu-Cuong Nguyen Abstract - With the development of e-commerce and global supply chains, there is a growing concern about fake or counterfeit products. Current methods for verifying product authenticity are often cumbersome ,time consuming, and vulnerable to tampering. In order to address these issues, for the purpose of this project, a QR code based "Fake Product Detection System" is introduced. In this system, the manufacturer creates an exclusive QR code for each product. The manufacturer then keeps the QR code in a database. If the QR code is scanned through the web-based application, the code is instantly verified. If the code is unique and has not been used be-fore, the product is genuine. But if the code is duplicated or used multiple times, the product is deemed counterfeit. The system is implemented using the Flask web development framework, SQLite database, and web interfaces using the HTML/CSS duo, which is lightweight and easy to use. Other notable features of the system are user authentication, history logging, suspicious image upload for the QR code, and detection of counterfeit items. Overall, this solution would offer a simple, economical, and efficient means to uncover Trojan products while fostering trust amongst consumers and aiding manufacturers to track counterfeit practices.
Authors - K. Thirupathi Reddy, K. Venkata Ajay Kumar, M. Kaveri Abstract - This study explores female creators’ subjective lived experiences navigating human–AI interaction (HAI) within generative design ecosystems. It examines how creators engage with intelligent systems during collaborative creation processes and how they negotiate creative agency between algorithmic outputs and personal meaning-making. Drawing on an Interpretative Phenomenological Analysis (IPA) approach, the study involves seven women who actively utilize Canva’s AI-enabled capabilities to produce professional digital content. Data were collected through in-depth semi-structured interviews and digital observation of design outputs distributed on Instagram. The findings indicate that participants interpret Canva AI as a collaborative creative partner that supports iterative dialogue, experimentation, and reflective decision-making. Rather than replacing human authorship, AI interaction functions as a mediated process in which creators provide prompts, reinterpret generated results, and refine instructions to align outcomes with their subjective intentions. This interaction fosters a sense of psychological safety, particularly among non-professional designers, enabling them to explore creative practices with greater confidence. Through this ongoing negotiation between human agency and algorithmic assistance, participants describe pathways toward professional identity formation and increased participation in contemporary digital creative cultures. Overall, the study highlights how intelligent design systems can shape meaning-making processes, reinforce creative self-efficacy, and support women’s evolving roles within AI-assisted visual communication practices.
Authors - Dudi Gnana Prasoona, Zeenathunnisa, Yamuna V, Pushyami B, Ramandeep Kaur, Navjot Kaur Abstract - In the global health sector, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work, in our data preprocessing stage, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
Authors - Lekshmipriya Vijayan, Bindu V R Abstract - In the present paper, a model on an EOQ policy for deteriorated inventory items with stock-sensitive demand pattern under inflation when the deterioration rate is considered to be a linear function of time. Partially backlogged shortages form is allowed to occur in this system. The required conditions are stated to validate the optimal solutions of the present model. Furthermore, the average cost function and decision variables such as shortages time-point and replenishment cycle have been computed with the help of a step-by-step solution procedure and Mathematica software 12.3. Finally, a numerical example as well as its post-optimal analysis for theoretical model is presented to illustrate the pro-posed work.
Authors - Windy Permata Suyono, Marsellisa Nindito, Dwi Handarini, Ratna Anggraini, Eka Septariana Puspa, Surya Anugrah, Sabo Hermawan, Rio Firnanda, Irima Rahmadani Abstract - Gas leaks pose a threat to safety because they can cause fires and damage to property, and they are sometimes fatal. Traditional detection methods are manually dependent or delayed in response, which means they are not always reliable and timely. This paper presents Safe- Gas system for gas leak detection, monitoring and automatic shut-off. The system uses low-cost gas sensor, flame sensors, load cell and an ESP32 microcontroller for local processing. The system is connected to the cloud via Firebase to send alerts, and it has a battery backup to keep it running when the internet or power goes off. The app supports both remote and autonomous valve shutoff. SafeGas is a name that stands for resilience and accuracy. The designers and developers of the device have tested it in the laboratory and in the field to ensure it meets the set standards. First, the system aims to reduce the number of false alarms. Second, it can operate without an internet connection. Third, it can take safety measures independently. The embedded system and cloud integration aspects of the project demonstrate how they can be combined to achieve the desired results.
Authors - Sachin Ratnaparkhi, Parikshit Mahalle, Pankaj Chandre Abstract - Spatial judgment, incorrect furniture size, and poor personalized decor advice are common issues in most interior design planning. The aim of this paper is to introduce an AI-powered Augmented Reality Interior Design Assistant that makes it possible for users to visualize furniture and decor in real spaces using accurate real-world measurements. Spatial mapping using SLAM based AR core plane detection and depth sensing allows for more accurate estimations in room sizes, identifies objects in the scene, and makes AI-driven suggestions on furniture size and styles. .A hybrid AI engine is built using K-nearest neighbours, collaborative filtering and feature extraction methods. The AR rendering process takes care of depth by modifying 3D assets to expected sizes to make sure everything is placed correctly. The AR application is based on Unity 3D with AR Foundation and ARCore, the backend services are provided by python(flask) connected through RESTful APIs, for user profile and catalog management Firebase/PostgreSQL is used. Scikit is used for building machine learning models which is supported with Numpy and Panda for data handling. The assistant will also provide design tips through a conversational AI feature that makes it accessible to everyone. Tests show a significant reduction in spatial errors, much faster design decisions, and better relevance of recommendations. These results indicate that real-scale visualization with AI suggestions tremendously increases design confidence and at the same time reduces the need for redesigns. This system connects AR visualization with AI interior support for a smooth and smart design experience.
Authors - Deepikaa R Ra, Sriram S, Sudhanthira G Abstract - Huntington’s disease is a devastating brain disorder. It gradually destroys nerve cells due to mutations in the HTT gene that disrupt gene functions. Years of research have not led to effective treatments that can slow or stop the disease. Clearly, we need faster ways to find new drugs. This paper introduced an AI-powered systems biology framework that examines both transcriptomic and clinical data to identify drugs that could be repurposed for Huntington’s disease. First, it uses ordinary least squares regression to remove any unusual variables followed by creating gene co-expression networks to closely examine the specific molecular disorder in the disease. Next, they conduct differential network analysis to identify pathways and transcriptional regulators that go awry and compare known drug effects with Huntington’s molecular signatures, rating each drug based on its ability to reverse those harmful gene changes. This helps them quickly focus on drugs that might actually be effective. The entire setup allows researchers to filter, rank, and test potential treatments efficiently, improving the process's reproducibility and reliance on real data.
Authors - Vani K S, Nanditha B, R Bharadhwaj, Rishika Ghai Abstract - Internet of Things (IoT) applications have experienced fast development resulting in massive interconnectivity of devices, and IoT networks have become susceptible to security risks of Sybil, flooding, and masquerading attacks. Conventional centralized security schemes lack flagella, lack the dynamism of trust evaluation, and are vulnerable to single-point failures, whereas the current blockchain-based systems impose too much extra computational and energy load to be applicable in resource-constrained IoT applications. These issues underscore the necessity to have a lightweight, decentralized, and trust-conscious security system that can be used to guarantee secure IoT communication in adversarial environments. The paper presents a lightweight framework of blockchain-based trust that can be exploited to provide security to IoT communication against network-level attacks. The suggested architecture combines a decentralized blockchain architecture and dynamic trust assessment operation to distinguish trustful nodes and isolate bad actors. It uses a trust-sensitive Proof-of-Work (PoW) architecture to verify block authenticity, in which a node trust score is calculated following communication behavior and history of interaction. Technique of order of preference similarity to Ideal solution (TOPSIS) is used to choose the high trust nodes to validate the transaction securely, which minimizes the amount of computation wasted and increases the network reliability.
Authors - Palungbam Roji Chanu, Venkata Sathish Kumar Badithala, Nepholar Chongtham, Arambam Neelima, Gulshan Gupta, Rohita Tyagi Abstract - Quantum computers are a major threat to the existing encryption mechanisms. In terms of security, the traditional encryption algorithm depends on complex problems like discrete logarithm as well as factorization of integer. Shor’s algorithm is believed to break the current Public Key Encryption algorithms such as Advanced Encryption Standard (AES). Therefore, several research are carried out in the area of PQC (Post Quantum Cryptography). PQC are based on very complex mathematical problems like Learning with error (LWE) which are robust against quantum computers. The National Institute of Standard and Technology (NIST) has initiated several rounds of standardization process for PQC algorithms, among which NTRU, SABER, CRYSTAL-KYBER are the leading candidates. CRYSTALS-KYBER (Kyber) is the first chosen PQC for standardization. This works explores the recent development in Crystals Kyber implementation and its optimization. Researchers can approach for new research challenges and target for improvement thereby increasing efficiency.
Authors - Taslima Ferdous Supty, Fahima Hossain, Era Aich, Ananna Datta, MD Sahadat Hossen Tanim Abstract - The Newborns mostly use infant crying as their main form of communication and it represents a great variety of physiological and emotional conditions. Despite the high potential of automated infant cry analysis in early diagnosis and support of caregivers, the application in real-life still has low usage rates because of environmental noise, imbalance of classes, low interpretability, and high computational cost. This paper is a compilation of an effective, interpretable, and real-time infant cry classification system using a two-step hierarchical methodology. The first stage involves a distinction of cry and non-cry sounds to reduce the rate of false alarms due to background noise. The second stage involves categorizing detected cries into a particular intent. An adaptive feature fusion strategy based on reinforcement learning, gives the cepstral and prosodic and qualitative acoustic features dynamic weights, and SHAP-based explainability offers explicit feature interpretations. Data augmentation, SMOTE-Audio, and model pruning are used to find solutions to the issues of class imbalance, noise robustness, and deployment constraints. Experimental evidence shows that the proposed approach outperforms single feature base-lines, it is also stable in noisy environments and also attains significant parameter reduction without significant loss in performance, making it possible to run in resource-constrained devices in real time. The system is tested on a publicly available infant cry dataset which contains 889 audio samples of cry and non-cry signals in five categories of cry intent and was recorded in realistic conditions.
Authors - S.Nagarjuna Reddy, B.Lakshmi Priyanka, E.Vamsi, G.Raja Shekar Reddy 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 - Shweta B. Barshe, Garima B. Shukla 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 - Ch.Venkata Narayana, T.Jhansi, D.Charan, K.Priskilla, D.Tejaaswani Abstract - This work proposes an intelligent system for automatic food-image-based recognition and calorie estimation to meet the emerging demand for accurate dietary monitoring and personalized nutrition recommendations. Conventional food-logging methods are cumbersome, prone to errors, and mostly fail to capture portion sizes, hence motivating an end-to-end computer vision and depth-based approach. The proposed system utilizes a custom-curated Indian food image dataset of eighty classes, collected, labeled, and preprocessed to make it robust enough to present various variations in lighting, background, etc. A deep learning model was then trained for detecting and classifying food with high precision. The overall classification accuracy achieved by the proposed system is ninety-seven percent. The depth understanding of the detected food regions will provide an approximation of volume and weight, leading to relatively better calorie calculations. Nutritional analysis gets integrated into the system by relating the type and estimated weight of food to the standard nutritional information for detailed insights in terms of calories, proteins, fats, car-bohydrates, fiber, and micronutrient content. The results for evaluation reveal strong detection, minimum estimation error, and efficient real-time processing, which clearly show its applications. In this paper, an approach that combines recognition by image, depth estimation by portion, and nutrition logic capable of leading to a strong solution for diet determination has been introduced.
Authors - Dipti Varpe, Gouri Kulkarni, Anish Sontakke, Anuj Patil, Prasanna Kekare Abstract - Inconsistent medication intake is a major issue, especially for elderly individuals and patients with memory problems [1]. The MediMitra: Voice Enabled Medicine Alert System seeks to tackle this problem by offering an automated, low-cost and user-friendly medication reminder solution. The system combines Raspberry Pi with Optical Character Recognition (OCR) technology to pull medicine names, dosage details and intake times directly from scanned prescriptions. This reduces manual input and user reliance. The information is stored in a central database and connected to a scheduler that sends timely voice alerts through smart speakers or Bluetooth devices. This ensures users receive reliable and easy-to-access reminders. The OCR module is designed for high accuracy in processing printed prescription images by using image preprocessing techniques like noise reduction and thresholding, which helps in effectively extracting key medication details [2]. The system focuses on accessibility, affordability and ease of use in home or clinical settings. Overall, MediMitra provides a useful technological solution to improve medication adherence and supports independent living. It also has potential for future integration with health-monitoring systems.
Authors - Mamy Haja Rakotobe, Remy Courdier Abstract - This article presents a neuro-symbolic modelling approach grounded in qualitative data collected from 25 sports clubs located in R´eunion. The study develops a methodological chain linking structured semantic extraction, ontological formalisation in OWL, and agent-based simulation implemented in NetLogo. Rather than modifying structural scenarios across experiments, the design introduces two contrasting organisational sensitivity profiles derived from field observations: a damped profile and a high-gain profile. The structural configurations remain identical between profiles; only the coefficients of the commitment update function vary, ensuring strict experimental comparability. Results indicate that identical structural conditions produce differentiated collective trajectories depending on internal sensitivity parameters. In highgain configurations, dominance-weighted interactions increase variance and generate polarised engagement distributions, whereas damped configurations maintain relative stability across scenarios. These findings suggest that modelling organisational sensitivity parameters is critical for understanding the robustness of digitally mediated collaboration in volunteer-based organisations.
Authors - Silvio Simani Abstract - This paper presents a hybrid diagnostic approach for an engine air-path benchmark characterised by environmental variability, limited labelled faults, and the need for reliable online decisions. The proposed method combines physics-guided residual features with datadriven temporal representation learning. Residuals derived from grey-box relations capture physically meaningful deviations, while a lightweight encoder extracts temporal patterns across operating regimes. To enhance robustness under changing ambient conditions, the model is explicitly conditioned on measured environmental variables and trained to favour stable representations across sessions. An open-set decision policy with calibrated rejection is incorporated to reduce misclassification when encountering unseen fault magnitudes or insufficient evidence. The method is evaluated under the official benchmark protocol using online processing constraints and standard metrics, including false alarm rate, detection rate, isolation rate, detection delay, and computational cost. Results show improved reliability compared to competitive baselines, with lower false alarms, higher detection and isolation performance, and stable behaviour across sessions. The approach remains computationally efficient and suitable for real-time deployment in practical diagnostic pipelines.
Authors - Zubayer Bin Ahamed, Umair Hossain, Umara Binte Masud, Abdullah Al Mamun, Md. Rohan Islam, Sadah Anjum Shanto Abstract - Gas leaks pose a threat to safety because they can cause fires and damage to property, and they are sometimes fatal. Traditional detection methods are manually dependent or delayed in response, which means they are not always reliable and timely. This paper presents Safe- Gas system for gas leak detection, monitoring and automatic shut-off. The system uses low-cost gas sensor, flame sensors, load cell and an ESP32 microcontroller for local processing. The system is connected to the cloud via Firebase to send alerts, and it has a battery backup to keep it running when the internet or power goes off. The app supports both remote and autonomous valve shutoff. SafeGas is a name that stands for resilience and accuracy. The designers and developers of the device have tested it in the laboratory and in the field to ensure it meets the set standards. First, the system aims to reduce the number of false alarms. Second, it can operate without an internet connection. Third, it can take safety measures independently. The embedded system and cloud integration aspects of the project demonstrate how they can be combined to achieve the desired results.
Authors - Shafa Salsabila Risfi Febrian, Ricardo Indra, Aura Meivia Safira Arsya, Aurellia Arthamevia Aisyah Abstract - This study examines the determinants of continuance intention in YouTube live streaming consumption among Indonesian Generation Z, focusing on social interaction, entertainment, passing time, and enjoyment. Drawing upon Uses and Gratifications Theory and Computer-Mediated Communication, this research situates live streaming as an interactive digital environment where audiences actively negotiate social and emotional experiences. A quantitative explanatory survey was conducted among 108 Generation Z subscribers of the Windah Basudara YouTube channel, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that social interaction and passing time significantly influence continuance intention, whereas entertainment and enjoyment do not demonstrate significant effects. These results suggest that sustained engagement in live streaming environments is driven more by interactive and habitual gratifications than by purely hedonic motivations. By highlighting the contextual dynamics of Indonesian gaming live streaming, this study extends the application of Uses and Gratifications Theory in synchronous digital media settings and offers practical implications for content creators seeking to strengthen audience retention strategies.
