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.
