Authors - Mohammad Kaif, Anshika Banyal, Rohitashwa Dey, Shashi Mehrotra Abstract - A Natural Language Interface (NLI) lets users ask questions to get data from a database without having to learn a new language like Structured Query Language. Structured data with text is needed for many applications in many fields, such as search engines, customer service, and healthcare. There are many problems that have been studied, such as the popularity of relational databases, the complexity of configuration, and the processing needs of algorithms. Translating plain language into database queries is only one of these problems. The resurgence of natural language to database queries research is driven by the increasing prevalence of querying systems and speech-enabled interfaces. The last poll on this topic was done six years ago, in 2013. As far as we know, there hasn't been any recent research that looks at the best natural language translation frameworks for both structured and unstructured query languages. We examined 47 frameworks from 2008 to 2018 in this report. 35 of the 47 were very useful for what we do. There are three kinds of SQL-based frameworks: connectionist, symbolic, and statistical. There are two types of NoSQL-based frameworks: semantic matching and pattern matching. After that, these frameworks are judged based on their language support, heuristic rule sys-tem, interoperability support, dataset scope, and overall performance. The results showed that 70% of the work to make natural language queries work with databases has been done for SQL. NoSQL languages like SPAROL, CYPHER, and GREMLIN only account for 15%, 10%, and 5% of the work, respectively. It has also been found that most of the frame-works only work with English.
Authors - Avisek Sharma, Arpita Dey, Buddhadeb Sau Abstract - The increasing adoption of intelligent transportation systems has high lighted the importance of preventive vehicle safety mechanisms that address critical human factors such as unauthorized access, alcohol impairment, and driver fatigue. This review presents a structured analysis of recent research on automated vehicle access and driver alert systems that integrate biometric au thentication, alcohol sensing, and vision-based drowsiness detection. Embedded platforms, particularly Raspberry Pi– based implementations, are examined alongside computer vision techniques for facial and eye-state analysis and MQ series sensors for alcohol detection. The study reviews and compares commonly used algorithms, including classical feature-based methods and deep learning ap proaches, in terms of detection accuracy, computational requirements, and real time suitability for embedded environments. Communication strategies for alert generation and remote notification are also discussed. The review identifies key challenges related to multi-module system integration, robustness under varying illumination conditions, and long-term sensor calibration. It concludes that an integrated, low-cost, and real-time embedded framework offers a practical and scalable approach to improving vehicular security and reducing road accidents by ensuring that only authorized, sober, and alert drivers operate vehicles.
Authors - Alena Rodicheva, Svetlana S. Bodrunova, Zaeem Yasin, Ivan S. Blekanov, Nikita Tarasov Abstract - Polycystic ovary syndrome (PCOS) is a complex of symptoms that affects many women and is estimated to affect 6 to 12% of women of childbearing age. This commonality makes it hard for healthcare professionals to give an accurate diagnosis of PCOS and thereby received adequate treatment. We created a computer system that converses with users and guides their understanding of PCOS. This system uses a language model called Ollama, which is similar to the LLaMA model. We also added a vast detailed database about PCOS categorized into 12 sections. It analyzes user questions to ensure that the responses are relevant and correct. The system was trialed with positive performance. It accurately detected PCOS related queries and formulated appropriate responses. Well, the system is very smart and can go through a huge amount of data to find for each question three most relevant answers. The most common application is augmenting LLM with scraping & performing other programming operations over the LLM to give more accurate answers than just a language model. We developed a computer program that can help PCOS patients without compromising their privacy. This system even has benefits for healthcare providers as it provides information that aids them in such treatments for women with PCOS. This project is a great example of using computer programs to help humans with PCOS and other similar things.
Authors - Lakshmi Priya G G, Gokulakrishnan. V, Nithin Joel. J, Padmalakshmi Govindarajan Abstract - Potatoes are among the most widely farmed crops globally. Healthy potato plants are crucial for the large-scale production of potato-derived foods. However, a vari ety of leaf diseases can harm potato plants, with Early Blight and Late Blight being the most prevalent. In this investigation, we employed a dataset of 1500 photos comprising healthy, early, and late blight leaves. For the diagnosis of leaf diseases, we used a transfer learning-based Ensemble Modeling. We selected Effi cientNetB0, ResNet50, MobileNetv2, and VGG16 as transfer learning models, integrating logistic regression as a meta-classifier within the Ensemble Model. We have attained 99.4% accuracy in distinguishing disease-affected leaves from healthy potato leaves, which is better than most of the recent works. For the per formance measurements, we employed accuracy, precision, recall, and F1-score. To ensure the credibility of our technique, we have integrated explainable AI (Grad-CAM) for our models, which indicates which parts of the image play a vital role in our model’s performance.
Authors - Muhamad Surya Nugraha, Dedy Rahman Wijaya, Tuntun Aditara Maharta Abstract - The widespread adoption of Kubernetes for orchestrating micro services has heightened monitoring complexity if we focus on identifying per formance degradation not visible at the level of infrastructure resource utiliza tion. In this paper, we present an application-centric AIOps framework that can be leveraged to detect “high-latency, low-resource” anomalies in Kubernetes microservices. Traditional autoscaling mechanisms that only rely on resource metrics (CPU and memory) fail to provide optimum response time with the emergence of reactive applications. The model for anomaly detection is trained using machine learning classifiers such as Random Forest, LightGBM, and Lo gistic Regression. This approach leads to a weak supervision-based approach to label datasets, with respect to Service Level Objective (SLO) violations. A course registration system is proposed to validate the application of this frame work under conditions of high concurrency and parallel simulation traffic. Ex perimental results show that the established machine learning model exhibits marked performance compared with normal threshold methods, leading to im proved operational steadiness and service robustness.
Authors - Y. Rama Devi, Panigrahi Srikanth, Devansh Makam Abstract - Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint–response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss level. The proposed approach is evaluated across ten Arabic large language models of varying architectures and parameter scales. While standard cross-entropy fine-tuning yields only modest improvements, severity-aware optimization consistently achieves larger gains. Using a balanced weighting configuration, performance improves from 54.04% to 66.14% for AraGPT2-Base, from 59.16% to 67.18% for AraGPT2-Medium, and from 57.83% to 66.86% for Qwen2.5-0.5B, with peak performance reaching 67.18%. Overall, severityaware fine-tuning delivers improvements of up to 12.10% over non-finetuned baselines, demonstrating robust and architecture-consistent gains.
Authors - Siddharth Jha, Mayur Jaiswal, Ajay Deshmukh, Kajal Joseph Abstract - The importance of agriculture for the survival of humans and the economic stability of the world continues to grow; however, at the same time, it has also come to face many severe problems due to increasing population figures, climate change, and the loss of natural resources. The traditional techniques for crop monitoring are mostly based on manual surveys and the use of vision for inspecting crops; thus, they are regarded as too labor-intensive, time-consuming, and passive in nature, thereby becoming ineffective for managing modern large-scale farming techniques. The avail-ability of the latest technological features, such as remote sensing, Internet of Things (IoT) devices, unmanned aerial vehicles (UAVs), artificial intelligence (AI) devices, and block chain technology, has transformed crop monitoring techniques into an intelligent and proactive process for farmers to monitor crops in an efficient and precise manner. Drones play an important role in crop monitoring by using high-resolution imaging devices for detecting early crop problems, such as crop stress, pest infestations, or nutrient deficiencies, whereas IoT devices are utilized for real-time monitoring of fluctuating environment parameters, such as soil, in an intelligent manner. All these innovations help towards a high and efficient agricultural system within a sustainable environment. Hence, there are still certain limitations and hindrances faced by these advanced techniques, including high initial cost, complexity, infrastructural constraints, and data privacy, limiting these techniques for small and marginal farmers. Hence, in this review paper, a detailed review of advanced crop monitoring techniques used in agriculture is discussed; further, a critical analysis of these techniques for achieving these requirements with efficiency and standards, and an understanding of these techniques for achieving a sustainable and robust ecosystem in an agricultural system is discussed.
Authors - Neha Kriti, Arti Devi, Sarthak Srivastava, Varun Dutt Abstract - Localization in Autonomous Underwater Vehicles (AUVs) continues to be a major challenge in GPS-denied environments, where inertial drift, low visibility and uncertain motion models frequently un dermine state estimation. In this paper, we present our visual-inertial odometry framework A-KIT VIO specifically designed for resilient pose tracking underwater. The system employs tightly coupled monocular camera observations with IMU data using an Extended Kalman Filter to maintain high-rate inertial propagation along with feature-based vi sual updates to avoid drift. To address the frequent covariance mismatch during non-stationary maneuvers, we added a transformer-based module to adaptively adjust IMU process noise based on the vehicle’s immediate motion context. This method of uncertainty modeling ensures filter sta bility in scenarios where standard, fixed-noise configurations typically diverge. Evaluated within a Gazebo-based underwater simulation, the framework demonstrated consistent state estimation and bounded drift over long-range trajectories, highlighting the efficacy of adaptive covari ance for reliable underwater localization.
Authors - Fatima Batool, Farzana Jabeen, Tahira Anwar Lashari, Mehvish Rashid Abstract - Autism Spectrum Disorder (ASD) is an invisible disorder that is of ten misdiagnosed in Pakistan due to unawareness and social stigma. There ex ist multiple technological digital interventions for children with autism designed to target their social, emotional or cognitive skills. However, recent studies have overlooked on the intervention integrating the psycho-social and behavioral skills of children with autism. This mixed-method study evaluates the effectiveness of a multi-modal learning framework, SHAAZ, for psycho-social and behavioral skills enhancement of children with ASD. Employing the proposed research design, the 7 week intervention was tested on autistic children with different severity level of disorder, aged 4 to 12 years. The results revealed that across observations, there is an improvement in users performance scores. The System Usability Scale (SUS)andAppQualityandImpactEvaluationbasedonMobileAppRatingScale (MARS) scores for the designed product was 89.16 and 4.27 respectively, imply ing high usability, user engagement and a positive impact on the targeted skills of the users.
Authors - Onkar Yende, Nayan Bhutada, Mohit Thakre, Sai Khadse, Mridula Korde Abstract -Reliable stock price forecasting remains challenging due to the noisy, nonlinear, and non-stationary characteristics of financial time-series data. Traditional statistical methods and deep learning models that rely solely on raw price data often struggle to capture short-term fluctuations and evolving market dynamics. To address these limitations, this study proposes a hybrid forecasting framework that integrates causal time-domain filtering, time–frequency feature extraction, and deep learning–based temporal modeling. The proposed approach employs Savitzky–Golay and Kalman filters to sup press high-frequency market noise while preserving important price trends in a causality-aware manner suitable for real-time forecasting. Localized spectral fea tures representing transient and time-varying market behavior are then extracted using the Short-Time Fourier Transform (STFT). These enhanced time-domain and frequency-domain features are combined and modeled using a Long Short Term Memory (LSTM) network, which effectively captures long-range depend encies and nonlinear temporal patterns in financial data. The framework is evaluated using standard performance metrics, including RMSE, MAPE, and R². Experimental results demonstrate that integrating causal filtering with STFT-based features significantly improves forecasting accuracy and robustness compared to baseline models, providing a reliable and practical solution for short-term and multi-step stock price prediction.
