Synthesis and Characterization of Silver Nanoparticles Conjugated with Folate and Curcumin for Their Anti-Cancer Activity

Nanoparticles are small particles with sizes ranging from 1 to 100 nanometers. Silver nanoparticles, composed of silver at the nanoscale, have been widely used in various fields including medicine, healthcare, food, and commercial industries. While silver nanoparticles can be harmful to normal cells depending on their concentration and exposure time, they are highly effective for wound healing and antibacterial applications. Historically, silver was used as a natural antibiotic. In this study, silver nanoparticles were conjugated with curcumin and folic acid using the glutaraldehyde method due to their anti-cancer properties. Curcumin is known for its ability to kill cancer cells, while folic acid—an organic form of vitamin B9—helps in the creation and preservation of healthy cells. The silver nanoparticles were first modified with polyethylene glycol (PEG), then conjugated with curcumin and folic acid. Curcumin was attached through the NH2 group, and folic acid was linked via the carbonyl group, both through PEG. The average crystalline size was calculated using X-ray diffraction (XRD), and functional groups were identified using Fourier-transform infrared spectroscopy (FTIR). These silver nanoparticles are considered to be more beneficial and less harmful than traditional chemotherapy or radiotherapy for targeting and destroying tumor cells

University Auto-Gate Management through AI-Driven License Plate Recognition

The rapid growth in the number of vehicles and transportation systems has made Automatic Number Plate Recognition (ANPR) an essential tool for modern traffic management and security. With the rising vehicle count, manual monitoring and control of traffic have become increasingly difficult. ANPR, a complex field within computer vision, faces challenges due to variations in license plate styles, sizes, orientations, and lighting conditions. License plate recognition, leveraging advanced image processing techniques, represents a promising research domain, especially in the context of IoT and smart city development. With the exponential rise in the number of vehicles, automated systems are essential for retaining vehicle information for various purposes. Researchers are increasingly focused on developing reliable ANPR systems, spurred by advancements in portable electronics and machine learning techniques. Although numerous ANPR approaches have been documented for surveillance systems and intelligent transportation applications, creating a robust system remains a challenging research problem. This research aims to investigate the utilization of ANPR for managing vehicle access at the entrance gates or parking areas of private or government universities and colleges. The system aims to maintain a record of vehicles entering and exiting the premises, as the performance of existing techniques depends on various factors and local conditions. The study introduces an AI-powered ANPR system that restricts access to authorized vehicles by capturing and identifying license plates. This technology can be used to track vehicle entry and exit at university campus gates, improving traffic regulation and security during peak hours

Extractive Text Summarization-Based Framework for Sindhi Language

This paper presents an extractive text summarization method specially designed for Sindhi, a culturally rich but low-resource Indo-Aryan language spoken widely in Pakistan. The study focuses on selecting the most relevant sentences from Sindhi texts, employing Natural Language Processing (NLP) techniques to generate concise summaries. The proposed system incorporates essential preprocessing steps, including text cleaning, tokenization, and stemming/lemmatization. For future extraction, it utilizes TF-IDF and sentence embeddings. After scoring the sentences, the most significant ones are selected to form the final summary. To evaluate the system\u27s performance in five test paragraphs, several metrics are used, including F1 score, precision, recall, cosine similarity, normalization level distance, and accuracy. The system demonstrates reliable and accurate summarization, and consistency achieving high precision (1.0), strong F1 score (0.89-0.92), a low a low normalized error (0.04), and an overall accuracy of 0.86. Graphic analysis further confirms the model\u27s coherence, semantic retention, and low error rates. By leveraging NLP for information summarization, this study contributes to preserving and promoting the Sindhi language—potential applications including digital accessibility, education, and content curation. Future research aims to enhance contextual understanding by exploring transformer-based models like BERT and extending the approach to abstraction summarization

AI Vision for Health Care: Virtual Keyboard and Mouse Empowering Partially Disabled Patients

This paper introduces a machine-learning-based virtual keyboard and mouse system designed to assist individuals with physical disabilities. The system recognizes hand gestures using computer vision techniques and translates them into keyboard inputs and mouse controls. By utilizing Convolutional Neural Networks (CNNs) and the YOLOv8 model, the system achieves real-time performance with an average accuracy of 92%, enabling touchless interaction with computers. The solution uses widely available hardware like standard webcams, making it accessible, affordable, and easy to deploy. This system improves the usability of computing devices for people with motor impairments, offering an innovative, touchless alternative to traditional input methods. It also supports essential tasks such as scrolling, clicking, and zooming through simple gestures. The framework is adaptable to various environments, ensuring it is easy to use in different settings. Our system offers a complete virtual keyboard and mouse solution using a common webcam and real-time gesture recognition, making computer use easier and more affordable for users with motor impairment

Novel Results on Refinement of Hermite-Hadamard Type Inequality with Applications Of ∆-Convex Function

In this paper, we recognized novel results on the integral inequalities type of Hermite-Hadamard to explore the applications of ∆-convex functions. Our conclusion extends several established theorems in the literature

Seismic Data Analysis and Earthquake Prediction with IoT Sensors and SmartGRU Model

Tectonic plate movement causes a slow accumulation of stress in the Earth’s lithosphere, especially around plate borders, leading to earthquakes. An earthquake occurs when this stress overcomes friction along a fault or exceeds the strength of the surrounding rock. Accurate earthquake prediction remains challenging due to the complexity of seismic data and the limitations of traditional methods. This creates a pressing need for models capable of real-time analysis and high prediction accuracy. The Internet of Things (IoT) provides a novel method for detecting earthquakes using a variety of sensors to collect vital seismic data, such as latitude, longitude, depth, magnitude, and time. IoT controllers and centralized systems process and analyze this data to enable efficient monitoring and forecasting. Furthermore, with the help of a machine learning model named Bidirectional Gated Recurrent Unit (Bi-GRU), which integrates sophisticated data fusion and advanced machine learning techniques. Our proposed study model, SmartGRU, demonstrates how to improve earthquake prediction systems by combining IoT sensors with a Bi-GRU machine learning model that incorporates an emerging approach

Exploring the Influence of Teacher\u27s Stress and Emotions on Student Behavior in Secondary Schools

The focus of this study is on the intricate relationship between the stress of the teacher, the emotion of the teacher, and the behavior of the workers in secondary school classrooms, stressing the bidirectional relation (between) the emotional well-being of (the teacher) and the engagement of (the worker). Qualitative data from interviews with teachers and students are drawn on to investigate how emotional exhaustion, workload pressures, and the absence of institutional support prevent teacher regulation of emotion, and inhibit effective classroom management. What they found was that students can pick up on teacher stress, often based on what students perceive to be nonverbal cues, tone, and expressions, and those interpretations lead to the classroom behaviors of the students. Positive emotions by teachers develop trust, focus, and engagement, whereas frustration, inconsistency, and unpredictability engender student disengagement, increased disruptive behaviors, and strained relationships between teachers and students. These same emotional dynamics are catalyzed further by cultural factors; for instance, hierarchical power structures influence the context within which students respond to teacher stress. The systemic institutional support mechanisms are also lacking resulting in a cascade of emotional exhaustion followed by diminished classroom effectiveness. This study highlights the importance of emotional intelligence training, proactive classroom management strategies, and robust institutional support systems in a bid to reduce teacher stress and create a stable, positive learning environment. These results add to existing literature on the emotional well-being of teachers and provide practical suggestions to educational policymakers, administrators, and teacher training programs on addressing the emotional and psychological demands of teachers in today’s classrooms

Analysis of Periodic Permeability on Free Convective Three-Dimensional Flow with Cattaneo-Christov heat transfer and Slip Effect

The present research paper contributes the slip effects on a three-dimensional viscous fluid flow for free convective boundary conditions with periodic permeability. Free convection fundamentally involves some heat transfer methods. In this work, the Cattaneo-Christov heat transfer method has been employed to develop the knowledge of heat transfer actions in complex flow porous system with periodic permeability. Moreover, the impact of the slip effect is investigated to more effectively deal with the boundary conditions. The mathematical model has designed for incompressible, viscous and laminar flow with free stream specifications. By using the regular perturbation approach, governing highly nonlinear partial differential equations are transferred into the ordinary differential equations in linear form together with certain linear partial differential equations. The separation variables approach is then used for transforming the linear PDEs to ODEs. Analytical solutions are obtained for the pressure, velocity field, components of skin friction, and temperature field. The influence of physical attributes existing in the mathematical representation of the physical occurrence is investigated and illustrated. Both the slip parameter and the Cattaneo-Christov heat flux have an impact of thickness on the thermal boundary layer of observed fluid flow

XDP-ML: A Game-Changer in Intrusion Detection Systems for Modern Cybersecurity

Intrusion Detection system (IDS) plays a vital role in cyber security. Traditional approaches are not good enough to detect properly the large threats. Machine learning provides a promising solution and good accuracy by providing large data adaptability.  This paper introduced an IDS approach using the XDP framework for real-time network traffic analysis. Objective: The primary goal of this paper is to improve IDS accuracy and effectiveness by integrating the IDS with the fast XDP-based machine learning approach. Motivation: Traditional IDS methods are defenseless to advanced attacks, so modern and adaptive solutions should be improvised. The XDP framework\u27s processing of the data at high speed makes it more resilient and ideal for real-time traffic analysis, enhancing IDS performance. Methodology: The proposed approach is evaluated using the CIC-IDS2017 and UNSW-NB15 datasets, which contain multiple network traffic features and attack labels. Results: The XDP-based machine learning approach enables real-time analysis and adapts to evolving threats. The XDP-based approach achieves a high detection rate of 98% to 99% with a low false positive rate. The performance is consistent and fast, demonstrating the productivity of the approach. Combining the IDS with XDP-based machine learning approaches makes more robust and scalable solutions for intrusion detection. The clear and accurate results show that it can handle advanced and more complex threats

A Robust Deep Learning Model for Early Glaucoma Detection Using Retinal Imaging

The Glaucoma Detection System is developed in such a way that it can enable early diagnosis of glaucoma by incorporating the latest technology with the patient-centric healthcare paradigm. It uses a user-friendly interface written in the Tkinter language and a Convolutional Neural Network (CNN) model, and is mostly useful in processing medical images. The purpose of the methodology is to democratize ocular care, focus on the insidious nature of glaucoma, and emphasize the need to have a highly accurate CNN model to detect the disease at the earliest stage. The key features are preset structures and real-time image processing, which will speed up detection and allow healthcare professionals to prioritize severe cases. The system encourages the development of multimodal integration and feedback of data in order to promote efficacy, proactive eye health, as well as the principles of fair access to care