Harnessing Artificial Intelligence for Disease Detection and Rapid Drug Discovery: A Path to Accelerated Medical Responses

The history of Artificial Intelligence (AI) in drug discovery spans decades, from rule-based systems to sophisticated machine learning and deep learning algorithms. Early applications included virtual screening and QSAR modeling, which paved the way for data-driven drug development. Today, systems like IBM Watson Health and DeepMind's AlphaFold are good at analyzing medical data, predicting molecular interactions, and accelerating the design of novel drugs. Yet in most AI solutions that already exist, they usually only solve the specific tasks rather than formulating a comprehensive framework in emerging disease management. This paper proposes the integration of disease symptom data, pathogen-level analysis, and treatment prediction via an AI-driven model about diseases with symptoms such as cold, cough, or fever. The system correlates new pathogens with stored datasets and identifies potential medicine combinations for rapid testing and refinement, thereby significantly reducing the timelines for drug development. Hence, this approach addresses the severe need for scalable, fast-response solutions in managing infectious diseases and future pandemics

Magnetic Nanoparticles Synthesis, Surface Coating, and Biomedical Applications: A Review

In recent years, the development of magnetic nanoparticles (MNPs) has attracted the attention of users worldwide due to their potential for use in many fields, including biomedical applications. The unique properties of MNPs, such as superparamagnetism, high saturation magnetization, and biocompatibility, make them ideal for many biomedical applications, including cancer therapy, magnetic resonance imaging (MRI), and drug delivery. Synthetic methods include ball milling, gas phase condensation (GPC), sol-gel, and thermal decomposition. Surface coating of MNPs is important to improve their biocompatibility, stability, and targeting ability. Various coating materials are discussed, including organic polymers, inorganic silica, and gold. Using MNPs as a contrast agent in MRI improves image quality and allows imaging of small tumors. MNPs also show promise in cancer treatment, including chemotherapy and hyperthermia. Biocompatibility and toxicity of MNPs are important factors to consider in their biomedical applications. Surface coating of MNPs plays an important role in reducing their toxicity and increasing their biocompatibility. The use of biocompatible materials such as polyethylene glycol (PEG) increases the safety of MNPs in biomedical applications. Future research should focus on overcoming challenges associated with mass synthesis, coating, and biomedical applications of MNPs

Automated System to Preventing Social Security Fund Misuse by Identifying Deceased Beneficiaries

Ensuring safe and convenient access to essential services, including pension retrieval, is crucial in the current digital era. Passwords and PINs are examples of traditional authentication systems that frequently expose people to fraud and identity theft. In order to replace these traditional methods with biometric verification (such as fingerprint and facial recognition), this project suggests a Web Biometric Credentialing System for pension retrieval. The system incorporates Auth0 for secure identity and   management   of   sessions and WebAuthn API for biometric authentication. This method greatly enhances security and user experience by enabling pensioners to verify their identity using biometric information. The technology makes sure that only authorized people can access sensitive financial data and, after successful verification, enables pensioners to safely retrieve their pension amounts. By lowering fraud, eliminating unwanted access, and streamlining the authentication procedure, the suggested solution improves security

Semantic Tag Clustering to Alleviate the Cold Start Problem in Learning Resource Recommendation: A Case Study on Delicious Dataset

This study explores a methodology for recommending learning resources, demonstrated through a case study on the Delicious dataset. Tags, representing keywords assigned to describe content, are semantically clustered using K-Means. Sentence Transformers are employed to generate dense vector representation of these tags, enabling more effective clustering. The system identifies meaningful tag groups to deliver relevant recommendations, even in the absence of user interaction history, effectively addressing the cold-start problem through predefined tag profiles. The proposed methodology personalizes resource recommendations for Deaf and Hard of Hearing (DHH) learners by leveraging their profile and resource Meta data. It enhances resource search during the cold start phase by identifying the most relevant tag cluster that matches with the learner’s search query and retrieving preferred content based on the learner profile.  Future extensions could incorporate dynamic preferences that evolve over time, enabling more adaptable and personalized recommendations. This work provides a robust foundation for clustering the resources based on their semantic meaning, thereby improving content-based search and retrieval of relevant learning resources

The Extreme Solar Storms of May 2024: A Comprehensive Analysis of Causes, Effects, and Historical Context

The solar storms of May 2024 represented one of the most significant space weather events of the 21st century, producing multiple X-class solar flares, a rare "cannibal" coronal mass ejection (CME), and G5-class geomagnetic storms that rivaled historical events like the Halloween Storms of 2003. This comprehensive analysis examines the underlying physics, technological impacts, societal consequences, and historical context of these extraordinary solar phenomena, while exploring their implications for our increasingly technology-dependent civilization

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Credit card fraud is a major concern for both financial institutions and consumers, leading to significant financial losses and a decline in trust. With the rise in online transactions and increasingly sophisticated fraudulent schemes, there is a pressing need for strong and effective fraud detection systems. This research explores how machine learning and deep learning algorithms, particularly Random Forest (RF) and K-Nearest Neighbors (KNN), can be applied to detect credit card fraud. The main goal is to assess and compare how well these algorithms perform in accurately spotting fraudulent transactions while keeping false positives to a minimum. To carry out this research, we use a publicly available dataset of credit card transactions, which is marked by an imbalanced class distribution, where fraudulent transactions are far fewer than legitimate ones. We apply various preprocessing techniques, such as data cleaning, feature scaling, and addressing class imbalance through resampling methods like SMOTE (Synthetic Minority Over-sampling Technique), to improve data quality and model performance. Random Forest is a powerful ensemble learning method that uses a collection of decision trees to boost prediction accuracy and cut down on overfitting. K-Nearest Neighbors (KNN) is a straightforward, instance-based learning algorithm that classifies transactions by looking at the majority class of their k-nearest neighbours in the feature space. To evaluate how well both algorithms perform, we look at various metrics like precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings show that Random Forest typically outshines K-Nearest Neighbors in overall accuracy and F1-score, especially when dealing with imbalanced datasets. This research emphasizes the need to tackle class imbalance and choose the right evaluation metrics for effective fraud detection

Comparative Analysis on Different Deepfake Detection Techniques

Advancements in deep learning have led to the emergence of highly realistic AI-generated videos known as deepfakes. These videos utilize generative models to expertly modify facial features, creating convincingly altered identities or expressions. Despite their complexity, deepfakes pose significant threats by potentially misleading or manipulating individuals, which can undermine trust and have repercussions on legal, political, and social frameworks. To address these challenges, researchers are actively developing strategies to detect deepfake content, essential for safeguarding privacy and combating the spread of manipulated media. This article explores current methods for generating deepfake images and videos, with a focus on facial features and expression alterations. It also provides an overview of publicly available deepfake datasets, crucial for developing and evaluating detection systems. Additionally, the research examines the challenges associated with identifying deepfake face swaps and expression changes, while proposing future research directions to overcome these hurdles. By offering guidance to researchers, the document aims to foster the development of robust solutions for deepfake detection, contributing to the preservation of the integrity and reliability of visual media

A Comparative Analysis of Machine Learning Models for Stroke Prediction

Stroke is a leading global health burden, and there is an urgent need for improvement in risk prediction and treatment. This paper examines the capability of several machine learning algorithms, including Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVMs), Elastic Nets, and Lasso, to predict stroke risk on four cardiovascular and stroke datasets. The results indicate that Decision Trees and Random Forests are always better than Neural Networks, although Neural Networks show promising accuracy. SVMs are consistent, while the Elastic Net and Lasso models give average results

Energy-Saving Triggering Series Low-Power, High-Performance Locking Systems for Element Design: Pseudo NMOS

Flip-flops represent a significant source of power dissipation within a system. The clocking system itself comprises sequential components, such as latches and flip-flops, alongside the network that delivers clock signals. The pseudo-NMOS technology, split path, and clock tree sharing schemes are employed to propose a positive edge triggering flip-flop that is designed for both speed and power efficiency. The flip-flop's latching section's floating node instability and inadequate circuit energy loss are solved via pseudo NMOS and split path approaches, respectively. By enabling the latching part of the flip-flop to share the clock provision network for gathering the data D, the clock tree sharing technique reduces the D-Q delay and the overall number of transistors required to construct the clock provision network. Cutting back on the number of clocked loads is one method that reduces dynamic power dissipation and switching activity. The flip-flop’s latching part is made using this process in the suggested design. This study evaluates the performance of a flip-flop circuit modeled using 0.12 nm CMOS process technology. According to the simulation comparison, the suggested register element design improves The power delay product increased from 56.86th% to 71.26th%, the energy delay product rose from 77.86th% to 82.4th%, and the power energy product (PEP) escalated from 56.22th% to 81.22th%. It conserves between 7.06th% and 32.83rd% of energy

Clayey Soil Stabilization by the Inclusion of Banana Fiber and Microorganisms in the MICP Technique

MICP is a process to produce bio cementation by using a hydrated lime and it is an inorganic compound of Ca(OH)₂ is a white crystal or white powder called as a quicklime. And which the calcium ions in the lime are present in the research. In this research the bacillus magateriumand Bacillusmycoides bacteria with lime upon the two separate ways by using the bacteria in the different molarities of 0.25, 0.75, 0.75, 1.Bananafibre is mixed with the clayey soil and the silt clay soil to maximize the strength to reduce the moisture content or to make it as dry moisture content. Puzzolona may mix with lime powder to maximize the strength. The experimental is to be maintained in the research of bacteria as (Bacillus magaterium) and (Bacillus mycoides), it is dissolving phosphorous and potassium, promoting growth and control the plant diseases. This may do mainly in the untreated soil and mainly done in the clayey soil, silt clay and fine loamed soil to find the strength according to the different molaties. The engineering behaviour is treated sand by using the bacteria in the unconfined test, CBR test, Specific gravity, PH test. The permeability of sand is minimized in the range of 6.3xE-3 to 3.2xE-5cm/s. It may use in the ground improvement process and other construction process to maximize the compression strength by testing in the unconfined compressive strength. The experimental findings with the help of using the scanning electron microscopy (SEM) and energy disperse x ray analysis (EDX). The research will finally show the maximum compression strength of molarity in the MICP process. The lime may attain the more compressive strength by using the banana fibre can be effectively adopted to enhance the engineering characteristics of sand