Latest Essays
The Role Of Sensory Modality In Lexical Decision And Semantic Priming
Overview The methods section outlines the systematic approach used to conduct the study, ensuring replicability and transparency. This section details the research design, participant characteristics, materials, procedures, and ethical considerations, providing a comprehensive overview of how the research was conducted. Participants The study involved a total of 120 participants, aged between 18 and 35 years, with a mean age of 25.4 years (SD = 4.2). Participants were recruited from a
Should Some Knowledge Not Be Sought On Ethical Grounds?
Object 1: Marina Abramović’s shocking Rhythm 0 performance Marina Abramovic’s Rhythm 0 deeply delves into the ToK prompt “Should some knowledge not be sought on ethical grounds?”. She is well known for her body art, for how she pushes the boundaries and for the relationship she upholds with her audience to make them delve into the possibilities of the human mind. Rhythm 0, performed in 1974, observed Marina standing still
The 10 University Libraries in the USA Worth a Visit
University libraries are essential pillars of higher education, serving as repositories of knowledge and intellectual development. These institutions offer vast collections of books, manuscripts, and digital archives that support academic research and learning. Beyond their academic role, many university libraries are architectural masterpieces that reflect the rich history and culture of their institutions. Some date back centuries, preserving historical documents and artifacts that provide insights into the past, while others
Improved Correlation Coefficients Of Pentapartitioned Neutrosophic Pythagorean Sets For MADM
Abstract: A correlation coefficient is a statistical measure which contributes to deciding the degree to which changes in one variable predict changes in another. Wang’s single-valued neutrosophic sets have still continued to improve to pentapartitioned neutrosophic sets. In this article, we analyze the characteristics of pentapartitioned neutrosophic Pythagorean sets with improved correlation coefficients. We’ve also used the same approach in multiple attribute decision-making methodologies, including one with a pentapartitioned neutrosophic
Enhancing TCP-PRN For Seamless Handoff Management In Wireless Networks Through Deep Learning And Hybrid Optimization
Abstract Handoff events, in which mobile nodes switch between various network access points, frequently result in performance degradation in wireless networks. TCP-PRN (Path Recovery Notification), a method designed to lessen the negative effects of temporary link disconnections, was developed in response to this challenge. In spite of its potential, precise handoff prediction and managing link disconnections continue to be major obstacles in actual deployment. This study introduces an improved TCP-PRN
A Metaheuristic Approach To Ensembled Deep Learning Based Wind Power Forecasting
Abstract In the field of renewable energy, the accurate forecasting of wind power generation is paramount for grid stability and efficient resource utilization. This paper presents a novel approach to enhance wind power forecasting (WPF) accuracy using an ensemble of deep learning (DL) models and a meta-heuristic framework. The proposed methodology encompasses a comprehensive pre-processing phase involving data cleaning and normalization through Box-Cox transformation, as well as data imputation to
Enhancing Urban Safety With Automated Crime Anomaly Detection Using Ensembled Deep Learning In Smart Cities
Abstract In the context of smart cities, ensuring safety and security is paramount, and the increasing deployment of surveillance cameras presents both an opportunity and a challenge. This paper introduces an automated Crime Anomaly Detection System (CADS) that harnesses the capabilities of Deep Learning (DL) and computer vision to address this challenge. CADS follows a systematic workflow, starting with the collection of video data from smart city surveillance cameras. It
Analysis Of ECG Signals For Ischemic Heart Disease Prediction Using Optimized Artificial Neural Network
Introduction The most prevalent dangerous illness is thought to be heart attacks. Medical practitioners perform numerous surveys on heart disease to acquire data on the illnesses, symptoms, and course of the condition [1]. Since cardiovascular disease is the most predictable disease in the world, it is a major worry for the medical sector in today’s heart disease research. Scaling down small and amazing gadgets that may be utilised while calculating
Secure Transfer Of Data In Cloud Computing Using DNA Cryptography With Cipher Techniques
Abstract Cloud computing is a type of distributed computing that allows organizations to store and access data on-demand. It is commonly used for various applications such as network services, storage, and platform services. However, many organizations are not excited about using it due to security concerns. Various researchers have been developing various methods to improve the security of cloud computing. One of these is the Bi-directional DNA encryption algorithm. However,
A Review Of Integration Of Fiber Bragg Grating Sensors In The Fourth Industrial Revolution (FBG-4IR)
Abstract Review of the Fiber Bragg Grating (FBG) sensor technology’s state-of-the-art. In the past three decades, FBG sensors have drawn a lot of attention because of their key benefits, which include immunity to electromagnetic interference, lightweight, compact size, high sensitivity, huge operation bandwidth, and excellent multiplexing capabilities. Temperature and strain are the two most extensively researched sense physical variables. This paper focuses on the operation of fiber bracket grating sensors
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