DENSITY BASED SMART TRAFFIC CONTROL SYSTEM USING CANNY EDGE DETECTION

The need for state-of-the-art equipment and technology to enhance traffic management is become more pressing as the problem of urban traffic congestion deteriorates. empirical evidence has shown that the traditional methods, such as timers and human control, are inadequate in effectively tackling this problem. The present study introduces a traffic control system that employs digital image processing and intelligent edge identification to enable real-time measurement of vehicle density. In contrast to earlier systems, this high-performance traffic control system offers a significant improvement in response time, automation, vehicle management, reliability, and overall efficiency. Furthermore, the whole process, including picture collection, edge recognition, and green signal allocation, is documented with suitable schematics and validated by hardware implementation using four illustrative images of different traffic situations

AN EFFICIENT IMAGE PROCESSING BASED IMAGE TO CARTOON GENERATION BASED ON DEEP LEARNING

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower computational cost

FORCASTING ACADMIC PERFORMANCE IN COMPUTER SCIENCE STUDENTS BASEDON FUTURE ANALYSIS METHOD

The ever increasing importance of education has drivenresearchers and educators to seek innovative methods forenhancing student performance and understanding the factorsthat contribute to academic success. This paper presents a methodology for predicting CGPA SGPA that leverages machine learning techniques to forecast students'academic achievements based on a variety of features, such asdemographic information, academic history, and behavioural patterns. The proposed students academic performance method utilizes a real-world collected dataset from multiple educational institutions toensure an accurate and comprehensive analysis. The proposed methodology starts with a data preparationstage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables ifnecessary. The feature analysis technique was used to select the most important features for the students academic performance model. A number ofmachine learning classifiers were tested, and the feature analysis was found to be the best performer. The results of this study demonstrate the potential of algorithms in predicting student performance andidentifying key factors that influence academic success. This information can be leveraged by educators and academicinstitutions to develop targeted intervention strategies, tailoredlearning experiences, and personalized recommendations forstudents, ultimately fostering a more effective learningenvironment and improving overall educational outcomes

THE CAUSE-AND-EFFECT PRINCIPLE: OPACITY OF SLAVERY IN TONI MORRISON’S BELOVED

When Toni Morrison embarked on her exploration of slavery, she grappled with two crucial inquiries. The first question delved into the resilience of her nation in enduring the unimaginable cruelties inflicted upon them. The second question probed the conspicuous absence of any mention in European historical records regarding the origins of the slave trade and the subsequent forced migration. The novel is a fearful picture of how bestiality and cruelty could come together to question the crude practices of sexual exploitation, emotional breakdown and physical torture in the name of developing the Western economy. The paper is an attempt to look at the circumstances that gave way to the genesis of one of the most important forerunners of slavery and its repercussions

This article has been retracted due to a serious plagiarism issue

This article has been retracted due to a serious plagiarism issu

Taguchi Analysis of Pervious Concrete Mixtures: A Way to Increase Strength and Permeability

A unique variety of concrete called pervious concrete is created by combining water, cement, and open-graded coarse particles. Usually, it contains very little to no fine aggregate concrete and only enough cement paste to coat the aggregate particles while preserving the interconnectedness of the spaces. The terms porous concrete, permeable concrete, no fines concrete, gap graded concrete, and improved porosity concrete are also used to describe pervious concrete. The experimental technique and findings for compressive strength, flexural strength, and permeability are presented in this work. Using Taguchi analysis, we were able to create an experiment with three variable factors—the mix proportion, the percentage of fine aggregates, and the percentage of human hairs as fibers—each with three levels. L9 arrays were utilized in this experiment. While varying the proportions of human hair as fibers of fine aggregate with coarse aggregates from 0.25%, 0.50%, and 0.75% of human hair of 0%, 5%, and 10% of fine aggregate accordingly in each proportion, the w/c ratio of 0.4 was used in this study. Whose findings show that the maximum compressive strength of M9 mix is between 1.45 and 3.48 N/mm2, the maximum flexural strength of M9 mix is between 0.135 and 2.11 N/mm2, and the maximum permeability of M8 mix is between 91.67 and 163.70 M/hr. That goes to show that adding more fibers helps to boost flexural strength, while adding more fine aggregates increases compressive strength at the same time

CHEMICAL PERSPECTIVES ON THE IMPACT OF CLIMATE CHANGE IN AFGHANISTAN: A COMPREHENSIVE REVIEW

: Climate change is a global phenomenon that has significant impacts on various aspects of human life, including the environment, economy, and social well-being. Afghanistan, one of the least developed and most vulnerable countries to climate change, is facing alarming effects due to its high dependence on agricultural livelihoods, fragile environment, poor socio-economic development, high frequency of natural hazards, and over four decades of conflict. This comprehensive review aims to provide an overview of the current state of knowledge on the impact of climate change on Afghanistan's environment, economy, and society, and to highlight the vulnerability of Afghanistan to climate change. The review also explores the chemical composition of air pollutants in Afghan cities, the impact of air and water pollution on human health and the environment, the influence of climate change on soil composition and nutrient availability, and the implications for water resources, including groundwater quality and availability. Finally, the review discusses the chemical aspects of climate change adaptation and mitigation efforts in Afghanistan, focusing on innovative technologies and practices to address climate-related challenges in the country

A EFFCIET DEEP FAKE FACE DETECTION USING DEEP INCEPTION NET LEARNING ALGORITHM

A Deep Fake Is Digital Manipulation Techniques That Use Deep Learning to Produce Deep Fake (Misleading) Images and Videos. Identifying Deep Fake Images Is the Most Difficult Part of Finding the Original. Due To the Increasing Reputation of Deep Fakes, Identifying Original Images and Videos Is More Crucial to Detect Manipulated Videos. This Paper Studies and Experiments with Different Methods That Can Be Used to Detect Fake and Real Images and Videos. The Convolutional Neural Network (Cnn) Algorithm Named Inception Net Has Been Used to Identify Deep Fakes. A Comparative Analysis Was Performed in This Work Based on Various Convolutional Networks. This Work Uses the Dataset from Kaggle With 401 Videos of Train Sample And 3745 Images Were Generated by Augmentation Process. The Results Were Evaluated with The Metrics Like Accuracy and Confusion Matrix. The Results of The Proposed Model Produces Better Results in Terms of Accuracy With 93% On Identifying Deep Fake Images and Videos

DETECTING PANCREATIC CANCER WITH MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used

Implementation of FIR Filters through Inner product Units and Parallel Accumulations

Finite Impulse Response (FIR) filters are pivotal in digital signal processing, finding applications in diverse fields like audio processing, telecommunications, and biomedical signal analysis. This work presents an enhanced implementation methodology for FIR filters utilizing inner product computation and parallel accumulations. In the existing, FIR filters are typically implemented using convolution techniques, basic adders, and multipliers, which involve sequential processing and intensive computational resources. This method often leads to latency issues and limits real-time applications. Moreover, traditional implementations suffer from inefficiencies in utilizing hardware resources optimally, leading to suboptimal performance. The proposed methodology overcomes these limitations by leveraging inner product computations and parallel accumulation techniques. By exploiting inherent parallelism in the filtering process, the proposed method significantly reduces latency and enhances throughput