Stress could be a state of mental pressure for particular person managing with issues from common and social thriving which prompts such incalculable contaminations. Energetic age is the fundamental time outline on the grounds that right presently youth faces bunches of changes in his/her life. They are supposed to be the elites within the common open. Thus, they have to be progress their weight the administrators capacities in arrange to carry on with a strong life consequent to entering the common open. At the point when a adolescent goes into the youthful age, they got to not fair alter to the modern life and modern climate however in expansion be know approximately numerous modern people, events, and things. The presence weight on them is noteworthy. In this way, understanding the wellsprings of push among them and how they can adjust to the weight is imperative. The pro found that the pressure essentially comes from academic tests, and calling examination. Such weight may commonly cause mental, physical, and social issues. This ponder tracks down the reasons for weight among youth and the board techniques to defeat the weight. The disclosures will offer assistance the solitary under studies, analysts, speakers, work and coordinating centers.

Keywords : Stress, Strategies, level, K nearest neighborhood Algorithm

Authors : R.nivedha

Title : Real Time Stress Monitoring and Analysis using Internet of Things

Volume/Issue : 2024;6(2 (March - April))

Page No : 41 - 43

In recent years, the proliferation of deepfake technology has raised significant concerns regarding the authenticity and trustworthiness of digital media. Deepfakes, which are highly realistic manipulations of audio and visual content created using artificial intelligence techniques, pose a serious threat to various aspects of society, including misinformation, privacy infringement, and cybersecurity breaches. Detecting and mitigating the spread of deepfakes has become a critical priority for researchers and practitioners in the field of computer vision and artificial intelligence. In this context, the development of advanced deep learning models for deepfake detection has emerged as a promising approach to address this pressing challenge.This research develops an advanced deep learning model to identify modified material in order to combat the rising issue of fraudulent videos. The process entails importing datasets, removing frames from films, locating landmarks on the face, and examining the frames to find abnormalities typical of deep fakes. There are several CNN architectures used, including ResNet-50 and EfficientNetB2. CNN and sequential architectures (e.g., InceptionV3 + GRU, EfficientNetB2 + GRU) are effective. The litmus test for the efficacy of our model lies in its evaluation using the Deep Fake Detection Challenge (DFDC) dataset—an industry benchmark recognized for gauging the prowess of deepfake detection systems. In addition to model refinement, we proffer a proactive strategy to counteract the pervasive dissemination of deepfakes.

Keywords : Fake Detection, Deep Learning, Text Analysis, Deep fake images, Feature Extraction.

Authors : D. Ramachandra Reddy

Title : VERIFAL: Comprehensice Fake Detection using Deep Learning

Volume/Issue : 2024;6(2 (March - April))

Page No : 34 - 40

This paper presents a comprehensive approach to addressing the diverse challenges encountered in urban infrastructure management, particularly during adverse weather conditions and night-time traffic congestion. By harnessing the capabilities of state-of-the-art technologies, including smart elevating bridges, intelligent streetlights, and dynamic traffic signal systems, it aims to fundamentally transform the urban landscape. The integration of these innovative solutions is designed to enhance urban safety, efficiency, and sustainability. Key components such as the Arduino Uno microcontroller, servo motors, LDR sensors, LEDs, ultrasonic sensors, and LCD displays serve as the backbone of these advanced systems, enabling seamless operation and integration into existing infrastructure. Through the strategic deployment of these technologies, the project endeavors to optimize the management of urban infrastructure, ensuring uninterrupted vehicular passage during floods, improving visibility and energy efficiency with intelligent street lighting, and optimizing traffic flow through dynamic signal control. Ultimately, the project seeks to pave the way for a more resilient, efficient, and sustainable urban environment.

Keywords : Smart City, Smart Bridges, Traffic Density Management System, Arduino UNO, Arduino Mega 2560

Authors : Sandhyarani

Title : Internet of Things Integrated Smart Urban Infrastructure

Volume/Issue : 2024;6(2 (March - April))

Page No : 28 - 33

In a rapidly evolving digital landscape, the construction industry is witnessing a paradigm shift towards online platforms. This application is poised to be a pioneering mobile application designed to streamline and revolutionize the procurement of construction materials. This app aims to connect construction professionals, contractors, and individuals with a user-friendly platform that facilitates the seamless purchase of a wide range of construction materials. This app seeks to reshape the way construction materials are sourced, making the process more convenient, cost-effective, and collaborative. The application boosts the store owners' capacity to present comprehensive details of each product and manage orders seamlessly, irrespective of the quantity of materials requested. Furthermore, the research tackles the recognized challenge of conventional methods in procuring product materials, encompassing the hurdles of locating construction materials, compiling product lists, comparing prices, and seeking product details.

Keywords : Construction industry, Procurement, Construction Materials, Cost Effective, Quantity, Stramline

Authors : A. Vanaja

Title : A Multi Platform Application for Ordering of Building Materials

Volume/Issue : 2024;6(2 (March - April))

Page No : 24 - 27

A neurological condition that affects millions of people is Parkinson's disease globally. The effects of Parkinson's disease (PD) sixty percent of persons over fifty. It is challenging for people Having Parkinson's illness in order to get to treatment and monitoring appointments since they have difficulty speaking and moving. It is feasible for Parkinson's disease (PD) sufferers to have normal lives with treatment. The necessity for precise, early, and remote PD identification is highlighted by the aging global population. The early identification and detection of Parkinson's disease has shown great promise in recent years thanks to machine learning methods. We describe a novel approach for the diagnosis of Parkinson's illness in this work using exception architecture and machine learning approaches. Specifically, we focus on the Parkinson's disease diagnosis illness.

Keywords : Parkinson's Disease, Neurological Disorder, Remote diagnosis, ML, Exception Architecture.

Authors : D. Dileep Kumar

Title : Neurodegenerative Disorder Detection using CNN

Volume/Issue : 2024;6(2 (March - April))

Page No : 20 - 23

In Now-a-Days we have more demand the Low power VLSI application. To design we are implementing of Built-In Self- Test (BIST) architecture using Verilog. We are proposed BIST architecture, in focuses on minimizing power consumption during the testing phase while maintaining high fault coverage. It leverages the capabilities of the Verilog hardware description language to model and simulate the design. The Project investigates various power reduction techniques, including test pattern compression, selective clock gating, and power-aware test scheduling, ta optimize power consumption during testing. Our project presents the design and implementation of a BIST architecture specifically tailored for low-power Very Large Scale Integration (VLSI) Applications. By implementing FPGA (Field programmable gate array) boards demonstrating the feasibility and practicality of the proposed design. The increasing demand for energy-efficient electronic devices and the proliferation of portable systems have necessitated the development of power-aware design techniques, BIST, a on chip testing technique, plays a crucial role in ensuring the quality and reliability of integrated circuits. The findings from this study can guide designers in developing energy-efficient and reliable integrated circuits, promoting sustainability, and extending battery life in portable devices. The proposed design is compatible with standard Verilog synthesis and is readily applicable to a wide range of low-power VLSI applications. This project presents a comprehensive study, design and implementation of a BIST architecture tailored for low-power VLSI applications, leveraging the Verilog hardware description language.

Keywords : BIST,FPGA, ATPG, Clock Pulses, RTL Schematic, Power.

Authors : Dr. K. Sai Venu Prathap

Title : Design and Implementation of BIST Architecture for Low Power Applications using VERILOG

Volume/Issue : 2024;6(2 (March - April))

Page No : 15 - 19

In this paper, we propose an Enhanced Link State Routing Protocol (OLSR) tailored specifically for real-time applications in Vehicular Ad-hoc Networks (VANETs). VANETs operate in dynamic and resource-constrained environments, requiring efficient routing protocols to facilitate communication among vehicles and infrastructure elements. Our protocol integrates the strengths of OLSR to provide robustness and scalability, while also introducing enhancements to address the unique challenges of real-time applications in VANETs. Through simulations and comparative analysis, we demonstrate the superiority of our proposed protocol over existing solutions in terms of latency, packet delivery ratio, and scalability. This paper underscores the efficacy of our Enhanced Link State Routing Protocol for enabling reliable and timely communication in VANETs, thus contributing to the advancement of real-time applications and safety-critical services in vehicular environments.

Keywords : VANETs, Mobility, Dynamic Network Topology, Low latency, High reliability, QoS, Routing Metrics.

Authors : k.Dinesh

Title : Enhanced Link State Routing Protocol for Real-Time Applications in Vehicular Ad-Hoc Networks

Volume/Issue : 2024;6(2 (March - April))

Page No : 8 - 14

Network intrusion detection technology plays a vital role in guaranteeing network security. The primary goal is to continually monitor the current state of the network, identify abnormal behaviour in the network state, and notify network administrators in time. The speed and accuracy of an intrusion detection system (IDS) are important to the availability and dependability of today's network. In response to the difficulties of high false alarm rates, poor detection efficiency, and restricted functionality prevalent in IDS, this study first studies the application of machine learning approaches to network intrusion detection. Since machine learning algorithms can automatically extract characteristics from intrusion data and prevent human feature extraction, an intrusion detection approach based on a decision tree classifier is presented. The approach has been enhanced by integrating the Inception module for optimum separation of intrusion functions. The initialization module employs a classification structure with distinct filters, utilizing varying size classification kernels in each row to operate in several layers, and the diverse features of network incursions in the dataset are identified and categorized by stacking.

Keywords : IDS, Feature extraction, Decision Tree Classifier, Network Intrusions.

Authors : N SATISH KUMAR

Title : Intrusion Detection System Using Machine Learning Techniques

Volume/Issue : 2024;6(2 (March - April))

Page No : 4 - 7

In recent years, significant advancements in deep learning, computer vision, and machine learning have the potential to revolutionize agricultural practices by modernizing crop management and yield prediction. A persistent challenge faced by farmers is the presence of invasive weeds, which can severely impact crop growth by competing for essential resources such as water, nutrients, and sunlight. Additionally, accurately predicting crop yield is crucial for farmers to optimize resource allocation, minimize costs, and maximize profits .Recent progress in computer vision offers a cost-effective approach to predicting crop yield using state-of-the-art algorithms. This project aims to address longstanding agricultural challenges by applying novel methodologies. Specifically, we will develop a robust methodology for collecting data on weed detection and establish an image processing pipeline. The collected data will be utilized to train advanced object detection models, such as the CNN Mobile, for accurate weed detection. Data will be leveraged in a CNN and deep learning-based model to distinguish between weeds and crops effectively. By leveraging these cutting-edge technologies, we aim to provide farmers with innovative solutions to enhance crop management and improve agricultural productivity.

Keywords : CNN, Deep Learning, Image Processing, Data Collection Methodology, Object Detection.

Authors : Mani golakoti

Title : Weed Detection In Paddy Fields Using Neural Network

Volume/Issue : 2024;6(2 (March - April))

Page No : 1 - 3