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Enhancing Human Safety Through IoT-Integrated Smart Helmet Technology

Enhancing Human Safety Through IoT-Integrated Smart Helmet T...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Venkatesh K Harshitha Jodu Sree Kanthi P Yatheesh Kumar Reddy K Sumalatha R Dept. Computer Science and Engineering (Data Science) Srinivasa Ramanujan Institute of Technology Anantapur India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

The smart helmet's characteristics are meant to identify and report whether or not a wearer has consumed alcohol while donning a helmet. Road accidents are increasing as a result of riders drinking alcohol and not wearing helmets. In the modern world, thousands of people lose their lives in auto accidents each year. Wearing a smart helmet can typically lower the number of accidents. The primary objective of this research study is to create a wearable helmet that can both detect alcohol and prevent accidents without the need for a helmet. Whether the user is wearing a helmet is determined by the limit switch. The rider's breath is tested by the Gas sensor to determine whether alcohol is present. If the rider has ingested alcohol or is not wearing a helmet, the bike will not start. Only when a helmet is on and there are no indications of intoxication can the bike be started. System construction is done by sensor actions.

关键词： Cloud computing Head Road accidents Switches Alcoholic beverages Position measurement Safety

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Enhancing Intrusion Detection Systems for Secure E-Commerce Communication Networks

Enhancing Intrusion Detection Systems for Secure E-Commerce ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Vullam, Nagagopiraju Roja, D. Rao, NagaMalleswara Vellela, Sai Srinivas Vuyyuru, Lakshma Reddy Kumar, K Kiran Chalapathi Institute of Engineering and Technology Dept. of Cse AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of Cse AP Guntur India Koneru Lakshmaiah Education Foundation Dept. of Cse AP Vaddeswaram India

ISBN: (纸本)9798350306088

Rapid advancements in information and communication technologies have permeated various sectors, leading to an exponential growth in Internet users. However, this surge in connectivity has also given rise to a corresponding increase in cyber-attacks. E-commerce applications, which have become integral to our daily lives, encompass online banking, marketing, trading, and numerous other online businesses. To safeguard these critical systems and networks, Network Intrusion Detection Systems (NIDS) play a pivotal role by thwarting unauthorized access and countering various cyber threats. Presently, the prevailing NIDS systems predominantly rely on the Backward Oracle Matching (BOM) algorithm. While effective in reducing false alarms, this algorithm has been associated with a high packet drop ratio. In light of these limitations, this paper examines the existing NIDS systems and the various pattern-matching techniques they employ, shedding light on their weaknesses and constraints. To overcome these shortcomings, the paper introduces an enhanced iteration of the BOM algorithm. This enhanced version leverages multiple pattern-matching methods, enhancing the overall performance of the NIDS system and subsequently fortifying network security. To validate the proposed solution, comprehensive simulations were conducted using established datasets such as Snort and NSL-KDD. The experimental results unequivocally demonstrate the superior performance of the proposed solution compared to existing approaches. Specifically, it achieved a remarkable 5.17% improvement in detection rate while concurrently reducing the false alarm rate by 0.22% compared to the existing solution. IN summary, this paper addresses critical issues inherent in current NIDS systems, providing a robust solution through an enhanced BOM algorithm that incorporates multiple pattern- Matching methods. The experimental findings underscore the effectiveness of this approach in bolstering network security, offering p

关键词： Network security

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An Enhancing Network Security: A Stacked Ensemble Intrusion Detection System for Effective Threat Mitigation 3

An Enhancing Network Security: A Stacked Ensemble Intrusion ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Vullam, Nagagopiraju Roja, D. Rao, Nagamalleswara Vellela, Sai Srinivas Vuyyuru, Lakshma Reddy Kumar, K Kiran Chalapathi Institute of Engineering and Technology Dept. of CSE AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of CSE AP Guntur India Koneru Lakshmaiah Education Foundation Dept. of CSE AP Vaddeswaram India

ISBN: (纸本)9798350343632

Modern cyber security relies heavily on intrusion detection in network traffic to quickly identify and mitigate security threats. Conventional intrusion detection systems frequently depend on lone, stand-alone models that could find it difficult to change with the ways that network attacks are evolving. In order to overcome this difficulty, this research study provides an ensemble-based strategy that improves threat detection effectiveness and accuracy by utilizing the strength of several intrusion detection models. Increasing security measures against data breaches and network invasions is essential due to the ever-growing usage of the Internet and networks. As intrusions are frequently hidden within of valid network packets, firewalls have a difficult time identifying and stopping them. Furthermore, the majority of network monitoring systems and algorithms find it increasingly difficult to handle the sheer volume of network traffic. Various intrusion detection strategies have been proposed in response to these issues, with machine learning techniques emerging as a possible route for handling these situations. This paper introduces an Intrusion Detection System (IDS) that leverages stacking ensemble learning. The core ensemble comprises three fundamental machine learning models: k-nearest-neighbours, Decision Tree, and Random Forest. To enhance classification performance, the proposed system combines a total of seven machine learning algorithms with preprocessing methods. Stacking ensembles improve system performance greatly by combining the results of these basic models with a meta-model represented by the Logistic Regression algorithm. The UNSW-NB15 dataset is used to assess the IDS's efficacy. With an astounding 96.16% accuracy rate in the training phase and an even greater 97.95% accuracy rate in the testing phase, the suggested IDS performs remarkably well. Additionally, the precision scores are remarkable, with training scores of 97.78% and testing scores of

关键词： Decision tree Intrusion detection system (IDS) k-nearest-neighbour, random forest stacking

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Third Generation Security System for Face Detection in ATM Machine Using Computer Vision 1

Third Generation Security System for Face Detection in ATM M...

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1st International Conference on Computer, Power and Communications, ICCPC 2022

作者： Sridevi, S. Monica, K.M. Senthil, G.A. Prabha, R. Sathya, A. Ramya, J. Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India Vit Chennai Campus Dept. of Computer Science and Engineering Chennai India Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Sri Sai Ram Institute of Technology Dept. of Artificial Intelligence and Data Science Chennai India Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397840

A workflow is proposed for the detection of unauthorized persons in an ATM. Certain assumptions, including the context of identification are made by training their features and storing the datasets. However, not every individual will be a suspect. Suspicious activity on ATMs that are located in remote areas and to reduce the risk of fraudulent transactions. such as using another person's card to withdraw money etc., Various video survey and image processing have been discussed in relation to surveillance methods. It discusses the various processes, preprocessing, classification, feature extraction and the corresponding video processing methods. © 2022 IEEE.

关键词： Face recognition

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Forecasting System of Wind Speed and Direction by Neural Network 9

Forecasting System of Wind Speed and Direction by Neural Net...

引用

9th IEEE Information Technology International Seminar, ITIS 2023

作者： Sari, Anggraini Puspita Yasuno, Takashi Prasetya, Dwi Arman Al Haromainy, Muhammad Muharrom Universitas Pembangunan Nasional 'Veteran' Jawa Timur Informatics Dept. Surabaya Indonesia Tokushima University Electrical and Electronic Engineering Dept Tokushima Japan Universitas Pembangunan Nasional 'Veteran' Jawa Timur Data Science Dept. Surabaya Indonesia

ISBN: (纸本)9798350306835

This paper discusses the neural network that is used to forecast wind speed and direction by combining methods of three-dimensional convolutional neural networks (3D-CNN), convolutional long short-term memory (CLSTM), and two-dimensional CNN (2D-CNN). Its method is called 3CN-CLSTM-2CN. Combining the three methods is to diminish the forecasted error and training time compared to the 3CN-CLSTM method. The input of the forecast system is the wind vector in the form of image data that indicates the image of time-sequential which is given by the Automated Meteorological data Acquisition System (AMeDAS), Japan. Evaluate the forecast system used by the root mean square error (RMSE) between observed data and forecast data. For verification, the usefulness of the proposed forecasted system is compared with the persistent system. The suggested forecasting system's objective is to increase prediction accuracy for each season, which is typical of Japan. The proposed forecasted system by 3CN-CLSTM-2CN for each season in one-year data can increase around 16.11-26.47% to wind speed and around 20.69-23.34% to wind direction over the persistent system, which has a greater accuracy than the 3CN-CLSTM system. So, it was suitable that the 3CN-CLSTM-2CN system be the most robust forecasting system. © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning Model for Intrusion Detection in SDN Networks 1

Deep Learning Model for Intrusion Detection in SDN Networks

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Jyothsna, V. Sandhya, E. Swetha, Thammisetty Lokesh Kumar Reddy, P. Jyothsna, B. Bhasha, P. Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept. of Data Science Tirupati India Madanapalle India Madanapalle India Tirupati India Viswam Engineering College Department of Cse Madanapalle India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Data Science Tirupati India

ISBN: (纸本)9798350328042

Network security has grown in importance as computer network technology has progressed. Today networking technologies are vulnerable to a variety of security risks, and intrusion detection has emerged as a key tool for identifying network attacks. The architecture of Software-Defined Networks (SDN) is utilized to offer network monitoring and function observation. In order to protect computer networks and systems, IDS serves a crucial role. In order to keep the SDN's security at a high level, an IDS is typically created to monitor the SDN's normal traffic. A SDN network's different security threats can be detected and managed using a number of strategies. For the purpose to identify the attacks, we combine LSTM with a deep convolution neural network (Deep CNN) driven by Grey Wolf Optimization (GWO). Effective intrusion detection requires a CNN feature extraction procedure, deep LSTM is used to detect network intrusion, and a deep CNN-LSTM is trained with a specially devised optimization strategy to improve detection performance. © 2023 IEEE.

关键词： Long short-term memory

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor

IoT-Enabled Covert Surveillance Camera Identification throug...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Shaik Ruksana Shaik Imran Shaik Fatima Farheen Krovi Mohan Kireeti K L Sailaja Dept. of Artificial Intelligence and Data Science VR Siddhartha Engineering College Vijayawada India Dept. of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adept.y spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all.

关键词： Wireless communication Night vision Privacy Wireless sensor networks Surveillance Detectors Cameras

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Kannada Word Detection in Heterogeneous Scene Images 10

Kannada Word Detection in Heterogeneous Scene Images

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Shikha, N. Pranav, R. Singh, Nidhi R Umadevi, V. Hussain, Muzammil B.M.S. College of Engineering Dept. of Computer Science Bengaluru India B.M.S. College of Engineering Dept. of Electrical and Electronics Bengaluru India B.M.S. College of Engineering Dept. of Medical Electronics Bengaluru India Tika Data Services Pvt. Ltd. Bengaluru India

ISBN: (纸本)9781665490993

Text extraction from scene images has started gaining a lot of traction in recent years in the computer vision field as its applications is manifold. One of its sub-categories is scene text detection. Factors like complex backgrounds, curvature, orientation, image quality, and various font styles and sizes makes it a difficult task. Moreover, building a single language scene text detector in a multilingual setting, especially Indian scripts, is more challenging in contrast to a general text detector. In this Work We attempt the task of Kannada text localization as Well as Word detection in natural scenes, Which is underexplored in comparison to other Well-known Indian languages. To achieve this, We fine tune a You Only Look Once (YOLOv4) based object detection model on our Kannada scene text images dataset collected. Experiments on local boards and sign images captured on smartphone, showed a mean average precision of 93.5% and per image average detection speed of 30 milli-seconds;pointing towards real time use and integration With mobile applications. © 2023 IEEE.

关键词： Object detection

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Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices 8

Deep Convolutional Neural Network Based Covid-19 Classificat...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Bhosale, Yogesh H. Zanwar, Shrinivas Ahmed, Zakee Nakrani, Mahendra Bhuyar, Devendra Shinde, Ulhas Dept. of Computer Science and Engineering Mesra Ranchi India Chh. Shahu College of Engineering Dept. of Artificial Intelligence and Data Science Csmss Aurangabad India WeAgile Software Solution Pvt. Ltd Dept. of Computer Vision Engineering Pune India

ISBN: (纸本)9781665408165

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub. © 2022 IEEE.

关键词： COVID-19

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Analysis and Ranking Resume using Machine Learning Algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： Machine learning algorithms Resumes Employment Natural languages Machine learning Software Portals Recruitment Testing Qualifications

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