1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves ...
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1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.
Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system levera...
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Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods fo...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively un...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.
Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing...
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Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,first we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient *** results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.
The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such a...
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Millions of individuals throughout the world suffer with diabetes mellitus, a chronic condition that can be effectively managed with early detection and precise prognosis. In this work, a machine learning method for p...
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Yoga practice offers numerous health benefits, but incorrect poses can lead to injuries and hinder progress. This project leverages the power of deep learning, specifically Convolutional Neural Networks (CNNs) and Ten...
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The patient health prediction system is the most critical study in medical research. Several prediction models exist to predict the patient's health condition. However, a relevant result was not attained because o...
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The exponential advancement in telecommunication embeds the Internet in every aspect of *** of networks all over the world impose monumental risks on the Internet.A Flooding Attack(FA)is one of the major intimidating ...
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The exponential advancement in telecommunication embeds the Internet in every aspect of *** of networks all over the world impose monumental risks on the Internet.A Flooding Attack(FA)is one of the major intimidating risks on the Internet where legitimate users are prevented from accessing network *** of the protective measures incorporated in the communication infrastructure,FA still persists due to the lack of global *** of the existing mitigation is set up either at the traffic starting point or at the traffic ending *** mitigation at one or the other end may not be a complete *** insist on better protection againstflooding attacks,this work proposes a cooperative multilevel defense *** proposed cooperative multilevel defense mechanism consists of two-level of *** thefirst level,it is proposed to design a Threshold-based rate-limiting with a Spoofing Resistant Tag(TSRT),as a source end countermeasure for High-Rate Flooding Attacks(HRFA)and spoofing *** the second level,the accent is to discriminate normal traffic after Distributed Denial of Service(DDoS)traffic and drop the DDoS traffic at the destination *** Congruence-based Selective Pushback(FCSP),as a destination-initiated countermeasure for the Low Rate Flooding Attack(LRFA).The source and the destination cooperate to identify and block the attack.A key advantage of this cooperative mechanism is that it can distinguish and channel down the attack traffic nearer to the starting point of the *** presentation of the agreeable cooperative multilevel safeguard mechanism is approved through broad recreation in *** investigation and the exploratory outcomes show that the proposed plan can effectively identify and shield from the attack.
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