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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Vivek, V. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

This researcher investigated effective much eye opening and pattern detection algorithms. Finally, this article used two frameworks to argue that geospatial investigation systems for patterns are necessary. One of most popular method for analyzing photographs using image processing and analysis math is probably visual configuration methods. Imagery description is also necessary for the evaluation of remote sensing tools used for thorough land investigation. The three main image configuration techniques that are often used for far detection are opinion piece visual analysis, controlled groupings, and solos visual categorization. The approach used in these image layout techniques is combined in the present study. The present study also briefly summarizes each approach's use in exploring distant sensing info while also outlining the steps for each way using stream outlines and frameworks. Generally speaking, a quick overview of image layout techniques is included in the test. © 2023 IEEE.

关键词： Image classification

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Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan Ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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An Innovation Development of Neuro Controller for Condition Monitoring and Smart Industrial Instrumentation 2

An Innovation Development of Neuro Controller for Condition ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Vanitha, K. Raja Praveen, K.N. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Neuro Controller is an innovative piece of industrial instrumentation designed to monitor conditions in smart industrial settings. It is a powerful and versatile controller that can be used to monitor, control, and manage industrial processes. The Neuro Controller is equipped with advanced sensors and actuators, enabling it to accurately measure and control a variety of conditions. The Neuro Controller is able to monitor and control temperature, pressure, humidity, flow, and other parameters in an industrial setting. It is also capable of detecting changes in these parameters, allowing it to respond to changes quickly and accurately. The Neuro Controller uses advanced algorithms to analyze data and make decisions, allowing it to optimize and control processes efficiently. Additionally, the controller is able to integrate data from numerous sources, providing a comprehensive view of a process. In this paper, a smart Neuro Controller for Condition Monitoring and smart industrial instrumentation. The Neuro Controller is designed to provide enhanced safety and efficiency in industrial settings. It is capable of detecting potentially hazardous conditions and alerting personnel to take action. Additionally, the controller can be used to automate processes, reducing the risk of accidents and improving process efficiency. © 2023 IEEE.

关键词： Controllers

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Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

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The Smart Development of Short Range Communication Protocol Development in Efficient Device Discovery 2

The Smart Development of Short Range Communication Protocol ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Narasimhayya, B.E. Lanke, Ravikumar Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The development of short-range communication protocols has been essential for efficient device discovery. Short-range communication allows two devices to communicate over short distances, typically up to 10 meters, using low-power radio frequencies. This type of communication is used extensively in applications such as Wi-Fi, Bluetooth, and Zigbee. Short-range communication protocols allow devices to discover each other without the need for a central server or intermediary. This is important for applications such as home automation, where it is necessary for various devices to be able to find each other and communicate. In order for efficient device discovery, short-range communication protocols must be reliable and secure. Reliable protocols are necessary to ensure that devices can find each other, while secure protocols are necessary to ensure that only authorized devices can access one another. Protocols such as Bluetooth, Wi-Fi, and Zigbee have been designed to be reliable and secure. The development of short-range communication protocols can be improved by using predictive algorithms. Predictive algorithms allow devices to automatically detect and respond to changes in the network environment. This enables devices to more efficiently discover each other and establish connections. © 2023 IEEE.

关键词： Zigbee

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An Innovation Development of Decentralized Prove Piercing Data Recuperation Using Improved Red in Mobile Grid Computing 2

An Innovation Development of Decentralized Prove Piercing Da...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Karthikeyan, S. Thomas, Merin Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The recent advancements in mobile computing have opened up the possibilities of decentralized data recovery in mobile grid computing. With the help of improved Red (Recovery of Erased Data) technique, data recovery can be done in a decentralized manner. This method is a combination of distributed computing, data replication, and data recovery algorithms which improves the data recovery process. The improved Red technique uses distributed computing to replicate the data and store it in multiple nodes in the grid. This helps to ensure that the data is distributed and available in multiple nodes for recovery. Data recovery is done by the Red algorithm which traverses the grid and identifies the nodes which have the data and then recovers it. The algorithm uses a technique called "greedy search"to identify the nodes which have the data. This helps to reduce the time and cost associated with the recovery process. The improved Red technique also makes use of data replication which helps to improve the data recovery process. The data is replicated and stored in multiple nodes which helps to improve the speed of data recovery. The replication process helps to ensure that the data is always available and can be recovered quickly. The improved Red technique also makes use of distributed computing to improve the scalability of the data recovery process. The distributed computing helps to ensure that the data recovery process can be done efficiently and quickly. The data recovery process can also be done on a large scale which helps to improve the speed of data recovery. © 2023 IEEE.

关键词： Recovery

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Novel Anomaly Detection Scheme for Cyber Physical System using C-GAN in Fog assisted IoT Environment 2

Novel Anomaly Detection Scheme for Cyber Physical System usi...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mathur, Ashwini Babu, S. Anantha Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technological developments. Despite the proliferation of security precautions, criminals have learned to circumvent them. We employ a Contingently Generative Adversarial Network in our research to detect harmful online behaviour (CC-GAN). Our proposed system employs CC-GAN, which can identify novel threats, to carry out intrusion detection based on anomalies. An Internet-of-Things layer, a fog layer, as well as a cloud layer make up the whole of our proposed fog-based infrastructure. The IoT layer consists of many connected devices, and it is here that the CC-GAN receives the training data it needs to learn to recognise potential security breaches. A convolutional neural network & encoder are trained on the cloud layer, where additional computing takes place. Adding a fog layer might help decrease the delay. Finally, the attack detection score is evaluated. For this exercise, we use the network simulator version 2.6. (NS 3.26). The simulation result shows that the proposed system achieves a higher detection rate than the state-of-the-art systems. © 2023 IEEE.

关键词： Cyber Physical System

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SW-Net: A novel few-shot learning approach for disease subtype prediction

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BIOCELL 2023年第3期47卷 569-579页

作者： YUHAN JI YONG LIANG ZIYI YANG NING AI Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao999078China Tencent Quantum Lab Shenzhen518000China

Few-shot learning is becoming more and more popular in many fields,especially in the computer vision *** inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with *** goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small *** disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical *** propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen *** model is built upon a simple baseline,and we modified it for genomic *** initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce ***,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence *** on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.

关键词： Few-shot learning Disease sub-type classification Feature selection Deep learning Meta-learning

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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A Way to Mark Attentance using Face Recognition using PL 3

A Way to Mark Attentance using Face Recognition using PL

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vivek, V. Tr, Mahesh Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350399264

The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful feature. This device helps students take attendance by utilizing both a biometric technology and artificial intelligence algorithms in one convenient package. When pupils enter the classroom, the face recognition software can identify them, making it useful for educational institutions of any kind. The existing system has a number of flaws that make it difficult to accurately record the attendance of both students and teachers. The approach makes it simpler to mark attendance and lowers the number of mistakes that might occur when doing *** students and teachers will gain a significant amount by utilizing these techniques. The ability to recognize people based on their faces is becoming increasingly significant in practically every industry. Face recognition techniques will be used to understand the faces. An attendance record is compared to the record that corresponds to it that was previously saved in the database. A student's photograph is used as a check point for this comparison. © 2023 IEEE.

关键词： Face recognition

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