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共 20250 条记录，以下是291-300 订阅

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The Rise of Intelligence: A Comprehensive Review on AI and AI Automation 3

The Rise of Intelligence: A Comprehensive Review on AI and A...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Bhardwaj, Cheshtaa Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331528201

This paper presents a comprehensive review of Artificial Intelligence (AI), Machine learning (ML), and Deep learning (DL) technologies, focusing on their impact in driving AI automation across diverse sectors with a special emphasis on robotics. As AI continues to evolve, its integration into automation systems has revolutionized industries, enhancing efficiency, precision, and decision-making. The role of AI automation in robotics is critically examined, covering key architectural frameworks that enable autonomous operation and intelligent decision-making. Furthermore, the security aspects associated with AI in robotics, identifying attack surfaces and potential vulnerabilities has been analysed. This review highlights the transformative potential of AI automation while addressing the challenges and security concerns that accompany its deployment in modern systems, particularly in robotics. © 2024 IEEE.

关键词： Adversarial machine learning

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A Study on Hardware-based Implementation of Homomorphic Encryption 3

A Study on Hardware-based Implementation of Homomorphic Encr...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Singh, Gurdeep Mittal, Sonam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

Homomorphic encryption is an innovative cryptographic mechanism that allows performing cryptographic operations even on encoded data without intermediate decoding. As a result, sensitive information cannot be compromised in case of dealing with outsourcing services. This article firstly provides the general background of HE and most notably discusses the types of HE such as partial homomorphic encryption, somewhat homomorphic encryption, and fully homomorphic encryption Each of these types has its advantages on drawbacks depending on the type of operations and the level of computation efficiency. The paper also considers research undertaken recently oriented towards increasing the feasibility of HE by all means, especially the lowering of the costs in computation. A comparative analysis matrix contains distinctive features of various approaches in HE and defines their complexity, security, and usability concerns. This and further surveys present the HE history and its above- and below-the-line progress and suggest paths for HE-morphed developmental works for applications such as cloud computing and machine learning that are inherently privacy-preserving. © 2025 IEEE.

关键词： Differential privacy

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intelligent Detection Method of Power Grid Fault Big data Based on Deep learning 3

Intelligent Detection Method of Power Grid Fault Big Data Ba...

引用

3rd international conference on New Energy and Power engineering, ICNEPE 2023

作者： Tian, Xiaolei Wang, Yue Liu, Yunchang State Grid Liaoning Electric Power Co. LTD. Liaoning Shenyang110006 China Liaoning Meteorological Equipment Support Center Liaoning Shenyang110006 China

ISBN: (纸本)9798350393224

To ensure the stable operation of the distribution network, a new approach is proposed for the intelligent detection of power grid faults using deep learning and big data analysis. The method involves establishing a loss function and iteratively processing the results obtained from data classification. These results are then input into the system, which utilizes deep learning techniques to extract relevant information from the power grid fault big data. By defining the power grid fault state and analyzing the transmission path of the traveling wave, the actual fault location can be determined by converting the average value. This enables intelligent detection of power grid faults using big data. Experimental results demonstrate that the proposed method achieves excellent performance, with a maximum detection accuracy of 98.5%. This approach is both accurate and practical. © 2023 IEEE.

关键词： Big data Deep learning intelligent detection Power grid failure Traveling wave transmission path

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intelligent Optimization Analysis of Automatic Pricing and Replenishment of Vegetable Products Based on Combinatorial Machine learning Models 3

Intelligent Optimization Analysis of Automatic Pricing and R...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Tang, Yuyang Xi'an University of Technology Xi'an China

ISBN: (纸本)9798350380989

Based on the idea of market demand and supply, this paper establishes a combined machine learning model by determining the indicators such as sales volume, demand, supply, etc., with the goal of maximizing the interests of the superstore, and adopts an intelligent optimization algorithm to realize the optimization analysis of automatic fixing and replenishment for vegetable commodities. First, Spearman correlation coefficient is used to quantitatively analyze the correlation between different categories to quantify the degree of correlation between different vegetable categories, and K-means cluster analysis is used to analyze the sales data of each category of vegetables, and different vegetables are divided into different clusters based on sales volume, so as to identify the vegetable items with similar sales volume. Then, XGBoost model and time series autoregressive model were used to construct a combined machine learning model, which realized the prediction of future price data based on historical price data and the prediction of future sales volume by combining the unit price and the price, and carried out the smoothing test and the seasonality test on the data to further optimize the predictive ability of the model. And it realized the prediction of future week's price, and the prediction of future week's price was realized based on the prediction results of the combined machine learning model. Based on the prediction results of the model, an automatic pricing strategy and replenishment strategy function with the goal of maximizing revenue is established, and genetic algorithm is applied to realize the solution of the model to obtain the corresponding optimal pricing. Finally, the objective function is shifted from the overall category to specific commodities, and the constraints adapted to it are set for specific time points, which are still solved by intelligent optimization algorithms and optimization analyses are carried out, and specific replenishment qua

关键词： genetic algorithm K-means cluster analysis spielman correlation coefficient time series autoregression XGBoost model

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Analysis of the Effect of Adversarial Training in Defending EfficientNet-B0 Model from DeepFool Attack 3

Analysis of the Effect of Adversarial Training in Defending ...

引用

3rd international conference on intelligent Communication and Computational Techniques, ICCT 2023

作者： Ashwin Muthuraman, A. Balaaditya, M. Dunston, Snofy D. Mary Anita Rajam, V. College of Engineering Guindy Anna University Department of Cse Chennai India

ISBN: (纸本)9781665453578

Medical image diagnosis is a time-consuming process when done manually, where the predictions are subjected to human error. Various Deep learning models have brought about an efficient and reliable automated system for medical image analysis. However, these models are highly vulnerable to attacks, upon exposure of which the models lose their reliability and misclassify the input images. Adversarial attack is one such technique which fools the deep learning models with deceptive data. DeepFool is an adversarial attack that efficiently computes perturbations that fool deep networks. With the help of two different datasets, we studied the impact of DeepFool attack on EfficientNet-B0 model in this research. There are several defense mechanisms to protect the model against various attacks. Adversarial training is one such defense method, which trains the model towards a particular attack. In this study, we have also analysed how effectively adversarial training would defend a model and make it robust. © 2023 IEEE.

关键词： Deep learning

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Deploying and Managing Web Application using Kubernetes 3

Deploying and Managing Web Application using Kubernetes

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhanwai, Aditya Bhagwat, Shivam Supekar, Kunal Deshmukh, Saurabh More, Amar School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

This project examines the deployment and management of web applications using Kubernetes, an open-source container orchestration platform. It provides an analysis of the core features of Kubernetes, including pods, services, and deployments, to simplify and optimize the deployment process of web-based systems. The study investigates various deployment strategies, such as rolling updates, to showcase Kubernetes’ adaptability and efficiency in maintaining continuous service availability during updates. By providing practical insights and guidelines, this project offers a comprehensive understanding of Kubernetes and its transformative potential in web application management. The findings are particularly valuable for developers, IT professionals, and anyone involved in the complex process of web application deployment. © 2025 IEEE.

关键词： Open source software

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Towards Sustainable Solar Energy: AI Based Multiple Anomaly Classification in PV Systems 3

Towards Sustainable Solar Energy: AI Based Multiple Anomaly ...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Singh, Lalit Kumar Rafiuddin, Nidal Azeem, Mohammad Fazle Zakir Hussain College of Engineering and Technology Amu Department of Electrical Engineering Uttar Pradesh Aligarh India

ISBN: (纸本)9798350387193

This paper investigates the use of Artificial intelligence (AI) techniques in the Solar Photovoltaic systems for measuring the health of the PV systems. It focusses on comparisons of different results generated by different deep learning networks models which are available in MATLAB like SqueezeNet;GoogLeNet. A total of eleven anomalies in PV module had been incorporated. The study also evaluates the accuracy and efficiency of these models in detecting faults and anomalies in solar PV modules. By analyzing the performance of CNN AI models, this research aims to provide insights into the effective approach for monitoring the health of PV systems. © 2024 IEEE.

关键词： Convolution neural network Deep learning Solar PV system anomalies

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Detection and Mitigation of DDOS Attack in SDN using Feature based RF & MLP Approach 3

Detection and Mitigation of DDOS Attack in SDN using Feature...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saranraj, K. Sumugar, M. Anantha Moorthy, R. Gowthami, N. Tamilselvi, S. National Engineering College Kovilpatti India

ISBN: (纸本)9798331527549

Software Defined Networking (SDN) is an architecture that decouples the control plane from the data plane, allowing for centralized oversight and flexible configuration of network resources via software. However, this centralization of the controller makes it especially susceptible to Distributed Denial of Service (DDoS) attacks. To effectively prevent and mitigate such attacks, deploying a robust machine learning model is crucial. In this project, the Genetic Algorithm and Chimp Optimization Algorithm were employed as optimal feature selection methods to identify and extract the most representative and distinguishing features from the SDN_DDoS_2020 dataset, which contains ICMP, TCP, and UDP attack traffic (sourced from Mendeley). The refined feature subset was then used to train and evaluate the performance of two machine learning algorithms commonly used are Random Forest (RF) and Multilayer Perceptron (MLP).Furthermore, a dedicated mitigation model was developed to block or redirect the flood requests from TCP, UDP, and ICMP attack traffic. Among the models tested, the combination of the Chimp Optimization Algorithm with the MLP classifier demonstrated superior performance, achieving an impressive accuracy of 98.68%. Notably, this model also exhibited a low false alarm rate of 0.74 compared to all other models, enhancing its reliability in real world scenarios where accurate attack detection and minimal false positives are essential. © 2025 IEEE.

关键词： Genetic algorithms

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Cyber Threat Attack Level Detection using Machine learning 3

Cyber Threat Attack Level Detection using Machine Learning

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rahul, R. Mythili, S. Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

This study focuses on creating a machine learning-based system for identifying cyber-attacks in real time using network data, system logs, and attack history. The work presents a thorough methodology that includes data collection, preprocessing, feature selection, model training, and real-time monitoring. The system uses a variety of machine learning techniques to classify attack types, including supervised learning methods such as Random Forest, Support Vector Machines (SVM), and Neural Networks, as well as unsupervised learning methods such as clustering to detect anomalies that indicate potential threats. The model was trained and evaluated on a dataset of 477 items, reaching an accuracy of 83.33%, with 470 entries identified as carrying threats. The study effectively proved the ability to detect numerous attack types, such as SQL injection, cross-site scripting (XSS), and DDoS attacks, as well as accurately classify and prioritize threat levels. Furthermore, the system was successfully deployed in a live environment, providing real-time threat detection, automatic response capabilities, and detailed alerts to security personnel. This research resulted in the creation of an efficient and scalable cyber threat detection framework capable of both detecting existing attacks and discovering novel, previously unknown threats. © 2025 IEEE.

关键词： Cyber attacks

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automated Lemon Quality Detection using DenseNet121: A Deep learning Approach 3

Automated Lemon Quality Detection using DenseNet121: A Deep ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kaushik, Pratham Khurana, Saniya Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331509675

Detection of lemon quality is an important process in agricultural and commercial sectors, optimizing the product standards and minimizing waste. This study explores the application of the DenseNet121 deep learning model for the classification of lemons into two categories: Bad Quality and Good Quality. Preprocessing of a dataset of 2,076 images from Kaggle was done using advanced techniques like resizing, normalization, CLAHE, and data augmentation to facilitate extraction of features and development of generalization of models. On the test set, the model was fine-tuned, and the accuracy was found to be 96%. The performance on all evaluation metrics, such as precision, recall, and F1-score, has always stayed at a very high level in both classes. Further analysis of the model using the confusion matrix revealed its reliability in reducing misclassifications. By automating the detection of lemon quality using deep learning, this study demonstrates the potential in automating processes in agriculture and supply chain management while also showcasing an efficient and scalable solution in the real world. © 2025 IEEE.

关键词： Citrus fruits

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