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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Faiz, Rayaan Agarwal, Tisha Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept. of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350375190

India's air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai's air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost's efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management. © 2024 IEEE.

关键词： Air quality

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Hybrid Platoon Control Based on Driving Characteristics 19th

Hybrid Platoon Control Based on Driving Characteristics

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Hu, Jingpeng Xiong, Zhiguo School of Computing Beijing Institute of Technology Zhuhai519000 China Software Engineering Technology Research Center Zhuhai519000 China School of Aviation Beijing Institute of Technology Zhuhai519000 China

ISBN: (纸本)9783031651229

Research is being conducted on the problem of nonlinear cooperative formation control of unmanned vehicles, considering sensor failures. Firstly, a nonlinear cooperative control system model that incorporates sensor failures is being established. Secondly, a model predictive controller is being designed based on this model using the Lyapunov method and linear matrix inequalities (LMIs) to optimize the control performance metrics of the vehicle fleet. This approach ensures both the asymptotic stability and queue stability of the fleet while further enhancing its control performance. Lastly, the effectiveness and practicality of the algorithm are being validated through an experimental fleet constructed with Arduino serving as the control unit. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Lyapunov methods

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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A Deep Learning Framework for Classifying Autism Spectrum Disorder from fMRI Images

A Deep Learning Framework for Classifying Autism Spectrum Di...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Varshney, Rajat Kishor Katiyar, Alok Johri, Prashant School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (纸本)9798331542344

The objective of this work is to propose a new machine learning based approach for ASD classification from fMRI data employing deep learning architecture and novel preprocessing method. Main datasets included in ABIDE I, ABIDE II, Human Connectome Project (HCP), and Social Functions and Autism Research Interface (SFARI). Many supplements include graph neural networks for brain segmentation, 3D-BRA for accurate mapping of the brain than just using conventional registration technique, EN-CC for feature selection. The approach incorporated graph neural network for segmentation of the brain, 3DBRA mapping that translates and rotates the brain precisely unlike the general technique of registration, EN-CC for selecting the features. The deep learning models under consideration were ResNet-50, VGG-16, Inception-v3, and Efficient Net which were improved through ABSC and MSTC. Classification accuracy of the proposed models was higher than 95% on all the datasets with Efficient Net scoring 99% on the SFARI dataset. This means that only the Stratified Group Cross-Validation (SGCV) was employed to make sure the results would be more reliable. The results present a considerable improvement in both accuracy and the interpretability of the derived connectivity profiles of the ASD-related brain. © 2024 IEEE.

关键词： Graph neural networks

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Interpreting Healthcare Insights: The Power of Explainable AI and Enhanced Data Analysis 8th

Interpreting Healthcare Insights: The Power of Explainable A...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Makubhai, Shahin Pathak, Ganesh R. Chandre, Pankaj R. Department of Computer Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India

ISBN: (纸本)9789819766833

Background. This study explores the potential of Explainable AI (XAI) and Enhanced Data Analysis in improving the understanding and trustworthiness of AI models. Methods: The research focuses on integrating XAI techniques with machine learning models to provide interpretable explanations for predictions. Additionally, advanced data analysis methods are employed to gain deeper insights into the dataset and its impact on model performance. Findings: The findings highlight the benefits of XAI in enhancing model interpretability and guiding decision-making processes. Novelty and applications: Moreover, the study underscores the significance of transparent AI systems for various domains, promoting ethical use and fostering wider adoption of AI technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data assimilation

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Reversible Data-Hiding Scheme Using Color Coding for Ownership Authentication 7th

Reversible Data-Hiding Scheme Using Color Coding for Ownersh...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Singh, Anuj Kumar Kumar, Sandeep Singh, Vineet Kumar School of Computing Science and Engineering Galgotias University Greater Noida India ABES Institute of Technology Ghaziabad Ghaziabad India

ISBN: (纸本)9789819995615

The concept of reversible data hiding (RDH) enables the full restoration of the cover image while simultaneously recovering the concealed data from a previously obscured image. Hence, it is the favored choice when complete restoration of the cover image is required in situations where the concealment of critical data is important. This work introduces a system that utilizes interpolation-based color coding method (CCM) to temporarily conceal data without permanent deletion. The refinement of the expanded form of the original image is achieved by the utilization of two distinct methods, namely enhanced neighbor mean interpolation (ENMI) and modified neighbor mean interpolation (MNMI). This procedure is conducted prior to the inclusion of any confidential data. The experimental findings indicate that the suggested approach has the potential to be implemented and exhibits superior performance compared to standard methods in relations of highest signal-to-noise ratio and data whacking size. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Interpolation

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Deepfake Detection System 2

Deepfake Detection System

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Shrivas, Shaswat Rai, Aditya Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet-pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet-pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies. © 2024 IEEE.

关键词： Biometrics

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Adversarial Attacks Using Carlini Wagner

Adversarial Attacks Using Carlini Wagner

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Baddukonda, Tarun Aditya Kanakam, Ronit Kumar Yasotha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the AI systems. This exposure has seen classification errors compromises and the undermining of the model performance. Understand the critical mechanisms of adversarial attacks is critical to developing strong defense mechanisms that would protect AI systems from the malevolent interventions. This abstract provides a summary of adversarial attacks as a prevalent threat to the dependability and resilience of AI technologies thus the need for continuous research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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High Accurate Gold Prize Rate Prediction Using Random Forest Regression Algorithm 7

High Accurate Gold Prize Rate Prediction Using Random Forest...

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7th IET Smart Cities Symposium, SCS 2023

作者： Sivasangari, A. Deepa, R. Geetha Rani, K. Surendran, R. Tamilvizhi, T. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai600123 India

ISBN: (纸本)9781839539831

In the past, one of the precious resources utilized to fund trading purchases was gold. The nations that have gold resources are regarded as rich countries. The nation's gold reserves show how strong its economy is. Not only have government departments participated in gold reserves, but also a number of private individuals and businesses. The routine of the world's top financial prudence affects gold rates. An increase in investors has been drawn to gold due to the recent fluctuations in price. Transactions involving gold are risky, and price fluctuations in gold are unpredictable due to the unpredictability of the gold market. The suggested uses machine learning to forecast the price of gold. Generally, machine learning is used to predict economic variables. To train a prediction model, we use supervised learning algorithms such as multiple linear regression and random forest regression. © The Institution of engineering & technology 2023.

关键词： Multiple linear regression

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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Computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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