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

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Organic and Recyclable Waste Classification Using Integrated Feature Selection Method

Organic and Recyclable Waste Classification Using Integrated...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Vijayaraj, A. Shreya, S. Deepana, G. Senthilvel, P. Gururama Jebakumar, R. Department of IT R.M.K Engineering College RSM Nagar Kavaraipettai Chennai601 206 India Department of Civil Engineering S.A. Engineering College Thiruverkadu Chennai600077 India Chennai602 105 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350305708

This paper suggests a new approach to enhancing waste classification performance using deep learning algorithms. Specifically, the proposed approach integrates an autoencoder network with a feature selection method in convolutional neural network models. This approach aims to identify valuable features from waste material images while reducing noise and improving the accuracy of the model. Autoencoder networks are utilised to learn a compressed representation of input data, which can be used as a feature vector for classification tasks. The feature selection method is then applied to remove irrelevant features, improving the accuracy of the model. The proposed approach has the potential to provide an efficient and effective solution for waste classification tasks, contributing to sustainable waste management and environmental conservation. Experimental results demonstrate the effectiveness of the proposed approach in enhancing the accuracy of waste classification tasks. © 2023 IEEE.

关键词： autoencoder network sustainable waste management Waste classification

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Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing 9

Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid ...

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9th International Conference on engineering and Emerging technology, ICEET 2023

作者： Asghar, Laiba Ahmed, Fawad Khan, Muhammad Shahbaz Arshad, Arshad Ahmad, Jawad Pakistan Navy Engineering College National University of Sciences and Technology Department of Cyber Security Karachi Pakistan School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350316926

To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates nonlinear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized hybrid chaotic maps are the logistic-tent and the logistic-sine-cosine map. The hybrid chaotic maps enhance the pseudorandom sequence generation and selection of substitution boxes, while the logistic-sine-cosine map induces non-linearity in the algorithm through random noise. This deliberate inclusion of noise contributes to increased resistance against cryptanalysis. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Extensive evaluation demonstrates the efficacy of the proposed scheme, with almost ideal values of entropy of 7.99 and correlation of -0.0040. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption. © 2023 IEEE.

关键词： Entropy

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A Novel Approach to Encrypt the Data Using DWT and Histogram Feature 9th

A Novel Approach to Encrypt the Data Using DWT and Histogram...

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9th International Conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Srivastava, Sandeep Kumar Katiyar, Sandhya Kumar, Sanjay Department of Information Technology Galgotias College of Engineering and Technology Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9789811666230

In recent years, digital image data techniques are used in very large scale. Data watermarking is the best technique that allows us to hide the secret information into an image. In this paper, discrete wavelet transfer (DWT) low frequency band is used for embedding watermark techniques. Binary code, i.e., 0, 1, is used to hide the data with watermark technique, which hides the information into an image. In this proposed work, information is hidden by carrier image where DWT feature hides the data by histogram feature shifting. By using this technique, information can be hidden point by point, and image can be recovered easily. Further verification is also done by taking authentic dataset image. These operations demonstrate the proposed work with SNR and PSNR with maximum secured information. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Parted Security Method for Secure Data Exchange in Heterogeneous Applications Using Wireless Sensor Networks 3

Parted Security Method for Secure Data Exchange in Heterogen...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Chandrika, G. Naga Ramesh, S. Balasamy, K. Anandaraj, A. Glory, E. Suganthi, R. Vnr Vignana Jyothi Institute Of Engineering & Technology Department Of It Hyderabad India Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur India Bannari Amman Institute Of Technology Department Of Ai&ds Erode India Sri Eshwar College Of Engineering Department Of Cse Coimbatore India Saveetha School Of Engineering Simats Department Of Biosciences Chennai India Panimalar Engineering College Department Of Ece Chennai India

ISBN: (纸本)9798350318609

In tackling the changing security issues in Wireless Sensor Network (WSN) applications, the Parted Secure Data Exchange Method (PSDEM) is a noteworthy development. In order to acknowledge the intrinsic heterogeneity of users and applications in WSNs, segment-based trust is crucial. PSDEM offers a sophisticated and adaptable approach to security by acknowledging that various areas of the network may require varying degrees of protection and customising security measures to those segments. PSDEM's primary novelty is its approach to reducing the erroneous rate of intrusions during data exchange periods. PSDEM presents a revolutionary paradigm where the vulnerability of one segment does not undermine the overall security of the entire data transmission. The proposed method is reliable in reducing the service latency, false rate, and service loss by 9.63%, 11.37%, and 10.68% for the maximum network regions used for data exchange. © 2024 IEEE.

关键词： Electronic data interchange

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft computing 2025年第7期29卷 3241-3251页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Information technology (IT) Governance Framework with Artificial Neural Network and Balance Scorecard to Improve the Success Rate of Software Projects 6

Information Technology (IT) Governance Framework with Artifi...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Ranesh, M. Maria Antony Samuel, S. Justin Natchadalingam, R. Jeyanthi, P. School of Computing Sathyabama Institute of Science and Technology Dept of Information Technology India Psn Engineering College Dept of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9781665482714

IT governance enables companies to plan and manage their IT investments in order to achieve their strategic objectives. Overall goal of this paper is to discuss about an approach to increase the success of the software projects through Artificial Intelligence. Standard governance standards are analyzed to understand the impact on software project success rates. Enterprise governance is a subset of IT governance that aids in the management of information technology resources. COBIT is a widely used methodology for developing effective enterprise IT governance. ISACA (Information Systems Audit and Control Association) stated implementation solution currently uses a sequentially ordered approach, which has raised issues. Several challenges have arisen as a result of top management's lack of commitment and misplaced solutions. Limitation on this investigation is the customized and confidential governance pattern used across various organization, hence there are very limited real time data for end-to-end analysis. Nonetheless, in combination with the agile project management paradigm, new project life-cycle solutions have evolved. Artificial Intelligence (AI) is an important element to address the increasing demand for machine learning systems. Artificial neural network (ANN) method is highly useful to arrive at best possible solution to improve the project success. This study found that ANN with Balance Score Card was exceptionally effective in providing the optimal solution to increase the project success rates. © 2022 IEEE.

关键词： Neural networks

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Research on a more safe image encryption communication algorithm

Research on a more safe image encryption communication algor...

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2024 International Conference on Computer Network Security and Software engineering, CNSSE 2024

作者： Liu, Lu Li, Yuhui Wang, Ting Qin, Yuhan Ren, Yuhan College of International Education SiChuan Normal University Chengdu610000 China School of Automotive Engineering Harbin Institute of Technology Harbin150001 China Faculty of Computing Harbin Institute of Technology Harbin150001 China Law School SiChuan Normal University Chengdu610000 China

ISBN: (纸本)9781510680234

Image encryption has become one of the key techniques for information protection in today's context of increasing number and rising importance of digital communication. The purpose of this paper is to explore a creative image encryption method based on Rijndael algorithm and chaos algorithm, which disambiguates the image, hash value calculation. Then encrypted by Rijndael algorithm and generates a secure ciphertext. Compared to traditional encryption, this algorithm greatly improves the security and speed of encryption by combining multiple encryption techniques, and by conducting several encryption and decryption experiments on this algorithm, we prove that this method has a great prospect for security applications. © 2024 SPIE.

关键词： Cryptography

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Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Optimized BIGRU-Attention Network for OSA and Cardiovascular Disease Prediction with Risk Factor Classification 4

Optimized BIGRU-Attention Network for OSA and Cardiovascular...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Yadav, Sameer Joseph, J. Sharmila Nagar, Mayank Dakshinamurthi, Vidyabharathi Usha, M. Dubey, Vedant Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Vit Bhopal University School of Computing Science and Engineering India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Sona College of Technology Dept. of Cse TN Salem India Measi Institute of Information Technology India Ggits MP India

ISBN: (纸本)9798331518981

Cardiac illness is a rapidly rising worldwide killer. The development of an automated prediction framework for OSA-related cardiovascular problems is tough given that to the extensive information needed to effectively forecast heart illness. Several algorithms have been developed for use in IoT-based medical oversight systems, which utilize sensor data to identify ailments *** deploy new learning models to improve prediction results. The existing methods lack the ability to forecast accurately, making them unsuitable for successful diagnosis. This study proposes BGEAN, a revolutionary framework that combines deep learning with computing devices to identify cardiac issues accurately. Data are pre-processed utilizing Studentized Residual Methods to address missing values and outliers. Improved Gated Recurrent Neural Networks help classify pre-processed data as normal or abnormal. Thorough testing and comparison to top deep learning techniques are conducted. Performance indicators, such as accuracy (99.99%), recall (99.35%), specificity (90.12%), precision (99.5), F1-score (99.99%), and Model Building Time (MBT) (0.212sec), are computed. The findings show that the technology that was suggested surpassed current algorithms in accuracy, error, and reaction time. With its many user situations, BGEAN provides the most diverse environment for forecasting cardiac diseases. © 2024 IEEE.

关键词： Recurrent neural networks

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