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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Murali, Ritwik Avudaiappan, Veeramanohar Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9798400704956

Generating 3D objects to produce art that diverges from conventional or existing artistic patterns is a major challenge. Given the growing prevalence of three-dimensional (3D) art in various domains such as architecture, game design, augmented reality, virtual reality, etc., it becomes imperative to devise strategies for generating diverse forms of 3D objects. This research explores the evolution of 3D artistic objects that are complex, aesthetically pleasing and abstract in nature. Eleven different fitness metrics adapted from existing 2D image evolution metrics are first used to generate the 3D models and subsequently 3 new metrics are proposed specifically for the 3D model generation. Then, 4 different methods are used to set the initial population of each of the 14 different metrics resulting in 56 different 3D model evolution techniques. Finally, user feedback (as a survey) is used to evaluate the aesthetics of the different models evolved by these fitness metrics. Results show that the models evolved by the proposed 3D specific fitness metrics were rated higher than others by the users. In a broader context, this research pushes the boundaries of 3D art generation and evaluation techniques, while also providing insights and opportunities for future in-depth exploration of 3D art generation. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Quantifying the Impact of Incentives on Service Availability at the Extreme Edge

Quantifying the Impact of Incentives on Service Availability...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Azmy, Sherif B. Allahham, M.H.D. Saria Zorba, Nizar Hassanein, Hossam S. Queen's University Department of Electrical and Computer Engineering KingstonON Canada Queen's University School of Computing KingstonON Canada Qatar University Electrical Engineering Department Doha Qatar

ISBN: (纸本)9798350351255

Edge computing seeks to optimize service provision over enterprise-owned infrastructure near the end-user at the network's edge. However, it misses out on the opportunity to utilize user-owned hardware at the extreme edge of the network as workers in a sharing economy. In this work, we build upon the existing Incentive Vacation Queueing (IVQ) model and develop the Virtual Kiosk Model (VKM) to analyze service availability and the dynamics of multiple workers' participation in the provision of a service on the extreme edge. We formulate an optimization problem to minimize total cost of incentive payments while maintaining service availability under temporal constraints. We propose the Model-based Incentive Strategy at the Edge (MISE) algorithm to iteratively adjust incentives in real-time. MISE is compared against traditional numerical optimizers and a baseline naive approach that greedily focuses on minimizing incentives. Our findings demonstrate that MISE ensures sustained service availability without overburdening the workers at a cost acceptable to the service provider, striking a crucial balance in the management of extreme edge computing resources. © 2024 IEEE.

关键词： Virtual corporation

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3D Modeling of Automated Robot for Seeding and Transplantation of Rice and Wheat Crops 1

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Krishna, G. Venkata Sai Amitasree, Palanki Manitha, P.V. Rajesh, M. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9789819923229

ISBN: (纸本)9789819923212

The most important sector of Indian economy which is identified as the backbone is agriculture. The major part of agriculture lies in paddy cultivation. In earlier days, agriculture was the major sector where most people were employed but nowadays, due to industrial growth and IT revolution, the employment in agriculture has become too less. The major part of economy which is vital for India’s economic growth is now facing serious issues due to the manpower shortage, lack of advancement in technological usage. This proposes a solution to meet out this issue by bringing in automation in the agriculture domain. Agricultural processes are chains of systematic, repetitive and time-dependent tasks. It requires lot of manual labor and accurate placement and managing of seeds and saplings. It’s a case where a human error is highly possible. In this work, an attempt is made to automate the process of distributing the seeds and once they are grown big enough to get transplanted, the robotic solution will transplant these plants. The automated solution will distribute the seeds in an organized and optimized way. The robotic platform will pluck the plants and replant them in an organized way. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Three dimensional computer graphics

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DeepUIR-Net: Underwater Image Restoration Using Residual-UNet with Optimized Efficient Channel Attention Network Integration 2nd

DeepUIR-Net: Underwater Image Restoration Using Residual-UNe...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Rayvanth, N. Amruth, S. Jaya Suryaa, E. Resmi, S. Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031845420

The underwater image restoration task targets retaining the true colors of the underwater scenarios captured in the images by rectifying the distorted colors to critically analyze the ocean scenarios and resources. This area has been explored using different deep learning techniques such as Convolutional neural networks, Residual Networks, and various other models that made use of post-processing techniques for the final enhanced outputs. Hence, this study proposes a novel approach by fine-tuning the Residual-UNet architecture and combining it with an Efficient Channel Attention network in an optimized architecture for the task of underwater image restoration. Channel attention mechanisms have a great ability to reduce model complexity by making use of fewer parameters and increasing the performance of the model. One such mechanism, called Efficient Channel Attention, has been incorporated into the Residual-UNet model to extract features efficiently and obtain an enhanced underwater image that is true to the ground truth image by making use of the HardSwish activation function. The dataset used to train the Residual-UNet model is LSUI (Large Scale Underwater Images), a benchmark dataset consisting of various real underwater images. The proposed model obtained SSIM and PSNR values of 0.9998 and 74.851 dB respectively and outperformed other models by a margin of 4%. To determine the superiority of the Efficient Channel Attention network optimally integrated into the Residual-UNet model, various base models without this attention module have also been trained and evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Enhancing Power Grid Security by Detecting Relay Setting Manipulation Attacks with Machine Learning 3

Enhancing Power Grid Security by Detecting Relay Setting Man...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Varghese, Samya Rebecca Jiji Venu, Ankita Vijaya Chandrakala, K.R.M. Gireesh Kumar, T. Arun Shankar, V.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Danfoss Industries Private Limited United States

ISBN: (纸本)9798350372120

As power grids undergo extensive digitalization, they become susceptible to diverse cybersecurity threats, including potentially manipulating protective relay settings. Traditional detection methods struggle to identify these attacks effectively, posing a significant risk of widespread disruptions. This research introduces a machine learning-based approach for detecting and categorizing attacks that manipulate relay settings. The experiments evaluate the efficacy of various algorithms, including Random Forest, XGBoost, and Decision Trees, and Random Forest emerged as the best-performing model, showcasing the capabilities of machine learning to enhance the security of power grid operations. © 2024 IEEE.

关键词： Anomaly detection

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A Machine Learning Approach for Detecting Fake News in Hindi 10

A Machine Learning Approach for Detecting Fake News in Hindi

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10th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2023

作者： Nagesh, Shreyas Sampath, Aditya N Guruprasad, Pradhumna Salai Sanjay, S. Jeyakumar, G. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350382471

Several solutions exist to detect whether English news articles are fake or not. However, in a country such as India, news articles are published in numerous regional languages too. There has been little research work done on regional languages such as Hindi. This work proposes a framework to detect Hindi fake news articles. A dataset of nearly 4500 news articles has been collected in Hindi, written using the Devanagari Script. These news articles are scraped using BeautifulSoup and Scrapy libraries. The IndicBERT and MuRIL libraries, developed earlier in Hindi, are used to generate the word embedding. SVM, Logistic Regression, and a simple CNN are used as classifiers. Extensive experiments have been methodically conducted in order to find the best combination to detect fake news in Hindi. The best combination was found to be MuRIL and SVM, yielding 98.58% classification accuracy. A standalone GUI running on a local host is built to demonstrate the working model. © 2023 IEEE.

关键词： Hindi Fake News IndicBERT Machine Learning MuRIL Natural Language Processing StanfordNLP

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Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4322-4343页

作者： Al-Maliki, Shawqi Qayyum, Adnan Ali, Hassan Abdallah, Mohamed Qadir, Junaid Hoang, Dinh Thai Niyato, Dusit Al-Fuqaha, Ala Division College of Science and Engineering Doha34110 Qatar Information Technology University Lahore54000 Pakistan University of New South Wales Sydney Sydney2052 Australia Qatar University Department of Computer Science and Engineering College of Engineering Doha34110 Qatar University of Technology Sydney School of Electrical and Data Engineering Sydney2052 Australia Nanyang Technological University College of Computing and Data Science 639798 Singapore

Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples - input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community. © 2020 IEEE.

关键词： Taxonomies

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Assessing the Efficacy of Symbolic Regression for Scientific Data Classification and Regression 14

Assessing the Efficacy of Symbolic Regression for Scientific...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Guruprasad, Pradhumna Nagesh, Shreyas Sampath, Aditya N Salai Sanjay, S. Jeyakumar, G. Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering India

ISBN: (纸本)9798350335095

Symbolic regression (SR) is a subdomain of evolutionary computing which aims to generate mathematical equations which best fit a given dataset. This is done with the help of genetic algorithms. Symbolic Regression is especially useful in cases where features in a dataset are known to have distinct mathematical relations to each other and its output. Therefore, this paper aims to compare and provide insight on the results of using SR on two datasets with two python libraries. These results are further compared with other ML models to assess their performance in classification and regression tasks. The detailed methodology and results are discussed further test metrics and obtained equations. The results show that symbolic regression can perform very well on certain datasets whose target outputs have a mathematical connection to the input features. SR is found to work better than other ML models in the classification and regression tasks. © 2023 IEEE.

关键词： Genetic algorithms

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PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud

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IEEE Transactions on Mobile computing 2025年第07期24卷 5632-5648页

作者： Wang, Haiyan Liu, Penghui Zhong, Xiaoxiong Luo, Fucai Xiao, Bin Yang, Yuanyuan Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Zhejiang Gongshang University School of Computer Science and Technology Hangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs. © 2025 IEEE.

关键词： Media Encryption Access Control Cloud computing Servers Passwords Keyword Search Copyright Protection Streaming Media Performance Evaluation Access Control Content Protection Media Cloud Password Searchable Encryption Time Sequence Access Policy Access Control Framework Computational Cost Cloud computing Communication Cost Storage Cost Encrypted Data Security Proof User Data Hash Function Computational Overhead Secret Key Public Key Access Structure Symmetric Encryption Data Owner Communication Overhead Key Material Encryption Scheme Number Of Keywords Cloud Users Encryption Key Bilinear Pairing Security Model Encrypted File Media Services Single Search L 2 Cache Internet Of Things

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Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning 1

Algorithmic Approaches, Practical Implementations and Future...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vaishali Sharma, Deepank Kumar Punia, Sanjeev Galgotias University School of Computing Science and Engineering Greater Noida India G L Bajaj Computer Science and Engineering Department Greater Noida India

ISBN: (纸本)9798350356816

Machine learning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this study examines the rapidly changing field of machine learning. It explores the fundamentals of supervised, unsupervised, and semi-supervised learning algorithms and highlights how they are used in a variety of fields, including autonomous systems, healthcare, and finance. The report highlights the potential of several important future research directions, such as explainable AI, robust and privacy-preserving learning, quantum machine learning, and multimodal AI, to overcome present constraints and open up hitherto unheard-of possibilities. These multidisciplinary research avenues emphasize the value of interdisciplinary cooperation in addressing difficult problems and guaranteeing the creation of morally sound and significant AI systems. In order to help academics and practitioners who want to progress the subject, this review attempts to give a thorough grasp of the present situation and potential future direction of machine learning. © 2024 IEEE.

关键词： Self-supervised learning

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