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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Krishnasamy, Lalitha Kiruthika, M. Manjunatha Swamy, C. Vigneshwaran, T. Sharon Roji Priya, C. Vijay Anand, D. School of Engineering and Technology Department of CSE Christ University Bengaluru India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu Kattankulathur India Kongu Engineering College Department of Information Technology Erode India

ISBN: (纸本)9798350304480

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. © 2023 IEEE.

关键词： bag of words cyberbullying feature extraction sentiment analysis SVM

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Comprehensive Analysis of Arabic Sentiment Analysis using Lexicon and Machine Learning based Approaches 2

Comprehensive Analysis of Arabic Sentiment Analysis using Le...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Maheswari, M. Shnain, Ammar Hameed Siddiqua, Ayesha Nagaraj, H.C. Shilpa, N. Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India School of Engineering Sr University Department of Ece Warangal India

ISBN: (纸本)9798350372892

Arabic sentiment analysis (ASA) is a vital field in Natural Language Processing (NLP) that utilizes Machine Learning (ML) techniques to understand the complex emotional tones that are present in Arabic text. This survey discovers the use of ML in Arabic sentiment analysis, which aims to implement various methodologies for sentiment classification. The goal is to develop a robust model capable of translating nuanced sentiments expressed in different dialects and intricate linguistic structures. The methodology involves creating a labeled dataset, using preprocessing techniques, and utilizing machine learning algorithms such as linear classifiers and deep learning models. By extracting features, training the model, and evaluating its performance, the methodology improves the accuracy of sentiment evaluations. The results of this study highlight the effectiveness of machine learning models in accurately categorizing sentiments in Arabic text, as demonstrated by performance metrics such as precision, recall, and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Applying convolutional attention mechanisms and Human Memory Search for effective English-Urdu translation

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engineering Applications of Artificial Intelligence 2025年 155卷

作者： Jayasakthi Velmurugan, K. Faheem Nikhat, H. Suresh, K. Hemavathi, S. Kavitha, V. Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Vandalur Tamilnadu 600 048 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai603203 India

In recent decades, machine translation has become a prominent area of research, with the primary objective of overcoming language barriers. Early approaches primarily centered on word-for-word translation between source and target languages. However, there has been a shift towards data-driven models, such as neural machine translation and statistical methods, driven by advancements in Artificial Intelligence (AI), communication and computing technologies. This paper introduces a novel Convolutional Attention Mechanism-based Human Memory Search (CAM-HMS) algorithm for translating English into Urdu, to achieve high-quality and effective machine translation. The proposed model consists of several key phases, including pre-processing, sentence padding, word embedding, encoding, decoding, and target text generation. A new spider web-based search strategy is also incorporated to enhance the translation process in neural machine translation. The performance is evaluated using a UMC005 English-Urdu dataset, Parallel corpus for English & Urdu language, and the Roman Urdu to English Translation Dataset. Various automatic evaluation metrics such as the Bilingual Evaluation Understudy (BLEU), National institute of Standards and Technology (NIST), Word Error Rate (WER), Accuracy, Precision, Recall, and F-Measure are used to assess the model's efficiency, and its output is compared to that of Google Translate. The proposed model achieves an average BLEU score of 82.14 %, NIST of 79.51 %, WER of 2.77 %, Accuracy of 98.99 %, Precision of 98.95 %, Recall of 98.90 %, and F-Measure of 98.92 %, demonstrating its effectiveness in producing high-quality machine translation results. © 2025

关键词： Deep learning

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Inverse reinforcement learning with the average reward criterion 23

Inverse reinforcement learning with the average reward crite...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Feiyang Wu Jingyang Ke Anqi Wu School of Computational Science and Engineering College of Computing Georgia Institute of Technology Atlanta Georgia

We study the problem of Inverse Reinforcement Learning (IRL) with an average-reward criterion. The goal is to recover an unknown policy and a reward function when the agent only has samples of states and actions from an experienced agent. Previous IRL methods assume that the expert is trained in a discounted environment, and the discount factor is known. This work alleviates this assumption by proposing an average-reward framework with efficient learning algorithms. We develop novel stochastic first-order methods to solve the IRL problem under the average-reward setting, which requires solving an Average-reward Markov Decision Process (AMDP) as a subproblem. To solve the subproblem, we develop a Stochastic Policy Mirror Descent (SPMD) method under general state and action spaces that needs O(1/ε) steps of gradient computation. Equipped with SPMD, we propose the Inverse Policy Mirror Descent (IPMD) method for solving the IRL problem with a O(1/ε2) complexity. To the best of our knowledge, the aforementioned complexity results are new in IRL literature with the average reward criterion. Finally, we corroborate our analysis with numerical experiments using the MuJoCo benchmark and additional control tasks.

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Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity 8

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, R. Vinston Siddiqui, Shavej Ali Avilasha, B.G. Kannan, L. Mohana Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Deparment of Applied Science and Humanities Lucknow India Dayanandasagar College of Engineering Deparment of Mechanical Engineering Bangalore India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India

ISBN: (纸本)9798350376425

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments. © 2024 IEEE.

关键词： Energy utilization

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Priority-Based Uplink Raw Slot Utilization in the IEEE 802.11ah Networks

Priority-Based Uplink Raw Slot Utilization in the IEEE 802.1...

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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Dang, Duc Ngoc Minh Tran, Anh Khoa Pham, Nhat Truong Computing Fundamental Department FPT University Ho Chi Minh Viet Nam Department of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819987023

The IEEE 802.11ah standard allows an Access Point (AP) to connect up to 8192 stations at a transmission range of up to 1 km. The goal of the IEEE 802.11ah standard is to maintain wide connectivity and energy efficiency. Some stations are allocated to RAW slots, but they do not have uplink data packets to transmit results in low channel efficiency. The new MAC protocol (PUT-MAC) allows stations to use adjacent RAW slots in a priority manner to improve channel access usage efficiency. The stations use different Arbitration Inter Frame Space and contention window values to contend channel in the same RAW slot. The paper conducts simulations to compare the network performance of the PUT-MAC protocol with the IEEE 802.11ah. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency

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Learned multiphysics inversion with differentiable programming and machine learning

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Leading Edge 2023年第7期42卷 474-486页

作者： Louboutin, Mathias Yin, Ziyi Orozco, Rafael Grady, Thomas J. Siahkoohi, Ali Rizzuti, Gabrio Witte, Philipp A. Møyner, Olav Gorman, Gerard J. Herrmann, Felix J. Georgia Institute of Technology School of Earth and Atmospheric Sciences AtlantaGA United States Georgia Insitute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology College of Computing GA Atlanta United States Utrecht University Utrecht Netherlands Microsoft Corp. RedmondWA United States SINTEF Trondheim Norway Imperial College London Department of Earth Science and Engineering London United Kingdom

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning. © 2023 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See.

关键词： Multiphysics

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An Efficient Method for Seeds Classification and Quality Testing Using Fast R-CNN 5

An Efficient Method for Seeds Classification and Quality Tes...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Ashwini, S. Rao, B. Srinivasa Shirgire, Anil Kannan, K.R. Kumar, T Ch Anil Sivashankar, G. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Dr D y Patil Institute of Technology Maharashtra Pune India Hindusthan Institute of Technology Department of Ece Tamilnadu Coimbatore India Vignan's Foundation for Science Technology and Research Department of Mechanical Engineering Andhra Pradesh Guntur India Srm University Department of Computational Intelligence Tamilnadu Chennai India

ISBN: (纸本)9798350321425

Research into agriculture is gaining an increasing attention in recent years. The newest innovations use a variety of computational technology to make agricultural tasks easier. Seed quality is the most important of many factors that determine agricultural output. Further insights into quality manufacturing, seed quality control, and impurity discovery can be gained by proper seed classification. Seeds have traditionally been sorted into different categories based on their appearance (including their color, texture, and shape). This is often done by experts who must visually inspect each sample, which is a laborious and time-consuming process. Seed sorting is a labor-intensive process that can be greatly improved by automating the inspection process. This study presents a classification system for seeds using Fast RCNN, inspired by the success of computational intelligence methods in other image classification challenges. The suggested method includes five stages: wiener filter preprocessing;HE image enhancement;binarization;adaptive watershed segmentation;and Rapid R-CNN model training. The proposed approach outperforms other two models such as CNN and RNN. © 2023 IEEE.

关键词： Image enhancement

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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