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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

computer Systems science & Engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

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SN computer science 2023年第3期4卷 1-13页

作者： Vamsi, Bandi Mahanty, Mohan Doppala, Bhanu Prakash Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Data Analytics Generation Australia 88 Phillip St Sydney 2000 NSW Australia

Nowadays identification of bird species is the main problem for ornithologists and ecologists. The increase in volumes of data labelling, still there is a scope to implement an automated bird classification through deep learning techniques. Identifying the nature of a bird and its flock aids in analyzing different other species in the natural environment. This is considered as the main role in the problem of ecology. The species of birds act accordingly to the changes in ecology and this became a challenging task to identify the growth of the bird species with advanced technologies. In this work, our aim is to classify the different bird species based on voice recordings across various locations using autoencoders and decoders. This proposed work is a four-step process: data preprocessing of bird acoustic, feature extraction using a deep network, classification by random forest (RF) and support vector machine (SVM) and identifying the pitch intensities of a bird in low, medium and high ranges. In data pre-processing, the recorded audio is converted into spectrograms through frequency waves. The Mel Spectrogram filter is used to remove the noise from the recorded bird audio. For extracting the features in the spectrogram image, encoders and decoder convolutions are used to construct a fully connected vector. These features are given to RF and SVM for classifying the bird species to obtain standard prediction accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bird classification CNN Decoder Encoder Mel spectrum

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Anthracnose Detection and Classification in Chilli Leaves Using Mobile Net: UAV Images 9

Anthracnose Detection and Classification in Chilli Leaves Us...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bandaru, Prudhvi Narayana Anuradha, G. Bora, Himavarshini Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence And Data Science Andra Pradesh India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science And Engineering Andra Pradesh India

ISBN: (纸本)9798350394474

The health of a plant has remained an indispensable factor for sustainable and enriching crop production to fulfill the Nation's food demands. A degradation in the health of the crop leads to low crop yields. Out of a wide range of factors affecting the health of the crop the most concerning factor remains the plant diseases and pests. More than 50% of India's population is engaged in agricultural practices. In the agricultural sector, India is among the top 3 exporters of Dried Chilli. However, Chilli exports have faced a descent in recent years owing to crop yield loss due to diseases caused by fungi or pests. Anthracnose is the most prevalent disease in Chilli plants. The process of anthracnose disease detection in Chilli crops is a tedious process due to a lack of laboratory facilities and expertise. Such a situation demands for development of an 'automated detection of anthracnose disease in Chilli plant' system which puts the process of early and precise detection of plant disease at ease. computer Vision advancements make it possible for automatic disease detection using visible symptoms on the crop. Keeping this in view, this paper proposes an automated model for the detection of anthracnose in the Chilli plant using a Mobile Net-based convolutional neural network (CNN). Over the last few years researchers such as Boulent et al. stated in their article that the integration of aerial image data and AI-based methods holds promise for offering an effective, high-throughput technique for detecting plant diseases in real-world conditions. Hence the dataset utilized for our model is aerial images using UAV (unnamed aerial vehicle). The proposed system for anthracnose detection in Chilli developed using Mobile Net achieved an accuracy of 99.6%. © 2024 IEEE.

关键词： Crops

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Machine Learning Approach for Onion Leaf Disease Detection: A Case Study in Maharashtra, India 15

Machine Learning Approach for Onion Leaf Disease Detection: ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Amondkar, Madhavi Bhoite, Sachin Vidyalankar School of Information Technology Department of Information Technology & Data Science Mumbai India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science & Applications Pune India

ISBN: (纸本)9798331300579

Maharashtra is a remarkable state in India that is a major producer of onions, accounting for a sizeable portion of the country's total production. An ideal climate for onion growth is provided in areas like Nashik, Ahmednagar, and Pune Despite its importance, growing onions is filled with difficulties, the most significant of which being the frequency of illnesses that damage onion crops. Serious risks to onion production include diseases which are downy mildew, purple blotch, bacterial blight, white rot & basal rot which result in output losses and financial hardship for farmers. Machine learning models have become extremely effective tools for managing and forecasting agricultural crop diseases such as tomato, corn, grape etc. While nearly all crop leaf diseases may be predicted by these algorithms, very little or none has been done for onions in Maharashtra. There is almost no research done focusing on the onion disease found in different parts of Maharashtra. The purpose of the study is to assist farmers in Maharashtra in recognizing various diseases during the cultivation of onions by utilizing a machine learning model that combines CNN with image classification. Using a dataset of plant leaf diseases, this model ran with 95% accuracy. Additionally, given that the suggested model may be trained on any kind of leaf disease picture dataset and assist in disease type prediction, it can be suggested to work on an onion disease dataset as well. © Grenze Scientific Society, 2024.

关键词： basal rot CNN leaf disease machine learning Maharashtra onion disease purple blotch

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VulnFusion: Exploiting Multimodal Representations for Advanced Smart Contract Vulnerability Detection 6

VulnFusion: Exploiting Multimodal Representations for Advanc...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Upadhya, Jiblal Sainju, Arpan Upadhyay, Kritagya Poudel, Samir Hasan, Md Nahid Poudel, Khem Ranganathan, Jaishree Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9798350351538

In the fast-evolving domain of blockchain technology, smart contracts have become instrumental, facilitating billions of dollars in transactions daily. However, the increasing prevalence of smart contract vulnerabilities poses significant risks, potentially undermining trust in this innovative technology. To address these security challenges, our research introduces a novel approach for detecting and mitigating vulnerabilities through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This multimodal analysis employs a Transformer architecture, VulnFusion, which uniquely integrates these modalities to provide a deeper understanding of both the semantic and syntactical nuances of smart contracts. We propose an interleaving-based data fusion technique that outperforms traditional fusion methods such as concatenation and cross-attention. Experimental evaluations demonstrate that our interleaving-based fusion technique achieves an impressive average F1 score of 89% across source code, opcode, and intermediate representations, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Smart contract

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Advanced Remote Sensing Techniques for Underwater Fin Whale Detection in the Indian Ocean 16

Advanced Remote Sensing Techniques for Underwater Fin Whale ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sanikommu, Vasavi Sura, Akshaya Chimirala, Pranavi Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Andhra Pradesh India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331505264

Accurate detection and tracking of cetaceans in the Indian Ocean remain some of the salient issues for marine conservation despite the abundant challenges that accompany the environment underwater. Such realistic avenues can go a long way in bridging the said challenges with advanced remote sensing technologies. In this study, hyperspectral imaging, satellite imagery, and thermal imaging combine to form an integrated system in whale detection. The hyperspectral imaging of whale skin provides spectral reflectance characteristics for detection under different oceanic conditions. Along with these, satellite imaging helps determine subsurface features while thermal imaging determines the thermal characteristics of whales. Machine learning algorithms analyze the data set amalgamation to enhance detection accuracy as well as reliability. The overall system effectively detected whales at the various depths and environmental conditions associated with the Indian Ocean. Hyperspectral imaging was sensitive to the turbidity levels of water and target reflectance, while the coastal blue band showed subsurface features of a whale. Thermal imaging also increased detection abilities in dim light as well. This three-in-one platform approached better than other methods with an accuracy of 0.96 and reliability by real-time whale monitoring. © 2024 IEEE.

关键词： Hyperspectral imaging

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Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

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computers, Materials & Continua 2022年第9期72卷 4755-4769页

作者： Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Shahbaz Ahmad Yazeed Ghadi Zubair nawaz Department of Computer Science National Textile UniversityFaisalabadPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia Department of Software Engineering and Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates Department of Data Science University of the PunjabPakistan

Social media provide digitally interactional technologies to facilitate information sharing and exchanging ***,in case of disasters,a massive corpus is placed on platforms such as *** accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is *** approaches have been developed to address this kind of *** recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic ***,these approaches suffer from limitations as they are dataset-dependent and not *** this paper,we proposed a method to identify eyewitnesses from *** experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the *** each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this *** algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words ***,keyword matching for each feature in tweets is *** a feature exists in a tweet,it is termed as 1;otherwise,***,for 13 features,we created a file that reflects features in each *** classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were *** approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest *** results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure *** the same time,the proposed approach gained a 0.88 value of *** results were comparable as the proposed approach is not ***,it can be used for the identification of eyewitness accounts.

关键词： Word dictionary social media eyewitness identification disasters

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LDA Topic Modeling for Bioinformatics Terms in arXiv Documents

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Procedia computer science 2024年 245卷 229-238页

作者： Andrea Stevens Karnyoto Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "computer science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology; thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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ROBUST ADVERSARIAL REINFORCEMENT LEARNING VIA BOUNDED RATIONALITY CURRICULA 12

ROBUST ADVERSARIAL REINFORCEMENT LEARNING VIA BOUNDED RATION...

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12th International Conference on Learning Representations, ICLR 2024

作者： Reddi, Aryaman Tölle, Maximilian Peters, Jan Chalvatzaki, Georgia D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Systems AI for Robot Learning Germany Center for Cognitive Science TU Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i.e., rational strategy, corresponds to a Nash equilibrium. However, finding Nash equilibria requires facing complex saddle point optimization problems, which can be prohibitive to solve, especially for high-dimensional control. In this paper, we propose a novel approach for adversarial RL based on entropy regularization to ease the complexity of the saddle point optimization problem. We show that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality, i.e., agents sometimes play random actions instead of optimal ones. Crucially, the connection between the entropy-regularized objective and QRE enables free modulation of the rationality of the agents by simply tuning the temperature coefficient. We leverage this insight to propose our novel algorithm, Quantal Adversarial RL (QARL), which gradually increases the rationality of the adversary in a curriculum fashion until it is fully rational, easing the complexity of the optimization problem while retaining robustness. We provide extensive evidence of QARL outperforming RARL and recent baselines across several MuJoCo locomotion and navigation problems in overall performance and robustness. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Implementation of Lightweight PRNG on PLC for Industrial Control Systems 3

Implementation of Lightweight PRNG on PLC for Industrial Con...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Sama, Andi Gaol, Ford Lumban Harso Supangkat, Suhono Ranti, Benny Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350394559

The Information Technology/Operational Technology convergence towards Industry 4.0 opens the opportunity to leverage recent advancements in Information Technology for Operational Technology, such as Cloud, Internet of Things, and Artificial Intelligence. Meanwhile, cyber-attacks are increasing for Operational Technology. The security aspect in Operational Technology systems has traditionally been a low priority, in contrast with speed. This introduces challenges as Industrial Control Systems such as programmable Logic Controller in Operational Technology have been traditionally optimized for speed rather than security due to limited computing power. As computations need to be as efficient as possible, security in Operational Technology has yet to be managed as robustly as in Information Technology. Operational Technology communication protocols for securing data transfer, for example, have no or just a few security capabilities, even for basic authentication and encryption. Common security algorithms rely on random numbers. However, Random Number Generator is not usually part of standard functions in programmable Logic Controllers, the core control component in Industrial Control Systems. In Industrial Control Systems, the Random Number Generator is mostly implemented as a software-based Pseudo Random Number Generator. This paper shows how to apply a Pseudo Random Number Generator in a Siemens Compact PLC S7-1200 using a modified lightweight XORshift algorithm. The XORshift algorithm can generate better randomness than the system's clock-based implementation in the Siemens Library of Generic Functions. © 2023 IEEE.

关键词： Internet of things

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