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

作者： Kiranagi, Soumya S Patil, Savitha Sharnbasva University Department of Artificial Intelligence and Data Science Kalaburagi India Sharnbasva University Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350372892

there are hundreds of kinds of plants on Earth, and many of them have medicinal or curative properties. Approximately 80% of the global population continues to rely on traditional medicine. In Ayurveda, the use of herbs as medicine dates back to Vedic times. Recognizable identification of the medicinal plants is crucial for the efficacy of ayurvedic, social, and homoeopathic treatments. For the treatment of skin conditions, lung infections, asthma, dyspepsia, and other ailments, many people throughout the globe rely on medicinal plants. One of the most important steps in making an Ayurvedic medicine is finding the right medicinal leaf. Misclassification or incorrect identification of medicinal leaves could affect Ayurveda's efficacy. Form, colour, and texture of leaves are the most basic characteristics that must be present to identify a medicinal plant. While the form and venation pattern of individual leaves might vary greatly, members of the same class of leaves tend to have commonalities. In light of this need, here is a model that makes use of an SVM Machine Learning Algorithm to reliably identify and categories medicinal leaves. © 2024 IEEE.

关键词： Plant diseases

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Efficient Image Captioning Based on Vision Transformer Models

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computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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A Robust data Security Model Development to Maintain the Integrity of Information Using Artificial Intelligence and Cyber Laws

A Robust Data Security Model Development to Maintain the Int...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, G. Muthu Hemanand, D. Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,

关键词： Risk assessment

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REVISITING ROBUSTNESS IN GRAPH MACHINE LEARNING 11

REVISITING ROBUSTNESS IN GRAPH MACHINE LEARNING

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11th International Conference on Learning Representations, ICLR 2023

作者： Gosch, Lukas Sturm, Daniel Geisler, Simon Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. However, because manual inspection of a graph is difficult, it is unclear if the studied perturbations always preserve a core assumption of adversarial examples: that of unchanged semantic content. To address this problem, we introduce a more principled notion of an adversarial graph, which is aware of semantic content change. Using Contextual Stochastic Block Models (CSBMs) and real-world graphs, our results uncover: i) for a majority of nodes the prevalent perturbation models include a large fraction of perturbed graphs violating the unchanged semantics assumption;ii) surprisingly, all assessed GNNs show over-robustness - that is robustness beyond the point of semantic change. We find this to be a complementary phenomenon to adversarial examples and show that including the label-structure of the training graph into the inference process of GNNs significantly reduces over-robustness, while having a positive effect on test accuracy and adversarial robustness. Theoretically, leveraging our new semantics-aware notion of robustness, we prove that there is no robustness-accuracy tradeoff for inductively classifying a newly added node. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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SafeCultural: A dataset for Evaluating Safety and Cultural Sensitivity in Large Language Models 8

SafeCultural: A Dataset for Evaluating Safety and Cultural S...

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8th International Conference on Information Technology, InCIT 2024

作者： Vongpradit, Pawat Imsombut, Aurawan Kongyoung, Sarawoot Damrongrat, Chaianun Phaholphinyo, Sitthaa Tanawong, Tanik Kasetsart University Department of Computer Science Bangkok Thailand Pathum Thani Thailand Chiangmai University Department of Data Science Chiangmai Thailand

ISBN: (纸本)9798350366303

The increasing use of Large Language Models (LLMs) in daily life raises important questions about ensuring their trustworthiness. While existing datasets are widely used to evaluate issues like safety and hallucinations, they often overlook important factors like social norms and cultural sensitivity. This paper introduces a dataset that incorporates regional cultural aspects, allowing LLMs to be more adaptable across diverse contexts. This dataset covers aspects of both safety and cultural sensitivity. The safety component includes seven dimensions: Unlawful Conduct, Toxicity, Violence, Privacy Violations, Harms to Minors, Adult Content, and Mental Health Issues. Cultural sensitivity is divided into three categories: Beliefs, Behaviors, and Safety. We tested across seven LLMs, the dataset aims to help models overcome cultural barriers, fostering user-friendly and culturally aware development. Furthermore, we provide guidelines for creating and evaluating culturally sensitive prompts to ensure they meet safety and cultural standards. The finding of this paper may help the development of more adaptable, culturally aware, and trustworthy LLMs for daily use. © 2024 IEEE.

关键词： Cultural LLMS Safety Trustworthiness

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Kathakali Mudras: An AI-Enhanced Predictive Approach 11

Kathakali Mudras: An AI-Enhanced Predictive Approach

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

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Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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Effectiveness of K-means Clustering-Based Outlier Handling Techniques in Colonic Condition Endoscopy Image Classification Using CNN 13

Effectiveness of K-means Clustering-Based Outlier Handling T...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Athiyah, Ummi Rustad, Supriadi Soeleman, Moch Arief Muljono Akrom, Muhamad Dian Nuswantoro University Faculty of Computer Science Semarang Indonesia Telkom University Department of Data Science Purwokerto Indonesia

ISBN: (纸本)9798350368086

Colorectal cancer is one of the leading causes of cancer death worldwide, so accurate early detection is needed. Endoscopic imaging technology plays a vital role in diagnosis, but the presence of outliers in endoscopic images can significantly affect the accuracy of the classification model. This study addresses this challenge by applying Histogram of Oriented Gradients (HOG) for feature extraction and K-means Clustering for outlier detection and removal. A dataset of colon endoscopic images was obtained from the public database Gastrovision, which was divided into two classes: normal and abnormal conditions. This study applied a two-step modeling process, first with the original dataset and then with the cleaned dataset after outlier removal. The results showcased a significant improvement in model performance after outlier handling, with accuracy increasing from 61% to 85%. The Area Under Curve (AUC) of the ROC curve also increased from 0.84 to 0.92, indicating a better model ability to distinguish between normal and abnormal conditions. These findings emphasize the importance of data quality and effective outlier management in training a machine-learning model for colon cancer endoscopic image conditions. © 2024 IEEE.

关键词： Diseases

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SAMPLING-FREE INFERENCE FOR AB-INITIO POTENTIAL ENERGY SURFACE NETWORKS 11

SAMPLING-FREE INFERENCE FOR AB-INITIO POTENTIAL ENERGY SURFA...

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11th International Conference on Learning Representations, ICLR 2023

作者： Gao, Nicholas Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function. At inference time, the surrogate avoids expensive Monte-Carlo integration by directly estimating the energy, accelerating the process from hours to milliseconds. In this way, we can accurately model high-resolution multi-dimensional energy surfaces for larger systems that previously were unobtainable via neural wave functions. Finally, we explore an additional inductive bias by introducing physically-motivated restricted neural wave function models. We implement such a function with several additional improvements in the new PESNet++ model. In our experimental evaluation, PlaNet accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74 %. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Wave functions

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