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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Chandel, Taniska Gangodkar, Ninad Gautam, Abhiraj Lohumi, Yogesh Ghazi, Mohd Rehan Goyal, Ashtha University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India

ISBN: (纸本)9798350375442

Due to the advancement of technology, information security and user privacy have become a critical issue resulting in the use of various encryption techniques. Especially with the increasing use of social networking, it has become necessary to secure multimedia messages and prevent data threats and data manipulation. Image Cryptography focuses on the encryption of images and securing them from unauthorized access and protect their confidentiality and integrity. In order to encrypt and decode pictures, this paper focuses on the Advanced Encryption Standard Algorithm (AES) which is a symmetric encryption algorithm. Furthermore, it reflects on the efficiency of the algorithm by determining the security of the algorithm using various parameters like entropy, NPCR, MSE and Histogram analysis. For better comparison all five of AES modes are presented in this paper;Electronic Codebook (ECB), Cipher Block Chaining (CBC), Cipher Feedback (CFB), Output Feedback (OFB), and Counter (CTR). Overall, it can be concluded that AES is a secure algorithm that can be used for safe data transfer. Furthermore, through the result of the parameters used to determine the security of each mode of the AES algorithm, it can further be concluded that CTR mode of the algorithm is the most secure. © 2024 IEEE.

关键词： Encryption algorithms

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Potato plant leaf diseases detection and identification using convolutional neural networks

Potato plant leaf diseases detection and identification usin...

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Gurusamy, Sriram Natarajan, B. Bhuvaneswari, R. Arvindhan, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Galgotias University India

ISBN: (纸本)9781032669663

Potato plant leaf diseases can cause significant crop losses and impact the quality of potato products. Early identification of these diseases can aid in timely intervention and treatment, minimizing the spread of diseases and reducing crop losses. In this work, we propose the incorporation of Convolutional Neural Networks (CNNs) for the automatic detection and identification of diseases present in potato plant leaves. We compiled a diverse dataset of images of potato plant leaves and used it to train and evaluate a CNN for the classification of potato leaf diseases. The results of the research work shows that the CNN was able to achieve high levels of precision score, F1 score, recall and accuracy over the training and testing splits of the dataset. Our findings demonstrate the potential of CNNs for the automatic identification of diseases present in potato plant leaves, and suggest that this approach could be applied to other types of plant diseases as well. The proposed method has the capability to improve crop management and disease control efforts by enabling the automatic and early detection of leaf diseases, which could have significant economic and environmental benefits. According to our result, the CNN offers a promising method for identifying and detecting leaf diseases on potato plants. This method may also be utilized to create an automated system for disease diagnostics in potato harvests. For the goal of identifying diseases in potato leaves, a Deep Learning (DL) base CNN model has been introduced in this research work. The proposed CNN technique has secured 98.1% accuracy rate for the Plant Village dataset, shows 3% improvement when compared to the existing methods. © 2024 The Author(s).

关键词： Convolutional neural networks

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Enabling Precise Diabetic Retinopathy Severity Prediction through Convolutional Neural Networks 10

Enabling Precise Diabetic Retinopathy Severity Prediction th...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Shruti, P. Reddy, Tanuja Konda Abhiram, S. Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Amrita Vishwa Vidyapeetam Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Diabetes retinopathy (DR) continues to be a major public health concern, needing efficient and reliable approaches for early identification and severity categorization. In this work, we used deep learning to improve the diagnosis procedure of diabetic retinopathy, which is blindness caused by diabetes. Deep learning algorithms, notably neural networks, have shown extraordinary performance in image analysis tasks, demonstrating their potential to transform medical picture classification. The dataset used was the APTOS-2019 dataset with 3930 retinal images and corresponding classes of severity of the disease. Using a custom-designed CNN architecture, we focused on diabetic retinopathy severity classification, a multiclass classification consisting of 5 classes. The suggested custom model outperformed existing pretrained models in terms of accuracy with training accuracy at a high 98% and testing accuracy of 93%, showing its efficacy in diabetic retinopathy severity categorization. Our discoveries revealed the potential of customized deep learning models to improve diagnostic speed and accuracy, providing a major advancement toward individualized and successful patient treatment in the field of managing diabetic retinopathy. © 2024 IEEE.

关键词： Convolutional neural networks

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An Automated Identification Approach for Partial Discharge Detection Using Density-Based Clustering Without User Inputs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 310-320页

作者： Rahman, Md Anisur Ang, Li-Minn Seng, Kah Phooi Charles Sturt University School of Computing Mathematics & Engineering BathurstNSW2795 Australia University of the Sunshine Coast School of Science and Engineering Sippy DownsQLD4556 Australia Xi'an Jiaotong Liverpool University School of AI & Advanced Computing Suzhou215123 China Queensland University of Technology School of Computer Science BrisbaneQLD4000 Australia

A recently published state-of-the-art density-based clustering technique called ICFSFDP for partial discharge (PD) detection requires various sensitive user-defined input parameters. This article presents a parameter-free clustering technique called PDAutoClust for PD detection. The PDAutoClust algorithm can produce high-quality clusters for PD datasets without requiring user-defined input parameters. PDAutoClust produces high-quality clustering results by utilizing a vein-based density clustering approach. The vein of a cluster is produced by using multivariate kernel density estimation and a unique neighborhood set. We compared the performance of PDAutoClust against ICFSFDP and seven other state-of-the-art density-based and non-density-based clustering techniques by using four PD datasets in terms of adjusted rand index, normalized mutual information, F1-score, and purity. Another contribution of the article is a novel merging technique used with PDAutoClust to merge small non-viable clusters that a clustering technique may produce. PDAutoClust produces the final clusters for a dataset by merging the non-viable clusters that a clustering technique may produce. We also evaluate the performance of PDAutoClust with merging technique versus PDAutoClust without merging technique using four datasets. Simulation results for PDAutoClust with the merging technique show good performance compared to ICFSFDP and seven other state-of-the-art clustering techniques. We also performed an ablation study to demonstrate the importance of the steps involved in PDAutoClust. © 2020 IEEE.

关键词： Merging

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DKWS: A Distributed System for Keyword Search on Massive Graphs (Extended Abstract) 40

DKWS: A Distributed System for Keyword Search on Massive Gra...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Jiang, Jiaxin Choi, Byron Huang, Xin Xu, Jianliang Bhowmick, Sourav S School of Computing National University of Singapore Singapore Hong Kong Baptist University Department of Computer Science Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Addressing the complexities of querying unstructured graphs such as knowledge graphs and social networks, this paper introduces D KWS, a novel distributed keyword search system. Leveraging a monotonic property, we ensure correct parallelization of our advanced keyword search algorithm, which incorporates tight pruning bounds and is divided into monotonic backward and forward search phases. The system is further augmented by the notify-push paradigm and the PINE programming model, facilitating asynchronous communication and preemptive searches to mitigate staleness in distributed environments. Extensive experiments on real-world datasets demonstrate DKWS's performance advantage, being up to two orders of magnitude faster and incurring 7.6 times lower communication costs than the existing systems. © 2024 IEEE.

关键词： Search engines

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Efficient allocation of independent gridlet on small, medium, and large grid

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Personal and Ubiquitous computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

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Emotion identification in human faces through ensemble of deep learning models

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9729-9752页

作者： Naveen Kmuar, M. Godfrey Winster, S. Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Computing Technologies Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. © 2023 - IOS Press. All rights reserved.

关键词： Emotion Recognition

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Geopolitical Risk Analysis and its Impact on Market Volatility 9

Geopolitical Risk Analysis and its Impact on Market Volatili...

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

作者： Rathinavel, Keshav Indira Kumar, A.K. Sahoo, Anshuman Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350394474

This study addresses the influence of geopolitical risks on financial markets, as noticed in recent events like the COVID-19 pandemic and the Russia-Ukraine war. This study showcases a model combining financial indices with the Geopolitical Risk Index (GPRI), employing Bi-LSTM networks with attention mechanisms to analyze the data. This approach captures the nuanced effects of geopolitical events on market volatility. Focused on the pandemic period, our model offers insights into market dynamics during times of geopolitical unrest and serves as a predictive tool for market behavior in response to global events. © 2024 IEEE.

关键词： Risk analysis

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Fortifying Web Applications: Advanced XSS and SQLi Payload Constructor for Enhanced Security 8th

Fortifying Web Applications: Advanced XSS and SQLi Payload C...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Ravindran, Rahulkrishnan Abhishek, S. Anjali, T. Shenoi, Ashwin Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India CRED Bangalore India

ISBN: (纸本)9789819712595

This SQL injection and cross-site scripting (XSS) extension is a Google chrome-based software program that is designed to detect and prevent SQLi and XSS attacks on web applications. These types of injections or attacks are among the most common and dangerous threats to web applications, as they can allow hackers a loophole to access and avail sensitive data or take control of a website, SQLi, and XSS extension typically works by analyzing incoming data and queries for suspicious patterns and characters, such as SQL commands or HTML code. An SQL based injection and a cross-site scripting XSS extension is an essential tool for developers, security analysts, and administrators who want to ensure the security and integrity of their web applications, prevent data breaches, and protect their users from potential harm. Web applications require rigorous security measures to protect them from various malicious attacks or injections and prevent data breaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensitive data

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