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检索条件"机构=Data Science Program Computer Science Department School of Computer Science"

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File Encryption Application using Menezes-Vanstone Elliptic Curve Cryptography Based on Python 8

File Encryption Application using Menezes-Vanstone Elliptic ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Heru, Edward Keitaro Faisal Pranoto, Hady Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Humans have been affected by the advent of information technology in many different ways. Information can be sent freely as a result of the conveniences offered. However, unauthorized parties may take advantage of this to retrieve and utilize the information without the owner's consent. In this paper, the authors perform text, image, audio, and video data security using a modified version of the Menezes-Vanstone elliptic curve cryptography algorithm. The study implements Menezes-Vanstone Elliptic Curve Cryptography in a program for file text, image, audio, and video encryption and decryption, while also providing performance data for both our program and alternative approaches for comparison. The outcome demonstrates that the encrypted file size is smaller than alternative approaches. Nevertheless, our program might benefit from some enhancements in terms of encryption and decryption times. © 2023 The Authors. Published by Elsevier B.V.

关键词： Elliptic curve cryptography(ECC) file encryption Menezes-Vanstone ECC

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Design of Company Financial Health Prediction System with Extra Trees Method Based on Fundamental Analysis 12

Design of Company Financial Health Prediction System with Ex...

引用

12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Novaldo, Wilson Gunawan, Alexander A S Ibrahim, Muhammad Amien Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350341102

The financial health of a company can be understood as the company's ability to obtain funds, fulfill its financial obligations, and generate sufficient profits to maintain its business operations. Analysis of company health can be done by fundamental analysis. This research deliberately uses machine learning to analyze the company's fundamental data to avoid traditional financial analysis methods, which often suffer from subjectivity and limitations in handling complex problems. This research uses fundamental indicator data from KOMPAS100. The results of this study compare several classification methods and the Extra Trees model is the best in terms of performance. The performance is in the form of 94% accuracy, 96% precision, 92% recall, and 94% F1-score. © 2023 IEEE.

关键词： Machine learning

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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Face-based age and gender classification using deep learning model 2

Face-based age and gender classification using deep learning...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Kumar, Rajiv Singh, Kuldeep Mahato, Dharmendra Prasad Gupta, Umesh School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science University of Delhi Delhi110-007 India Department of Computer Science and Engineering National Institute of Technology Hamirpur India

The classification of the age and gender of face image data is a significant study subject with numerous applications. Convolution Neural Networks (CNNs) model is the effective technique to classify data, particularly for unrestricted real-world faces. In this current study, we introduce an innovative CNN-based method specifically designed for the extraction of distinguishing features from real-world facial data. These extracted features play a crucial role in the categorization and classification of images, with a focus on age and gender. We perform pre-training on facial images using the IMDb-WIKI dataset and subsequently classify both age and gender using unrestricted real-life facial data. To avoid over-fitting and enable the proposed model to generalise on the test images, we additionally chose a drop-out and data augmentation regularisation strategy. We demonstrate that optimised training hyper-parameters and well-planned network architecture produce better outcomes. The proposed technique achieved the 84.8 percent age group classification accuracy rate while the compared CNN2ELM model achieve the 52.3 percent accuracy rate. The suggested model demonstrates significantly better performance when contrast on the CNN2ELM model on the OIU-Adience dataset, especially in the context of age group classification and overall accuracy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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Implementation of Dijkstra Algorithm in Vehicle Routing to Improve Traffic Issues in Urban Areas 3

Implementation of Dijkstra Algorithm in Vehicle Routing to I...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Utomo, Derrick Daniel Aurelia, Maria Tanasia, Steffi Maria Nurhasanah Handoyo, Alif Tri Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Many studies have been conducted on Dijkstra Algorithm, and one of the implementations of those studies includes vehicle path selection or routing. A traditional Dijkstra Algorithm can compute simple path routing problems;however, it is not suitable for complex situations. This paper proposes an improved method of Dijkstra Algorithm that analyzes influencing factors, such as: traffic congestion, travel time reliability, weight of each equivalent path and then produces the best path according to each situation. This new method is a beneficial improvement in efficiency for shortest path search algorithms and reduces time complexity, and so, this paper applies the Dijkstra Algorithm to analyze travel routes between Bung Karno Stadium and Merlynn Park. A node diagram is created based on cost tables and transformed into cost metrics. The goal is to find the shortest route while considering traffic data and analyzing differences with road distances. The cost metrics are inserted into the Dijkstra Algorithm, resulting in minimum costs for each vertex from the source node. Two optimal travel paths are determined based on different datasets, highlighting the impact of traffic data. Further evaluations consider travel times and costs with and without traffic. Incorporating predicted traffic times into the algorithm improves travel cost predictions. This research concludes that the Dijkstra Algorithm is effective in solving the shortest route problem and can be applied to urban traffic networks with the inclusion of traffic predictions, addressing drawbacks and enhancing route planning effectiveness. © 2023 IEEE.

关键词： Traffic congestion

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Feature Selection using Grey Wolf Optimization Algorithm on Light Gradient Boosting Machine

Feature Selection using Grey Wolf Optimization Algorithm on ...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Kumiadi, Felix Indra Wulandari, Ajeng Arifin, Samsul Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

high dimensional data provide a major problem to supervised learning. In identifying high dimensional data, the learning models usually exhibit overfitting and become less understandable. One way to find the ideal features on high-dimensional data implemented feature selection on dataset Feature selection is one of the crucial aspects on data preprocessing step. Several algorithms for feature selection were proposed over the decades such as wrapper method, filter, and embedded method. In this research, we implemented wrapper method with Grey Wolf Optimization. We implemented Grey Wolf Optimization on wrapper method because the algorithm is efficient, simple and had lower computational time. We are also compared Grey Wolf Optimization to other meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithms. The result showed the GWO provide better computational time with the average time from four different dataset was 6.1125s. The accuracy result showed the GWO performed better on Ionosphere dataset. © 2023 IEEE.

关键词： Feature Selection

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Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective 38

Stabilize the Latent Space for Image Autoregressive Modeling...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhu, Yongxin Li, Bocheng Zhang, Hang Li, Xin Xu, Linli Bing, Lidong School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Zhejiang University China

Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encode pixels into a more compact latent space and learn the data distribution in the latent space instead of directly from pixels. However, this practice raises a pertinent question: Is it truly the optimal choice? In response, we begin with an intriguing observation: despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. This finding contrasts sharply with the field of NLP, where the autoregressive model GPT has established a commanding presence. To address this discrepancy, we introduce a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling by applying K-Means on the latent features of self-supervised learning models. Experimental results show that image autoregressive modeling with our tokenizer (DiGIT) benefits both image understanding and image generation with the next token prediction principle, which is inherently straightforward for GPT models but challenging for other generative models. Remarkably, for the first time, a GPT-style autoregressive model for images outperforms LDMs, which also exhibits substantial improvement akin to GPT when scaling up model size. Our findings underscore the potential of an optimized latent space and the integration of discrete tokenization in advancing the capabilities of image generative models. The code is available at https://***/DAMO-NLP-SG/DiGIT. © 2024 Neural information processing systems foundation. All rights reserved.

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Prediction of Heart Disease using Machine Learning Technique 1

Prediction of Heart Disease using Machine Learning Technique

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

作者： Mall, Shachi Singh, Jitendra Nath Malik, Anshika Mahur, Lakshay Mundher Adnan, Myasar Galgotias University School of Computer Science Engineering Greater Noida India Galgotias University School of Computer Science U.P Gr. Noida India Raj Kumar Goel Institute of Technology Department of Computer Science U.P Ghaziabad India The Islamic University Department of Computer Science Najaf Iraq

ISBN: (纸本)9798350356816

heart disease is a leading global cause of death, and predicting it is complex, requiring advanced expertise beyond doctors' ease. The medical environment remains "Information rich"and "Information poor". There is a lot of medical information available on the internet. However, there are no good analytical tools for discovering relationships and patterns in the data. Automated diagnostic systems offer greater efficiency and cost-effectiveness. This web application uses Cleveland Medical Research Foundation and Kaggle heart disease data to predict the likelihood of disease occurrence. The goal is to apply knowledge mining techniques to the dataset to extract hidden patterns important to heart disease and predict whether a patient has heart disease where heart disease is dedicated to something. Modern technology struggles to process and analyze the massive data volumes required for accurate heart disease prediction, highlighting the need for advanced tools to handle such complex healthcare challenges. Our goal is to find suitable machine learning methods with high performance and accuracy in predicting heart disease. data mining enables the discovery of hidden patterns and relationships in large datasets by combining statistical analysis, machine learning, and advanced database techniques for insightful and efficient data interpretation. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Performance Analysis of Javascript Frontend Web Frameworks 3

Comparative Performance Analysis of Javascript Frontend Web ...

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Tong, Jason Jikson, Ricky Rivaldo Gunawan, Alexander Agung Santoso School of Computer Science Bina Nusantara University Mathematic Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

One of the most popular tools today for building engaging, robust, and easy to manage websites is the JavaScript programming language. Over the past 10 years, numerous front-end frameworks have been developed on top of JavaScript, making it simpler to develop, design, and maintain interactive websites. Currently, the most popular JavaScript front-end frameworks are *** and ***. *** is a relatively new framework that is also catching developers' attention and growing in popularity. This paper assesses the performance of these prominent front-end JavaScript frameworks, analyzing aspects like browser performance, speed index, interactive time, and the efficiency of specific DOM operations such as adding, editing, and removing elements. Using benchmark applications tailored for each framework, we conducted extensive testing to gauge their performance across different DOMs, leveraging technology-based data. Finally, our study of three front-end frameworks concluded that *** offers superior performance, ***'s focus on JavaScript dynamics leads to latency, and *** exhibits average speed, particularly in 'to-do list' and 'image gallery' project contexts. © 2023 IEEE.

关键词： Websites

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A Blockchain-Based Architecture for Interoperable Healthcare data Exchange

A Blockchain-Based Architecture for Interoperable Healthcare...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sahi, Niranjan Liang, Anda McHale, Sean Van Devanter, Wyn Zhang, Peng Vanderbilt University Department of Computer Science NashvilleTN United States Vanderbilt University Department of Computer Science and Data Science Institute NashvilleTN United States

ISBN: (纸本)9798350337488

Patients and healthcare providers often face challenges when dealing with creating, reading, updating, sharing, and deleting operations on individual healthcare data due to incompatible electronic medical record systems and the inability to provide necessary data. These challenges can drastically prolong both a patient's diagnosis time and the time until the operation. There are many informational discrepancies when attempting to provide adequate healthcare background - especially with new providers when a full healthcare history is required. To aid in increasing interoperability, we propose a decentralized data-sharing approach. While current centralized solutions own patients' healthcare data, we seek to put data back in the hands of the patients using blockchain as a decentralized database. In essence, our data-sharing architecture and set of rules leverage a permissioned blockchain network, Hyperledger Fabric, to give each patient on the network the ability to own and control the flow of their healthcare data. © 2023 IEEE.

关键词： data sharing healthcare interoperability multisignature transactions permissioned blockchain

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