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The Implementation of Octave-S in the Risk Management Measurement of Information System in the Department of Communication, Informatics, and Statistics 4th

The Implementation of Octave-S in the Risk Management Measur...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Sukardi, Oggsa Gaol, Ford Lumban Matsuo, Tokuro Master of Information System Management Binus Graduate Programs Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

An information system is an important part in an organization to support business processes and to achieve its vision and mission. The information system nowadays has been one of the assets that ought to be protected by an organization. The Department of Communication, Informatics, and Statistics has the information system supporting its various operational activities. In order to protect the information system, OCTAVE-S method as the management method is used. It is expected that the OCTAVE-S method can manage the risks to the information system as the asset by specifying the possible risks and developing countermeasures and mitigation plans for each of the risks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Dual Mode Host-Based and Cloud-Based Smartphone Intrusion Detection System 1

Dual Mode Host-Based and Cloud-Based Smartphone Intrusion De...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Gopala, B. Hanumanthappa, M. Kumar, Manish Bangalore University Department of Computer Science and Applications Bangalore India M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India

ISBN: (纸本)9781665450461

Smartphones are one of the most important devices in our daily life. It produces transfers and stores a significant amount of data about individuals such as banking transactions, authentication credentials, location details, call logs, etc. If the security of smartphones is compromised, it can severely affect the individual's life. As a result, the security system must be both efficient and dependable. We present a novel dual-mode Intrusion Detection System (IDS) that can operate both offline (without internet) and online (cloud mode). It collects application run-time data and analyses it locally on a smartphone or in the cloud, based on the user's configuration, to predict the app's behaviors and detect any intrusion. © 2022 IEEE.

关键词： Smartphones

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Equivalence Between 1-D Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions and Neural Networks with ReLU Activation 1

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Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 23

作者： Bede, Barnabas Kreinovich, Vladik Toth, Peter DigiPen Institute of Technology 9931 Wilows Rd. NE RedmondWA98012 United States Department of Computer Science University of Texas at El Paso 500 W University Ave El PasoTX79968 United States

ISBN: (数字)9783031467783

ISBN: (纸本)9783031467776

In the present paper we establish the equivalence between a neural network with one input and one hidden layer with ReLU activation and a Takagi-Sugeno fuzzy system with triangular membership functions. The results verify, both from theoretical and practical point of view, that we can convert a neural network into a TS fuzzy system and make it more explainable via this method. Possible extensions to multiple dimensions and multiple layers are also discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy systems

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Experimental Approach to Identify the Optimal Deep CNN Models to Early Detection of Glaucoma from Fundus CT-Scan Images

Experimental Approach to Identify the Optimal Deep CNN Model...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： S Sumitha S Gokila Master of Computer Applications Hindustan Institute of Technology & Science Chennai India Department of Computer Applications Hindustan Institute of Technology & Science Chennai India

Glaucoma is an eye disorder that causes the optic nerve to be affected in glaucoma cause of improved Intraocular fluid Pressure (IOP) in the eye. The only way to prevent blindness is to detect glaucoma early, which can only be done through regular eye examinations. Additionally, earlier diagnosis of glaucoma is challenging to determine and requires several costly vision examination protocols and skillful ophthalmologists. The proposed work’s goal is to detect Glaucoma using Convolution Neural Networks (CNNs) to speed up the diagnosis process for timely prevention. CNNs have proven effective in discovering even subtle changes produced by glaucoma. The experimental study using three different Deep Learning (DL) algorithms CNN, MobileNet, and ResNet are carried out to analyses the best model. All these models are pretrained on the image data generator for detecting and classifying glaucoma from the fundus photographs. A Gaussian filter is applied in order to remove noise from the images. Three different datasets ORIGA, SCES, and G1020, which contains images of both healthy and advanced glaucoma, are used to train the model. Also, these databases contain colored images of retina and retinal fundus images that are used for testing, and screening the glaucoma. The proposed method attained the best accuracy of 95% from the ResNet-50 algorithm. Also, each suggested model’s performance has been evaluated with the results of each CNN model. Additionally, a custom web application has been developed with CNN-based models to classify Glaucoma. The outcomes show that the CNN algorithms have the ability to develop into a time-saving and cost-efficient tool for analyzing populations intended to prevent irreparable blindness caused by glaucoma.

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

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AI-Driven Innovations in Modern Cloud Computing

arXiv

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arXiv 2024年

作者： Kumar, Animesh Master of Computer Science Illinois Institute of Technology IL United States Bachelor of Engineering in Computer Technology Nagpur University Maharashtra India

The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the digital ecosystem has brought in numerous innovations with integration of Artificial Intelligence commonly known as AI. This paper explores how AI and cloud computing intersect to deliver transformative capabilities for modernizing applications by providing services and infrastructure. Harnessing the combined potential of both AI & Cloud technologies, technology providers can now exploit intelligent resource management, predictive analytics, automated deployment & scaling with enhanced security leading to offering innovative solutions to their customers. Furthermore, by leveraging such technologies of cloud & AI businesses can reap rich rewards in the form of reducing operational costs and improving service delivery. This paper further addresses challenges associated such as data privacy concerns and how it can be mitigated with robust AI governance *** Codes 68Txx - Artificial Intelligence Copyright © 2024, The Authors. All rights reserved.

关键词： Cloud platforms

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Utilizing YoLov8 Architecture for Sharp-Edge Segmentation

Utilizing YoLov8 Architecture for Sharp-Edge Segmentation

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International Conference on Advances in Information technology (ICAIT)

作者： Laxmi Pujari Srushti Kashappa Dodawad Prasanna H. B Dept. of Master of Computer Applications KLE Institute of Technology Hubballi India Dept. of Computer Science KLE Institute of Technology Hubballi India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

In computer vision applications, such as object identification, scene comprehension, and robotic manipulation, accurate object segmentation is essential. In complicated contexts, traditional segmentation techniques frequently have trouble distinguishing sharp objects with accuracy. This work declare a novel approach to robust and exact object segmentation utilizing deep learning techniques built around the YOLOv8 architecture using an annotated collection of sharp object photos, the suggested methodology entails pre-processing the images with the CVAT(computer Vision Annotation Tool) tool to create binary masks. The YOLOv8 model is then trained on these annotated photos, and its behavior is compared with conventional segmentation techniques using a various image datasets. With its potential to advance the realm of computer vision, the YOLOv8 architecture presents a promising option for precise and effective sharp object segmentation in real-world scenarios.

关键词： Deep learning Image segmentation computer vision Accuracy Annotations Computational modeling computer architecture

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Virtual Mouse Using Face Gesture and NLP 2

Virtual Mouse Using Face Gesture and NLP

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Zad, Ajay Jagannatha, S. Kumar, Manish Ajitha, S. Prakash, B.R. M S Ramaiah Institute of Technology Department of Masters of Computer Applications Bangalore India East Point College of Engineering Department of Computer Applications Bangalore India M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

The mouse plays a very important role in interacting with laptop's and computer system, to perform necessary operations. In this paper, we are proposing a method to carry out mouse operations on the computer system without a physical mouse by utilizing voice commands and face-motion gestures. The system employs an internal webcam to detect facial gestures and an internal microphone to receive voice commands from the user. By employing a virtual mouse based on face gestures and natural language processing (NLP), we can accomplish all the fundamental functionalities of a mouse by issuing voice commands to the computer system. The system accepts these commands and executes corresponding mouse events such as "click,""right-click,""double-click,""scroll-up,""scroll-down,""minimize,"and "close."The mobility of the mouse cursor on the monitor is achieved through face motion gestures, enabling the cursor to move up, down, left, and right, respectively. © 2023 IEEE.

关键词： Natural language processing systems

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Artistic Style Transfer Using Generative Adversarial Networks: A Pix2Pix Implementation

Artistic Style Transfer Using Generative Adversarial Network...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Kumar, Dubasi Pavan Bhadula, Shuchi Al-Farouni, Mohammed Varshney, Neeraj Sharma, Vishal Saraswat, Manish National Institute Of Technology Integrated Master Of Science Mathematics Department Of Mathematics Patna India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This investigation explores the viability of four noticeable models, specifically Pix2Pix, Neural Style Transfer (NST), Fast Neural Style Transfer (FastNST), and CycleGAN, within the space of aesthetic style exchange. The think about envelops a fastidious assessment of these models, investigating their capabilities in generating outwardly engaging and elaborately reliable pictures. Through broad tests and quantitative evaluations, Pix2Pix developed as a strong choice, accomplishing a Crest Signal-to-Noise Ratio (PSNR) of 26.78 dB, a Basic Likeness List (SSIM) of 0.832, and a Fréchet Initiation Separate (FID) of 54.21. NST, exceeding expectations in style devotion, achieved a PSNR of 29.45 dB, an SSIM of 0.905, and an FID of 72.03. FastNST, known for its real-time preparation, illustrated an adjusted execution with a PSNR of 28.12 dB, an SSIM of 0.892, and an FID of 68.54. CycleGAN, outlined for unpaired picture interpretation, accomplished a PSNR of 27.65 dB, an SSIM of 0.874, and an FID of 63.72. The results give profitable experiences into the comparative qualities and weaknesses of these models, educating their appropriateness in different artistic assignments. © 2024 IEEE.

关键词： Generative adversarial networks

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