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检索条件"机构=Computer Engineering and Software Engineering Dept."

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Design and Optimization of an IR-Based Automation System with Telegram Bot Integration for Remote Device Control 4

Design and Optimization of an IR-Based Automation System wit...

引用

4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Adnan Sharar Oyon, Md. Shihab Himel, Abid Hasan Mredul, Khaliduzzaman Ahmed, Abir Ali, M Tanseer Hossainl, Chowdhury Akram Rahman, Md. Abdur Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Dept. of Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

Automation is advancing rapidly, with infrared (IR) communication being a key method for controlling electronic devices. Despite this, challenges remain in optimizing IR controllers for consistent performance across varying angles and task loads. A 3D design and a robust algorithm have been developed to enhance the system's accuracy, while security is ensured by matching a unique TOKEN with the Telegram bot for each session. Error rates were analyzed across different angles, with specific zones determined based on performance. Minimal error rates defined the working zone, while higher rates indicated critical and invalid zones. Error rates increased as the transmitter moved further from the optimal angles, such as a 0-6 % error rate when operating within a 0° - 5° range. Reaction time was calculated across multiple tasks, showing an average of 5 seconds per command. Mismatch rates, observed during higher task loads, rose sharply when handling many tasks. For instance, the mismatch rate was 0 % for 12 tasks but 20 % when executing 20 tasks. Performance evaluations demonstrated optimal IR signal strength for reliable control within a distance of 10 to 15 inches, with diminishing effectiveness beyond 20 inches. These findings provide a foundation for developing more reliable and efficient IR control systems for practical applications. © 2025 IEEE.

关键词： Bot (Internet)

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CADS: Cyber-Attack Detection System Using Decision Tree for Automotive Cyber-Physical Systems 4

CADS: Cyber-Attack Detection System Using Decision Tree for ...

引用

4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Park, Seo-Hee Baek, Youngmi Dept. of Computer Software Engineering Changshin University Changwon-si Korea Republic of

ISBN: (纸本)9798350369694

Modern vehicles are equipped with a large number of electronic control units (ECUs) to provide more convenient and safe services for drivers. These ECUs are required to share the various control data for driving using the CAN (Controller Area Network) protocol that is one of the communication protocols for in-vehicle networks. Recently, as vehicles become capable of real-time, two-way communication with various elements on the road, modern vehicles faces a wide range of security issues. The in-vehicle networks are already suffered from the cyber-attacks exploiting newly exposed security vulnerabilities. However, it cannot guarantee the safety and security of the vehicles from as the CAN is designed without considering security. Therefore, this paper proposes a machine learning-based cyber-attack detection system (CADS) in CAN to ensure driving safety. This CADS learns the data pattern of the CAN frames generated while the driving and is able to distinguish between regular messages and cyber-attack messages. The performance of the fully trained detection model of the CADS is evaluated using a vast number of testing dataset. The results show that our CADS in terms of accuracy, precision, recall, and F1 score has 100% of the detection rate for regular messages, replay attacks, brute-force attacks except for modification messages. © 2023 IEEE.

关键词： Cyber Physical System

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A Double MCDM-GIS Analysis for Solar Power Plant Site Selections in Disaster-Prone Area: A case study of Hawkes Bay

A Double MCDM-GIS Analysis for Solar Power Plant Site Select...

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2023 International Conference on Power and Renewable Energy engineering, PREE 2023

作者： Rahayani, Rizki Dian Nair, Nirmal-Kumar C University of Auckland Computer and Software Engineering Dept. Electrical Auckland New Zealand

ISBN: (纸本)9798350321906

Selecting an appropriate site for solar power plants is a critical long-term investment decision to maximize solar energy generation while minimizing investment and operational costs and ensuring environmental sustainability. While solar irradiance has traditionally been the primary factor in site selection, accounting for disaster risk assessment is becoming increasingly crucial, particularly in countries prone to natural disasters. Hawkes Bay, New Zealand, holds great promise as a region with high solar potential. Yet, it is susceptible to natural disasters like earthquakes, landslides, and floods. This research aims to identify the most suitable solar farm location in Hawkes Bay by integrating estimated solar energy production with disaster risk assessment. The study employs a two-stages multi-criteria decision-making (MCDM) approach, combining data envelopment analysis (DEA) and the analytical hierarchy process (AHP), alongside the utilization of geographical information system (GIS) technology. The finding, among 15 candidate locations, the area in a 5 km radius of Te Haroto weather station has the most potential for solar PV farms. 82 % of the location has low disaster risk, and only 7% of the area is considered high risk. © 2023 IEEE.

关键词： Disasters

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GPU Accelerated Modelling and Real-time Rendering of Fluid Motion 38

GPU Accelerated Modelling and Real-time Rendering of Fluid M...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Valentine, William Mukundan, Ramakrishnan University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes a fluid rendering pipeline that uses OpenGL-4 shaders to employ the parallel processing capabilities of the GPU. The fluid's surface mesh is produced using tessellation shader stages where the input patches are assigned tessellation levels based on the fluid heightmap's curvature. The curvature is stored using a texture buffer object which allows access by shaders, thus allowing the tessellation calculations to be carried out in parallel. Use of this adaptive tessellation method increases both the simulation's framerate as well as its capacity to handle a greater number of primitives. Furthermore, it more optimally distributes the mesh's vertices to effectively increase the level of detail without using more primitives. Polygon culling using the geometry shader further optimises the number of primitives used to define the fluid surface. The Phong-Blinn model is used for surface lighting. We propose two GPU-based fluid surface flow visualisation methods. Texture buffer objects can be used to store and update a surface texture. Alternatively, particle positions are updated each frame using the geometry shader and stored in a buffer object using transform feedback. These flow visualisation techniques are particularly effective for communicating the swirling motion of vortices. © 2023 IEEE.

关键词： Graphics processing unit

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-based Visual Attention Scenario Identification Model in Military Environment 4

AI-based Visual Attention Scenario Identification Model in M...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sapariya, Ankur Ravikumar, R.N. Bhatt, Urvi Singh, Suraj Prakash Wanglen, Soram Singh, Sushil Kumar Marwadi University Dept. of Computer Engineering Rajkot India School of Management Sciences Dept. of Computer Engineering Varanasi India

ISBN: (纸本)9798350343670

In response to the evolving landscape of modern military operations, where drone technology now constitutes an estimated 70% of Indian military missions, as evidenced by recent statistics, our proposed AI-based Visual Attention Scenario Identification Model takes center stage. This groundbreaking model is meticulously designed to augment the safety and efficiency of military endeavors in challenging terrains. Our model harnesses advanced deep-learning CNN techniques by processing real-time images from various sources, including drones, strategically focusing on critical details within these visuals. This approach enables the timely detection of potential dangers, even in complex environments, with the primary objective of significantly enhancing situational awareness for military personnel. Positioned as a valuable asset in the military's toolkit, our model, with 85% accuracy, proves particularly effective in identifying potential threats in forested and mountainous regions, ultimately minimizing risks faced by soldiers on the ground. © 2024 IEEE.

关键词： CNN Deep Learning Image Processing Military Operations Visual Attention AI model

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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Architectural Model of Deep Convolution Neural Network Approach for Brain Tumor Prediction

Architectural Model of Deep Convolution Neural Network Appro...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panigrahi, Raghunath Sahu, Rekha Nayak, Suvendu Chandan Kabat, Manas Ranjan IMIT Dept. of Computer Science and Engineering Cuttack India Silicon University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Magnetic Resonance Imaging (MRI) is most commonly used technique to detect a tumor by physicians in medical science. The MRI images are three-dimensional images with the number of slices that are analyzed to identify the defective cells in the naked eye that leads to a time-consuming process. Machine Learning (ML) techniques are investigated as a solution to improve tumor identification in a better way. Early and accurate detection is crucial for effective treatment planning and improving patient outcomes. Traditionally, brain tumor diagnosis relies on manual analysis of MRI (Magnetic Resonance Imaging) scans by radiologists, which can be time-consuming and prone to human error. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has opened new avenues for automating and improving the accuracy of brain tumor prediction. In this paper, we study the Convolutional Neural Network(CNN) model which is popular in image analysis. Similar way, we develop a CNN model to process and classify the MRI images to detect the type of brain tumor. We examine the model's performance by using 5712 MRI images from a standard dataset available in the public domain. The aim of the work is to classify the tumor in four classes like: Glioma, Meningioma, Pituitary and No Tumour using the proposed CNN model. Our model follows data preprocessing, image encoding, filtering and classification. The proposed model scores 78% accuracy as its performance in the dataset which needs more investigations and comparison with other techniques in further study. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence to Combat Audio Fraud: A Flask-Deployed Hybrid Deep Learning System 12

Artificial Intelligence to Combat Audio Fraud: A Flask-Deplo...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Solanki, Shashank Ravikumar, R. Devataval, Tushar Gajera, Jeet Kumar Singh, Sushil Gupta, Himanshu Marwadi University Dept. of Computer Engineering Rajkot India Marwadi University Dept. of Computer Engineering-AI Rajkot India

ISBN: (纸本)9798350340204

As the use of synthetic communication is growing widely spread, the ability to accurately identify counterfeit spoken words is now more important than ever. In this study, we have developed an advanced system using deep learning techniques that can differentiate between real and modified audio files. Our research employs Kaggle's DeepFake Voice Recognition Dataset and explores different deep learning architectures such as "Artificial Neural Networks (ANN)", "Convolutional Neural Networks (CNN)", and "Recurrent Neural Networks (RNN)". One crucial novelty of our work lies in merging these models with XGBoost, a machine learning algorithm, to create a hybrid system which greatly enhances the accuracy of detecting false audio tracks. What is surprising, however, is that RNN and XGBoost showed accuracy rate of 99.32% outperforming other settings. Additionally, we created an easy-to-use web application using Flask enabling users to upload and verify authenticity of voice records easily. This system tackles both the critical problem of voice forgery as well as advances in the field of sound authentication technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Advanced Approaches: A Comprehensive Analysis of Machine Learning and Deep Neural Networks in Spectrum Sensing Applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. This survey paper presents an indept. exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine Learning (ML) and Deep Neural Network (DNN) approaches. The study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. This paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) methodologies. The discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 IEEE.

关键词： Hidden Markov models

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