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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Saha, Rupak Sadad, Nahin Ul Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering. © 2024 IEEE.

关键词： Graphics processing unit

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing 26

Movie Genre Prediction Using AI-Generated Data and Natural L...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Abontee, Ayesha Parpia Rayhan, Md. Abu Rahman, Md. Fuadur Sultana, Tasnim Ahmed, Ajwa Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved. © 2023 IEEE.

关键词： Embedding GPT-3.5 Neural Network NLP Text Vectorization

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An Artificial Intelligence Approach in 5G New Radio Beam Enhancement 3

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Upadhyay, Deepak Mittal, Saksham Jain, Ayushi Sharma, Ravi Bagla, Piyush Tripathi, Neha Tiwari, Pallavi Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India National Institute of Technology Computer Science & Engineering Dept. Punjab Jalandhar India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300710

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems. © 2023 IEEE.

关键词： Beamforming

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A Motor Activity Analysis as a Depression Indicator: Predictive Approach Using A Hybrid CNN and LSTM Network 6

A Motor Activity Analysis as a Depression Indicator: Predict...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sutedja, Vinsensius Odilo Andreas, Frans Nabiilah, Ghinaa Zain Moniaga, Jurike V. Dept. Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Depression is a prevalent mental disorder affecting behavior, cognition, and physical activity. Early detection and intervention are crucial to mitigating its severe impacts, including suicidal ideation or suicide attempts. This study proposes a novel predictive model using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to analyze motor activity data as an indicator of depression. Utilizing the "Depresjon" dataset, which comprises activity levels from both depressed and non-depressed individuals, the model extracts features through CNN and processes these with LSTM to identify depression patterns. To address class imbalance in the dataset, Synthetic Minority Over-sampling Technique (SMOTE) was employed, enhancing the model's predictive accuracy. The final model achieved a high accuracy rate of 97.89%, significantly outperforming the other model created. This research demonstrates the potential of combining CNN and LSTM for effective depression prediction, providing a valuable tool for early diagnosis and intervention. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Iridology: Deep Learning for Predictive Health Insights

Smart Iridology: Deep Learning for Predictive Health Insight...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Avhad, Vedika Bakal, Jagdish W. Dept. of Computer Engineering Maharashtra India

ISBN: (纸本)9798350388602

This study aims to develop a reliable, non-invasive method for predicting early health issues through iridology by leveraging computer vision and deep learning techniques. Iridology, which involves the analysis of iris patterns, is explored for its potential to provide early indications of health conditions. The methodology involved capturing high-quality iris images using a specialized camera, followed by processing and analysis using advanced computer vision techniques. A deep learning model was implemented to detect early stages of conditions such as diabetes, hypertension, and heart diseases. The model achieved an average accuracy of 93.7%, demonstrating superior performance in precision, sensitivity, and specificity compared to other leading machine learning approaches. These findings suggest that the proposed method could serve as a robust tool for early health issue detection, with implications for enhancing public health outcomes and reducing healthcare costs. © 2024 IEEE.

关键词： Contrastive Learning

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A High Power Density Active-Clamp Forward-Flyback Converter with Three Planar Transformer for xEV Low Voltage DC-DC Converter

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Transactions of the Korean Institute of Electrical Engineers 2025年第3期74卷 468-476页

作者： Kwon, Gyeonghyun Lee, Dongin Jeong, Seongwook Jeong, Yeonho Youn, Hanshin Dept. of Electrical Engineering Incheon National University Korea Republic of Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States

This paper proposes an Active-Clamp Forward-Flyback (ACFF) converter with three planar transformers to achieve 4 kW output power. As autonomous driving technology rapidly advances, the demand for higher output power from low-voltage DC-DC converters (LDC) in electric vehicles has significantly increased. Conventionally, two 2 kW ACFF converters with four magnetic cores connected in series-parallel have been used to meet 4 kW power requirement, but this configuration has proven inefficient in terms of size, power density, and cost. The proposed converter improves power density by integrating the four magnetic cores of the conventional 4 kW ACFF converter into three, reducing the overall magnetic volume and allowing for a more compact design. Furthermore, the use of planar magnetic components enables the winding to be embedded in a 4-layer PCB. Copyright © The Korean Institute of Electrical Engineers.

关键词： High frequency transformers

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Bui, Thanh Duc Panda, Brajendra University of Arkansas Dept. of Computer Science and Computer Engineering Fayetteville United States

ISBN: (纸本)9798350327595

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset. © 2023 IEEE.

关键词： Cloud Computing Cyber Security Data Mining Machine Learning

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Classification Flower Images Based on Deep Learning and Machine Learning 8

Classification Flower Images Based on Deep Learning and Mach...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Mustfa, Asia Kamal Abdulateef, Salwa Khalid Hameed, Qabas Amer Ahmed, Mohamed Aktham Al-Qaysi, Zaydoon Computer Science and Mathematics College Tikrit University Dept. Computer Science Tikrit Iraq

ISBN: (纸本)9798350354423

Image classification has grown increasingly popular due to the growing significance of machine learning and deep learning. Flower images may sometimes exhibit resemblances in terms of hue, form, and visual characteristics. The problem lies in the classification of flowers. This work employs a hybrid approach that integrates deep learning and machine learning techniques to classify 17 discrete flower species. In order to do this, we utilised the ResNet_50, PCA, and SVM architecture to classify several species from the 'Oxford-17' dataset. With this goal in mind, we have made efforts to improve our model in order to get more accuracy compared to similar methods. Prior to inputting our images into our pretrained model, we resized them, and subsequently fine-tuned the model. The dataset was partitioned into two distinct sets: a training set and a testing set. We attained a precision rate of 95.58% while utilising the 'Oxford-17' dataset. Our approach outperformed previous machine learning and deep learning-based methods on this dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Human computer Interaction (HCI) Through Machine Learning Application 8

Enhancing Human Computer Interaction (HCI) Through Machine L...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Yaseen, Fahad Abdulateef Ahmed, Mohammad Shihab Khudhur, Mokhalad Eesee College Tikrit University Dept. Computer Science Computer Science and Mathematics Tikrit Iraq

ISBN: (纸本)9798350354423

Integrating human-computer interaction with machine learning is an important development that contributes and opens the way for many experiences that serve the user. This research presents the scientific benefits and effects resulting from integrating human-computer interaction and machine learning, and on the other hand, discusses the common applications between them and what challenges will arise from data privacy and ethical considerations. Thus, this interaction will contribute to enhancing and encouraging user participation and to a greater increase in intelligence and responsiveness. This research aims to present and propose innovative solutions that address the challenges that will face this interaction, in addition to presenting a concept and a methodological model that increases the power of human-computer interaction in a more intelligent and smooth way. © 2024 IEEE.

关键词： Adversarial machine learning

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State of the art: Security Testing of Machine Learning Development Systems 14

State of the art: Security Testing of Machine Learning Devel...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Das, Saikat Krishnamurthy, Bhargavi Das, Raktim Ranjan Shiva, Sajjan G Utah Valley University Dept. of Computer Science Orem United States Siddaganga Institute of Technology Dept. of Computer Science Karnataka Tumakuru India Stamford University Bangladesh Dept. of Computer Science Dhaka Bangladesh University of Memphis Dept. of Computer Science Memphis United States

ISBN: (纸本)9798350360134

In recent days, machine learning (ML) systems have become integral to nearly all mainstream applications. Understanding the underlying logic that contributes to the desired behavior in ML systems can be challenging. Humans play a crucial role in providing the necessary data samples for training ML models to ensure accurate predictions. The complexity of ML testing arises from various factors such as data dependency, dynamic model behavior, the absence of a test oracle, a vast input space, and the lack of a specific testing life cycle tailored for ML. Testing ML systems is not a straightforward process, as it not only involves verifying the code and their corresponding data but also is further complicated by the dynamic nature of these systems. A typical ML model undergoes seven stages, including eliciting business needs/requirements, gathering data, selecting a model, training the model, testing the model, deploying the model, and monitoring the model. Throughout the ML Development Life Cycle (MLDLC), each stage introduces various security risks. This paper explores the current state of the literature on security attacks and defense approaches concerning data, model, and prediction output of ML models. The paper also addresses potential security attacks at each stage of the MLDLC and the corresponding security measures to mitigate those attacks. © 2024 IEEE.

关键词： Life cycle

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