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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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A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression 3

A Comprehensive Review of Deep Learning Methods for Hyperspe...

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

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Rapid advancement in the development of hyperspectral image (HSI) sensors has significantly enhanced the capabilities of capturing detailed spectral information across a wide range of wavelengths, enabling more precise analysis and interpretation of the observed scenes. Managing the storage of these extensive images poses a crucial challenge, requiring the application of compression techniques. Deep Learning (DL) algorithms have seen a massive rise in popularity over the past few years for HSI compression as well as HSI analysis. This review paper provides a comprehensive summary of the current literature on deep learning methods for HSI compression. Additionally, it explores the challenges, opportunities, and potential directions for future research in HSI compression. © 2024 IEEE.

关键词： Image enhancement

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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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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AI Driven Brain Tumor Classification and Localization using Faster R-CNN 8

AI Driven Brain Tumor Classification and Localization using ...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Gospelin Reena, E. Kavitha, T. Dept. of Computer Science and Engineering Karunya University coimbatore India Dept. of Computer Science and Engineering Karunya University coimbatore India

ISBN: (纸本)9798331512248

The increasing prevalence of brain tumors necessitates the development of accurate, automated methods for early diagnosis and localization. Traditional approaches, which rely on manual MRI interpretation by radiologists, are prone to errors due to varying expertise levels and limited resources. To overcome these limitations, this study proposes an AI-driven solution for brain tumor classification and localization using Faster R-CNN, a deep learning architecture known for its robust object detection capabilities. The proposed model utilizes public MRI datasets to train a Faster R-CNN model, which effectively identifies and locates brain tumors with high precision. The model's performance is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and Intersection over Union (IoU), demonstrating superior results compared to conventional methods. A user-friendly web-based interface developed using Streamlit allows radiologists to upload MRI images and receive real-time diagnostic results, enhancing clinical workflows. Despite its high performance, the study acknowledges the need for improvements in image resolution and model interpretability. Future work will explore the integration of 3D MRI data and explainable AI techniques to further enhance the model's transparency and clinical applicability. © 2025 IEEE.

关键词： Artificial Intelligence Brain Tumor Detection Deep Learning Faster R-CNN Medical Imaging MRI Classification Region Proposal Network

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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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Detecting Ransomware Threats Using Machine Learning Approach 8

Detecting Ransomware Threats Using Machine Learning Approach

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shetty, Sanmitha Chitharanjan Bastian, Rose Khader, Sarah Abdul St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Medical Image Analysis: Leveraging Transfer Learning in Convolutional Neural Networks for Tuberculosis Detection 9

Enhancing Medical Image Analysis: Leveraging Transfer Learni...

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

作者： Dahiwadkar, Piyush Pankaj Bhamre, Hrishin Mandar Sahitya, Aarti Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350394474

Advancements in medical imaging have paved the way for image-based computer-aided diagnosis (CADx) systems, revolutionizing the detection of lung abnormalities, including lung nodules and diffuse lung diseases. This paper explores the potential of convolutional neural networks (CNNs) in image-based CADx without the reliance on image-feature extraction, presenting a powerful alternative to feature-based CADx methods. Additionally, we introduce an image-based computer-aided detection (CADe) algorithm employing regions with CNN features (R-CNN) for precise identification of lung abnormalities. We conduct comprehensive evaluations of our image-based CADx utilizing CNN and CADe employing R-CNN, demonstrating their effectiveness across various lung abnormalities. © 2024 IEEE.

关键词： computer aided diagnosis

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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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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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