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Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Reji K Resmi R Rohini S Padmavathi S Sreevidya C Rekha P Vishwanathan Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Electrical and Biomedical Engineering RMIT University Melbourne VIC Australia

In this paper different machine learning algorithms are used to predict the energy performance of residential buildings. Prediction of energy consumption has a major role in energy conservation, management, and planning. The data is classified using a variety of supervised and unsupervised techniques. For generating predictive models, predictive modelling algorithms are employed for regression as well as classification. It is examined to determine how well the various predictive models perform and contrast with each other. A comparative analysis was implemented to assess the surmising performance of the model. From the results, we can infer that the Decision Tree Classifier performs the best in terms of classification and in clustering algorithms produced on the dataset, the AdaBoost algorithm performs better compared to the K-Means algorithm. While in Ensemble methods, a combination of Decision tree and Logistic regression exhibits higher performance Finally, this paper substructures the expediency of using machine learning algorithms to estimate the energy performance of buildings as a beneficial and authentic approach.

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Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. 5

Real-time Efficacy of Features Extraction using Machine Lear...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Hafeez, Yasir Ali, Syed Saad Azhar Amin, Hafeez Ullah Naqvi, Syed Faraz Adil, Syed Hasan Boon, Tang Tong University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Nottingham Malaysia Faculty of Science and Engineering School of Computer Science Semenyih43500 Malaysia Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

The frontal alpha asymmetry represents as the neuromarker for stress. Stress is the psycho-physiological state of brain in response to some event or a demand. The continuous monitoring of mental stress is necessary to avoid chronic health issues. The real-time monitoring of frontal alpha asymmetry is necessary in daily life and to help in the therapy for example neurofeedback. In this paper, different approaches of machine learning and deep learning were adopted to extract the frontal alpha asymmetry features. The results analysis was based on the efficacy and the comparison of techniques for feature extraction has also been presented. © 2022 IEEE.

关键词： Feature extraction

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social Sciences (7.8%), engineering (3.9%), computer Science (2.6%), Environmental Science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Electromagnetic Imaging of Rough Dielectric Surface Profiles using a Single-Frequency Reverse Time Migration Method

Electromagnetic Imaging of Rough Dielectric Surface Profiles...

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Antennas and Propagation Society International Symposium

作者： Ahmet Sefer Ali Yapar Hakan Bagci Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Electrical and Electronics Engineering Işιk University Istanbul Turkey Department of Electronics and Communications Engineering Istanbul Technical University Istanbul Turkey

An electromagnetic imaging scheme, which makes use of a single-frequency reverse time migration (RTM) technique to reconstruct two-dimensional (2D) rough surface profiles from the scattered field data, is formulated and implemented. The unknown surface profile, which is expressed as a one-dimensional height function, is the interface between two dielectric media. It is assumed that the profile is illuminated from one side and the scattered fields are “measured” along a line on this same side. RTM is used to construct a cross-correlation imaging functional that is numerically evaluated to yield an image of the investigation domain. The maxima of this functional yields an accurate reconstruction of the rough dielectric surface profile.

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Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN

arXiv

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arXiv 2025年

作者： Tanbhir, Gazi Shahriyar, Md. Farhan Shahed, Khandker Raihan Chy, Abdullah Md Adnan, Md Al Department of Computer Science and Engineering World University of Bangladesh Dhaka1230 Bangladesh Department of Electrical and Electronics Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Southern University Bangladesh Chattogram Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali3814 Bangladesh

Smishing is a social engineering attack using SMS containing malicious content to deceive individuals into disclosing sensitive information or transferring money to cybercriminals. Smishing attacks have surged by 328%, posing a major threat to mobile users, with losses exceeding $54.2 million in 2019, yet the issue remains significantly under-addressed despite its growing prevalence. This paper presents a novel hybrid machine learning model for detecting Bangla smishing texts, combining Bidirectional Encoder Representations from Transformers (BERT) with Convolutional Neural Networks (CNNs) for enhanced character-level analysis. Our model addresses multi-class classification by distinguishing between Normal, Promotional, and Smishing SMS. Unlike traditional binary classification methods, our approach integrates BERT’s contextual embeddings with CNN’s character-level features, improving detection accuracy. Enhanced by an attention mechanism, the model effectively prioritizes crucial text segments. Our model achieves 98.47% accuracy, outperforming traditional classifiers, with high precision and recall in Smishing detection, and strong performance across all categories. © 2025, CC BY.

关键词： Convolutional neural networks

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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PWM based Software defined radio Modeling

PWM based Software defined radio Modeling

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Ahmed Sakr Aziza I. Hussein Ghazal A. Fahmy Mahmoud A. Abdelghany Research Engineer Minia Egypt Electrical and Computer Engineering Department Effat University Jeddah KSA Electronics Department National Telecommunications Institute Cairo Egypt Electrical Engineering Department Minia University Minia Egypt

Modern wireless networks are allocated in different frequency bands and have different specifications. However, many wireless devices are required to support different standards which is the case for mobile devices and IoT. For example, a modern mobile device usually supports 5G or 4G besides older generations back to 2G, Wi-Fi standards 802.11 a/b/g/n/ac and Bluetooth with its different versions. To support all these standards, an increasing complexity is added to the design of RF front-end that should be more flexible and more ***-defined-radio SDR aims to achieve a flexible front-end that is fully programmable and flexible in order to support more standards in different frequency bands and with different specifications. It takes advantage of the increasing enhancements in IC fabrication processes which enables performing signal processing in digital domain with high speed and accuracy more than what analog signal processing can *** this work, a review of traditional receivers as well as multistandard receivers and SDRs are performed, then an SDR based on Pulse-width-modulation PWM RF-to-digital receiver is demonstrated. The chosen PWM based SDR is modeled using MATLAB Simulink.

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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Constrained Sorter Design using Zero-One Principle

Constrained Sorter Design using Zero-One Principle

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sangil Han Jaehee Kim Dongyun Kam Byeong Yong Kong Mijung Kim Young-Seok Kim Youngjoo Lee Department of Electrical Engineering POSTECH Pohang Korea Memory Business Samsung Electronics Hwaseong Korea Division of Electrical Electronic and Control Engineering Kongju National University Cheonan Korea Sports AIX Graduate Program POSTECH Pohang Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

To derive efficient sorting architectures constrained to application-specific input/output conditions, we present in this paper a systematic design methodology that can effectively prune dispensable compare-and-swap (CAS) units. Unlike the previous works resorting to heuristic approaches, the proposed framework exploits the zero-one principle to validate the pruning of a CAS unit at a time, generating the cost-optimized sorter architecture in an iterative manner with a reasonable complexity. In addition to the given input/output constraints, we newly develop the architecture options for the proposed framework, allowing more design spaces for finding the most attractive constrained-sorter design. For 8-list polar decoders, the proposed framework successfully reduces 70% of CAS units in the baseline full sorter, relaxing the area-time complexity by 35% compared with the state-of-the-art solutions.

关键词： Systematics Circuits and systems Design methodology computer architecture Hardware Complexity theory Decoding

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Collaborative Multi-Agent Proximal Policy Based Resource Allocation in Clustered IoV Networks

Collaborative Multi-Agent Proximal Policy Based Resource All...

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Smart World Congress (SWC), IEEE

作者： Arif Raza Junfan Xiang Salabat Khan Shivanshu Shrivastava M. Wasim Abbas Ashraf Lu Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Computer and Information Engineering Qilu Institute of Technology Jinan China Department of Electrical and Electronics Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi India School of Mathematics and Computer Science Hanjiang Normal University Shiyan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This paper presents a novel multiagent reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm for optimizing resource allocation in clusters of cells within IoV networks. The framework dynamically allocates transmission power and bandwidth across base stations, each acting as a collaborative agent. Extensive simulations demonstrate that the multi-agent PPO approach outperforms other reinforcement learning algorithms, including Soft Actor-Critic (SAC) and Advantage Actor-Critic (A2C), regarding throughput, power efficiency, dynamic bandwidth allocation, and overall network performance.

关键词： Base stations Heuristic algorithms Clustering algorithms Collaboration Reinforcement learning Bandwidth Resource management Vehicle dynamics Optimization Internet of Vehicles

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