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Improving agility in projects using machine learning algorithm

Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Highly-efficient(>70%)and Wide-spectral(400-1700 nm)sub-micron-thick InGaAs photodiodes for future high-resolution image sensors

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Light(Science & Applications) 2024年第12期13卷 3224-3235页

作者： Dae-Myeong Geum Jinha Lim Junho Jang Seungyeop Ahn SeongKwang Kim Joonsup Shim Bong Ho Kim Juhyuk Park Woo Jin Baek Jaeyong Jeong SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer(AL).Therefore,we propose a guided-mode resonance(GMR)structure to enhance the quantum efficiency(QE)of the InGaAs PDs in the SWIR region with only sub-micron-thick *** TiOx/Au-based GMR structure compensates for the reduced AL thickness,achieving a remarkably high QE(>70%)from 400 to 1700 nm with only a 0.98μm AL InGaAs PD(defined as 1μm AL PD).This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high ***,the rapid transit time is highly expected to result in decreased electrical *** effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness,simultaneously enhancing the transit *** breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

关键词： thick resolution image

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Mixing detection on Bitcoin transactions using statistical patterns

IET Blockchain

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IET Blockchain 2023年第3期3卷 136-148页

作者： Shojaeinasab, Ardeshir Motamed, Amir Pasha Bahrak, Behnam School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Electrical and Computer Engineering University of Victoria Victoria Canada Tehran Institute of Advanced Studies Tehran Iran

Cryptocurrencies, particularly Bitcoin, have garnered attention for their potential in anonymous transactions. However, their anonymity has often been compromised by deanonymization attacks. To counter this, mixing services have been introduced. While they enhance privacy, they obscure fund traceability. This study seeks to demystify transactions linked to these services, shedding light on pathways of concealed and laundered money. We propose a method to identify and classify transactions and addresses of major mixing services in Bitcoin. Unlike previous research focusing on older techniques like CoinJoin, we emphasize modern mixing services. We gathered labelled data by transacting with three prominent mixers (MixTum, Blemder, and CryptoMixer) and identified recurring patterns. Using these patterns, an algorithm was created to pinpoint mixing transactions and distinguish mixer-related addresses. The algorithm achieved a remarkable recall rate of 100%. Given the lack of clear ground truth and the vast number of unlabelled transactions, ensuring accuracy was a challenge. However, by analyzing a set of non-mixing transactions with our model, it was confirmed that the high recall rate was not misleading. This work provides a significant advancement in monitoring mixing transactions, presenting a valuable tool against fraud and money laundering in cryptocurrency networks. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Room-temperature waveguide-integrated photodetector using bolometric effect for mid-infrared spectroscopy applications

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光：科学与应用（英文版） 2025年第5期14卷 1300-1311页

作者： Joonsup Shim Jinha Lim Inki Kim Jaeyong Jeong Bong Ho Kim Seong Kwang Kim Dae-Myeong Geum SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-RoYuseong-GuDaejeon 34141Republic of Korea Department of Electrical & Computer Engineering Inha University100Inha-roMichuhol-guIncheon 22212Republic of Korea

Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)*** various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible *** leap beyond these limitations,the bolometric effect-a thermal detection mechanism-is introduced into the waveguide *** importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into ***,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with ***,our device exhibits a broadband responsivity of 28.35％/mW across 4030-4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10-7W/Hz0.5 at 4180 *** further demonstrate label-free sensing of gaseous carbon dioxide(CO2)using our integrated photodetector and sensing waveguide on a single *** approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.

关键词： applications temperature integrated room bolometric effect mid-infrared photodetector spectroscopy using

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Coupon-based Demand Response for Consumers Facing Flat-rate Retail Pricing

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 1887-1900页

作者： Hongxun Hui Yi Ding Kaining Luan Tao Chen Yonghua Song Saifur Rahman State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao 999078China. College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Grid Jiangsu Electric Power Co. LTD.Nanjing 210000China School of Electrical Engineering Southeast UniversityNanjing 210096China Advanced Research Institute and Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityVA 22203USA

Even though smart meters have been widely used in power systems around the world,many consumers are still finding it hard to participate in demand response(DR)due to flat-rate retail pricing *** address this issue,this paper proposes a coupon-based demand response(CDR)scheme to achieve equivalent dynamic retail prices to inspire consumers’inherent ***,a security-constrained unit commitment optimization model is developed in the day-ahead market to obtain coupon rewards,which are then broadcast to consumers to motivate them to reschedule their power consumption *** evaluate the adjustment value of consumers’power consumption,a collective utility function is proposed to formulate the relationship between power quantity and coupon *** this basis,the security-constrained economic dispatch model is developed in the intra-day market to reschedule generating units’output power according to real-time load demands and fluctuating renewable *** the operation interval,a settlement method is developed to quantify consumers’electricity fees and coupon benefits on a monthly *** proposed CDR scheme avoids real-time iterative bidding process and effectively decreases the difficulty of massive,small consumers participating in *** proposed CDR is implemented in a realistic DR project in China to verify consumers’energy cost and renewables’curtailment can both be decreased.

关键词： Coupons demand response flat-rate retail pricing renewable energies small consumers

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Multiple-Masks Error Correction Code Transformer for Short Block Codes

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2518-2529页

作者： Park, Seong-Joon Kwak, Hee-Youl Kim, Sang-Hyo Kim, Sunghwan Kim, Yongjune No, Jong-Seon Institute of Artificial Intelligence Pohang37673 Korea Republic of University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Kyonggi University School of Electronic Engineering Suwon16227 Korea Republic of Department of Electrical Engineering Pohang37673 Korea Republic of Yonsei University Institute for Convergence Research and Education in Advanced Technology Seoul03722 Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

With the broadening applications of deep learning, neural decoders have emerged as a key research focus, specifically aimed at improving the decoding performance of conventional decoding algorithms. In particular, error correction code transformer (ECCT), which utilizes the transformer architecture, has achieved state-of-the-art performance among neural network-based decoders. We present three technical contributions to significantly enhance the performance of ECCT. First, we propose a novel transformer architecture of ECCT, termed the multiple-masks ECCT (MM ECCT). We employ multiple masked self-attention blocks with different mask matrices in a parallel manner to learn diverse relationships among the codeword bits. Second, we discover that constructing mask matrices based on systematic parity check matrices (PCMs) can make the attention maps sparse, which not only enhances the decoding performance but also reduces computational complexity. Finally, we propose using complementary mask matrices derived from cyclic permutations of the systematic PCM. These complementary mask matrices are specifically designed to enhance the decoding of cyclic codes. Our extensive simulation results show that the proposed MM ECCT architecture with carefully designed mask matrices outperforms the original ECCT by a large margin, achieving state-of-the-art decoding performance among neural decoders. The source code is available at https://***/iil-postech/mm-ecct. © 1983-2012 IEEE.

关键词： Decoding

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Inferring Electrocardiography from Optical Sensing Using Lightweight Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3535-3550页

作者： Li, Yuenan Tian, Xin Zhu, Qiang Wu, Min University of Maryland College ParkMD20742 United States Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States Meta Inc. Menlo ParkCA94025 United States University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20742 United States

This article presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG). While some smartwatches now allow users to obtain a 30-s ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of cardiac functions. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of cardiac abnormalities. To alleviate the need for continuous user attention and active participation, we design a lightweight neural network that infers ECG from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. We also develop a diagnosis-oriented training strategy to enable the neural network to capture the pathological features of ECG, aiming to increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs). We also leverage model interpretation to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40 000 parameters. © 2020 IEEE.

关键词： Electrocardiograms

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN computer Science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

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