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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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An Efficient Neural Cell Architecture for Spiking Neural Networks

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 599-612页

作者： Khalil, Kasem Kumar, Ashok Bayoumi, Magdy University of Mississippi Electrical and Computer Engineering Department OxfordMS38677 United States Assiut University Electrical Engineering Department Asyut71515 Egypt University of Louisiana at Lafayette The Center for Advanced Computer Studies LafayetteLA70504 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LafayetteLA70504 United States

Neurons in a Spiking Neural Network (SNN) communicate using electrical pulses or spikes. They fire or trigger conditionally, and learning is sensitive to such triggers' timing and duration. The Leaky Integrate and Fire (LIF) model is the most widely used SNN neuron model. Most existing LIF-based neurons use a fixed spike frequency, which prevents them from attaining near-optimal accuracy. A research challenge is to design energy and area-efficient SNN neural cells that provide high learning accuracy and are scalable. Recently, the idea of tuning the spiking pulses in SNN was proposed and found promising. This work builds on the pulse-tuning idea by proposing an area and energy-efficient, stable, and reconfigurable SNN cell that generates spikes and reconfigures its pulse width to achieve near-optimal learning. It auto-adapts spike rate and duration to attain near-optimal accuracies for various SNN applications. The proposed cell is designed in mixed-signal, known to be beneficial to SNN, implemented using 45-nm technology, occupies an area of 27 μm2, incurs 1.86 μW, and yields a high learning performance of 99.12%, 96.37%, and 78.64% in N-MNIST, MNIST, and N-Caltech101 datasets, respectively. The proposed cell attains higher accuracy, scalability, energy, and area economy than the state-of-the-art SNN neurons. Its energy efficiency and compact design make it highly suitable for sensor network applications and embedded systems requiring real-time, low-power neuromorphic computing. © 2020 IEEE.

关键词： Neurons

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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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Active Voltage Balancing of Series-Connected SiC MOSFETs During Body-Diode Turn-Off Using an Active Gate Driver

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IEEE Transactions on Power Electronics 2025年第8期40卷 10609-10622页

作者： Rai, Ajay Ganesan, P. Hatua, Kamalesh Bhattacharya, Subhashish Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram India Department of Electrical and Computer Engineering NC State University Raleigh NC USA

The series connection of fast-switching SiC MOSFETs presents a promising approach to enhance the efficiency, compactness, and simplicity of Medium Voltage (MV) power converters. However, managing voltage imbalances during the turn-off of MOSFETs and their body diodes introduces significant challenges. While existing literature covers passive and active voltage balancing strategies for switch turn-off (e.g., MOSFETs/IGBTs), the issue of uneven voltage distribution during body diode turn-off remains largely unexplored. This gap limits the widespread adoption of series-connected devices in MV power converters, as achieving reliable performance with minimal additional components and low switching losses remains difficult. This article presents an Active Voltage Balancing technique for handling body diode turn-off events by regulating the gate charge of parallel MOSFETs during voltage rise transients in the closedloop using Active Gate Drivers (AGD). This approach ensures uniform voltage distribution among the body diodes of seriesconnected MOSFETs. A comprehensive analysis and modelling of gate charge control during voltage transients is presented. The proposed method is experimentally validated on two seriesconnected SiC MOSFETs operating in buck mode (DC-DC) and half-bridge inverter mode (DC-AC) at a 1.5 kV DC bus voltage, with switching frequencies up to 40 kHz. © 1986-2012 IEEE.

关键词： Diodes

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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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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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