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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil Engineering 2025年第7期26卷 3083-3098页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T.C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah United Arab Emirates Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Construction schedule prediction Gradient boosting machine (GBM) Interpretability in machine learning Project performance analytics

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Predictions of H-mode access and edge pedestal instability in the EHL-2 spherical torus

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Plasma science and Technology 2025年第2期27卷 54-63页

作者： Yumin WANG Kai LI Zhuo HUANG Yiliang LIU Shuyu DAI Jie ZHANG Yanqing HUANG Xiang GU Yihang ZHAO Shuai XU Erhui WANG Dong GUO Yuejiang SHI Huasheng XIE Yunfeng LIANG Minsheng LIU the EHL-2 Team Hebei Key Laboratory of Compact Fusion Langfang 065001People’s Republic of China ENN Science and Technology Development Co. Ltd.Langfang 065001People’s Republic of China Centre for Theoretical and Computational Physics College of PhysicsQingdao UniversityQingdao 266071People’s Republic of China College of Computer Science South-Central Minzu UniversityWuhan 430074People’s Republic of China Key Laboratory of Materials Modification by Laser Ion and Electron Beams(Ministry of Education)School of PhysicsDalian University of TechnologyDalian 116024People’s Republic of China Department of Plasma Physics and Fusion Engineering University of Science and Technology of ChinaHefei 230026People’s Republic of China College of Physics and Electronic Engineering Hengyang Normal UniversityHenyang 421002People’s Republic of China Forschungszentrum Jülich GmbH Institute of Fusion Energy and Nuclear Waste Management-PlasmaphysikJülich 52425Germany

The EHL-2 spherical torus is designed to demonstrate proton-boron(p-11B)fusion within a compact spherical *** planned heating system includes a negative ion-based neutral beam injection(N-NBI),two positive ion-based NBI systems(P-NBI),electron cyclotron resonance heating(ECRH),ion cyclotron resonance heating(ICRH),and high harmonic fast wave(HHFW),with a total power output of 31 *** to scaling law estimates,the device is capable of achieving H-mode *** plasma density,,n_(e,min)at the minimum L-H power threshold,P_(lh),is estimated to be 4.4×10^(19)m^(-3).The pedestal parameters were calculated using the REPED *** B as the primary impurity ion,the predicted pedestal width and height are lower compared to the typical case with carbon *** pedestal collisionality for EHL-2 is estimated to range between 0.06 and 0.17,indicating the potential for significant energy loss due to edge localized modes(ELMs).The heat flux on the divertor plate has been calculated using the JOREK *** peak heat fluxes during ELM bursts are approximately 31.0 MW/m^(2)at the lower inboard target and 39.5 MW/m^(2)at the lower outboard target.A preliminary design of the resonant magnetic perturbation(RMP)coils has been completed to both control type-I ELMs and correct error *** system comprises 16 coils arranged into 24 *** ELM control mode,a 14/2 component is generated at 1.7 G/kAt,with a current of 4.9 kA required to achieveσChirikow=1 at the resonant surface,where the normalized poloidal magnetic flux is *** error field(EF)modulation mode,2/1 and 3/1 components are generated at 3.5 G/kAt and 2.8 G/kAt,respectively.

关键词： H-mode spherical torus(ST) EHL-2 pedestal resonant magnetic perturbations

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The Impact of Advanced Neural Network Architectures for Extracting Elusive Information

The Impact of Advanced Neural Network Architectures for Extr...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Renu Kachhoria Love Mittal Prakash Divakaran Vinay Singh N. Thangarasu S. Rajarajeswari Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility.

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Fault Detection in Solar Power System with Internet of Things using Multi resolution Sinusoidal Neural Network - Snow Geese Optimization Approach 4

Fault Detection in Solar Power System with Internet of Thing...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kumar, Pramod Purushothaman, K.E. Arunmozhi, R.V. Jayachitra, S. Rani, D. Leela Kaliappan, S. Ganga Institute of Technology and Management Department of Computer Science and Engineering Haryana Kablana124001 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai600062 India Karpagam Academy of Higher Education Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641021 India S.A. Engineering College Department of Chemistry Tamil Nadu Chennai600077 India Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Lovely Professional University Division of Research and Development Delhi Punjab Jalandhar144411 India

ISBN: (纸本)9798331540364

Fault detection in power systems, including Photovoltaic (PV) systems, using Internet of Things (IoT) involves deploying sensors to monitor key parameters and analyzing the data identify anomalies. The accuracy of these systems can be compromised by issues such as noisy or low-quality sensor data, which may lead to false positives or missed faults. Additionally, the complexity of managing large volumes of data from numerous IoT devices can strain the system, further affecting detection accuracy. To overcome these drawbacks, this paper proposes an efficient approach for fault uncovering in solar power system grounded on IoT. The data are collected from solar power generation dataset. Subsequently, the data are nourished to preprocessing. In preprocessing segment removes the absent values in the data utilizing Reverse Lognormal Kalman Filter (RLKF). The consequence after the preprocessing data is transported to the Multiresolution Sinusoidal Neural Network (MSNN). The line-line faults, ground faults, line-ground faults and partial shading are successfully predicted and classified by using MSNN. The Snow Geese Optimization (SGO) is rummage-sale to optimize the weight structure of MSNN. The projected MSNN-SGO is utilized within the MATLAB platform. Performance metrics counting precision, accuracy, Root Mean Squared Error (RMSE), F1-score, and Mean Squared Error (MSE) were examined in order to determine the proposed method. The proposed DAGCN-COA technique yields 14.89%, 16.89%, and 18.23% higher precision, 16.65%, 18.85%, and 17.89% higher accuracy, 22.36%, 15.42% and 28.27% lower RMSE when analysed the existing methods. The proposed MSNN-SGO technique is associated with the existing methods for instance spatial-temporal recurrent graph neural network (STRGNN), convolutional neural network (CNN), and long short-term memory network (LSTMN), respectively. © 2024 IEEE.

关键词： Mean square error

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Augmented Reality for Enhanced Surgical Precision: A Framework for Real-Time Visualization and Navigation

Augmented Reality for Enhanced Surgical Precision: A Framewo...

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Advances in computer science, Electrical, Electronics, and Communication technologies (CE2CT), International Conference on

作者： Jagbir Singh Jagadeesh Vedula Hariharan Ragothaman Shojonov Muzaffar Urazbaeva Yulduz Satendra Pratap Singh India Digital Transformation Lead Meghaz Inc. Houston Texas USA Head of the Information Security department of the Digital Education Technologies Center of Urgench State University Pedagogy and psychology department Mamun university Khiva Uzbekistan Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The advancement of Augmented reality (AR) technology has made it possible to use it in surgical precision and better the lives of patients. This paper outlines a real-time visualization and navigation framework where AR is integrated into surgical work. CT and MRI imaging data are used in the system to create accurate and rather detailed preoperative 3D anatomical models. These models are overlaid on the surgeon's vision, being the integral part of the system, providing the surgeon with dynamic constant assistance during procedures. To surmount the problem of misalignment of the virtual overlays with the patient's anatomy due to movements of the patient in the operating room, the framework uses complex tracking algorithms as well as machine learning techniques. It highlights planner pre-operative, intra-operative visualization and decision support mainly in complicated surgical cases. In the case of minimally invasive surgery, the framework was used to position the instruments correctly with improved accuracy, cutting short on time to operate, and countering complications. This research shows how AR can benefit today's surgery, as an origination of increased accuracy and a closing of the gap between conception and operations. It has also discussed some of the issues like scalability, cost, and the required training for the surgeon for the use of such system and has therefore provided a direction for the large-scale use of AR-assisted surgical systems.

关键词： Solid modeling Accuracy Three-dimensional displays Minimally invasive surgery Costs Navigation Surgery Machine learning Real-time systems Augmented reality

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IoT Based Alcohol Detection and Vehicle Control System

IoT Based Alcohol Detection and Vehicle Control System

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining Control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

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Revolutionizing Aerospace and Defense: The Impact of AI and Robotics on Modern Warfare 4

Revolutionizing Aerospace and Defense: The Impact of AI and ...

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4th International Conference on Information management and Machine Intelligence, ICIMMI 2022

作者： Shetty, Dasharathraj K Prerepa, Gayathri Naik, Nithesh Bhat, Ritesh Sharma, Jayant Mehrotra, Parva Department of Humanities and Management Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics and Communication Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Mechanical and Industrial Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (纸本)9781450399937

Integrating artificial intelligence (AI) and robotics in the aerospace and defense industry has revolutionized how things are done. This has led to cost savings, faster design and prototyping, and improved manufacturing efficiency. The growth in air traffic has prompted industry to explore new ways of automating production. This includes the development of unmanned autonomous aircraft to reduce time and cost, human error and environmental pollution. The application of AI in security strategies such as perimeter protection, threat detection, and automated response to network attacks has significantly enhanced defense capabilities. This article reviews the impact of AI and robotics on the aerospace and defense sector, highlighting the key technological advancements and inventions shaping military operations' future. © 2022 ACM.

关键词： Robotics

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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IEEE Globecom Workshops

作者： Rani Kumari Dinesh Kumar Sah Korhan Cengiz Ali Nauman Nikola Ivkovi Ivan Mihaljevi Department of Computer Science and Engineering Birla Institutue of technology Ranchi India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Bhuwanswar India Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea Faculty of Organization and Informatics University of Zagreb Varaždin Croatia

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases.

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Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks

arXiv

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arXiv 2023年

作者： Huang, Chung-Ju Wang, Leye Han, Xiao Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework. © 2023, CC BY-SA.

关键词： Distillation

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering Optimization algorithm

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