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International Conference on Technological Innovations in Multidisciplinary engineering and Sciences, TIMES 2024

作者： Arpitha, L.M. Kumar, R. Anil Fathima, Zoya Yeshaswini, R. Dhanyashree, G. Roshini, D. M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Civil Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India TCSiON Tata Consultancy Services Karnataka Bengaluru India Department of Computer Science and Design BGS College of Engineering and Technology Karnataka Bengaluru India

ISBN: (纸本)9783031837494

Crack detection is critical to the safety and performance of engineering structures including highways, bridges, and buildings. This paper reviews the different types of image processing techniques for crack detection which includes traditional, advanced and machine learning based methods. One of the sixty works chosen and written from 1999–2023. The results indicate that machine learning is preferred simply because it can be more precise, given its automated nature. The paper reviews different methodologies applied, their performances based on the datasets employed as well as limitations governing one approach over another to realize real-time crack detection. It also highlights the need for standardized methods of categorizing and measuring cracks, as well as efficient ways to proceed in many different situations. Specifically, the study addresses potential benefits and limitations of using machine learning in infrastructure management with respect to data-driven approaches linked to big data sets as well as computing costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

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Intelligent Automation & Soft Computing 2023年第1期35卷 755-767页

作者： Ziad M.Ali Ahmed M.Galal Salem Alkhalaf Imran Khan College of Engineering at Wadi Addawaser Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia Electrical Engineering Department Aswan Faculty of EngineeringAswan UniversityAswan81542Egypt Production Engineering and Mechanical Design Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassSaudi Arabia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal *** traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is ***, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

关键词： Load forecasting distribution network machine learning renewable energy

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images 9

Identification of Diabetic Retinopathy by Transfer Learning ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sagili, Sreenivas Reddy Goswami, Chandrashekhar Bharathi, V.C. Ananthi, S. Rani, K. Sathya, R. Rochester Institute Of Technology Department of computer science Lomb Memorial Drive RochesterNY14623 United States Mit School of Computing Mit Art Design and Technology University Department of Computer Science & Engineering Maharashtra Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350377972

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported. © 2024 IEEE.

关键词： Convolutional neural networks

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Non-knowledge Based Decision Support System 3rd

Non-knowledge Based Decision Support System

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3rd International Conference on Sustainable Expert Systems, ICSES 2022

作者： Taranath, N.L. Prabhu, B. P. Aniruddha Dani, Rakesh Tiwari, Devesh Darshan, L.M. Department of Computer Science and Engineering Alliance college of Engineering and Design Alliance University Bengaluru India Department of CS & E Graphic Era Hill University Uttarakhand Dehradun India Department of Hospitality Management Graphic Era Deemed to be University Uttarakhand Dehradun India School of CSE REVA University Karnataka Bengaluru India

ISBN: (纸本)9789811978739

The Medical Decision Support System (MDSS) delineates knowledge and supports optimal therapeutic decisions to assist physicians, clinicians, or other healthcare professionals with knowledge and person-specific information in making clinical decisions using targeted medical knowledge which reduces prescribing errors. MDSS are frequently classified as Knowledge base or learning base. Learning bases derive mapping using machine learning (ML), artificial intelligence (AI), or statistical pattern recognition from knowledge-driven human-engineered mapping of literature based, patient-oriented, and practically based data to recommendations. This study offers a merging decision-making support framework that combines a knowledge-based system with a learning-based approach to give and realms medical aid for decision-making despite of events with missing information, with a powerful solution to the information challenge. This work integrates the idea of the data mining of knowledge bases (KB) with artificial intelligence in this context intended to support a combined module of integrated medical decision (AI) In healthcare choices around drug prescribing and evolving medical conditions, poorly experienced inexperienced medical providers can develop an infrastructure to address issues of highly complexity. The rule-based system has its drawbacks, such that the basis of the knowledge uses an explicit system to administer patent medicines with knowledge of each field. However, where the knowledge base available data is unknown, the machine learning strategies are used to respond to a query. The skeleton is query oriented and can be adapted to be used for various user interfaces such as desktops, web browsers and mobile apps. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms

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Procedia computer Science 2023年 230卷 879-889页

作者： Jayavadivel Ravi Rajkumar N Viji C Loganathan D K S N Sushma Stalin M Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Information Science & Engineering Cambridge Institute of Technology Bangalore Karnataka India Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner.... 详细信息

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures .

关键词： Crow search algorithm Imperialist competitive algorithm. Task scheduling Nature-inspired and Cloud computing

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A Review of Advanced Technologies for Intelligent Sensing, Diagnostics, and Personalized Healthcare Solutions

A Review of Advanced Technologies for Intelligent Sensing, D...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： G S Pradeep Ghantasala Rajesh Sharma R Akey Sungheetha Anitha P Selvalakshmi B Pradeepa K Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of ECE Sri Sai Renganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering St. Michael College of Engineering & Technology Sivaganga Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intelligent Health is a revolutionary approach to modern healthcare that uses the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies to transform patient-centric solutions. This study examines how these technologies are combining to change patient monitoring, illness management, personalized treatment, and healthcare delivery. Wearable sensors, medical equipment, and IoT-enabled infrastructure allow for real-time, continuous monitoring of patient health data, enabling the early identification of anomalies and the implementation of personalized interventions. IoT platforms enable remote connectivity and data-driven insights by easily integrating sensor data into healthcare systems. AI algorithms analyses large amounts of sensor data to maximize clinical decision-making, forecast health outcomes, and extract insightful information. Intelligent health solutions offer many advantages, such as better patient outcomes and a better patient experience. However, to fully utilize these technologies, issues like data privacy, interoperability, and regulatory compliance need to be resolved. Future directions in intelligent health include the establishment of standards and regulatory frameworks, patient-centric design, integration with electronic health records, and advances in AI and machine learning. With individualized, data-driven solutions, intelligent health holds great promise for revolutionizing the delivery of healthcare and enabling people to take charge of their own health and well-being

关键词： Patient monitoring Data privacy Reviews Data security Medical services Transforms Ubiquitous computing Internet of Things Wearable sensors Interoperability

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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Proposal of Machine Learning Algorithm for the Construction and Demolition Waste Prediction Rate

Proposal of Machine Learning Algorithm for the Construction ...

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International Conference on Technological Innovations in Multidisciplinary engineering and Sciences, TIMES 2024

作者： Arpitha, L.M. Kumar, R. Anil Fathima, Zoya Yeshaswini, R. Dhanyashree, G. M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Civil Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India TCSiON Tata Consultancy Services Karnataka Bengaluru India Department of Computer Science and Design BGS College of Engineering and Technology Karnataka Bengaluru India

ISBN: (纸本)9783031837494

The construction field in India is growing rapidly due to urbanization, industrialization, modernization and population expansion. This expansion results in significant construction and demolition (C&D) waste produced when structures are constructed, renovated, repaired or demolished. Artificial Intelligence (AI) and Machine Learning (ML) plays an important role in solving this problem by turning C&D waste into construction materials, thereby promoting a circular and sustainable *** optimizes the entire process of turning waste into construction materials, from collection and classification to material processing and construction. Advanced analytics and ML algorithms are leveraged to effectively manage waste, reduce environmental impact and promote sustainability. This article proposes a process for developing ML algorithms to analyze C&D waste generation rates and explores future trends in this field. Integrating AI into waste management in the construction field promises a greener and more efficient future. A methodology for implementation of various Machine Learning model like support vector machines (SVM), K-nearest neighbors (KNN), random forests (RF), artificial neural networks (ANN), and linear regression (LR) etc., are given in the form of an algorithm. As waste C&D increases rapidly, waste management becomes necessary so information about waste generation is needed. In this article, different methods applied to predict C&D waste by applying AI will be explained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Demolition

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Railway Passenger Flow and Sales Prediction Using Advanced Machine Learning Techniques

Railway Passenger Flow and Sales Prediction Using Advanced M...

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BIT Conference (BITCON), International

作者： Nalli Vinaya Kumari G S Pradeep Ghantasala Rajesh Sharma R Pellakuri Vidyullatha Ananthanagu U Saliha Bathool Department of Computer Science and Engineering Malla Reddy Engineering College for Women JNTUH Hyderabad Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Effective passenger waft control is pivotal for the highest quality operation of railway structure, impacting each operational performance and passenger satisfaction. This study delves into the software of system gaining knowledge of strategies to investigate and forecast passenger waft and price tag income inside railway networks. Utilizing historic tour statistics, sensor statistics, and outside influences, we increase predictive fashions that offer treasured insights into passenger conduct and decorate operational decision-making. Our findings spotlight the extensive enhancements in prediction accuracy and operational performance that system gaining knowledge of can deliver, providing sizeable blessings for railway control.

关键词： Support vector machines Knowledge engineering Accuracy Decision making Transportation Predictive models Rail transportation Software Real-time systems Random forests

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Optimizing Athletic Performance through Nutrition and Machine Learning: A Comprehensive Review

Optimizing Athletic Performance through Nutrition and Machin...

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BIT Conference (BITCON), International

作者： K. Venkata Narayana Nalli Vinaya Kumari G S Pradeep Ghantasala Rajesh Sharma R Pellakuri Vidyullatha Ananthanagu U Department of Physical Education MLR Institute of Technology JNTUH Hyderabad Department of Computer Science and Engineering Malla Reddy Engineering College for Women JNTUH Hyderabad Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Nutrition plays a pivotal function in athletic general overall performance, influencing training, competition, and healing. This paper examines the influence of macronutrients (proteins, carbohydrates, fats), micronutrients (vitamins, minerals), and hydration on general overall performance, integrating insights from sports activities sports era and device learning. Recent advances in periodized vitamins and personalized dietary strategies are more with the resource of the use of wearable generation and information analytics, providing real- time monitoring and optimization of athletes` nutritional status. Current evidence on dietary nutritional dietary supplements for enhancing general overall performance and healing is reviewed. Findings emphasize the importance of tailored vitamins plans based totally mostly on sport-precise wishes and character wishes. Practical pointers for optimizing general overall performance thru strategic vitamins and leveraging device learning tools are provided, underscoring vitamin’s vital function in maximizing athletic capability and long-term health.

关键词： Performance evaluation Training Support vector machines Proteins Reviews Decision trees Random forests Optimization Monitoring Sports

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