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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Anslam Sibi, S. Sundaresan, C. Surya, S. Sujith, J.P. Srm Valliammai Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Medicinal plants have been integral to traditional medicine, offering a wide range of health benefits and natural remedies. However, accurate identification is essential for safe use and the preservation of this valuable knowledge. This project proposes an AI-based system that combines EfficientNet and XGBoost models to enhance the accuracy of medicinal plant identification from images. While traditional systems rely on manual identification or basic models like CNN and SVM, they often fail to account for variations in plant appearance due to growth stages or environmental conditions. To address these limitations, the proposed system leverages EfficientNet for robust feature extraction from plant images, utilizing its superior ability to handle image variations across diverse conditions. These features are then passed to an XGBoost classifier, which excels in handling structured data and fine-tuning predictions. Together, this combination ensures high identification accuracy across a wide variety of plant species. Additionally, the system integrates a Hugging Face Transformer for natural language understanding and generation, allowing it to provide comprehensive information about each plant's medicinal properties and uses. This approach bridges the gap between accurate identification and practical application, promoting safe usage and preserving traditional medicinal knowledge. © 2025 IEEE.

关键词： Medicinal chemistry

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HDR Reconstruction from LDR Scenes Using Basic Image Formation Procedure 14

HDR Reconstruction from LDR Scenes Using Basic Image Formati...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sisira, C.S. Ann Thomas, Princy Government Engineering College Computer Science & Engineering Department of Computer Science & Engineering Thrissur India Government Engineering College Department of Computer Science & Engineering Thrissur India

ISBN: (纸本)9798350335095

High dynamic range (HDR) images are the one's which includes all the objects from the original scene with correct exposure. The HDR content can be very helpful in analysis purposes as it possess all features of the scene as it is. Although it is useful the creation of HDR is very much expensive. Low dynamic range (LDR) images are less expensive but may include saturation regions leads to the absence of some content. For this reason there is a necessity to think of an alternative. The reconstruction of HDR from LDR content can be the alternative solution for the problem. This process can be done using different *** studies mainly represents different kinds of machine learning techniques. The methods include machine learning , deep learning , different kinds of CNN architectures specially designed for particular purposes , GAN models , reverse tone mapping operators etc. This survey also analyses evaluation techniques for assessing quality of images and techniques like image declipping, tone mapping operator etc. Some of the analyzed methods also deals with reconstruction of HDR videos from LDR videos rather than from raw *** in the survey reveals accuracy of different methods. © 2023 IEEE.

关键词： Inverse problems

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Correction to: Adaptive learning-enhanced lightweight network for real-time vehicle density estimation (The Visual computer, (2024), 10.1007/s00371-024-03572-3)

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Visual computer 2024年第6期41卷 4393-4394页

作者： Qin, Ling-Xiao Sun, Hong-Mei Duan, Xiao-Meng Che, Cheng-Yue Jia, Rui-Sheng College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Correction: The Visual computer https://***/10.1007/s00371-024-03572-3 The publication of this article unfortunately contained mistakes. Figure 3 was not correct. The corrected figure is given below. Wrong figure 3: (Figure presented.) Correct figurure 3: (Figure presented.) The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Integrated Road Safety System for Real-Time Waterlogging Detection and Traffic Management Using Google Maps API

Integrated Road Safety System for Real-Time Waterlogging Det...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vignesh, R. Pabitha, C. Shamritha, V. Prathija, A. Srm Valliammai Engineering College Department Of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Potholes, waterlogging, and possible electrical hazards have a significant impact on urban traffic safety, especially during the monsoon season. Road damage, traffic jams, car accidents, and even potentially fatal electrical leaks from damaged power lines in flooded areas are all consequences of these situations. A thorough machine learning-based method for detecting potholes in real time, determining the extent of waterlogging, and identifying electrical leaks in water is presented in this study. The device measures the risk of water accumulation, estimates pothole depth, and records road conditions using computer vision algorithms. It also finds electrical leaks in water and sends out timely alerts to reduce the risk of electrocution. Using the Google Maps API, the system automatically reroutes traffic to safer routes, reducing the amount of time that cars and pedestrians are exposed to dangerous situations. Our system provides a strong, scalable foundation for enhancing road safety and urban mobility during inclement weather by combining pothole recognition, waterlogging severity assessment, and electrical danger detection. © 2025 IEEE.

关键词： Risk assessment

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Technological advancements in the detection and diagnosis of migraine: A review 4th

Technological advancements in the detection and diagnosis of...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kaur, Shivneet Kaur, Parneet Gosain, Monika Kaur, Roopkamal Department of Computer Science & Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Headaches are the most prevalent medical condition in the globe. The migraine is one of the various types of headaches. Along with their excruciating headaches, migraineurs often have nausea, vomiting fits, and light and sound sensitivities. Individuals have unique symptoms, some of which may be more intense than others. A migraine episode is about to begin when an aura, a symptom, appears. Based on the nature of symptoms, migraines can be divided into two basic categories: aura-producing and aura-less. Every individual has different triggers for migraine. This study provides an overview of the state-of-the-art deep learning and machine learning techniques for migraine identification. The primary subjects of this study include migraine kinds, phases, causes, and symptoms. © 2025 the Author(s).

关键词： Diseases

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Detection of Vehicle Crashes on Roads using Deep Learning 2

Detection of Vehicle Crashes on Roads using Deep Learning

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pokkuluri, Kiran Sree Sssn Usha Devi, N. Prasad, M. Raja Rao, Pbv Varma, Ch Phaneedra Ramesh Babu, G. Shri Vishnu Engineering College for Women Dept. of Computer Science and Engineering Bhimavaram India University College of Engineering College Dept. of Computer Science and Engineering JNTU Kakinada India

ISBN: (纸本)9798350371314

As the modern life is highly dependent on the modern transport system, road safety became a high priority. Most of the systems are aiming for very less reporting time to reduce the severity of the accidents. This paper presents an in-depth analysis of crash detection on roads using deep learning(DL). We have use one of the mechanisms of deepening i.e. Convolutional Neural Networks (CNNs) to detect the same. These DL models are used to classify the traffic accidents and it automatically recognize accidents with minimum effort. We have integrated automatic SMS alert with the system, which send SMS automatically after detecting the crash. This feature enhances the acceleration of emergency response, which makes the system more robust. We have achieved an accuracy of 96.3% which relatively good compared with the existing baseline methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification of Therapeutic Plants Using Machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rohini, S. Keerthana Shree, M.U. Jayalakshmi, R. Malathi, M. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 IEEE.

关键词： Machine learning

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Real Time Traffic Light Timing Optimisation Using Reinforcement Learning

Real Time Traffic Light Timing Optimisation Using Reinforcem...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Shri Vaisalini, L.C.K. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

A major problem especially in urban areas is traffic congestion which causes increased travel times, pollution, and compromises safety. This paper presents the design of the Smart Traffic light timing Management System, optimized in real-time traffic light timings using reinforcement learning techniques. This system scans data from different sources, including vehicle count in a network and waiting time of the vehicle at a traffic stop, which are utilized to continuously to alter traffic signals based on current situations. Utilizing reinforcement learning algorithms, the system continuously learns and adapts to changing traffic scenarios, striving for minimal congestion. The simulations run in SUMO (Simulation of Urban Mobility) indicate a noticeable reduction in wait times and improved flow across roads. The results form the basis for the inclusion of advanced data analytics in traffic management: it represents an effective method of mitigating urban congestion problems. The study concludes that by leveraging real-time data and machine learning, the traditional traffic systems can be transformed into an intelligent responsive network, capable of enhancing the efficiency and safety of urban transportation. © 2025 IEEE.

关键词： Traffic congestion

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Speech Recording Analysis for Parkinson’s Detection Using Machine Learning Approach 8th

Speech Recording Analysis for Parkinson’s Detection Using M...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Deepika, G. Vinothiyalakshmi, P. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India

ISBN: (纸本)9783031736162

Parkinson’s Disease (PD) is a neuro-degenerative disorder that affects the motor skills of a person when the production of dopamine is reduced and eventually motor dysfunction. Dopamine is a chemical produced by brain cells, which is responsible for controlling movements. Thus, if Parkinson’s disease is detected earlier, then the progression and the effects of its symptoms can be controlled. The proposed Parkinson’s Disease Detection (PDD) model used for the detection of Parkinson’s Disease using various Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Voting Classifier, and Random Forest (RF). The algorithms are applied on balanced data scales and on features selected using LDA and PCA from the speech recording dataset. On experimental analysis, the SVM algorithm is found to be more efficient on the balanced dataset and PCA extracted features, giving an accuracy of 96.73% and 95.42% respectively as compared to the other classification algorithms. While the Random Forest algorithm gives the best results on LDA applied dataset giving an accuracy of 95.85%. © IFIP International Federation for Information Processing 2025.

关键词： Random forests

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Detection of fungal infection in dogs using deep learning techniques 4th

Detection of fungal infection in dogs using deep learning te...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sunaina Kaur, Sukhpreet Kaur, Sukhdeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Fungal infections at the skin of puppies, including dermatophytosis, are commonplace and pose great health dangers to each the animals and their proprietors. Although these infections do no longer affect human skin immediately, rapid detection remains important for well-timed remedy and spreading prevention. The currently established diagnosis process frequently includes the visual confirmation of the infection by medical professionals and fungal culture and is often referred to as time-intensive and highly dependent on medical professionals’ qualifications. Over the past few years, deep learning has become quite a promising area of research, R&D, and deployment in multiple fields, including medical imaging. The current research develops a deep learning model for fungal infection detection on the skin of dogs based on a variety of architectures and variations of Convolutional Neural Networks to classify image patches as healthy and infected. Transfer learning for the model structuring is reviewed to determine the impact of pre-trained models on the model’s learning efficiency. © 2025 the Author(s).

关键词： Convolutional neural networks

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