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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nitisha, S. Mohith, N. Vinay, R. Agarwal, Monika Sinha, Aparajita Mokshitha, M. Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India National Institute of Technology Dept. Computer Science Engineering Agartala India

ISBN: (纸本)9798331540821

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security. © 2024 IEEE.

关键词： Agriculture Classification CNN Computer Vision Plant diseases

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Intelligent Management Information System: Web Approach 15

Intelligent Management Information System: Web Approach

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Devi, S. Renuka Subha, S. Sree Raju, D. Naveen Loganathan, V. Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Aims to develop web application for information management and sharing which helps educational institutions broadcast information to the students and their faculties. A college generates and uses a large amount of data where it needs to be stored in the centralized repository. This data must be communicated appropriately from the authorities to students, faculty, and HOD. This web application resolves to store, manage, and distribute this information. It is intended for information management and sharing in a way that can manage and manipulate data in a centralized place which helps us reduce the time required for updating, storing, sharing the information a college is generating. And eliminates duplication and maintains consistency in data and using one of the latest and powerful concepts called session which helps us store the user information across multiple web pages. The goal of the of web application for information management and sharing is to achieve centralized data management and accessibility. © Grenze Scientific Society, 2024.

关键词： Information hiding

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Review on Event Extraction for BioNLP with a Survey 2

Review on Event Extraction for BioNLP with a Survey

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Pattankar, Veena V Priyanga, P. Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India

ISBN: (纸本)9781665475174

The scientific literature contains essential information connected to proteins, drugs, and symptoms. Researchers are extracting organized, brief, and understandable information from unstructured texts to keep up with the growing number of publications. Within the context of the biomedical area, this study investigates event extraction and natural language comprehension. We will build a flexible description of an event by first outlining several terminological methods. Second, we demonstrate event extraction, its challenges, and annotated corpora. Third, we investigate representative methodologies and provide an up-to-date analysis with performance discussion. We provide a taxonomy to assist academics in navigating the deluge of event extraction works. Fourth, we compare biomedical solutions to those in other domains to uncover fresh research opportunities and approaches. Finally, we discuss potential applications, explainability, and knowledge injection. © 2023 IEEE.

关键词： Semantics

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Dynamic Service Caching Aided Computation Offloading Optimization Algorithm for Mobile Edge Networks

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IEEE Internet of Things Journal 2025年第13期12卷 24789-24802页

作者： Xie, Bo Xie, Jinhua Cui, Haixia He, Yejun Guizani, Mohsen South China Normal University School of Electronic Science and Engineering School of Microelectronics Foshan528225 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi99163 United Arab Emirates

The widespread adoption of computation-and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on Deep Dueling Double Q-Network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multi-user game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments. © 2014 IEEE.

关键词： Optimization Servers Heuristic algorithms Resource management Internet of Things Computational efficiency Vehicle dynamics Deep reinforcement learning Computer architecture Cloud computing

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Understanding Red Wine Quality with Feature Importance in machine learning and Explainable AI 15

Understanding Red Wine Quality with Feature Importance in Ma...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： John, Ayain Damodar, Navya Mohith, N. Vijayan, Bharathy Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India Dayananda Sagar University Dept. Computer Science Engineering Bangalore India

ISBN: (纸本)9798350370249

The research work investigates the factors that influence the quality of red wine and how they contribute to positive outcomes. It uses machine learning techniques like Support Vector machines (SVM) and K-nearest neighbors (KNN) to classify and assess red wine quality data. Traditionally, red wine quality has been evaluated subjectively, which presents challenges. machine learning algorithms offer an objective alternative. This study compares SVM and KNN algorithms in assessing red wine quality and aims to determine the crucial attributes that affect its quality. To evaluate the classification performance of different machine learning algorithms, metrics like specificity, sensitivity, selectivity, and F1 measure are used. Additionally, grid search algorithms are employed for parameter optimization. In the red wine datasets, the Support Vector machine (SVM) model obtains an accuracy rate of 89%, surpassing the K-Nearest Neighbors (KNN) model's accuracy rate of 87%, especially regarding predictive specificity. Furthermore, using grid search algorithms improves the overall performance of the classifiers. This study highlights the effectiveness of machine learning in assessing red wine quality, providing valuable information for producers to optimize wine production processes and ensure high-quality products for consumers. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep learning Algorithm 5

Automatic Human Life Rescue Analysis Model with Incorporatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kalaiselvi, V.K.G. Prithikhaa, S. Divya Darshini, J. Reddy, S Bharath Anandkumar, B. Hariharan, Shanmugasundaram Sri Sairam Engineering College Department of Information Technology Tamilnadu India KG Reddy College of Engineering and Technology Department of CSE Telangana India Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331530013

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities. © 2024 IEEE.

关键词： Convolutional neural networks

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End-to-End Hyperspectral Image Classification Using Hybrid GCNN with SWIN Transformer 1

End-to-End Hyperspectral Image Classification Using Hybrid G...

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1st IEEE International Conference on Ambient intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Ghotekar, Rahul Shaw, Kailash Rout, Minakhi Kiit University Department of Computer Science and Engineering Odisha India Department of Artificial Intelligence and Machine Learning Maharashtra Pune Lavale India

ISBN: (纸本)9798350316469

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional machine learning and Deep learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets. © 2023 IEEE.

关键词： Deep learning Graph Convolution Neural Network Hybrid Model Hyperspectral Image Classification SWIN Transformer

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Detection of Medicinal Plants Using machine learning 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Customer Behaviour Analysis: Tools Overview 2

Customer Behaviour Analysis: Tools Overview

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharti, Jigyasa Dongre, Snehlata Thorat, Mayuri G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India PES's Modern College of Engineering Dept. of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350383287

Analytics is a process of collecting and analyzing data that can help in taking meaningful decisions. The data is collected from various sources, this raw data is then processed and converted to dashboards which assists in business analyzation. Analytics are of various types such as descriptive, diagnostic, predictive, prescriptive and text. One area of analytics is Business intelligence which is a broader concept of using various tools and methods that involves presentation of business data to support decision making. Customer behavioral analysis can be conducted by having insights from web analytics. It is a process of understanding how a customer is responding to a website. Regular improvements can be done to increase customer interaction, enhance user experience and data-driven decision-making. In this paper, we will be discussing two major tools that are used for behavior analysis. © 2024 IEEE.

关键词： Decision making

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Revolutionizing Image Analysis for Precision Diagnosis and Recognition Using Cutting-Edge Visual Encoding

Revolutionizing Image Analysis for Precision Diagnosis and R...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Rajalakshmi, D. Anita, C.S. Sasikumar, R. Sudharson, K. Alekhya, Badi Rose, S. Remya R.M.D. Engineering College Department of Computer Science and Engineering Chennai India R.M.D. Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350348460

This groundbreaking study introduces a revolutionary image analysis method, showcasing unprecedented accuracy in precision diagnosis and recognition. The innovative approach seamlessly integrates feature detection and description, demonstrating exceptional proficiency in identifying crucial local features. The advanced visual encoding architecture, surpassing conventional methods, utilizes intricate convolutional layers and the renowned Haar algorithm, achieving a remarkable 99% success rate. This comprehensive suite of advancements propels image analysis to new heights, with potential applications ranging from medical diagnostics to facial recognition. Strongly accepted for its cutting-edge techniques and impressive success rate. © 2024 IEEE.

关键词： facial recognition feature detection image analysis precision diagnosis visual encoding

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