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4th International Conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Uchimuthu, Muthaiah Ramanathan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349856

The four most prevalent diseases that affect apple leaves are black rot, cedar rust, scab and leaf blotch. The spread of infection can be stopped and the healthful growth of the apple sector ensured by early detection and correct identification of apple leaf diseases. The Convolutional Neural Network (CNN) achieved a commendable accuracy of 89.42%. In comparison, the utilization of Inception v3 demonstrated a substantial enhancement, yielding an impressive accuracy of 92.50%. These results underscore the superiority of Inception v3 in accurately classifying apple leaf diseases, marking a significant advancement in predictive modeling for agricultural applications. The result is more superiority of MobileNet in classifying apple leaf disease more accurately with accuracy of 96.88%. In comparison of all three algorithms, MobileNet has more accuracy to offer a valuable tool for precise disease detection and informed decision-making for farmers, ultimately contributing to enhanced agricultural productivity and sustainable farming practices. © 2024 IEEE.

关键词： Decision making

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Online Food Order Management System With Recommendation Integrated *** and Microsoft SQL Server 15

Online Food Order Management System With Recommendation Inte...

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

作者： Kulkarni, Abhishek V. Ravindra, Addanki Ranga Sangeethi, Maheswarla Sai Sreevidya, B. Rajesh, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Online food ordering management system with recommendation is a full-fledged software model based on the Database Management, SQL and *** to minimize the manual work for the workers of the hotel. This helps to even customers to order their food easily in this evolving software world. This model has many features like Ordering food, Menu and Category updating by the Admin, Customers order history, Total payment done in a day, selling report of the hotel from a date to date, Login and mainly the recommendation for the customer. The software and hardware requirements for this software model are easily available and easy to work with. This helps us to keep track of all the data for long years. As the KNN classifier recommends the user. This software is completely based on the administration level as the DB admin can manage. The total workflow of the project and the work of DB admin explained in the methodology section. © 2024 IEEE.

关键词： DBMS Python Recommendationsystem SQL

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Emotion Detection from Text Using ML Framework

Emotion Detection from Text Using ML Framework

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Jasvitha, Bommisetty Durga Kanagaraj, Kanaganandini Murali, Keerthana Venugopalan, Manju Department Of Computer Science And Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350371567

With technology improving at astonishing strides, most people have access to Internet for communication via text, image, audio and video. Emotions are expressed through words, gestures, expressions and in today's world through tweets and comments. With the applications tending to be more user personalised this data needs to be processed in real time. Their behaviour needs to be analysed by their emotions. The dataset is sourced from Kaggle where the data has been streamed from X (formerly known as Twitter). This data is first converted into a vectorised form by the TF-IDF algorithm and then supervised classification techniques such as such as K-means, Naive Bayes, SVM and Ensemble Learning Techniques like Gradient Boosting, Random Forest, Decision Tree, XG Boost, Cat Boost and deep learning network named Multi-Layer Perceptron is used to determine and correctly classify the data into one of the 6 labels: sad, joy, love, anger, fear and surprise. The data balancing technique SMOTE is applied to enhance the performance of classifiers. MLP classifier was able to achieve the best results followed by the tree-based classifiers Random Forest, Decision Tree, and SVM with 0.99 F-measure. © 2024 IEEE.

关键词： Decision trees

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： Image segmentation

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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

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IEEE Access 2025年 13卷 95753-95796页

作者： Ahmed Fime, Awal Mahmud, Saifuddin Das, Arpita Islam, Md. Sunzidul Kim, Jong-Hoon Southern Illinois University School of Computing CarbondaleIL62901 United States Bradley University Department of Computer Science and Information Systems PeoriaIL61625 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh Kent State University Advanced Telerobotics Research Laboratory Department of Computer Science KentOH44240 United States

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation. © 2013 IEEE.

关键词： Deep learning

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Federated Multi-Agent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2

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IEEE Internet of Things Journal 2025年第13期12卷 23402-23417页

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Ajmal, Mahnoor Kim, Dongkyun Srivastava, Gautam Kyungpook National University School of Computer Science and Engineering Dongseo University Republic of Korea and the School of Computer Science Busan Daegu Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications. © 2014 IEEE.

关键词： Resource allocation

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AffectiveLearn: Emotion Enhanced Online Learning 8th

AffectiveLearn: Emotion Enhanced Online Learning

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Aiswariya Milan, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819766833

This research introduces an approach to online learning by incorporating deep facial expression analysis to discern primary and secondary emotions, such as frustration, anxiety, boredom, and confusion. Using an advanced deep face algorithm, the system accurately detects and classifies facial expressions, providing nuanced insights into users’ emotional states during online learning. Additionally, the work explores the integration of blink rate as a supplementary metric to enhance emotion recognition accuracy. The goal is to create a responsive online learning environment by leveraging emotional cues, fostering adaptive strategies according to individual emotional states. The system features a user-friendly Graphical User Interface (GUI) and has undergone testing with an existing dataset, ensuring its efficacy. This research marks a significant step toward revolutionizing online education, offering a dynamic and emotionally intelligent learning experience for improved user engagement and educational outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Big Data Analytics and Weather Insights for Improved Crop Planning in India 15th

Big Data Analytics and Weather Insights for Improved Crop Pl...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Bikku, Thulasi Anusha, Mupparaju Sreelekha, Pavuluri Jhansi Mamatha, Choppara Sri, Vinnakota Phani Computer Science and Engineering School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9783031889912

The purpose of this research is to gather and analyze a wide range of data related to weather and crop production in specific regions of India. The data includes information on temperature, rainfall, sand, grain, crop production, moisture content, and wind velocity, among others. The aim is to use this information to help farmers improve their crop yields and make more informed decisions about planting and harvesting crops. To achieve this goal, the study employs a method known as big data analytics for weather-based crop prediction. This approach utilizes historical weather data, crop yield, and soil moisture information to forecast future crop yields. Machine learning algorithms are applied to analyze large datasets and identify patterns and trends that can be used to make predictions about future crop yields. The research involves several stages. First, the data is processed using Python to remove any unwanted or noisy data. Next, the MapReduce framework is used to analyze and process the large amount of data collected. Then, k-means clustering is applied to the MapReduce outcomes to group similar data points together and obtain an expected average for the data, which helps in assessing its accuracy. The research also involves the development of a customized approach for predicting and displaying harvest forecasts through a user-friendly interface based on Flask. This approach allows users to make informed decisions about farming practices based on information such as temperature, rainfall, moisture content, and wind velocity. Furthermore, the study recommends three crops for farming and is configurable, allowing for the prediction of the best farms for each region. The research is essential for policymakers and farmers in India as it provides valuable insights into the potential impact of climate change on agricultural production. The study also offers a method for using data analytics to improve crop production, which can help increase food security in India an

关键词： Global warming

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