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检索条件"机构=Dept. Informatics Engineering of Computer Science"

共 36957 条记录，以下是401-410 订阅

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DubasAlert: AI-Powered Detection System for Dubas Infestation on Palm Leaves

DubasAlert: AI-Powered Detection System for Dubas Infestatio...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Nagaraju, Y. Venkatesh Uvce Bangalore University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different stages of their growth. Existing models perform ineffectively and have large trained weights, making integration with edge devices difficult. To address these issues, we propose a pruned YOLOv7 model which is lighter and has fewer parameters, resulting in 75% lower FLOPs. Pruned Yolov7 outperforms baseline models with a mAP@0.5 of 80.1%. Our approach enables real-time detection and seamless integration into edge devices, resulting in practical solutions to combat Dubas insect infestations. © 2024 IEEE.

关键词： Plant diseases

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms 1

Smart Agriculture Inventory Management System Using Machine ...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kanyal, Hoshiyar Singh Jain, Harshit Tyagi, Harsh Yadav, Satyarth Prakash ABES Institute of Technology Ghaziabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350353648

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices. © 2024 IEEE.

关键词： Agricultural economics

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GNN-based Disease Detection and Classification in Cassava Leaf 5

GNN-based Disease Detection and Classification in Cassava Le...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Meenalochini, M. Amalroshini, A. Nandhini, K L Megha Murugan, C R Sogil Kongu Engineering College Dept. of Computer Science and Design Erode India

ISBN: (纸本)9798331518097

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches 9

Text-Based Sentimental Analysis to Understand User Experienc...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gokulapriya, R. Sambandam, Rakoth Kandan Balamurugan, M. Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general *** primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. © 2023 IEEE.

关键词： Python

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Intelligent Video Surveillance System 4

Intelligent Video Surveillance System

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Aryan, Ansh Singh, Dharya Pratap Kumar, Nitish Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350385793

This research study provides the utilization of an intelligent video surveillance system, a sophisticated technology that integrates video cameras, sensors, and cutting-edge software to monitor and analyze activities within a designated area or locale. By seamlessly combining compute r vision and machine learning techniques, it goes beyond conventional video surveillance installations to provide real-time insights along with automated alerts. Intelligent video surveillance systems harness the power of machine learning and computer vision algorithms to analyze video feeds in real-time, empowering them to detect, track, and comprehend objects, individuals, and occurrences captured within the video frames. Furthermore, these systems can recognize a variety of objects, including cars, people, animals, and particular objects, even in complex and crowded settings. They also offer a variety of analytical tools and insights that are very helpful to organizations and security operations, such as traffic patterns, heatmaps, and other data. This research study provides a thorough explanation of the modern uses of machine learning in the field of surveillance systems. Two crucial issue statements are addressed by it in practice: the identification of human falls and the detection of auto accidents. This study's main goal is to demonstrate an end-to-end system that will act as a beacon for law enforcement and medical personnel, giving them the ability to quickly identify traffic accidents and serious injuries and guarantee that the necessary assistance is provided on time. © 2024 IEEE.

关键词： Deep learning

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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Leukaemia: A Comparative Analysis of Deep Learning Models Using ALL Dataset 6

Leukaemia: A Comparative Analysis of Deep Learning Models Us...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sudheer, Rhea Vivek, Rapaka Ramesh, Richa Sarvade, Apoorva Kakarla, Sada Preethi, P. Pes University Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350381689

White blood cells, also known as leukocytes, or WBCs, spread abnormally in the bone marrow and blood, resulting in leukaemia (blood cancer). Leukaemia can be identified by pathologists by examining a patient's blood sample under a microscope. By counting different blood cells and physical characteristics, they can identify and classify leukaemia. This method takes a lot of time to forecast leukaemia. The pathologist's professional qualifications and experiences may also have an impact on this process. Traditional machine learning and deep learning techniques in computer vision are useful road maps that improve the precision and speed of identifying and categorizing medical images, such as minuscule blood cells. This paper offers a thorough analysis of the different Deep CNN models for identification and classification of WBCs and acute leukaemia in microscopic blood cells. © 2024 IEEE.

关键词： Contrastive Learning

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Utilizing Genetic-Neural System Fusion for Advanced Prediction and Classification of Damage Intensity in Road Traffic Accidents 2

Utilizing Genetic-Neural System Fusion for Advanced Predicti...

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

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350371314

This study proposes an analysis and prediction framework for road traffic accidents (RTAs) in the hilly terrain of Himachal Pradesh and several states in, India, integrating machine learning techniques such as neural networks (NNs) and genetic algorithms (GAs). Utilizing a comprehensive dataset comprising historical accident records, geographical features, weather conditions, and demographic information specific to Himachal Pradesh, the framework aims to identify patterns and correlations contributing to RTAs. Neural networks are employed to discern complex relationships within the data, while genetic algorithms optimize model parameters for enhanced predictive accuracy. By forecasting accident-prone areas and periods, this approach enables proactive interventions and targeted road safety measures. The outcomes of this research provide valuable insights for stakeholders and policymakers, facilitating the reduction of RTAs and mitigating their impact in the challenging terrain of Himachal Pradesh including all states of India. © 2024 IEEE.

关键词： Genetic algorithms

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An Effective Pre-Owned Car Price Prediction Based on Multi Linear Regression Technique 1

An Effective Pre-Owned Car Price Prediction Based on Multi L...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Kumar, Aman Singh, Dheeraj Chauhan, Keshav Guru Prasad, M.S. Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341737

The market for the purchase and sale of second- hand automobiles has seen a trend towards expansion in recent years. Used vehicle buyers and sellers are able to conduct business at rates that are acceptable and fair thanks to scientific examination of automobile prices. This contributes to a reduction in the risks associated with conducting transactions in the used car market and encourages the market to continue to grow in a sustainable manner. Even though a lot of effort has been done on predicting the price of used cars, there are still a lot of obstacles standing in the way of an accurate and reliable estimate. Predictions of the prices of used automobiles that are accurate can assist individuals in making wise choices and avoiding the inflated costs of used vehicles on the market to the greatest extent possible. In this work multi linear regression technique is used, which is a type of supervised machine learning algorithm to build a model for predicting the price of pre-owned cars effectively. The dataset used for the prediction was collected from Kaggle webpage. After training and testing the model, the proposed model deployed using flask framework. The accuracy of the proposed model is 89.23% based on testing data. © 2023 IEEE.

关键词： Automobiles

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