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Automated Detection and Classification of Cotton Leaf Diseases: A computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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

作者： Swastick Ganguly Jathin S M Mohak M Sanjay BM Somashekhar N Shobharani Department of Computer Science Amrita School of Computing Mysuru Campus Dept of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru Campus

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability

关键词： Deep learning Training Plant diseases Pathogens Accuracy Computational modeling Crops

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University's Educational Environment Personalization Based on the Ontological Models 23

University's Educational Environment Personalization Based o...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Pustovalova, Natalya V. Avdeenko, Tatiana V. Pustovalova, Alexandra V. Novosibirsk State Technical University Dept. Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Dept. Applied Mathematics and Computer Science Novosibirsk Russia

ISBN: (纸本)9781665498043

The paper presents a work in progress. Personalization of the educational environment allows improving the quality of the educational process, raising it to a new level. Our development is intended to diagnose and recommend educational content, depending on the students' personality. This article describes a system of ontological models (Learner Model, educational content model and cognitive functions model) designed to diagnose students' characteristics and the subsequent issuance of educational content, taking into account these characteristics. The learner model, implemented as a Protege ontology, is based not only on human professional experience but also on student testing results. This approach, based on the use of various IQ tests, allows reflecting in the ontology the dependencies identified due to linear regression methods, as well as other types of analysis. By this way we will reduce the dependence of the model on the human expert's personality and make it more objective. Using the Protege tools, we will combine three ontologies in the architecture of a personalized environment through rules and axioms. © 2022 IEEE.

关键词： Ontology

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Bone Tumour Detection and Classification Using Deep Learning Based on Computed Tomography

Bone Tumour Detection and Classification Using Deep Learning...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： S Rajath R A Archana Kunaal Shivakumar Kevin S Mathew Mohit Krishna Dept of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Bone Tumours may develop in any part of the bone and they usually occur in young adults or adolescents. Diagnosis requires at least a radiologist, surgeon, pathologist, and oncologist. For any suspected bone tumour lesion, radiography, CT, and magnetic resonance imaging are required. A groundbreaking initiative is underway in the realm of medical imaging, geared towards achieving early and accurate detection of bone tumours. By combining deep learning techniques to create an accessible diagnostic instrument for health care providers and patients. Three distinct deep learning models, including a basic Convolutional Neural Network (CNN), VGG-16, and DenseNet, will be implemented and trained for bone tumour classification. These models will leverage transfer learning to optimize their performance. The ultimate deliverable will be a user-friendly web platform built using Flask, offering a seamless interface for users to access and benefit from the trained models. This project is poised to make a significant impact in the realm of medical diagnostics, potentially revolutionizing bone tumour detection by facilitating early intervention and improving patient care.

关键词： Deep learning Training Accuracy Computed tomography Transfer learning Bones Medical diagnosis Convolutional neural networks Medical diagnostic imaging Tumors

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Towards Content Dissemination in Subway Mobility: Re-Evaluating the Performance of Power and Interest Aware PeopleRank

Towards Content Dissemination in Subway Mobility: Re-Evaluat...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Sara Elsingergy Soumaia Al Ayyat Sherif G. Aly Computer Science and Engineering Dept. The American University in Cairo Cairo Egypt

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

In this paper, we explore the performance of an opportunistic network forwarding algorithm, namely Power and Interest Aware PeopleRank (PIPeR) in a subway mobility environment. PIPeR is known to perform well according to known metrics in delivering data taking into consideration both the interest in the disseminated data, along with power conservation. The algorithm was only tested however using pedestrian mobility models. Subway mobility is a candidate for many other useful cases for opportunistic content dissemination such as that of armed conflicts where civilian populations live in subway environments for safety without fixed network infrastructure. In this work, we implement and evaluate the PIPeR algorithm in a subway model using the AnyLogic simulator. Our results show a significant increase in the f-measure by 61% and decrease in the delay by 41% in comparison to the pedestrian mobility environment. In addition to that, we pinpoint some areas of interest where content dissemination happens vigorously yet at the expense of some increase in cost and power consumption, which we subsequently alleviate by using a particular hibernation model that decreases the power consumption by 43 % at the expense of only 6% reduction in delivery ratio.

关键词： Measurement Wireless communication Power demand Costs Pedestrians Mobility models Hybrid power systems Delays Safety Public transportation

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Multivariate analysis of the influence of students' characteristics on academic performance 8

Multivariate analysis of the influence of students' characte...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Pustovalova, Natalya Avdeenko, Tatiana Novosibirsk State Technical University Dept. Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Dept. Applied Mathematics and Computer Science Novosibirsk Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Modern universities, developing a digital environment, rely on a student-centric approach relevant to pedagogical and didactic practices. Personalization of the educational environment allows improving the quality of the educational process, raising it to a new level. This article presents the usage of multivariate modeling to identify common relationships and patterns between academic performance and psychometric characteristics. The study sample includes psychometric testing data of students from Novosibirsk State Technical University (NSTU), which determines cognitive, motivational and personal characteristics, and also data on their academic performance. The results obtained are further used for diagnostic and prognostic purposes to recommend educational content depending on the student's personality. © 2022 IEEE.

关键词： Students

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ML-based Digital Twin for anomaly detection: a case-study on Turtle soccer robots 49

ML-based Digital Twin for anomaly detection: a case-study on...

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Liu, Xingyu Van Den Brand, Mark Muctadir, Hossain Muhammad Van De Molengraft, Rene Eindhoven University of Technology Set Cluster Dept. of Mathematics & Computer Science Eindhoven Netherlands Eindhoven University of Technology Department of Mechanical Engineering Eindhoven Netherlands

ISBN: (纸本)9798350342352

In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in the anomaly detection domain. A search-based literature survey revealed that the majority of the case studies focus on large-scale systems (i.e., nuclear power plant, aerospace, and power grid) producing extensive data. Our work aims to investigate the performance of this technology in smaller-scale systems that generate less data. In this case study, we developed a ML-based DT of the mobility system, the omni wheels, of the Turtle soccer robots. The DT is capable of analyzing historical data collected from the physical robots and differentiating between damaged and undamaged wheels. Our experiments suggest that ML-based DT of small-scale systems is indeed capable of achieving relatively accurate results for anomaly detection use-cases. © 2023 IEEE.

关键词： Sports

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Harmony Forecast: Using Machine Learning to Predict Relationship Quality

Harmony Forecast: Using Machine Learning to Predict Relation...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Harshitha Sai Nallagonda Varun Teja Kondra Srinivas Gorla Mohankrishna Samantula Dept. of Computer Science and Engineering GITAM Deemed to be University Visakhapatnam India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Several things influence human relations. Many of each other's personality traits can have an impact on a relationship between two people. These characteristics have the power to strengthen their love or spark conflict. Personality traits can be used to evaluate the quality of the relationship between the partners. To determine whether the individual will maintain or end the relationship, the relationship's quality has been evaluated based on personality traits. A thorough analysis has been conducted on the personality traits that impact relationships to determine whether they will have an impact, might have an impact or have no impact at all. A study was carried out to investigate the impact of personality traits on relationships. The relationship quality was assessed based on the personality trait which will make the person stay in the relationship, might or might not stay, or not stay in the relationship. The data collected and the pre-existing data combined was used to calculate the relationship quality.

关键词： Renewable energy sources Accuracy Machine learning algorithms Sensitivity Computational modeling Power control Predictive models

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Fabrication and Database Management of Cubesat

Fabrication and Database Management of Cubesat

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Khulal, Nibesh Mergoju, Bhanu Kiran Ranganayakulu, S.V. Guru Nanak Institutions Technical Campus Dept. of Computer Science Engineering Hyderabad India Guru Nanak Institutions Technical Campus Dept. of Mechanical Engineering Hyderabad India Guru Nanak Institutions Technical Campus Research & Development Hyderabad India

ISBN: (纸本)9798350348460

The continuous evolution of technology necessitates the creation of cutting-edge innovations for the betterment of mankind. One remarkable achievement that emerged in 1999 is the concept of CubeSats, drastically reducing the cost of satellite development and launch, leading to numerous space experiment applications. CubeSats have emerged as a groundbreaking solution in the realm of satellite technology, comprising modular cubic units, each measuring 10cm x 10cm x 10cm. This standardized form factor enables unparalleled flexibility in design, empowering researchers and institutions to customize CubeSats for diverse missions. This paper presents the conceptualization and development of a CubeSat equipped with an advanced environmental parameter sensing system, including temperature, pressure, humidity, altitude, accelerometer, and gyroscope. The collected data is efficiently stored in a centralized XAMMP database management system, functioning as a compact ground station server. With externally mounted antennas and a dedicated onboard microcontroller, the CubeSat boasts an innovative local database system, offering unparalleled features. These CubeSats offer an affordable means of implementing coordinated sensing, low-bandwidth communications, and efficient local database management. The cost-effective local database management server provides compelling evidence that local database systems are a viable and dependable means of storing and maintaining mission-critical data for cube satellites. By showcasing the potential of small-scale satellite projects, this development empowers individuals and educational institutions to participate in hands-on space exploration, fostering creativity and inspiring future generations of space enthusiasts. © 2024 IEEE.

关键词： Cubesat local database management mission-critical data Sensors Space XAMPP Server

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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Rough Set Based Recipe Clustering Approach

Rough Set Based Recipe Clustering Approach

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Priyanka Das Chandrani Mukherjee Samiksha Gupta Sayan Roy Khushi Shah Shreya Singh Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique.

关键词： Uncertainty Communication systems Decision making Rough sets Clustering algorithms Food products Information systems

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