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

Automated Detection and Classification of Cotton Leaf Diseas...

引用

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

作者： Ganguly, Swastick Jathin, S. Mohak, M. Sanjay, M. Somashekhar, B.M. Shobharani, N. Amrita School Of Computing Department Of Computer Science Mysuru Campus India Gitam School Of Technology Gitam University Dept Of Computer Science And Engineering Bengaluru Campus India

ISBN: (纸本)9798350371567

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. © 2024 IEEE.

关键词： Cotton

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An Exhaustive Analysis on Security Issues concerning IoT using Blockchain

An Exhaustive Analysis on Security Issues concerning IoT usi...

引用

2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Vats, Gaurav Sharma, Pankaj Kumar Abes Engineering College Dept of Computer Science Ghaziabad India

ISBN: (纸本)9798350323887

In today's world, the Internet of Things (IoT) has become an important part of daily life. Due to the exchange of information or data generated by sensors and various technologies used in IoT, the safety of an IoT system is becoming a major issue in technology-driven areas. On the other hand, blockchain, which started with Bitcoin, is now being actively explored for smart contracts that remove the need for a third- party and improve processes and transparency in different areas such as asset management, supply chain management, and healthcare networks. Furthermore, it has the potential to improve the quality of analytical systems by enabling users to trace immutable data from beginning to *** merger of both technologies, IoT and Blockchain, is one of the most noticeable advancements empowering digital transformation in every possible way. Blockchain endorses IoT devices, increasing reliability and clarity in the IoT system. With blockchain, IoT platforms, devices, and apps can run in a decentralized, scalable environment © 2023 IEEE.

关键词： Internet of things

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Facial Detection and Anonymization for Privacy Protection from Social Networking Sites(SNS) 14

Facial Detection and Anonymization for Privacy Protection fr...

引用

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

作者： Charan, Srungaram Sai Kumar, K. Kishor Kakatiya University Dept of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350335095

A social networking site (SNS) is an incredible information exchange platform that helps advance and shares a lot of useful information regarding items, news, training, travel, medical care, etc. However, there are a lot of risks involved with protecting a person's safety and security. Posts of photos online can be shared, labeled, and reposted again without the permission of the individuals pictured. Using facial recognition, we propose a framework for identifying each person and examining the web-based account connections between people. Replicated, labeled, or replicated posts will be checked, regardless of whether they're by a known or unknown individual. Whenever a person is obscure, the images are convolved through a low-pass filter in order to eliminate clamor and edges that obstruct the image. The proposed solution used deep-learning-based VGG19 engineering to characterize pictures, Haar overflow viola jones calculations for location, and PC vision-based obscuring methods to remove objects from the picture. Thus, a region under the ROC curve (AUC) precision of 88% was achieved. There was a 91.53% accuracy achieved by using the proposed model, and we also assessed the model using Precision, Accuracy, Review, and F1-Score matrices, which yielded an accuracy of 70.83%, 82%, 71%, and 0.68% respectively. © 2023 IEEE.

关键词： computer vision

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Sugarcane Classification of Image Processing and Convolutional Neural Networks 2

Sugarcane Classification of Image Processing and Convolution...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Kavitha, K. Umasankari, E. IFET College of Engineering Dept of Computer Science Villupuram India

ISBN: (纸本)9798350348002

Sugarcane is a significant crop with several uses in the food, bio-energy, and bio-based product sectors. Many elements, including climate, soil fertility, and plant diseases, can have an impact on the quality of sugarcane. In this study, it suggest a sugarcane classification system based on convolutional neural networks (CNNs) and image processing to automatically evaluate sugarcane crops' quality automatically. Using image processing techniques, the system uses digital photographs of sugarcane to extract attributes including color, texture, and form. These attributes are then sent into a CNN to be categorized into various grades or quality levels. By testing the proposed system using a sizable collection of photographs of sugarcane, comparing the outcomes to those attained using conventional visual inspection techniques. The findings indicate that the suggested system can classify sugarcane crops with a high degree of accuracy and dependability. The sugarcane industry may employ the suggested approach to increase the efficacy and precision of sugarcane quality evaluation. This research highlights the possibility of fusing deep learning models with image processing methods for agricultural applications. © 2023 IEEE.

关键词： Support vector machines

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Energy management system for mitigating buildings' power exchanges with the grid 2

Energy management system for mitigating buildings' power exc...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Kelepouris, Nikolaos S. Kothona, Despoina Kelepouris, Dimitrios Magkos, Petros Tsigkouni, Sofia Bouhouras, Aggelos S. Christoforidis, Georgios C. University of Western Macedonia Dept.of Electrical and Computer Engineering Kozani Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Energy Management Systems (EMSs) have gained significant attention over the last years, considering the integration of Renewable Energy Sources and the flexibility provided by the storage systems and the schedulable household appliances. In this paper a residential EMS with two different approaches is proposed. The formulation of the first and the second approach is Mix Integer Linear Programming (MILP) and Mixed Integer Quadratically Constrained Program (MIQCP), respectively. Both approaches schedule the operation of storage systems and flexible appliances in order to balance the local demand and production power. However, the formulation of the EMSs' objective function differs relating to each contribution at the peak prevention. The efficiency of each approach is assessed at both the building and the grid level. The results point out that the MIQCP mathematical formulation outperforms since could reduce the grid losses by alleviating the imported and exported peak power. © 2022 IEEE.

关键词： Energy management systems

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An Automated System for Wrong-Way Vehicle Detection using YOLO and DeepSORT 5

An Automated System for Wrong-Way Vehicle Detection using YO...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Siddique Mahi, Abu Bakar Eshita, Farhana Sultana Helaly, Tanjina University of Asia Pacific Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Wrong-way driving is one of the main causes of wrong-way crashes. This also contributes to increased traffic flow and accidents. As a consequence, every year thousands of people die in road accidents, and valuable working hours are wasted. Bangladesh is a highly populated country where the traffic police are still handling traffic monitoring manually. Consequently, they often overlook or ignore the wrong-way vehicle. To address these problems, the paper introduces an automated wrong-way vehicle detection system that can be used for monitoring wrong-way vehicles. The proposed system has three phases: Detect objects from the video clips, track the detected objects using DeepSORT, and lastly, identify the vehicles driving in the wrong direction. Additionally, we count the number of vehicles traveling in each direction, which can be used for further statistical analysis. Our proposed wrong-way detection model achieved an accuracy of 97.53%. The model is also converted to TFLite and Core ML model so that the model can be deployed on Android and macOS/iOS devices. The proposed solution will be a fundamental tool for vehicle monitoring, lowering road accidents, traffic law violations, criminal activities, and traffic congestion. © 2023 IEEE.

关键词： Vehicles

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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Detection of Predatory Publishing Using Mixed Dataset: Proposal of a Data Model 11

Detection of Predatory Publishing Using Mixed Dataset: Propo...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Sharma, Garima Tripathi, Vikas Singh, Vijay University Dept. Of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350326147

Research Publications typically showcases new findings and conclusions. To ensure the reliability of publication, various authentic databases have been curated such as enlisting scholarly articles, research papers, etc. One such key database is Scopus. It is one of the largest scholarly directories of peer-reviewed literature in scientific, technical, medical, and social science. In this work, data from varied authentic publishing sources such as Scopus, DOAJ, Beall's, and Open Dataset for Predatory Publishers is collected, merged, and transformed into a single schema, and validation of publishers and their titles is analyzed. The publisher and their titles being classified into three categories: green, orange, and red nodes based on their title's presence in different authentic sources. All the green nodes publishers have every single title placed under the legitimate category in all our focused data sources, orange ones have either of their titles in the suspicious category and red ones are completely illegitimate ones. The results show the change in the percentage of legitimate publishers individually and produced single schema with a significant difference. With the proposed strategy the possibility of finding the legitimation of publishers becomes more accurate for further scientific assignments. © 2023 IEEE.

关键词： Citrus fruits

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Pharmacy Location and Recommendation Services

Pharmacy Location and Recommendation Services

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Valli, R. Prathiba, Sahaya Beni School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Kcg College of Technology Dept.of Mathematics Chennai India Manakula Vinayagar Institute of Technology Dept.of Electronics and Communication Puducherry India

ISBN: (纸本)9798350315127

The medical sector has continually advanced in recent years. The integration of medical services with a centralized system, on the other hand, is effective in enhancing people's lives thanks to the technology that is currently available. This system will help people to make out their medicinal requirements in their nearby location. If the exact medicine is not available alternative medicines are recommended based on user searched medicine combination it is achieved by using the rank-based algorithm to find similar medicine. This approach will computerize all pharmacy records so that customers and pharmacists can access them whenever and wherever they choose. The proposed system is to lay out a plan of action to the consumers plus pharmacist and do the representation of web services between them. This system intensifies the engagement of unorganized pharmacies into the computerized system to lift medical assistance. In such a way, the system will become the forthcoming way of e-medicinal services and advances the approach of medicinal assistance that are given to the patients by hospitals and doctors. It also helps to get drug availability in every locality © 2023 IEEE.

关键词： Medicine

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Machine Learning Framework for Women Safety Prediction using Decision Tree 5

Machine Learning Framework for Women Safety Prediction using...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmika, P. Sareena Rao, S.Siva Nageswara Rafi, Shaik Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

In every city, harassment and violence becomes one of the major problems for women. Further, women's personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women's safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations. © 2023 IEEE.

关键词： Decision trees

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