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Multi Task Learning for Plant Leaf Segmentation and Counting

Multi Task Learning for Plant Leaf Segmentation and Counting

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Chaudhury, Bharathi Joshi, Vasudha Mitra, Pabitra Sahadevan, Anand S. Indian Institute of Technolog Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Space Research Organization Space Applications Centre Ahmedabad India

ISBN: (纸本)9781665461634

In this paper, we propose an efficient method to segment the leaves and count the number of leaves in digital plant images, important tasks in plant phenotyping. However, for large cropping lands, automated leaf counting and segmentation are challenging and time-consuming due to the plant's rapid growth, varying size of leaves and shape, and presence of occlusions, which pose the task challenging to even human experts. Deep learning methods show considerable improvement when employed in image-based plant phenotyping tasks like plant leaf counting and leaf segmentation. However, most of the research related to plant phenotyping was designed to perform a single-task at a time. Therefore, a multi-task deep learning (MTL) framework is proposed to infer the two tasks simultaneously (i)leaf segmentation and (ii)leaf counting. Compared to single-task learning, MTL achieved the segmentation mean intersection over union score of 0.7478 (improved by 2%), and the difference in the count is decreased by two times when employed in agricultural field images. © 2023 IEEE.

关键词： Image enhancement

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Increasing Productivity in Software Development Through the Use of Docker Technology 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Information Technology Guru Ghashi Das University Bilaspur Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The Docker technology makes the process of developing software substantially more effective by resolving issues related to deployment and hosting. Through the use of virtualization, it is able to function independently of the underlying hardware infrastructure, and it relies on its own code to carry out the execution of programs. The encapsulation of an application's whole environment together with its source code is at the heart of Docker's methodology. This enables Docker to efficiently eliminate the problems that are associated with having a variety of working environments, but it may also result in the images having a greater file size. "Images" is the term that Docker uses to refer to these bundled environments. Docker was used in this investigation to carry out a variety of tests, which allowed identical code to be executed on a variety of platforms, so permitting extensive comparisons. Software developers made use of Docker's ability to analyze the outcomes, which enabled them to make decisions based on accurate information. This study simplifies the software development and deployment procedures for the development community by providing significant insights into the benefits and considerations of Docker across numerous platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Sugar Learning: Deep Learning for Rapid Detection and Classification of Sugarcane Diseases

Sugar Learning: Deep Learning for Rapid Detection and Classi...

引用

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Brar, Simrat Kaur Sharma, Rishabh Vats, Satvik Kukreja, Vinay Chandigarh Group of Colleges Department of Computer Applications Landran India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350311204

Sugarcane is a widely cultivated crop due to its high demand and supply in various industries. However, the increase in production levels has led to an increase in the number of diseases that affect the crop. One of the most devastating diseases is sugarcane red rot (SRR) disease. A multi-layer perceptron (MP) based deep learning (DL) model was built for the identification and classification of SRR illness using a dataset of 20,000 photos of sugarcane leaves in order to address this problem. This model was trained on 20,000 photographs of sugarcane leaves. The dataset was classified into 5 different disease severity levels. The proposed model achieved an accuracy rate of 97.97% for binary classification and an accuracy rate of 98.03% for overall multi-classification. Furthermore, a comparison of the different SRR illness stages was carried out, and it was demonstrated that the proposed model is an effective tool for accurately categorizing sugarcane images based on the severity of SRR disease. This study contributes to the development of an efficient and accurate model for the early detection and diagnosis of SRR disease in sugarcane crops, which is essential for improving crop yield and preventing economic losses. © 2023 IEEE.

关键词： Diseases

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Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kolli, Srinivas Ranjani, M. Kavitha, P. Daniel, D. Arul Pon Chandramauli, Awadhesh VNR Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Computer Applications Jawaharlal Nehru College for Women Ulundurpet India Muthayammal Center for Advanced Research Muthayammal College of Arts & Science India Loyola College of Arts & Science Department of Computer Science and Applications India Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehrradun India

ISBN: (纸本)9798350348217

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the 'chemical, physical, and biological' quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply's quality. They're Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an 'smart water quality monitoring system by using wireless sensonrs' for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water's pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3. © 2023 IEEE.

关键词： Water quality

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A Way to Design Fog Computing Model for 5G Network using Vanet 4

A Way to Design Fog Computing Model for 5G Network using Van...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Maruthamuthu, R. Patel, Neeraj Yawanikha, T. Jayasree, S. Alsalami, Zaid Subbarao, S.P.V. Madanapalle Institute of Technology & Science Computer Applications Andhrapradesh Madanapalle India Ies Institute of Technology and Management Ies University Computer Science & Engineering Department M.P Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350360165

Intelligent cities rely heavily on smart automobiles, providing portable computing as well as communication capabilities via a wireless link. By Mobile spontaneous groups, computer fog acts as a middleman between automobiles and the web, giving them access to storing, retrieving and abilities to network. With VANET, which stands processing the request to the vehicle takes fewer minutes since it is a latency-sensitive tech. The most well-known problems with fogged and VANETs are congestion and delays. In this study, we created a priority-based fogged approach to intelligent metro automobile journeys, which lowers fog compute time and postpone, to address such issues. 5G localised Multi-Access edge information workstations was also used to enhance the computer fog infrastructures in meeting timely and The caliber of Level standards, which has greatly reduced the latency and wait. In lieu of the study conducted utilizing simply a web-based design, we achieved a 20% reduction in data time. Additionally, we achieved a 35% reduction in CPU usage as compared to the use of a public cloud platform. © 2024 IEEE.

关键词： 5G mobile communication systems

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Diagnosing Phytophthora infestans infestations on Solanum tuberosum leaves using Machine Learning Classifiers

Diagnosing Phytophthora infestans infestations on Solanum tu...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Text-Based Recommendation System for E-Commerce Apparel Stores 2

Text-Based Recommendation System for E-Commerce Apparel Stor...

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

作者： De, Utpal Chandra Dash, Bibhuti Bhusan Behera, Trupti Mayee Samant, Tapaswini Banerjee, Shobhan Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9781665475174

Ever since digitalization, online shopping has been trending at its peak. This shopping is in the form of gadgets, food items, clothes, daily use products & whatnot. Due to such a high demand of netizens, like the market-basket analysis in physical offline stores, the online E-commerce stores also need certain recommendation systems using which, around 35% of their revenues get generated as per surveys. In this paper, we have used & presented content i.e., text-based methods that would recommend ladies' apparel using product similarity measures, based on different aspects of the available text content. © 2023 IEEE.

关键词： Inverse problems

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Advanced Fake Review Detection via Aspect Extraction and TPA-BiLSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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Monitoring and Analysis of Encrypted Attack Traffic Based on Machine Learning

Monitoring and Analysis of Encrypted Attack Traffic Based on...

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2023 Human-Centered Cognitive Systems, HCCS 2023

作者： Zeng, Yingming Chen, Pengfei Zhang, Jingpeng Xidian University Xi'an Beijing Institute of Computer Technology and Applications School of Cyber Engineering Beijing China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China

ISBN: (纸本)9798350359183

With the growing significance of network security, an increasing number of websites and applications are opting to encrypt their transmitted traffic. In order to provide an effective method for analyzing encrypted traffic, this paper uses the C4.5 decision tree algorithm to establish a model for classifying encrypted traffic. Based on the Moore feature set, we perform feature extraction for monitoring and analyzing encrypted traffic. We use Cfssubseteval, Gainratioattributeeval and Infogainattributeeval as evaluation functions to remove redundant features to obtain the optimal feature subset, thereby improving the performance of model detection. Experimental results show that the C4.5 decision tree algorithm after feature extraction can reduce the training time of the original algorithm by 50% while ensuring a certain accuracy. In addition, compared to other deep learning models, although the traditional C4.5 decision tree method may not have high accuracy, it is more efficient in processing large-scale data and holds potential research value in the future. © 2023 IEEE.

关键词： Feature extraction

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Big Data Analytics for Healthcare Resource Allocation

Big Data Analytics for Healthcare Resource Allocation

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sharma, Brijesh Kumar Jayant, Khel Prakash Pujari, Purvi Vikram, Devendra Saraswat, Manish Kathuria, Kajol Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engieering UP Ghaziabad India Dy Patil University Vijay Patil School of Management Department of Management Navi Mumbai India Abes Institute of Technology Computer Science & Engieering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Dronacharya Group of Institutions Dept Computer Science and Information Technology UP Greater Noida India

ISBN: (纸本)9798331508685

Big Data Analytics for Healthcare Resource Allocation is the new mantra as it integrates vast amounts of complementary data to allocate healthcare resources efficiently. There is an increasing demand for healthcare services, and resources are in limited supply, so doing something as simple as allocating resources can ensure that care quality remains high. The process includes gathering and organizing large datasets from several resources, including electronic health records, patient demographics, service utilization and detailed analysis to generate meaningful findings. Utilizing advanced statistical and analytical techniques, by definition, amongst others, Big Data Analytics can help you decipher the data to show you patterns in resource utilization (how resources are being used), areas where inefficiencies occur which have gone unnoticed long enough that cause them become ripe for incremental year over year cost savings targets or staff reactions if not corrected as then gain media attention likely) some even assembled through different techniques to predict future needs based on past use cases. The advantages of this are manifold. It will help identify high-risk patients, enhance clinical decision-making, and facilitate evidence-based resource allocation. It can also help quantify how well-existing resource distribution strategies work and influence resource planning. Big Data Analytics for Healthcare Resource Allocation: Better Outcome, Cutting Cost and Effective Utilization of Resources This have the potential to revolutionize healthcare resource allocation and improve national healthcare systems' efficiency and effectiveness. © 2025 IEEE.

关键词： Electronic health record

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