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DFNet: Dense fusion convolution neural network for plant leaf disease classification

Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Revolutionizing Malaria Diagnosis: Precision Segmentation of Thin Blood Smears via Advanced U-Net Convolutional Architectures

Revolutionizing Malaria Diagnosis: Precision Segmentation of...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Muhammad Shameem, P. Malarvel, Muthukumaran Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The most recent advances in deep learning have demonstrated considerable promise for medical image analysis, especially for the segmentation of thin blood smears, which are microscopic and used to diagnose malaria. This paper presents an improved Convolutional Neural Network (CNN) model for the segmentation of malaria parasites in thin blood smear images, which is based on the U-Net architecture. Our model aims to address the problems that are frequently present in microscopic images, such as overlapping cells and variable stain intensity. Specifically, our model is quite good at overcoming the inherent difficulties in microscopy imaging, especially when it comes to segmentation in conditions with overlapping cells, poor staining quality, and low contrast. Our model stands out for its ability to handle these complex scenarios, which is a major advancement over traditional segmentation strategies. The U-Net architecture is a perfect fit for this purpose because of its efficiency in segmenting biomedical images. We provide an extensive analysis of the model's performance, contrasting it with conventional segmentation techniques in terms of accuracy, sensitivity, and specificity. © 2024 IEEE.

关键词： Medical imaging

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An IoT and Blockchain-Based Framework for Sustainable Vertical Farming 2

An IoT and Blockchain-Based Framework for Sustainable Vertic...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Munim, Kazi Md. Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350360523

With ongoing global challenges like population growth, resource deficiency, climate change, and global warming, the demand for efficient procedures to produce sustainable, efficient, and safe food is increasing. Thus vertical farming, a pioneer emerging technology may play a vital role in addressing such challenges in agriculture. This article presents an innovative framework integrating IoT and blockchain to offer an efficient, reliable, and secure ecosystem for vertical farming. The IoT devices allow real-time monitoring and controlling of essential parameters like temperature, moisture, light, nutrient levels, and alike, while the Blockchain technology provides a transparent and immutable ledger of the records regarding crop growth and resource consumption of the farm, enhancing the framework's traceability. A prototypical system of this framework was developed and simulated in the lab environment to evaluate its performance for a specified use case. By generating required control instructions to maintain the ideal environment, the prototypical system proved to be effective, efficient, and reliable. © 2024 IEEE.

关键词： Blockchain

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Emotional Evaluation of Facial Tension Using LSTM for Psychological Assessments

Emotional Evaluation of Facial Tension Using LSTM for Psycho...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sreekrishna, M. Madhumitha, M.V. Simretha, R. Gopika, G.S. Supriya, S. Sathyabama Institute Of Science And Technology Department Of Computer Science Engineering Chennai India

ISBN: (纸本)9798350364828

Recent years have seen a significant invisible stress during and after the pandemic, making emotion a hot topic. The current economy's inflation and rising cost of living are making this worse;many workers are negatively impacted, which has led to many families experiencing heartbreaking situations and a sharp decline in productivity. Growing stress is detrimental to a person's well-being as well as the development of the business and nation. While using a single model to identify emotion is less accurate, using many models can result in processing delays and potentially misleading findings if the input model data is not correctly filtered and segregated. This paper aims to examine, analyse, and theoretically compare research works on emotion recognition using 15 facial expression methods and 17 voice approaches. It will go over the advantages and disadvantages of each technique as well as the accuracy of certain independent and hybrid techniques for recognizing emotions. While some techniques (such CNN, KNN, and SVM) can be used to many models, their levels of strength vary. Finding this is crucial in order to improve or replace the weaker level when using the same technique on several models. For simplicity of visual comparison, this article will also show the various popularity levels of the procedures in each model. Ideally, it will enable novice researchers to quickly choose the best technique for interpreting a facial expression or vocal cue to identify an emotion. © 2024 IEEE.

关键词： Emotion Recognition

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Silent Alert: Advancing Women's Security through Smart Sign Recognition and AI

Silent Alert: Advancing Women's Security through Smart Sign ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Senthilkumar, Dheeptha Bhavana, Lone Ranjana, P. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

In today's world, many technologically innovative solutions are developed to prioritize women's safety solutions, especially during nighttime. This research's main aim is to focus on addressing crucial safety issues like emergencies and harassment in public spaces. To build an efficient model the proposed system uses technologies like MediaPipe and machine learning algorithms like random forest, decision tree, support vector machine (SVM), gaussian naive bayes, k-nearest neighbor (KNN), gradient boosting and stacking classifier (hybrid model). Rigorous testing ensures the selection of the most effective model, assessed through metrics like F1 score, precision, and recall. By integrating technology and machine learning, apart from raising awareness and fostering safe environments the proposed work also aims to empower women and enhance their safety. MediaPipe significantly enhances the accuracy of sign language recognition and also plays a vital role in improving the system's effectiveness. At the same time, OpenCV enables real-time video capture for comparison with the trained model. To enhance the system, predictions are made based on the recognized sign language gestures. In the system, predictions are improved by analyzing recognized sign language gestures. It also incorporates a database matching system that activates an alarm and sends a mail with a screenshot upon detecting predefined gestures. This function ensures prompt notifications, enabling authorities to respond promptly to identified incidents concerning women's safety. The system's ability to instantly recognize and react to specific gestures makes it an effective tool for addressing safety issues for women. © 2024 IEEE.

关键词： Decision trees

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Enhancing Diagnostic Precision: Comparative Analysis of BERT and Machine Learning Algorithms for Multi-Disease Prediction from Symptom Narratives 3

Enhancing Diagnostic Precision: Comparative Analysis of BERT...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kumar, Y Udit Kumar, K Pradeep Vijayarani, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

This study examines the prediction of diseases through the analysis of 1,200 symptom narratives across 24 different diseases. Employing Huggingface's 'bert-base-cased' transformer model, renowned for its ability in natural language understanding, this research study capitalizes on its extensive training on diverse English language corpora. Utilizing the BERT model's advanced capabilities in next sentence prediction and masked language modeling, the study aims to deduce probable diseases from intricate symptom descriptions. Additionally, the research rigorously evaluates established machine learning models such as AdaBoostClassifier, SVM, and RandomForest, juxtaposing their predictive efficacy against the more sophisticated BERT model. The overarching objective of this comparative analysis is to develop a comprehensive predictive model for multiple diseases, transcending conventional methods focused on individual disease detection. © 2024 IEEE.

关键词： Forecasting

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EnviroTech: Revolutionizing Waste Management with IoT-Driven Automated Segregation and Alerts

EnviroTech: Revolutionizing Waste Management with IoT-Driven...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Thiyaga Vinnarasu, V. Bobby Farel, P. Balakrishnan, Mathiarasi Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The swift rise in urbanization has resulted in growing difficulties with garbage management, calling for creative approaches to lessen its negative effects on the environment. The system accomplishes real-time garbage recognition and segregation into biodegradable and non-biodegradable categories on a Raspberry Pi 4. It is built using the OpenCV library and the MobileNetV3 algorithm. The system uses a mix of sensors, load cells, and cameras to measure garbage, weigh, and keep an eye on levels in different bins. The utilization of OpenCV improves computational performance and makes it appropriate for deployment on edge devices, while the inclusion of MobileNetV3 guarantees high accuracy of 96.32 % in waste classification. A deodorizing capability is integrated for biodegradable trash to reduce offensive odours, improving user experience, and promoting environmentally friendly waste management practices. The non-biodegradable garbage bin also has a fire detection sensor built in, which allows for early fire detection and notifies authorities of the situation. Thorough statistics illustrate the usefulness and effectiveness of the waste segregation system, including accuracy measurements, real-world testing results, and computational efficiency studies. Through its scalable and flexible approach to supporting sustainable urban environments, this research advances smart waste management systems. © 2024 IEEE.

关键词： Object detection

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Image Processing Based Proposed System for Malnutrition Using Deep Learning 7

Image Processing Based Proposed System for Malnutrition Usin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Mary Posonia, A. Priyadarshi, Hariom Jai Adithya, B. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

In many nations, malnutrition is the primary cause of death for children under five. An inventive way to handle the problem of hunger in a non-invasive and effective way is through the use of deep learning in image processing for malnutrition diagnosis. The system trains a deep learning model, such as a convolutional neural network, on a huge dataset of photographs of people with various degrees of malnutrition in order to find patterns and features in the photos that indicate malnutritionFor ease of usage by medical practitioners, the trained model is then made available on a web-based platform or mobile device. The suggested system may supplement current practices and offer more data for a thorough evaluation, especially in environments with limited resources. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection and Prevention of Black Hole Attack using Tree Hierarchical Deep Convolutional Neural Network and Enhanced Identity-based Encryption in Vehicular Ad Hoc Network

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IEIE Transactions on Smart Processing and Computing 2024年第1期13卷 41-50页

作者： Narayanan, K. Lakshmi Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai603203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai603203 India

A method for the recognition and prevention of a black hole attack is proposed using a tree hierarchical deep convolutional neural network (THDCNN)and enhanced identity based encryption in a vehicular ad hoc network (VANET). Automobiles are organized with a cluster formula using an improved k-means clustering algorithm. Following the cluster formation, cluster head (CH) selection is done using a balancing composite motion optimization (BCMO) algorithm. After selecting the cluster head, the entrance of a spiteful node occurs in the cluster. The THDCNN is proposed for classifying a cluster node as i) a black hole attack node or ii) a normal node. If a black hole attack node is found, the attack node information is communicated to an individual CH, which makes a final decision. Otherwise, the standard node data is encrypted with the enhanced identity-based encryption algorithm. Finally, the proposed method prevents the normal node data from the attacker. Thus, the proposed method attains higher accuracy and lower computational time than other methods. Copyrights © 2024 The institute of Electronics and Information Engineers.

关键词： K-means clustering

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Efficient Diagnosis of Intracranial Tumor on MRI Scans 10

Efficient Diagnosis of Intracranial Tumor on MRI Scans

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Amshavalli, R.S. Ramkumar, P. Reshath, M. Sheena, B. Gracelin Kumar, V. Nithish Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

The accurate and timely diagnosis of intracranial tumors is crucial for effective treatment and management. In recent years, magnetic resonance imaging (MRI) has emerged as a valuable tool for detecting and diagnosing intracranial tumors. The objective of this paper is to review the current state of research on the diagnosis of intracranial tumors using MRI scans and to provide an overview of the various techniques and methodologies employed. MRI has proven to be highly sensitive and specific in the detection of intracranial tumors. It provides detailed images of the brain, allowing for the identification of tumor location, size, and characteristics. Various MRI sequences, such as T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced imaging, can be utilized to enhance the visualization and detection of tumor tissues. Moreover, advanced MRI techniques, including magnetic resonance spectroscopy (MRS) and functional MRI (fMRI), have shown promise in evaluating tumor metabolism and functional status. Several studies have focused on developing computer-aided diagnosis (CAD) systems to assist radiologists in interpreting MRI scans and improving diagnostic accuracy. These CAD systems utilize machine learning algorithms and image processing techniques to analyze MRI data and help distinguish between normal brain tissue and tumor abnormalities. Furthermore, the integration of MRI with other imaging modalities, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), has been explored to enhance tumor detection and characterization. In conclusion, MRI plays a crucial role in the diagnosis and detection of intracranial tumors. The development and utilization of advanced imaging techniques, as well as the integration of MRI with other modalities, hold great promise in improving early detection, accurate diagnosis, and effective treatment planning for patients with intracranial tumors. © 2024 IEEE.

关键词： Magnetic resonance imaging

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