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Journal of Biotech Research 2024年 16卷 22-31页

作者： Muflikhah, Lailil Iskandar, Agustin Yudistira, Novanto Nadlori, Isbat Uzzin Dewanto, Bambang Nur Faculty of Computer Science Brawijaya University Malang India Faculty of Medicine Brawijaya University Malang India Department of Informatics Engineering and Computer Politeknik Elektronika Negeri Surabaya India Faculty of Technology Information Merdeka University Malang India

Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition. © (2024), (Bio Tech System). All Rights Reserved.

关键词： Patient treatment

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Secure Fog Enabled Blockchain Based Healthcare Model 1

Secure Fog Enabled Blockchain Based Healthcare Model

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

作者： Awasthi, Charu Awasthi, Satya Prakash Mishra, Prashant Kumar Poornima University Department of Computer Engineering Jaipur India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350353648

Cloud Computing introduced high latency, mobility, bandwidth, etc., which Fog Computing addressed. Fog Computing streamlines remote IoT device and sensor management. However, Fog-Cloud has security and privacy issues. Blockchain gives trustless security, subsequently its utilization in various fields is developing. We propose an electronic healthcare framework that oversees basic and non-basic patients all the while. Basic and non-basic fog bunches make up the fog layer. Basic patients are treated in basic fog bunches for fast reaction, while non-basic patients are taken care of utilizing blockchain innovation to save patient wellbeing records. The proposed technique reduces key message response time and offloads cloud infrastructure with minimal IoT ecosystem changes. The results demonstrate that the suggested method protects privacy while maintaining network settings. The aim of the research is to create a safe healthcare system that combines blockchain with fog computing. © 2024 IEEE.

关键词： Electronic health record

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Brinjal Leaf Diseases Detection Based on Deep Convolutional Neural Network 4

Brinjal Leaf Diseases Detection Based on Deep Convolutional ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anu Priya, A. Rajaram, K. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Brinjal leaf diseases, such as leaf spot, mosaic virus, and wilt, would critically reduce crop yield quality if the disease is spread during an early stage. Such reasons have made early identification imperative to ensure effective prevention and management. This article, therefore, aims at coming up with an effective technique of identifying brinjal leaf diseases using VGG16 CNN model. Traditional identification methods are based on manually examining plant diseases, an effort known to be highly labour-consuming, subjective, and even unreliable. In addition, most of the available machine learning methods fail in scalability, computational cost, and specific domain problems. However, our technique makes use of the high ability of VGG16 architecture to extract automatically relevant features from leaf images and then classify them with high accuracy. Used Eggplant Leaf Disease Dataset which contains six classes. The central idea of our approach is to employ deep convolutional layers of VGG16 to extract discriminative features from the images followed by classification using fully connected layers and softmax activation. The approach obtains 98.2% accuracy for determining of Brinjalleaf diseases. This work will impact the agricultural sector by means of providing a fast automated solution that gives accurate insights about early detection of leaf disease occurrence, hence reducing losses to crops and enhancing production. © 2025 IEEE.

关键词： Convolutional neural networks

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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A Real time Application of Virtual Reality in Indian Agriculture

A Real time Application of Virtual Reality in Indian Agricul...

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2024 International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024

作者： Aradhya, Sumalatha Navya, V. Siddaganga Institute of Technology Tumakuru Department of Computer Science and Engineering India

ISBN: (纸本)9798350364040

Agriculture is the backbone of the economic system for any country and for ages, agriculture has been related with the production of vital food crops to satisfy the needs of consumers. Farmers must meet the changing needs of end users, customers, regulators, food supply chain bodies and retailers. Despite the important role that the farmer plays in the economic growth of the country, not many initiatives are taken to face the challenges in the agriculture market. The challenges include environmental factors such as climatic changes, rising demands for food and higher quality, supply chain issues, keeping track of the crops both grown, sold, and purchased, delineation of agriculture lands, and securing the crops till it reaches the agriculture market or end user etc. There is a need to educate farmers on the importance of digitization and how farmers can benefit in the right way by using modern tools. The challenges faced by the farmers can be handled by using an easy platform where they can know the accurate prices of different crops in and near their city. Through the Virtual Reality based platform, the farmers can easily explore the weather trends of their farm locations and can visualize the agriculture market dynamically from any remote location. By combining the power of virtual reality with agricultural knowledge, one can revolutionize agriculture practices and crop marketing. The framework enables different farming techniques, equipment operation, and crop management through interactive simulations with 98.8% accuracy. © 2024 IEEE.

关键词： Crops

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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Hybrid RNN Based Text Classification Model for Unstructured Data

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SN computer Science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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Exploring Transfer Learning in Brain Tumor Classification with Deep Neural Networks 11

Exploring Transfer Learning in Brain Tumor Classification wi...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rawat, Vikas Singh Johar, Sakshi Kumar, Amit Singh, Ninni University Department of Computer Science and Engineering Dehradun India Cmr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350357

In today's era, medical advancements have reached their peak but still, some bugs led to the emergence and involvement of advanced techniques like Machine learning, Artificial Intelligence, and Deep Learning. These three are interconnected and have strong relationships that work as such, to increase the growth of work in any domain with rapid accuracy and without any error. This paper deals with the identification and categorization of tumors. Brain tumors are a result of abnormal behavior of cell division which leads to uncontrolled growth of cells and results in the formation of masses and lumps in the brain. The detection of tumors is done using radiology with the help of MRI techniques or MRI scans. Traditional scans sometimes lead to errors causing misdiagnoses. This process is also timeconsuming and as such requires high expertise. To elevate this process, in this paper, we used a multi-layered CNN model which takes the MRI images as input and classifies it into one of the four major categories i.e., 'Glioma', 'Meningioma', 'Pituitary' or 'No Tumor'. The publicly available dataset used to train this model contains over 7000 images which have been segregated into testing and training where both classes contain 4 different segmentations depicting the different brain tumors. The training folder contains nearly 5200 images, and the testing folder has over 1500 images. The accuracy of the model has been tested to over 96 percent. Then we converted the above model to the VGG16 model by using the study of Transfer Learning and the model gave up to 98.04% accuracy and validation accuracy was approximately 94.73%. © 2024 IEEE.

关键词： Tumors

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Neuro-Cybernetics: Enabling Human–Machine Interaction Through Brain Signals

Neuro-Cybernetics: Enabling Human–Machine Interaction Throu...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Kulkarni, Mukund Lautawar, Tanmay Bhalgat, Kunal Nandankar, Kuntesh Lad, Yogita Langade, Niranjan Department of Computer Engineering BRACT’s Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819780952

In an era where human–machine interaction is at the forefront of technological advancement, the search for a seamless and responsive interface between human cognition and robotic control is supreme. This paper delves into the creation of an IoT-enabled mind-controlled robot, an innovation poised to redefine the boundaries of human–robot collaboration. The challenge addressed here is the establishment of Brain-computer Interface (BCI) which is real-time, having precise control over a robotic platform using EEG-based brain signals. Leveraging NodeMcu devices and wireless communication, this study pioneers a Server-Client Model for facilitating uninterrupted communication between the user and the robot. Within this framework, the user interface NodeMcu captures EEG signals via specialized sensors, while signal processing algorithms extract meaningful patterns. These patterns are mapped to predefined commands, which, when transmitted to the robotic platform NodeMcu, are transformed into motor control signals, driving the robot's movements. This dynamic interaction not only showcases the potential of mind-controlled robotics but also opens new horizons for intuitive and efficient human–robot collaboration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain computer interface

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