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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

SN computer science 2023年第6期4卷 710页

作者： Patil, Manoj A. Manohar, M. Department of Computer Science and Engineering Christ (Deemed to be University) School of Engineering and Technology Kengeri Karnataka Bengaluru 560074 India Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad 500104 India

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Hybrid classifier LSTM SISS-OB

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Deep Learning based Human Interactions using Hand Gestures and Voice Commands 14

Deep Learning based Human Interactions using Hand Gestures a...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kumar, Prasanna K.R. Divyashree, U.M. Chaitra, M. Gaurav, Kumar Siddaganaga Institute of Technology Computer Science & Engineering Tumkur India

Nowadays, completing tasks fast is essential. computers are necessary to complete numerous tasks more quickly. The "Voice and gesture-controlled system" that we demonstrated allows users to operate the computer by using voice commands and hand gestures. The suggested method instantly permits mouse activities as soon as it recognizes speech and gestures. Hand gesture recognition is crucial in interactions between people and computers. As we can see, many new technological advancements are occurring, such as the widespread use of biometric authentication on smartphones. Similarly, hand gesture recognition is a modern form of human-computer interaction, which allows us to control a computer system by waving our hands in front of a webcam. © Grenze Scientific Society, 2023.

关键词： Speech recognition

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Prediction of Covid-19 Recovery Chances Using Supervised Machine Learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. The suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. Thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes 5

Development of Cancer Drug Targets and Potential Anti-Cancer...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Balamurugan, K.S. Ramesh, T. Sathish Kumar, P.J. Surendran, R. Tamilvizhi, T. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets. © 2024 IEEE.

关键词： Mammals

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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Comparative Study of Clustering Algorithms: Scenario Based on Boston Crime Dataset 26

Comparative Study of Clustering Algorithms: Scenario Based o...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Jerin, Jubaida Quader Khan, Nusrat Kaniz Biswas, Swapnil Sharmin, Nusrat Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350359015

In the study of crime analysis, using clustering techniques to understand patterns in crime-prone areas is crucial. This research paper focuses on grouping regions with higher crime rates using various clustering methods and then thoroughly compares and evaluates the results. We have applied six different clustering methods: K-Means, K-Prototypes, Agglomerative Hierarchical Clustering, DBSCAN Clustering, Fuzzy C-Means clustering, and Gaussian Mixture Models (GMMs) to the Boston City Crime Dataset. These methods were carefully examined to understand their performance. For example, K-Means and K-Prototypes create clusters, Agglomerative Clustering organizes data hierarchically, Fuzzy C-Means extends the principles of K-Means for softer clustering, DBSCAN uses a unique approach based on graph density, and GMM effectively handles complex data structures while allowing for probabilistic *** research enhances the field by introducing inventive algorithmic applications, systematically assessing their effectiveness, rigorously evaluating their performance, and providing practical insights into their implementation for analyzing crime data on extensive datasets, along with aiming to demonstrate the effectiveness of different clustering techniques in identifying areas or clusters of higher crime rates. © 2023 IEEE.

关键词： Agglomerative Clustering Crime Zone DBSCAN Fuzzy C-Means Gaussian mixture models (GMMs) K-Means Clustering K-Prototypes Clustering

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Face Recognition Based on Cascade Classifier Using Deep Learning 1

Face Recognition Based on Cascade Classifier Using Deep Lear...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Mannepalli, Praveen Kumar Singh Kushwaha, Devendra Kalamdhar, Sanjay Nagrale, Vishakha Rajpoot, Vikram GH Raisoni Institute of Engineering and Technology Department of Computer Science & Engineering Nagpur India Madhav Institute of Technology & Science Gwalior India

ISBN: (纸本)9798350345957

The human face is significant because it is used as a tool of identification in our daily lives. One kind of biometric identification is Face Recognition (FR), which works by memorizing and using a person's unique facial features to identify that person in the future. Numerous researchers have been interested in biometric facial recognition technology due to its widespread use. Because of the non-contact nature of the process, FR technology surpasses other biometric-based authentication methods like fingerprint, palm print, & iris recognition. One such potential use for facial recognition (FR) algorithms is in remote identification when no physical touch or interaction with the target individual is required. This study proposes a method for constructing an FR system with a learning strategy depending on the DCNN algorithm. The approach of Deep Learning (DL) is presented for learning & assessing all samples & new inputs in a scheme. The ORL and YALE face datasets are used in studies that compare the findings to those produced by the Deep CNN approach. The proposed DL method uses a systematic approach to learning from and assessing new inputs and samples. The proposed model Deep CNN obtained 96.04 percentage points (or a validation accuracy of 62.2%) over the ORL dataset after 100 iterations with a loss of 17.33 percent. DCNN achieves a 99.99% training accuracy and a 99.89% validation accuracy without any data loss after 100 epochs of training on the Extended Yale B dataset. The proposed DL method analyses and rates are existing data from the system and fresh face inputs. In addition, it gathers information from the facial features & enhances the D-CNN algorithm for facial classification. Utilizing CNN (Convolutional Neural Networks), the accomplishments in several contests are advancing and becoming the focus of study. © 2023 IEEE.

关键词： Face recognition

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Blockchain Ballotbox: Empowering Democracy Through Tamper-Proof E-Voting 3

Blockchain Ballotbox: Empowering Democracy Through Tamper-Pr...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Madhuri, A. Lakshmi, Patnala Rajya John, Patchala Ganesh, Tadapaneni Asma, Shaik Nahila, Pathan PVP Siddhartha Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350343632

Election administration has undergone a revolution as a result of the development of digital technology, with electronic voting (eVoting) systems emerging as a significant and ground-breaking replacement for the conventional paper-based procedures. Due to its potential to increase security, transparency, and voter confidence in the electoral process, the integration of blockchain technology into eVoting systems has attracted a lot of attention in this context. In order to address the inherent difficulties and weaknesses prevalent in conventional voting systems, this research offers an electronic voting system (eVoting system) that makes use of blockchain technology's capabilities. The suggested system makes use of the core characteristics of blockchain, such as decentralization, immutability, and cryptographic security, to provide a robust and open platform for the collection and tabulation of votes. This eVoting system's noteworthy characteristics include real-time result verification, anonymous and tamper-proof vote recording, and secure voter authentication. Blockchain's distributed ledger technology makes sure that every vote is recorded in a way that is transparent, verifiable, and immune to manipulation - all while preserving the voter's privacy. Additionally, the use of smart contracts in this system automates a number of voting-related tasks, doing away with the need for middlemen and improving the effectiveness of the overall election process. In summary, incorporating blockchain technology into electronic voting has the potential to revolutionize the electoral landscape by introducing unprecedented levels of security and transparency. By facilitating secure and streamlined remote voting while preserving the integrity of the process, this blockchain-powered eVoting system seeks to strengthen democratic practices and instill confidence in the electoral process in our digital era. © 2023 IEEE.

关键词： Blockchain Technology Decentralization Digital Technology Election Administration Immutability Security Transparency

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An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4574-4584页

作者： Fan, Zongwen Gou, Jin Weng, Shaoyuan College of Computer Science and Technology Huaqiao University Xiamen361021 China School of Software Engineering Xiamen Institute of Software Technology Xiamen361024 China

Concrete is a vital component in modern construction, prized for its strength, durability, and versatility. Accurately determining the quantities of concrete components is crucial in civil engineering applications to optimize resources (e.g., manpower and financial resources). In this article, we propose an unbiased fuzzy-weighted relative error support vector machine (UFW-RE-SVM) for reverse prediction of concrete components. First, we add an unbiased term to the target function of UFW-RE-SVM for obtaining an unbiased model. Second, we design a fuzzy-weighted operation to indicate sample importance by incorporating the fuzzy membership values into the UFW-RE-SVM. The nth root operation is introduced to address the exponential explosion issue in the fuzzy-weighted operation. Finally, considering the UFW-RE-SVM is sensitive to its hyperparameters for multioutput prediction, the whale optimization algorithm (WOA) is utilized for hyperparameter optimization for its effectiveness in optimization tasks. We design the fitness function based on the results from multiple components to balance the performance of multioutput predictions. Experimental results show that the performance of our proposed model outperforms existing works in predicting concrete components in terms of mean absolute relative error, standard deviation, and root mean square error. Further, the statistical test shows the WOA and two other metaheuristics can significantly improve the prediction performance. This indicates that the unbiased term, fuzzy-weighted operation, and WOA are effective for improving the proposed model for reverse prediction concrete components. With these promising results, the proposed model could provide decision-makers with a valuable tool for determining concrete component quantities based on desired concrete qualities. © 2020 IEEE.

关键词： Forecasting

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Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis 14

Comparative Analysis of Multi-Model and Uni-Model Approaches...

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

作者： Gupta, Krati Parmar, Mahesh Madhav Institute of Technology & Science Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350335095

The sentiment of the data contained in a text can be determined by analysis. This sentiment can be good, negative, or neutral. When carrying out this research, one may choose to employ either a uni-model or a multi-model technique. In the uni-model method, both the training and the analysis are performed using just a single model. The application of this strategy is uncomplicated and uncomplicated, but it might not be able to make sense of complicated or different facts, which would lead to wrong findings. Instead, the multi-model technique utilizes a number of different models, each of which is designed to examine a particular aspect of the data or a particular kind of attitude. This allows the multi-model strategy to produce more accurate results. The results of several different models might be combined in order to obtain a more in-depth understanding of feelings. This method is more accurate overall and captures more complex expressions of emotion, but it requires more time and money to train personnel and put into practice. The evaluation of metrics was carried out using Multimodal and mono Model with Time Distributed Bidirectional LSTM in this research. Both the loss and the accuracy. The Multi-Model achieves the highest accuracy of 99.71 percent for the train, while the validation accuracy achieves 99.32 percent, and the Multi-Model achieves the least loss of 0.010 percent. © 2023 IEEE.

关键词： Sentiment analysis

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