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Crop Disease Detection using CNN Architecture VGG16

Crop Disease Detection using CNN Architecture VGG16

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Sandeep Raj Surinder Kaur Neetu Singh Monica Gupta Department of Artificial Intelligence & Machine Learning Dronacharya College of Engineering Farrukh Nagar Haryana Department of Information Technology Bharati Vidyapeeth’s College of Engineering New Delhi Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The Agriculture plays a major part in any nation for economy through the production of various crops and it’s an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using image processing, machine learning, and deep learning. The deep learning plays an essential part in the field of image processing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture.

关键词： Deep learning Plant diseases Transfer learning Neural networks Crops Transportation Production Computer architecture Agriculture Object recognition

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Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy

Deepfake Detection: Leveraging InceptionResNetV2 and LSTM fo...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Aradhna Saini Attiuttama Sheenam Naaz Anushka Shivhare Gaurav Dhuriya Neha Yadav Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Artificial Intelligence and Machine Learning G L Bajaj Institute of Technology and Management Greater Noida Computer Science and Engineering Sharda University Greater Noida Computer Science and Engineering Netaji Subhas University of Technology Delhi

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The antique adage "seeing is believing" is simply no longer actual, which has profound implications for a ramification of sides of our lives. Deepfakes are becoming less complicated and simpler to create as technology advances. In fact, with the right software program, you can do a part of it right out of your palm. it is challenging to pick out deepfakes. The capability of the human eye to differentiate among deepfakes has reduced. however, a few academics have tried to identify deepfake. Deepfakes are media that can be generated by synthetic intelligence (AI) using algorithms. artificial intelligence algorithms are designed to understand the traits of both the supply and the target photo. Subsequently, the target image is positioned on the pinnacle of the supply photograph. Our goal is to pick out video deepfakes by way of utilizing deep getting-to-know neural networks which include InceptionResNetV2 and LSTM. With the usage of transfer learning and the pre-trained InceptionResNetV2 CNN for characteristic extraction and vector production, we were able to efficaciously assemble a deepfake detection version. The capabilities had been used to train the LSTM layer, and the confusion matrix that was produced offers us the accuracy of testing and validation. The corresponding version's accuracy for 20 and 40 epochs was 84.75% and 91.48%, respectively.

关键词： Deepfakes Accuracy Neural networks Transfer learning Streaming media Vectors artificial intelligence Long short term memory Faces Testing

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Handling Data and Model Drift for World Application using Big Data

Handling Data and Model Drift for World Application using Bi...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Singh, Rajesh Gehlot, Anita Daniel Shadrach, Finney Prabu, S. Nirmalan, R. Sunil Kumar, V. Uttaranchal Institute of Technology Dehradun India Kpr Institute of Engineering and Technology Department of ECE Coimbatore India Mahendra Institute of Technology Department of ECE Namakkal India Kalasalingam Academy of Research and Education Department of Cse Krishnankoil India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9781665456371

It's still unclear how to effectively extract the information concealed inside vast and massive amounts of data. The problem of 'idea drift' in stream data flows is one of the difficulties. Random walk is a common issue in data analytics where the statistical features of the characteristics and the categories they are intended for change with time, decreasing the accuracy of the trained model. There are numerous approaches that have been put out for bulk data mining. A new generation of data mining methods called stream mining updates the model in a single pass whenever fresh data is received. Because of its inherent adaptability, this one-pass mechanism may be properly capable than its predecessors of coping with idea drift in data streams. In this study, we assess a group of algorithms for mining the data streams using decision trees. The collection of rules which can be derived from the induced model is the benefit of decision tree learning. The predicate logics that represent the extracted rules can then be applied in a variety of decision-supporting applications. Even in the face of concept drift, the induced decision tree has to be precise and condensed. When dealing with concept-drift data, we evaluate how well three common incremental decision tree methods (random forest, isolation forest, and forest tree) perform. In the experiment, drift data from both synthetic and actual environments are employed. It is discovered that optimization method with big data technique i.e., MapReduce yields better outcomes. © 2022 IEEE.

关键词： MapReduce

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Enhanced Prediction of Rheumatoid Arthritis Flares Through Ensemble learning Methods

Enhanced Prediction of Rheumatoid Arthritis Flares Through E...

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International Conference on Advances in Information technology (ICAIT)

作者： K. Priyadharshini S. Vinitha Sivaprakash S S. Kayalvili Thilakavathy. P M. Vasavi Department of Computing Technologies School of Computing and SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Artificial Intelligence Kongu Engineering College Erode Computer Science and engineering Vels Institute of Science Technology and Advanced Studies Chennai ET department CVR College of Engineering Hyderabad

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective.

关键词： Analytical models Accuracy Sensitivity analysis Stacking Receivers Predictive models Prediction algorithms Data models Arthritis Ensemble learning

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Preserving Correlation in Multi-Modal Data: Challenges and Strategies

Preserving Correlation in Multi-Modal Data: Challenges and S...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Vaishali Koria Nikita Bhatt Nirav Bhatt Purvi Prajapati U & PU. Patel Department of Computer Engineering CSPIT CHARUSAT Anand India Department of Artificial Intelligence and Machine Learning CSPIT CHARUSAT Anand India Department of Information Technology CSPIT CHARUSAT Anand India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

With the exponential growth of social media, an immense volume of data is generated across diverse modalities, each exhibiting distinct statistical properties. Despite these differences, the modalities often convey semantic similarities, representing the same concepts or topics. Preserving these semantic connections poses a significant challenge but holds immense potential for applications such as cross-modal retrieval, recommendation systems, and fraud detection. This paper explores various correlation methods like Canonical correlation analysis (CCA), kernel Canonical correlation analysis (KCCA) and deep Canonical correlation analysis (DCCA), which preserves the semantic similarities between various modalities of data. Experiments are performed on well-known Multi-Modal datasets like MIRFLICKR-25K, NUS-WIDE-10k and XMediaNet. The experiments conclude that DCCA outperforms CCA and KCCA as DCCA has the capability to preserve complex non-linear relationship exhibit in the Multi-Modal data. Consequently, DCCA emerges as a robust choice for applications involving multi-modal data, ensuring the preservation of correlations.

关键词： Technological innovation Correlation Social networking (online) Semantics Fraud Fourth Industrial Revolution Kernel

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artificial intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network

Artificial Intelligence powered chatbots for the speech disa...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Subhayan Mukherjee Anusuya Chatterjee Khushbu Chandrakar Sandeep Saxena Artificial Intelligence and Machine Learning Asansol Engineering College Asansol India Electronics and Communication Engineering Asansol Engineering College Asansol India Computer Science and Engineering Asansol Engineering College Asansol India Computer Science and Engineering Greater Noida Institute of Technology Delhi India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled ’artificial intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network’ provides a novel integration of artificial intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities.

关键词： Training Auditory system Speech recognition User interfaces Chatbots Software Real-time systems Feedforward neural networks artificial intelligence Speech processing

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A machine learning approach for estimating compressive strength of concrete structures using an artificial electric field algorithm-based neuro-fuzzy predictor 19

A machine learning approach for estimating compressive stren...

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19th Orissa Information technology Society International Conference on Information technology, OCIT 2021

作者： Nayak, Sarat Chandra Dash, Ch. Sanjeev Kumar Behera, Ajit Kumar Mishra, Bijan Bihari CMR College of Engineering & Technology Department of Artificial Intelligence Engineering & Machine Learning Hyderabad501401 India Silicon Institute of Technology Department of Computer Science & Engineering Silicon Hills Patia Bhubaneswar751024 India

ISBN: (纸本)9781665416641

machine learning methods are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need of exploring an effective and accurate predictor for this domain. This article proposes an artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) for prediction of compressive strength of concrete structures. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using Gaussian triangular membership function with a degree of membership to different classes. The model parameters are decided by AEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the AEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes are in favour of AEFA+NFN, hence it can be suggested as a promising forecasting tool. © 2021 IEEE.

关键词： Electric fields

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： G N Keshava Murthy Manjula G Harshitha P Achyutha Prasad N Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka India Department of CSE BGS College of Engineering and Technology Bangalore India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked.

关键词： Telemedicine Wireless networks Metaheuristics Watermarking Medical services Elliptic curve cryptography Discrete wavelet transforms

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Discussion and Application of Virtual Garment Design technology 2

Discussion and Application of Virtual Garment Design Technol...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Venkatasubramanian, R. Vidhya, V. Sankar Ganesh, S. Khadjibekov, S. Alawady, Ahmad Hussein Uma Reddy, N.V. New Prince Shri Bhavani College Of Engineering And Technology India M.E.T Engineering College Nagercoil India Medchal India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan College of technical engineering The Islamic university Najaf Iraq Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Ring Road Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

As science and technology have advanced, more people have begun to use computer technology. Virtual reality technology has received attention and extensive references from the apparel design industry because of its superiority in accuracy and authenticity, its ease of preservation and modification, and its capacity to inspire designers' creative inspiration. This is due to the fact that it is the new technology that has been experienced the most intensely. As a result, research will be conducted on conceptualizing and implementing technologies for virtual clothing design. As a result, research will be conducted on conceptualizing and implementing technologies for virtual clothing design. First, the gene coding of an interactive genetic algorithm for product concept design was shown, and then the picture evaluation method of a virtual reality environment was presented. Both of these were presented in the same order. In conclusion, the findings of optimization experiments on synthetic lines were acquired by the use of experiments. Experiments have demonstrated that the optimized line is more conducive to the visual identification of images and graphics. © 2023 IEEE.

关键词： Costume designing Genetic algorithm Virtual reality

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