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Detection of Potato Leaves Using Convolutional Neural Networks (CNNs) 3

Detection of Potato Leaves Using Convolutional Neural Networ...

3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Kaushik, Priyanka Kakkar, Manav Yadav, Meenakshi Sathish, M. Mago, Beenu Kaushik, Manas Manglik Chandigarh University Dept. of Cse (AIML) Chandigarh India Dhanalakshmi Srinivasan College Dept. of Civil Engineering Coimbatore India Skyline University College Dept. of Computer Science Sharjah United Arab Emirates Manipal Institute of Technology Dept. of Electrical and Electronics Karnataka India

ISBN: (纸本)9798331521691

Agricultural productivity plays a vital role in India's economy, contributing approximately 17-18% to the nation's GDP and serving as a primary livelihood for a significant portion of the population. With India possessing the largest net cropped area globally, effective pest and disease management is crucial to ensuring stable agricultural yields. This study utilizes advanced technologies, including machine learning, computer vision, and deep learning, to detect and identify common diseases such as early blight and late blight in potato plant leaves. Using a Kaggle-sourced dataset comprising roughly 2,000 images, rigorous data augmentation techniques were applied to enhance the robustness of the model. A customized convolutional neural network (CNN) was deployed, achieving an impressive accuracy of 97.12% after just 32 epochs. This performance surpasses many existing Kaggle models, which typically reach around 95% accuracy. © 2025 IEEE.

关键词： data Augmentation Deep Learning Kaggle dataset Machine Learning Pests Potato Disease Detection

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A Blockchain-Based Credentials for Food Traceability in Agricultural Supply Chain

A Blockchain-Based Credentials for Food Traceability in Agri...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Manoj T Krishnamoorthi Makkithaya Narendra V G Dept. of Computer Science & Engg. Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

In recent years the prominence of food product traceability has significantly increased due to growing concerns regarding food safety and ensuring equitable market access for farmers. Presently, supply chain information systems collect, transmit and store records in centralized systems. These conventional digital systems create fragmented data silos vulnerable to a single point of failure. These limitations pose significant challenges in establishing trust factors such as transparency, traceability, integrity, and privacy among the various actors involved in the agri-supply chain. To overcome these challenges, a decentralized technology like blockchain has immense potential to enhance traceability by addressing security and privacy aspects. This study proposes a blockchain-based framework called AgriFoodCredChain for food product traceability using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Initially, the study outlines a secure mechanism for establishing DID communication channels, assuring robust and reliable data exchange. Furthermore, methods for privacy-preserved issuance and verification of credentials for food traceability are developed and presented. The implementation of the framework is conducted utilizing the identity blockchain named Hyperledger Indy and Aries library, considering particular workflow in the food supply chain. Evaluating the framework w.r.t credential issue and verifying time proves that the solution implemented is scalable and satisfies the requirements for real-time adoption.

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Improvement in Facial Emotion Recognition using Synthetic data Generated by Diffusion Model

arXiv

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arXiv 2024年

作者： Roy, Arnab Kumar Kathania, Hemant Kumar Sharma, Adhitiya Dept. of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim India Dept. of Electronics and Communication Engineering National Institute of Technology Sikkim India

Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in available datasets, which can hinder both model performance and generalization. In this paper, we tackle the issue of data imbalance by incorporating synthetic data augmentation and leveraging the ResEmoteNet model to enhance the overall performance on facial emotion recognition task. We employed Stable Diffusion 2 and Stable Diffusion 3 Medium models to generate synthetic facial emotion data, augmenting the training sets of the FER2013 and RAF-DB benchmark datasets. Training ResEmoteNet with these augmented datasets resulted in substantial performance improvements, achieving accuracies of 96.47% on FER2013 and 99.23% on RAF-DB. These findings shows an absolute improvement of 16.68% in FER2013, 4.47% in RAF-DB and highlight the efficacy of synthetic data augmentation in strengthening FER models and underscore the potential of advanced generative models in FER research and applications. The source code for ResEmoteNet is available at https://***/ArnabKumarRoy02/ResEmoteNet © 2024, CC BY.

关键词： Human computer interaction

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RDI-SD: An Efficient Rice Disease Identification based on Apache Spark and Deep Learning Technique

RDI-SD: An Efficient Rice Disease Identification based on Ap...

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2022 International Conference on Artificial Intelligence and data Engineering, AIDE 2022

作者： Prasad, M S Guru Pratap, M.S. Jain, Prithviraj Gujjar, J Praveen Kumar, M Anand Kukreti, Anurag Dept. of Computer Science & Engg Dehradun India School of Computing and Information Technology Reva University Bengaluru India SDM Institute of Technology Dept. of Computer Science & Engg Ujire India Faculty of Management Studies Bengaluru India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665493048

Early and accurate detection of diseases affecting rice plants may have devastating impacts on productivity, and early and accurate detection of these diseases is essential for mitigating their consequences. However, the current methods for diagnosing rice diseases are neither precise nor effective. When it comes to diagnosing plant diseases, convolutional neural networks (CNNs) are the gold standard. The difficulty of using CNN for large-scale data analysis using conventional methods remains a major barrier to its wider adoption. In our work for massive data processing, we have adapted the CNN technique for use with pySpark. We used a dataset of 3,472 images of rice diseases to train and evaluate our suggested system. The proposed system will provide a tailored answer to the challenge of assessing rice diseases by employing smartphone plant images as its major data source. With the proposed model for identifying different rice diseases, a total accuracy of 99% was reached, which is pretty impressive given that some of the diseases look similar. © 2022 IEEE.

关键词： Convolutional neural networks

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Categorization and Interpretation of Satellite Image Scenes Employing AI Approaches

Categorization and Interpretation of Satellite Image Scenes ...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Manjula Gururaj Rao Shaun Noronha Ritesh Shetty B M Ahamed Shafeeq K Hemant Kumar Reddy Ch Sree Kumar NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala dept. of CSE Manipal Institute of Technology Manipal Academy of Higher Education Manipal dept. of CSE VIT AP University Department of Computer Science NIST Berhampur

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Scene identification in Very High-Resolution (VHR) photography presents a formidable challenge. Although Convolutional Neural Networks (CNNs) have enhanced accuracy in feature learning, their deep layers often struggle to accurately depict object relationships within images. To address this limitation, the paper introduces an advanced Multilayer Perceptron (MLP) acting as a deep classifier, utilizing RMSprop and Adadelta optimizers for classification. Our proposed model, CNN-MLP, merges the strengths of MLP and CNN methods. It utilizes a pre-trained CNN, devoid of fully-connected layers, for feature generation, supplemented by data augmentation (DA) techniques to enrich the training dataset. The resulting feature maps undergo classification using an MLP, achieving an outstanding classification performance. The model excels in identifying barren and farm land, even within the same image, showcasing its efficacy in scene classification. This success is demonstrated using three publicly available VHR image datasets UC-Merced, Aerial Image (AID), RSI CB 128, NWPURESISC45 combined to and also create a blended dataset with the overall $96.5 \%$ percentage of the accuracy

关键词： Training Representation learning Photography Knowledge engineering Scene classification Accuracy Multilayer perceptrons

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Interactive Volume Rendering Module using Two-Dimensional Transfer Function on Cone-Beam Computed Tomography data

Interactive Volume Rendering Module using Two-Dimensional Tr...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Shreenidhi H Bhat K S Hareesha Abhay T Kamath Adarsh Kudva H Shashank Dept. of Data Science and Applications Manipal Academy Higher Education Manipal Institute of Technology Manipal Karnataka India Dept. of Dental Sciences KMC Hospitals Balmatta Mangalore Karntaka India Dept. of Oral Maxillofacial Surgery Manipal College of Dental Sciences Manipal Academy Higher Education Manipal Karnataka India Dept. Information and Communication Technology Manipal Academy Higher Education Manipal Institute of Technology Manipal Karnataka India

The transfer function’s design has a direct impact on the outcome of the medical image visualization. A well-designed transfer function can boost both image quality as well as visualization speed. In this paper, a new approach is proposed for developing an interactive ubiquitous volume rendering module using a 2D histogram-based transfer function on medical data. The set of direct manipulation widgets in VTK is used to demonstrate interactive and intuitive 2D transfer functions. Also, the interaction in the histogram was done by adding multiple control points, which act as the boundary-emphasizing function designed to identify regions of the transfer function. The CBCT dataset of the dentofacial images is used to evaluate the outcome of the proposed 2D transfer function by highlighting the required features in the dentofacial volume. By comparing with the 3D Slicer open-source software, the effectiveness and quality were much more accurate, and more detailed clear information can be visualized from the proposed approach.

关键词： Histograms Three-dimensional displays Software algorithms Transfer functions data visualization Surgery Signal processing algorithms

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E-Voting System Using Blockchain technology 4

E-Voting System Using Blockchain Technology

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Nagesh, H.R. Guru Prasad, M.S. Shivaraj, B.G. Jain, Dhawal Puneeth, B.R. Anadkumar, M. Sahyadri College of Engg. & Management Dept. of Computer Science & Engg. Mangaluru India Dept. of Computer Science & Engg. Dehradun India Mangalore Institute of Technology & Engg. Dept. of Computer Science & Engg. Moodabidri India Sdm Institute of Technology Dept. of Computer Science & Engg. Ujire India Department of Mca Nitte Karnataka India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665474368

In every nation, democratic elections are a momentous and weighty occurrence, and the voting system that is now in place requires the use of ballots or electronic voting machines (EVM). Transparency, poor turnout, vote manipulation, distrust of electoral organizations, fabrication of unique IDs (voting party IDs), and delays in posting results are some of the issues that arise as a result of these procedures. The matter of safety is of the utmost importance. When considering the installation of a computerized voting system, voter confidentiality has always been one of the most important concerns. There is no question regarding the system's capability to secure itself in contrast to prospective assaults and safeguard data in the face of such big choices. Utilization of blockchain technology is one approach that might be taken to resolve security concerns. The blockchain technology has an endless number of different uses that might be implemented. The technology known as blockchain is a distributed ledger that makes it possible for peer-to-peer networks all over the world to handle digital assets. In this context, distributed ledger technology represents an intriguing development. A grouping of all transactions is referred to as a block. Immutability, decentralisation, security, transparency, and anonymity are some of the outstanding properties offered by blockchain technology. The combination of blockchain technology with smart contracts has shown promise as a viable option for the development of trustworthy and open-source electronic voting systems. In this article, we demonstrate how to use blockchain technology with the help of a wallet and the Solidity programming language to build an electronic voting application. The programme was designed as a smart contract for the Ethereum network. In order to avoid having the same person vote twice, the user's wallet will only hold a certain number of tokens (gas), which will be depleted each time the user casts a vote. Thi

关键词： Blockchain

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment data using the Machine Learning Algorithm and data Mining Techniques

Investigation on Nail Denting or Crumbling from Nail Psorias...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： K. Janani S. P. Santhoshkumar D. Vikram S. Priya M. Vijayaraj M. Priyanga Department of Data science and Computer Applications Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it; reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient’s nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, a

关键词： Training Accuracy Machine learning algorithms Image color analysis Psoriasis Nails Vectors Topology data mining Biomedical image processing

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