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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Surya, M. Manohar, S. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

An entire country's economy, population security, nutrition, and health all depend on its agricultural productivity. In an effort to increase agricultural output, farmers have been adopting new technologies like blockchain and the Internet of Things in recent years. Blockchain has been used to address issues in a variety of industries. Blockchain is being used in agriculture to speed up transaction times and improve food safety. The complex mechanism that keeps agricultural products moving throughout the market is called an agricultural supply chain. Agricultural commercial resources play a crucial role in meeting the demand for agricultural products and preserving their quality and safety as they transport agricultural items across the market. Many existing methods have explored the use of blockchains in the supply chain, although they are unable to enhance the process's overall efficiency. An efficient crop yield prediction and demand prediction system is proposed with secure block chain technology. The proposed system has the following three phases: 1) crop yield prediction system;2) supply chain process with blockchain;and 3) demand prediction system. The first phase consists of the following steps: dataset collection, preprocessing, feature extraction, feature selection, and training. In the second phase, farmers, distributors, retailers, and consumers are involved. Finally, for demand prediction, pre-processing, feature extraction, feature reduction, and prediction are carried out. All three phases are linked to a blockchain and securely stored in the cloud. © 2024 IEEE.

关键词： Internet of things

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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Meal Magic: An Image-Based Recipe-Generation System 8th

Meal Magic: An Image-Based Recipe-Generation System

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Sravya, Pemmasani Pariga, Swetha Swetha, S. Devi, Prasanna Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789819748945

The growing popularity of food photography on social media platforms has seen a surge in demand for recipe ideas and cooking inspiration. However, creating a recipe from scratch can be a daunting task, especially for someone with little cooking experience. To address this issue, our study proposes Meal Magic, a web-based image-to-recipe generator that generates recipes from the images of dishes. The image-based recipe creation system employs Inception v3, a Convolutional Neural Network (CNN) that achieves an accuracy of 78.1% when applied to the ImageNet dataset. It has a more efficient and deeper network and is computationally less expensive than the Inception v1 and v2 models. When compared to its predecessors, the model has an extremely low error rate. The web-based application is designed to be user-friendly, allowing users to simply upload an image of a dish that they wish to recreate and receive a recipe in return. As a result, it saves users’ time and streamlines the recipe creation process by eliminating the need for manual ingredient searching and recipe browsing. The image-to-recipe generator has many potential applications, including assisting home cooks in meal planning and aiding chefs in creating new recipes. Overall, this system represents a promising approach to the generation of recipes that has the potential to transform the way we approach cooking and recipe creation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Intelligent Automation and Monitoring of Environmental Characteristics for Hydroculture Systems using IoT 4

Intelligent Automation and Monitoring of Environmental Chara...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Seth, Saksham Mishra, Sachin Kumar Malarselvi, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

The need for adopting Hydroculture systems (viz. hydroponics, aeroponics, aquaponics) is increasing subsequently with the increase in demand for Sustainable farming solutions for populous countries like India. Industrial Hydroculture systems do exist at this current time, but these systems are still far from the reach of smaller scale implementations in terms of cost. The Ambient temperature, temperature of water, pH of water and Electrical Conductivity are considered as the key factors for the growth of plants in such systems. Some of these factors can be controlled and governed as per the requirements of plants. This work aims to develop a proof of concept and automate the process of monitoring, logging and controlling these factors with IoT. The implementation eliminates rudimentary manual monitoring of environmental characteristics, while logging data on an Azure cloud server, with insights delivered via PowerBI. © 2024 IEEE.

关键词： Environmental monitoring

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Deep Adversarial Network Based Dental Inlay Restoration Using Point Cloud Segmenntation 3

Deep Adversarial Network Based Dental Inlay Restoration Usin...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Lakshmi, S. Shanmuganathan, C. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

computer Aided Design (CAD) and dental industry are going hand-in-hand due to the increasing requirements for digitalizing the process of dental diagnosis and treatment. The crucial step in dental treatment is the identification of the individual affected teeth and gingiva region. These days 3D intraoral scanners are widely used to take the inputs for analysis and design of the tooth. High accuracy and resolution play a predominant role in providing efficient and hassle-free treatment to the patients. Since the intraoral scanners provide low quality input images in deep intraoral regions, current automated systems are facing the challenge of automated tooth labeling. The problem of segmentation of teeth and gingiva region of 3D IOS mesh scans are complex to process and produce desired output. The design of dental inlay restoration requires a fully automated end-to-end network to be developed. We propose a deep learning method called DPCNet to produce segmentation outputs that can automate the construction of a realistic surface of the defective tooth. Our goal was to start using point clouds as input to the neural network, which are obtained from mesh scans. DPCNet employs graph-based learning modules to acquire local features, which are summarized and fed into a stack of convolutional layers to achieve global features. Our approach is effective in real-time datasets, with minimal need for manual adjustment scenarios encountered. © 2024 IEEE.

关键词： Mesh generation

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Generative AI: A Transformative Force in Business Intelligence 2

Generative AI: A Transformative Force in Business Intelligen...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Thiruneelakandan, A. Umamageswari, A. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

In an era where data is the cornerstone of strategic decision-making, Generative Artificial Intelligence (Gen AI) is revolutionizing the Business Intelligence (BI) landscape. This study investigates the multifaceted role of Generative AI in BI, encompassing data generation and augmentation, predictive analytics, reporting automation, product and service innovation, and personalization of customer experiences. The work employs a combination of synthetic data generated by Generative Adversarial Networks (GANs) and Seasonal Autoregressive Integrated Moving Average (SARIMA) predictive modeling techniques to demonstrate the potent capabilities of Generative AI in enhancing data-driven insights. The findings reveal that Generative AI not only significantly improves the accuracy of predictive models, especially in scenarios with limited historical data but also streamlines reporting processes and catalyzes innovation by uncovering latent customer needs. The research underscores the transformative impact of Generative AI in BI, while also addressing the attendant ethical considerations and the necessity for rigorous data governance. Through this exploration, the study provides a roadmap for businesses to harness the power of Generative AI, paving the way for smarter, more agile, and more personalized business strategies. © 2024 IEEE.

关键词： Data Augmentation Ethical Artificial Intelligence Generative Adversarial Networks Generative Artificial Intelligence Reporting Automation

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Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

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SN computer science 2024年第8期5卷 1052页

作者： Deena, G. Ancy Breen, W. Raja, K. Shanmuganathan, C. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Ramapuram Chennai India

Question classification is a critical task in natural language processing, as well as applications in question-answering systems and intelligent personal assistants. This study presents novel machine learning approach for automatic question classification based on Bloom’s taxonomy, a hierarchical structure of cognitive levels. Traditional methods rely on feature engineering and classic machine learning models, but these methods often lack the ability to understand complex patterns and connections in natural language data. This study proposes a novel approach using a convolutional neural network (CNN) model, which can automatically learn relevant features from input data without extensive feature engineering. The model classifies questions into pre-established categories or labels using an embedding layer, convolutional and pooling layers, and a fully linked layer for classification. We assess the model’s efficacy on a publicly accessible dataset generated by the subject expert and compare it to conventional machine learning methods like logistic regression and support vector machines. The CNN-based model outperforms conventional techniques, achieving greater recall, accuracy, precision, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bloom’s Taxonomy Convolutional Neural Network Machine Learning Word embedding

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through Automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 IEEE.

关键词： Sales

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Deep learning based predicting urban traffic congestion with RGB-coded images using GRU-CNN and LSTM

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Multimedia Tools and Applications 2024年第38期83卷 86261-86280页

作者： P, Rajesh Azhagiri, M. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600 089 India

Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GRU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a RGB coded image which is the input to the GRU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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