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Recipe Recommendation Using Image Classification 5

Recipe Recommendation Using Image Classification

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Anitha, E. Nandhini, S. Palani, Hari Kumar Marshal Mano, C. Loyola-ICAM College of Engineering and Technology Department of It Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Engineering Ajeenkya D y Patil University Pune India

ISBN: (纸本)9798350321425

As the digital era develops, smart food technologies are more in demand because they make meal planning and preparation simpler and more enjoyable. Recipe recommendation systems have become widespread in order to provide clients with customized recipe recommendations based on their dietary preferences, level of cooking proficiency, and availability of ingredients. On the other hand, the majority of recipe recommendation systems rely on textual inputs or pre-defined categories of ingredients, which might not accurately reflect what the user has in their pantry. This research study proposes a recipe suggestion system based on user-provided images of readily available ingredients that combine image recognition and convolutional neural networks (CNNs). It will recommend recipes based on the image provided. This will make finding recipes more quick and simple than it would otherwise be. In this instance, text-to-speech guided technology is used so that the user can just replay the procedure as needed rather than reading it repeatedly. This research work will include additional elements like redirecting to a grocery cart by clicking a link. This concept will be very helpful to those who don't know what to cook. Thanks to this initiative, users won't have to waste time thinking about the recipes. © 2023 IEEE.

关键词： Image classification

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Plant Identification and Analysis of Medicinal Properties Using Image Processing and CNN with MobileNetV2 and AlexNet

Plant Identification and Analysis of Medicinal Properties Us...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Bandla, Rakesh Priyanka, J. Swetha dumpali, Yashash Chandra Hattaraki, Sunil Kumar M. Dept. of ECE Vardhaman College of Engineering Hyderabad India Dept. of ECE B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura586103 India

ISBN: (纸本)9798331505134

Medicinal plants have been valued for their diverse botanical traits and healing properties, also playing a crucial role in pharmacy and ecological conservation. Identification of these medicinal plants leaves and with their properties is necessary to increase the traditional methods of using a particular leaf. Machine learning (Ml) based algorithms were used to analyze the detecting plant leaves. This study aims to streamline the identification process, enhancing accuracy and accessibility of plant leaves particularly. The existing method performance was analyzed using various metrics namely accuracy, f1-score, recall and precision. The outcomes of this study have the potential to support the preservation of traditional knowledge, contribute to pharmaceutical advancements and aid in the conservation of biodiversity. Accurate and rapid identification of these plants is essential for various purposes, including the development of herbal medicine, scientific research, and environmental protection. © 2024 IEEE.

关键词： Medicinal chemistry

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Machine learning Model used Efficiently to Manage the Smart Grid and Renewable Energy

Machine Learning Model used Efficiently to Manage the Smart ...

引用

2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Narayan, Yogendra Selvaraj, Sivakumar Suryawanshi, Mahesh A. Ramya, S. Chandigarh University Department of Ece Mohali India VelTech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Bharati Vidyapeeth College of Engineering Department of Chemical Engineering Navi Mumbai India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350317060

A new technology, a Smart Grid Management System (SGMS), explains how it uses machine learning algorithms to distribute power more effectively. This article overviews intelligent grid management systems, including their design, advantages, and problems. Machine learning methods, including decision trees, neural networks, and Support Vector Machines (SVM), are also discussed in this study as they are employed in intelligent grid management systems. Increased efficiency, less waste, enhanced dependability, and decreased expenses are a few benefits of using deep learning techniques in intelligent energy systems for management. Protection of data, Security, and capacity are only some issues that arise when applying machine learning techniques to innovativegrid management systems. The report finishes with a discussion of potentialnext steps for machine research learning-based intelligent control systems for grids. © 2023 IEEE.

关键词： Support vector machines

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Novel Applications of Cloud Computing and deep learning Techniques for data Analysis 15

Novel Applications of Cloud Computing and Deep Learning Tech...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajan, V.Aravinda Tiwari, devesh Parveen, H. Summia Verma, Aditya Shukla, Rishi Prakash Pawar, Vikas V Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Tamil Nadu Srivilliputhur626126 India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Eshwar College of Engineering Department of Computer Science Engineering Tamil Nadu Coimbatore641202 India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India Jaipuriya Institute of Management Jaipur India Dr. D Y Patil Vidyapeeth Centre for Online Learning Pune India

ISBN: (纸本)9798350370249

In recent years, cloud computing and deep getting to know strategies have emerged as effective gear for information analysis. This combination permits for the efficient processing of large amounts of statistics, permitting researchers to gain precious insights and make informed selections. This technical abstract will talk the unconventional packages of cloud computing and deep learning strategies for statistics evaluation and how they've revolutionized the sphere. Cloud computing has made it feasible to save and get admission to big amounts of statistics from any region, eliminating the need for costly and cumbersome hardware. This has opened up new possibilities for data evaluation as researchers can now access and examine information from a couple of resources: social media, medical studies, and monetary statistics. deep studying techniques, then again, have proven terrific fulfillment in processing complicated and unstructured statistics. Via mimicking the shape and functions of the human brain, deep mastering fashions can identify styles and traits in information that traditional methods may additionally leave out. © 2024 IEEE.

关键词： Computing Hardware Information Mimicking Statistics

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Automatic Irrigation and Crop Monitoring using Machine learning and Internet of Things 4

Automatic Irrigation and Crop Monitoring using Machine Learn...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kaur, Inderpreet Patil, Snehal Pradeep Shankar, Bhukya Beenu, G. H. Kerinab Kumar, G. Suresh Reddy, d. V. lokeswar Ramachandran, G. Rayat Bahra University Mohali India D. Y. Patil Institute of Master of Computer Applications and Management Maharashtra Pune India Cvr College of Engineering Department of Electronics and Communication Engineering Ibrahimpatnam Hyderabad India Velammal Engineering College Surapet India Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Deemed to be University Salem India Jntu College Of Engineering Humanities and Basic Sciences Department Andhra Pradesh Kadapa516390 India

ISBN: (纸本)9798331540364

The Crop environmental monitoring system with intelligence multidimensional and multi-angle crop environment monitoring system is created to satisfy the demands of contemporary society for agricultural crop environmental monitoring. The system gathers, processes, and forwards data to the network module by connecting the appropriate data gathering sensors. Ultimately, the data is sent to the information processing center for accurate processing and analysis before being visually displayed. Ultimately, the system's ability to achieve the desired result, have a high application value, and effectively increase agricultural environment surveillance efficiency is fully demonstrated by the testing and verification of its operation. low-cost intelligent modules for smart irrigation systems powered by IoT and AI In India, agriculture is the primary source of income for the majority of people. It is essential to use cutting edge technologies to safeguard priceless water supplies. In addition to serving as Industry 4.0's cornerstone, IoT improves smart agriculture. The goal of the research is to develop a cutting-edge, affordable system for intelligent irrigation. Self-talk and inter-device networking are made possible by IoT. © 2024 IEEE.

关键词： Smart agriculture

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Integrating deep learning and Meta-Heuristics for Healthcare: A Survey

Integrating Deep Learning and Meta-Heuristics for Healthcare...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： K. Pathmapriya P. M. Joe Prathap Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

data mining is used in healthcare organizations for surveillance, diagnosis, and prognosis. According to Health Catalyst, deep learning (dl) is the game-changing technology that will revolutionize healthcare. deep learning algorithms are far more successful than regular computing algorithms when used in the healthcare industry. deep learning models do, however, have several drawbacks that impact their overall performance. These include choosing proper model parameter values, choosing the best architecture and feature representation, and figuring out the ideal weight and bias values. The study has brought up a number of concerns about the top deep learning models for a variety of illness datasets and how to solve the issue by improving feature selection for improved performance. Therefore, the present level of current practice in this subject may be further optimized by deep learning models that include computer concepts inspired by biology. New recently, deep learning models have gained attention in the medical field. To improve algorithm visualization, a dataset compiled from the surveyed research publications is compared. An improved description of upcoming work in sustainable healthcare sectors is given by this survey. Better and quicker results will be obtained by combining dl approaches with meta-heuristic algorithms.

关键词： deep learning Surveys Computational modeling Biological system modeling Surveillance Metaheuristics Medical services Organizations Prognostics and health management Mobile computing

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Reinforcement learning-Based Autonomous Collision Prediction and Avoidance System for Vehicles

Reinforcement Learning-Based Autonomous Collision Prediction...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Jagan, S. Jadhav, Ganesh K Priya, C. Adhikary, Priyabrata Bhatt, Upendra Mohan Jayasri, V. Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Mechanical Engineering Dr. D y Patil Institute of Engineering Management & Research Akurdi Pune India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India St. Martins Engineering College Department of AI&DS Telangana Secunderabad India

ISBN: (纸本)9798350319200

This study investigates the feasibility developing an Autonomous Collision Prediction and Avoidance System (Rl-ACPAS) based on Reinforcement learning automobiles in light of the increasing prevalence of autonomous vehicles on the road. In this research, we used a thorough approach that included data gathering from many sensors, Rl agent training using deep reinforcement learning algorithms, and performance assessment. The findings show that Rl-ACPAS has high rates of avoiding collisions, fast reaction times, and effective decision-making, all of which bode well for enhancing road safety, traffic efficiency, and economic sustainability. These results, combined with hypothetical data and practical consequences, illustrate the transformational potential of Rl-ACPAS. The real-world effects on security, effectiveness, economy, and trust are emphasized throughout the presentation. Insights into technological developments, road safety awareness, regulatory issues, and consumer advantages are also provided to readers. Future directions include things like improved learning algorithms, sensor integration, field testing, ethics, human-AI interaction, regulatory frameworks, data sharing, and constant progress. The future of Rl-ACPAS is one of experimentation, cooperation, and dedication to making autonomous transportation systems more secure and dependable. © 2023 IEEE.

关键词： Autonomous Vehicles Avoidance Systems Collision Prediction deep Reinforcement learning Real-Time decision-Making Regulatory Frameworks Reinforcement learning Rl-ACPAS Traffic Efficiency Vehicle Safety

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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An Efficient High Throughput Block Chain Specification 4

An Efficient High Throughput Block Chain Specification

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sharmila, V. Subha lakshmi, P. Karthikeyan, M. Kavitha, G. Sivakumar, A. R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Salem India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Blockchain was thought of as a revolutionary platform with implementation potential in diverse industries. Thus, different blockchain networks have been implemented to analyze their utility in multiple scenarios. In this article, a comprehensive assessment of blockchain efficiency by classifying all the solutions into two broad categories;an empirical study and computational modeling is conducted. The latest methodological blockchain approach includes benchmarking, testing, experimental research and then modeling. It is comparatively tested for stochastic models of computational modeling which is used to test the efficiency of conventional blockchain consensus algorithms. By contrasting, comparing, and grouping the various approaches together, significant parameters can be used to choose the most effective measurement technology to maximize blockchain device performance based on the detection of the bottlenecks. Finally, the proposed approach lists suggested recommendations for possible inquiries. © 2023 IEEE.

关键词： Blockchain

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Oral lesion Cell Segmentation and Classification using Convolution Neural Network Technique 3

Oral Lesion Cell Segmentation and Classification using Convo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Amutha, J. Priyadarsini, S. Prasanth, S. Senkumar, M.R. Ramnath, M. Jasperline, T. P.S.R Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Dr.G.U.POPE College of Engineering Sawyerpuram Department of Computer Science and Engineering Tamil Nadu Tuticorin India

ISBN: (纸本)9798350384598

Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancer diagnosis and other medical image processing applications. In this study, we train deep convolutional neural networks (CNNs) to detect oral cancer early. The suggested approach makes use of a large database of pictures of the mouth and its cavities. develop and refine a convolutional neural network (CNN) model using the use of radiographs, histology slides, and intraoral images. Oral carcinogenic tumours, both precancerous and malignant, may be identified and classified automatically using this approach. The process begins with picture pre-processing, continues with dataset supplementation for better model generalizability, and culminates in the use of complex neural networks for feature extraction and learning. Transfer learning methods modify previously taught models for this medical imaging task. The system's performance, sensitivity, and accuracy are evaluated using a set of images that have pre-existing diagnoses. deep learning CNNs were able to identify oral cancer with an efficiency and accuracy of 93.62%, according to the research. The trained model is a valuable tool for early cancer screening because of its high sensitivity and specificity. By automating the process, oral cancer may be detected and treated more quickly, which improves patient outcomes and decreases the disease burden. © 2024 IEEE.

关键词： Convolutional neural networks

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