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Online Grading of Fruits using Deep learning Models and Computer Vision 4

Online Grading of Fruits using Deep Learning Models and Comp...

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4th international conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Rajmohan, Shathanaa Mendem, Mani Tej Vanam, Shankar Sreenu thalapally, Pavan Kumar Indian Institute of Information Technology Sri City Chittoor India

ISBN: (纸本)9781450399937

Fruit quality evaluation is an important task in many industrial applications especially, in processing units. In Industries separating bad quality fruits manually is expensive and time consuming. the classification and grading of fruits when done manually is not precise. this work presents an efficient methodology for fruit classification using deep learning. Most existing works done to address the fruits classification based on quality focus on a single variety of fruit and a more general system with good accuracy is not available. In this paper, a Convolutional Neural Network based quality evaluation system for multiple fruits is presented. the proposed work is evaluated by comparing with state-of-the-art works based on two datasets and it achieves an accuracy of 99.12% and 97.67% for those. To make the proposed work available to public a web application has been created and the classification model is integrated to that. © 2022 ACM.

关键词： Fruits

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StrokeAlert: Early Detection of Minor Strokes

StrokeAlert: Early Detection of Minor Strokes

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Shruti Vasa Simoni Savani Anmol thaker Pradnya Bhangale Department of Computer Engineering KJ Somaiya Institute of Technology Mumbai Maharashtra India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Stroke is a critical global health issue, with minor strokes often going undetected due to subtle symptoms. these minor events are key indicators of potential major cerebrovascular issues and, if untreated, can lead to severe health deterioration. this paper presents StrokeAlert, a mobile application for the early detection of minor strokes using the FAST criteria (Face, Arms, Speech, Time). the app employs image analysis, speech recognition and arm weakness detection technologies to detect stroke symptoms in real time using deep learning algorithms. Testing demonstrated an 83.84% accuracy in facial drooping detection, 96.33% in speech difficulty evaluation and 88% in arm weakness detection. StrokeAlert offers an accessible, user-friendly solution that could significantly enhance early intervention and reduce stroke-related complications.

关键词： Accuracy Image analysis Telemedicine Prevention and mitigation Speech recognition Real-time systems Mobile applications Reliability Public healthcare Testing

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837951

the proceedings contain 109 papers. the special focus in this conference is on Advanced Network Technologies and Intelligent Computing. the topics include: Exploratory Study on Different Transformer Models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models;human Action Recognition Using Mediapipe Holistic Keypoints: A Deep learning Approach;machine learning for Waste-to-Energy: optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced Machine learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid Machine learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised Machine learning Based Speech Recognition Techniques;improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction through Machine learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and optimization for B5G Networks;file Prefetching Accuracy Enhancement Using Deep learning Approaches;Mamba Based Conditional GAN for Optical Cloud Removal of Satellite Images;a Hybrid Quantum Machine learning Model for Multi-class Classifier;adaptive Resource Management in Massive Machine-Type Communications Using Reinforcement learning;Advanced Machine learning and Features Based Data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks;deep Featu

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Deep learning for Alzheimer's Disease Detection using Multimodal MRI-PET Fusion 4

Deep Learning for Alzheimer's Disease Detection using Multim...

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4th international conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Suma, K.V. Raghavan, Dharini Ganesh, Puneeth Ramaiah Institute of Technology Dept. of e & C Bangalore India

ISBN: (纸本)9798350397475

Alzheimer's disease (AD) is an irremediable brain disorder that is progressive and causes irreparable damage to brain cells, neurotransmitters, and nerves. this in turn severely affects brain functionalities and ultimately leads to dementia. Although there is currently no cure for AD, there are treatments that can slow down the disease's development. Hence, early diagnosis of AD is the need of the hour and researchers across the world have shifted their focus on developing robust and intelligent systems that can aid in early and accurate diagnosis of AD this has been the main motivation behind this study. the main objective of this paper is to present a comparative study of 2D and 3D Convolutional Neural Network (CNN) architectures for AD classification and to choose the most robust model for AD classification. the models are trained on MRI and PET individually and with the fusion of MRI and PET. 2D feature fusion is performed using pre-trained neural networks and 3D fusion involves a series of operations such as skull-stripping, image segmentation, and co-registration. 2D CNN provided the highest accuracy of 91.29% on MRI images followed by 3D CNN with an accuracy of 91.07%. Comparing the performance on multimodal fusion, 3D MRI -PET fusion exhibited a significantly good accuracy of 86.90%. this paper briefly describes the GUI developed for easy visualization of AD classification and the possibilities of integrating the trained machine learning models with various mobile and web applications and with instruments that facilitate real-time diagnosis and classification of AD. © 2022 IEEE.

关键词： Magnetic resonance imaging

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AI in Agriculture: Yield optimization and Food Security

AI in Agriculture: Yield Optimization and Food Security

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Lavanya Chavan Rishikesh Kumbhalkar Poorva Agrawal Nitin Rakesh R. Parvathi Mohammad Shahid Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology (VIT) Vellore India Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Artificial Intelligence (AI) is remarkably automating the agricultural methods. In this process, AI is helping to optimize yield, and improving food security in a world with growing population and environmental concern(s). AI technology integration in agriculture is the focus of this paper which includes predictive analytics, machine learning and IoT. these applications led to precision farming and resource management which lead to efficient use of resources. the discussion on the benefits of AI in increasing productivity and sustainability is based on the summarization of the latest literature and empirical studies. Yet, these technologies bring along their own issues such as initial capital investment to implement the systems in emergence economies as well as the need for the rapid upgrading of the infrastructure and human capital. this research demonstrates that AI has the potential to change agriculture radically, while at the same time acknowledging the pending problems that hinder full-fledged implementation.

关键词： Training Food security Agriculture Resource management Artificial intelligence Sustainable development Socioeconomics optimization Farming Investment

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Machine learning applications for Digital Advertising optimization

Machine Learning Applications for Digital Advertising Optimi...

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international conference on Computing and Networking Technology (ICCNT)

作者： K.V. Jamuna Amit Kumar Shrivastav Jyoti Seth Department of Forensic Science School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Office of Registrar ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Virtual advertising optimization is an increasing number of crucial things in digital advertising. Device getting-to-know strategies can offer an effective manner to enhance online advertising and marketing campaigns and provide a higher return on investment. Device learning algorithms can be used to model complicated relationships and identify styles in user behavior information with the purpose of higher identifying goal audiences, creating greater a success campaigns, and optimizing advert shipping in real-time. by way of making use of supervised and unsupervised getting-to-know methods, groups can study from past campaigns and use predictive models to recognize how changes in variables inclusive of campaign timing, innovative, ad placement, and concentrated on can cause higher person engagement and better return on investment. System mastering models may be used to examine the anticipated overall performance of a given campaign and, in flip, better installation assets to maximize performance. Additionally, herbal language processing and sentiment evaluation can be used to apprehend better the impact of advertising and marketing messages on customers

关键词： Performance evaluation Predictive models Prediction algorithms Real-time systems Timing Object recognition Advertising Predictive analytics optimization Investment

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Exploration on Intelligent Detection Methods for Substation Equipment Based on Deep learning

Exploration on Intelligent Detection Methods for Substation ...

引用

Power Electronics, Computer applications (ICPECA), IEEE international conference on

作者： Wenchao Xuan Zhanghongtai Li Hongtao Chen Haichao Peng Yanchun Guo Xianda Wang Lin Song He Wang Songyuan Power Supply Company State Grid Jilin Electric Power Co Ltd Songyuan Jilin China

With the continuous development of industrial automation and intelligence, traditional methods of substation equipment detection face challenges of low efficiency and accuracy. this study aims to explore a deep learning-based intelligent detection method for substation equipment, aiming to improve detection efficiency, accuracy, and reduce manual costs. the advanced object detection model YOLOv5 is employed in this exploration. through training and testing on a large number of substation equipment images, rapid and accurate identification of equipment is achieved. Experimental results show that this method outperforms traditional approaches in terms of accuracy, recall, and processing speed, demonstrating promising application prospects. Furthermore, the study delves into the performance of the YOLOv5 model in handling complex scenarios, including its ability to recognize equipment states under various weather conditions and different lighting conditions. By optimizing the model structure and adjusting training strategies, the robustness of the model is enhanced when facing challenges such as occlusion, reflection, and equipment aging. Particularly, the model's capability to detect minor faults is significantly improved, which is crucial for early fault warning and maintenance decision support. In conclusion, the successful application of deep learning-based intelligent detection technology for substation equipment provides new ideas and tools for the safe operation and intelligent management of power systems. Looking ahead, with ongoing technological advancements and further accumulation of data, we believe these intelligent detection technologies will play an increasingly important role in ensuring grid reliability, optimizing operational processes, and reducing operating costs.

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Machine learning for PV System Operational Fault Analysis: Literature Review 4th

Machine Learning for PV System Operational Fault Analysis: L...

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4th international conference on Intelligent Technologies and applications (INTAP)

作者： Zenebe, Tarikua Mekashaw Midtgard, Ole-Morten Voller, Steve Cali, Umit Norwegian Univ Sci & Technol Trondheim Norway

ISBN: (纸本)9783031105258;9783031105241

this review paper aims to discover the research gap and assess the feasibility of a holistic approach for photovoltaic (PV) system operational fault analysis using machine learning (ML) methods. the analysis includes the detection and diagnosis of operational faults in a PV system. Even if standard protective devices are installed in PV systems, they fail to clear various faults because of low current during low mismatch levels, high impedance fault, low irradiance, etc. this failure will increase the energy loss and endanger the PV system's reliability, stability, and security. As a result of the ML method's ability to handle a non-linear relationship, distinguishing features with similar signatures, and their online application, they are getting attractive in recent years for fault detection and diagnosis (FDD) in PV systems. In this paper, a review of literature on ML-based PV system FDD methods is provided. It is found that considering their simplicity and performance accuracy, Artificial Neural networks such as Multi-layer Perceptron are the most promising approach in finding a central PV system FDD. Besides, the review paper has identified main implementation challenges and provides recommendations for future work.

关键词： Ensemble learning Fault detection and diagnosis Machine learning PV system fault Transfer learning

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Assessing the Complexity and Real-Time Performance of Anomaly Detection algorithms in Resource-Constrained Environments

Assessing the Complexity and Real-Time Performance of Anomal...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Romarick Yatagha Oumayma Mejri Karl Waedt Christoph Ruland Framatome Erlangen Germany University of Siegen Siegen Germany

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

In the context of Industrial Internet of things, the deployment of AI-based anomaly detection algorithms in resource-constrained environments is critical. this paper systematically evaluates the complexity and real-time performance of widely used machine learning models-Random Forests, Support Vector Machines, Linear Regression, XGBoost, Neural Networks, and Autoencoders. through comprehensive theoretical analysis and empirical measurements on four diverse datasets, we identify the strengths and limitations of each model in terms of computational demands and practical feasibility. Our findings highlight that while complex models like Neural Networks and Autoencoders offer high accuracy on time series datasets, their deployment on edge devices necessitates significant optimization for real-time performance. In contrast, simpler models, though less resource-intensive, may fall short in critical applications. this study provides a decision matrix and structured evaluation framework to guide the selection of efficient anomaly detection models tailored to specific operational constraints. these contributions aim to assist developers and system administrators in achieving balanced AI deployments that optimize performance, security, explainability, and resource consumption.

关键词： Support vector machines Performance evaluation Accuracy Machine learning algorithms Computational modeling Neural networks Time series analysis Real-time systems Anomaly detection optimization

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A Comprehensive Study on Software Engineering and Emerging Technologies

A Comprehensive Study on Software Engineering and Emerging T...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Vivek Bhatnagar Tejinder Kaur Gulbir Singh Ritu Mishra Department of MMICTBM Maharishi Markaneshwar (Deemed to be University) Mullana Ambala Haryana India Department of Computer Science and Engineering Graphic Era Hill University Haldwani Campus Nainital Uttarakhand India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Recently, software engineering field is undergoing a significant transformation driven by the rapid advancement of emerging technologies. AI introduces intelligent automation in code generation, testing, and debugging by significantly enhancing the development efficiency and reducing human error. Machine learning (ML) algorithms further enhance the predictive maintenance and performance optimization. Block chain technology enables the creation of decentralized applications (DApps) and smart contracts, ensuring enhanced security, transparency, and automation. Internet of things (IoT) brings a paradigm shift with an exponential increase in connected devices, necessitating robust software solutions for real-time data processing and analytics, while also highlighting the security challenges.

关键词： Technological innovation Software algorithms Smart contracts Decentralized applications Software Real-time systems Security Internet of things Software engineering Testing

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