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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Poonam Shourie Vatsala Anand Deepak Upadhyay Swati Devliyal Sheifali Gupta Computer Science & Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Electronics and Communication Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

The healthy vision is seriously threatened by retinal diseases include drusen, diabetic macular edoema, and choroidal neovascularization (CNV). Immediate identification and treatment are essential to reducing their effects and retain healthy lives. In this work, model provide a deep learning method based on the VGG16 architecture for the automatic identification of retinal injury using a dataset of retinal OCT (optical coherence tomography) pictures. retinal OCT images into these four categories by preprocessing the images and training a VGG16 convolutional neural network (CNN) model. The retinal OCT dataset is used to fine-tune the pretrained VGG16 model, which is well-known for its efficiency in image classification tasks, so that it can adjust to the unique properties of retinal pictures. By means of comprehensive testing, the model assesses the accuracy performance of the proposed model across several epochs. The suggested model improves validation accuracy from 87.89% to 92.01% and training accuracy from 97.22% to 100%. The findings show that the VGG16-based method is helpful in accurately detecting retinal abnormalities, providing ophthalmologists with a possible tool to help them diagnose retinal illnesses quickly and reliably. By offering a dependable and automated technique for identifying retinal damage from OCT pictures, this work advances computer-aided diagnosis tools for retinal illnesses and eventually advances early intervention and better patient outcomes and hence improve quality of life.

关键词： Training Accuracy Optical coherence tomography Training data Retina Market research Data models Convolutional neural networks Diseases Testing

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Real-Time Food Detection and Nutritional Tracking Application for Personalized Health Management Using MobileNetV2

Real-Time Food Detection and Nutritional Tracking Applicatio...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Divyabhavani Ganpisetty Chetana R Reddy Navyashree Ganpisetty Anitha J Dept of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India Dept of Electronics and Communication Engineering RV Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Accurate and timely dietary assessment is essential for health management. This study presents a deep learning-based approach for real-time, personalized food analysis through image analysis. The preparation processaccurately predicts the content of food products based on images captured by a smartphone camera. The model is trained on a comprehensive database of food images along with their corresponding menu items. To enhance user experience and engagement, the system integrates a mobile application that allows users to easily record their meals by taking photos. Test resultsshow the system's effectiveness in accurately estimating macronutrients and calories, providing users with a better understanding of making dietary decisions. This research contributes to the development of smart health devices and has the potential to impact public health through health promotion.

关键词： Image analysis Databases Smart healthcare Cameras Real-time systems User experience Mobile applications Food products Public healthcare Information technology

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Revolutionizing Bus Tracking and Arrival Prediction in Real Time for Both Public and Private Sectors, Powered by Firebase

Revolutionizing Bus Tracking and Arrival Prediction in Real ...

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Distributed Computing and Electrical Circuits and electronics (ICDCECE), IEEE International Conference on

作者： Mohammed Shafiulla Abdul Lateef Haroon P S Shaik Mohammed Naveed Suhail Ahmed Mahika Dhar M Utkarsh Kumar Department of Computer Science and Engineering Ballari Institute of Technology & Management Ballari India Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Public transport systems often face challenges related to reliability and efficiency, leading to inconvenience and uncertainty for passengers. Existing solutions for real-time bus monitoring typically rely on expensive Internet of Things (IoT) components. This paper presents Track N Go, a cost-effective alternative for real-time monitoring of public buses and taxis using mobile phone location data. The system utilizes a robust backend system built with Java and *** for the user interface. Integration of GPS via the Map Box API and Firebase database ensures accurate location tracking. Track N Go provides accurate real-time monitoring, addressing challenges associated with public transport and improving overall urban mobility. The system enhances convenience and peace of mind for passengers by delivering reliable and user-centric solutions. The evaluation of Track N Go demonstrates its effectiveness in improving urban mobility and transit services. By offering a reliable and cost-effective solution for real-time bus monitoring, Track N Go aims to streamline daily commutes and make public transport more efficient and predictable.

关键词： Uncertainty Smart cities Scalability User interfaces Real-time systems Mobile handsets Reliability

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AI-Driven Mentor Service Advising Application for Timely Course Completion using RNN/LSTM

AI-Driven Mentor Service Advising Application for Timely Cou...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Sumathy K Surendran R Maha Sweta Sathiamurthy Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

To be precise, time is a very sensitive factor that concerns course completion in colleges. MOOCs and other such sources of online learning have begun to make education accessible to everyone. One of the significant problems that exist in the course of studying is the fact that so many students flunk. The study recommends an AI-based mentor service thus integrating an advising app that will ensure that students complete their courses on time. The program applies the AI and ML to provide the academic planning of performance and direction, immediate assistance and constant feedback to students, resulting in higher scores and enhanced satisfaction levels. Recurrent Neural Networks (RNN s) and in particular the implementation of Long Short-Term Memory (LSTM) are applied to an innovative virtual mentor service which allows the generation of individual learning pathways and enhancement of completion time for online students. The LSTM network determines and categories how the students are transitioning throughout the course content and distinction between the zones of strength and areas that might require the students to be guided. Possible Challenges With the help of the technology, a student might indicate such problems as being lost at a topic or lagging behind Growth of learning patterns can be estimated. Proposing specific actions As regards barrier-related knowledge deficits, the system provides specific recommendations in terms of special approaches. Such interventions may comprise of suggesting new resources that include products, or skillful practice sessions, or even different learning resources. Accelerate Completion The technology to complete courses in time based on students' performance as well as recommendations. It was used for Comparing therapies and suggestion using experiments hence used as AlB Tool. The rationale of this approach is to enhance timely completion of courses and consequently the results of learning processes.

关键词： Recurrent neural networks Education Medical treatment Transforms Learning (artificial intelligence) Transformers Planning Expert systems Long short term memory

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Multimodal Transformer Fusion for Sentiment Analysis using Audio, Text, and Visual Cues

Multimodal Transformer Fusion for Sentiment Analysis using A...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： S. Peerbasha Mohammed I. Habelalmateen Ramachandra A C Revathi P T. Saravanan Department of Computer Science Jamal Mohamed College (Autonomous) Affiliated to Bharathidasan University Tiruchirapalli India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In modern era, the increased growth in social media platforms and technologies such as Artificial Intelligence (AI) have gained interest towards multimodal sentiment analysis that includes text, audio and visual cues for extraction of useful insights. Although these systems are capable of analyzing sentiment, but also faces certain challenges in synchronization of multimodal inputs, data fusion and reliability under various contexts. Hence, this research proposes an effective Multimodal Transformer Fusion (MTF) which combines the strengths of various modalities to recognize human emotions. Initially, the data is collected from CMU-Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset. Further, the data is preprocessed with Empirical Mode Decomposition (EMD) and Min-max normalization, stop words removal, and lemmatization to remove noise and improve the quality of sentiment analysis. Then, the features are extracted for different input modalities by using Mel-Frequency Cepstral Coefficients (MFCC), 3D Residual Network (ResNet 3D) and Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). After that, extracted features are further fed into proposed MTF to classify the sentiment of a customer into positive, negative or neutral. From the results, the proposed MTF achieved outstanding results in F1 Score (80.76%) as well as Mean Absolute Error (0.376) when compared with the existing Tensor Fusion Network (TFN).

关键词： Sentiment analysis Visualization Three-dimensional displays Empirical mode decomposition Tensors Customer services Noise Feature extraction Transformers Mel frequency cepstral coefficient

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Cane Vision: A Cutting-Edge Mobility Assistance System for the Visually Impaired

Cane Vision: A Cutting-Edge Mobility Assistance System for t...

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2024 International Conference on communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Waghmode, Rushikesh Ramchandra Bhonsle, Sanketvardhan Satyajeet Shetty, Ruchit Ramesh Iyer, Deepti Jayananth Kumar, Sachin Chakrabartty, Shubhro D Y Patil International University School of Computer Science Engineering and Applications Pune India Galgotias College of Engineering and Technology Department of Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798331541583

People with visual impairments face difficulties navigating unfamiliar areas, restricting their daily activities. They often require help from others, guide dogs, or expensive electronic aids to move safely. In this paper, an advanced mobility assistance system for the visually impaired people is created to address the challenges. This affordable, lightweight device uses an image processing concept to alert users about obstacles and puddles through vibrations. It also precisely measures the distance to obstacles, with different vibration patterns indicating varying levels of proximity. This functionality is achieved by combining an ultrasonic sensor, vibration motor, and ESP32 CAM module, all controlled by an ESP32 microcontroller. These components, strategically placed on the cane, provide efficient guidance and improved mobility for visually impaired users. © 2024 IEEE.

关键词： Microcontrollers

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Plant Disease Classification Using Fusion Cauchy Reverse Learning Strategy Based Orca Predation Algorithm with DenseNet-121

Plant Disease Classification Using Fusion Cauchy Reverse Lea...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： Revati Vishwanath P Manjula B M Saef Wbaid Suvendu Kumar Jena Department of Electronics and Communication Engineering Visvesvaraya technological University Belagavi India Department of Electronics and Communication Engineering Sir M Visvesvaraya College of Engineering Raichur India Affiliated to Visvesvaraya Technological University Belagavi India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Computer Science Engineering and Applications Indira Gandhi Institute of technology Dhenkanal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Plant diseases represents specific spots with various colors and locations, that have a significant impact on leaves lead to affect the plants and reduced the crop yield. Thus, an accurate plant disease classification is essential corresponding to the distinct spot shape, size, etc. However, the existing Deep Learning (DL) models have several limitations that make the model difficult to distinguish between various plant diseases due to similar characteristics. To overcome this limitation, a Fusion cauchy reverse learning strategy based Orca Predation Algorithm (FOPA-DenseNet-121) is proposed to identify and classify the plant leaf disease accurately. The proposed DenseNet-121 model has the advantage of feature reusability that helps to learn the information about various diseases effectively. Also, the FOPA based hyper parameter tuning model adjusts the DenseNet-121 model parameters to learn the difference between various diseases which helps to differentiate the diseases and enhance performance of classification model. Experimental results of the proposed model achieved accuracy of 98.92% which is greater than the existing approach like Visual Graph Geometry -16 (VGG-16).

关键词： Geometry Deep learning Plant diseases Visualization Accuracy Feature extraction Classification algorithms Integrated circuit modeling Tuning Crop yield

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Exploring the Benefits of Deep Learning for Data science Practices 15

Exploring the Benefits of Deep Learning for Data Science Pra...

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

作者： Sharma, Mohit Kumar Kumar, Ritesh Sinha, Deepak Kumar Senthilkumar, K.K. Dhabliya, Dharmesh Ahluwalia, Gunveen Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction. © 2024 IEEE.

关键词： Economic and social effects

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Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers

arXiv

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arXiv 2024年

作者： Paneru, Biplov Paneru, Bishwash Shah, Krishna Bikram Department of Electronics & Communication Engineering Nepal Engineering College Pokhara University Nepal Department of Applied Sciences and chemical Engineering Tribhuvan University Institute of Engineering Nepal Department of Computer Science and Engineering Pokhara University Nepal

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. A thorough investigation of deep learning methods is carried out using the UCI dataset in order to create a reliable and effective model that can correctly identify a range of rice diseases. The suggested transfer learning models perform better at identifying subtle features and complex patterns in the dataset, which results in extremely accurate disease classification. Moreover, the study goes beyond the creation of models by incorporating an intuitive Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, and MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101, only an accuracy of 54% could be achieved, along with only 33% on efficientNetB0. A MobileNetV2-trained model was successfully deployed on a TKinter GUI application to make predictions using image or real-time video capture. © 2024,

关键词： Convolutional neural networks

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