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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, Gollapalli Madhu Anand, Rakam Sankeerth, Thalloju Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

Image exploration adopts several dimensionalities with web-based tasks, integrating Streamlit and OpenCV to create an interactive platform for advanced image enhancement. Alongside the transformative tool enabling conversion of black and white images into vibrant compositions, an innovative styling suite offers diverse artistic filters. Notably, the addition of a camera option allows users to capture photos directly within the interface. Through Streamlit's intuitive interface and OpenCV's robust image processing, users experience real-time previews for seamless colorization and filtering. The proposed methodology involves designing a responsive web interface, optimizing Streamlit widgets for file uploads and style selection, and leveraging OpenCV for image processing. Results demonstrate the application's effectiveness, providing immediate insights into style impact. Challenges include processing speed optimization, with future research directions exploring advanced Machine Learning (ML) models for dynamic style transfer. In summary, this research advances interactive image processing, offering a refined platform for creative visual content enhancement. The results obtained is promising leading to effective image colorization and styling, thereby improving the quality of the image. © 2024 IEEE.

关键词： Image enhancement

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ResNet-CNN Model for Plant Disease Classification for E-Agriculture Applications

ResNet-CNN Model for Plant Disease Classification for E-Agri...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, Arava Vamsi Sreenivasu, S.V.N. Narasimha Reddy, Kandi Venkata Narasaraopeta Engineering College Department of Computer Science & Engineering Narasaraopet India

ISBN: (纸本)9798350360660

The advancement of an economy's innovation capacity is closely tied to the progress in agriculture. Effective plant disease management is crucial for optimizing crop yields and ensuring agricultural sustainability. This project focuses on developing a Plant Disease Detection and Classification Network (PDDC-Net) by integrating deep learning models. The preprocessing stage standardized the dataset images by eliminating various types of interference. The PDDC-Net employs a Residual Network (ResNet)-Convolution Neural Network (CNN), featuring residual networks, to enhance feature extraction and classification accuracy. The PDDC-Net model demonstrated exceptional performance in detecting and classifying plant leaf diseases. Specifically, it achieved an accuracy rate of 99.84% across various crop diseased leaves, including those from potato, tomato, and pepper plants. Further, the suitable pesticide also suggested for the recognised disease. The proposed PDDC-Net effectively addresses the challenge of plant disease identification with high accuracy, underscoring its potential as a robust tool for agricultural disease management and innovation. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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Multimodal Deep Learning Framework for Non-Intrusive Autism Spectrum Disorder Detection 3

Multimodal Deep Learning Framework for Non-Intrusive Autism ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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MedConnect - A Comprehensive AI-driven Platform for Seamless Patient care and Optimization of Medical Services 8

MedConnect - A Comprehensive AI-driven Platform for Seamless...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ida Christy, J. Abisha, D. Navedha Evanjalin, R. Sriranjani, K. Dimple Abirami, S. Guru Varshini, B. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

In the rapidly evolving landscape of healthcare, technological advancements play a pivotal role in enhancing patient care and optimizing medical services. New solutions are being developed and implemented, both those catered for treatment of organ-specific diseases, MedConnect's aim is to improve the process of treatment delivery. It helps the user streamline an 'in-matching' selection process with ways of overcoming the challenges posed by manual processes, having to wait over long periods, and more. Modifying a GPT-3 API within chatbot components increased the applications power and responsiveness as well as made use of the turbo version. The implementation offered users a straightforward health information retrieval system when patients could retrieve their medical records, look for relevant diseases and information on their prevention. Results also demonstrated a substantial enhancement in user satisfaction and usability of operations. Such results are of great importance, indicating clearly how transformational technology that incorporates AI can be in healthcare. MedConnect seeks to enhance the use of available medical facilities in as much as it improves the system of medical services provision;it has proved to be a success story in the application of technology in fighting for patients' needs. © 2024 IEEE.

关键词： Electronic health record

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Exploring AI Ability to Generate Artistic Content, Music Literature, and Other Creative Works

Exploring AI Ability to Generate Artistic Content, Music Lit...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Ahuja, Harsehaj Gupta, Dipika Kumar, Manish Chugh, Jatin Punjab Engineering College Department of Computer Science Engineering Chandigarh India

ISBN: (纸本)9798350364088

Creative work means doing work that has not been done or thought to date. It involves your logical and reasoning ability. It includes making creative efforts in sculpture, painting, sketching, etc. Creativity allows people to synthesize their imagination and intelligence, which inspires them to tell new stories and convey original viewpoints. It's an adventure where the logical skills of the mind merge with the whims of creativity, giving birth to uniqueness in a variety of artistic expressions and problem-solving pursuits. © 2024 IEEE.

关键词： Applications of AI Artificial Intelligence Human FeedBack

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Transforming Human-Machine Interaction: Generative AI Virtual Asst

Transforming Human-Machine Interaction: Generative AI Virtua...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Babu, Gotru Jeevan Safrinfathima, S. Reddy, K. Deekshith Sen, Sanjay Kumar Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350359688

By creating a sophisticated virtual assistant (VA), this study investigates how generative AI might revolutionise human-machine interaction (HMI). Our suggested solution overcomes conventional constraints by utilising cutting-edge generative models, which allow the VA to produce contextually relevant responses and adjust to a variety of user inputs. The project's goals include increasing user engagement, broadening the Virtual Assistant's scope of work, and maximising the effectiveness of human-machine interactions overall. The paper explores the technical aspects of putting generative AI into practice, emphasising its real-time understanding of user intent, context, and sentiment. We tackle issues including potential biases, ethical concerns, and the requirement for ongoing learning to guarantee the responsible and efficient use of generative AI in virtual assistant applications. By means of this study, we hope to add to the current conversation on the direction of HMI by illuminating the revolutionary potential of Generative AI in producing more perceptive, flexible, and human-like virtual assistants. © 2024 IEEE.

关键词： Chatbots

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Voice Assisted Facial Emotion Recognition System For Blind Peoples With Tensorflow Model

Voice Assisted Facial Emotion Recognition System For Blind P...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Ashmad Ahemed, S. Jayakumar, D. Ifet College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350348460

This research project presents the development of a novel Voice-Assisted Facial Emotion Recognition System tailored to address the unique needs of blind individuals. Leveraging the power of deep learning and the TensorFlow framework, the system employs computer vision techniques to detect and interpret facial emotions, enabling blind users to comprehend the emotional cues of those they interact with. The project encompasses data collection, preprocessing, model training, and evaluation, resulting in a robust TensorFlow model capable of real-time emotion recognition from facial images. A voice assistant integrates seamlessly into the system, offering spoken feedback to users, thus enhancing their understanding of emotional contexts during social interactions. The system prioritizes user accessibility and privacy, ensuring a user-friendly interface and adherence to data security standards. Extensive testing and user feedback are essential components of the project, allowing for continuous improvement and optimization to meet the specific needs of blind individuals. This research offers a promising solution to empower the visually impaired community by providing them with a valuable tool for enhanced social interaction and emotional understanding. Real-World Application, Emphasize the practicality and real-world applicability of the system, showcasing how it can make a meaningful impact on the daily lives of blind individuals by helping them navigate social and emotional contexts more effectively. Multimodal Interaction, Highlight the multimodal nature of the system, which combines visual facial emotion recognition with voice-based interaction to provide a comprehensive and accessible user experience. © 2024 IEEE.

关键词： Convolutional Neural Network Emotion identification Tensorflow

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Implications of Virtual Reality on Environmental Sustainability in Restaurants based on AI 10

Implications of Virtual Reality on Environmental Sustainabil...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Adithya Narayanan, C. Bharath, E. Vijayakumar, R. Rajalakshmi Engineering College Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350353068

This talks about the various ways that augmented reality (AR) affects environmental sustainability in the restaurant business. When combined with artificial intelligence (AI), augmented reality has the power to completely change the restaurant industry by improving eco-friendly operations and changing many facets of their operations. This article examines how AR and AI might help restaurants become more environmentally sustainable. It focuses on important topics including menu design, waste management, customer interaction, and operational efficiency. Easy access to menus and simplicity are two important elements that make restaurant food ordering simpler. The eating experience for customers is entirely revolutionized by the HOLOGRAM menu. Current programs offer an application that eateries can utilize to include their menus into tablets with CONTROL & HOLOGRAM functionality, making it simpler for customers to swipe, swipe, and tap through menus. The objective of this project is to give the restaurants a tablet menu that suggests items using a novel recommendation algorithm. We store the database on a cloud-based server, which is safe & affordable. © 2024 IEEE.

关键词： Augmented reality

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Breast cancer diagnosis using machine learning model 4th

Breast cancer diagnosis using machine learning model

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. This study proposes a lightweight Machine Learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. The model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep Learning (DL) models, focusing on both accuracy and computational efficiency. The results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep Learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny Machine Learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

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