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TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions

TexAVi: Generating Stereoscopic VR Video Clips from Text Des...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Jayaraman, Shruti Bhavya, R. Srihari, Vriksha Mary Anita Rajam, V. College of Engineering Dept. of Computer Science and Engineering Guindy Chennai India

ISBN: (纸本)9798350376876

While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.

关键词： Virtual environments

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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DDOS Detection Using ML and Deep Learning Approaches 8

DDOS Detection Using ML and Deep Learning Approaches

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sumukh, S. Sandhya, S. R V College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

DDoS has a huge potential of threat towards the availability and performance of webservices. The attack system makes the systems unavailable to the valid users by flooding them with malignant traffic. Traditional defense mechanisms are mostly ill-designed to counter such fast-changing and increasing attacks, so service disruption, financial loss, and reputational damage are expected results. This work explores the potential of real-time DDoS attack detection and mitigation using machine learning and deep learning, with a particular emphasis on scalability, accuracy, and adaptability. The research involves the creation and assessment of several machine learning (ML) methods, such as Random Forests and Decision Trees, in addition to deep learning (DL) models like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. The study attempts to determine the best models for real-time DDoS detection by comparing and contrasting these methods. The methodology consists of comprehensive data collecting and preprocessing, model building, training, and evaluation. The performance of the models on parameters such as detection speed, accuracy, and resource efficiency is compared and analyzed. © 2024 IEEE.

关键词： Long short-term memory

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

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

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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Recommendation System For Social Platform Using Deep Learning Techniques 8

Recommendation System For Social Platform Using Deep Learnin...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nishchal Narayan, S. SriVidya, M.S. Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations (Google, Explorer, Microsoft Edge), music recommendations (Wynk), video recommendations (YouTube), movie recommendations (Amazon Prime), and product recommendations (Flipkart, Amazon). The study employs similarity coefficient calculations like Jaccard Distance and Cosine Similarity, alongside ranking measures such as PageRank. Furthermore, the system's performance is evaluated using advanced deep learning techniques, including Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Comprehensive experimentation involves measuring the F'l-Score and comparing the accuracy of various machine learning and deep learning algorithms to identify the most effective methods for friend recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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Genetic Data for Early Detection and Treatment of Cystic Fibrosis through Machine Learning 8

Genetic Data for Early Detection and Treatment of Cystic Fib...

引用

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

作者： Deepthi, P. Bhuvaneshwari, M. Chithra, K. Dhanasree, L.P. Jananee, V. Layola, J.A. Adlin Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Cystic Fibrosis (CF) is a genetic disorder that significantly impacts the respiratory and digestive systems, requiring early detection and precise management to improve patient outcomes. This research study presents a machine learning model designed to predict the presence, severity, and potential treatments for Cystic Fibrosis using genetic data. By leveraging collaborative filtering techniques, our model not only identifies individuals at risk but also enables personalized treatment plans based on genetic predispositions. The proposed model aims to revolutionize Cystic Fibrosis diagnosis and management by automating the screening process on a mass scale. This automation facilitates the identification of demographic areas and genetic populations with a higher susceptibility to Cystic Fibrosis, enabling targeted interventions. Through large-scale genetic data analysis, our approach ensures that at-risk populations receive timely and appropriate care, ultimately improving the quality of life for Cystic Fibrosis patients. The findings suggest that the implementation of this predictive model in clinical settings can lead to earlier and accurate diagnoses, personalized treatment plans, and significant advancements in Cystic Fibrosis care. The implications of this research extend beyond Cystic Fibrosis, offering a framework for applying machine learning in the diagnosis and treatment of other genetic disorders. © 2024 IEEE.

关键词： Personalized medicine

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm 4

SARCASAM Analysis in Social Networks Using Deep Learning Alg...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Priya, M. Kumar, Vijaya K Vennila, P. Prasanna, M.A. Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Women's Engineering College Puducherry605008 India Dept. of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy621112 India

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one's own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers' opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). © 2025 The Authors.

关键词： Tweets

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Streamline Pro Next-Gen Recruitment Solution

Streamline Pro Next-Gen Recruitment Solution

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Vinod Sangeetha, G. Gopal, K. Kumar, P. Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

An innovative full-stack project called 'StreamlinePro Next-Gen Recruitment Solution' aimsto transform the hiring process. With its modern technologies, this fully inclusive platform enhances the hiring process to satisfy the demands of administrators, HR professionals, and job seekers. The Job Board Application is developed around a powerful system for posting jobs and accepting applications. Experts in human resources can readily post job openings, andneed to offer corporate papers to validate their skills. This ensures that reliable sources are used to publish job openings. Conversely, candidates can use the website to look for jobs that fit their qualifications and apply immediately. Posting resumes is required, and an applicant tracking system (ATS) reviews them after are uploaded. After reviewing the entries, the ATS rates the resumes according to theirquality and applicability. One of the primary aspects of the StreamlinePro Next-Gen Recruitment Solution is its exclusive referral mechanism. Employees in a company may indicate that are willing to suggest others. Job searchers can contact these references, and if accepted, both parties can communicate through an integrated chat system. This recommendation system encourages networking and enhances job-seeking opportunities by leveraging existing staff networks. The application also offers the capability of real-time job status tracking. Applicants can monitor the progress of their applications and receive alerts regarding their receipt, review, and acceptance by employers. This tool helps clarity and informs job seekers during the hiring process. © 2024 IEEE.

关键词： Apprentices

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Predictive Precision: A Comparative Study of ML in Alzheimer's Diagnosis 1

Predictive Precision: A Comparative Study of ML in Alzheimer...

引用

1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Sujatha, E. Subiksha, M. Saveetha Engineering College Dept.of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350307795

Alzheimer's disease has become a significant contemporary health concern, impacting approximately 45 million individuals. This degenerative brain condition, prevalent among the elderly, lacks a well-defined etiology and pathogenesis. Dementia serves as the primary instigator, progressively inflicting damage upon brain cells and leading to a decline in cognitive functions such as thinking and reading. Those afflicted typically experience an average of 8 years of lifespan after the discernible onset of symptoms. However, this duration may vary, spanning from four to 20 years, contingent upon factors such as age and overall health. Addressing this critical issue, the integration of ML systems gives a great solution for disease prediction. The primary objective is to identify Dementia in diverse patient populations. This study encapsulates the outcomes and analyses derived from the detection of Dementia from many ML algorithms. The dataset collected from the Open Access Series of Imaging Studies (OASIS) serves as the foundational resource for system development. Comprising MRI data from 150 patients from the age gap of 60 to 96, this also undergoes comprehensive scrutiny and application across multiple machine learning *** ensuing comparative analysis seeks to ascertain the efficacy of different models in delivering optimal results. Through this investigation, the aim is to delineate the model that exhibits superior performance for the detection of Alzheimer's disease within the context of the OASIS dataset. © 2024 IEEE.

关键词： Diagnosis

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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