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Synergizing Artistry and Technology by Unveiling the Integration of Matte Painting Techniques in Crafting Precise and Immersive Visual Effects Backgrounds 4th

Synergizing Artistry and Technology by Unveiling the Integra...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Animation and Game Design Amity School of Engineering and Technology Amity University Punjab India

ISBN: (纸本)9789819752300

The film industry has evolved because of visual effects (VFX). Matte painting is a component of VFX and is important to VFX. Matte painting is a successful method for producing realistic visual effects and backgrounds for movies and television. To produce a scene or setting that can be utilized as a background in a movie or television show, this technique entails painting on a flat surface, such as a canvas or board. Filmmakers can depict a setting that is not actually there with the use of matte painting. The set will cost a lot of money to produce. This makes it possible to carry out actions that aren’t possible at the shooting site. This study paper’s aim is to investigate the background, methods, and applications of matte painting in the film and television industries. This essay also looks at the benefits and drawbacks of matte painting as well as its efficiency in producing realistic visual effects backdrops. A single work of art can now incorporate digital, photographic, and computer-generated imagery, providing artists with infinite options to create unique and cutting-edge pieces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Television applications

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A Deep Learning Approach for Multimodal Deception Detection 19th

A Deep Learning Approach for Multimodal Deception Detection

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Krishnamurthy, Gangeshwar Majumder, Navonil Poria, Soujanya Cambria, Erik Singapore Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031237928

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multimodal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Ergonomic Factors Impacting Industrial Operator Performance, Productivity, and Musculoskeletal Health

Ergonomic Factors Impacting Industrial Operator Performance,...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Shahabadkar, Krutika Dani, Sourabh Shahabadkar, Ramesh Kulkarni, Ambarish Swinburne University of Technology Dept of Computer Science HawthornVIC Australia Swinburne University of Technology Dept of Product Design Engineering HawthornVIC Australia Amc Education Dept of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350338522

This paper investigates the intricate relationship between workplace factors and their influence on the occurrence of Work-Related Musculoskeletal Disorders (WMSDs). It delves into the factors affecting industry operators' performance, emphasizing the role of organizational, ergonomic, and individual factors as triggering elements. To quantitatively assess these factors' contributions to a person's work productivity, a novel grid model is proposed, enabling a structured evaluation. The study involved a cohort of ten actively participated in the study. Participants provided insights into workplace conditions significantly affecting WMSDs through a meticulously designed questionnaire. The mathematical model presented here adopts a systematic and analytical approach, employing algebraic equations to represent both hyperbolic and linear relations. This model establishes a structured framework for the assessment and optimization of workplace conditions, ultimately aiming to reduce the incidence of WMSDs. Parametric studies were conducted to evaluate the model's effectiveness, facilitating the prediction of different factors' impacts on the performance index linked to WMSD reduction. Results indicate that ergonomic factors affected 60% of the participants' performance. Similarly, individual factors affected 20% and organisational factors affected 50% of the participant's performance. This paper sheds light on the dynamic interplay of these factors, offering valuable insights for the prevention of WMSDs in various occupational settings. © 2023 IEEE.

关键词： Ergonomics

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Countering DoS Threats in WSN: Inventive Machine Learning Strategies with a Minimalistic Twist 1

Countering DoS Threats in WSN: Inventive Machine Learning St...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Chidambaranathan, Senthilnathan Vanitha Sasireka, C.S. Jose, Naduvathezhath Nessariose Rao, K. Madhusudhana Devi, T. Lakshmi Architecture and Design Virtusa India Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering India Srm Institute of Science and Technology Department of Computer Science and Engineering India Regent Strategic Solutions Rochester United States Kkr&ksr Institute Of Technology & Science A.P Guntur India Department of Data Science Telangana Hyderabad501506 India

ISBN: (纸本)9798350377002

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes. © 2024 IEEE.

关键词： Sensor nodes

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Effect of Water Temperature on Water Quality Variables in Urban Catfish Cultivation Based on Association Rule Mining 10

Effect of Water Temperature on Water Quality Variables in Ur...

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10th International Conference on Information Technology, computer, and Electrical engineering, ICITACEE 2023

作者： Siswanto, Boby Mardiyana, Bubun Dani, Yasi Morika, Doni Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Maintenance Engineering Staff Building Management Jakarta Indonesia Bina Nusantara University School of Design Interior Design Department Jakarta Indonesia

ISBN: (纸本)9798350322729

Fish is a good food commodity for human consumption. Catfish are one type of fish that can be cultivated. Cultivating catfish can now be done through urban farming, which does not require large land areas. Water quality is an essential variable in the survival of catfish farming. Various methods have been used to analyze water quality variables, including IoT tools and applying machine learning. This study uses a public dataset from the Kaggle database published in the Data in Brief journal. Water temperature, pH, turbidity, and ammonia levels are variables considered in water quality. The research focuses on the effect of temperature variables on other water quality variables. This study analyzes the possibility of using association rule mining on the factors that affect the water temperature variable. Association rule mining is proven to explore the relationship between water temperature variables and other variables. The ideal water temperature for catfish is 24-26° C to maintain other variables within the recommended range of values. © 2023 IEEE.

关键词： Water temperature

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Smart Captures- An Advanced Attendance System using Facial Recognition 1

Smart Captures- An Advanced Attendance System using Facial R...

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1st International Conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Gunjan, Reena Mital, Anannya Jain, Muskan Pangaonkar, Satyajit Shete, Virendra Rokade, Prakash MIT School of Computing MIT Art Design and Technology University Department of Computer Science Engineering Pune India School of Computer Science and Engineering Avantika University Madhya Pradesh Ujjain India MIT School of Engineering and Science MIT Art Design and Technology University Department of Electronics and Communication Engineering Pune India Cloud and Infrastructure Services Infosys Ltd Maharashtra Pune India

ISBN: (纸本)9798350332803

Facial Recognition is one of the most productive and important techniques in image processing. It uses Machine Learning and Artificial Intelligence techniques to identify, collect, store, and evaluate face characteristics so that they can be matched to photos of people in a database. Nowadays, many kinds of research are focusing on human detection mainly in the context of marking students' attendance. Marking attendance is a procedure of recognizing students' faces and marking their attendance for that class. The need for this application was generated because after the pandemic use of touch biometric systems was suspended in many places and the manual process of taking attendance and maintaining paper records was a very tedious job and this could be manipulated very easily by anyone. Some attendance systems with face recognition already got developed but they were very basic and only had the feature of just recognizing the face once and marking the attendance of that student. So, by bringing in some unique features, here, developed 'Smart Captures', an advanced attendance system for universities, schools, and education-based places. Smart Captures was entitled to capturing the students' movement from the classroom (in out) and marking their attendance according to the set constraints using IoT, thereafter, making it visible to the faculties on their mobile application. This provided the data analytics for regular attendance on the dashboard to the admin with the administrative power using the application. © 2023 IEEE.

关键词： Students

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches 21

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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21st International Learning and Technology Conference, L and T 2024

作者： Usha Nandhini, A. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Ben Dhaou, Imed Vistas School of Computing Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won't get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification. © 2024 IEEE.

关键词： Machine learning

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm 21

A Novel Method to Predict Chronic Kidney Disease using Optim...

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21st International Learning and Technology Conference, L and T 2024

作者： Preethi, I. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Dhaou, Imed Ben Vistas School of Computing Sciences Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andhra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

关键词： Classification (of information)

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STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 224-238页

作者： Lujing Chen Rui Liu Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei tional and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China

In recent years,human motion prediction has become an active research topic in computer ***,owing to the complexity and stochastic nature of human motion,it remains a challenging *** previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive ***,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in ***,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each *** overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous *** experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction *** effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

关键词： Human motion prediction Transformer Gragh convolutional network

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Auto-encoder Convolutıonal Neural Network (AECNN) for Apple Fruit Flower Detection 1

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Congress on Smart Computing Technologies, CSCT 2023

作者： Rajagopal, Manikandan Sathesh Kumar, K. Nagaraja, P. Sivasakthivel, Ramkumar Sivaraman, G. Bangalore India Computer Science and Engineering Alliance College of Engineering and Design Alliance University Central Campus Anekal Main Road Karnataka Bangalore India Bangalore India Bangalore India Department of Computer Science M.G.R. College Tamil Nadu Hosur India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided through the bloom frequency, i.e., the quantity of the flowers that are present in a plantation. The intensity of bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend closely on designed procedures which function just under explicit conditions and with restricted execution. This work comprises four significant advances, (I) system preparing for Fully Convolutional Network (FCN), (ii) preprocessing, (iii) component extraction, (iv) division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. The next phase of the work handles the highlight extraction for diminishing the quality of the prime assets which are needed for handling without compromising on data applicable. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate an image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired score maps to figure out the final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision (P) and Recall (R). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing unit

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