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Optimizing deep learning architectures for novel view synthesis: Investigating the impact of NeRF MLP parameters on complex scenes

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International Journal of Information technology (Singapore) 2024年第4期16卷 2295-2305页

作者： Singla, Kanika Nand, Parma Department of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida India

Machine learning combined with geometric reasoning is a promising approach for generating new perspectives of a scene using limited image captures, known as neural rendering techniques. Neural radiance fields (NeRF) represent complex 3D scenes by learning the radiance implicit function and have shown impressive performance in various applications. However, the influence of NeRF's parameters on its overall performance remains largely unexplored. In this paper, we conduct an extensive grid search on NeRF's multi-layer perceptron (MLP) parameters to examine their impact on image quality. We analyze the results by varying parameters related to the MLP architecture, such as hidden layer count and neurons per layer. Our findings reveal that the choice of MLP architecture significantly affects image quality. Our proposed approach outperforms NeRF's default settings on challenging scenes, including synthetic and real-world datasets. These results shed light on the impact of different parameters on NeRF's performance, assisting practitioners in selecting suitable settings for their specific applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Neural radiance fields (NeRF) Neural rendering Novel view synthesis Peak Signal to Noise Ratio (PSNR)

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Adaptive Health Records Protection Using Modular Encryption Standard In Cloud Computing 1

Adaptive Health Records Protection Using Modular Encryption ...

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

作者： Kumar, R.G. Suresh, A. Amrutha, G. Himaja, S. Bhanu, T. Dinesh, C. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350377002

The healthcare industry has benefited greatly from cloud computing (CC), but its expansion is being impeded by privacy and security concerns. In order to realize its full scope and effective utilization, such concerns demand the most urgent attention. Global, regional, and local health record security is crucial. Health record (HR) security is an on-going process that changes as the healthcare environment does. For small health centres, identifying the vulnerabilities and safeguarding the HR can be difficult and demanding. The goal of this project is to offer appropriate security techniques to guarantee HR security. It is essential to implement the required security procedures for the prevention of security breaches and vulnerabilities in order to effectively utilize the health services. Therefore, the Modular Encryption Standard (MES), which is based on the layered modeling security mechanisms for medical images and data, may perform better than the security measures for this method. This tiered modeling security strategy may provide multilayer security, lowering the risk of breaches. This outperforms other frequently utilized programs in opposition to the protection of information at the CC circumstance in terms of enhanced performance and additional qualitative security ensuring features. © 2024 IEEE.

关键词： Electronic health record

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An Optimal Disassembly Sequence Planning for Complex Products using Enhanced Deep Reinforcement Learning Framework

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SN computer science 2024年第5期5卷 1-24页

作者： Chand, Mirothali Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Disassembly Sequence Planning (DSP) is a crucial problem in the field of repair and maintenance. There is a pressing need for an efficient technique to solve the Complete Disassembly Sequence Planning (CDSP) problem for large, highly complex products without compromising time and computational resources. Since exact methods fail to handle complex products, and meta-heuristic approaches often do not produce optimal results, its solution requires a deep reinforcement learning approach. This work proposes a novel Enhanced Deep Reinforcement Learning (EDRL) approach in which the Actor-Critic Networks with an attention mechanism is employed in both networks to assign weightage to important actions, generating optimal sequences based on these actions. To further improve the model and accurately predict the loss value, a hybrid loss function is developed by combining the Categorical Cross-entropy and Log-Cosh loss functions. This work considers DSP attributes like stability, liaison, geometric feasibility, and precedence for experiments on various products. The proposed EDRL-CDSP method outperforms existing techniques in terms of optimality while requiring less time to generate the solution. The optimized disassembly sequence, when followed, consumes less time for the total disassembly of all the parts of the product. These findings suggest that the proposed EDRL-CDSP approach can effectively address the challenges in DSP and provide practical benefits for industrial applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Disassembly Hybrid loss function Optimal sequence planning Optimization

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Finding the structure of parliamentary motions in the Swedish Riksdag 1971–2015

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Quality and Quantity 2024年第4期58卷 3275-3301页

作者： Bruinsma, Bastiaan Johansson, Moa Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

The current increase in the number of large, open sets of unstructured textual data has created both opportunities and challenges for social scientists. Here, we explore if and how we can use such data by looking at a dataset of over 144,000 documents used by parliamentary committees in Sweden. Of these, we aim to understand: (a) the topical content of these motions, (b) how these topics have changed over time, and (c) how these topics differ across political parties. To do so, we use a Structural Topic Model, which allows us to not only find the topics using the textual data itself, but also to include the documents’ metadata, such as authorship and date of publication. Doing so, we find 30 topics, which we combine into 9 broader themes. We find that these themes often rise and fall in popularity in line with historical events, and relate to the various political parties as we would expect. Throughout our analysis, we provide a step-by-step overview of how to use structural topic models in practice and also how to handle the type of dataset we use here. © 2023, The Author(s).

关键词： Open data Parliamentary motions Structural topic model

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Automated Disease Detection and Classification in Jackfruit Leaves: An Efficient Deep Learning-Based Approach 3rd

Automated Disease Detection and Classification in Jackfrui...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Saha, Dip Kumar Mahmud Joy, Md. Ashif Reza, Md. Rokonuzzaman Shovon, Reduanul Bari Department of Computer Science and Engineering American International University-Bangladesh Kuratoli Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering The International University of Scholars Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Jackfruit is the national fruit of Bangladesh, and one of the most consumed fruits in India, Sri Lanka, Philippines, Indonesia, Malaysia, Australia, and many more countries. The every year due to diseases jackfruit production stays lower than the expectation. The early identification of these diseases is crucial for agricultural productivity, and our research showcases the effectiveness of deep learning techniques in addressing this issue. This study introduces an innovative approach to detect and classify two common jackfruit leaf diseases, algal spot and black spot, combining the YOLOv8 object detection model and the EfficientNetB7 pre-trained deep convolutional neural network model. The models trained on 6,360 images of those two diseases and our methodology achieved an accuracy of 99.9% on training and 99.5% on testing. This work represents a valuable contribution to agriculture, emphasizing the synergy between cutting-edge technology and age-old challenges, with the potential to positively impact the agricultural industry and global food security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Citrus fruits

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Time Complexity of Population-Based Metaheuristics

Mendel

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Mendel 2023年第2期29卷 255-260页

作者： Omran, Mahamed G. H. Engelbrecht, Andries Computer Science Department Gulf University for Science & Technology Kuwait Department of Industrial Engineering and Computer Science Division Stellenbosch University South Africa Centre for Applied Mathematics and Bioinformatics Gulf University for Science & Technology Kuwait

This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamen-tal concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known meta-heuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics’ time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed. © 2023, Brno University of technology. All rights reserved.

关键词： Optimization

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Tri-factorized Modular Hypergraph Autoencoder for Multimodal Semantic Analysis

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SN computer science 2024年第7期5卷 1-12页

作者： Malik, Shaily Dhand, Geetika Sheoran, Kavita Jatain, Divya Garg, Vaani Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology Delhi India

For image-to-text and text-to-image classifications, the features of data collected from various imaging devices, sensors, and their text descriptions must be mapped into a common latent space with reduced dimensions. The low-dimensional features are supposed to provide the most information with the least amount of *** this paper we propose a cross-modal semantic autoencoder that uses nonnegative matrix factorization (NMF) to factorize the features into a lower rank. Due to two matrix factorization, the traditional NMF is unable to translate all of the information into lower space. This is addressed by a unique tri-factorized NMF with hypergraph regularization. Instead of using the feature adjacency matrix in hypergraph regularization, a more information-rich modularity matrix is suggested. The Wiki dataset is used to evaluate this tri-factorized hypergraph regularized multimodal autoencoder for image-to-text and text-to-image conversion. In order to lower the feature dimension, Multimodal Conditional Principal label space transformation (MCPLST) is also enabled by this novel autoencoder. Comparing the proposed autoencoder against the semantic autoencoder, the former showed an improvement in classification accuracy of up to 1.8%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multimodal retrieval Multimodality Nonnegative matrix factorization Semantic analysis TriNMF Wiki dataset

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A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region

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Environmental science and Pollution Research 2024年第49期31卷 59430-59445页

作者： Kumar, Gaurav Patil, Deshbhushan Kumar, Akshay Gupta, Rajiv Department of Computer Science and Engineering SCSE Galgotias University Greater Noida India Department of Civil Engineering Walchand College of Engineering Sangli India Civil Engineering Department Birla Institute of Technology and Science Pilani Rajasthan Jhunjhunu333031 India

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Surface waters

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Number Plate Recognition Using OpenCV 6

Number Plate Recognition Using OpenCV

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Roopa Chandrika, R. Vanitha, K. Thahaseen, Aafiya Chandramma, R. Neerugatti, Vikram Mahesh, T.R. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306613

The advancement of automated number plate recognition (ANPR) systems has garnered noteworthy attention in recent times owing to their diverse applications across multiple domains, including traffic management, parking management, and law enforcement. This paper presents an innovative ANPR method using OpenCV, an open-source computer vision library. Developing ANPR systems is a great fit for OpenCV since it offers a flexible platform for image processing and computer vision tasks. The suggested method effectively detects and recognizes number plates from images and video streams by utilizing OpenCV's capabilities for feature extraction, image preprocessing, and machine learning. Character segmentation, optical character recognitlon (OCR), and licence plate localization are important stages in the process. The ANPR system's accuracy and effectiveness are increased by using these strategies. A variety of real-world datasets, including different lighting conditions, camera angles, and vehicle types, are used in extensive experiments to assess the system's performance. The outcomes show how well the OpenCV-based ANPR system performs in tasks requiring license plate recognition, attaining high accuracy and resilience. Additionally, covered in the paper are possible uses for the ANPR system in security systems, vehicle tracking, and traffic monitoring. To make sure the system is workable in a variety of situations, scalability and real-time implementation are also taken into account. © 2024 IEEE.

关键词： machine learning number plate OpenCV recognition

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Patient-centric information management in blockchain and interplanetary storage

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 85-96页

作者： Dewangan, Narendra Chandrakar, Preeti Department of Computer Science and Engineering National Institute of Technology Raipur G.E. Road Chhattisgarh Raipur492010 India

In the healthcare sector, the protection of patient information is an essential factor in terms of secrecy and privacy. This information is very useful for industrial and research purposes. Electronic medical records (EMR) hold data related to patients and their treatments. According to GDPR (general data protection regulation), patient information should be anonymous. There should not be any disclosure of patients private information during data transfer. For hiding patients private information, encryption is required. For developing countries, the treatment cost is also a matter for health services. Blockchain technology provides facilities like distributed and immutable data storage and peer-to-peer data transfer. We need cheaper blockchain-based solutions for the healthcare sector. IPFS provides a secure way to store the content of the files in a decentralized manner by using the Merkle tree. By using IPFS with blockchain, we can counter the use of fake medical records or the forge of EMR. In this paper, the interplanetary file system (IPFS) is used to store EMR in compliance with GDPR. We work on the treatment cycle of patients with a blockchain-maintained hospital and doctor. Our proposed system enhances the security and transparency of patients’ treatment information. We compared our proposed system with previously developed systems and provided analytical results in terms of security, privacy, and cost. We implemented our proposed system on a Python-customized blockchain with a customized consensus algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Electronic health record

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