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Tri-factorized Modular Hypergraph Autoencoder for Multimodal Semantic Analysis

SN computer science 2024年第7期5卷 867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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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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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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IOT-Enabled Smart Crop Recommendation System Using Machine Learning 5

IOT-Enabled Smart Crop Recommendation System Using Machine L...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Saranya, S.S. Dharani, A.S. Kavitha, M.N. Selya Dharsnee, M. Pragatheeswari, E. Selya Varsnee, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Technology - Ug Perundurai India Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based on soil quality and geographic location. By providing data-driven insights, the proposed system aims to enhance crop productivity, promote sustainable farming practices, and empower farmers to make informed decisions. © 2024 IEEE.

关键词： Smart agriculture

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Attack on lattice shortest vector problem using K-Nearest Neighbour

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Iran Journal of computer science 2024年第3期7卷 515-531页

作者： Singh, Shaurya Pratap Chaurasia, Brijesh Kumar Tripathi, Tanmay Pal, Ayush Gupta, Siddharth Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

Lattice-based cryptography is now the most effective and adaptable branch of post-quantum cryptography. The prime number factoring assumption or the presumption that the discrete logarithm problem is intractable are the two assumptions that underlie nearly all cryptographic security systems. Lattice-based cryptography has recently gained popularity to improve security as the world prepares for quantum computing. Lattices are used to secure the systems;however, one of the problems is the Shortest vector problem. In this work, we addressed the attack on lattice problems, especially two-dimensional, four-dimensional, and ten-dimensional, with the help of the machine learning algorithm K-Nearest Neighbour (KNN). Results and analysis findings demonstrate that the suggested approach can achieve accuracy of upto 78% and 58% on self-prepared datasets over two-dimensional and ten- dimensional, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Euclidean distance K-Nearest Neighbour algorithm (KNN) Lattice-based cryptography (LBC) Lenstra, Lenstra, and Lovasz (LLL) Post-Quantum Cryptography (PQC) Shortest vector problem (SVP)

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Human Age and Gender Prediction from Facial Images Using Deep Learning Methods 15

Human Age and Gender Prediction from Facial Images Using Dee...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Dey, Puja Mahmud, Tanjim Chowdhury, Mohammad Sanaullah Hossain, Mohammad Shahadat Andersson, Karl Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Department of Computer Science and Engineering Rangamati Science and Technology University Bangladesh Lulea University of Technology Skelleftea Sweden

Human age and gender prediction from facial images has garnered significant attention due to its importance in various applications. Traditional models struggle with large-scale variations in unfiltered images. Convolutional Neural Networks (CNNs) have emerged as effective tools for facial analysis due to their robust performance. This paper presents a novel CNN approach for robust age and gender classification using unconstrained real-world images. The CNN architecture includes convolution, pooling, and fully connected layers for feature extraction, dimension reduction, and mapping to output classes. Adience and UTKFace datasets were utilized, with the best training and testing accuracies achieved using an 80% training and 20% testing data split. Robust image pre-processing and data augmentation techniques were applied to handle dataset variations. The proposed approach outperformed existing methods, achieving age prediction accuracies of 86.42% and 81.96%, and gender prediction accuracies of 97.65% and 96.32% on the Adience and UTKFace datasets, respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolution

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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