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5th International Conference on Computing and Data science, CONF-CDS 2023

作者： Chaturvedi, Rajnish Kumar Sahu, Dinesh Prasad Tyagi, Manoj Kumar Diwakar, Manoj Singh, Prabhishek Shankar, Achyut Sathishkumar, V.E. Schoolof Computer Science Engineering and Technology Bennett University Greater Noida India Galgotias University India CSE Dept. Graphic Era Deemed to Be University Dehradun India University of Warwick United Kingdom Department of Software Engineering Jeonbuk National University Jeonju-si Jeollabuk-do Korea Republic of

Nowadays Internet of Things (IoT) and Machine Learning (ML) are growing fields. One application of these two fields is object detection, which detects semantic objects using digital images and videos of classes like humans, vehicles, buildings, etc. Visual object detection systems are very effective and accurate due to the appearance information obtained from the cameras. But they face the problem of a limited Field of View. This paper aims to tackle this issue by using audio data to localize the object. A microphone is used to estimate the angular position of the object emitting the sound. Objects currently in the Field of View of a camera are detected and tracked using optical flow, but when they go out of the Field of View, the sound emitted by the object is used by the microphone to calculate the object's angular position. Once the angle is calculated, the camera is rotated in that direction. This thus ensures that the object can be located even if it goes out of frame. Once the object is located through IoT devices, we use ML to identify the person's face. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Using Deep Learning depth Maps to Improve Monocular SLAM

Using Deep Learning Depth Maps to Improve Monocular SLAM

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Simon Hollows Richard Green Dept. Mechatronics Engineering University of Canterbury Christchurch New Zealand Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes the use of a neural network to generate depth maps from 2D frames, offering an alternative to depth cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate depth data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between depth sensor depth maps in test datasets and the generated depth maps. The MiDaS neural network was used to generate estimated depth maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated depth maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM depth maps had a root mean square error (RMSE) or 0.24 m while the generated depth maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between depth maps causes this approach to fail in slow-moving scenes.

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Self-Reported Learning Strategies and Preferences in Health Informatics

Self-Reported Learning Strategies and Preferences in Health ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Rohani, Narjes Gallagher, Michael Gal, Kobi Banas, Kasia Manataki, Areti Usher institute University of Edinburgh United Kingdom Moray House School of Education and Sport University of Edinburgh United Kingdom School of Informatics University of Edinburgh United Kingdom Dept. of Software and Information Systems Engineering Ben-Gurion University Israel School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781643685335

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education. © 2024 The Authors.

关键词： Contrastive Learning

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare

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Procedia computer science 2024年 241卷 570-575页

作者： Ammerha Naz Muhammad Ali Sehrish Munawar Cheema Ivan Miguel Pires Dept. of Computer Science University of Sialkot Sialkot Punjab Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries.

关键词： Healthcare Hospital information systems (HIS) Electronic health records (EHRs) Cloud computing. Data transmission

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN)

Fingerprint generation and authentication though Adaptive co...

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International Multi-Topic ICT Conference (IMTIC)

作者： Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Alina Baber Maria Andleeb Siddiqui Dept. of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan Dept of Software Engineering Sir Syed University of Engineering and Technology karachi Pakistan

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data.

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Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

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Dynamic View Synthesis of Thin Structures with Short-term Movements from Monocular Videos Using Neural Radiance Fields

Dynamic View Synthesis of Thin Structures with Short-term Mo...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Uchitha Rajapaksha Hamid Laga Dean Diepeveen Mohammed Bennamoun Ferdous Sohel School of Information Technology Murdoch University Murdoch WA Australia Dept. of Computer Science and Software Engineering The University of Western Australia Crawley WA Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with air and wind. When given a set of RGB images or videos of a scene with moving thin structures as input, existing methods that map the scene to its corresponding canonical space for rendering novel views fail as the object movements are too subtle compared to the background. Disentangling the objects with thin parts from the background scene is also challenging when the parts show fast and rapid motions. To address these issues, we propose a Neural Radiance Field (NeRF)-based framework that accurately reconstructs thin structures such as leaves and captures their subtle, fast motions. The framework learns the geometry of a scene by mapping the dynamic images to a canonical scene in which the scene remains static. We propose a ray masking network to further decompose the canonical scene into foreground and background, thus enabling the network to focus more on foreground movements. We conducted experiments using a dataset containing thin structures such as leaves and petals, which include image sequences collected by us and one public image sequence. Experiments show superior results compared to existing methods. Video outputs are available at https://***/.

关键词： Geometry Three-dimensional displays Tracking Dynamics Neural radiance field Rendering (computer graphics) Image sequences Image reconstruction Optical flow Videos

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Performance Evaluation of Quantum-Resistant TLS for Consumer IoT Devices

Performance Evaluation of Quantum-Resistant TLS for Consumer...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jessica Bozhko Yacoub Hanna Ricardo Harrilal-Parchment Samet Tonyali Kemal Akkaya Dept. of Computer Science Auburn University Auburn USA Dept. of Electrical and Computer Engineering Florida International University Miami USA Dept. of Software Engineering Gumushane University Gumushane Turkey

Post-quantum (PQ) cryptographic algorithms are currently being developed to be able to resist attacks by quantum computers. The practical use of these algorithms for securing networks will depend on their computational and communication efficiency. In particular, this is critical for the security of wireless communications within the context of consumer IoT devices that may have limited computational power and depend on a constrained wireless bandwidth. To this end, there is a need to evaluate the performance of widely used application layer security standards such as transport layer security (TLS) to understand the use of the existing PQ algorithms that are being evaluated by NIST as a replacement to the current cryptographic algorithms. This paper focuses on two widely used IoT standards Bluetooth Low Energy (BLE) and WiFi to find out the optimal performing PQ algorithm for their security when used in end-to-end connections over the Internet. By implementing the capability for IP over BLE and all options of TLS connection establishment, we developed a client-server IoT testbed to measure the efficiency of PQ key encapsulation mechanisms (KEMs) and PQ digital signature algorithms. The test results showed that Kyber512 is the ideal KEM while Falcon-512 and Dilithium2 are the best signatures for BLE and WiFi devices. Based on this outcome, we developed a mechanism for IoT devices with multiple communication interfaces, that dynamically chooses a PQ KEM algorithm based on the MAC layer protocol being used at the time.

关键词： Wireless communication Performance evaluation Encapsulation Heuristic algorithms Resists Internet of Things Cryptography

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Md. Kalim Amzad Chy Abdul Kadar Muhammad Masum Mohammed Shamsul Alam Md. Abul Kalam Azad Md. Maruf Hassan Touhid Bhuiyan Dept. of Computer Science and Engineering International Islamic University Chittagong Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Bangladesh Dept. of Business and Technology Management Islamic University of Technology Gazipur Bangladesh School of IT Washington University of Science and Technology VA USA

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation.

关键词： Deep learning Analytical models Data integrity Computational modeling Predictive models Data models Macroeconomics Stock markets Forecasting Long short term memory

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Decentralized Drug Transaction Tracking: A Novel Hybrid Blockchain-IPFS Platform

Decentralized Drug Transaction Tracking: A Novel Hybrid Bloc...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Mouhamadou Mouctar Gueye Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Superieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

Counterfeit drugs are a serious global problem that poses threats to public health. It occurs when falsified or counterfeit drugs are intentionally manufactured and introduced into the pharmaceutical supply chain, masquerading as genuine products. This is due to the complexity of the supply chain that involves independent entities. As a result, a drug transits through several stages before reaching the patient. Current drug management systems are centralized, thus creating data transparency and authenticity risks within the supply chain. To bypass this issue, we propose in this paper a novel hybrid and distributed drug transaction tracking platform that leverages a combination of the Interplanetary File System (IPFS) protocol and Ethereum blockchain. Relying on IPFS guarantees the integrity of drug traceability data, while the blockchain ensures the authenticity of this data. We developed our platform and, through experiments, we prove that our method is more efficient, in terms of process costs, than the conventional blockchain-based drug tracking system.

关键词： Drugs Costs Supply chains Smart contracts Public security Blockchains InterPlanetary File System

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