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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Survival Prediction for Patients with Cirrhosis

Survival Prediction for Patients with Cirrhosis

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sowmiya, X.L. Sanjana, R. Nishintha, A. Pavithra, S. Kannan, Shree Charan Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331510022

Cirrhosis is one of the major causes of death around the world. Hence, the possibility of survival prediction among patients affected by this disease constitutes the principal factor in properly planning treatment and management procedures. This work aims to design a predictive model of the probability of survival of patients who have cirrhosis based on patient data, including clinical parameters, medical history, and laboratory findings of these patients. The data-driven machine learning algorithm improves the predictive accuracy and significantly enhances the clinical decision-making process. This considers multiple predictors: the patient's liver function tests, the demographic factors, and the indicators of disease progression in the body. It will be validated for reliability and accuracy using real-world patient case data. Therefore, this tool could aid clinicians in devising treatment plans that improve patient outcomes and efficiently utilise healthcare resources. This study also contributes to the emerging genre of AI-assisted care because chronic liver disease management, specifically cirrhosis, in this case, requires constituting predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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Efficient Hyperspectral Data Compression using 3D Convolutional Autoencoder 3

Efficient Hyperspectral Data Compression using 3D Convolutio...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Hyperspectral image has large size due to abundance of spectral bands preventing efficient storage and transmission in real-time applications. This paper introduces a 3dimensional convolutional autoencoder (CAE) for hyperspectral data compression, utilizing CAE for both compression and subsequent decompression. Our method achieves improved Compression Ratio (CR) values of 982 and 960 on the KSC and Botswana datasets, along with better Peak Signal-to-Noise Ratio (PSNR) results of 55.29 and 52.64, surpassing existing 2D convolutional autoencoder-based HSI compression. Our approach also shows negligible differences in classification accuracy between original image and the reconstructed version of the compressed image, confirming its effectiveness in real-world applications. © 2024 IEEE.

关键词： Convolution

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Carriageway Edge Detection for Unmarked Urban Roads using Deep Learning Techniques

Carriageway Edge Detection for Unmarked Urban Roads using De...

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2023 Smart Cities Symposium Prague, SCSP 2023

作者： Vasudha, E. Chilukuri, Bhargava Rama College of Engineering Guindy Campus Anna University Dept. of Computer Science and Engineering Chennai India IIT Madras Dept. of Civil Engineering Chennai India

ISBN: (纸本)9798350321623

Traffic infrastructure segmentation is widely used in for digitization of urban areas for smart city applications. In this paper, we use transfer learning for carriageway edge detection for Indian urban roads where lane edge markings are often invisible. Also, the edge of the pavement often overlaps with unpaved shoulders posing challenges for image-based edge detection. The images obtained from various routes in Chennai, India are annotated and their masks were trained using DeepLabV3with ResNet101 and MobileNetV3 as backbones to predict the classes for each pixel. Two performance metrics, AUROC and F1 score, were used to evaluate the methods. Results show that DeepLabV3 with ResNet101 outperformed DeepLabV3 with MobileNetV3. © 2023 IEEE.

关键词： Pavements

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Designing a Graphics Processing Unit for 2D Rendering on FPGA for Educational Purpose 3

Designing a Graphics Processing Unit for 2D Rendering on FPG...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Saha, Rupak Sadad, Nahin Ul Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering. © 2024 IEEE.

关键词： Graphics processing unit

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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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Multispectral Satellite Data Classification Using Rank Correlation Similarity Index and Automatic Labeling Technique

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SN computer science 2024年第8期5卷 1-20页

作者： Ain, Arindom Gogoi, Minakshi Chutia, Dibyajyoti Dept. of Computer Science and Engineering GIMT Under Assam Science and Technology University Assam Guwahati India Dept. of Computer Science and Engineering Girijananda Chowdhury University Assam Guwahati India NESAC Umium Shillong India

Multispectral satellite image classification is crucial in remote sensing and image analysis, serving various applications like water area extraction, urban region classification, and vegetation monitoring. Model-based classification, particularly supervised learning approaches, heavily relies on manually annotated training samples, which are labor-intensive and subject to human bias. However, solely depending on features derived from individual pixels may fail to capture spatial contextual information effectively and manage mixed pixels and various types of noise, resulting in classification inaccuracies. To address these challenges, this research proposes a novel unsupervised learning approach and automatic labeling technique (ALT). This approach utilizes collective information from groups of pixels instead of relying solely on individual pixels for identification and extraction of meaningful unique patterns which serves as the most significant factor for satellite image classification. A new similarity index, the patch similarity index (PSI), is generated by incorporating the rank correlation similarity index (RCSI). The PSI is used to determine the similarity factor both between the patches and within the patch. Subsequently, a clustering algorithm is employed to group pixels into four distinct classes: vegetation, water, built-ups, and open land. Additionally, ALT streamlines the post-classification process by reducing the need for manual intervention in mapping clusters to the actual class label. The proposed method offers a more accurate and reliable unsupervised classification for multispectral satellite imagery, promising enhanced efficiency and reduced manual labor. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automatic labeling technique Frequent masking Multispectral Patch similarity index Rank Correlation

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities 3

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Benjamine, Daniel Anto Madhavan, K. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem. © 2025 IEEE.

关键词： Cybersecurity frameworks IoT smart city technologies urban

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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Datta, Lipika College of Engineering and Management Dept. of Computer Science and Engineering West Bengal Kolaghat India

ISBN: (纸本)9798331517892

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm. © 2024 IEEE.

关键词： Virtual machine

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On-site Visual Construction Management System Based on the Integration of SLAM-based AR and BIM on a Handheld Device

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KSCE Journal of civil engineering 2023年第11期27卷 4688-4707页

作者： Hsieh, Chang-Cheng Chen, Hung-Ming Wang, Sheng-Kai Dept. of Civil & Construction Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Recently, Simultaneous Localization and Mapping (SLAM) has been integrated into the development of AR systems. Using conventional SLAM adoption method to overlay a building information model (BIM) onto the image of a real scene on handheld device in a construction site will result in poor model overlaying accuracy owing to the relatively unique environment and monotonous texture of such environment. This paper proposes an adoption method of SLAM suitable for construction environment to improve positioning accuracy for the fitting of BIMs for the AR presentation. In addition, a AR system to visualize construction progress on-site is developed. It allows site personnel to input on-site work progress through an AR device, then compares the originally planned construction progress with the actual on-site progress, and presents BIM components using different colors in the AR mode to show whether work progress on each component is ahead or behind schedule intuitively. © 2023, Korean Society of civil Engineers.

关键词： Augmented reality

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