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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Arjun, P. Geethan, Daniel Anandha Kanniga Devi, R. Sanjay K, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

In today's fast-paced healthcare environment, efficient management of patient records and seamless patient-provider communication is crucial. This study proposes a comprehensive solution integrating blockchain technology (Ethereum), frontend web interfaces, natural language processing (NLP), and adverse drug event detecting functions. Through a user-friendly *** interface, patients input health data securely, while NLP automates medical information extraction from conversations. ADE detectors identify drug interactions and adverse events, enhancing care and safety. Ethereum integration via Metamask ensures secure record storage, with IPFS storage and CIDs enabling encrypted data sharing. A smart contract on Ethereum automates data management for transparency and accountability. © 2024 IEEE.

关键词： Records management

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X-Ray Based Lung Disease Classification Using Fine-Tuned VGG16 Model 2

X-Ray Based Lung Disease Classification Using Fine-Tuned VGG...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gehani, Ronit Victor, Akila Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350327533

The COVID-19 pandemic was a global outbreak of a viral that caused widespread to people's well-being. COVID-19 primarily affects the respiratory system, leading to various lung diseases and complications. During this unprecedented time, the need for accurate and efficient classification systems for lung diseases, including COVID-19, has become more crucial than ever. During the pandemic, accurate and timely identification of lung diseases became crucial in order to effectively manage the healthcare system and provide appropriate treatment to patients. Therefore, early detection using state-of-The-Art technology such as deep learning and computer vision algorithms can greatly assist in the classification of lung diseases. Concurrently there has been an unprecedented increase in the incidence of other respiratory ailments, such as Pneumonia, which further emphasizes the need for accurate and efficient classification systems. In light of this urgent requirement, this study has proposed a novel model for image classification by demonstrating the ability to differentiate COVID-19, viral pneumonia, and normal categories. The classification model presented in this study is constructed by fine-Tuning a VGG16 architecture that has undergone extensive training, validation, and evaluation processes. The proposed dataset contains a large set of images, which includes 3616 COVID-19 images, 1345 Pneumonia and 10k normal training images. According to the experimental findings, the proposed model has achieved an accuracy of 95.79%. To verify the performance, the training and validation loss curves are outlined. © 2024 IEEE.

关键词： Bayesian Optimizer COVID-19 Deep learning Feature Extraction Feature Selection Machine learning Prediction X-ray images

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NEMESIS: NORMALIZING THE SOFT-PROMPT VECTORS OF VISION-LANGUAGE MODELS 12

NEMESIS: NORMALIZING THE SOFT-PROMPT VECTORS OF VISION-LANGU...

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12th International Conference on Learning Representations, ICLR 2024

作者： Fu, Shuai Wang, Xiequn Huang, Qiushi Zhang, Yu Department of Computer Science and Engineering Southern University of Science and Technology China Computer Science Research Centre University of Surrey United Kingdom

With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downstream tasks. However, few works delve into the inherent properties of learnable soft-prompt vectors, specifically the impact of their norms to the performance of VLMs. This motivates us to pose an unexplored research question: "Do we need to normalize the soft prompts in VLMs?" To fill this research gap, we first uncover a phenomenon, called the Low-Norm Effect by performing extensive corruption experiments, suggesting that reducing the norms of certain learned prompts occasionally enhances the performance of VLMs, while increasing them often degrades it. To harness this effect, we propose a novel method named Normalizing the soft-prompt vectors of vision-language models (Nemesis) to normalize soft-prompt vectors in VLMs. To the best of our knowledge, our work is the first to systematically investigate the role of norms of soft-prompt vector in VLMs, offering valuable insights for future research in soft-prompt tuning. The code is available at https://***/ShyFoo/Nemesis. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Vectors

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A Novel Approach for Detection of Malicious Attack during Post Disaster Using UAV Assisted Delay Tolerant Network 2

A Novel Approach for Detection of Malicious Attack during Po...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Chakrabarti, Chandrima Narula Institute Of Technology Department Of Computer Science And Engineering Kolkata India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

Utilizing delay tolerant networks (DTN) via smartphones is a practical approach, especially when traditional networks are compromised. The use of short-range communication technologies like Bluetooth or WiFi ensures connectivity among ground nodes, albeit with limitations in determining efficient routes across the entire disaster area. Incorporating Unmanned Aerial Vehicles (UAVs) into the network introduces a dynamic element that can significantly enhance message delivery speed and route determination However, the introduction of UAVs also brings new challenges, particularly in ensuring the security and reliability of communication. Malicious attacks, such as message dropping or manipulation, can disrupt the network's overall functionality and effectiveness. This paper proposes the use of monitor nodes to track both UAVs and ground nodes, identifying and mitigating malicious nodes to ensure successful message delivery. Simulation is done using Opportunistic Network Environment (ONE). The simulator provides valuable insights into the proposed methods' effectiveness. © 2024 IEEE.

关键词： Delay tolerant networks

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A Comparative Analysis of Pancreatic Tumor Detection using VGG16, ResNet, and DenseNet 3

A Comparative Analysis of Pancreatic Tumor Detection using V...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepthi, Gannamani Anusha Bamini, A.M. Praveen, Yenumala Joseph Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350375190

This study explores the transformative potential of image classification algorithms like VGG16, ResNet, and DenseNet, for the early detection of pancreatic tumors using medical imaging. One of the main causes of cancer-related deaths globally is pancreatic cancer. However, pancreatic cancer detected at early stages can be cured. This study navigates through the evolution of CNNs in medical image analysis and their specific applications in pancreatic cancer detection. Pancreatic cancer poses a significant global health challenge due to its high mortality rates, often attributed to late-stage detection. The primary objective is to enhance diagnostic accuracy and enable timely intervention. The obtained results demonstrated promising accuracy levels for each model. VGG16 achieved an accuracy of 96%, ResNet demonstrated an accuracy of 98.31%, and DenseNet showcased an accuracy of 99%. Precision values for VGG16, ResNet, and DenseNet were 95%, 96%, and 95.2%, respectively. Recall values were 98% for VGG16, 95% for ResNet, and 97% for DenseNet. The F1 score for 98% for VGG16, 97% for ResNet, and 98.1% for DenseNet. The study concludes that VGG16, ResNet, and DenseNet are valuable tools for pancreatic tumor detection, with each architecture exhibiting unique advantages. By aiding in the early detection of pancreatic cancer, the suggested models may enhance patient outcomes. The research provides insights into the comparative performance of these deep learning architectures, guiding future developments in medical image analysis. © 2024 IEEE.

关键词： Image classification

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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A Comprehensive Evaluation Process by Analytical Hierarchical Process for Identification of Potential Groundwater Zones 2

A Comprehensive Evaluation Process by Analytical Hierarchica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gudadhe, Amit Anil Reddy, K.T.V. Faculty of Engineering and Technology Computer Science and Design Department Wardha India Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798331528713

For social and economic development of any region, groundwater plays a very vital role. Surface water infiltration depends on various parameters of the earth. The parameters includes Slope, Geology, Soil Type, Land Use/Land Cover, Rainfall, Drainage Density based on the study area under consideration. There exists various techniques for recognizing of groundwater probable regions. This paper focuses specifically on the recognizing of the subsurface water probable regions by Analytical Hierarchy Process. The steps involved in the process are discussed in detailed and shows how the potential zone identification is based on value of weights evaluated. © 2024 IEEE.

关键词： Land use

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Deep Learning-Based Predictive Model of Molecular Properties 6

Deep Learning-Based Predictive Model of Molecular Properties

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Yang, Mingyu Chen, Xiangyang Lv, Weijie School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

With the rapid development of computer hardware and machine learning algorithms in recent decades, artificial intelligence has created a boom in various industries. computer technology has been widely used in all stages of drug discovery, from machine learning to deep learning, and scientists have conducted numerous studies on the physicochemical properties of drugs. With the advancement of computer technology and deep learning algorithms, it has become possible to obtain large-scale molecular characterization data, extract different types of molecular representations, and construct more subtle models. Among them, molecular graph structure data is increasingly used in deep learning to predict molecular properties. It directly extracts the two-dimensional structural information of molecules, avoiding the defects of indirect structural information extraction in traditional methods. However, the performance of different algorithms based on molecular fingerprints, descriptors, molecular pictures and molecular graph structures is currently controversial. Some studies have found that descriptor-based models outperform graph-based models, while others do the opposite. This shows the difference in the impact of different tasks and datasets on the models. In this experiment, molecular graphs and molecular fingerprints are combined, and molecular graphs are fed into graph neural networks to extract features after the attention mechanism, molecular fingerprints are fed into a fully connected layer to extract features, and the features extracted from the two molecular representations are fused together and a specific activation function is adopted to see if the accuracy and stability of the model can be effectively improved. © 2024 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Employee Attrition Prediction using Deep Learning and Fully Homomorphic Encryption

Privacy-Preserving Employee Attrition Prediction using Deep ...

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

作者： Rajesh, R. Harshavardhan, S. Kirthana, B. Shreya, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Employee attrition presents a significant challenge for organizations, impacting both finances and operations. Mean-while, the automation of human resource processes, including recruitment and performance monitoring, heavily relies on vast amounts of personal data. Traditional predictive models for employee attrition have increasingly turned to deep learning techniques due to their superior accuracy in deciphering complex data patterns. However, these methods raise serious privacy concerns due to their reliance on sensitive employee information. This paper introduces a novel solution by integrating Fully Homomorphic Encryption (FHE) with deep learning, ensuring data privacy without compromising predictive performance. This integration not only enhances attrition prediction accuracy but also maintains employee data confidentiality, potentially improving organizational efficiency and trust in HR processes. © 2024 IEEE.

关键词： Differential privacy

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Selected Replay with Distillation for Continual Semantic Segmentation 3

Selected Replay with Distillation for Continual Semantic Seg...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Luo, Yuan Liu, Jun Zeng, Shuqi Hu, Zhuoya Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331506582

Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the typical challenges commonly encountered in deep learning models. This issue becomes apparent when new training data introduces new categories to adapt to environmental changes, while the old training data is no longer available. As a result, the model tends to forget previously learned knowledge. In recent years, a variety of solutions and incremental learning frameworks have been proposed to address this issue, particularly showing effectiveness in classification and detection tasks. In this paper, we extend the problem to semantic segmentation, which involves pixel-level classification. We conducted an in-depth analysis of the root causes of catastrophic forgetting and identified two key issues arising from the unavailability of old data samples during the new training phase. First, previously learned knowledge is often mistakenly labeled as background in the new training phase, causing a 'background shift' phenomenon. Second, the data distribution of old and new classes becomes highly imbalanced. To tackle these issues, we propose a strategy that combines replay and knowledge distillation to effectively alleviate catastrophic forgetting. © 2024 IEEE.

关键词： Deep reinforcement learning

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