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Label Aggregation with Self-Supervision Enhanced Graph Transformer 26

Label Aggregation with Self-Supervision Enhanced Graph Trans...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Jiacheng Tang, Feilong Hou, Xiaofeng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Aggregating noisy labels produced by the crowd of workers to generate true labels is a challenging problem in crowdsourcing. The key behind label aggregation is to effectively utilize the hidden information (e.g., characteristics of workers and questions which are often missing) in the labeling process. Existing methods mainly generated aggregation models based on the complicated Bayesian model or some strong assumptions. Recently, deep learning-based methods attempt to automate label aggregation but need various labels. These all make them hard to deploy to real-world applications. In fact, abundant information in the process of crowdsourcing itself can be extremely helpful to aggregate the labels. In this paper, we propose ATHENA (lAbel aggregaTion witH sElf-supervision eNhanced grAph transformer) to aggregate labels by utilizing the self-supervision signals in crowdsourcing. Firstly, we propose a transformer-based graph neural network that can learn from the crowdsourcing topology and features. Then, we use self-supervision signals inherently included in the dataset to help to aggregate the labels. To be specific, we identify the answer-based self-supervision signal that can predict the answer of any user given to different tasks. In our evaluations, we compare the proposed ATHENA with the other 11 representative methods on 10 datasets. Our experimental results demonstrate that ATHENA is highly effective in aggregating labels and obtains much better performance than existing methods. © 2023 The Authors.

关键词： Aggregates

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Sleep Quality Prediction and Estimation by using Deep Belief Networks-Bayesian Optimization 1

Sleep Quality Prediction and Estimation by using Deep Belief...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Mamatha Bai, B.G. Koti, Valli Madhavi Abbas, Haider Mohammed Sivakumar, V. Jansirani, D. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Giet Degree College Department of Computer Science and Engineering Rajahmundry India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Department of Information Technology Virudhunagar India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Sufficient sleep is important for individuals who are suffering with chronic diseases, as sleep scarcity severely impacts emotional, physical and mental well-being, worsening health complications. The existing convolutional network for sleep quality prediction and estimation has computational complexity and over fitting by which the model didn't effectively predict the quality of sleep. Therefore, this research proposes Deep Belief Networks-Bayesian Optimization (DBN-BO) for predicting and estimating the quality of sleep. Initially, Sleep study data is used as input and this data contains outliers which are preprocessed by using local outlier factor and effectively reduced impact of noise while detecting the outliers. Then, the sleep quality is predicted by using DBN which learns the complex features from raw data. After that, BO is used for estimating the quality of sleep and explores the various factors related to sleep. This proposed DBN-BO effectively reduced the computational complexity and over fitting by training one layer at a time, proposed DBN-BO achieved better results in terms of accuracy 9 4. 4 5 %, sensitivity 9 7. 0 9 %, and specificity of 9 3. 4 5 %and F1-Score of 91.12 %when compared with existing convolutional layers. © 2024 IEEE.

关键词： Sleep research

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Real-Time Convolutional Neural Networks for Emotion and Gender Classification 2

Real-Time Convolutional Neural Networks for Emotion and Gend...

引用

2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Singh, Jagendra Singh, Akansha Singh, Krishna Kant Lal, Bechoo Verma, Harsh Samudre, Niranjan Raperia, Harsh School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Delhi Technical Campus Greater Noida201310 India Department of Computer Science and Engineering Konenru Lakshmaiah Education Foundation vaddeswaram India Department of Electronics and Computer Science Atharva College of Engineering Mumbai India

Emotion and gender recognition are important areas of research in the field of computer vision and human-computer interaction. The proposed CNN architecture is designed to extract features from facial images and classify them into six basic emotions (happy, sorrow, anger, fear, surprise, and disgust) and two genders (male and female) in real-time. To extract and categorize characteristics from facial photographs, the suggested CNN architecture consists of convolutional layers, pooling layers, and fully connected layers. The suggested system performs at the cutting edge for both emotion and gender recognition tasks when tested on publicly accessible datasets. The proposed real- time CNN architecture has potential applications in various fields, including social robotics, human-computer interaction, and affective computing. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolution

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database Under Siege: The Hidden Menace of SQL Injection Attacks 14th

Database Under Siege: The Hidden Menace of SQL Injection Att...

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14th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Ala, Rajitha Ramana, Attili Venkata Venu, Vasantha Sandhya Bejjam, Kiranmai Kanagala, Sai Sriharsha Punyamurthy, Bheemeshwar Sangeam, Ruchitha Department of CSE Vardhaman College of Engineering Hyderabad India Department of CSE-Data Science Geethanjali College Of Engineering and Technology Hyderabad India Department of CSE Keshav Memorial Institute of Technology Hyderabad India

ISBN: (纸本)9783031811708

The report begins by providing a detailed explanation of SQL injection attacks, illustrating how attackers exploit vulnerabilities in application code to inject malicious SQL queries. Attackers use various methods to bypass security and access data. In addition, the report delves into the underlying causes that make databases susceptible to SQL injection attacks, including poor input validation, lack of parameterized queries, and inadequate access controls. It highlights the importance of secure coding practices and ongoing vulnerability assessments as proactive measures to mitigate risk. To address this growing menace, the report explores a range of defense strategies and best practices. It discusses the implementation of robust input validation techniques, the utilization of parameterized queries, and the adoption of web application firewalls. Furthermore, it emphasizes the significance of educating developers, administrators, and users about SQL injection risks and the adoption of a security-first mindset. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Access control

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Block chain and Deep Learning Based Secure Communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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Green and Network-Aware Smart IoT Logistics Applications 11

Green and Network-Aware Smart IoT Logistics Applications

引用

11th International Conference on Information Technology, ICIT 2023

作者： Alharbi, Hatem A. College of Computer Science and Engineering Department of Computer Engineering Taibah University Al-Madinah42353 Saudi Arabia

ISBN: (纸本)9798350320060

Internet of Things (IoT) plays a vital role in delivering outstanding logistical services to release any obstacles for people and provide a good experience. IoT logistic solutions are great assets for multiple sectors in smart cities. These solutions including but not limited to inventory tracking and analytics, real-time fleet management, Predictive maintenance. However, the IoT applications for logistics operations is a challenging task, especially, when it aims to reduce energy consumption along with their environmental and economic impacts. Thus, there is a need to have a system that performs IoT applications for logistics operations in an efficient manner. In the light of this challenge, this paper investigates how IoT applications for logistics operations would be efficiently offloaded to minimize the energy consumption and network traffic in fog-cloud systems. Therefore, an energy-efficient approach is proposed to collect data from several sensors (e.g., monitor people locations, health conditions, etc.) and process them either in fog or cloud in a real-time manner. This approach will help in reducing the energy consumption and network traffic to process IoT applications for logistics operations efficiently. The results show that the proposed fog/cloud approach outperforms the traditional approach in terms of decreasing the total power consumption and the Internet network traffic by 55% and 50%, respectively. © 2023 IEEE.

关键词： Fog computing

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Blockchain-Powered Student Feedback for Enhanced Learning Experiences

Blockchain-Powered Student Feedback for Enhanced Learning Ex...

引用

2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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Hybrid Deep Learning Models with Genetic Algorithm Optimization for Enhanced Kidney Tumor Detection 5

Hybrid Deep Learning Models with Genetic Algorithm Optimizat...

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

作者： Rajagopal, K. Kumari, V. Sheeja Saraswathy, S. Kumar, V. Suresh Ponmaniraj, S. Deepa, A. Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Chennai Thandalam India Saveetha Institute of Medical and Technical Sciences Simats University Department of Computer Science and Engineering Chennai Thandalam India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Institute of Ece Saveetha Institute of Medical and Technical Sciences Simats University Department of Computational Intelligence Chennai Thandalam India Saveetha Engineering College Department of Computer Science and Engineering Chennai Thandalam India

ISBN: (纸本)9798350379945

Accurate and timely segmentation of kidney tumors from medical images is crucial for effective treatment and prognosis. This research study proposes a novel hybrid deep learning framework incorporating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for enhanced kidney tumor segmentation. To optimize model performance, Genetic Algorithms (GA) are employed to fine-tune hyperparameters and network architecture. The proposed method effectively captures both local and global image features, leading to improved segmentation accuracy compared to existing techniques. Experimental results on a publicly available dataset demonstrate the superiority of our approach in terms of recall, F1-score, and precision. This research contributes to the advancement of computer-aided diagnosis for kidney cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Predictive Modeling in High Dimensional data Using Hybrid Feature Selection 5

Enhancing Predictive Modeling in High Dimensional Data Using...

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

作者： Ambhika, C. Gayathri, S. Anish, T.P. Sheena, B. Gracelin Nalini, M. Siva Subramanian, R. Rmd Engineering College Dept of Aiml India S.A. Engineering College Department of Computer Science and Engineering India Rmk College of Engg and Tech Dept of Cse India Sathyabama Institute of Science and Technology India S A Engineering College Dept of Cse India

ISBN: (纸本)9798350379945

Healthcare data has become invaluable for patients care enhancement as well as outcomes which have been influenced by the expanding volume and complexity of health informatics. The current research discusses the design of a hybrid feature selection(HFS) methodology which is based on a combination of filter & wrapper methods to detect the necessary variables for predictive modeling using machine learning algorithms. The study takes the data set on Parkinson's disease as a use case. The performance of KNN, Decision Trees, and Random Forest models is assessed based on this data set. Findings reveal that integration of feature selection impacts models' efficacy, where Random Forest performs best among all techniques. KNN helped to lower the total number of errors but to increase sensitivity, while the Decision Trees (DT) applied changes not only to sensitivity but to specificity also. The findings bring feature selection into the limelight making healthcare data analysis more revealing in disease prediction and personalized medicine. The application of filter and wrapper methods by providers makes the data exploring process in a high-dimensional data easier and useful in getting actionable insights for improved patient care. Additional studies on feature selection methods and network architecture evolution should be carried out in order to increase the efficiency of medical data mining and enhance predictive model accuracy. © 2024 IEEE.

关键词： Personalized medicine

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Distributed File Systems for Cloud Storage Design and Evolution 1

Distributed File Systems for Cloud Storage Design and Evolut...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Sabitha, R. Sydulu, Sk. John Karthik, S. Kavitha, M.S. SNS College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350342796

This paper deals the scalable, flexible, and cost-effective solutions. Distributed file systems play a crucial role in the design and evolution of cloud storage systems, enabling across multiple servers and data centers. This research paper explores the design principles, key components, and evolution of distributed file systems in the context of cloud storage. We examine the challenges faced by distributed file systems, explore their evolution over time, and discuss emerging trends and future directions in this field. © 2023 IEEE.

关键词： Cloud storage design Distributed file systems evolution over time

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