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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rashmi, K. Anandhi, R.J. Singh, Navdeep Kumar, V. Surya Pavan Gupta, Krashnkant Albawi, Ali Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India Amity University Greater Noida Department Of Electronics & Communication Engineering India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

A complete wireless network system for flexible resource sharing, spectrum management, security, and cross-layer optimization is described in this paper. Five algorithms comprise the system. These are ADS, QoS-ML, CLO, and DSM. Wireless networks adapt and grow, and these applications solve issues. Real-time resource allocation using predictive models enhances network performance, latency, and efficiency with the PRA algorithm. DSM uses adaptive spectrum utilization to reduce interference. ADS uses machine learning to detect unusual activity and implement flexible safety measures to make networks safer. QoS-ML improves routing options using real-time QoS data to ensure network-wide QoS. CLO alters layer connections, utilizes metrics across layers, and is continually searching for network improvements. A detailed analysis of the research examined each program's distinct qualities and how they function together. The discussion focused on how well the framework performed in various settings and was adaptable. In the conclusion, the algorithms' strengths and potential in distributed computing environments were highlighted. The recommended technique outperformed others in speed, latency, security, energy efficiency, scalability, and customer satisfaction. Performance trends, parameter distributions, and trade-offs were shown, making the structure more dependable. By offering a full, adaptive, and successful method, this study advances wireless network management. Optimizations, scalability, and real-world applications might be studied to ensure the system works in various conditions. In challenging and changing situations, the proposed architecture supports wireless network expansion. © 2024 IEEE.

关键词： Machine learning

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology 11

Enhancing Facility Safety for Autonomous Gas Inspection Dron...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Latha, S. Asha, P. Srinivasan, V. Prasanna Elangovan, K. Thamizhamuthu, R. Sujatha, S. Department of Biotechnology Tamil Nadu Salem India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To increase facility safety by integrating autonomous gas inspection drones with Convolutional Neural Networks (CNNs) and Internet of Things (loT) technologies. Due to their ability to reach difficult situations, unmanned aerial vehicles are increasingly used for gas inspection in industry. However, drone safety and gas detection accuracy remain major issues. It offers a comprehensive method that uses CNN s for real-time image analysis and classification to help the drone locate gas leaks with high accuracy. CNN is trained on a broad dataset of gas leak situations and environmental conditions. loT technology also allows the drone and centralized monitoring system to communicate in real-time for data sharing and decision-making. Our method can adjust to changing lighting and weather and discriminate between innocuous abnormalities and gas leaks. Advanced navigation algorithms let the drone traverse complicated industrial sites while avoiding obstacles and maximizing coverage. Experimental findings show the suggested technology improves facility safety by lowering gas leak reaction times and operator risk. CNNs with loT technologies increase gas leak detection and allow proactive maintenance, improving industrial facility safety and efficiency. It advances autonomous systems for industrial applications by tackling gas inspection safety problems. The suggested framework lays the groundwork for integrating innovative technology to improve autonomous gas inspection drone capabilities and safety. © 2024 IEEE.

关键词： Gases

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Computational Complexity of Flattening Fixed-Angle Orthogonal Chains 34

Computational Complexity of Flattening Fixed-Angle Orthogona...

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34th Canadian Conference on Computational Geometry, CCCG 2022

作者： Demaine, Erik D. Ito, Hiro Lynch, Jayson Uehara, Ryuhei Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States School of Informatics and Engineering The University of Electro-Communications Japan Cheriton School of Computer Science University of Waterloo Canada School of Information Science JAIST Japan

Planar/flat configurations of fixed-angle chains and trees are well studied in the context of polymer science, molecular biology, and puzzles. In this paper, we focus on a simple type of fixed-angle linkage: every edge has unit length (equilateral), and each joint has a fixed angle of 90° (orthogonal) or 180° (straight). When the linkage forms a path (open chain), it always has a planar configuration, namely the zig-zag which alternating the 90° angles between left and right turns. But when the linkage forms a cycle (closed chain), or is forced to lie in a box of fixed size, we prove that the flattening problem - deciding whether there is a planar noncrossing configuration - is strongly NP-complete. Back to open chains, we turn to the Hydrophobic- Hydrophilic (HP) model of protein folding, where each vertex is labeled H or P, and the goal is to find a folding that maximizes the number of H-H adjacencies. In the well-studied HP model, the joint angles are not fixed. We introduce and analyze the fixed-angle HP model, which is motivated by real-world proteins. We prove strong NP-completeness of finding a planar noncrossing configuration of a fixed-angle orthogonal equilateral open chain with the most H-H adjacencies, even if the chain has only two H vertices. (Effectively, this lets us force the chain to be closed.) © 2022 Yeganeh Bahoo and Konstantinos Georgiou.

关键词： Molecular biology

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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An efficient and autonomous dynamic resource allocation in cloud computing with optimized task scheduling

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Procedia computer science 2024年 246卷 3654-3663页

作者： Safa Rabaaoui Héla Hachicha Ezzeddine Zagrouba University of Tunis El Manar Higher Institute of Computer Science Laboratory of Informatics Modeling and Information and Knowledge Processing (LIMTIC) 2 Rue Abou Rayhane Bayrouni Ariana 2080 Tunisia College of Computer Science and Engineering - University of Jeddah

Resource allocation, scheduling, and provisioning are indeed critical and complex issues in cloud computing environments, as they directly impact both the user experience and the profitability of cloud service providers. Also, the excessive use of resources and the fact that the required resources may exceed those available have raised additional challenges. The effective and optimal allocation of resources in this dynamic cloud environment poses a significant challenge due to the substantial increase in cloud utilization. Then, an energy-efficient task scheduling algorithm is required to improve the efficiency of the resource allocation process. This paper proposed an efficient and autonomous dynamic resource allocation in cloud computing with optimized task scheduling. We aim to minimize the cost of virtual machines and the makespan. Furthermore, its impact on the best response time and energy consumption has been studied. The simulation shows that our method gave better results than the former ones.

关键词： Cloud Computing Dynamic Resource allocation Multi-Agent System Energy consumption Task scheduling Cost Makespan

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Real-Time Blood Pressure Monitoring and Hypertension Prediction based on Multitask Temporal Convolutional Network 1

Real-Time Blood Pressure Monitoring and Hypertension Predict...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Musnmusawi, Muntather Deepa, R. Shankar, Smitha Sharath Nirupashri, G. Khanam, P. Katheeja The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq St.Joseph's Institute of Technology Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Bio Medical Engineering Mamallapuram India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, blood pressure monitoring and hypertension prediction have become essential for effective measurement and management. Continuously tracking blood pressure helps in making informed decisions like changing medication or dosage. However, some challenges are raised while monitoring such as blood pressure readings being prone to errors, variability in measurements if a patient moves and environmental noise. Therefore, this research proposes Multitask Temporal Convolutional Network (MTCN) for realtime blood pressure monitoring and hypertension prediction. Initially, data is collected from medical information Mart for Intensive Care III (MIMIC-III) and preprocessed by using Min- Max Normalization which rescales numeric data to definite range. After that, features like time frequency coefficients, power spectral density, Scalogram features are extracted by using Continuous Wavelet Transform (CWT). Finally, blood pressure monitoring and hypertension prediction is done by using proposed MTCN which provides accurate hypertension prediction and personalized risk assessments. The proposed MTCN achieved better results in terms of Mean Absolute Error (MAE) of 2.01, Root Mean Square Error (RMSE) of 3.26 when compared with existing Random Forest Regression model. © 2025 IEEE.

关键词： Wavelet transforms

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Naive Bayes-based Autonomous Illumination System for Urban Green Spaces with Cloud Assistance 4

Naive Bayes-based Autonomous Illumination System for Urban G...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Tamilselvi, M. Neelima, A. Amarnath, J. Jegan Lalitha, K. Ganesh, E.N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Sri Sairam Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India R&d Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Improving urban cities' quality of life is one of the primary functions of urban green spaces. Efficient lighting solutions in these areas enhance safety and ambiance. This research introduces a new method for autonomously controlling the lighting in urban parks with the help of a Naive Bayes classifier and cloud computing. The system uses information gathered from several sensors to adapt the brightness of the lights in response to changes in the surrounding environment and the presence or absence of people. The system can generate accurate choices in real-time using machine learning methods, including the Naive Bayes algorithm. With the help of the cloud, remote monitoring and administration are made easier, which improves efficiency and scalability. The experimental findings prove that the proposed approach is reliable and efficient in enhancing urban green areas' energy efficiency and user enjoyment. Intelligent algorithms combined with cloud computing provide a viable option for smart city lighting control, opening the door to more responsive and environmentally friendly cityscapes. © 2024 IEEE.

关键词： Ambiance Energy efficiency Environmental conditions Lighting management Real-time control Remote monitoring Safety

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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Exploring the Hidden Potential of Cyber Security Oriented Intelligent Data Hiding in Encrypted Images 1

Exploring the Hidden Potential of Cyber Security Oriented In...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Nirmaladevi, K. Nagarajan, Anandh Tamizharasi, A. Ramya, R. Allirani, P. Sreenivasa Reddy, M. Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Andhrapradesh Madanapalle India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India MLR institute of technology Department of EEE Telangana Hyderabad India

ISBN: (纸本)9798350364590

In recent years, there has been a surge of interest in the possibility of reversible data hiding inside encrypted images due to the promising practical uses of this technology, which include content owner identification, privacy protection, and encrypted picture authentication. Data hiding refers to the process of concealing data within a cover material. It is possible to conceal data in visual, auditory, textual, and graphical formats. Pictures, particularly digital pictures, can be used to conceal data. This data may be authentication details or author information, or it might be any text that is relevant to the image. The receiving end has to be able to decipher the encrypted data and restore the original picture. Advanced Cipher Logic based Data concealing (ACLDH) is a data concealing approach that meets this condition;it allows for accurate recovery of the host image. The losslessness of this method makes it well-suited for use in the medical field, the military, and cloud storage. After extracting the concealed data from an encrypted image, the suggested technique, known as ACLDH, may restore the original image to its original state without introducing any deformation. These days, reversible data hiding in encrypted graphics is all the rage because of its great quality: you can have your original cover back after removing the hidden data. It safeguards the initial data as well. Reversible Data Hiding (RDH) techniques that are now available for encrypted photos may be classified into two groups: those that do not preprocess the images and those that does. In this research, we show how to use adaptive thresholding to increase data concealing capacity and how to retrieve the original picture from a tagged image without distortion. © 2024 IEEE.

关键词： ACLDH Advanced Cipher Logic Cyber Security Data Hiding Encrypted Images Intelligent Data Hiding RDH Reversible Data Hiding

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