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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Subramanian, M. Gokulakrishnan, K. Nirmala, B. Vijaya Vijay Shankar, S. Krishnan, R. Santhana Vinoth Rajkumar, G. Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India College of Engineering Guindy Anna University Department of Electronics And Communication Engineering Chennai India Psna College of Engineering And Technology Department of Information Technology Dindigul India Sona College of Technology Department of Electrical And Electronics Engineering Salem India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India J.P. College of Engineering Department of Electronics And Communication Engineering Thenkasi India

ISBN: (纸本)9798350359299

The proposed patient monitoring system is a substantial progression in healthcare technology. It integrates various sensors and communication modules to provide real-time patient data to caregivers and medical professionals. The array of sensors includes a non-contact temperature sensor, a rain sensor, a Max30100 sensor, and a load cell sensor. The non-contact temperature sensor vigilantly tracks the patient's body temperature, alerting caregivers to any deviations. The rain sensor serves as a communication channel for patients unable to express needs, detecting urination. The Max30100 sensor monitors critical health metrics like heart rate and blood oxygen levels for early detection of conditions. The load cell sensor observes saline levels in IV bags, triggering alerts on nearing critical thresholds. Powered by an Arduino microcontroller, the system aggregates data, relaying it to a dedicated server via Wi-Fi for remote accessibility. The Isolation Forest-based IsoSentry Algorithm enhances anomaly detection, identifying aberrations in health readings. Adding reliability, a GSM module connected to Arduino dispatches real-time alerts via text messages. In essence, "GuardianForest"epitomizes a transformative leap in healthcare, integrating cutting-edge hardware, wireless communication, and cloud-based data management to enhance overall healthcare responsiveness and efficacy. © 2024 IEEE.

关键词： Internet of things

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Hybrid Routing and Energy Management for Performance Optimization in Mobile Ad-hoc Networks 2

Hybrid Routing and Energy Management for Performance Optimiz...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Nirmala, B. Vijaya Francis Raj, J. Relin Krishnan, R. Santhana Lavanya, V. Pitchirani, A. Kumar, V. Vinoth PSNA College of Engineering and Technology Department of Information Technology Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331505745

Mobile Ad-hoc Networks (MANETs) encounter challenges related to dynamic topologies, energy constraints, and link instability. To enhance performance, a hybrid model integrates Ad-hoc On-demand Multipath Distance Vector (AOMDV), Power-Efficient Gathering in Sensor Information System (PEGASIS), and Deep Q-Network (DQN). OMNeT++, a discrete event simulation framework, facilitates comprehensive data collection, capturing key network parameters such as node density, signal strength, packet transmission rate, residual energy, routing paths, link breakage frequency, and packet delivery status. Stored in scalar and vector files, the dataset enables energy-efficient communication modeling and adaptive routing optimization. Data preprocessing involves cleaning, normalizing, and handling missing values using interpolation techniques while filtering noisy data through a sliding window averaging method. Feature extraction focuses on hop count, queue length, residual energy, and link expiration time, ensuring consistency and facilitating faster convergence. The hybrid model architecture integrates AOMDV for reliable multipath routing, PEGASIS for energy-efficient communication, and DQN for adaptive path optimization. Simulation results indicate a 15% improvement in throughput, a 10% rise in packet delivery ratio, a 20% reduction in end-to-end delay, and a 30% decrease in average energy consumption. Applications extend to military operations, disaster recovery, vehicular networks, IoT sensor networks, and smart city infrastructure, ensuring efficient and adaptive communication. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Optimized Traffic Management in Software Defined Networking 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Lece Elizabeth Rani, J. Jeyarohini, R. Emil Selvan, G.S.R. Arun Karthick, S. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9783031472244

ISBN: (纸本)9783031472237

In recent years, the data center network has improved its rapid exchanging abilities. Software Defined Network (SDN) architecture adds functionality to the node globally by means of separation of the control and data plane. Switches and routers are network devices that are inflexible to deal with different loads of traffic in a network and experience limitations on routing and load balancing. Data centers are utilized whenever high user traffic is encountered. Network operators manage the network by controlling the traffic and providing optimal resource allocation strategies to their applications. A path optimization algorithm is proposed to increase the performance of SDN. The proposed model’s efficiency is demonstrated and simulated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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Load Balancing and Energy Efficient Routing in Software-Defined Networking 1st

Load Balancing and Energy Efficient Routing in Software-Defi...

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Rani, J. Lece Elizabeth Jeyarohini, R. Selvan, G. S. R. Emil Konar, Rahul Shiva Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031472237

Software-defined networking (SDN), a newly developed architecture that alters the route (paths) of traffic packets, is a flexible and logically centralized control plane that provides a reduction in the network’s energy usage. SDN is designed in such a way that the switches and links can handle a large amount of traffic. Their energy use is not proportional to traffic. Due to the increase in network communication, SDN devices demand much more load and energy. The SDN controller has to be modeled efficiently. The system model of the controller is proposed which efficiently distributes the load. This model helps in traffic routing with minimal energy consumption for networking devices. In addition to the above, it balances the load based on the heterogeneous traffic distribution. The proposed method optimizes the energy consumed by the networking devices by means of using the few active switches, minimizing the number of device state transitions, and handling the volume of traffic. Load balancing is performed for multiple controllers. The switch migration technique is used for migrating the traffic from one controller to the other. Energy-efficient routing is done by the proposed algorithm with reduced device state transition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

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Journal of Optical Communications 2024年第2期45卷 347-354页

作者： Meenu Sharma, Anshu Malhotra, Rahul Department of Electronics and Communication Engineering CT Institute of Engineering Research and Technology Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology & Research Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology and Management Shahpur Near Lambra Punjab Jalandhar144020 India

In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Fog

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Investigating Routing Protocol Attacks on Low Power and Lossy IoT Networks

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SN computer Science 2024年第4期5卷 393页

作者： Kiran, Usha Maurya, Poonam Sharma, Himanshu Department of Computer Science Indian Institute of Technology Bhilai Bhilai India Department of Electronics Systems Aalborg University Aalborg Denmark Department of Electronics and Communication Noida Institute of Engineering and Technology Greater Noida India

Internet-of-things (IoT) networks are distinguished by nodes with limited computational power and storage capacity, making Low Power and Lossy Networks (LLNs) protocols essential for effective communication in resource-constrained environments. One such protocol is the Routing Protocol for Low-Power and Lossy networks (RPL), which establishes and manages routes in RPL-based networks. RPL contributes to optimized routing and reduced network overhead in LLNs. However, the RPL-based protocol is susceptible to various internal and external vulnerabilities that require thorough exploration and mitigation. Experimental results illustrate the impact of several RPL attacks, including the DODAG Information Solicitation (DIS) attack, version number attack, decreased rank attack, and worst parent selection (WPS) attack. For simulation purposes, we employed the Contiki Cooja network simulator. Further, we conduct a comparative analysis of these RPL-based attacks, revealing that the WPS attack has a significant impact on the network performance compared to other attacks mentioned in the paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Decreased rank attack DIS attack Intrusion detection system RPL attacks Version number attack WPS attack

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Parasitic Patch for UWB Application 3

A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Pa...

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

作者： Razib, Mohammad Rahman Das, Debprosad Murshed, Abu Hena Istiaque, Arif Hossain, Md. Azad Chittagong University of Engineering & Technology Department of Electronics and Telecommunication Engineering Bangladesh The University of Texas at Dallas Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350388282

This paper presents a performance comparison of a comb-shaped Vivaldi antenna for ultrawideband (UWB) applications, which is antipodal in nature. The antenna utilizes a 50 Ω microstrip feed line for energizing the antenna. To achieve ultrawide-band behavior, a parasitic elliptical-shaped patch and corrugated slots are employed. The patch controls frequency shifting along with high-gain behavior via electromagnetic coupling. To validate the proposed antenna, Rogers RT5880 substrate is used ( =2.2., tan .δ=0.0009). The proposed antenna operates in a frequency range from 5 to 28 G H z , with the highest gain of 12.5dBi at 27.5GHz. It has a total radiation efficiency of 97\% at 21.25GHz. The proposed Vivaldi antenna is a suitable candidate for high-gain wireless applications. The CST Studio Suite is employed to extract the simulation results of the antenna. © 2024 IEEE.

关键词： Microstrip antennas

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