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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Border Detection for Fishermen Using IoT 4

Border Detection for Fishermen Using IoT

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Vijayalakshmi, K. Alivn Ancy, A. Priyadharshini, J. NaveenKrishna, B.B. Harshavardhan, R. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

At present, the marine security issue includes the protection of fishers and address the rescue in case of illegal border infiltration. To detect the border for fisherman, this study conducted a method of enhancement in border detection essentially utilising the Automatic Identification System (AIS) with the assistance of the Internet of Things (IoT). In the middle sea, to provide boat detection with high precision, the proposed model incorporates Very High Frequency (VHF) communication inspired by X-band radar. The system is able to detect and monitor the boats at sea. To provide the middle sea with a high level of detection and monitoring of boats, system architecture included VHF (Very High Frequency) communication, which was inspired by the X band radar system. The range of AIS was increased using VHF communication. The coastal monitoring stations receive the position and status data from fishing boats with the help of IoT devices. The new AIS technology includes IoT-enabled devices installed on fishing boats. The IoT devices situated on boats send information to coastal monitoring station after sending data. The advanced technology increases the software capabilities of AIS, which will be helpful in the situation where the only AIS information was not enough. The advanced software capabilities improved the range of AIS and the definition capability of the vessels which were identified in the ocean near borders. The Automatic Identification System (AIS) that is being used in the present day has been upgraded and the Internet of Things (IoT) has been introduced. As a result, the system is now capable of much more comprehensive and real-time tracking and monitoring, as well as communication for marine vessels. This combination of technologies strengthens the safety, efficiency, and management of maritime operations by giving ships the Opportunity to connect and exchange data with different sensors, devices, and networks, and thus offering a more detailed and dynam

关键词： Fishing vessels

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Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

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Intelligent Automation & Soft Computing 2023年第5期36卷 1603-1612页

作者： Mahendrakumar Subramaniam V.Vedanarayanan Azath Mubarakali S.Sathiya Priya Department of Electronics and Communications Engineering Velalar College of Engineering and TechnologyErodeTamil NaduIndia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennaiTamil NaduIndia College of Computer Science King Khalid UniversityAbhaSaudi Arabia Department of Electronics and Communications

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business *** of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and ***-sor data gathering is an efﬁcient solution to collect information from spatially dis-seminated IoT *** Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this ***,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efﬁciency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select *** incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time *** MS is used to col-lect the data from CH,thus minimizing the network *** sleep and awake scheduling is used for minimizing the CH dead in the *** work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line *** prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.

关键词： Wireless sensor network quality of service parameters Internet of things reinforcement learning mobile sink

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Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy Detection

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computers, Materials & Continua 2023年第4期75卷 927-942页

作者： Swagata Boruah Archit Dehloo Prajul Gupta Manas Ranjan Prusty A.Balasundaram School of Electronics Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

Diabetic Retinopathy (DR) is a serious hazard that can result inirreversible blindness if not addressed in a timely manner. Hence, numeroustechniques have been proposed for the accurate and timely detection ofthis disease. Out of these, Deep Learning (DL) and computer Vision (CV)methods for multiclass categorization of color fundus images diagnosed withDiabetic Retinopathy have sparked considerable attention. In this paper,we attempt to develop an extended ResNet152V2 architecture-based DeepLearning model, named ResNet2.0 to aid the timely detection of DR. TheAPTOS-2019 datasetwas used to train the model. This consists of 3662 fundusimages belonging to five different stages of DR: no DR (Class 0), mild DR(Class 1), moderate DR (Class 2), severe DR (Class 3), and proliferativeDR (Class 4). The model was gauged based on ability to detect stage-wiseDR. The images were pre-processed using negative and positive weightedGaussian-based masks as feature engineering to further enhance the qualityof the fundus images by removing the noise and normalizing the images. Upsamplingand data augmentation methods were used to address the skewnessof the original dataset. The proposed model achieved an overall accuracyof 91% and an area under the receiver-operating characteristic curve (AUC)score of 95.1%, outperforming existing Deep Learning models by around 10%.Furthermore, the class-wise F1 score for No DR was 92%, Mild DR was 82%,Moderate DR was 66%, Severe was DR 89% and Proliferative DR was 80%.

关键词： Diabetic retinopathy deep learning transfer learning image processing image classification

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Music Emotion Classification using Harris Hawk Optimization based LightGBM Classifier 10

Music Emotion Classification using Harris Hawk Optimization ...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Balasubramaniam, Anishkaa Shanmugasundaram, Lackesh Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350350951

This research investigates the relationship between music and human emotions, focusing on emotion classification in Turkish music and the role of acoustics in music emotion recognition (MER). Leveraging a unique dataset stored in the UCI-MLR, the study employs machine learning techniques, including Linear Discriminant Analysis and Gradient Boost Classifier, to build a robust model. The accuracy enhancement is primarily driven by the feature selection process, which is powered by the Harris Hawks optimizer. This optimizer, when used with the LightGBM model, enables a more precise selection of features, thereby significantly improving the model's accuracy. The research achieves notable accuracies - 81.25% for Linear Discriminant Analysis and 83.75% for the Gradient Boosting Classifier - surpassing previous records. The paper discusses findings in the context of affective computing, emphasizing potential applications in music recommendation systems and therapy, concludes with a comprehensive analysis, and outlines future possibilities. This research contributes to advancing MER, laying the groundwork for innovations in diverse domains, including the potential integration with IoT-based intelligent systems for real-time emotion analysis and personalized music experiences based on camera frames. © 2024 IEEE.

关键词： Emotion Recognition

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An Effective Anti-Money Laundering System Using Block Chain technology

An Effective Anti-Money Laundering System Using Block Chain ...

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2024 International Conference on Smart Systems for Electrical, electronics, Communication and computer engineering, ICSSEEC 2024

作者： Divya, B. Thara, P. Kavitha, S. Alvin Ancy, A. Preethi Sri, R. Dhanalakshmi, D. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Dept of Computer Science and Engineering India India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

In the developed financial services industry, identity is defined using government-issued identification cards. Nevertheless, the primary obstacles in the consolidation and authentication of these data in connection with large-scale anti-money laundering (AML) requirements have significantly increased the challenges for individuals seeking to enter the financial system. This renders it exceedingly unprofitable and a greater risk for banking systems to engage with developing economies that might lack even the most primitive forms of identification. Furthermore, the process of establishing a banking account is packed with difficulty as a result of protracted processing times, substantial report obligations, and ambiguity surrounding the utilization of confidential customer data. The objective of cash laundering is to return illegal funds to their source;therefore, money launderers typically employ robust financial structures to transport funds. Additionally, the level of money laundering activity may become more geographically concentrated as the amount of money laundered increases. For many years, the financial quarter has been working to reevaluate the concept of identity. Blockchain is an emerging computing structure that enables the integration of disparate data points from authoritative source vendors into a unified digital ledger of validated, unalterable, transparent, and cryptographically protected data. Furthermore, secrecy can be ensured by letting the appropriate person or group control how their identity is shared. A blockchain is a distributed, virtual ledger that is unchangeable and keeps track of transactions in chronological order and close to real time. In order for later transactions to be added to the ledger the community members also known also nodes, must agree on them. This establishes an ongoing management mechanism that oversees issues related to manipulation, errors, and statistics. After dispatch Information contained within a block cannot be

关键词： Laundering

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MotionGuard: ESP32 CAM-Powered Smart Flooring System for Enhanced Security

MotionGuard: ESP32 CAM-Powered Smart Flooring System for Enh...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Marathe, Abha Ashutosh Sharma, Laksh Sailo, Lalropuia Limdiyawala, Mustafa Mate, Akhil Vishwakarma Institute of Technology Department Of Electronics and Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331541170

Traditional home security systems often face limitations in effectively addressing evolving security threats, such as theft and intrusion. These systems typically rely on conventional sensors and surveillance techniques, which may overlook crucial aspects of security, such as real-time monitoring and comprehensive threat detection. For instance, some systems may have limited coverage of entry points or may rely solely on single-factor authentication methods, rendering them vulnerable to sophisticated intrusions. In response to these shortcomings, our proposed solution introduces a novel anti-theft security system leveraging modern IoT (Internet of Things) technologies. One key feature of our system is the integration of ESP32 CAM, a versatile microcontroller for real-time monitoring of the premises. This allows homeowners to monitor their property remotely and receive instant alerts in case of any suspicious activity. Additionally, we incorporate a Telegram bot interface, enabling seamless communication between the system and the homeowner for immediate response to security threats. By addressing the limitations of previous systems, our proposed solution aims to enhance home security by providing multi-modal threat detection and proactive surveillance capabilities. Through a systematic approach to security system design and implementation, we seek to establish a robust and adaptable framework capable of safeguarding residential premises against unauthorized access and intrusion. © 2024 IEEE.

关键词： Cams

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Water Quality Monitoring and Control in Urban Areas in Real-Time via IoT and Mobile Applications 3

Water Quality Monitoring and Control in Urban Areas in Real-...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohanasundaram, R. Sagar, Raghav Kumar Khandelwal, Animan Upadhyay, Diwas Poddar, Harshit Rajagopal, Sivakumar Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The presence of impurities in water resulting from the expansion of industrial activities and the disregard of regulations aimed at protecting the environment presents considerable threats to the well-being of the general population. The establishment of reliable access to uncontaminated drinking water assumes paramount importance in the realm of disease prevention. The continuous monitoring of the quality of water in real-time serves as an indispensable measure for ensuring its safety. The objective of this article is to introduce a system that capitalizes on the Internet of Things (IoT) to automate the distribution and management of water resources within urban areas. By making use of embedded technology, this system allows for the uninterrupted surveillance and regulation of water quality. To further streamline the management process, an application compatible with the Android operating system has been developed to promptly notify users of any irregularities, thereby enabling remote control. Moreover, a web-based application has been designed to cater to the needs of iOS users, thereby broadening accessibility. This comprehensive approach not only heightens the level of user awareness but also provides them with greater control over their water supply, thereby enhancing public health outcomes. © 2024 IEEE.

关键词： Internet of things

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A CNN Approach for Brain Tumor Detection Using Diverse Optimizers 3

A CNN Approach for Brain Tumor Detection Using Diverse Optim...

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

作者： Revathi, Ar Venkataraman, Rishab Naveen Moola, Maitreya School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350375190

The ability to recognize brain tumors via medical imaging is crucial for brain tumor treatment. This proposed work focused on a technique for detecting brain tumors by employing Convolutional Neural Networks (CNNs) to achieve precise and prompt diagnosis of brain tumors. This frame utilizes a variety of optimizers such as Stochastic Gradient Descent, RMSprop, Adam (Adaptive Moment Estimation), Adadelta, Adagrad, Adamax, and Nadam. The model's performance is evaluated using accuracy, sensitivity, specificity, precision, and recall as the primary metrics. Leveraging a dataset of 6951 MRI images, results reveal varying performance across optimizers, with a peak accuracy of 98.8% emphasizing the need for careful selection based on specific requirements and dataset characteristics. Advancing brain tumor identification, aiming to enhance early intervention and patient outcomes through the integration of cutting-edge technology, particularly Convolutional Neural Networks (CNNs), which offer high precision in analyzing intricate patterns within medical imaging data. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI-driven Solutions for Autonomous Maintenance and Fault Detection in Electrical Power Grids 4

AI-driven Solutions for Autonomous Maintenance and Fault Det...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kanimozhi, S. Enakshi Chhabra, Rohan Rai, Shashank Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus School of Electronics Engineering Vellore Institute of Technology Chennai Campus

ISBN: (纸本)9798350354218

Efficient and reliable power grids are crucial for the functioning of modern society. However, increasing energy demand and renewable sources can lead to power failures. Traditional methods struggle. AI, particularly Random Forest Classifiers, offers a promising approach. These classifiers analyse vast amounts of grid data to identify patterns indicative of faults. This allows for real-time anomaly detection. This research investigates the efficacy of Random Forest models for fault classification, focusing on optimising data preprocessing and collection. Transparency in data collection and model development is crucial for data privacy. While simulated environments pose challenges for early fault detection, real-world data presents even greater complexities. The effectiveness of Random Forest Classifiers in early fault detection is critical for grid reliability, reducing power outages, and enhancing long-term operational efficiency. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Autonomous Maintenance Data Security Fault Detection Power Grid Predictive Maintenance Random Forest

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