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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Zope, Minal A. deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant d. Kalinga University Raipur India Dept. of Computer Engineering AISSMS's Institute of Information Technology Pune India Kalinga University Raipur India Indira College of Engineering and Management Pune India Dr.D.Y.Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350361155

Recent machine learning Ad predictions employed feature selection and extraction methods. The literature research revealed several attempts to identify Ad by using different deep learning layer and micro-simulation techniques. However, identifying important features that can detect Ad extremely early remains a difficult task. Automation, poor classification accuracy, over fitting, etc. are the other challenges while using machine learning algorithms. The recent progression of using deep learning methods has received the significant attention for the automatic Ad classification. CNN's strategy, while successful, has certain drawbacks: (1) The CNN model uses local receptive fields, shares weights, and subsamples, (2) deep neural networks need a lot of labeled sample training models to avoid over fitting, which may cause the loss of crucial information like representative features at various frequencies and orientations;and (3) the higher computational overhead is another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of Ad from the input medical images. © 2024 IEEE.

关键词： Feature extraction

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AUTONOMOUS IOT MOVEMENT IN HOSTIlE AREAS USING ROBOTICS ANd dEEP FEdERATEd AlGORITHMS

AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Archana, U. Roslin dayana, K. Jayadurga, R. Sahaana, G. Praveena, K. Saravanan, V. Department of Computer Science and Design R.M.K. Engineering College Tamilnadu Kavaraipettai India Department of Computer Science and Engineering R.M.D. Engineering College Tamilnadu Kavaraipettai India Campus Karnataka Bengaluru India Department of ECE Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College Tamilnadu Chennai India Department of ECE Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Science Dambi Dollo University Oromia Region Dambi Dollo Ethiopia

ISBN: (纸本)9798350305708

Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. © 2023 IEEE.

关键词： Autonomous IoT deep Federated Algorithms Hostile Areas Robotics

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designing of Solid State SIW Antenna for 5G Application 1

Designing of Solid State SIW Antenna for 5G Application

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Bhatt, Maharshi K. Borisagar, Komal R. Kotak, Nirali A. Sedani, Bhavin S. Electronics & Communication Engineering Department Government Engineering College Bhuj India Graduate School of Engineering and Technology Gujarat Technological University Ahmedabad India Electronics & Communication Engineering Department L.D.College of Engineering Ahmedabad India

ISBN: (纸本)9781607685395

This experimental research paper presents the plan of a solid single band microstrip patch Substrate Integrated Waveguide(SIW) antenna which might be suitable candidate for 5G wireless application working at 3.5GHz. Right now, the full world is within the surge of satisfying the request of capacity boosting highlights of remote eras such as huge data rate in conjunction with moo inactivity, ultra-reliable communications, and enormous network. These all over said highlights of 5th era remote innovation might be for the most part held within the 3.5GHz band. The plan of SIW antenna proposed in this paper is carried on Roger RT3203 with 3.02 dielectric, 0.0016 misfortune digression and 1 Oz thickness utilizing inset bolstering. The overall dimension of the patch antenna is 60.4523×34mm2. In this work rectangular fix is planned with U-shape opened radio wire with utilizing SIW methodology. The antenna is outlined and recreated and the investigation was carried out on computer Simulation Technology (CST) studio. The antenna attains gain of 2.61dB at 3.5GHz frequency which is the most preferred feature that facilitates to implement this antenna plan in the 5G applications. © The Electrochemical Society

关键词： dielectric Filled Waveguide Empty SIW Microstrip Patch Antenna SIW antenna

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Exploring K-Means Meta-Heuristic Techniques For Prediction of Anomalies In IoT-Enabled Industrial Systems 15

Exploring K-Means Meta-Heuristic Techniques For Prediction o...

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

作者： Gnanasekaran, T. Girinath, S. Venkatesh, K. Valarmathi, N. Bandili, Sunil Kumar Balasubramani, S. R.M.K. Engineering College Department of Ece Tamilnadu Kavaraipettai India Department of Computer Applications AP Tirupati India Aandra Pradesh Anantapur India M.Kumarasamy College of Engineering Department of Information Technology Karur Thalavapalayam India Saint Louis University School for Professional Studies Department of Computer and Information Sciences St. LouisMO63103 United States R.M.D. Engineering College Department of Ece Tamilnadu India

ISBN: (纸本)9798350370249

due to the broad implementation of the Internet of Things (IoT) in industrial systems, the introduction of Industry 4.0 resulted in a significant increase in operational efficiency. However, it also resulted in the introduction of new challenges when it came to identifying abnormalities. This paper presents a new method for boosting the flexibility and precision of anomaly detection models by integrating the best aspects of K-Means clustering with meta-heuristic approaches. The utilisation of K-Means meta-heuristic techniques is the driving force behind this research, which is being driven by the growing demand for reliable anomaly prediction in industrial systems that are enabled by the IoT. Aiming to discover methods that can improve the reliability of anomaly detection algorithms is the objective of this research. K-Means clustering is systematically integrated with metaheuristic techniques in this study, which allows for the optimisation of the clustering algorithm for the purpose of detecting abnormalities in industrial data that is enabled by the IoT. We use real-world datasets from a wide range of businesses in order to measure the effectiveness of the proposed strategy in comparison to more traditional ways. The results demonstrate that the K-Means meta-heuristic strategy greatly enhances the accuracy of anomaly detection in comparison to the approaches that are considered conventional. Recall, false positive rates, and precision are all areas in which it excels beyond its capabilities. The results indicate that this unique approach has the potential to be useful when applied to the challenges of anomaly detection in industrial systems that are enabled by the IoT. © 2024 IEEE.

关键词： Anomaly detection Industrial Systems IoT K-Means Meta-Heuristic

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Maximizing IoT Security: An Examination of Cryptographic Algorithms

Maximizing IoT Security: An Examination of Cryptographic Alg...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Arya, leena Sharma, Yogesh Kumar Kumar, Ramakrishna Padmanaban, Harish devi, Suman Tyagi, lalit Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Guntur India College of Engineering National University of Science and Technology Department of Ece Muscat Oman Site Reliability and Software Engineering Professional Investment Banking HoustonTX United States Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India G L Bajaj Institute of Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350357769

The proliferation of Internet of Things (IoT) devices has created a ubiquitous network of interconnected sensors and devices that generate and exchange vast amounts of data. With this increased connectivity comes a pressing need for robust security measures to safeguard IoT communications' integrity, confidentiality, and authenticity. This paper categorizes cryptographic algorithms commonly employed in IoT security, including symmetric and asymmetric encryption methods, digital signatures, and key exchange protocols. By examining the attributes of these algorithms, their strengths, and potential vulnerabilities, this paper assesses their effectiveness within the unique context of IoT. Furthermore, the paper highlights the significance of cryptographic algorithm optimization in IoT security. Strategies for enhancing algorithm efficiency, such as algorithm selection, key management, and hardware acceleration are thoroughly explored. Through these case studies, this paper underscores the practical implications of cryptographic algorithm selection and optimization, illustrating how they can prevent security breaches and enhance IoT security. © 2023 IEEE.

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Enhancing secrecy using hybrid elliptic curve cryptography and diffie Hellman key exchange approach and Young's double slit experiment optimizer based optimized cross layer in multihop wireless network

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Measurement: Sensors 2024年 31卷

作者： Bhagappa divyashree, H.S. Avinash, N. Manjunatha, B.N. Vishesh, J. Mamatha, M. Department of Computer Science and Engineering Brindavan College of Engineering Karnataka Bengaluru India Department of Information Science and Engineering Brindavan College of Engineering Karnataka Bengaluru India Department of Computer Science and Engineering R. L. Jalappa Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru India

Wireless Network is usually a non-infrastructure multi-hop network, in which the communication between the nodes are carried implicitly or explicitly with the help of intermediate nodes. The two primary considerations when creating a wireless network are energy efficiency and secrecy, which are currently hot topics in study. Power-related issues make the conventional tiered approach ineffective. We suggest a novel solution to address those problems: the Young's double slit experiment optimizer (YdEO) based optimized cross layer routing protocol. This method computes parameters like data success rate, average energy loads, and traffic indices to determine consistency and energy efficacious routing paths. The secrecy of the network can be enhanced with the proposed innovative approach known as Hybrid Elliptic Curve Cryptography and diffie Hellman Key Exchange (HECC-dH). This greatly enhances data integrity and security by offering dual authentications with key exchange techniques. In addition to this HECC-dH, bilinear maps are used for secure communication. To verify the effectiveness of the suggested work, experiments are performed with the NS2 simulator and contrasted with state-of-the-art works. When it comes to communication, our method performs better than the others and with greater satisfaction. © 2023 The Authors

关键词： Energy efficiency

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disease Classification Model using Capsule Networks based on White Blood Cells 5

Disease Classification Model using Capsule Networks based on...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gaikwad, Prajwal S. deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant d. Kalinga University Raipur India Dept. of Computer Engineering AISSMS's Institute of Information Technology Pune India Kalinga University Raipur India Indira College of Engineering and Management Pune India Dr.D.Y.Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350361155

As a substitute for recurrent neural networks (RNNs), deep learning (dls) has shown promise in text processing in the last several years. In this study, we looked at the recently announced capsule networks (CapsNets), which are gaining a lot of interest since they outperform deep learning (dls) in image analysis, and we found that they function well for language categorization and sentiment analysis in some instances. Our experimental findings demonstrate that the suggested well-tuned CapsNets model may serve as a suitable (and, in some cases, superior and more cost-effective) alternative to sentence categorization models based on RNNs and dls. We ran a battery of tests with randomly shuffled test data to see whether CapsNets could learn the sequence of words sequentially. Overall, our CapsNets model outperforms dl and RNN models in terms of classification performance and resilience to adversarial assaults. © 2024 IEEE.

关键词： Cost effectiveness

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A Hybrid data Sensing in Intra cluster Communication by using Mac Protocols in Wireless Communication

A Hybrid Data Sensing in Intra cluster Communication by usin...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Vinutha, C.B. Geetha, N. Padmini, Viriyala Sri Anima Nayak, Sanjib Kumar Chaithra, N.K. Sri Siddhartha Institute of Technology Ssahe Department of Electronics and Communication Engineering Tumakuru India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Wireless Sensor Networks (WSNs) have become increasingly popular due to their numerous beneficial features, such as low power consumption, adaptability, fast response time, reliability, dynamic nature, affordability, and ease of installation, making them applicable in various fields for different purposes. However, effective data sensing in WSNs requires the use of proper Medium Access Control (MAC) protocols. In order to conserve energy and minimize redundant sensing, cluster members transmit their data to the leader node, which is facilitated by appropriate clustering methods. In this study, we propose the Enhanced Energy-efficient Real-time MAC (EERMAC) protocol. The EERMAC protocol uses a combination of two different access methods: CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) for real-time data and vTdMA (variable Time division Multiple Access) for periodic data. The proposed EERMAC protocol outperforms existing protocols in terms of packet delivery ratio (PdR), throughput, jitter, and packet drop rates. The queuing model implemented by EERMAC effectively prevents congestion, leading to reduced packet delay. This study presents an innovative solution for addressing the challenges of data sensing in WSNs and showcases the potential of EERMAC to enhance the performance of WSNs for various applications. © 2023 IEEE.

关键词： Carrier sense multiple access

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Automated drone-Based Imaging Systems For Plant Health Monitoring Using deep learning Techniques

Automated Drone-Based Imaging Systems For Plant Health Monit...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sundravadivelu, K. Senthilvel, P. Gururama Thirupurasundari, d.R. Rajesh Kumar, K. Palani, Hari Kumar School of Information Technology Madurai Kamaraj University Department of Computer Science Tamil Nadu Madurai625021 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Department of Electronics and Communication Engineering Chennai Tamilnadu Kundrathur India School of Engineering Ajeenkya D y Patil University India

ISBN: (纸本)9798350394580

Accurate and scalable plant health monitoring has entered a new age with the combination of automated drone-based imaging systems with cutting-edge deep learning algorithms. This cutting-edge method uses drones' mobility and adaptability to get detailed photographs, which reveal previously unattainable information on plant health and disease. We have made great progress in identifying plant illnesses, nutritional deficits, and growth irregularities using state-of-the-art deep-learning algorithms. To analyze drone video in real-time, our approach uses convolutional neural networks (CNNs) trained on large datasets of annotated plant images. The ability to quickly diagnose and pinpoint the exact location of plant health problems is a major step forward in developing effective intervention techniques and improving agricultural practices. The system's ability to detect even minute differences in plant health has been greatly improved by incorporating multispectral imaging capabilities, which allows it to go beyond the bounds of simple visual spectral analysis. This study is a breakthrough in agricultural monitoring since it offers a practical, economical, and ecologically sound approach to the problem of ensuring the world's population has access to enough to eat. When combined with deep learning, automated drone-based imaging systems have the potential to completely transform plant health assessment, providing invaluable assistance to farmers, agronomists, and researchers as they work to increase crop yield while decreasing resource inputs and negative environmental effects. Insights into the full scope of this game-changing technology and its potential to profoundly alter the face of contemporary farming are presented in this research. © 2023 IEEE.

关键词： deep learning Techniques drone-based Imaging Systems Plant Health Monitoring

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Small Single Board computers based Smart Manhole Monitoring and detection System 4

Small Single Board Computers based Smart Manhole Monitoring ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vikram, N. Raman, Ramakrishnan Babu, J. Jagan Srividhya, E. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Maharashtra Pune India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Accidents now frequently culminate from missing or broken maintenance manhole covers. In developing countries, maintenance holes receive insufficient care. Both fatalities and serious injuries may result from incidents of this nature. This research study provides a solution to this problem. Several sensors are coordinated to screen the sewage vent cap and prevent similar incidents thoroughly. This recommended solution includes a gas cover to keep track of the gas's toxicity that the sewage systems discharge. A tilt sensor also shows whether the maintenance hole may tilt. A float sensor also alerts individuals when the water level rises too high. If any parameters cause an alert, they SMS a selected municipal corporation mobile number and upload it on the IOT website. Also, all parameters are regularly updated on the webpage. The suggested real-time, cost-effective, and low-maintenance IoT system is a maintenance hole that alerts the management station when it exceeds specific threshold values. Not only does this technology help the general population, but it also reduces the death risk for people who manually clean underground drainage. © 2023 IEEE.

关键词： Internet of things

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