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LoRaWAN Mesh Networks: An efficient mesh formation of LoRa devices in rural energy system

LoRaWAN Mesh Networks: An efficient mesh formation of LoRa d...

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2022 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2022

作者： Farzhana, I. Chandrasekar, A. Diwakaran, S. Hemavathi, S. Ramkumar, G. Department of Computer Science and Engineering VelTechRangarajanDr.Sagunthala R and D Institute of Science and Technology Avadi Chennai India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai602117 India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Srivilliputhur626128 India Tamil Nadu Chennai600 113 India Academy of Scientific & Innovative Research Uttar Pradesh Ghaziabad201 002 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

For Iot systems, LoRa (Long Range) offers the lowest wide-area network solution. The use of Iot systems for remote monitoring and control is growing in popularity with the growth of the Internet of Things. Compared to conventional metres, digital metres have several benefits, and smart metering is one of the applications of the Internet of Things (IoT). Because of its distant load measurement and management abilities, it aids in the integration of new concepts into the old power system, such as smart grids, blockchain technologies, and automated systems. However, because of the high installation and operational costs and the unpredictability of smart metres, classic analogue metres are still favored in many applications. Because of the absence of dependable and economical communication systems, these difficulties may be solved by deploying a LoRa-based network. The rural energy system is discussed in this article in relation to LoRa technology and how it might be used to address smart metering issues. As part of a simulation-based research, a rural microgrid has been examined to see whether the LoRa technology can be used in various architectures, as well as to discover the most cost-effective and reliable approach to integrate smart metres. © 2022 IEEE.

关键词： Internet of things

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A deep learning approach for Indian sign language gestures classification with different backgrounds 1

A deep learning approach for Indian sign language gestures c...

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1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Dhiman, R. Joshi, G. Rama Krishna, C. Department of Computer Science and Engineering NITTTR Chandigarh India Department of Electronics and Communication Engineering UIET Panjab University Chandigarh India

The sign language recognition system recently has drawn the attention of various researchers as there is no universal sign language, moreover, it consists of many patterns and postures. Many methods for extracting features and classifying sign language have been proposed in the literature, most of them are based on machine learning techniques. In this article, a deep learning method has been adopted by designing a Convolution Neural Network (CNN) model to extract the sign language features where for classification softmax layer is used. All alphabets in simple as well as complex backgrounds have been considered, where data is collected from 100 subjects in different lighting conditions. The effect of various optimization techniques (Adam, Sgdm, RMSProp), activation functions (ReLU and Leaky ReLU) for generalization ability is also observed. The proposed approach has succeeded in attaining the testing accuracy of 99.10%, 92.69%, and 95.95% on the Indian sign language dataset with simple, complex backgrounds and on a mixed background scenarios, respectively. The model is also tested on NUS dataset-I, NUS dataset-II, their combination, and achieved the accuracy of 100%, 95.95%, and 97.22% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Neural network models

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Smart Design and Implementation of Self Adjusting Robot using Arduino

Smart Design and Implementation of Self Adjusting Robot usin...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Vidhya, R.G. Singh, Kamlesh Paul, P John Sai Srinivas, T. Aditya Prasad Patra, Jyoti Sagar, K.V.Daya HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Graphic EraHill University Department of Computer Science Engineering Dehradun Campus Dehradun India Ellenki College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India G Pulla Reddy College of Engineering Department of Computer Science and Engineering Andhra Pradesh Kurnool India Odisha University of Technology and Research Faculty of Electrical OUTR Mahalaxmi ViharGhatikia Techno campus Odisha Bhubaneswar India Koneru Lakshmaiah Education Foundation Department of ECE Guntur Andhrapradesh Vaddeswaram India

ISBN: (纸本)9781665489621

Self-adjusting robots are an excellent approach for keeping up with breakthroughs and developments in advanced mechanics. This idea is based on the basic pendulum theory. A framework goes through a cycle of self-adjustment to reinforce itself from inside. This study intends to overcome the difficulties of stabilizing an unstable structure by supplying the robot with the essential components for self-adjustment and balance. Data from the accelerometer and gyrator are used to compute the robot's accurate three-dimensional location, which is then relayed to a controller. The regulator then uses its own code to legally direct the wheel rotation of the drive module, boosting the robot's stability. This robot has an advantage over others with four wheels since it can spin faster and around obstacles more readily. As a result, it is a necessary tool in many current scenarios. © 2022 IEEE.

关键词： Accelerometers

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Intelligent Drug Release System Development and Evaluation via Support Vector Machine Algorithm

Intelligent Drug Release System Development and Evaluation v...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Kalyani G Venkatesh Jayaraman Sathish Kumar P J Surendran R Balamurugan K S Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare.

关键词： Support vector machines Radio frequency Temperature measurement Drugs Machine learning algorithms Medical services Artificial neural networks Predictive models Biomedical monitoring Monitoring

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Enhancing Classification Accuracy and Speed with ML-DSP: A Game-Changer in Genomic Sequence Analysis

Enhancing Classification Accuracy and Speed with ML-DSP: A G...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： Rakesh M D Madhura R Shashidhar R Krutthika Hirebasur Krishnappa Department of Electronics and Communication Engineering JSS Science and Trechnology University Mysuru India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Computer Science Southern University and A&M College Baton Rouge Louisina USA

Even with a wide range of software tools at our disposal for comparing, analyzing, identifying, and categorizing genomic sequences, the complexity of the task remains, the task of classifying these sequences remains challenging. This difficulty is attributed to the vast datasets and inherent issues associated with classification. To overcome the limitations of existing alignment-centric and alignment-independent methods, a novel approach and software tool are required. In this research, we introduce ML-DSP, an innovative fusion of guided Machine Learning and processing of digital signals. This hybrid technique is developed to facilitate rapid, precise, and scalable classification of genetics across various taxonomic levels. The evaluation of ML-DSP involves the classification of 7396 complete mitochondrial genetics, resulting in an average accuracy of over 97% from kingdom to genus levels. Through a thorough comparison with classification software tools, using both small standard datasets and a larger dataset containing 4322 vertebrate mtDNA genetics, we demonstrate that machine learning digital signal processing beats the alignment-centric software MEGA7 (employing MUSCLE or CLUSTALW for alignment) into processing speed, during achieving kindred or even superior sorting accuracy across the data inventory. In contrast to the alignment-independent software FFP, Machine learning processing exhibits enhanced sorting accuracy and faster aggregate performance. Furthermore, starting experiments underscore the potential of ML-DSP in diverse datasets, achieving perfect accuracy in categorizing 4271 entire dengue virus genetic information into variants and 95.5% accuracy in classifying 4,710 bacterial genetic information into phyla.

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CNN-LSTM Based on Crow Search Optimization Algorithm for Product Review Classification

CNN-LSTM Based on Crow Search Optimization Algorithm for Pro...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Jothi, B. Srinivasan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Cse Chennai India Saveetha School of Engineering Sriperumbudur Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Product reviews means an everyone's opinion or views towards particular products or services that delivered to a specific company. In modern global, reviews of online product play a vital part in the estimation process of product in a company and their customers. The extracted features are selected by using the Chi-Squared technique and for the classification of product review proposed a model by utilizing the combined deep learning techniques like Convolutional Neural Network (CNN) and Long- Short Term Memory (LSTM) and it optimized by Crow Search Algorithm (CSA). CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. By utilizing the benefits of CNN and LSTM and it is optimized by CSA for the effective classification of product review. The proposed model performance is estimated by using the performance metrics like Accuracy, Recall, Specificity, Sensitivity and F1-score are 98.35%, 93.20%, 97.57%, 94.82% and 95.78% respectively. The proposed model is compared with other existing model in terms of performance metrics which shows the better performance results. © 2023 IEEE.

关键词： Chi-Squared Convolutional Neural Network Crow Search Algorithm Long-Short Term Memory and Product Review

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A Comprehensive Analysis of Various Deep Learning Based Multi Class Plant Disease Classification Techniques

A Comprehensive Analysis of Various Deep Learning Based Mult...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepika Kala Divya Punia Geeta Sikka Kushagra Sikka Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering National Institute of Technology Delhi Delhi India Department of Information Science and Engineering University of Florida Gainesville Gainesville USA

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

In modern agriculture, deep learning has become increasingly essential to identify plant diseases in the field of plant disease identification with leaf images, where convolutional neural networks (CNN) are garnering a lot of interest. Plant disease iden-tification and prevention are essential for producing healthy crops and maintaining farmers' livelihoods. This study uses leaf images to examine the effectiveness of deep learning (DL) algorithms for multi class plant leaf disease classification. The training data was expanded using data augmentation techniques, which eliminated the need for further image collecting. This paper uses three models namely InceptionV3, VGG16, and EfficientNetBO that have been compared using the plant Village data set. The three models that achieved the highest accuracy were EfficientNetBO (98.51 %), InceptionV3 (97.78%), and VGG16 (95.37%) for plant detection, and EfficientNetBO (97.58%), InceptionV3 (96.83%), and VGG 16 (94.35 %) for plant detection. Notably, EfficientNetBO performed better for disease classification than the other two algorithms. Farmers are able to take prompt action to stop the disease's spread and reduce crop losses since the technology can deliver fast and precise results.

关键词： Deep learning Plant diseases Prevention and mitigation Crops Training data Livelihood Data augmentation Data models Classification algorithms Convolutional neural networks

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Assessment of the Effects of Blockchain Based Protection in Network Performance 1

Assessment of the Effects of Blockchain Based Protection in ...

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Murali Mohan Babu, Y. Radhika, K. Saravanan, T.R. Periasamy, J.K. Department of Electronics and Communication Engineering Tirumala Engineering College Narasaraopet AP Guntur India Department of Electronics and Communication Engineering Geethanjali Institute of Science and Technology Andhra Pradesh Kovur India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

Blockchain is popular as the distributed directory under Blockchain, which safeguards the transaction history in a completely linked network of peers. For double-dollar secrecy, a Bitcoin transaction is only called irrevocable after it's on the blockchain, a chain comprising at least six mined blocks - shortly, "six confirmations."In addition to the usage of blockchain technologies for securing funding, the Internet of Things and the ad hoc vehicle network are now paired with other new technologies (VANET). Nevertheless, these modern apps could have relatively poor Internet access because of the integration of handheld devices that may be placed outside network networks. This paper discusses the influence of latency on forking actions by blockchain and potential breaches of the above six transaction acceptance confirmations convention. We simplify the data structure of blockchain and prevent detailed calculations needed for proof of operating to speed up our simulations (Pow). By simulating, we prove that the convention of six confirmations is vulnerable to the latency of the peer-to-peer system besides also illustrate how easy it is disrupted by simpler mine-mining problems. It is not shocking that the lateness of the underlying peer-to-peer network has seriously impacted the tempo at which all nodes converge in the blockchain. It is also seen to the degree nodes with more regular Internet access will benefit unfairly from proof-of-work mining benefits. We therefore propose to track fairness with heterogeneous latency profiles across nodes, which can prohibit any nodes from being miners. © Published under licence by IOP Publishing Ltd.

关键词： Blockchain

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Smart Ambulance Management System with Real-Time Patient Monitoring

Smart Ambulance Management System with Real-Time Patient Mon...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： M Nithyashree Krupa P Nadgir Pavithra N R Sindhu Rajendran Computer Science and Engineering R. V. College of Engineering Bangalore India Electronics and Communication Engineering R. V. College of Engineering Bangalore India

Delayed medical assistance and poor ambulance management are major factors that contribute to preventable deaths. To mitigate these critical issues, this paper proposes an innovative and comprehensive Smart Ambulance Management System (SAMS). The implementation of the proposed system aims to significantly optimize the medical response time, by enabling the hospital authorities to instantaneously access vital patient health information and track the ambulance's location in real-time. It comprises a user-friendly mobile application to facilitate the booking and arrival of the ambulance to the patient's spot. The system also integrates primary health sensors such as ECG, temperature, heart rate, and SpO2 monitors, providing continuous, real-time patient health data which is relayed to the hospital authorities through a cloud-based web interface thereby providing the healthcare sectors with the information efficiently in order to administer suitable medical intervention upon the patient's arrival.

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Intelligent Recognition of Optical Modulation Formats in Dynamic communication Networks

Intelligent Recognition of Optical Modulation Formats in Dyn...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： D Gnanakumar V Anusooya R Muthupradeepa S Gobalakrishnan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Humanitie and Sciences (Physics) Rajalakshmi Engineering College Thandalam India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Machine learning-based optical modulation format recognition is essential for dynamic optical networks. Convolutional Neural Networks (CNNs) can analyze signal space diagrams directly from raw data. Specifically, CNNs excel in image classification due to their ability to extract features and learn autonomously. In this study, signal space diagrams for various modulation formats (BPSK, QPSK, 8-QAM, 16-QAM, 32-QAM, and 64-QAM) are generated using an electrical constellation visualizer in Optisystem software, with the Optical Signal-to-Noise Ratio (OSNR) varying from 10 to 40 dB and the noise figure ranging from 5 to 25 dB. The proposed VGG-16 CNN architecture achieves 100% accuracy in modulation format recognition (MFR) for all six formats, utilizing minimal training data and fewer epochs. This approach can significantly enhance next-generation dynamic optical communication networks by enabling automatic detection and recognition of modulation formats.

关键词： Bit error rate Training data computer architecture Optical imaging Feature extraction Optical modulation Optical noise Convolutional neural networks Nonlinear optics Signal to noise ratio

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